1
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Billings LK, Jablonski KA, Pan Q, Franks PW, Goldberg RB, Hivert MF, Kahn SE, Knowler WC, Lee CG, Merino J, Huerta-Chagoya A, Mercader JM, Raghavan S, Shi Z, Srinivasan S, Xu J, Florez JC, Udler MS. Increased genetic risk for β-cell failure is associated with β-cell function decline in people with prediabetes. Diabetes 2024:db230761. [PMID: 38758294 DOI: 10.2337/db23-0761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Accepted: 05/09/2024] [Indexed: 05/18/2024]
Abstract
Partitioned polygenic scores (pPS) have been developed to capture pathophysiologic processes underlying type 2 diabetes (T2D). We investigated the influence of T2D pPS on diabetes-related traits and T2D incidence in the Diabetes Prevention Program. We generated five T2D pPS (β-cell, proinsulin, liver/lipid, obesity, lipodystrophy) in 2,647 participants randomized to intensive lifestyle, metformin or placebo arms. Associations were tested using general linear models and Cox regression adjusted for age, sex, and principal components. Sensitivity analyses included adjustment for BMI. Higher β-cell pPS was associated with lower insulinogenic index and corrected insulin response at one year follow-up adjusted for baseline measures (effect per pPS standard deviation (SD) -0.04, P=9.6 x 10-7; -8.45 uU/mg, P=5.6 x 10-6, respectively) and with increased diabetes incidence adjusted for BMI at nominal significance (HR 1.10 per SD, P=0.035). The liver/lipid pPS was associated with reduced one-year baseline-adjusted triglyceride levels (effect per SD -4.37, P=0.001). There was no significant interaction between T2D pPS and randomized groups. The remaining pPS were associated with baseline measures only. We conclude that despite interventions for diabetes prevention, participants with a high genetic burden of the β-cell cluster pPS had worsening in measures of β-cell function.
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Affiliation(s)
- Liana K Billings
- Division of Endocrinology, Department of Medicine, NorthShore University HealthSystem/Endeavor Health, Skokie, IL, USA
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL, US
| | | | - Qing Pan
- George Washington University Biostatistics Center, Washington D.C
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University. Jan Waldenströmsgata 35, Building 60, Floor 13, Skåne University Hospital, 20502, Malmö, Sweden
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Marie-France Hivert
- Division of Chronic Disease Research Across the Lifecourse (CoRAL), Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Steven E Kahn
- Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, VA Puget Sound Health Care System and University of Washington, Seattle, WA, USA
| | - William C Knowler
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Christine G Lee
- Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institutes of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland
| | - Jordi Merino
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Alicia Huerta-Chagoya
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Josep M Mercader
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Sridharan Raghavan
- Department of Veterans Affairs Eastern Colorado Health Care System, Aurora, CO, USA
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Zhuqing Shi
- Program for Personalized Cancer Care, NorthShore University Health System, Evanston, Illinois, USA
| | - Shylaja Srinivasan
- Department of Pediatrics, University of California, San Francisco, San Francisco, California
| | - Jianfeng Xu
- Program for Personalized Cancer Care, NorthShore University Health System, Evanston, Illinois, USA
| | - Jose C Florez
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Miriam S Udler
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine and Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
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2
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Li JH, Perry JA, Jablonski KA, Srinivasan S, Chen L, Todd JN, Harden M, Mercader JM, Pan Q, Dawed AY, Yee SW, Pearson ER, Giacomini KM, Giri A, Hung AM, Xiao S, Williams LK, Franks PW, Hanson RL, Kahn SE, Knowler WC, Pollin TI, Florez JC. Identification of Genetic Variation Influencing Metformin Response in a Multiancestry Genome-Wide Association Study in the Diabetes Prevention Program (DPP). Diabetes 2023; 72:1161-1172. [PMID: 36525397 PMCID: PMC10382652 DOI: 10.2337/db22-0702] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022]
Abstract
Genome-wide significant loci for metformin response in type 2 diabetes reported elsewhere have not been replicated in the Diabetes Prevention Program (DPP). To assess pharmacogenetic interactions in prediabetes, we conducted a genome-wide association study (GWAS) in the DPP. Cox proportional hazards models tested associations with diabetes incidence in the metformin (MET; n = 876) and placebo (PBO; n = 887) arms. Multiple linear regression assessed association with 1-year change in metformin-related quantitative traits, adjusted for baseline trait, age, sex, and 10 ancestry principal components. We tested for gene-by-treatment interaction. No significant associations emerged for diabetes incidence. We identified four genome-wide significant variants after correcting for correlated traits (P < 9 × 10-9). In the MET arm, rs144322333 near ENOSF1 (minor allele frequency [MAF]AFR = 0.07; MAFEUR = 0.002) was associated with an increase in percentage of glycated hemoglobin (per minor allele, β = 0.39 [95% CI 0.28, 0.50]; P = 2.8 × 10-12). rs145591055 near OMSR (MAF = 0.10 in American Indians) was associated with weight loss (kilograms) (per G allele, β = -7.55 [95% CI -9.88, -5.22]; P = 3.2 × 10-10) in the MET arm. Neither variant was significant in PBO; gene-by-treatment interaction was significant for both variants [P(G×T) < 1.0 × 10-4]. Replication in individuals with diabetes did not yield significant findings. A GWAS for metformin response in prediabetes revealed novel ethnic-specific associations that require further investigation but may have implications for tailored therapy.
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Affiliation(s)
- Josephine H. Li
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - James A. Perry
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD
| | - Kathleen A. Jablonski
- Department of Epidemiology and Biostatistics, George Washington University Biostatistics Center, Washington, DC
| | - Shylaja Srinivasan
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, University of California, San Francisco, San Francisco, CA
| | - Ling Chen
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
| | - Jennifer N. Todd
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
- Division of Endocrinology, Department of Pediatrics, Boston Children’s Hospital, Boston, MA
| | - Maegan Harden
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
| | - Josep M. Mercader
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Qing Pan
- Department of Epidemiology and Biostatistics, George Washington University Biostatistics Center, Washington, DC
| | - Adem Y. Dawed
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee, U.K
| | - Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA
| | - Ewan R. Pearson
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee, U.K
| | - Kathleen M. Giacomini
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA
| | - Ayush Giri
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN
| | - Adriana M. Hung
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Shujie Xiao
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, MI
| | - L. Keoki Williams
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, MI
| | - Paul W. Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - Robert L. Hanson
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - Steven E. Kahn
- Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, VA Puget Sound Health Care System and University of Washington, Seattle
| | - William C. Knowler
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - Toni I. Pollin
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD
| | - Jose C. Florez
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
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3
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Gyamfi-Bannerman C, Jablonski KA, Blackwell SC, Tita ATN, Reddy UM, Jain L, Saade GR, Rouse DJ, Clark EAS, Thorp JM, Chien EK, Peaceman AM, Gibbs RS, Swamy GK, Norton ME, Casey BM, Caritis SN, Tolosa JE, Sorokin Y, VanDorsten JP. Evaluation of Hypoglycemia in Neonates of Women at Risk for Late Preterm Delivery: An Antenatal Late Preterm Steroids Trial Cohort Study. Am J Perinatol 2023; 40:532-538. [PMID: 34044454 PMCID: PMC8626537 DOI: 10.1055/s-0041-1729561] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
OBJECTIVE In the antenatal late preterm steroids (ALPS) trial betamethasone significantly decreased short-term neonatal respiratory morbidity but increased the risk of neonatal hypoglycemia, diagnosed only categorically (<40 mg/dL). We sought to better characterize the nature, duration, and treatment for hypoglycemia. STUDY DESIGN Secondary analysis of infants from ALPS, a multicenter trial randomizing women at risk for late preterm delivery to betamethasone or placebo. This study was a reabstraction of all available charts from the parent trial, all of which were requested. Unreviewed charts included those lost to follow-up or from sites not participating in the reabstraction. Duration of hypoglycemia (<40 mg/dL), lowest value and treatment, if any, were assessed by group. Measures of association and regression models were used where appropriate. RESULTS Of 2,831 randomized, 2,609 (92.2%) were included. There were 387 (29.3%) and 223 (17.3%) with hypoglycemia in the betamethasone and placebo groups, respectively (relative risk [RR]: 1.69, 95% confidence interval [CI]: 1.46-1.96). Hypoglycemia generally occurred in the first 24 hours in both groups: 374/385 (97.1%) in the betamethasone group and 214/222 (96.4%) in the placebo group (p = 0.63). Of 387 neonates with hypoglycemia in the betamethasone group, 132 (34.1%) received treatment, while 73/223 (32.7%) received treatment in placebo group (p = 0.73). The lowest recorded blood sugar was similar between groups. Most hypoglycemia resolved by 24 hours in both (93.0 vs. 89.3% in the betamethasone and placebo groups, respectively, p = 0.18). Among infants with hypoglycemia in the first 24 hours, the time to resolution was shorter in the betamethasone group (2.80 [interquartile range: 2.03-7.03) vs. 3.74 (interquartile range: 2.15-15.08) hours; p = 0.002]. Persistence for >72 hours was rare and similar in both groups, nine (2.4%, betamethasone) and four (1.9%, placebo, p = 0.18). CONCLUSION In this cohort, hypoglycemia was transient and most received no treatment, with a quicker resolution in the betamethasone group. Prolonged hypoglycemia was uncommon irrespective of steroid exposure. KEY POINTS · Hypoglycemia was transient and approximately two-thirds received no treatment.. · Neonates in the ALPS trial who received betamethasone had a shorter time to resolution than those with hypoglycemia in the placebo group.. · Prolonged hypoglycemia occurred in approximately 2 out of 100 late preterm newborns, irrespective of antenatal steroid exposure..
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Affiliation(s)
| | - Kathleen A Jablonski
- Department of Epidemiology, George Washington University Biostatistics Center, Washington, District of Columbia
| | - Sean C Blackwell
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Texas Health Science Center, Children's Memorial Hermann Hospital, Houston, Texas
| | - Alan T N Tita
- Department of Obstetrics and Gynecology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Uma M Reddy
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland
| | - Lucky Jain
- Department of Pediatrics, Emory University, Atlanta, Georgia
| | - George R Saade
- Department of Obstetrics and Gynecology, University of Texas Medical Branch, Galveston, Texas
| | - Dwight J Rouse
- Department of Obstetrics and Gynecology, Brown University, Providence, Rhode Island
| | - Erin A S Clark
- Department of Obstetrics and Gynecology, University of Utah Health Sciences Center, Salt Lake City, Utah
| | - John M Thorp
- Department of Obstetrics and Gynecology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Edward K Chien
- Department of Obstetrics and Gynecology Specialists, MetroHealth Medical Center, Case Western Reserve University, Cleveland, Ohio
| | - Alan M Peaceman
- Department of Obstetrics and Gynecology, Northwestern University, Chicago, Illinois
| | - Ronald S Gibbs
- Department of Obstetrics and Gynecology, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, Colorado
| | - Geeta K Swamy
- Department of Obstetrics and Gynecology, Duke University, Durham, North Carolina
| | - Mary E Norton
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Stanford University, Stanford, California
| | - Brian M Casey
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Steve N Caritis
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Jorge E Tolosa
- Department of Obstetrics and Gynecology, Oregon Health and Science University, Portland, Oregon
| | - Yoram Sorokin
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan
| | - J Peter VanDorsten
- Department of Obstetrics and Gynecology, Medical University of South Carolina, Charleston, South Carolina
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4
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Maxwell TJ, Franks PW, Kahn SE, Knowler WC, Mather KJ, Florez JC, Jablonski KA. Quantitative trait loci, G×E and G×G for glycemic traits: response to metformin and placebo in the Diabetes Prevention Program (DPP). J Hum Genet 2022; 67:465-473. [PMID: 35260800 PMCID: PMC10102970 DOI: 10.1038/s10038-022-01027-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 02/16/2022] [Accepted: 02/17/2022] [Indexed: 11/09/2022]
Abstract
The complex genetic architecture of type-2-diabetes (T2D) includes gene-by-environment (G×E) and gene-by-gene (G×G) interactions. To identify G×E and G×G, we screened markers for patterns indicative of interactions (relationship loci [rQTL] and variance heterogeneity loci [vQTL]). rQTL exist when the correlation between multiple traits varies by genotype and vQTL occur when the variance of a trait differs by genotype (potentially flagging G×G and G×E). In the metformin and placebo arms of the DPP (n = 1762) we screened 280,965 exomic and intergenic SNPs, for rQTL and vQTL patterns in association with year one changes from baseline in glycemia and related traits (insulinogenic index [IGI], insulin sensitivity index [ISI], fasting glucose and fasting insulin). Significant (p < 1.8 × 10-7) rQTL and vQTL generated a priori hypotheses of individual G×E tests for a SNP × metformin treatment interaction and secondarily for G×G screens. Several rQTL and vQTL identified led to 6 nominally significant (p < 0.05) metformin treatment × SNP interactions (4 for IGI, one insulin, and one glucose) and 12G×G interactions (all IGI) that exceeded experiment-wide significance (p < 4.1 × 10-9). Some loci are directly associated with incident diabetes, and others are rQTL and modify a trait's relationship with diabetes (2 diabetes/glucose, 2 diabetes/insulin, 1 diabetes/IGI). rs3197999, an ISI/insulin rQTL, is a possible gene damaging missense mutation in MST1, is associated with ulcerative colitis, sclerosing cholangitis, Crohn's disease, BMI and coronary artery disease. This study demonstrates evidence for context-dependent effects (G×G & G×E) and the complexity of these T2D-related traits.
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Affiliation(s)
- Taylor J Maxwell
- Computational Biology Institute, The George Washington University, Ashburn, VA, USA.
| | - Paul W Franks
- Genetic & Molecular Epidemiology Unit, Lund University Diabetes Center, Lund, Sweden
| | - Steven E Kahn
- VA Puget Sound Health Care System and University of Washington, Seattle, WA, USA
| | - William C Knowler
- National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA
| | - Kieren J Mather
- Center for Diabetes and Metabolic Diseases & Division of Endocrinology & Metabolism, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jose C Florez
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kathleen A Jablonski
- The Biostatistics Center, The Milken Institute of Public Health, The George Washington University, Rockville, MD, USA
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5
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Park DE, Watson NL, Focht C, Feikin D, Hammitt LL, Brooks WA, Howie SRC, Kotloff KL, Levine OS, Madhi SA, Murdoch DR, O'Brien KL, Scott JAG, Thea DM, Amorninthapichet T, Awori J, Bunthi C, Ebruke B, Elhilali M, Higdon M, Hossain L, Jahan Y, Moore DP, Mulindwa J, Mwananyanda L, Naorat S, Prosperi C, Thamthitiwat S, Verwey C, Jablonski KA, Power MC, Young HA, Deloria Knoll M, McCollum ED. Digitally recorded and remotely classified lung auscultation compared with conventional stethoscope classifications among children aged 1-59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) case-control study. BMJ Open Respir Res 2022; 9:9/1/e001144. [PMID: 35577452 PMCID: PMC9115042 DOI: 10.1136/bmjresp-2021-001144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 04/28/2022] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Diagnosis of pneumonia remains challenging. Digitally recorded and remote human classified lung sounds may offer benefits beyond conventional auscultation, but it is unclear whether classifications differ between the two approaches. We evaluated concordance between digital and conventional auscultation. METHODS We collected digitally recorded lung sounds, conventional auscultation classifications and clinical measures and samples from children with pneumonia (cases) in low-income and middle-income countries. Physicians remotely classified recordings as crackles, wheeze or uninterpretable. Conventional and digital auscultation concordance was evaluated among 383 pneumonia cases with concurrently (within 2 hours) collected conventional and digital auscultation classifications using prevalence-adjusted bias-adjusted kappa (PABAK). Using an expanded set of 737 cases that also incorporated the non-concurrently collected assessments, we evaluated whether associations between auscultation classifications and clinical or aetiological findings differed between conventional or digital auscultation using χ2 tests and logistic regression adjusted for age, sex and site. RESULTS Conventional and digital auscultation concordance was moderate for classifying crackles and/or wheeze versus neither crackles nor wheeze (PABAK=0.50), and fair for crackles-only versus not crackles-only (PABAK=0.30) and any wheeze versus no wheeze (PABAK=0.27). Crackles were more common on conventional auscultation, whereas wheeze was more frequent on digital auscultation. Compared with neither crackles nor wheeze, crackles-only on both conventional and digital auscultation was associated with abnormal chest radiographs (adjusted OR (aOR)=1.53, 95% CI 0.99 to 2.36; aOR=2.09, 95% CI 1.19 to 3.68, respectively); any wheeze was inversely associated with C-reactive protein >40 mg/L using conventional auscultation (aOR=0.50, 95% CI 0.27 to 0.92) and with very severe pneumonia using digital auscultation (aOR=0.67, 95% CI 0.46 to 0.97). Crackles-only on digital auscultation was associated with mortality compared with any wheeze (aOR=2.70, 95% CI 1.12 to 6.25). CONCLUSIONS Conventional auscultation and remotely-classified digital auscultation displayed moderate concordance for presence/absence of wheeze and crackles among cases. Conventional and digital auscultation may provide different classification patterns, but wheeze was associated with decreased clinical severity on both.
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Affiliation(s)
- Daniel E Park
- Department of Environmental and Occupational Health, The George Washington University, Washington, District of Columbia, USA
| | | | | | - Daniel Feikin
- Department of International Health, Johns Hopkins University International Vaccine Access Center, Baltimore, Maryland, USA
| | - Laura L Hammitt
- Department of International Health, Johns Hopkins University International Vaccine Access Center, Baltimore, Maryland, USA,Kenya Medical Research Institute - Wellcome Trust Research Programme, Kilifi, Kenya
| | - W Abdullah Brooks
- International Centre for Diarrhoeal Disease Research Bangladesh, Dhaka and Matlab, Bangladesh,Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Stephen R C Howie
- Medical Research Council Unit, Basse, Gambia,Department of Paediatrics, The University of Auckland, Auckland, New Zealand
| | - Karen L Kotloff
- Department of Pediatrics, University of Maryland Center for Vaccine Development, Baltimore, Maryland, USA
| | - Orin S Levine
- Department of International Health, Johns Hopkins University International Vaccine Access Center, Baltimore, Maryland, USA,Bill & Melinda Gates Foundation, Seattle, Washington, USA
| | - Shabir A Madhi
- South African Medical Research Council Vaccines and Infectious Diseases Analytics Research Unit, University of the Witwatersrand, Johannesburg, Gauteng, South Africa,Department of Science and Innovation/National Research Foundation: Vaccine Preventable Diseases Unit, University of the Witwatersrand, Johannesburg, Gauteng, South Africa
| | - David R Murdoch
- Department of Pathology and Biomedical Science, University of Otago, Christchurch, New Zealand,Microbiology Unit, Canterbury Health Laboratories, Christchurch, New Zealand
| | - Katherine L O'Brien
- Department of International Health, Johns Hopkins University International Vaccine Access Center, Baltimore, Maryland, USA
| | - J Anthony G Scott
- Kenya Medical Research Institute - Wellcome Trust Research Programme, Kilifi, Kenya,Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Donald M Thea
- Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, USA
| | | | - Juliet Awori
- Kenya Medical Research Institute - Wellcome Trust Research Programme, Kilifi, Kenya
| | - Charatdao Bunthi
- Division of Global Health Protection, Thailand Ministry of Public Health – US CDC Collaboration, Royal Thai Government Ministry of Public Health, Bangkok, Thailand
| | - Bernard Ebruke
- Medical Research Council Unit, Basse, Gambia,International Foundation Against Infectious Disease in Nigeria, Abuja, Nigeria
| | - Mounya Elhilali
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Melissa Higdon
- Department of International Health, Johns Hopkins University International Vaccine Access Center, Baltimore, Maryland, USA
| | - Lokman Hossain
- International Centre for Diarrhoeal Disease Research Bangladesh, Dhaka and Matlab, Bangladesh
| | - Yasmin Jahan
- International Centre for Diarrhoeal Disease Research Bangladesh, Dhaka and Matlab, Bangladesh
| | - David P Moore
- South African Medical Research Council Vaccines and Infectious Diseases Analytics Research Unit, University of the Witwatersrand, Johannesburg, South Africa,Department of Paediatrics and Child Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Justin Mulindwa
- Department of Paediatrics and Child Health, University Teaching Hospital, Lusaka, Zambia
| | - Lawrence Mwananyanda
- Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, USA,Right to Care - Zambia, Lusaka, Zambia
| | | | - Christine Prosperi
- Department of International Health, Johns Hopkins University International Vaccine Access Center, Baltimore, Maryland, USA
| | - Somsak Thamthitiwat
- Division of Global Health Protection, Thailand Ministry of Public Health – US CDC Collaboration, Royal Thai Government Ministry of Public Health, Nonthaburi, Thailand
| | - Charl Verwey
- South African Medical Research Council Vaccines and Infectious Diseases Analytics Research Unit, University of the Witwatersrand, Johannesburg, Gauteng, South Africa,Department of Paediatrics and Child Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | | | - Melinda C Power
- Department of Epidemiology, The George Washington University, Washington, District of Columbia, USA
| | - Heather A Young
- Department of Epidemiology, The George Washington University, Washington, District of Columbia, USA
| | - Maria Deloria Knoll
- Department of International Health, Johns Hopkins University International Vaccine Access Center, Baltimore, Maryland, USA
| | - Eric D McCollum
- Global Program in Respiratory Sciences, Eudowood Division of Pediatric Respiratory Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, USA,Department of International Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
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6
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Carpenter JR, Jablonski KA, Koncinsky J, Varner MW, Gyamfi-Bannerman C, Joss-Moore LA. Antenatal Steroids and Cord Blood T-cell Glucocorticoid Receptor DNA Methylation and Exon 1 Splicing. Reprod Sci 2022; 29:1513-1523. [PMID: 35146694 PMCID: PMC9010373 DOI: 10.1007/s43032-022-00859-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 01/18/2022] [Indexed: 02/03/2023]
Abstract
Antenatal administration of glucocorticoids such as betamethasone (BMZ) during the late preterm period improves neonatal respiratory outcomes. However, glucocorticoids may elicit programming effects on immune function and gene regulation. Here, we test the hypothesis that exposure to antenatal BMZ alters cord blood immune cell composition in association with altered DNA methylation and alternatively expressed Exon 1 transcripts of the glucocorticoid receptor (GR) gene in cord blood CD4+ T-cells. Cord blood was collected from 51 subjects in the Antenatal Late Preterm Steroids Trial: 27 BMZ, 24 placebo. Proportions of leukocytes were compared between BMZ and placebo. In CD4+ T-cells, methylation at CpG sites in the GR promoter regions and expression of GR mRNA exon 1 variants were compared between BMZ and placebo. BMZ was associated with an increase in granulocytes (51.6% vs. 44.7% p = 0.03) and a decrease in lymphocytes (36.8% vs. 43.0% p = 0.04) as a percent of the leukocyte population vs. placebo. Neither GR methylation nor exon 1 transcript levels differed between groups. BMZ is associated with altered cord blood leukocyte proportions, although no associated alterations in GR methylation were observed.
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Affiliation(s)
| | - Kathleen A. Jablonski
- Milken School of Public Health, Biostatistics Center, George Washington University, Washington, D.C, USA
| | | | - Michael W. Varner
- Obstetrics & Gynecology, University of Utah, Salt Lake City, Utah, USA
| | | | - Lisa A. Joss-Moore
- Pediatrics, University of Utah, Salt Lake City, Utah, USA,Corresponding author: Lisa Joss-Moore, Ph.D., University of Utah, Department of Pediatrics, 295 Chipeta Way, Salt Lake City, Utah, 84108, USA, Ph: 1-801-213-3494,
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7
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Maxwell TJ, Franks PW, Kahn SE, Knowler WC, Mather KJ, Florez JC, Jablonski KA. Correction to: Quantitative trait loci, G×E and G×G for glycemic traits: response to metformin and placebo in the Diabetes Prevention Program (DPP). J Hum Genet 2022; 67:503. [PMID: 35411098 DOI: 10.1038/s10038-022-01034-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Taylor J Maxwell
- Computational Biology Institute, The George Washington University, Ashburn, VA, USA.
| | - Paul W Franks
- Genetic & Molecular Epidemiology Unit, Lund University Diabetes Center, Lund, Sweden
| | - Steven E Kahn
- VA Puget Sound Health Care System and University of Washington, Seattle, WA, USA
| | - William C Knowler
- National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA
| | - Kieren J Mather
- Center for Diabetes and Metabolic Diseases & Division of Endocrinology & Metabolism, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jose C Florez
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.,Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kathleen A Jablonski
- The Biostatistics Center, The Milken Institute of Public Health, The George Washington University, Rockville, MD, USA
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8
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McCaffery JM, Jablonski KA, Pan Q, Astrup A, Revsbech Christiansen M, Corella D, Corso LM, Florez JC, Franks PW, Gardner C, Hansen T, Kilpeläinen TO, Knowler WC, Lindström J, Saris WH, Sørensen TI, Tuomilehto J, Uusitupa M, Wing RR, Agurs-Collins T. Genetic Predictors of Change in Waist Circumference and Waist-to-Hip Ratio With Lifestyle Intervention: The Trans-NIH Consortium for Genetics of Weight Loss Response to Lifestyle Intervention. Diabetes 2022; 71:669-676. [PMID: 35043141 PMCID: PMC9114721 DOI: 10.2337/db21-0741] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 01/04/2022] [Indexed: 11/13/2022]
Abstract
Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) associated with waist circumference (WC) and waist-to-hip ratio (WHR) adjusted for BMI (WCadjBMI and WHRadjBMI), but it remains unclear whether these SNPs relate to change in WCadjBMI or WHRadjBMI with lifestyle intervention for weight loss. We hypothesized that polygenic scores (PS) comprised of 59 SNPs previously associated with central adiposity would predict less of a reduction in WCadjBMI or WHRadjBMI at 8-10 weeks in two lifestyle intervention trials, NUGENOB and DiOGenes, and at 1 year in five lifestyle intervention trials, Look AHEAD, Diabetes Prevention Program, Diabetes Prevention Study, DIETFITS, and PREDIMED-Plus. One-SD higher PS related to a smaller 1-year change in WCadjBMI in the lifestyle intervention arms at year 1 and thus predicted poorer response (β = 0.007; SE = 0.003; P = 0.03) among White participants overall and in White men (β = 0.01; SE = 0.004; P = 0.01). At average weight loss, this amounted to 0.20-0.28 cm per SD. No significant findings emerged in White women or African American men for the 8-10-week outcomes or for WHRadjBMI. Findings were heterogeneous in African American women. These results indicate that polygenic risk estimated from these 59 SNPs relates to change in WCadjBMI with lifestyle intervention, but the effects are small and not of sufficient magnitude to be clinically significant.
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Affiliation(s)
- Jeanne M. McCaffery
- Department of Allied Health Sciences, University of Connecticut, Storrs, CT
- Corresponding author:
| | - Kathleen A. Jablonski
- Department of Epidemiology, The Biostatistics Center, George Washington University, Rockville, MD
| | - Qing Pan
- Department of Epidemiology, The Biostatistics Center, George Washington University, Rockville, MD
| | - Arne Astrup
- Healthy Weight Center, Novo Nordisk Foundation, Hellerup, Denmark
| | - Malene Revsbech Christiansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Dolores Corella
- Department of Preventive Medicine and Public Health and CIBER Physiopathology of Obesity and Nutrition, University of Valencia, Valencia, Spain
| | - Lauren M.L. Corso
- Department of Allied Health Sciences, University of Connecticut, Storrs, CT
| | - Jose C. Florez
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Paul W. Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Lund University, Malmö, Sweden
- Harvard T.H. Chan School of Public Health, Boston, MA
| | | | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tuomas O. Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - William C. Knowler
- National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - Jaana Lindström
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Wim H.M. Saris
- Department of Human Biology, NUTRIM, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Thorkild I.A. Sørensen
- Novo Nordisk Foundation Center for Basic Metabolic Research and Department of Public Health, Section of Epidemiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jaakko Tuomilehto
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Matti Uusitupa
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Rena R. Wing
- Weight Control and Diabetes Research Center, The Miriam Hospital and Warren Alpert School of Medicine at Brown University, Providence, RI
| | - Tanya Agurs-Collins
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD
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9
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Varner MW, Costantine MM, Jablonski KA, Rouse DJ, Mercer BM, Leveno KJ, Reddy UM, Buhimschi C, Wapner RJ, Sorokin Y, Thorp JM, Ramin SM, Malone FD, Carpenter M, O’sullivan MJ, Peaceman AM, Dudley DJ, Caritis SN. Sex-Specific Genetic Susceptibility to Adverse Neurodevelopmental Outcome in Offspring of Pregnancies at Risk of Early Preterm Delivery. Am J Perinatol 2020; 37:281-290. [PMID: 30731481 PMCID: PMC6685763 DOI: 10.1055/s-0039-1678535] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
OBJECTIVE To evaluate sex-specific genetic susceptibility to adverse neurodevelopmental outcome (ANO, defined as cerebral palsy [CP], mental, or psychomotor delay) at risk for early preterm birth (EPTB, < 32 weeks). STUDY DESIGN Secondary case-control analysis of a trial of magnesium sulfate (MgSO4) before anticipated EPTB for CP prevention. Cases are infants who died by the age of 1 year or developed ANO. Controls, matched by maternal race and infant sex, were neurodevelopmentally normal survivors. Neonatal DNA was evaluated for 80 polymorphisms in inflammation, coagulation, vasoregulation, excitotoxicity, and oxidative stress pathways using Taqman assays. The primary outcome for this analysis was sex-specific ANO susceptibility. Conditional logistic regression estimated each polymorphism's odds ratio (OR) by sex stratum, adjusting for gestational age, maternal education, and MgSO4-corticosteroid exposures. Holm-Bonferroni corrections, adjusting for multiple comparisons (p < 7.3 × 10-4), accounted for linkage disequilibrium between markers. RESULTS Analysis included 211 cases (134 males; 77 females) and 213 controls (130 males; 83 females). An interleukin-6 (IL6) polymorphism (rs2069840) was associated with ANO in females (OR: 2.6, 95% confidence interval [CI]: 1.5-4.7; p = 0.001), but not in males (OR: 0.8, 95% CI: 0.5-1.2; p = 0.33). The sex-specific effect difference was significant (p = 7.0 × 10-4) and was unaffected by MgSO4 exposure. No other gene-sex associations were significant. CONCLUSION An IL6 gene locus may confer susceptibility to ANO in females, but not males, after EPTB.
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Affiliation(s)
- Michael W. Varner
- Department of Obstetrics and Gynecology, University of
Utah, Salt Lake City, Utah
| | - Maged M. Costantine
- Department of Obstetrics and Gynecology, University of
Texas Medical Branch, Galveston, Texas
| | - Kathleen A. Jablonski
- Department of Epidemiology and Biostatistics, George
Washington University Biostatistics Center, Washington, Disctrict of Columbia
| | - Dwight J. Rouse
- Department of Obstetrics and Gynecology, University of
Alabama at Birmingham, Birmingham, Alabama
| | - Brian M. Mercer
- Department of Obstetrics and Gynecology, MetroHealth
Medical Center, Case Western Reserve University, Cleveland, Ohio
| | - Kenneth J. Leveno
- Department of Obstetrics and Gynecology, University of
Texas Southwestern Medical Center, Dallas, Texas
| | - Uma M. Reddy
- Eunice Kennedy Shriver National Institute of Child Health
and Human Development, Bethesda, Maryland
| | - Catalin Buhimschi
- Department of Obstetrics and Gynecology, The Ohio State
University, Columbus, Ohio
| | - Ronald J. Wapner
- Department of Obstetrics and Gynecology, Thomas Jefferson
University, Philadelphia, Pennsylvania
- Department of Obstetrics and Gynecology, Drexel
University, Philadelphia, Pennsylvania
| | - Yoram Sorokin
- Department of Obstetrics and Gynecology, Wayne State
University, Detroit, Michigan
| | - John M. Thorp
- Department of Obstetrics and Gynecology, University of
North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Susan M. Ramin
- Department of Obstetrics and Gynecology, University of
Texas Health Science Center at Houston, Houston, Texas
| | - Fergal D. Malone
- Department of Obstetrics and Gynecology, Columbia
University, New York, New York
| | - Marshall Carpenter
- Department of Obstetrics and Gynecology, Brown
University, Providence, Rhode Island
| | - Mary J. O’sullivan
- Department of Obstetrics and Gynecology, University of
Miami, Miami, Florida
| | - Alan M. Peaceman
- Department of Obstetrics and Gynecology, Northwestern
University, Chicago, Illinois
| | - Donald J. Dudley
- Department of Obstetrics and Gynecology, University of
Texas Health Science Center, San Antonio, Texas
| | - Steve N. Caritis
- Department of Obstetrics and Gynecology, University of
Pittsburgh, Pittsburgh, Pennsylvania
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10
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Merino J, Jablonski KA, Mercader JM, Kahn SE, Chen L, Harden M, Delahanty LM, Araneta MRG, Walford GA, Jacobs SB, Ibebuogu UN, Franks PW, Knowler WC, Florez JC. Interaction Between Type 2 Diabetes Prevention Strategies and Genetic Determinants of Coronary Artery Disease on Cardiometabolic Risk Factors. Diabetes 2020; 69:112-120. [PMID: 31636172 PMCID: PMC6925585 DOI: 10.2337/db19-0097] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 10/17/2019] [Indexed: 01/09/2023]
Abstract
Coronary artery disease (CAD) is more frequent among individuals with dysglycemia. Preventive interventions for diabetes can improve cardiometabolic risk factors (CRFs), but it is unclear whether the benefits on CRFs are similar for individuals at different genetic risk for CAD. We built a 201-variant polygenic risk score (PRS) for CAD and tested for interaction with diabetes prevention strategies on 1-year changes in CRFs in 2,658 Diabetes Prevention Program (DPP) participants. We also examined whether separate lifestyle behaviors interact with PRS and affect changes in CRFs in each intervention group. Participants in both the lifestyle and metformin interventions had greater improvement in the majority of recognized CRFs compared with placebo (P < 0.001) irrespective of CAD genetic risk (P interaction > 0.05). We detected nominal significant interactions between PRS and dietary quality and physical activity on 1-year change in BMI, fasting glucose, triglycerides, and HDL cholesterol in individuals randomized to metformin or placebo, but none of them achieved the multiple-testing correction for significance. This study confirms that diabetes preventive interventions improve CRFs regardless of CAD genetic risk and delivers hypothesis-generating data on the varying benefit of increasing physical activity and improving diet on intermediate cardiovascular risk factors depending on individual CAD genetic risk profile.
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Affiliation(s)
- Jordi Merino
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Research Unit on Lipids and Atherosclerosis, CIBERDEM, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Reus, Spain
| | - Kathleen A. Jablonski
- The Biostatistics Center, Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, The George Washington University, Rockville, MD
| | - Josep M. Mercader
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA
| | - Steven E. Kahn
- Division of Metabolism, Endocrinology and Nutrition, VA Puget Sound Health Care System and University of Washington, Seattle, WA
| | - Ling Chen
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA
| | - Maegan Harden
- Genomics Platform, Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA
| | - Linda M. Delahanty
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Maria Rosario G. Araneta
- Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA
| | - Geoffrey A. Walford
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
| | - Suzanne B.R. Jacobs
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA
| | - Uzoma N. Ibebuogu
- Division of Cardiovascular Diseases, Department of Medicine, The University of Tennessee Health Science Center, Memphis, TN
| | - Paul W. Franks
- Genetic & Molecular Epidemiology Unit, Lund University Diabetes Centre, Malmo, Sweden
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - William C. Knowler
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - Jose C. Florez
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA
- Programs in Metabolism and Medical and Population Genetics, Eli and Edythe L. Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
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11
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Srinivasan S, Jablonski KA, Knowler WC, Dagogo-Jack S, E. Kahn S, Boyko EJ, Bray GA, Horton ES, Hivert MF, Goldberg R, Chen L, Mercader J, Harden M, Florez JC. A Polygenic Lipodystrophy Genetic Risk Score Characterizes Risk Independent of BMI in the Diabetes Prevention Program. J Endocr Soc 2019; 3:1663-1677. [PMID: 31428720 PMCID: PMC6694040 DOI: 10.1210/js.2019-00069] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Accepted: 06/18/2019] [Indexed: 01/24/2023] Open
Abstract
CONTEXT There is substantial heterogeneity in insulin sensitivity, and genetics may suggest possible mechanisms by which common variants influence this trait. OBJECTIVES We aimed to evaluate an 11-variant polygenic lipodystrophy genetic risk score (GRS) for association with anthropometric, glycemic and metabolic traits in the Diabetes Prevention Program (DPP). In secondary analyses, we tested the association of the GRS with cardiovascular risk factors in the DPP. DESIGN In 2713 DPP participants, we evaluated a validated GRS of 11 common variants associated with fasting insulin-based measures of insulin sensitivity discovered through genome-wide association studies that cluster with a metabolic profile of lipodystrophy, conferring high metabolic risk despite low body mass index (BMI). RESULTS At baseline, a higher polygenic lipodystrophy GRS was associated with lower weight, BMI, and waist circumference measurements, but with worse insulin sensitivity index (ISI) values. Despite starting at a lower weight and BMI, a higher GRS was associated with less weight and BMI reduction at one year and less improvement in ISI after adjusting for baseline values but was not associated with diabetes incidence. A higher GRS was also associated with more atherogenic low-density lipoprotein peak-particle-density at baseline but was not associated with coronary artery calcium scores in the Diabetes Prevention Program Outcomes Study. CONCLUSIONS In the DPP, a higher polygenic lipodystrophy GRS for insulin resistance with lower BMI was associated with diminished improvement in insulin sensitivity and potential higher cardiovascular disease risk. This GRS helps characterize insulin resistance in a cohort of individuals at high risk for diabetes, independent of adiposity.
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Affiliation(s)
- Shylaja Srinivasan
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, University of California at San Francisco, San Francisco, California
| | - Kathleen A Jablonski
- Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, George Washington University, Washington, DC
| | - William C Knowler
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona
| | - Samuel Dagogo-Jack
- Division of Endocrinology, Diabetes and Metabolism, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Steven E. Kahn
- Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, VA Puget Sound Health Care System and University of Washington, Seattle, Washington
| | - Edward J Boyko
- Division of General Internal Medicine, University of Washington, Seattle, Washington
| | - George A Bray
- Division of Clinical Obesity and Metabolism, Pennington Biomedical Research Center, Baton Rouge, Louisiana
| | | | - Marie-France Hivert
- Diabetes Research Center, Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Ronald Goldberg
- Diabetes Research Institute, University of Miami Health System, Miami, Florida
| | - Ling Chen
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Josep Mercader
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Maegan Harden
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Jose C Florez
- Diabetes Research Center, Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, Massachusetts
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12
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Buhimschi CS, Jablonski KA, Rouse DJ, Varner MW, Reddy UM, Mercer BM, Leveno KJ, Wapner RJ, Sorokin Y, Thorp JM, Ramin SM, Malone FD, Carpenter MW, O'Sullivan MJ, Peaceman AM, Saade GR, Dudley D, Caritis SN, Buhimschi IA. Cord Blood Haptoglobin, Cerebral Palsy and Death in Infants of Women at Risk for Preterm Birth: A Secondary Analysis of a Randomised Controlled Trial. EClinicalMedicine 2019; 9:11-18. [PMID: 31143877 PMCID: PMC6510719 DOI: 10.1016/j.eclinm.2019.03.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 03/08/2019] [Accepted: 03/11/2019] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Antenatal exposure to intra-uterine inflammation results in precocious Haptoglobin (Hp) expression (switch-on status). We investigated the relationships between foetal Hp expression at birth with newborn and childhood outcomes. METHODS We evaluated cord blood samples from 921 newborns of women at imminent risk for preterm delivery randomised to either placebo (n = 471, birth gestational age (GA) median [min-max]: 31 [24-41] weeks) or magnesium sulphate (n = 450, GA 31 [24-42] weeks]). Primary outcome was infant death by 1 year and/or cerebral palsy (CP) ≥ 2 years of corrected age. Adjusted odd ratios (aOR) for neonatal and childhood outcomes were calculated controlling for GA, birth weight, sex, and magnesium exposure. FINDINGS Primary outcome occurred in 2.8% of offspring. Newborns were classified in three pre-defined categorisation groups by cord blood Hp switch status and IL-6 levels: inflammation-nonexposed (Category 1, n = 432, 47%), inflammation-exposed haptoglobinemic (Category 2, n = 449, 49%), and inflammation-exposed anhaptoglobinemic or hypohaptoglobinemic (Category 3, n = 40, 4%). Newborns, found anhaptoglobinemic or hypohaptoglobinemic (Category 3) had increased OR for intraventricular haemorrhage (IVH) and/or death (aOR: 7.0; 95% CI: 1.4-34.6, p = 0.02) and for CP and/or death (aOR: 6.27; 95% CI: 1.7-23.5, p = 0.006) compared with Category 2. Foetal ability to respond to inflammation by haptoglobinemia resulted in aOR similar to inflammation-nonexposed newborns. Hp1-2 or Hp2-2 phenotypes protected against retinopathy of prematurity (aOR = 0.66; 95% CI 0.48-0.91, p = 0.01). INTERPRETATION Foetal ability to switch-on Hp expression in response to inflammation was associated with reduction of IVH and/or death, and CP and/or death. Foetuses unable to mount such a response had an increased risk of adverse outcomes.Trial Registration: clinicaltrials.gov Identifier: NCT00014989.
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Affiliation(s)
- Catalin S. Buhimschi
- Department of Obstetrics and Gynaecology, The Ohio State University, Columbus, OH, United States of America
- Corresponding author at: Department of Obstetrics & Gynecology, University of Illinois at Chicago, Chicago, IL 60612, United States of America.
| | - Kathleen A. Jablonski
- The George Washington University Biostatistics Center, Washington, DC, United States of America
| | - Dwight J. Rouse
- University of Alabama at Birmingham, Birmingham, AL, United States of America
| | | | - Uma M. Reddy
- the Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, United States of America
| | - Brian M. Mercer
- Case Western Reserve University-MetroHealth Medical Center, Cleveland, OH, United States of America
- University of Tennessee, Memphis, TN, United States of America
| | | | - Ronald J. Wapner
- Thomas Jefferson University, Philadelphia, PA, United States of America
- Drexel University, Philadelphia, PA, United States of America
| | - Yoram Sorokin
- Wayne State University, Detroit, MI, United States of America
| | - John M. Thorp
- University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Susan M. Ramin
- University of Texas Health Science Center at Houston, Houston, TX, United States of America
| | | | | | | | | | - George R. Saade
- University of Texas Medical Branch, Galveston, TX, United States of America
| | - Donald Dudley
- University of Texas at San Antonio, San Antonio, TX, United States of America
| | - Steve N. Caritis
- University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Irina A. Buhimschi
- Centre for Perinatal Research, The Research Institute at Nationwide Children's Hospital, Columbus, OH, United States of America
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13
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Hivert MF, Christophi CA, Jablonski KA, Edelstein SL, Kahn SE, Golden SH, Dagogo-Jack S, Mather KJ, Luchsinger JA, Caballero AE, Barrett-Connor E, Knowler WC, Florez JC, Herman WH. Genetic Ancestry Markers and Difference in A1c Between African American and White in the Diabetes Prevention Program. J Clin Endocrinol Metab 2019; 104:328-336. [PMID: 30358859 PMCID: PMC6300069 DOI: 10.1210/jc.2018-01416] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 10/19/2018] [Indexed: 01/18/2023]
Abstract
PURPOSE HbA1c levels are higher in blacks than non-Hispanic whites (NHWs). We investigated whether genetics could explain this difference in Diabetes Prevention Program (DPP) participants. METHODS We tested (i) genetic variants causing hemoglobinopathies, (ii) a genetic risk score (GRS) based on 60 variants associated with HbA1c from genome-wide association meta-analysis, and (iii) principal component (PC) factors that capture continental ancestry derived from genetic markers distributed across the genome. RESULTS Of 2658 eligible DPP participants, 537 (20%) self-identified as black and 1476 (56%) as NHW. Despite comparable fasting and 2-hour glucose levels, blacks had higher HbA1c (mean ± SD = 6.2 ± 0.6%) compared with NHWs (5.8 ± 0.4%; P < 0.001). In blacks, the genetic variant causing sickle cell trait was associated with higher HbA1c [β (SE) = +0.44 (0.08)%; P = 2.1 × 10-4]. The GRS was associated with HbA1c in both blacks and NHWs. Self-identified blacks were distributed along the first PC axis, as expected in mixed ancestry populations. The first PC explained 60% of the 0.4% difference in HbA1c between blacks and NHWs, whereas the sickle cell variant explained 16% and GRS explained 14%. CONCLUSIONS A large proportion of HbA1c difference between blacks and NHWs was associated with the first PC factor, suggesting that unidentified genetic markers influence HbA1c in blacks in addition to nongenetic factors.
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Affiliation(s)
- Marie-France Hivert
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, Massachusetts
- Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | | | | | - Sharon L Edelstein
- The Biostatistics Center, George Washington University, Rockville, Maryland
| | - Steven E Kahn
- Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, VA Puget Sound Health Care System and University of Washington, Seattle, Washington
| | - Sherita Hill Golden
- Department of Medicine, Division of Endocrinology, Diabetes, and Metabolism and Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland
| | - Samuel Dagogo-Jack
- Department of Medicine, Division of Endocrinology, Diabetes & Metabolism, The University of Tennessee Health Science Center, Memphis, Tennessee
| | - Kieren J Mather
- Indiana University School of Medicine, Indianapolis, Indiana
| | - José A Luchsinger
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, New York
| | | | | | - William C Knowler
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona
| | - Jose C Florez
- Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, Massachusetts
| | - William H Herman
- Departments of Internal Medicine and Epidemiology, University of Michigan, Ann Arbor, Michigan
- Correspondence and Reprint Requests: William H. Herman, MD, MPH, c/o Diabetes Prevention Program Coordinating Center, George Washington University Biostatistics Center, 6110 Executive Boulevard, Suite 750, Rockville, Maryland 20852. E-mail:
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14
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Tita AT, Jablonski KA, Bailit JL, Grobman WA, Wapner RJ, Reddy UM, Varner MW, Thorp JM, Leveno KJ, Caritis SN, Iams JD, Saade G, Sorokin Y, Rouse DJ, Blackwell SC, Tolosa JE, Wallace M, Northen A, Grant J, Colquitt C, Mallett G, Ramos-Brinson M, Roy A, Stein L, Campbell P, Collins C, Jackson N, Dinsmoor M, Senka J, Paychek K, Peaceman A, Talucci M, Zylfijaj M, Reid Z, Leed R, Benson J, Forester S, Kitto C, Davis S, Falk M, Perez C, Hill K, Sowles A, Postma J, Alexander S, Andersen G, Scott V, Morby V, Jolley K, Miller J, Berg B, Dorman K, Mitchell J, Kaluta E, Clark K, Spicer K, Timlin S, Wilson K, Moseley L, Santillan M, Price J, Buentipo K, Bludau V, Thomas T, Fay L, Melton C, Kingsbery J, Benezue R, Simhan H, Bickus M, Fischer D, Kamon T, DeAngelis D, Mercer B, Milluzzi C, Dalton W, Dotson T, McDonald P, Brezine C, McGrail A, Latimer C, Guzzo L, Johnson F, Gerwig L, Fyffe S, Loux D, Frantz S, Cline D, Wylie S, Shubert P, Moss J, Salazar A, Acosta A, Hankins G, Hauff N, Palmer L, Lockhart P, Driscoll D, Wynn L, Sudz C, Dengate D, Girard C, Field S, Breault P, Smith F, Annunziata N, Allard D, Silva J, Gamage M, Hunt J, Tillinghast J, Corcoran N, Jimenez M, Ortiz F, Givens P, Rech B, Moran C, Hutchinson M, Spears Z, Carreno C, Heaps B, Zamora G, Seguin J, Rincon M, Snyder J, Farrar C, Lairson E, Bonino C, Smith W, Beach K, Van Dyke S, Butcher S, Thom E, Zhao Y, McGee P, Momirova V, Palugod R, Reamer B, Larsen M, Spong C, Tolivaisa S, VanDorsten J. Neonatal outcomes of elective early-term births after demonstrated fetal lung maturity. Am J Obstet Gynecol 2018; 219:296.e1-296.e8. [PMID: 29800541 DOI: 10.1016/j.ajog.2018.05.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Revised: 11/09/2016] [Accepted: 05/14/2018] [Indexed: 10/16/2022]
Abstract
BACKGROUND Studies of early-term birth after demonstrated fetal lung maturity show that respiratory and other outcomes are worse with early-term birth (370-386 weeks) even after demonstrated fetal lung maturity when compared with full-term birth (390-406 weeks). However, these studies included medically indicated births and are therefore potentially limited by confounding by the indication for delivery. Thus, the increase in adverse outcomes might be due to the indication for early-term birth rather than the early-term birth itself. OBJECTIVE We examined the prevalence and risks of adverse neonatal outcomes associated with early-term birth after confirmed fetal lung maturity as compared with full-term birth in the absence of indications for early delivery. STUDY DESIGN This is a secondary analysis of an observational study of births to 115,502 women in 25 hospitals in the United States from 2008 through 2011. Singleton nonanomalous births at 37-40 weeks with no identifiable indication for delivery were included; early-term births after positive fetal lung maturity testing were compared with full-term births. The primary outcome was a composite of death, ventilator for ≥2 days, continuous positive airway pressure, proven sepsis, pneumonia or meningitis, treated hypoglycemia, hyperbilirubinemia (phototherapy), and 5-minute Apgar <7. Logistic regression and propensity score matching (both 1:1 and 1:2) were used. RESULTS In all, 48,137 births met inclusion criteria; the prevalence of fetal lung maturity testing in the absence of medical or obstetric indications for early delivery was 0.52% (n = 249). There were 180 (0.37%) early-term births after confirmed pulmonary maturity and 47,957 full-term births. Women in the former group were more likely to be non-Hispanic white, smoke, have received antenatal steroids, have induction, and have a cesarean. Risks of the composite (16.1% vs 5.4%; adjusted odds ratio, 3.2; 95% confidence interval, 2.1-4.8 from logistic regression) were more frequent with elective early-term birth. Propensity scores matching confirmed the increased primary composite in elective early-term births: adjusted odds ratios, 4.3 (95% confidence interval, 1.8-10.5) for 1:1 and 3.5 (95% confidence interval, 1.8-6.5) for 1:2 matching. Among components of the primary outcome, CPAP use and hyperbilirubinemia requiring phototherapy were significantly increased. Transient tachypnea of the newborn, neonatal intensive care unit admission, and prolonged neonatal intensive care unit stay (>2 days) were also increased with early-term birth. CONCLUSION Even with confirmed pulmonary maturity, early-term birth in the absence of medical or obstetric indications is associated with worse neonatal respiratory and hepatic outcomes compared with full-term birth, suggesting relative immaturity of these organ systems in early-term births.
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15
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Flannick J, Fuchsberger C, Mahajan A, Teslovich TM, Agarwala V, Gaulton KJ, Caulkins L, Koesterer R, Ma C, Moutsianas L, McCarthy DJ, Rivas MA, Perry JRB, Sim X, Blackwell TW, Robertson NR, Rayner NW, Cingolani P, Locke AE, Tajes JF, Highland HM, Dupuis J, Chines PS, Lindgren CM, Hartl C, Jackson AU, Chen H, Huyghe JR, van de Bunt M, Pearson RD, Kumar A, Müller-Nurasyid M, Grarup N, Stringham HM, Gamazon ER, Lee J, Chen Y, Scott RA, Below JE, Chen P, Huang J, Go MJ, Stitzel ML, Pasko D, Parker SCJ, Varga TV, Green T, Beer NL, Day-Williams AG, Ferreira T, Fingerlin T, Horikoshi M, Hu C, Huh I, Ikram MK, Kim BJ, Kim Y, Kim YJ, Kwon MS, Lee J, Lee S, Lin KH, Maxwell TJ, Nagai Y, Wang X, Welch RP, Yoon J, Zhang W, Barzilai N, Voight BF, Han BG, Jenkinson CP, Kuulasmaa T, Kuusisto J, Manning A, Ng MCY, Palmer ND, Balkau B, Stančáková A, Abboud HE, Boeing H, Giedraitis V, Prabhakaran D, Gottesman O, Scott J, Carey J, Kwan P, Grant G, Smith JD, Neale BM, Purcell S, Butterworth AS, Howson JMM, Lee HM, Lu Y, Kwak SH, Zhao W, Danesh J, Lam VKL, Park KS, Saleheen D, So WY, Tam CHT, Afzal U, Aguilar D, Arya R, Aung T, Chan E, Navarro C, Cheng CY, Palli D, Correa A, Curran JE, Rybin D, Farook VS, Fowler SP, Freedman BI, Griswold M, Hale DE, Hicks PJ, Khor CC, Kumar S, Lehne B, Thuillier D, Lim WY, Liu J, Loh M, Musani SK, Puppala S, Scott WR, Yengo L, Tan ST, Taylor HA, Thameem F, Wilson G, Wong TY, Njølstad PR, Levy JC, Mangino M, Bonnycastle LL, Schwarzmayr T, Fadista J, Surdulescu GL, Herder C, Groves CJ, Wieland T, Bork-Jensen J, Brandslund I, Christensen C, Koistinen HA, Doney AS.F, Kinnunen L, Esko T, Farmer AJ, Hakaste L, Hodgkiss D, Kravic J, Lyssenko V, Hollensted M, Jørgensen ME, Jørgensen T, Ladenvall C, Justesen JM, Käräjämäki A, Kriebel J, Rathmann W, Lannfelt L, Lauritzen T, Narisu N, Linneberg A, Melander O, Milani L, Neville M, Orho-Melander M, Qi L, Qi Q, Roden M, Rolandsson O, Swift A, Rosengren AH, Stirrups K, Wood AR, Mihailov E, Blancher C, Carneiro MO, Maguire J, Poplin R, Shakir K, Fennell T, DePristo M, de Angelis MH, Deloukas P, Gjesing AP, Jun G, Nilsson P, Murphy J, Onofrio R, Thorand B, Hansen T, Meisinger C, Hu FB, Isomaa B, Karpe F, Liang L, Peters A, Huth C, O'Rahilly SP, Palmer CNA, Pedersen O, Rauramaa R, Tuomilehto J, Salomaa V, Watanabe RM, Syvänen AC, Bergman RN, Bharadwaj D, Bottinger EP, Cho YS, Chandak GR, Chan JCN, Chia KS, Daly MJ, Ebrahim SB, Langenberg C, Elliott P, Jablonski KA, Lehman DM, Jia W, Ma RCW, Pollin TI, Sandhu M, Tandon N, Froguel P, Barroso I, Teo YY, Zeggini E, Loos RJF, Small KS, Ried JS, DeFronzo RA, Grallert H, Glaser B, Metspalu A, Wareham NJ, Walker M, Banks E, Gieger C, Ingelsson E, Im HK, Illig T, Franks PW, Buck G, Trakalo J, Buck D, Prokopenko I, Mägi R, Lind L, Farjoun Y, Owen KR, Gloyn AL, Strauch K, Tuomi T, Kooner JS, Lee JY, Park T, Donnelly P, Morris AD, Hattersley AT, Bowden DW, Collins FS, Atzmon G, Chambers JC, Spector TD, Laakso M, Strom TM, Bell GI, Blangero J, Duggirala R, Tai ES, McVean G, Hanis CL, Wilson JG, Seielstad M, Frayling TM, Meigs JB, Cox NJ, Sladek R, Lander ES, Gabriel S, Mohlke KL, Meitinger T, Groop L, Abecasis G, Scott LJ, Morris AP, Kang HM, Altshuler D, Burtt NP, Florez JC, Boehnke M, McCarthy MI. Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls. Sci Data 2018; 5:180002. [PMID: 29360107 PMCID: PMC5779067 DOI: 10.1038/sdata.2018.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
This corrects the article DOI: 10.1038/sdata.2017.179.
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16
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Jason F, Fuchsberger C, Mahajan A, Teslovich TM, Agarwala V, Gaulton KJ, Caulkins L, Koesterer R, Ma C, Moutsianas L, McCarthy DJ, Rivas MA, Perry JRB, Sim X, Blackwell TW, Robertson NR, Rayner NW, Cingolani P, Locke AE, Tajes JF, Highland HM, Dupuis J, Chines PS, Lindgren CM, Hartl C, Jackson AU, Chen H, Huyghe JR, van de Bunt M, Pearson RD, Kumar A, Müller-Nurasyid M, Grarup N, Stringham HM, Gamazon ER, Lee J, Chen Y, Scott RA, Below JE, Chen P, Huang J, Go MJ, Stitzel ML, Pasko D, Parker SCJ, Varga TV, Green T, Beer NL, Day-Williams AG, Ferreira T, Fingerlin T, Horikoshi M, Hu C, Huh I, Ikram MK, Kim BJ, Kim Y, Kim YJ, Kwon MS, Lee J, Lee S, Lin KH, Maxwell TJ, Nagai Y, Wang X, Welch RP, Yoon J, Zhang W, Barzilai N, Voight BF, Han BG, Jenkinson CP, Kuulasmaa T, Kuusisto J, Manning A, Ng MCY, Palmer ND, Balkau B, Stančáková A, Abboud HE, Boeing H, Giedraitis V, Prabhakaran D, Gottesman O, Scott J, Carey J, Kwan P, Grant G, Smith JD, Neale BM, Purcell S, Butterworth AS, Howson JMM, Lee HM, Lu Y, Kwak SH, Zhao W, Danesh J, Lam VKL, Park KS, Saleheen D, So WY, Tam CHT, Afzal U, Aguilar D, Arya R, Aung T, Chan E, Navarro C, Cheng CY, Palli D, Correa A, Curran JE, Rybin D, Farook VS, Fowler SP, Freedman BI, Griswold M, Hale DE, Hicks PJ, Khor CC, Kumar S, Lehne B, Thuillier D, Lim WY, Liu J, Loh M, Musani SK, Puppala S, Scott WR, Yengo L, Tan ST, Taylor HA, Thameem F, Wilson G, Wong TY, Njølstad PR, Levy JC, Mangino M, Bonnycastle LL, Schwarzmayr T, Fadista J, Surdulescu GL, Herder C, Groves CJ, Wieland T, Bork-Jensen J, Brandslund I, Christensen C, Koistinen HA, Doney ASF, Kinnunen L, Esko T, Farmer AJ, Hakaste L, Hodgkiss D, Kravic J, Lyssenko V, Hollensted M, Jørgensen ME, Jørgensen T, Ladenvall C, Justesen JM, Käräjämäki A, Kriebel J, Rathmann W, Lannfelt L, Lauritzen T, Narisu N, Linneberg A, Melander O, Milani L, Neville M, Orho-Melander M, Qi L, Qi Q, Roden M, Rolandsson O, Swift A, Rosengren AH, Stirrups K, Wood AR, Mihailov E, Blancher C, Carneiro MO, Maguire J, Poplin R, Shakir K, Fennell T, DePristo M, de Angelis MH, Deloukas P, Gjesing AP, Jun G, Nilsson P, Murphy J, Onofrio R, Thorand B, Hansen T, Meisinger C, Hu FB, Isomaa B, Karpe F, Liang L, Peters A, Huth C, O'Rahilly SP, Palmer CNA, Pedersen O, Rauramaa R, Tuomilehto J, Salomaa V, Watanabe RM, Syvänen AC, Bergman RN, Bharadwaj D, Bottinger EP, Cho YS, Chandak GR, Chan JCN, Chia KS, Daly MJ, Ebrahim SB, Langenberg C, Elliott P, Jablonski KA, Lehman DM, Jia W, Ma RCW, Pollin TI, Sandhu M, Tandon N, Froguel P, Barroso I, Teo YY, Zeggini E, Loos RJF, Small KS, Ried JS, DeFronzo RA, Grallert H, Glaser B, Metspalu A, Wareham NJ, Walker M, Banks E, Gieger C, Ingelsson E, Im HK, Illig T, Franks PW, Buck G, Trakalo J, Buck D, Prokopenko I, Mägi R, Lind L, Farjoun Y, Owen KR, Gloyn AL, Strauch K, Tuomi T, Kooner JS, Lee JY, Park T, Donnelly P, Morris AD, Hattersley AT, Bowden DW, Collins FS, Atzmon G, Chambers JC, Spector TD, Laakso M, Strom TM, Bell GI, Blangero J, Duggirala R, Tai ES, McVean G, Hanis CL, Wilson JG, Seielstad M, Frayling TM, Meigs JB, Cox NJ, Sladek R, Lander ES, Gabriel S, Mohlke KL, Meitinger T, Groop L, Abecasis G, Scott LJ, Morris AP, Kang HM, Altshuler D, Burtt NP, Florez JC, Boehnke M, McCarthy MI. Sequence data and association statistics from 12,940 type 2 diabetes cases and controls. Sci Data 2017; 4:170179. [PMID: 29257133 PMCID: PMC5735917 DOI: 10.1038/sdata.2017.179] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Accepted: 11/02/2017] [Indexed: 02/08/2023] Open
Abstract
To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.
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Affiliation(s)
- Flannick Jason
- Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts, USA,Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA,
J.F. ()
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Anubha Mahajan
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Tanya M. Teslovich
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Vineeta Agarwala
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Kyle J. Gaulton
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Lizz Caulkins
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Ryan Koesterer
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Clement Ma
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Loukas Moutsianas
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Davis J. McCarthy
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Department of Statistics, University of Oxford, Oxford, UK
| | - Manuel A. Rivas
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - John R. B. Perry
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK,MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK,Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Xueling Sim
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Thomas W. Blackwell
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Neil R. Robertson
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - N William Rayner
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK,Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Pablo Cingolani
- School of Computer Science, McGill University, Montreal, Quebec, Canada,McGill University and Génome Québec Innovation Centre, Montreal, Quebec, Canada
| | - Adam E. Locke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Juan Fernandez Tajes
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Heather M. Highland
- Human Genetics Center, The University of Texas Graduate School of Biomedical Sciences at Houston, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Josee Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA,National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, USA
| | - Peter S. Chines
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Cecilia M. Lindgren
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA,Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Christopher Hartl
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Anne U. Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Han Chen
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA,Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Jeroen R. Huyghe
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Martijn van de Bunt
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Richard D. Pearson
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Ashish Kumar
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Chronic Disease Epidemiology, Swiss Tropical and Public Health Institute, University of Basel, Basel, Switzerland
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universität, Munich, Germany,Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Germany,DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany
| | - Niels Grarup
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Heather M. Stringham
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Eric R. Gamazon
- Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, Illinois, USA
| | - Jaehoon Lee
- Department of Statistics, Seoul National University, Seoul, Republic of Korea
| | - Yuhui Chen
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Robert A. Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Jennifer E. Below
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Peng Chen
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore
| | - Jinyan Huang
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Min Jin Go
- Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Republic of Korea
| | - Michael L. Stitzel
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
| | - Dorota Pasko
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Stephen C. J. Parker
- Departments of Computational Medicine & Bioinformatics and Human Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Tibor V. Varga
- Department of Clinical Sciences, Lund University Diabetes Centre, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden
| | - Todd Green
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Nicola L. Beer
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Aaron G. Day-Williams
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Teresa Ferreira
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Tasha Fingerlin
- Department of Epidemiology, Colorado School of Public Health, University of Colorado, Aurora, Colorado, USA
| | - Momoko Horikoshi
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Cheng Hu
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Iksoo Huh
- Department of Statistics, Seoul National University, Seoul, Republic of Korea
| | - Mohammad Kamran Ikram
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore,The Eye Academic Clinical Programme, Duke-NUS Graduate Medical School, Singapore, Singapore
| | - Bong-Jo Kim
- Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Republic of Korea
| | - Yongkang Kim
- Department of Statistics, Seoul National University, Seoul, Republic of Korea
| | - Young Jin Kim
- Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Republic of Korea
| | - Min-Seok Kwon
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Juyoung Lee
- Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Republic of Korea
| | - Selyeong Lee
- Department of Statistics, Seoul National University, Seoul, Republic of Korea
| | - Keng-Han Lin
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Taylor J. Maxwell
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Yoshihiko Nagai
- McGill University and Génome Québec Innovation Centre, Montreal, Quebec, Canada,Department of Human Genetics, McGill University, Montreal, Quebec, Canada,Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | - Xu Wang
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore
| | - Ryan P. Welch
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Joon Yoon
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK,Department of Cardiology, Ealing Hospital NHS Trust, Southall, Middlesex, UK
| | - Nir Barzilai
- Departments of Medicine and Genetics, Albert Einstein College of Medicine, New York, USA
| | - Benjamin F. Voight
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania—Perelman School of Medicine, Philadelphia, Pennsylvania, USA,Department of Genetics, University of Pennsylvania—Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Bok-Ghee Han
- Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Republic of Korea
| | - Christopher P. Jenkinson
- Department of Medicine, University of Texas Health Science Center, San Antonio, Texas, USA,Research, South Texas Veterans Health Care System, San Antonio, Texas, USA
| | - Teemu Kuulasmaa
- Faculty of Health Sciences, Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Johanna Kuusisto
- Faculty of Health Sciences, Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland,Kuopio University Hospital, Kuopio, Finland
| | - Alisa Manning
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Maggie C. Y. Ng
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA,Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Nicholette D. Palmer
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA,Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA,Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Beverley Balkau
- Centre for Research in Epidemiology and Population Health, Inserm U1018, Villejuif, France
| | - Alena Stančáková
- Faculty of Health Sciences, Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Hanna E. Abboud
- Department of Medicine, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Heiner Boeing
- German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala, Sweden
| | | | - Omri Gottesman
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, USA
| | - James Scott
- National Heart and Lung Institute, Cardiovascular Sciences, Hammersmith Campus, Imperial College London, London, UK
| | - Jason Carey
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Phoenix Kwan
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - George Grant
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Joshua D. Smith
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington, USA
| | - Benjamin M. Neale
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA,Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Shaun Purcell
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA,Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA,Department of Psychiatry, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Adam S. Butterworth
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Joanna M. M. Howson
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Heung Man Lee
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Yingchang Lu
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, USA
| | - Soo-Heon Kwak
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Wei Zhao
- Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John Danesh
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK,Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK,NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Vincent K. L. Lam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Kyong Soo Park
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Danish Saleheen
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Wing Yee So
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Claudia H. T. Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - Uzma Afzal
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - David Aguilar
- Cardiovascular Division, Baylor College of Medicine, Houston, Texas, USA
| | - Rector Arya
- Department of Pediatrics, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore,The Eye Academic Clinical Programme, Duke-NUS Graduate Medical School, Singapore, Singapore
| | - Edmund Chan
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore
| | - Carmen Navarro
- Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain,CIBER Epidemiología y Salud Pública (CIBERESP), Spain,Unit of Preventive Medicine and Public Health, School of Medicine, University of Murcia, Spain
| | - Ching-Yu Cheng
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore,Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore,The Eye Academic Clinical Programme, Duke-NUS Graduate Medical School, Singapore, Singapore
| | - Domenico Palli
- Cancer Research and Prevention Institute (ISPO), Florence, Italy
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Joanne E. Curran
- South Texas Diabetes and Obesity Institute, Regional Academic Health Center, University of Texas Health Science Center at San Antonio/University of Texas Rio Grande Valley, Brownsville, Texas, USA
| | - Dennis Rybin
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Vidya S. Farook
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, USA
| | - Sharon P. Fowler
- Department of Medicine, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Barry I. Freedman
- Department of Internal Medicine, Section on Nephrology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Michael Griswold
- Center of Biostatistics and Bioinformatics, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Daniel Esten Hale
- Department of Pediatrics, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Pamela J. Hicks
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA,Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA,Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Chiea-Chuen Khor
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore,Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore,Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore,Division of Human Genetics, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Satish Kumar
- South Texas Diabetes and Obesity Institute, Regional Academic Health Center, University of Texas Health Science Center at San Antonio/University of Texas Rio Grande Valley, Brownsville, Texas, USA
| | - Benjamin Lehne
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | | | - Wei Yen Lim
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore
| | - Jianjun Liu
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore,Division of Human Genetics, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Marie Loh
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK,Institute of Health Sciences, University of Oulu, Oulu, Finland,Translational Laboratory in Genetic Medicine (TLGM), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Solomon K. Musani
- Jackson Heart Study, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Sobha Puppala
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, USA
| | - William R. Scott
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - Loïc Yengo
- CNRS-UMR8199, Lille University, Lille Pasteur Institute, Lille, France
| | - Sian-Tsung Tan
- Department of Cardiology, Ealing Hospital NHS Trust, Southall, Middlesex, UK,National Heart and Lung Institute, Cardiovascular Sciences, Hammersmith Campus, Imperial College London, London, UK
| | - Herman A. Taylor
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Farook Thameem
- Department of Medicine, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Gregory Wilson
- College of Public Services, Jackson State University, Jackson, Mississippi, USA
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore,Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore,The Eye Academic Clinical Programme, Duke-NUS Graduate Medical School, Singapore, Singapore
| | - Pål Rasmus Njølstad
- KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway,Department of Pediatrics, Haukeland University Hospital, Bergen, Norway
| | - Jonathan C. Levy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK,NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London, UK
| | - Lori L. Bonnycastle
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Thomas Schwarzmayr
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - João Fadista
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
| | | | - Christian Herder
- Institute of Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany,German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Christopher J. Groves
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Thomas Wieland
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jette Bork-Jensen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ivan Brandslund
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark,Department of Clinical Biochemistry, Vejle Hospital, Vejle, Denmark
| | - Cramer Christensen
- Department of Internal Medicine and Endocrinology, Vejle Hospital, Vejle, Denmark
| | - Heikki A. Koistinen
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland,Abdominal Center: Endocrinology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland,Minerva Foundation Institute for Medical Research, Helsinki, Finland,Department of Medicine, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland
| | - Alex S. F. Doney
- Division of Cardiovascular and Diabetes Medicine, Medical Research Institute, Ninewells Hospital and Medical School, Dundee, UK
| | - Leena Kinnunen
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
| | - Tõnu Esko
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA,Estonian Genome Center, University of Tartu, Tartu, Estonia,Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA,Division of Endocrinology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Andrew J. Farmer
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Liisa Hakaste
- Abdominal Center: Endocrinology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland,Folkhälsan Research Centre, Helsinki, Finland,Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland
| | - Dylan Hodgkiss
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Jasmina Kravic
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
| | - Valeri Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
| | - Mette Hollensted
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Torben Jørgensen
- Research Centre for Prevention and Health, Capital Region of Denmark, Glostrup, Denmark,Department of Public Health, Institute of Health Sciences, University of Copenhagen, Copenhagen, Denmark,Faculty of Medicine, Aalborg University, Aalborg, Denmark
| | - Claes Ladenvall
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
| | - Johanne Marie Justesen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Annemari Käräjämäki
- Department of Primary Health Care, Vaasa Central Hospital, Vaasa, Finland,Diabetes Center, Vaasa Health Care Center, Vaasa, Finland
| | - Jennifer Kriebel
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Wolfgang Rathmann
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany
| | - Lars Lannfelt
- Department of Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala, Sweden
| | - Torsten Lauritzen
- Department of Public Health, Section of General Practice, Aarhus University, Aarhus, Denmark
| | - Narisu Narisu
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Allan Linneberg
- Research Centre for Prevention and Health, Capital Region of Denmark, Glostrup, Denmark,Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Olle Melander
- Department of Clinical Sciences, Hypertension and Cardiovascular Disease, Lund University, Malmö, Sweden
| | - Lili Milani
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Matt Neville
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK,Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK
| | - Marju Orho-Melander
- Department of Clinical Sciences, Diabetes and Cardiovascular Disease, Genetic Epidemiology, Lund University, Malmö, Sweden
| | - Lu Qi
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA,Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Qibin Qi
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA,Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, USA
| | - Michael Roden
- Institute of Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University, Düsseldorf, Germany,German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Division of Endocrinology and Diabetology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Olov Rolandsson
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Amy Swift
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Anders H. Rosengren
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
| | - Kathleen Stirrups
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Andrew R. Wood
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | | | - Christine Blancher
- High Throughput Genomics, Oxford Genomics Centre, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Mauricio O. Carneiro
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Jared Maguire
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Ryan Poplin
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Khalid Shakir
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Timothy Fennell
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Mark DePristo
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Martin Hrabé de Angelis
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Center of Life and Food Sciences Weihenstephan, Technische Universität München, Freising-Weihenstephan, Germany
| | - Panos Deloukas
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK,William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK,Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia
| | - Anette P. Gjesing
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Goo Jun
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA,Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Peter Nilsson
- Department of Clinical Sciences, Medicine, Lund University, Malmö, Sweden
| | - Jacquelyn Murphy
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Robert Onofrio
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Barbara Thorand
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Torben Hansen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark,Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Christa Meisinger
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Frank B. Hu
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA,Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Bo Isomaa
- Folkhälsan Research Centre, Helsinki, Finland,Department of Social Services and Health Care, Jakobstad, Finland
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK,Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK
| | - Liming Liang
- Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA,Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Annette Peters
- DZHK (German Centre for Cardiovascular Research), partner site Munich Heart Alliance, Munich, Germany,German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Cornelia Huth
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Stephen P O'Rahilly
- Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Colin N. A. Palmer
- Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, Medical Research Institute, Ninewells Hospital and Medical School, Dundee, UK
| | - Oluf Pedersen
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Rainer Rauramaa
- Foundation for Research in Health, Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
| | - Jaakko Tuomilehto
- Center for Vascular Prevention, Danube University Krems, Krems, Austria,Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia,Dasman Diabetes Institute, Dasman, Kuwait,National Institute for Health and Welfare, Helsinki, Finland
| | - Veikko Salomaa
- National Institute for Health and Welfare, Helsinki, Finland
| | - Richard M. Watanabe
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA,Department of Physiology & Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA,Diabetes and Obesity Research Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Ann-Christine Syvänen
- Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Richard N. Bergman
- Cedars-Sinai Diabetes and Obesity Research Institute, Los Angeles, California, USA
| | - Dwaipayan Bharadwaj
- Functional Genomics Unit, CSIR-Institute of Genomics & Integrative Biology (CSIR-IGIB), New Delhi, India
| | - Erwin P. Bottinger
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, USA
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon, Republic of Korea
| | - Giriraj R. Chandak
- CSIR-Centre for Cellular and Molecular Biology, Hyderabad, Telangana, India
| | - Juliana CN Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China,Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China,Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Kee Seng Chia
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore
| | - Mark J. Daly
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK,MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
| | - Kathleen A. Jablonski
- The Biostatistics Center, The George Washington University, Rockville, Maryland, USA
| | - Donna M. Lehman
- Department of Medicine, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Weiping Jia
- Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Ronald C. W. Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China,Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China,Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Toni I. Pollin
- Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, and Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Manjinder Sandhu
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK,Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Nikhil Tandon
- Department of Endocrinology and Metabolism, All India Institute of Medical Sciences, New Delhi, India
| | - Philippe Froguel
- CNRS-UMR8199, Lille University, Lille Pasteur Institute, Lille, France,Department of Genomics of Common Disease, School of Public Health, Imperial College London, London, UK
| | - Inês Barroso
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK,Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Yik Ying Teo
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore,Life Sciences Institute, National University of Singapore, Singapore, Singapore,Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
| | - Eleftheria Zeggini
- Department of Human Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, UK
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, The Icahn School of Medicine at Mount Sinai, New York, USA
| | - Kerrin S. Small
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Janina S. Ried
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ralph A. DeFronzo
- Department of Medicine, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Harald Grallert
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Benjamin Glaser
- Endocrinology and Metabolism Service, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | | | - Nicholas J. Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Mark Walker
- The Medical School, Institute of Cellular Medicine, Newcastle University, Newcastle, UK
| | - Eric Banks
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Christian Gieger
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Erik Ingelsson
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Hae Kyung Im
- Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, Illinois, USA
| | - Thomas Illig
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Hannover Unified Biobank, Hannover Medical School, Hanover, Germany,Department of Human Genetics, Hannover Medical School, Hanover, Germany
| | - Paul W. Franks
- Department of Clinical Sciences, Lund University Diabetes Centre, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden,Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, USA,Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Gemma Buck
- High Throughput Genomics, Oxford Genomics Centre, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Joseph Trakalo
- High Throughput Genomics, Oxford Genomics Centre, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - David Buck
- High Throughput Genomics, Oxford Genomics Centre, Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Inga Prokopenko
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK,Department of Genomics of Common Disease, School of Public Health, Imperial College London, London, UK
| | - Reedik Mägi
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Yossi Farjoun
- Data Sciences and Data Engineering, Broad Institute, Cambridge, Massachusetts, USA
| | - Katharine R. Owen
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK,Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK
| | - Anna L. Gloyn
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK,Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Chair of Genetic Epidemiology, IBE, Faculty of Medicine, LMU Munich, Germany
| | - Tiinamaija Tuomi
- Abdominal Center: Endocrinology, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland,Folkhälsan Research Centre, Helsinki, Finland,Research Programs Unit, Diabetes and Obesity, University of Helsinki, Helsinki, Finland,Finnish Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
| | - Jaspal Singh Kooner
- Department of Cardiology, Ealing Hospital NHS Trust, Southall, Middlesex, UK,National Heart and Lung Institute, Cardiovascular Sciences, Hammersmith Campus, Imperial College London, London, UK,Imperial College Healthcare NHS Trust, Imperial College London, London, UK
| | - Jong-Young Lee
- Center for Genome Science, Korea National Institute of Health, Chungcheongbuk-do, Republic of Korea
| | - Taesung Park
- Department of Statistics, Seoul National University, Seoul, Republic of Korea,Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Peter Donnelly
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Department of Statistics, University of Oxford, Oxford, UK
| | - Andrew D. Morris
- Clinical Research Centre, Centre for Molecular Medicine, Ninewells Hospital and Medical School, Dundee, UK,The Usher Institute to the Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | | | - Donald W. Bowden
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA,Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA,Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Francis S. Collins
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Gil Atzmon
- Departments of Medicine and Genetics, Albert Einstein College of Medicine, New York, USA,Department of Natural Science, University of Haifa, Haifa, Israel
| | - John C. Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK,Department of Cardiology, Ealing Hospital NHS Trust, Southall, Middlesex, UK,Imperial College Healthcare NHS Trust, Imperial College London, London, UK
| | - Timothy D. Spector
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Markku Laakso
- Faculty of Health Sciences, Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland,Kuopio University Hospital, Kuopio, Finland
| | - Tim M. Strom
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Graeme I. Bell
- Departments of Medicine and Human Genetics, The University of Chicago, Chicago, Illinois, USA
| | - John Blangero
- South Texas Diabetes and Obesity Institute, Regional Academic Health Center, University of Texas Health Science Center at San Antonio/University of Texas Rio Grande Valley, Brownsville, Texas, USA
| | | | - E. Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore, Singapore,Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore,Cardiovascular & Metabolic Disorders Program, Duke-NUS Medical School Singapore, Singapore, Singapore
| | - Gilean McVean
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Craig L. Hanis
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - James G. Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Mark Seielstad
- Department of Laboratory Medicine & Institute for Human Genetics, University of California, San Francisco, San Francisco, California, USA,Blood Systems Research Institute, San Francisco, California, USA
| | - Timothy M. Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - James B. Meigs
- General Medicine Division, Massachusetts General Hospital and Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Nancy J. Cox
- Department of Medicine, Section of Genetic Medicine, The University of Chicago, Chicago, Illinois, USA
| | - Rob Sladek
- McGill University and Génome Québec Innovation Centre, Montreal, Quebec, Canada,Department of Human Genetics, McGill University, Montreal, Quebec, Canada,Division of Endocrinology and Metabolism, Department of Medicine, McGill University, Montreal, Quebec, Canada
| | - Eric S. Lander
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Stacey Gabriel
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Karen L. Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany,Institute of Human Genetics, Technische Universität München, Munich, Germany
| | - Leif Groop
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden,Finnish Institute for Molecular Medicine, University of Helsinki, Helsinki, Finland
| | - Goncalo Abecasis
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Laura J. Scott
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Andrew P. Morris
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Estonian Genome Center, University of Tartu, Tartu, Estonia,Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Hyun Min Kang
- Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - David Altshuler
- Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts, USA,Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA,Department of Genetics, Harvard Medical School, Boston, Massachusetts, USA,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA,Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA,Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Noël P. Burtt
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA
| | - Jose C. Florez
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA,Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA,Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Mark I. McCarthy
- Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK,Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, Oxford, UK
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17
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Mercader JM, Liao RG, Bell AD, Dymek Z, Estrada K, Tukiainen T, Huerta-Chagoya A, Moreno-Macías H, Jablonski KA, Hanson RL, Walford GA, Moran I, Chen L, Agarwala V, Ordoñez-Sánchez ML, Rodríguez-Guillen R, Rodríguez-Torres M, Segura-Kato Y, García-Ortiz H, Centeno-Cruz F, Barajas-Olmos F, Caulkins L, Puppala S, Fontanillas P, Williams AL, Bonàs-Guarch S, Hartl C, Ripke S, Tooley K, Lane J, Zerrweck C, Martínez-Hernández A, Córdova EJ, Mendoza-Caamal E, Contreras-Cubas C, González-Villalpando ME, Cruz-Bautista I, Muñoz-Hernández L, Gómez-Velasco D, Alvirde U, Henderson BE, Wilkens LR, Le Marchand L, Arellano-Campos O, Riba L, Harden M, Gabriel S, Abboud HE, Cortes ML, Revilla-Monsalve C, Islas-Andrade S, Soberon X, Curran JE, Jenkinson CP, DeFronzo RA, Lehman DM, Hanis CL, Bell GI, Boehnke M, Blangero J, Duggirala R, Saxena R, MacArthur D, Ferrer J, McCarroll SA, Torrents D, Knowler WC, Baier LJ, Burtt N, González-Villalpando C, Haiman CA, Aguilar-Salinas CA, Tusié-Luna T, Flannick J, Jacobs SBR, Orozco L, Altshuler D, Florez JC. A Loss-of-Function Splice Acceptor Variant in IGF2 Is Protective for Type 2 Diabetes. Diabetes 2017; 66:2903-2914. [PMID: 28838971 PMCID: PMC5652606 DOI: 10.2337/db17-0187] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Accepted: 08/13/2017] [Indexed: 12/12/2022]
Abstract
Type 2 diabetes (T2D) affects more than 415 million people worldwide, and its costs to the health care system continue to rise. To identify common or rare genetic variation with potential therapeutic implications for T2D, we analyzed and replicated genome-wide protein coding variation in a total of 8,227 individuals with T2D and 12,966 individuals without T2D of Latino descent. We identified a novel genetic variant in the IGF2 gene associated with ∼20% reduced risk for T2D. This variant, which has an allele frequency of 17% in the Mexican population but is rare in Europe, prevents splicing between IGF2 exons 1 and 2. We show in vitro and in human liver and adipose tissue that the variant is associated with a specific, allele-dosage-dependent reduction in the expression of IGF2 isoform 2. In individuals who do not carry the protective allele, expression of IGF2 isoform 2 in adipose is positively correlated with both incidence of T2D and increased plasma glycated hemoglobin in individuals without T2D, providing support that the protective effects are mediated by reductions in IGF2 isoform 2. Broad phenotypic examination of carriers of the protective variant revealed no association with other disease states or impaired reproductive health. These findings suggest that reducing IGF2 isoform 2 expression in relevant tissues has potential as a new therapeutic strategy for T2D, even beyond the Latin American population, with no major adverse effects on health or reproduction.
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Affiliation(s)
- Josep M Mercader
- Broad Metabolism Program and Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Barcelona Supercomputing Center, Joint BSC-CRG-IRB Research Programme in Computational Biology, Barcelona, Spain
| | - Rachel G Liao
- Broad Metabolism Program and Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Avery D Bell
- Department of Genetics, Harvard Medical School, Boston, MA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Zachary Dymek
- Broad Metabolism Program and Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Karol Estrada
- Broad Metabolism Program and Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Taru Tukiainen
- Department of Genetics, Harvard Medical School, Boston, MA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA
| | - Alicia Huerta-Chagoya
- Consejo Nacional de Ciencia y Tecnología (CONACYT), Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Hortensia Moreno-Macías
- Unidad de Biología Molecular y Medicina Genómica, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México/Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
- Universidad Autónoma Metropolitana, Mexico City, Mexico
| | | | - Robert L Hanson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Geoffrey A Walford
- Broad Metabolism Program and Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Ignasi Moran
- Department of Medicine, Imperial College London, London, U.K
| | - Ling Chen
- Broad Metabolism Program and Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
| | - Vineeta Agarwala
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - María Luisa Ordoñez-Sánchez
- Consejo Nacional de Ciencia y Tecnología (CONACYT), Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Rosario Rodríguez-Guillen
- Consejo Nacional de Ciencia y Tecnología (CONACYT), Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Maribel Rodríguez-Torres
- Consejo Nacional de Ciencia y Tecnología (CONACYT), Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Yayoi Segura-Kato
- Consejo Nacional de Ciencia y Tecnología (CONACYT), Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | | | | | | | - Lizz Caulkins
- Broad Metabolism Program and Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Sobha Puppala
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX
| | - Pierre Fontanillas
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Amy L Williams
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY
| | - Sílvia Bonàs-Guarch
- Barcelona Supercomputing Center, Joint BSC-CRG-IRB Research Programme in Computational Biology, Barcelona, Spain
| | - Chris Hartl
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Stephan Ripke
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA
- Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | | | - Katherine Tooley
- Department of Genetics, Harvard Medical School, Boston, MA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Jacqueline Lane
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Carlos Zerrweck
- Clínica Integral de Cirugía para la Obesidad y Enfermedades Metabólicas, Hospital General Tláhuac, Mexico City, Mexico
| | | | | | | | | | - María E González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Pública, Mexico City, Mexico
| | - Ivette Cruz-Bautista
- Departamento de Endocrinología y Metabolismo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Liliana Muñoz-Hernández
- Departamento de Endocrinología y Metabolismo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Donaji Gómez-Velasco
- Departamento de Endocrinología y Metabolismo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Ulises Alvirde
- Departamento de Endocrinología y Metabolismo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Brian E Henderson
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Lynne R Wilkens
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI
| | - Loic Le Marchand
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI
| | - Olimpia Arellano-Campos
- Departamento de Endocrinología y Metabolismo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Laura Riba
- Departamento de Endocrinología y Metabolismo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Maegan Harden
- The Genomics Platform, Broad Institute, Cambridge, MA
| | | | | | | | - Hanna E Abboud
- Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX
| | | | - Cristina Revilla-Monsalve
- Unidad de Investigación Médica en Enfermedades Metabólicas, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Sergio Islas-Andrade
- Unidad de Investigación Médica en Enfermedades Metabólicas, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Xavier Soberon
- Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - Joanne E Curran
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX
| | - Christopher P Jenkinson
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Edinburg, TX
| | - Ralph A DeFronzo
- Division of Diabetes, Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX
| | - Donna M Lehman
- Departments of Medicine and Cellular & Structural Biology, University of Texas Health Science Center at San Antonio, San Antonio, TX
| | - Craig L Hanis
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX
| | - Graeme I Bell
- Department of Medicine, The University of Chicago, Chicago, IL
- Department of Human Genetics, The University of Chicago, Chicago, IL
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI
| | - John Blangero
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX
| | - Ravindranath Duggirala
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Edinburg, TX
| | - Richa Saxena
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Daniel MacArthur
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Jorge Ferrer
- Department of Medicine, Imperial College London, London, U.K
- Genomic Programming of Beta Cells and Diabetes, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
- CIBERDEM, Barcelona, Spain
| | - Steven A McCarroll
- Department of Genetics, Harvard Medical School, Boston, MA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - David Torrents
- Barcelona Supercomputing Center, Joint BSC-CRG-IRB Research Programme in Computational Biology, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
| | - William C Knowler
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Leslie J Baier
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ
| | - Noel Burtt
- Broad Metabolism Program and Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Clicerio González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Pública, Mexico City, Mexico
| | | | - Carlos A Aguilar-Salinas
- Departamento de Endocrinología y Metabolismo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Teresa Tusié-Luna
- Unidad de Biología Molecular y Medicina Genómica, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México/Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Jason Flannick
- Broad Metabolism Program and Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Molecular Biology, Harvard Medical School, Boston, MA
| | - Suzanne B R Jacobs
- Broad Metabolism Program and Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
| | - Lorena Orozco
- Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - David Altshuler
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Genetics, Harvard Medical School, Boston, MA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Molecular Biology, Harvard Medical School, Boston, MA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA
| | - Jose C Florez
- Broad Metabolism Program and Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
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18
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Bustos ML, Caritis SN, Jablonski KA, Reddy UM, Sorokin Y, Manuck T, Varner MW, Wapner RJ, Iams JD, Carpenter MW, Peaceman AM, Mercer BM, Sciscione A, Rouse DJ, Ramin SM. The association among cytochrome P450 3A, progesterone receptor polymorphisms, plasma 17-alpha hydroxyprogesterone caproate concentrations, and spontaneous preterm birth. Am J Obstet Gynecol 2017; 217:369.e1-369.e9. [PMID: 28522317 DOI: 10.1016/j.ajog.2017.05.019] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 05/07/2017] [Indexed: 12/13/2022]
Abstract
BACKGROUND Infants born <37 weeks' gestation are of public health concern since complications associated with preterm birth are the leading cause of mortality in children <5 years of age and a major cause of morbidity and lifelong disability. The administration of 17-alpha hydroxyprogesterone caproate reduces preterm birth by 33% in women with history of spontaneous preterm birth. We demonstrated previously that plasma concentrations of 17-alpha hydroxyprogesterone caproate vary widely among pregnant women and that women with 17-alpha hydroxyprogesterone caproate plasma concentrations in the lowest quartile had spontaneous preterm birth rates of 40% vs rates of 25% in those women with higher concentrations. Thus, plasma concentrations are an important factor in determining drug efficacy but the reason 17-alpha hydroxyprogesterone caproate plasma concentrations vary so much is unclear. Predominantly, 17-alpha hydroxyprogesterone caproate is metabolized by CYP3A4 and CYP3A5 enzymes. OBJECTIVE We sought to: (1) determine the relation between 17-alpha hydroxyprogesterone caproate plasma concentrations and single nucleotide polymorphisms in CYP3A4 and CYP3A5; (2) test the association between progesterone receptor single nucleotide polymorphisms and spontaneous preterm birth; and (3) test whether the association between plasma concentrations of 17-alpha hydroxyprogesterone caproate and spontaneous preterm birth varied by progesterone receptor single nucleotide polymorphisms. STUDY DESIGN In this secondary analysis, we evaluated genetic polymorphism in 268 pregnant women treated with 17-alpha hydroxyprogesterone caproate, who participated in a placebo-controlled trial to evaluate the benefit of omega-3 supplementation in women with history of spontaneous preterm birth. Trough plasma concentrations of 17-alpha hydroxyprogesterone caproate were measured between 25-28 weeks of gestation after a minimum of 5 injections of 17-alpha hydroxyprogesterone caproate. We extracted DNA from maternal blood samples and genotyped the samples using TaqMan (Applied Biosystems, Foster City, CA) single nucleotide polymorphism genotyping assays for the following single nucleotide polymorphisms: CYP3A4*1B, CYP3A4*1G, CYP3A4*22, and CYP3A5*3; and rs578029, rs471767, rs666553, rs503362, and rs500760 for progesteronereceptor. We adjusted for prepregnancy body mass index, race, and treatment group in a multivariable analysis. Differences in the plasma concentrations of 17-alpha hydroxyprogesterone caproate by genotype were evaluated for each CYP single nucleotide polymorphism using general linear models. The association between progesterone receptor single nucleotide polymorphisms and frequency of spontaneous preterm birth was tested using logistic regression. A logistic model also tested interaction between 17-alpha hydroxyprogesterone caproate concentrations with each progesterone receptor single nucleotide polymorphism for the outcome of spontaneous preterm birth. RESULTS The association between CYP single nucleotide polymorphisms *22, *1G, *1B, and *3 and trough plasma concentrations of 17-alpha hydroxyprogesterone caproate was not statistically significant (P = .68, .44, .08, and .44, respectively). In an adjusted logistic regression model, progesterone receptor single nucleotide polymorphisms rs578029, rs471767, rs666553, rs503362, and rs500760 were not associated with the frequency of spontaneous preterm birth (P = .29, .10, .76, .09, and .43, respectively). Low trough plasma concentrations of 17-alpha hydroxyprogesterone caproate were statistically associated with a higher frequency of spontaneous preterm birth (odds ratio, 0.78; 95% confidence ratio, 0.61-0.99; P = .04 for trend across quartiles), however no significant interaction with the progesterone receptor single nucleotide polymorphisms rs578029, rs471767, rs666553, rs503362, and rs500760 was observed (P = .13, .08, .10, .08, and .13, respectively). CONCLUSION The frequency of recurrent spontaneous preterm birth appears to be associated with trough 17-alpha hydroxyprogesterone caproate plasma concentrations. However, the wide variation in trough 17-alpha hydroxyprogesterone caproate plasma concentrations is not attributable to polymorphisms in CYP3A4 and CYP3A5 genes. Progesterone receptor polymorphisms do not predict efficacy of 17-alpha hydroxyprogesterone caproate. The limitations of this secondary analysis include that we had a relative small sample size (n = 268) and race was self-reported by the patients.
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Affiliation(s)
- Martha L Bustos
- Department of Obstetrics and Gynecology at University of Pittsburgh, Pittsburgh, PA
| | - Steve N Caritis
- Department of Obstetrics and Gynecology at University of Pittsburgh, Pittsburgh, PA.
| | | | - Uma M Reddy
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD
| | | | - Tracy Manuck
- University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | | | | | | | | | - Brian M Mercer
- Case Western Reserve University-MetroHealth Medical Center, Cleveland, OH
| | | | | | - Susan M Ramin
- University of Texas Health Science Center at Houston, Houston, TX
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19
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Billings LK, Jablonski KA, Warner AS, Cheng YC, McAteer JB, Tipton L, Shuldiner AR, Ehrmann DA, Manning AK, Dabelea D, Franks PW, Kahn SE, Pollin TI, Knowler WC, Altshuler D, Florez JC. Variation in Maturity-Onset Diabetes of the Young Genes Influence Response to Interventions for Diabetes Prevention. J Clin Endocrinol Metab 2017; 102:2678-2689. [PMID: 28453780 PMCID: PMC5546852 DOI: 10.1210/jc.2016-3429] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Accepted: 04/21/2017] [Indexed: 11/19/2022]
Abstract
Context Variation in genes that cause maturity-onset diabetes of the young (MODY) has been associated with diabetes incidence and glycemic traits. Objectives This study aimed to determine whether genetic variation in MODY genes leads to differential responses to insulin-sensitizing interventions. Design and Setting This was a secondary analysis of a multicenter, randomized clinical trial, the Diabetes Prevention Program (DPP), involving 27 US academic institutions. We genotyped 22 missense and 221 common variants in the MODY-causing genes in the participants in the DPP. Participants and Interventions The study included 2806 genotyped DPP participants randomized to receive intensive lifestyle intervention (n = 935), metformin (n = 927), or placebo (n = 944). Main Outcome Measures Association of MODY genetic variants with diabetes incidence at a median of 3 years and measures of 1-year β-cell function, insulinogenic index, and oral disposition index. Analyses were stratified by treatment group for significant single-nucleotide polymorphism × treatment interaction (Pint < 0.05). Sequence kernel association tests examined the association between an aggregate of rare missense variants and insulinogenic traits. Results After 1 year, the minor allele of rs3212185 (HNF4A) was associated with improved β-cell function in the metformin and lifestyle groups but not the placebo group; the minor allele of rs6719578 (NEUROD1) was associated with an increase in insulin secretion in the metformin group but not in the placebo and lifestyle groups. Conclusions These results provide evidence that genetic variation among MODY genes may influence response to insulin-sensitizing interventions.
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Affiliation(s)
- Liana K. Billings
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02114
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114
- Department of Medicine, NorthShore University HealthSystem, Evanston, Illinois 60201
- Department of Medicine, Pritzker School of Medicine, University of Chicago, Chicago, Illinois 60637
| | | | - A. Sofia Warner
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114
| | - Yu-Chien Cheng
- Department of Medicine, NorthShore University HealthSystem, Evanston, Illinois 60201
- Department of Medicine, Pritzker School of Medicine, University of Chicago, Chicago, Illinois 60637
| | - Jarred B. McAteer
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114
| | - Laura Tipton
- Biostatistics Center, George Washington University, Rockville, Maryland 20852
| | - Alan R. Shuldiner
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland 21201
| | - David A. Ehrmann
- Department of Medicine, Pritzker School of Medicine, University of Chicago, Chicago, Illinois 60637
| | - Alisa K. Manning
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02114
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142
| | - Dana Dabelea
- Department of Epidemiology, Colorado School of Public Health, University of Colorado, Denver, Colorado 80045
| | - Paul W. Franks
- Department of Clinical Sciences, Genetic, and Molecular Epidemiology Unit, Lund University Diabetes Center, Skåne University Hospital Malmö, SE-205 02 Malmö, Sweden
| | - Steven E. Kahn
- Division of Metabolism, Endocrinology, and Nutrition, VA Puget Sound Health Care System and University of Washington, Seattle, Washington 98195
| | - Toni I. Pollin
- Departments of Medicine (Division of Endocrinology, Diabetes, and Nutrition) and Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland 21201
| | - William C. Knowler
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona 85014
| | - David Altshuler
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02114
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142
- Vertex Pharmaceuticals, Boston, Massachusetts 02210
| | - Jose C. Florez
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02114
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142
| | - for the Diabetes Prevention Program Research Group
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02114
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114
- Department of Medicine, NorthShore University HealthSystem, Evanston, Illinois 60201
- Department of Medicine, Pritzker School of Medicine, University of Chicago, Chicago, Illinois 60637
- Biostatistics Center, George Washington University, Rockville, Maryland 20852
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland 21201
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142
- Department of Epidemiology, Colorado School of Public Health, University of Colorado, Denver, Colorado 80045
- Department of Clinical Sciences, Genetic, and Molecular Epidemiology Unit, Lund University Diabetes Center, Skåne University Hospital Malmö, SE-205 02 Malmö, Sweden
- Division of Metabolism, Endocrinology, and Nutrition, VA Puget Sound Health Care System and University of Washington, Seattle, Washington 98195
- Departments of Medicine (Division of Endocrinology, Diabetes, and Nutrition) and Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland 21201
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona 85014
- Vertex Pharmaceuticals, Boston, Massachusetts 02210
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20
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Varga TV, Winters AH, Jablonski KA, Horton ES, Khare-Ranade P, Knowler WC, Marcovina SM, Renström F, Watson KE, Goldberg R, Florez JC, Pollin TI, Franks PW. Comprehensive Analysis of Established Dyslipidemia-Associated Loci in the Diabetes Prevention Program. ACTA ACUST UNITED AC 2016; 9:495-503. [PMID: 27784733 DOI: 10.1161/circgenetics.116.001457] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Accepted: 10/03/2016] [Indexed: 01/19/2023]
Abstract
BACKGROUND We assessed whether 234 established dyslipidemia-associated loci modify the effects of metformin treatment and lifestyle intervention (versus placebo control) on lipid and lipid subfraction levels in the Diabetes Prevention Program randomized controlled trial. METHODS AND RESULTS We tested gene treatment interactions in relation to baseline-adjusted follow-up blood lipid concentrations (high-density lipoprotein [HDL] and low-density lipoprotein-cholesterol, total cholesterol, and triglycerides) and lipoprotein subfraction particle concentrations and size in 2993 participants with pre-diabetes. Of the previously reported single-nucleotide polymorphism associations, 32.5% replicated at P<0.05 with baseline lipid traits. Trait-specific genetic risk scores were robustly associated (3×10-4>P>1.1×10-16) with their respective baseline traits for all but 2 traits. Lifestyle modified the effect of the genetic risk score for large HDL particle numbers, such that each risk allele of the genetic risk scores was associated with lower concentrations of large HDL particles at follow-up in the lifestyle arm (β=-0.11 µmol/L per genetic risk scores risk allele; 95% confidence interval, -0.188 to -0.033; P=5×10-3; Pinteraction=1×10-3 for lifestyle versus placebo), but not in the metformin or placebo arms (P>0.05). In the lifestyle arm, participants with high genetic risk had more favorable or similar trait levels at 1-year compared with participants at lower genetic risk at baseline for 17 of the 20 traits. CONCLUSIONS Improvements in large HDL particle concentrations conferred by lifestyle may be diminished by genetic factors. Lifestyle intervention, however, was successful in offsetting unfavorable genetic loading for most lipid traits. CLINICAL TRIAL REGISTRATION URL: https://www.clinicaltrials.gov. Unique Identifier: NCT00004992.
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Affiliation(s)
- Tibor V Varga
- Dept of Clinical Sciences, Genetic & Molecular Epidemiology Unit, Lund Univ, Malmö, Sweden
| | - Alexandra H Winters
- Division of Endocrinology, Diabetes & Nutrition, Dept of Medicine & Program in Genetics & Genomic Medicine, Univ of Maryland School of Medicine, Baltimore
| | | | - Edward S Horton
- Dept of Medicine, Harvard Medical School.,Joslin Diabetes Center, Boston, MA
| | | | - William C Knowler
- Diabetes Epidemiology & Clinical Research Section, NIDDK, Phoenix, AZ
| | - Santica M Marcovina
- Northwest Lipid Metabolism & Diabetes Research Laboratories, Univ of Washington, Seattle, WA
| | - Frida Renström
- Dept of Clinical Sciences, Genetic & Molecular Epidemiology Unit, Lund Univ, Malmö, Sweden.,Dept of Biobank Research, Umeå Univ, Umeå, Sweden
| | | | - Ronald Goldberg
- Lipid Disorders Clinic, Division of Endocrinology, Diabetes & Metabolism, Leonard M. Miller School of Medicine, Univ of Miami, Miami, FL.,The Diabetes Research Institute, Leonard M. Miller School of Medicine, Univ of Miami, Miami, FL
| | - José C Florez
- Dept of Medicine, Harvard Medical School.,Program in Medical & Population Genetics, Broad Institute of Harvard & MIT, Cambridge.,Center for Human Genetic Research, Diabetes Unit, MGH.,Diabetes Research Center, Diabetes Unit, MGH
| | - Toni I Pollin
- Division of Endocrinology, Diabetes & Nutrition, Dept of Medicine & Program in Genetics & Genomic Medicine, Univ of Maryland School of Medicine, Baltimore
| | - Paul W Franks
- Dept of Clinical Sciences, Genetic & Molecular Epidemiology Unit, Lund Univ, Malmö, Sweden.,Dept of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.,Dept of Public Health & Clinical Medicine, Umeå Univ, Umeå, Sweden
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21
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Livingstone KM, Celis-Morales C, Papandonatos GD, Erar B, Florez JC, Jablonski KA, Razquin C, Marti A, Heianza Y, Huang T, Sacks FM, Svendstrup M, Sui X, Church TS, Jääskeläinen T, Lindström J, Tuomilehto J, Uusitupa M, Rankinen T, Saris WHM, Hansen T, Pedersen O, Astrup A, Sørensen TIA, Qi L, Bray GA, Martinez-Gonzalez MA, Martinez JA, Franks PW, McCaffery JM, Lara J, Mathers JC. FTO genotype and weight loss: systematic review and meta-analysis of 9563 individual participant data from eight randomised controlled trials. BMJ 2016; 354:i4707. [PMID: 27650503 PMCID: PMC6168036 DOI: 10.1136/bmj.i4707] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
OBJECTIVE To assess the effect of the FTO genotype on weight loss after dietary, physical activity, or drug based interventions in randomised controlled trials. DESIGN Systematic review and random effects meta-analysis of individual participant data from randomised controlled trials. DATA SOURCES Ovid Medline, Scopus, Embase, and Cochrane from inception to November 2015. ELIGIBILITY CRITERIA FOR STUDY SELECTION Randomised controlled trials in overweight or obese adults reporting reduction in body mass index, body weight, or waist circumference by FTO genotype (rs9939609 or a proxy) after dietary, physical activity, or drug based interventions. Gene by treatment interaction models were fitted to individual participant data from all studies included in this review, using allele dose coding for genetic effects and a common set of covariates. Study level interactions were combined using random effect models. Metaregression and subgroup analysis were used to assess sources of study heterogeneity. RESULTS We identified eight eligible randomised controlled trials for the systematic review and meta-analysis (n=9563). Overall, differential changes in body mass index, body weight, and waist circumference in response to weight loss intervention were not significantly different between FTO genotypes. Sensitivity analyses indicated that differential changes in body mass index, body weight, and waist circumference by FTO genotype did not differ by intervention type, intervention length, ethnicity, sample size, sex, and baseline body mass index and age category. CONCLUSIONS We have observed that carriage of the FTO minor allele was not associated with differential change in adiposity after weight loss interventions. These findings show that individuals carrying the minor allele respond equally well to dietary, physical activity, or drug based weight loss interventions and thus genetic predisposition to obesity associated with the FTO minor allele can be at least partly counteracted through such interventions. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42015015969.
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Affiliation(s)
- Katherine M Livingstone
- Human Nutrition Research Centre, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne NE4 5PL, UK Deakin University, Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Victoria, Australia
| | - Carlos Celis-Morales
- Human Nutrition Research Centre, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne NE4 5PL, UK BHF Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Science, University of Glasgow, Glasgow, UK
| | - George D Papandonatos
- Department of Biostatistics, Brown University School of Public Health, Providence, RI, USA
| | - Bahar Erar
- Department of Biostatistics, Brown University School of Public Health, Providence, RI, USA
| | - Jose C Florez
- Diabetes Unit and Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Kathleen A Jablonski
- George Washington University Department of Epidemiology and Biostatistics The Biostatistics Center, Rockville, MD, USA
| | - Cristina Razquin
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain CIBER Fisiopatologia de la Obesidad y Nutricion and PREDIMED Network from Instituto de Salud Carlos III Spanish Government, Spain
| | - Amelia Marti
- CIBER Fisiopatologia de la Obesidad y Nutricion and PREDIMED Network from Instituto de Salud Carlos III Spanish Government, Spain Department of Nutrition, Food Science and Physiology, University of Navarra, Pamplona, Spain
| | - Yoriko Heianza
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA
| | - Tao Huang
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA Epidemiology Domain, Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Frank M Sacks
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Mathilde Svendstrup
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section on Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark Danish Diabetes Academy, Odense, Denmark
| | - Xuemei Sui
- Department of Exercise Science, University of South Carolina, Columbia, SC, USA
| | - Timothy S Church
- Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, USA
| | - Tiina Jääskeläinen
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland Department of Medical and Clinical Genetics, University of Helsinki, Finland
| | - Jaana Lindström
- Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
| | - Jaakko Tuomilehto
- Dasman Diabetes Institute, Dasman, Kuwait City, Kuwait Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Matti Uusitupa
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Tuomo Rankinen
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Wim H M Saris
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre +, Maastricht, Netherlands
| | - Torben Hansen
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section on Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section on Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Arne Astrup
- Department of Nutrition, Exercise and Sports, Copenhagen University, Rolighedsvej 30, Frederiksberg C, Denmark
| | - Thorkild I A Sørensen
- Novo Nordisk Foundation Centre for Basic Metabolic Research, Section on Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospitals, The Capital Region, Denmark
| | - Lu Qi
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - George A Bray
- Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA, USA
| | - Miguel A Martinez-Gonzalez
- CIBER Fisiopatologia de la Obesidad y Nutricion and PREDIMED Network from Instituto de Salud Carlos III Spanish Government, Spain Department of Nutrition, Food Science and Physiology, University of Navarra, Pamplona, Spain
| | - J Alfredo Martinez
- CIBER Fisiopatologia de la Obesidad y Nutricion and PREDIMED Network from Instituto de Salud Carlos III Spanish Government, Spain Department of Nutrition, Food Science and Physiology, University of Navarra, Pamplona, Spain Food Science and Physiology, Centre for Nutrition Research, University of Navarra, Pamplona, Spain
| | - Paul W Franks
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Skåne University Hospital Malmö, Malmö, Sweden
| | - Jeanne M McCaffery
- The Miriam Hospital and the Alpert School of Medicine, Brown University, Providence, USA
| | - Jose Lara
- Human Nutrition Research Centre, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne NE4 5PL, UK Department of Applied Sciences, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - John C Mathers
- Human Nutrition Research Centre, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne NE4 5PL, UK
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22
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Zhou K, Yee SW, Seiser EL, van Leeuwen N, Tavendale R, Bennett AJ, Groves CJ, Coleman RL, van der Heijden AA, Beulens JW, de Keyser CE, Zaharenko L, Rotroff DM, Out M, Jablonski KA, Chen L, Javorský M, Židzik J, Levin AM, Williams LK, Dujic T, Semiz S, Kubo M, Chien HC, Maeda S, Witte JS, Wu L, Tkáč I, Kooy A, van Schaik RHN, Stehouwer CDA, Logie L, Sutherland C, Klovins J, Pirags V, Hofman A, Stricker BH, Motsinger-Reif AA, Wagner MJ, Innocenti F, 't Hart LM, Holman RR, McCarthy MI, Hedderson MM, Palmer CNA, Florez JC, Giacomini KM, Pearson ER. Variation in the glucose transporter gene SLC2A2 is associated with glycemic response to metformin. Nat Genet 2016; 48:1055-1059. [PMID: 27500523 PMCID: PMC5007158 DOI: 10.1038/ng.3632] [Citation(s) in RCA: 131] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 06/30/2016] [Indexed: 02/06/2023]
Abstract
Metformin is the first-line antidiabetic drug with over 100 million users worldwide, yet its mechanism of action remains unclear1. Here the Metformin Genetics (MetGen) Consortium reports a three-stage genome wide association study (GWAS), consisting of 13,123 participants of different ancestries. The C-allele of rs8192675 in the intron of SLC2A2, which encodes the facilitated glucose transporter GLUT2, was associated with a 0.17% (p=6.6x10-14) greater metformin induced HbA1c reduction in 10,577 participants of European ancestry. rs8192675 is the top cis-eQTL for SLC2A2 in 1,226 human liver samples, suggesting a key role for hepatic GLUT2 in regulation of metformin action. In obese individuals C-allele homozygotes at rs8192675 had a 0.33% (3.6mmol/mol) greater absolute HbA1c reduction than T-allele homozygotes.This is about half the effect seen with the addition of a DPP-4 inhibitor, and equates to a dose difference of 550mg of metformin, suggesting rs8192675 as a potential biomarker for stratified medicine.
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Affiliation(s)
- Kaixin Zhou
- School of Medicine, University of Dundee, Dundee, UK
| | - Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA
| | - Eric L Seiser
- Division of Pharmacotherapy and Experimental Therapeutics, Center for Pharmacogenomics and Individualized Therapy, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Nienke van Leeuwen
- Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Amanda J Bennett
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Christopher J Groves
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Ruth L Coleman
- Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Amber A van der Heijden
- Department of General Practice, EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands
| | - Joline W Beulens
- Department of Epidemiology and Biostatistics, EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands.,Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Linda Zaharenko
- Latvian Genome Data Base (LGDB), Riga, Latvia.,Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Daniel M Rotroff
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA.,Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Mattijs Out
- Treant Zorggroep, Location Bethesda, Hoogeveen, the Netherlands.,Bethesda Diabetes Research Centre, Hoogeveen, the Netherlands
| | | | - Ling Chen
- Diabetes Unit and Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | - Jozef Židzik
- Faculty of Medicine, Šafárik University, Košice, Slovakia
| | - Albert M Levin
- Department of Public Health Sciences, Henry Ford Health System, Detroit, Michigan, USA
| | - L Keoki Williams
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan, USA.,Department of Internal Medicine, Henry Ford Health System, Detroit, Michigan, USA
| | - Tanja Dujic
- School of Medicine, University of Dundee, Dundee, UK.,Faculty of Pharmacy, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Sabina Semiz
- Faculty of Pharmacy, University of Sarajevo, Sarajevo, Bosnia and Herzegovina.,Faculty of Engineering and Natural Sciences, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Michiaki Kubo
- RIKEN Center for Integrative Medical Sciences (IMS), Yokohama, Japan
| | - Huan-Chieh Chien
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA
| | - Shiro Maeda
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Nishihara, Japan.,Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Nishihara, Japan
| | - John S Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA.,Institute for Human Genetics, University of California, San Francisco, San Francisco, California, USA.,Department of Urology, University of California, San Francisco, San Francisco, California, USA.,UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, USA
| | - Longyang Wu
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA
| | - Ivan Tkáč
- Faculty of Medicine, Šafárik University, Košice, Slovakia
| | - Adriaan Kooy
- Treant Zorggroep, Location Bethesda, Hoogeveen, the Netherlands.,Bethesda Diabetes Research Centre, Hoogeveen, the Netherlands
| | - Ron H N van Schaik
- Department of Clinical Chemistry, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Coen D A Stehouwer
- Department of Internal Medicine and Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Lisa Logie
- School of Medicine, University of Dundee, Dundee, UK
| | | | | | | | | | - Janis Klovins
- Latvian Genome Data Base (LGDB), Riga, Latvia.,Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Valdis Pirags
- Latvian Biomedical Research and Study Centre, Riga, Latvia.,Faculty of Medicine, University of Latvia, Riga, Latvia.,Department of Endocrinology, Pauls Stradins Clinical University Hospital, Riga, Latvia
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Bruno H Stricker
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.,Inspectorate of Healthcare, Heerlen, the Netherlands
| | - Alison A Motsinger-Reif
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - Michael J Wagner
- Center for Pharmacogenomics and Individualized Therapy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Federico Innocenti
- Division of Pharmacotherapy and Experimental Therapeutics, Center for Pharmacogenomics and Individualized Therapy, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Leen M 't Hart
- Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, the Netherlands.,Department of Epidemiology and Biostatistics, EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands.,Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Rury R Holman
- Diabetes Trials Unit, Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.,Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.,Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Monique M Hedderson
- Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | | | - Jose C Florez
- Diabetes Unit and Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA.,Program in Metabolism, Broad Institute, Cambridge, Massachusetts, USA.,Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA.,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Kathleen M Giacomini
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California, USA.,Institute for Human Genetics, University of California, San Francisco, San Francisco, California, USA
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23
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Hall KT, Jablonski KA, Chen L, Harden M, Tolkin BR, Kaptchuk TJ, Bray GA, Ridker PM, Florez JC, Mukamal KJ, Chasman DI. Catechol-O-methyltransferase association with hemoglobin A1c. Metabolism 2016; 65:961-967. [PMID: 27282867 PMCID: PMC4924514 DOI: 10.1016/j.metabol.2016.04.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Revised: 03/10/2016] [Accepted: 04/07/2016] [Indexed: 01/06/2023]
Abstract
AIMS Catecholamines have metabolic effects on blood pressure, insulin sensitivity and blood glucose. Genetic variation in catechol-O-methyltransferase (COMT), an enzyme that degrades catecholamines, is associated with cardiometabolic risk factors and incident cardiovascular disease (CVD). Here we examined COMT effects on glycemic function and type 2 diabetes. METHODS We tested whether COMT polymorphisms were associated with baseline HbA1c in the Women's Genome Health Study (WGHS), and Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC), and with susceptibility to type 2 diabetes in WGHS, DIAbetes Genetics Replication And Meta-analysis consortium (DIAGRAM), and the Diabetes Prevention Program (DPP). Given evidence that COMT modifies some drug responses, we examined association with type 2 diabetes and randomized metformin and aspirin treatment. RESULTS COMT rs4680 high-activity G-allele was associated with lower HbA1c in WGHS (β=-0.032% [0.012], p=0.008) and borderline significant in MAGIC (β=-0.006% [0.003], p=0.07). Combined COMT per val allele effects on type 2 diabetes were significant (OR=0.98 [0.96-0.998], p=0.03) in fixed-effects analyses across WGHS, DIAGRAM, and DPP. Similar results were obtained for 2 other COMT SNPs rs4818 and rs4633. In the DPP, the rs4680 val allele was borderline associated with lower diabetes incidence among participants randomized to metformin (HR=0.81 [0.65-1.00], p=0.05). CONCLUSIONS COMT rs4680 high-activity G-allele was associated with lower HbA1c and modest protection from type 2 diabetes. The directionality of COMT associations was concordant with those previously observed for cardiometabolic risk factors and CVD.
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Affiliation(s)
- Kathryn T. Hall
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Corresponding author at: Division of Preventive Medicine, Brigham and Women’s Hospital, 900 Commonwealth Avenue, Boston, MA, 02215, USA. Tel.: +1 617 278 0938; fax: +1 617 731 3843. (K.T. Hall)
| | | | - Ling Chen
- Center for Human Genetic Research and Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, MA, USA
| | - Maegan Harden
- Genomics Platform, Broad Institute, Cambridge, MA, USA
| | - Benjamin R. Tolkin
- Division of General Medicine and Primary Care, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Ted J. Kaptchuk
- Harvard Medical School, Boston, MA, USA
- Division of General Medicine and Primary Care, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - George A. Bray
- Pennington Biomedical Research Center, Baton Rouge, LA 70808
| | - Paul M. Ridker
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Jose C. Florez
- Harvard Medical School, Boston, MA, USA
- Center for Human Genetic Research and Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | | | - Kenneth J. Mukamal
- Harvard Medical School, Boston, MA, USA
- Division of General Medicine and Primary Care, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Daniel I. Chasman
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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24
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Beck JM, Schloss PD, Venkataraman A, Twigg H, Jablonski KA, Bushman FD, Campbell TB, Charlson ES, Collman RG, Crothers K, Curtis JL, Drews KL, Flores SC, Fontenot AP, Foulkes MA, Frank I, Ghedin E, Huang L, Lynch SV, Morris A, Palmer BE, Schmidt TM, Sodergren E, Weinstock GM, Young VB. Multicenter Comparison of Lung and Oral Microbiomes of HIV-infected and HIV-uninfected Individuals. Am J Respir Crit Care Med 2016; 192:1335-44. [PMID: 26247840 DOI: 10.1164/rccm.201501-0128oc] [Citation(s) in RCA: 102] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
RATIONALE Improved understanding of the lung microbiome in HIV-infected individuals could lead to better strategies for diagnosis, therapy, and prophylaxis of HIV-associated pneumonias. Differences in the oral and lung microbiomes in HIV-infected and HIV-uninfected individuals are not well defined. Whether highly active antiretroviral therapy influences these microbiomes is unclear. OBJECTIVES We determined whether oral and lung microbiomes differed in clinically healthy groups of HIV-infected and HIV-uninfected subjects. METHODS Participating sites in the Lung HIV Microbiome Project contributed bacterial 16S rRNA sequencing data from oral washes and bronchoalveolar lavages (BALs) obtained from HIV-uninfected individuals (n = 86), HIV-infected individuals who were treatment naive (n = 18), and HIV-infected individuals receiving antiretroviral therapy (n = 38). MEASUREMENTS AND MAIN RESULTS Microbial populations differed in the oral washes among the subject groups (Streptococcus, Actinomyces, Rothia, and Atopobium), but there were no individual taxa that differed among the BALs. Comparison of oral washes and BALs demonstrated similar patterns from HIV-uninfected individuals and HIV-infected individuals receiving antiretroviral therapy, with multiple taxa differing in abundance. The pattern observed from HIV-infected individuals who were treatment naive differed from the other two groups, with differences limited to Veillonella, Rothia, and Granulicatella. CD4 cell counts did not influence the oral or BAL microbiome in these relatively healthy, HIV-infected subjects. CONCLUSIONS The overall similarity of the microbiomes in participants with and without HIV infection was unexpected, because HIV-infected individuals with relatively preserved CD4 cell counts are at higher risk for lower respiratory tract infections, indicating impaired local immune function.
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Affiliation(s)
- James M Beck
- 1 Department of Medicine, University of Colorado Denver, Aurora, Colorado.,2 Veterans Affairs Eastern Colorado Health Care System, Denver, Colorado
| | - Patrick D Schloss
- 3 Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Arvind Venkataraman
- 3 Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Homer Twigg
- 4 Department of Medicine, Indiana University, Indianapolis, Indiana
| | - Kathleen A Jablonski
- 5 Department of Epidemiology and Biostatistics, George Washington University, Washington, District of Columbia
| | | | - Thomas B Campbell
- 1 Department of Medicine, University of Colorado Denver, Aurora, Colorado
| | - Emily S Charlson
- 7 Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ronald G Collman
- 7 Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Kristina Crothers
- 8 Department of Medicine, University of Washington, Seattle, Washington
| | - Jeffrey L Curtis
- 3 Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan.,9 Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan
| | - Kimberly L Drews
- 5 Department of Epidemiology and Biostatistics, George Washington University, Washington, District of Columbia
| | - Sonia C Flores
- 1 Department of Medicine, University of Colorado Denver, Aurora, Colorado
| | - Andrew P Fontenot
- 1 Department of Medicine, University of Colorado Denver, Aurora, Colorado
| | - Mary A Foulkes
- 5 Department of Epidemiology and Biostatistics, George Washington University, Washington, District of Columbia
| | - Ian Frank
- 7 Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Elodie Ghedin
- 10 Department of Computational and Systems Biology and
| | - Laurence Huang
- 11 Department of Medicine, University of California San Francisco, San Francisco, California; and
| | - Susan V Lynch
- 11 Department of Medicine, University of California San Francisco, San Francisco, California; and
| | - Alison Morris
- 12 Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Brent E Palmer
- 1 Department of Medicine, University of Colorado Denver, Aurora, Colorado
| | - Thomas M Schmidt
- 3 Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | - Erica Sodergren
- 13 The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut
| | | | - Vincent B Young
- 3 Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
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25
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Hivert MF, Christophi CA, Franks PW, Jablonski KA, Ehrmann DA, Kahn SE, Horton ES, Pollin TI, Mather KJ, Perreault L, Barrett-Connor E, Knowler WC, Florez JC. Lifestyle and Metformin Ameliorate Insulin Sensitivity Independently of the Genetic Burden of Established Insulin Resistance Variants in Diabetes Prevention Program Participants. Diabetes 2016; 65:520-6. [PMID: 26525880 PMCID: PMC4747453 DOI: 10.2337/db15-0950] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2015] [Accepted: 10/27/2015] [Indexed: 12/15/2022]
Abstract
Large genome-wide association studies of glycemic traits have identified genetics variants that are associated with insulin resistance (IR) in the general population. It is unknown whether people with genetic enrichment for these IR variants respond differently to interventions that aim to improve insulin sensitivity. We built a genetic risk score (GRS) based on 17 established IR variants and effect sizes (weighted IR-GRS) in 2,713 participants of the Diabetes Prevention Program (DPP) with genetic consent. We tested associations between the weighted IR-GRS and insulin sensitivity index (ISI) at baseline in all participants, and with change in ISI over 1 year of follow-up in the DPP intervention (metformin and lifestyle) and control (placebo) arms. All models were adjusted for age, sex, ethnicity, and waist circumference at baseline (plus baseline ISI for 1-year ISI change models). A higher IR-GRS was associated with lower baseline ISI (β = -0.754 [SE = 0.229] log-ISI per unit, P = 0.001 in fully adjusted models). There was no differential effect of treatment for the association between the IR-GRS on the change in ISI; higher IR-GRS was associated with an attenuation in ISI improvement over 1 year (β = -0.520 [SE = 0.233], P = 0.03 in fully adjusted models; all treatment arms). Lifestyle intervention and metformin treatment improved the ISI, regardless of the genetic burden of IR variants.
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Affiliation(s)
- Marie-France Hivert
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, MA Department of Medicine, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | | | - Paul W Franks
- Genetic & Molecular Epidemiology Unit, Lund University Diabetes Center, Department of Clinical Sciences, Lund University, Malmö, Sweden Department of Nutrition, Harvard School of Public Health, Boston, MA Department of Public Health and Clinical Medicine, Division of Medicine, Umeå University, Umeå, Sweden
| | | | - David A Ehrmann
- Department of Medicine, The University of Chicago School of Medicine, Chicago, IL
| | - Steven E Kahn
- Division of Metabolism, Endocrinology & Nutrition, VA Puget Sound Health Care System and University of Washington, Seattle, WA
| | - Edward S Horton
- Section on Clinical, Behavioral & Outcomes Research, Joslin Diabetes Center, Boston, MA Department of Medicine, Harvard Medical School, Boston, MA
| | - Toni I Pollin
- Departments of Medicine and Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD
| | - Kieren J Mather
- Division of Endocrinology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Leigh Perreault
- Department of Medicine, Division of Endocrinology, Metabolism and Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Elizabeth Barrett-Connor
- Division of Epidemiology, Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA
| | - William C Knowler
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - Jose C Florez
- Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, MA Department of Medicine, Harvard Medical School, Boston, MA Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
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26
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Papandonatos GD, Pan Q, Pajewski NM, Delahanty LM, Peter I, Erar B, Ahmad S, Harden M, Chen L, Fontanillas P, Wagenknecht LE, Kahn SE, Wing RR, Jablonski KA, Huggins GS, Knowler WC, Florez JC, McCaffery JM, Franks PW. Genetic Predisposition to Weight Loss and Regain With Lifestyle Intervention: Analyses From the Diabetes Prevention Program and the Look AHEAD Randomized Controlled Trials. Diabetes 2015; 64:4312-21. [PMID: 26253612 PMCID: PMC4657576 DOI: 10.2337/db15-0441] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 08/04/2015] [Indexed: 12/22/2022]
Abstract
Clinically relevant weight loss is achievable through lifestyle modification, but unintentional weight regain is common. We investigated whether recently discovered genetic variants affect weight loss and/or weight regain during behavioral intervention. Participants at high-risk of type 2 diabetes (Diabetes Prevention Program [DPP]; N = 917/907 intervention/comparison) or with type 2 diabetes (Look AHEAD [Action for Health in Diabetes]; N = 2,014/1,892 intervention/comparison) were from two parallel arm (lifestyle vs. comparison) randomized controlled trials. The associations of 91 established obesity-predisposing loci with weight loss across 4 years and with weight regain across years 2-4 after a minimum of 3% weight loss were tested. Each copy of the minor G allele of MTIF3 rs1885988 was consistently associated with greater weight loss following lifestyle intervention over 4 years across the DPP and Look AHEAD. No such effect was observed across comparison arms, leading to a nominally significant single nucleotide polymorphism×treatment interaction (P = 4.3 × 10(-3)). However, this effect was not significant at a study-wise significance level (Bonferroni threshold P < 5.8 × 10(-4)). Most obesity-predisposing gene variants were not associated with weight loss or regain within the DPP and Look AHEAD trials, directly or via interactions with lifestyle.
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Affiliation(s)
| | - Qing Pan
- The Biostatistics Center, George Washington University, Rockville, MD
| | - Nicholas M Pajewski
- Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC
| | - Linda M Delahanty
- Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, MA Department of Medicine, Harvard Medical School, Boston, MA
| | - Inga Peter
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Bahar Erar
- Center for Statistical Sciences, Brown University, Providence, RI
| | - Shafqat Ahmad
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Skåne University Hospital Malmö, Malmö, Sweden
| | | | - Ling Chen
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Pierre Fontanillas
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | | | - Lynne E Wagenknecht
- Look AHEAD Coordinating Center, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC
| | - Steven E Kahn
- Division of Metabolism, Endocrinology & Nutrition, Department of Medicine, VA Puget Sound Health Care System and University of Washington, Seattle, WA
| | - Rena R Wing
- Weight Control and Diabetes Research Center, The Miriam Hospital and The Warren Alpert Medical School of Brown University, Providence, RI
| | | | - Gordon S Huggins
- Center for Translational Genomics, Molecular Cardiology Research Institute, Tufts Medical Center, Boston, MA
| | - William C Knowler
- National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - Jose C Florez
- Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, MA Department of Medicine, Harvard Medical School, Boston, MA Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Jeanne M McCaffery
- Weight Control and Diabetes Research Center, The Miriam Hospital and The Warren Alpert Medical School of Brown University, Providence, RI
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Lund University, Skåne University Hospital Malmö, Malmö, Sweden Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
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27
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Nilakanta H, Drews KL, Firrell S, Foulkes MA, Jablonski KA. A review of software for analyzing molecular sequences. BMC Res Notes 2014; 7:830. [PMID: 25421430 PMCID: PMC4258797 DOI: 10.1186/1756-0500-7-830] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2014] [Accepted: 11/11/2014] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Over the past ten years, there has been an explosion of microbiome research. Many software packages for analyzing microbial sequences such as the 16S gene from 454 sequencers and Illumina platforms are available. But for a new researcher, it is difficult to know which package to choose. We present a systematic review of packages for the analysis of molecular sequences used to describe and compare microbial communities. This review gives students and researchers information to help choose the best analytic pipeline for their project. To the best of our knowledge, this is the first review of such software. FINDINGS Seven software packages met our inclusion criteria of being cost free and publically available, offering analysis functions from platform sequencing to results presentation, and included documentation and data security. We installed and executed each of the software packages and describe the installation, documentation, features, and functions of each. CONCLUSIONS For the user, pipeline choices may be limited because some packages only run on select operating systems. Users should be aware of the availability of features and functions of each package. Of utmost importance is that the user must be aware of the default settings and underlying assumptions of each function. All packages are lacking sufficient methods for longitudinal analysis.Researchers can do well using any one of these seven packages. However, two packages are outstanding; mothur and QIIME, due not only to the comprehensive suite of functions and procedures incorporated into the pipelines but also because of the accompanying documentation.
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Affiliation(s)
| | | | | | | | - Kathleen A Jablonski
- The Biostatistics Center, The George Washington University, 6110 Executive Boulevard 750, Rockville, MD 20852-3943, USA.
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28
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Delahanty LM, Pan Q, Jablonski KA, Aroda VR, Watson KE, Bray GA, Kahn SE, Florez JC, Perreault L, Franks PW. Effects of weight loss, weight cycling, and weight loss maintenance on diabetes incidence and change in cardiometabolic traits in the Diabetes Prevention Program. Diabetes Care 2014; 37:2738-45. [PMID: 25024396 PMCID: PMC4170126 DOI: 10.2337/dc14-0018] [Citation(s) in RCA: 83] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE This study examined specific measures of weight loss in relation to incident diabetes and improvement in cardiometabolic risk factors. RESEARCH DESIGN AND METHODS This prospective, observational study analyzed nine weight measures, characterizing baseline weight, short- versus long-term weight loss, short- versus long-term weight regain, and weight cycling, within the Diabetes Prevention Program (DPP) lifestyle intervention arm (n = 1,000) for predictors of incident diabetes and improvement in cardiometabolic risk factors over 2 years. RESULTS Although weight loss in the first 6 months was protective of diabetes (hazard ratio [HR] 0.94 per kg, 95% CI 0.90, 0.98; P < 0.01) and cardiometabolic risk factors (P < 0.01), weight loss from 0 to 2 years was the strongest predictor of reduced diabetes incidence (HR 0.90 per kg, 95% CI 0.87, 0.93; P < 0.01) and cardiometabolic risk factor improvement (e.g., fasting glucose: β = -0.57 mg/dL per kg, 95% CI -0.66, -0.48; P < 0.01). Weight cycling (defined as number of 5-lb [2.25-kg] weight cycles) ranged 0-6 times per participant and was positively associated with incident diabetes (HR 1.33, 95% CI 1.12, 1.58; P < 0.01), fasting glucose (β = 0.91 mg/dL per cycle; P = 0.02), HOMA-IR (β = 0.25 units per cycle; P = 0.04), and systolic blood pressure (β = 0.94 mmHg per cycle; P = 0.01). After adjustment for baseline weight, the effect of weight cycling remained statistically significant for diabetes risk (HR 1.22, 95% CI 1.02, 1.47; P = 0.03) but not for cardiometabolic traits. CONCLUSIONS Two-year weight loss was the strongest predictor of reduced diabetes risk and improvements in cardiometabolic traits.
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Affiliation(s)
- Linda M Delahanty
- Diabetes Research Center, Massachusetts General Hospital, Boston, MA Department of Medicine, Harvard Medical School, Boston, MA
| | - Qing Pan
- The Biostatistics Center, George Washington University, Rockville, MD
| | | | - Vanita R Aroda
- MedStar Health Research Institute, Hyattsville, MD, and Georgetown University School of Medicine, Washington, DC
| | - Karol E Watson
- The David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA
| | - George A Bray
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA
| | - Steven E Kahn
- Veterans Affairs Puget Sound Health Care System and University of Washington, Seattle, WA
| | - Jose C Florez
- Diabetes Research Center, Massachusetts General Hospital, Boston, MA Department of Medicine, Harvard Medical School, Boston, MA Center for Human Genetic Research, Department of Medicine, Massachusetts General Hospital, Boston, MA Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
| | - Leigh Perreault
- Department of Medicine, Division of Endocrinology, Metabolism and Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Paul W Franks
- Department of Clinical Sciences, Lund University, Malmö, Sweden Department of Nutrition, Harvard School of Public Health, Boston, MA
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29
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Vimaleswaran KS, Cavadino A, Berry DJ, Jorde R, Dieffenbach AK, Lu C, Alves AC, Heerspink HJL, Tikkanen E, Eriksson J, Wong A, Mangino M, Jablonski KA, Nolte IM, Houston DK, Ahluwalia TS, van der Most PJ, Pasko D, Zgaga L, Thiering E, Vitart V, Fraser RM, Huffman JE, de Boer RA, Schöttker B, Saum KU, McCarthy MI, Dupuis J, Herzig KH, Sebert S, Pouta A, Laitinen J, Kleber ME, Navis G, Lorentzon M, Jameson K, Arden N, Cooper JA, Acharya J, Hardy R, Raitakari O, Ripatti S, Billings LK, Lahti J, Osmond C, Penninx BW, Rejnmark L, Lohman KK, Paternoster L, Stolk RP, Hernandez DG, Byberg L, Hagström E, Melhus H, Ingelsson E, Mellström D, Ljunggren O, Tzoulaki I, McLachlan S, Theodoratou E, Tiesler CMT, Jula A, Navarro P, Wright AF, Polasek O, Wilson JF, Rudan I, Salomaa V, Heinrich J, Campbell H, Price JF, Karlsson M, Lind L, Michaëlsson K, Bandinelli S, Frayling TM, Hartman CA, Sørensen TIA, Kritchevsky SB, Langdahl BL, Eriksson JG, Florez JC, Spector TD, Lehtimäki T, Kuh D, Humphries SE, Cooper C, Ohlsson C, März W, de Borst MH, Kumari M, Kivimaki M, Wang TJ, Power C, Brenner H, Grimnes G, van der Harst P, Snieder H, Hingorani AD, Pilz S, Whittaker JC, Järvelin MR, Hyppönen E. Association of vitamin D status with arterial blood pressure and hypertension risk: a mendelian randomisation study. Lancet Diabetes Endocrinol 2014; 2:719-29. [PMID: 24974252 PMCID: PMC4582411 DOI: 10.1016/s2213-8587(14)70113-5] [Citation(s) in RCA: 257] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND Low plasma 25-hydroxyvitamin D (25[OH]D) concentration is associated with high arterial blood pressure and hypertension risk, but whether this association is causal is unknown. We used a mendelian randomisation approach to test whether 25(OH)D concentration is causally associated with blood pressure and hypertension risk. METHODS In this mendelian randomisation study, we generated an allele score (25[OH]D synthesis score) based on variants of genes that affect 25(OH)D synthesis or substrate availability (CYP2R1 and DHCR7), which we used as a proxy for 25(OH)D concentration. We meta-analysed data for up to 108 173 individuals from 35 studies in the D-CarDia collaboration to investigate associations between the allele score and blood pressure measurements. We complemented these analyses with previously published summary statistics from the International Consortium on Blood Pressure (ICBP), the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, and the Global Blood Pressure Genetics (Global BPGen) consortium. FINDINGS In phenotypic analyses (up to n=49 363), increased 25(OH)D concentration was associated with decreased systolic blood pressure (β per 10% increase, -0·12 mm Hg, 95% CI -0·20 to -0·04; p=0·003) and reduced odds of hypertension (odds ratio [OR] 0·98, 95% CI 0·97-0·99; p=0·0003), but not with decreased diastolic blood pressure (β per 10% increase, -0·02 mm Hg, -0·08 to 0·03; p=0·37). In meta-analyses in which we combined data from D-CarDia and the ICBP (n=146 581, after exclusion of overlapping studies), each 25(OH)D-increasing allele of the synthesis score was associated with a change of -0·10 mm Hg in systolic blood pressure (-0·21 to -0·0001; p=0·0498) and a change of -0·08 mm Hg in diastolic blood pressure (-0·15 to -0·02; p=0·01). When D-CarDia and consortia data for hypertension were meta-analysed together (n=142 255), the synthesis score was associated with a reduced odds of hypertension (OR per allele, 0·98, 0·96-0·99; p=0·001). In instrumental variable analysis, each 10% increase in genetically instrumented 25(OH)D concentration was associated with a change of -0·29 mm Hg in diastolic blood pressure (-0·52 to -0·07; p=0·01), a change of -0·37 mm Hg in systolic blood pressure (-0·73 to 0·003; p=0·052), and an 8·1% decreased odds of hypertension (OR 0·92, 0·87-0·97; p=0·002). INTERPRETATION Increased plasma concentrations of 25(OH)D might reduce the risk of hypertension. This finding warrants further investigation in an independent, similarly powered study. FUNDING British Heart Foundation, UK Medical Research Council, and Academy of Finland.
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Affiliation(s)
- Karani S Vimaleswaran
- Population, Policy and Practice, UCL Institute of Child Health, London, UK; Hugh Sinclair Unit of Human Nutrition, Department of Food & Nutritional Sciences, School of Chemistry, Food & Pharmacy, University of Reading, Reading, UK
| | - Alana Cavadino
- Population, Policy and Practice, UCL Institute of Child Health, London, UK
| | - Diane J Berry
- Population, Policy and Practice, UCL Institute of Child Health, London, UK
| | | | - Rolf Jorde
- Tromsø Endocrine Research Group, Department of Clinical Medicine, University of Tromsø, Tromsø, Norway
| | - Aida Karina Dieffenbach
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | - Chen Lu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Alexessander Couto Alves
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK; Institute of Health Sciences, University of Oulu, Oulu, Finland
| | - Hiddo J Lambers Heerspink
- Department of Clinical Pharmacology, University Medical Center, University of Groningen, Groningen, Netherlands
| | - Emmi Tikkanen
- Institute for Molecular Medicine Finland, Tukholmankatu, Finland; Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland; Hjelt Institute, University of Helsinki, Helsinki, Finland
| | - Joel Eriksson
- Centre for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing, University College London, London, UK
| | - Massimo Mangino
- Department of Twin Research & Genetic Epidemiology, King's College London, St Thomas' Campus, London, UK
| | - Kathleen A Jablonski
- Biostatistics Center, Department of Epidemiology and Biostatistics, School of Public Health, George Washington University, Rockville, MD, USA
| | - Ilja M Nolte
- Department of Epidemiology, University Medical Center, University of Groningen, Groningen, Netherlands
| | - Denise K Houston
- Gerontology and Geriatric Medicine, Department of Internal Medicine, and J Paul Sticht Center on Aging, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Tarunveer Singh Ahluwalia
- Metabolic Genetics, Novo Nordisk Foundation Centre for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Copenhagen Prospective Studies on Asthma in Childhood, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Danish Pediatric Asthma Center, Gentofte Hospital, Copenhagen, Denmark
| | - Peter J van der Most
- Department of Epidemiology, University Medical Center, University of Groningen, Groningen, Netherlands
| | - Dorota Pasko
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | - Lina Zgaga
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK; Department of Public Health and Primary Care, Trinity College Dublin, Dublin, Ireland
| | - Elisabeth Thiering
- Institute of Epidemiology I, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Division of Metabolic Diseases and Nutritional Medicine, Ludwig Maximilian University of Munich, Dr von Hauner Children's Hospital, Munich, Germany
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Ross M Fraser
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK
| | - Jennifer E Huffman
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Rudolf A de Boer
- Department of Cardiology, University Medical Center, University of Groningen, Groningen, Netherlands
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | - Kai-Uwe Saum
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Oxford NIHR Biomedical Research Centre, Oxford, UK
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA; National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA
| | - Karl-Heinz Herzig
- Institute of Biomedicine, University of Oulu, Oulu, Finland; Biocenter Oulu, University of Oulu, Oulu, Finland; Medical Research Center Oulu, Oulu University Hospital, Oulu, Finland
| | - Sylvain Sebert
- Biocenter Oulu, University of Oulu, Oulu, Finland; Institute of Health Sciences, University of Oulu, Oulu, Finland
| | - Anneli Pouta
- Obstetrics and Gynecology, Department of Clinical Sciences, Oulu University Hospital, Oulu, Finland; National Institute for Health and Welfare, Oulu, Finland
| | - Jaana Laitinen
- Finnish Institute of Occupational Health, Helsinki, Finland
| | - Marcus E Kleber
- Medical Clinic V (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Mannheim Medical Faculty, University of Heidelberg, Mannheim, Germany
| | - Gerjan Navis
- Department of Internal Medicine, Division of Nephrology, University Medical Center, University of Groningen, Groningen, Netherlands
| | - Mattias Lorentzon
- Centre for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Karen Jameson
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Nigel Arden
- NIHR Oxford Musculoskeletal Biomedical Research Unit, University of Oxford, Oxford, UK; MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Jackie A Cooper
- Cardiovascular Genetics, BHF Laboratories, Institute of Cardiovascular Science, University College London, London, UK
| | - Jayshree Acharya
- Cardiovascular Genetics, BHF Laboratories, Institute of Cardiovascular Science, University College London, London, UK
| | - Rebecca Hardy
- MRC Unit for Lifelong Health and Ageing, University College London, London, UK
| | - Olli Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland; Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Liana K Billings
- Center for Human Genetic Research and Diabetes Research Center, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; NorthShore University HealthSystem, Evanston, IL, USA
| | - Jari Lahti
- Institute of Behavioural Sciences, University of Helsinki, Helsinki, Finland
| | - Clive Osmond
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Brenda W Penninx
- Department of Psychiatry, EMGO Institute, VU University Medical Centre, Amsterdam, Netherlands
| | - Lars Rejnmark
- Department of Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Kurt K Lohman
- Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Lavinia Paternoster
- MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Ronald P Stolk
- Department of Epidemiology, University Medical Center, University of Groningen, Groningen, Netherlands
| | - Dena G Hernandez
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Liisa Byberg
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Emil Hagström
- Uppsala Clinical Research Centre, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Håkan Melhus
- Uppsala Clinical Research Centre, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Erik Ingelsson
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Uppsala Clinical Research Centre, Department of Medical Sciences, Uppsala University, Uppsala, Sweden; Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Dan Mellström
- Centre for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Osten Ljunggren
- Uppsala Clinical Research Centre, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Ioanna Tzoulaki
- Faculty of Medicine, School of Public Health, Imperial College London, London, UK
| | - Stela McLachlan
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK
| | - Evropi Theodoratou
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK
| | - Carla M T Tiesler
- Institute of Epidemiology I, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany; Institute of Medical Informatics, Biometry and Epidemiology, Ludwig Maximilian University of Munich, Dr von Hauner Children's Hospital, Munich, Germany
| | - Antti Jula
- Department of Chronic Disease Prevention, National Institute for Health and Welfare, Turku, Finland
| | - Pau Navarro
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Alan F Wright
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Ozren Polasek
- Croatian Centre for Global Health, University of Split Medical School, Split, Croatia
| | | | | | | | | | - James F Wilson
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK
| | - Igor Rudan
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK
| | - Veikko Salomaa
- National Institute for Health and Welfare, Helsinki, Finland
| | - Joachim Heinrich
- Institute of Epidemiology I, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Harry Campbell
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK
| | - Jacqueline F Price
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK
| | - Magnus Karlsson
- Clinical and Molecular Osteoporosis Research Unit, Department of Clinical Sciences and Orthopaedic Surgery, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Lars Lind
- Uppsala Clinical Research Centre, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Karl Michaëlsson
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | | | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, Exeter, UK
| | - Catharina A Hartman
- Department of Psychiatry, University Medical Center, University of Groningen, Groningen, Netherlands
| | - Thorkild I A Sørensen
- Metabolic Genetics, Novo Nordisk Foundation Centre for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Institute of Preventive Medicine, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Stephen B Kritchevsky
- Gerontology and Geriatric Medicine, Department of Internal Medicine, and J Paul Sticht Center on Aging, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Bente Lomholt Langdahl
- Department of Endocrinology and Internal Medicine, Aarhus University Hospital, Aarhus, Denmark
| | - Johan G Eriksson
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland; Vasa Central Hospital, Vasa, Finland; Folkhälsan Research Centre, Helsinki, Finland; Unit of General Practice, Helsinki University Central Hospital, Helsinki, Finland
| | - Jose C Florez
- Center for Human Genetic Research and Diabetes Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Tim D Spector
- Department of Twin Research & Genetic Epidemiology, King's College London, St Thomas' Campus, London, UK
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories and School of Medicine, University of Tampere, Tampere, Finland
| | - Diana Kuh
- MRC Unit for Lifelong Health and Ageing, University College London, London, UK
| | - Steve E Humphries
- Cardiovascular Genetics, BHF Laboratories, Institute of Cardiovascular Science, University College London, London, UK
| | - Cyrus Cooper
- NIHR Oxford Musculoskeletal Biomedical Research Unit, University of Oxford, Oxford, UK; MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Claes Ohlsson
- Centre for Bone and Arthritis Research, Institute of Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Winfried März
- Medical Clinic V (Nephrology, Hypertensiology, Rheumatology, Endocrinology, Diabetology), Mannheim Medical Faculty, University of Heidelberg, Mannheim, Germany; Synlab Academy, Mannheim, Germany; Department of Internal Medicine, Division of Endocrinology and Metabolism, and Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
| | - Martin H de Borst
- Department of Internal Medicine, Division of Nephrology, University Medical Center, University of Groningen, Groningen, Netherlands
| | - Meena Kumari
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Thomas J Wang
- Division of Cardiovascular Medicine, Vanderbilt University, Nashville, TN, USA
| | - Chris Power
- Population, Policy and Practice, UCL Institute of Child Health, London, UK
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | - Guri Grimnes
- Tromsø Endocrine Research Group, Department of Clinical Medicine, University of Tromsø, Tromsø, Norway
| | - Pim van der Harst
- Department of Cardiology, University Medical Center, University of Groningen, Groningen, Netherlands
| | - Harold Snieder
- Department of Epidemiology, University Medical Center, University of Groningen, Groningen, Netherlands
| | | | - Stefan Pilz
- Department of Internal Medicine, Division of Endocrinology and Metabolism, and Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
| | | | - Marjo-Riitta Järvelin
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK; MRC-PHE Centre for Environment & Health, Imperial College London, London, UK; Biocenter Oulu, University of Oulu, Oulu, Finland; Institute of Health Sciences, University of Oulu, Oulu, Finland; Unit of Primary Care, Oulu University Hospital, Oulu, Finland; National Institute for Health and Welfare, Oulu, Finland
| | - Elina Hyppönen
- Population, Policy and Practice, UCL Institute of Child Health, London, UK; School of Population Health, Sansom Institute for Health Research, University of South Australia, Adelaide, SA, Australia; South Australian Health and Medical Research Institute, Adelaide, SA, Australia.
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30
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Billings LK, Jablonski KA, Ackerman RJ, Taylor A, Fanelli RR, McAteer JB, Guiducci C, Delahanty LM, Dabelea D, Kahn SE, Franks PW, Hanson RL, Maruthur NM, Shuldiner AR, Mayer-Davis EJ, Knowler WC, Florez JC. The influence of rare genetic variation in SLC30A8 on diabetes incidence and β-cell function. J Clin Endocrinol Metab 2014; 99:E926-30. [PMID: 24471563 PMCID: PMC4010688 DOI: 10.1210/jc.2013-2378] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
CONTEXT/OBJECTIVE The variant rs13266634 in SLC30A8, encoding a β-cell-specific zinc transporter, is associated with type 2 diabetes. We aimed to identify other variants in SLC30A8 that increase diabetes risk and impair β-cell function, and test whether zinc intake modifies this risk. DESIGN/OUTCOME: We sequenced exons in SLC30A8 in 380 Diabetes Prevention Program (DPP) participants and identified 44 novel variants, which were genotyped in 3445 DPP participants and tested for association with diabetes incidence and measures of insulin secretion and processing. We examined individual common variants and used gene burden tests to test 39 rare variants in aggregate. RESULTS We detected a near-nominal association between a rare-variant genotype risk score and diabetes risk. Five common variants were associated with the oral disposition index. Various methods aggregating rare variants demonstrated associations with changes in oral disposition index and insulinogenic index during year 1 of follow-up. We did not find a clear interaction of zinc intake with genotype on diabetes incidence. CONCLUSIONS Individual common and an aggregate of rare genetic variation in SLC30A8 are associated with measures of β-cell function in the DPP. Exploring rare variation may complement ongoing efforts to uncover the genetic influences that underlie complex diseases.
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Affiliation(s)
- Liana K Billings
- Center for Human Genetic Research (L.K.B., R.J.A., A.T., R.R.F., J.B.M., J.C.F.) and Diabetes Research Center (Diabetes Unit) (L.K.B., L.M.D., J.C.F.), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114; Department of Medicine (L.K.B., L.M.D., J.C.F.), Harvard Medical School, and Department of Nutrition (P.W.F.), Harvard School of Public Health, Boston, Massachusetts 02115; Department of Medicine (L.K.B.), NorthShore University HealthSystem, Evanston, Illinois 60201; University of Chicago (L.K.B.), Pritzker School of Medicine, Chicago, Illinois 60637; The Biostatistics Center (K.A.J.), George Washington University, Rockville, Maryland 20852; Program in Medical and Population Genetics (A.T., J.B.M., C.G., J.C.F.), Broad Institute, Cambridge, Massachusetts 02142; Department of Epidemiology (D.D.), Colorado School of Public Health, University of Colorado, Denver, Colorado 80045; Division of Metabolism, Endocrinology, and Nutrition (S.E.K.), VA Puget Sound Health Care System and University of Washington, Seattle, Washington 98108; Department of Clinical Sciences (P.W.F.), Genetic and Molecular Epidemiology Unit, Lund University, SE-200 41 Malmö, Sweden; Diabetes Epidemiology and Clinical Research Section (R.L.H., W.C.K.), National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona 85014; Department of Medicine (N.M.M.), Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205; Department of Medicine (A.R.S.), Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland 21201; and Department of Nutrition (E.J.M.-D.), University of North Carolina, Gillings School of Global Public Health, Chapel Hill, North Carolina 27599
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Goldfine AB, Jablonski KA, Shoelson SE, Creager MA. Response to comment on Goldfine et al. Targeting inflammation using salsalate in patients with type 2 diabetes: effects on flow-mediated dilation (TINSAL-FMD). Diabetes care 2013;36:4132-4139. Diabetes Care 2014; 37:e112. [PMID: 24757239 PMCID: PMC4876756 DOI: 10.2337/dc14-0222] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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32
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Sullivan SD, Jablonski KA, Florez JC, Dabelea D, Franks PW, Dagogo-Jack S, Kim C, Knowler WC, Christophi CA, Ratner R. Genetic risk of progression to type 2 diabetes and response to intensive lifestyle or metformin in prediabetic women with and without a history of gestational diabetes mellitus. Diabetes Care 2014; 37:909-11. [PMID: 24271189 PMCID: PMC3964494 DOI: 10.2337/dc13-0700] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
OBJECTIVE The Diabetes Prevention Program (DPP) trial investigated rates of progression to diabetes among adults with prediabetes randomized to treatment with placebo, metformin, or intensive lifestyle intervention. Among women in the DPP, diabetes risk reduction with metformin was greater in women with prior gestational diabetes mellitus (GDM) compared with women without GDM but with one or more previous live births. RESEARCH DESIGN AND METHODS We asked if genetic variability could account for these differences by comparing β-cell function and genetic risk scores (GRS), calculated from 34 diabetes-associated loci, between women with and without histories of GDM. RESULTS β-Cell function was reduced in women with GDM. The GRS was positively associated with a history of GDM; however, the GRS did not predict progression to diabetes or modulate response to intervention. CONCLUSIONS These data suggest that a diabetes-associated GRS is associated with development of GDM and may characterize women at risk for development of diabetes due to β-cell dysfunction.
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33
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Barzilay JI, Jablonski KA, Fonseca V, Shoelson SE, Goldfine AB, Strauch C, Monnier VM. The impact of salsalate treatment on serum levels of advanced glycation end products in type 2 diabetes. Diabetes Care 2014; 37:1083-91. [PMID: 24255104 PMCID: PMC3964486 DOI: 10.2337/dc13-1527] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Salsalate is a nonacetylated salicylate that lowers glucose levels in people with type 2 diabetes (T2D). Here we examined whether salsalate also lowered serum-protein-bound levels of early and advanced glycation end products (AGEs) that have been implicated in diabetic vascular complications. RESEARCH DESIGN AND METHODS Participants were from the Targeting Inflammation Using Salsalate for Type 2 Diabetes (TINSAL-T2D) study, which examined the impact of salsalate treatment on hemoglobin A1c (HbA1c) and a wide variety of other parameters. One hundred eighteen participants received salsalate, 3.5 g/day for 48 weeks, and 109 received placebo. Early glycation product levels (HbA1c and fructoselysine [measured as furosine]) and AGE levels (glyoxal and methylglyoxal hydroimidazolones [G-(1)H, MG-(1)H], carboxymethyllysine [CML], carboxyethyllysine [CEL], pentosidine) were measured in patient serum samples. RESULTS Forty-eight weeks of salsalate treatment lowered levels of HbA1c and serum furosine (P < 0.001) and CML compared with placebo. The AGEs CEL and G-(1)H and MG-(1)H levels were unchanged, whereas pentosidine levels increased more than twofold (P < 0.001). Among salsalate users, increases in adiponectin levels were associated with lower HbA1c levels during follow-up (P < 0.001). Changes in renal and inflammation factor levels were not associated with changes in levels of early or late glycation factors. Pentosidine level changes were unrelated to changes in levels of renal function, inflammation, or cytokines. CONCLUSIONS Salsalate therapy was associated with a reduction in early but not late glycation end products. There was a paradoxical increase in serum pentosidine levels suggestive of an increase in oxidative stress or decreased clearance of pentosidine precursor.
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Franks PW, Christophi CA, Jablonski KA, Billings LK, Delahanty LM, Horton ES, Knowler WC, Florez JC. Common variation at PPARGC1A/B and change in body composition and metabolic traits following preventive interventions: the Diabetes Prevention Program. Diabetologia 2014; 57:485-90. [PMID: 24317794 PMCID: PMC4154629 DOI: 10.1007/s00125-013-3133-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2013] [Accepted: 11/19/2013] [Indexed: 12/21/2022]
Abstract
AIMS/HYPOTHESIS PPARGC1A and PPARGCB encode transcriptional coactivators that regulate numerous metabolic processes. We tested associations and treatment (i.e. metformin or lifestyle modification) interactions with metabolic traits in the Diabetes Prevention Program, a randomised controlled trial in persons at high risk of type 2 diabetes. METHODS We used Tagger software to select 75 PPARGCA1 and 94 PPARGC1B tag single-nucleotide polymorphisms (SNPs) for analysis. These SNPs were tested for associations with relevant cardiometabolic quantitative traits using generalised linear models. Aggregate genetic effects were tested using the sequence kernel association test. RESULTS In aggregate, PPARGC1A variation was strongly associated with baseline triacylglycerol concentrations (p = 2.9 × 10(-30)), BMI (p = 2.0 × 10(-5)) and visceral adiposity (p = 1.9 × 10(-4)), as well as with changes in triacylglycerol concentrations (p = 1.7 × 10(-5)) and BMI (p = 9.9 × 10(-5)) from baseline to 1 year. PPARGC1B variation was only associated with baseline subcutaneous adiposity (p = 0.01). In individual SNP analyses, Gly482Ser (rs8192678, PPARGC1A) was associated with accumulation of subcutaneous adiposity and worsening insulin resistance at 1 year (both p < 0.05), while rs2970852 (PPARGC1A) modified the effects of metformin on triacylglycerol levels (p(interaction) = 0.04). CONCLUSIONS/INTERPRETATION These findings provide several novel and other confirmatory insights into the role of PPARGC1A variation with respect to diabetes-related metabolic traits. TRIAL REGISTRATION ClinicalTrials.gov NCT00004992.
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Affiliation(s)
- Paul W Franks
- Department of Clinical Science, Genetic and Molecular Epidemiology Unit, Lund University, Malmö, Sweden,
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35
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Goldfine AB, Buck JS, Desouza C, Fonseca V, Chen YDI, Shoelson SE, Jablonski KA, Creager MA. Targeting inflammation using salsalate in patients with type 2 diabetes: effects on flow-mediated dilation (TINSAL-FMD). Diabetes Care 2013; 36:4132-9. [PMID: 24130358 PMCID: PMC3836144 DOI: 10.2337/dc13-0859] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To test whether inhibiting inflammation with salsalate improves endothelial function in patients with type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS We conducted an ancillary study to the National Institutes of Health-sponsored, multicenter, randomized, double-masked, placebo-controlled trial evaluating the safety and efficacy of salsalate in targeting inflammation to improve glycemia in patients with T2D. Flow-mediated, endothelium-dependent dilation (FMD) and endothelium-independent, nitroglycerin-mediated dilation (NMD) of the brachial artery were assessed at baseline and 3 and 6 months following randomization to either salsalate 3.5 g/day or placebo. The primary end point was change in FMD at 6 months. RESULTS A total of 88 participants were enrolled in the study, and data after randomization were available for 75. Patients in the treatment and control groups had similar ages (56 years), BMI (33 kg/m(2)), sex (64% male), ethnicity, current treatment, and baseline HbA1c (7.7% [61 mmol/mol]). In patients treated with salsalate versus placebo, HbA1c was reduced by 0.46% (5.0 mmol/mol; P < 0.001), fasting glucose by 16.1 mg/dL (P < 0.001), and white blood cell count by 430 cells/µL (P < 0.02). There was no difference in the mean change in either FMD (0.70% [95% CI -0.86 to 2.25%]; P = 0.38) or NMD (-0.59% [95% CI -2.70 to 1.51%]; P = 0.57) between the groups treated with salsalate and placebo at 6 months. Total and LDL cholesterol were 11 and 16 mg/dL higher, respectively, and urinary albumin was 2.0 µg/mg creatinine higher in the patients treated with salsalate compared with those treated with placebo (all P < 0.009). CONCLUSIONS Salsalate does not change FMD in peripheral conduit arteries in patients with T2D despite lowering HbA1c. This finding suggests that salsalate does not have an effect on vascular inflammation, inflammation does not cause endothelial dysfunction in T2D, or confounding effects of salsalate mitigate favorable effects on endothelial function.
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36
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Pan Q, Delahanty LM, Jablonski KA, Knowler WC, Kahn SE, Florez JC, Franks PW. Variation at the melanocortin 4 receptor gene and response to weight-loss interventions in the diabetes prevention program. Obesity (Silver Spring) 2013; 21:E520-6. [PMID: 23512951 PMCID: PMC4023472 DOI: 10.1002/oby.20459] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2012] [Accepted: 03/05/2013] [Indexed: 02/01/2023]
Abstract
OBJECTIVE To assess associations and genotype × treatment interactions for melanocortin 4 receptor (MC4R) locus variants and obesity-related traits. DESIGN AND METHODS Diabetes prevention program (DPP) participants (N = 3,819, of whom 3,356 were genotyped for baseline and 3,234 for longitudinal analyses) were randomized into intensive lifestyle modification (diet, exercise, weight loss), metformin or placebo control. Adiposity was assessed in a subgroup (n = 909) using computed tomography. All analyses were adjusted for age, sex, ethnicity and treatment. RESULTS The rs1943218 minor allele was nominally associated with short-term (6 month; P = 0.032) and long-term (2 year; P = 0.038) weight change. Eight SNPs modified response to treatment on short-term (rs17066856, rs9966412, rs17066859, rs8091237, rs17066866, rs7240064) or long-term (rs12970134, rs17066866) reduction in body weight, or diabetes incidence (rs17066829) (all Pinteraction < 0.05). CONCLUSION This is the first study to comprehensively assess the role of MC4R variants and weight regulation in a weight loss intervention trial. One MC4R variant was directly associated with obesity-related traits or diabetes; numerous other variants appear to influence body weight and diabetes risk by modifying the protective effects of the DPP interventions.
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Affiliation(s)
- Qing Pan
- The Biostatistics Center, George Washington University, Rockville, Maryland
| | - Linda M. Delahanty
- Diabetes Research Center, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | | | - William C. Knowler
- National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona
| | - Steven E. Kahn
- VA Puget Sound Health Care System and University of Washington, Seattle, WA
| | - Jose C. Florez
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Center for Human Genetic Research and Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
| | - Paul W. Franks
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts
- Department of Public Health & Clinical Medicine, Umeå University, Umeå, Sweden
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Abstract
BACKGROUND Short-duration studies show that salsalate improves glycemia in type 2 diabetes mellitus (T2DM). OBJECTIVE To assess 1-year efficacy and safety of salsalate in T2DM. DESIGN Placebo-controlled, parallel trial; computerized randomization and centralized allocation, with patients, providers, and researchers blinded to assignment. (ClinicalTrials.gov: NCT00799643). SETTING 3 private practices and 18 academic centers in the United States. PATIENTS Persons aged 18 to 75 years with fasting glucose levels of 12.5 mmol/L or less (≤225 mg/dL) and hemoglobin A1c (HbA1c) levels of 7.0% to 9.5% who were treated for diabetes. INTERVENTION 286 participants were randomly assigned (between January 2009 and July 2011) to 48 weeks of placebo (n = 140) or salsalate, 3.5 g/d (n = 146), in addition to current therapies, and 283 participants were analyzed (placebo, n = 137; salsalate, n = 146). MEASUREMENTS Change in hemoglobin A1c level (primary outcome) and safety and efficacy measures. RESULTS The mean HbA1c level over 48 weeks was 0.37% lower in the salsalate group than in the placebo group (95% CI, -0.53% to -0.21%; P < 0.001). Glycemia improved despite more reductions in concomitant diabetes medications in salsalate recipients than in placebo recipients. Lower circulating leukocyte, neutrophil, and lymphocyte counts show the anti-inflammatory effects of salsalate. Adiponectin and hematocrit levels increased more and fasting glucose, uric acid, and triglyceride levels decreased with salsalate, but weight and low-density lipoprotein cholesterol levels also increased. Urinary albumin levels increased but reversed on discontinuation; estimated glomerular filtration rates were unchanged. LIMITATION Trial duration and number of patients studied were insufficient to determine long-term risk-benefit of salsalate in T2DM. CONCLUSION Salsalate improves glycemia in patients with T2DM and decreases inflammatory mediators. Continued evaluation of mixed cardiorenal signals is warranted.
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Affiliation(s)
- Allison B Goldfine
- Joslin Diabetes Center and Harvard Medical School, Boston, Massachusetts 02215, USA.
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Mather KJ, Christophi CA, Jablonski KA, Knowler WC, Goldberg RB, Kahn SE, Spector T, Dastani Z, Waterworth D, Richards JB, Funahashi T, Pi-Sunyer FX, Pollin TI, Florez JC, Franks PW. Common variants in genes encoding adiponectin (ADIPOQ) and its receptors (ADIPOR1/2), adiponectin concentrations, and diabetes incidence in the Diabetes Prevention Program. Diabet Med 2012; 29:1579-88. [PMID: 22443353 PMCID: PMC3499646 DOI: 10.1111/j.1464-5491.2012.03662.x] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
AIMS Baseline adiponectin concentrations predict incident Type 2 diabetes mellitus in the Diabetes Prevention Program. We tested the hypothesis that common variants in the genes encoding adiponectin (ADIPOQ) and its receptors (ADIPOR1, ADIPOR2) would associate with circulating adiponectin concentrations and/or with diabetes incidence in the Diabetes Prevention Program population. METHODS Seventy-seven tagging single-nucleotide polymorphisms (SNPs) in ADIPOQ (24), ADIPOR1 (22) and ADIPOR2 (31) were genotyped. Associations of SNPs with baseline adiponectin concentrations were evaluated using linear modelling. Associations of SNPs with diabetes incidence were evaluated using Cox proportional hazards modelling. RESULTS Thirteen of 24 ADIPOQ SNPs were significantly associated with baseline adiponectin concentrations. Multivariable analysis including these 13 SNPs revealed strong independent contributions of rs17366568, rs1648707, rs17373414 and rs1403696 with adiponectin concentrations. However, no ADIPOQ SNPs were directly associated with diabetes incidence. Two ADIPOR1 SNPs (rs1342387 and rs12733285) were associated with ∼18% increased diabetes incidence for carriers of the minor allele without differences across treatment groups, and without any relationship with adiponectin concentrations. CONCLUSIONS ADIPOQ SNPs are significantly associated with adiponectin concentrations in the Diabetes Prevention Program cohort. This observation extends prior observations from unselected populations of European descent into a broader multi-ethnic population, and confirms the relevance of these variants in an obese/dysglycaemic population. Despite the robust relationship between adiponectin concentrations and diabetes risk in this cohort, variants in ADIPOQ that relate to adiponectin concentrations do not relate to diabetes risk in this population. ADIPOR1 variants exerted significant effects on diabetes risk distinct from any effect of adiponectin concentrations.
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Affiliation(s)
- K J Mather
- Division of Endocrinology and Metabolism, Indiana University, Indianapolis, IN, USA.
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Florez JC, Jablonski KA, McAteer JB, Franks PW, Mason CC, Mather K, Horton E, Goldberg R, Dabelea D, Kahn SE, Arakaki RF, Shuldiner AR, Knowler WC. Effects of genetic variants previously associated with fasting glucose and insulin in the Diabetes Prevention Program. PLoS One 2012; 7:e44424. [PMID: 22984506 PMCID: PMC3439414 DOI: 10.1371/journal.pone.0044424] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2012] [Accepted: 08/03/2012] [Indexed: 11/19/2022] Open
Abstract
Common genetic variants have been recently associated with fasting glucose and insulin levels in white populations. Whether these associations replicate in pre-diabetes is not known. We extended these findings to the Diabetes Prevention Program, a clinical trial in which participants at high risk for diabetes were randomized to placebo, lifestyle modification or metformin for diabetes prevention. We genotyped previously reported polymorphisms (or their proxies) in/near G6PC2, MTNR1B, GCK, DGKB, GCKR, ADCY5, MADD, CRY2, ADRA2A, FADS1, PROX1, SLC2A2, GLIS3, C2CD4B, IGF1, and IRS1 in 3,548 Diabetes Prevention Program participants. We analyzed variants for association with baseline glycemic traits, incident diabetes and their interaction with response to metformin or lifestyle intervention. We replicated associations with fasting glucose at MTNR1B (P<0.001), G6PC2 (P = 0.002) and GCKR (P = 0.001). We noted impaired β-cell function in carriers of glucose-raising alleles at MTNR1B (P<0.001), and an increase in the insulinogenic index for the glucose-raising allele at G6PC2 (P<0.001). The association of MTNR1B with fasting glucose and impaired β-cell function persisted at 1 year despite adjustment for the baseline trait, indicating a sustained deleterious effect at this locus. We also replicated the association of MADD with fasting proinsulin levels (P<0.001). We detected no significant impact of these variants on diabetes incidence or interaction with preventive interventions. The association of several polymorphisms with quantitative glycemic traits is replicated in a cohort of high-risk persons. These variants do not have a detectable impact on diabetes incidence or response to metformin or lifestyle modification in the Diabetes Prevention Program.
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Affiliation(s)
- Jose C. Florez
- Center for Human Genetic Research and Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail: (DPPRG); (JCF)
| | - Kathleen A. Jablonski
- The Biostatistics Center, George Washington University, Rockville, Maryland, United States of America
| | - Jarred B. McAteer
- Center for Human Genetic Research and Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
| | - Paul W. Franks
- Lund University Diabetes Center, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Clinton C. Mason
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona, United States of America
| | - Kieren Mather
- Division of Endocrinology, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
| | - Edward Horton
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
- Joslin Diabetes Center, Boston, Massachusetts, United States of America
| | - Ronald Goldberg
- Lipid Disorders Clinic, Division of Endocrinology, Diabetes, and Metabolism, and the Diabetes Research Institute, Leonard M. Miller School of Medicine, University of Miami, Miami, Florida, United States of America
| | - Dana Dabelea
- Department of Preventive Medicine and Biometrics, University of Colorado at Denver and Health Sciences Center, Denver, Colorado, United States of America
| | - Steven E. Kahn
- Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, VA Puget Sound Health Care System and University of Washington, Seattle, Washington, United States of America
| | - Richard F. Arakaki
- Department of Medicine Clinical Research, University of Hawaii, Honolulu, Hawaii, United States of America
| | - Alan R. Shuldiner
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - William C. Knowler
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona, United States of America
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Florez JC, Jablonski KA, Taylor A, Mather K, Horton E, White NH, Barrett-Connor E, Knowler WC, Shuldiner AR, Pollin TI. The C allele of ATM rs11212617 does not associate with metformin response in the Diabetes Prevention Program. Diabetes Care 2012; 35:1864-7. [PMID: 22751958 PMCID: PMC3425006 DOI: 10.2337/dc11-2301] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE The C allele at the rs11212617 polymorphism in the ataxia-telangiectasia-mutated (ATM) gene has been associated with greater clinical response to metformin in people with type 2 diabetes. We tested whether this variant modified the effect of metformin in the Diabetes Prevention Program (DPP), in which metformin reduced diabetes incidence by 31% in volunteers with impaired glucose tolerance. RESEARCH DESIGN AND METHODS We genotyped rs11212617 in 2,994 DPP participants and analyzed its effects on diabetes incidence and related traits. RESULTS Contrary to expectations, C carriers enjoyed no preventive advantage on metformin; their hazard ratio, compared with A carriers, was 1.17 ([95% CI 0.96-1.42], P = 0.13) under metformin. There were no significant differences by genotype in metformin's effects on insulin sensitivity, fasting glucose, glycated hemoglobin, or disposition index. CONCLUSIONS The reported association of rs11212617 with metformin response was not confirmed for diabetes prevention or for effects on relevant physiologic parameters in the DPP.
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Delahanty LM, Pan Q, Jablonski KA, Watson KE, McCaffery JM, Shuldiner A, Kahn SE, Knowler WC, Florez JC, Franks PW. Genetic predictors of weight loss and weight regain after intensive lifestyle modification, metformin treatment, or standard care in the Diabetes Prevention Program. Diabetes Care 2012; 35:363-6. [PMID: 22179955 PMCID: PMC3263869 DOI: 10.2337/dc11-1328] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We tested genetic associations with weight loss and weight regain in the Diabetes Prevention Program, a randomized controlled trial of weight loss-inducing interventions (lifestyle and metformin) versus placebo. RESEARCH DESIGN AND METHODS Sixteen obesity-predisposing single nucleotide polymorphisms (SNPs) were tested for association with short-term (baseline to 6 months) and long-term (baseline to 2 years) weight loss and weight regain (6 months to study end). RESULTS Irrespective of treatment, the Ala12 allele at PPARG associated with short- and long-term weight loss (-0.63 and -0.93 kg/allele, P ≤ 0.005, respectively). Gene-treatment interactions were observed for short-term (LYPLAL1 rs2605100, P(lifestyle*SNP) = 0.032; GNPDA2 rs10938397, P(lifestyle*SNP) = 0.016; MTCH2 rs10838738, P(lifestyle*SNP) = 0.022) and long-term (NEGR1 rs2815752, P(metformin*SNP) = 0.028; FTO rs9939609, P(lifestyle*SNP) = 0.044) weight loss. Three of 16 SNPs were associated with weight regain (NEGR1 rs2815752, BDNF rs6265, PPARG rs1801282), irrespective of treatment. TMEM18 rs6548238 and KTCD15 rs29941 showed treatment-specific effects (P(lifestyle*SNP) < 0.05). CONCLUSIONS Genetic information may help identify people who require additional support to maintain reduced weight after clinical intervention.
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Affiliation(s)
- Linda M Delahanty
- Diabetes Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
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Kilpeläinen TO, Qi L, Brage S, Sharp SJ, Sonestedt E, Demerath E, Ahmad T, Mora S, Kaakinen M, Sandholt CH, Holzapfel C, Autenrieth CS, Hyppönen E, Cauchi S, He M, Kutalik Z, Kumari M, Stančáková A, Meidtner K, Balkau B, Tan JT, Mangino M, Timpson NJ, Song Y, Zillikens MC, Jablonski KA, Garcia ME, Johansson S, Bragg-Gresham JL, Wu Y, van Vliet-Ostaptchouk JV, Onland-Moret NC, Zimmermann E, Rivera NV, Tanaka T, Stringham HM, Silbernagel G, Kanoni S, Feitosa MF, Snitker S, Ruiz JR, Metter J, Larrad MTM, Atalay M, Hakanen M, Amin N, Cavalcanti-Proença C, Grøntved A, Hallmans G, Jansson JO, Kuusisto J, Kähönen M, Lutsey PL, Nolan JJ, Palla L, Pedersen O, Pérusse L, Renström F, Scott RA, Shungin D, Sovio U, Tammelin TH, Rönnemaa T, Lakka TA, Uusitupa M, Rios MS, Ferrucci L, Bouchard C, Meirhaeghe A, Fu M, Walker M, Borecki IB, Dedoussis GV, Fritsche A, Ohlsson C, Boehnke M, Bandinelli S, van Duijn CM, Ebrahim S, Lawlor DA, Gudnason V, Harris TB, Sørensen TIA, Mohlke KL, Hofman A, Uitterlinden AG, Tuomilehto J, Lehtimäki T, Raitakari O, Isomaa B, Njølstad PR, Florez JC, Liu S, Ness A, Spector TD, Tai ES, Froguel P, Boeing H, Laakso M, Marmot M, Bergmann S, Power C, Khaw KT, Chasman D, Ridker P, Hansen T, Monda KL, Illig T, Järvelin MR, Wareham NJ, Hu FB, Groop LC, Orho-Melander M, Ekelund U, Franks PW, Loos RJF. Physical activity attenuates the influence of FTO variants on obesity risk: a meta-analysis of 218,166 adults and 19,268 children. PLoS Med 2011; 8:e1001116. [PMID: 22069379 PMCID: PMC3206047 DOI: 10.1371/journal.pmed.1001116] [Citation(s) in RCA: 392] [Impact Index Per Article: 30.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2011] [Accepted: 09/23/2011] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The FTO gene harbors the strongest known susceptibility locus for obesity. While many individual studies have suggested that physical activity (PA) may attenuate the effect of FTO on obesity risk, other studies have not been able to confirm this interaction. To confirm or refute unambiguously whether PA attenuates the association of FTO with obesity risk, we meta-analyzed data from 45 studies of adults (n = 218,166) and nine studies of children and adolescents (n = 19,268). METHODS AND FINDINGS All studies identified to have data on the FTO rs9939609 variant (or any proxy [r(2)>0.8]) and PA were invited to participate, regardless of ethnicity or age of the participants. PA was standardized by categorizing it into a dichotomous variable (physically inactive versus active) in each study. Overall, 25% of adults and 13% of children were categorized as inactive. Interaction analyses were performed within each study by including the FTO×PA interaction term in an additive model, adjusting for age and sex. Subsequently, random effects meta-analysis was used to pool the interaction terms. In adults, the minor (A-) allele of rs9939609 increased the odds of obesity by 1.23-fold/allele (95% CI 1.20-1.26), but PA attenuated this effect (p(interaction) = 0.001). More specifically, the minor allele of rs9939609 increased the odds of obesity less in the physically active group (odds ratio = 1.22/allele, 95% CI 1.19-1.25) than in the inactive group (odds ratio = 1.30/allele, 95% CI 1.24-1.36). No such interaction was found in children and adolescents. CONCLUSIONS The association of the FTO risk allele with the odds of obesity is attenuated by 27% in physically active adults, highlighting the importance of PA in particular in those genetically predisposed to obesity.
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Affiliation(s)
- Tuomas O Kilpeläinen
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Cambridge, United Kingdom
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Londy FJ, Lowe S, Stein PD, Weg JG, Eisner RL, Leeper KV, Woodard PK, Sostman HD, Jablonski KA, Fowler SE, Hales CA, Hull RD, Gottschalk A, Naidich DP, Chenevert TL. Comparison of 1.5 and 3.0 T for contrast-enhanced pulmonary magnetic resonance angiography. Clin Appl Thromb Hemost 2011; 18:134-9. [PMID: 21993980 DOI: 10.1177/1076029611419840] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE In a recent multi-center trial of gadolinium contrast-enhanced magnetic resonance angiography (Gd-MRA) for diagnosis of acute pulmonary embolism (PE), two centers utilized a common MRI platform though at different field strengths (1.5T and 3T) and realized a signal-to-noise gain with the 3T platform. This retrospective analysis investigates this gain in signal-to-noise of pulmonary vascular targets. METHODS Thirty consecutive pulmonary MRA examinations acquired on a 1.5T system at one institution were compared to 30 consecutive pulmonary MRA examinations acquired on a 3T system at a different institution. Both systems were from the same MRI manufacturer and both used the same Gd-MRA pulse sequence, although there were some protocol adjustments made due to field strength differences. Region-of-interests were manually defined on the main pulmonary artery, 4 pulmonary veins, thoracic aorta, and background lung for objective measurement of signal-to-noise, contrast-to-noise, and bolus timing bias between centers. RESULTS The 3T pulmonary MRA protocol achieved higher spatial resolution yet maintained significantly higher signal-to-noise ratio (≥13%, p = 0.03) in the main pulmonary vessels relative to 1.5T. There was no evidence of operator bias in bolus timing or patient hemodynamic differences between groups. CONCLUSION Relative to 1.5T, higher spatial resolution Gd-MRA can be achieved at 3T with a sustained or greater signal-to-noise ratio of enhanced vasculature.
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Majithia AR, Jablonski KA, McAteer JB, Mather KJ, Goldberg RB, Kahn SE, Florez JC. Association of the SLC30A8 missense polymorphism R325W with proinsulin levels at baseline and after lifestyle, metformin or troglitazone intervention in the Diabetes Prevention Program. Diabetologia 2011; 54:2570-4. [PMID: 21779873 PMCID: PMC3444290 DOI: 10.1007/s00125-011-2234-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2011] [Accepted: 06/08/2011] [Indexed: 10/18/2022]
Abstract
AIMS/HYPOTHESIS Individuals with impaired glucose tolerance have increased proinsulin levels, despite normal glucose or C-peptide levels. In the Diabetes Prevention Program (DPP), increased proinsulin levels predicted type 2 diabetes and proinsulin levels were significantly reduced following treatment with metformin, lifestyle modification or troglitazone compared with placebo. Genetic and physiological studies suggest a role for the zinc transporter gene SLC30A8 in diabetes risk, possibly through effects on insulin-processing in beta cells. We hypothesised that the risk allele at the type 2 diabetes-associated missense polymorphism rs13266634 (R325W) in SLC30A8 would predict proinsulin levels in individuals at risk of type 2 diabetes and may modulate response to preventive interventions. METHODS We genotyped rs13266634 in 3,007 DPP participants and examined its association with fasting proinsulin and fasting insulin at baseline and at 1 year post-intervention. RESULTS We found that increasing dosage of the C risk allele at SLC30A8 rs13266634 was significantly associated with higher proinsulin levels at baseline (p = 0.002) after adjustment for baseline insulin. This supports the hypothesis that risk alleles at SLC30A8 mark individuals with insulin-processing defects. At the 1 year analysis, proinsulin levels decreased significantly in all groups receiving active intervention and were no longer associated with SLC30A8 genotype (p = 0.86) after adjustment for insulin at baseline and 1 year. We found no genotype × treatment interactions at 1 year. CONCLUSIONS/INTERPRETATION In prediabetic individuals, genotype at SLC30A8 predicts baseline proinsulin levels independently of insulin levels, but does not predict proinsulin levels after amelioration of insulin sensitivity at 1 year.
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Affiliation(s)
- A R Majithia
- Center for Human Genetic Research and Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, MA, USA
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McCaffery JM, Jablonski KA, Franks PW, Dagogo-Jack S, Wing RR, Knowler WC, Delahanty L, Dabelea D, Hamman R, Shuldiner AR, Florez JC. TCF7L2 polymorphism, weight loss and proinsulin:insulin ratio in the diabetes prevention program. PLoS One 2011; 6:e21518. [PMID: 21814547 PMCID: PMC3144193 DOI: 10.1371/journal.pone.0021518] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2011] [Accepted: 06/02/2011] [Indexed: 01/17/2023] Open
Abstract
Aims TCF7L2 variants have been associated with type 2 diabetes, body mass index (BMI), and deficits in proinsulin processing and insulin secretion. Here we sought to test whether these effects were apparent in high-risk individuals and modify treatment responses. Methods We examined the potential role of the TCF7L2 rs7903146 variant in predicting resistance to weight loss or a lack of improvement of proinsulin processing during 2.5-years of follow-up participants (N = 2,994) from the Diabetes Prevention Program (DPP), a randomized controlled trial designed to prevent or delay diabetes in high-risk adults. Results We observed no difference in the degree of weight loss by rs7903146 genotypes. However, the T allele (conferring higher risk of diabetes) at rs7903146 was associated with higher fasting proinsulin at baseline (P<0.001), higher baseline proinsulin∶insulin ratio (p<0.0001) and increased proinsulin∶insulin ratio over a median of 2.5 years of follow-up (P = 0.003). Effects were comparable across treatment arms. Conclusions The combination of a lack of impact of the TCF7L2 genotypes on the ability to lose weight, but the presence of a consistent effect on the proinsulin∶insulin ratio over the course of DPP, suggests that high-risk genotype carriers at this locus can successfully lose weight to counter diabetes risk despite persistent deficits in insulin production.
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Affiliation(s)
- Jeanne M McCaffery
- The Biostatistics Center, The George Washington University, Rockville, Maryland, United States of America.
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Pollin TI, Jablonski KA, McAteer JB, Saxena R, Kathiresan S, Kahn SE, Goldberg RB, Altshuler D, Florez JC. Triglyceride response to an intensive lifestyle intervention is enhanced in carriers of the GCKR Pro446Leu polymorphism. J Clin Endocrinol Metab 2011; 96:E1142-7. [PMID: 21525158 PMCID: PMC3205512 DOI: 10.1210/jc.2010-2324] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
CONTEXT Glucokinase regulatory protein (GCKR) regulates the trafficking and enzymatic activity of hepatic glucokinase, the rate-limiting enzyme in glycogen synthesis and glycolysis. The intronic single-nucleotide polymorphism (SNP) rs780094 (intron 16) and the missense SNP rs1260326 (P446L) in the GCKR gene are strongly associated with increased circulating triglyceride and C-reactive protein levels and, paradoxically, reductions in diabetes incidence, fasting glucose levels, and insulin resistance. OBJECTIVE, SETTING, AND PATIENTS: We sought to replicate these associations and evaluate interactions with lifestyle and metformin interventions in the multiethnic Diabetes Prevention Program (DPP). INTERVENTIONS AND MAIN OUTCOME MEASURES We genotyped the two GCKR SNP in 3346 DPP participants and evaluated association with progression to diabetes and both baseline levels and changes in triglycerides, homeostasis model assessment of insulin resistance (HOMA-IR), oral disposition index, and inflammatory markers along with their interactions with DPP interventions. RESULTS GCKR variation did not predict development of type 2 diabetes. At baseline, the 446L allele was associated with higher triglyceride and C-reactive protein levels (both P < 0.0001) and lower fasting glucose (P = 0.001) and HOMA-IR (P = 0.06). The lifestyle intervention was associated with a decrease in magnitude of the effect of the 446L allele on triglyceride levels (interaction P = 0.04). Metformin was more effective in reducing HOMA-IR in carriers of the P446 allele (interaction P = 0.05). CONCLUSIONS Intensive lifestyle intervention appears to partially mitigate the effect of the 446L allele on higher triglycerides, whereas the P446 allele appears to enhance responsiveness to the HOMA-IR-lowering effect of metformin.
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Affiliation(s)
- Toni I Pollin
- Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland21201, USA.
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Hivert MF, Jablonski KA, Perreault L, Saxena R, McAteer JB, Franks PW, Hamman RF, Kahn SE, Haffner S, Meigs JB, Altshuler D, Knowler WC, Florez JC. Updated genetic score based on 34 confirmed type 2 diabetes Loci is associated with diabetes incidence and regression to normoglycemia in the diabetes prevention program. Diabetes 2011; 60:1340-8. [PMID: 21378175 PMCID: PMC3064108 DOI: 10.2337/db10-1119] [Citation(s) in RCA: 136] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Over 30 loci have been associated with risk of type 2 diabetes at genome-wide statistical significance. Genetic risk scores (GRSs) developed from these loci predict diabetes in the general population. We tested if a GRS based on an updated list of 34 type 2 diabetes-associated loci predicted progression to diabetes or regression toward normal glucose regulation (NGR) in the Diabetes Prevention Program (DPP). RESEARCH DESIGN AND METHODS We genotyped 34 type 2 diabetes-associated variants in 2,843 DPP participants at high risk of type 2 diabetes from five ethnic groups representative of the U.S. population, who had been randomized to placebo, metformin, or lifestyle intervention. We built a GRS by weighting each risk allele by its reported effect size on type 2 diabetes risk and summing these values. We tested its ability to predict diabetes incidence or regression to NGR in models adjusted for age, sex, ethnicity, waist circumference, and treatment assignment. RESULTS In multivariate-adjusted models, the GRS was significantly associated with increased risk of progression to diabetes (hazard ratio [HR] = 1.02 per risk allele [95% CI 1.00-1.05]; P = 0.03) and a lower probability of regression to NGR (HR = 0.95 per risk allele [95% CI 0.93-0.98]; P < 0.0001). At baseline, a higher GRS was associated with a lower insulinogenic index (P < 0.001), confirming an impairment in β-cell function. We detected no significant interaction between GRS and treatment, but the lifestyle intervention was effective in the highest quartile of GRS (P < 0.0001). CONCLUSIONS A high GRS is associated with increased risk of developing diabetes and lower probability of returning to NGR in high-risk individuals, but a lifestyle intervention attenuates this risk.
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Affiliation(s)
- Marie-France Hivert
- Division of Endocrinology, Department of Medicine, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | | | - Leigh Perreault
- Department of Medicine, Division of Endocrinology, Metabolism and Diabetes, University of Colorado at Denver School of Medicine, Aurora, Colorado
| | - Richa Saxena
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
| | - Jarred B. McAteer
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
| | - Paul W. Franks
- Department of Public Health and Clinical Medicine, Division of Medicine, Genetic Epidemiology and Clinical Research Group, Umeå University Hospital, Umeå, Sweden
- Department of Clinical Sciences, Lund University Diabetes Center, Lund University, Malmö, Sweden
| | - Richard F. Hamman
- Department of Epidemiology, Colorado School of Public Health, University of Colorado at Denver, Aurora, Colorado
| | - Steven E. Kahn
- Division of Metabolism, Endocrinology and Nutrition, Veterans’ Affairs Puget Sound Health Care System and the University of Washington, Seattle, Washington
| | | | | | - James B. Meigs
- General Medicine Unit, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - David Altshuler
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Department of Genetics, Harvard Medical School, Boston, Massachusetts
- Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - William C. Knowler
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona
| | - Jose C. Florez
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Corresponding author: Jose C. Florez, and
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48
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Sostman HD, Jablonski KA, Woodard PK, Stein PD, Naidich DP, Chenevert TL, Weg JG, Hales CA, Hull RD, Goodman LR, Tapson VF. Factors in the technical quality of gadolinium enhanced magnetic resonance angiography for pulmonary embolism in PIOPED III. Int J Cardiovasc Imaging 2011; 28:303-12. [PMID: 21347594 DOI: 10.1007/s10554-011-9820-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2010] [Accepted: 01/24/2011] [Indexed: 12/14/2022]
Abstract
In a multi-center trial, gadolinium enhanced magnetic resonance angiography (MRA) for diagnosis of acute pulmonary embolism (PE) had a high rate of technically inadequate images. Accordingly, we evaluated the reasons for poor quality MRA of the pulmonary arteries in these patients. We performed a retrospective analysis of the data collected in the PIOPED III study. We assessed the relationship to the proportion of examinations deemed "uninterpretable" by central readers to the clinical centers, MR equipment platform and vendors, degree of vascular opacification in different orders of pulmonary arteries; type, frequency and severity of image artifacts; patient co-morbidities, symptoms and signs; and reader characteristics. Centers, MR equipment vendor and platform, degree of vascular opacification, and motion artifacts influenced the likelihood of central reader determinations that images were "uninterpretable". Neither the reader nor patient characteristics (age, body mass index, respiratory rate, heart rate) correlated with the likelihood of determining examinations "uninterpretable". Vascular opacification and motion artifact are the principal factors influencing MRA interpretability. Some centers obtain better images more consistently, but the reasons for differences between centers are unclear.
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Affiliation(s)
- H Dirk Sostman
- Office of the Dean and Department of Radiology, Weill Cornell Medical College and The Methodist Hospital, Houston, Texas, USA
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49
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Woodard PK, Chenevert TL, Sostman HD, Jablonski KA, Stein PD, Goodman LR, Londy FJ, Narra V, Hales CA, Hull RD, Tapson VF, Weg JG. Signal quality of single dose gadobenate dimeglumine pulmonary MRA examinations exceeds quality of MRA performed with double dose gadopentetate dimeglumine. Int J Cardiovasc Imaging 2011; 28:295-301. [PMID: 21337023 DOI: 10.1007/s10554-011-9821-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2010] [Accepted: 01/24/2011] [Indexed: 12/19/2022]
Abstract
During a recent multi-center trial assessing gadolinium (Gd)-enhanced magnetic resonance angiography (MRA) for diagnosis of acute pulmonary embolism (PE), the Food and Drug Administration announced a risk of nephrogenic sclerosing fibrosis in patients with renal insufficiency who had received intravenous Gd-based MR contrast agents. Although no patients in this trial had renal insufficiency, in cautious response to this announcement, the trial protocol was changed from an intravenous administration of 0.2 mmol/Kg of a conventional Gd-based MR contrast agent to 0.1 mmol/Kg of gadobenate dimeglumine. The study described herein compares the signal quality of pulmonary MRA performed with double dose conventional agent to single dose gadobenate dimeglumine. This study is a retrospective analysis of data from a prospective, multicenter study in men and women ≥18 years with documented presence or absence of PE. The study was approved by the Institutional Review Board at all participating centers, and all patients provided written indication of informed consent. We performed both objective and subjective analysis of pulmonary artery image quality. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the main pulmonary artery were assessed in single and double dose protocols and compared. SNR and CNR of the main PA were correlated with subjective quality assessment of main/lobar, segmental and subsegmental pulmonary arteries. Although there were individual outliers, both SNR (P = 0.01) and CNR (P = 0.008) were higher in all quartiles for examinations using gadobenate dimeglumine than with gadopentetate dimeglumine. Subjective quality of vascular signal intensity at each vessel order was significantly better for gadobenate dimeglumine (P < 0.0001), and correlated well with SNR and CNR at each order (<0.001). Because of agent high relaxivity, a single dose of gadobenate dimeglumine provides better pulmonary MRA signal quality than double dose of a conventional Gd-based MR contrast agent.
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Affiliation(s)
- Pamela K Woodard
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S. Kingshighway Blvd., St. Louis, MO 63110, USA.
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50
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Jablonski KA, McAteer JB, de Bakker PIW, Franks PW, Pollin TI, Hanson RL, Saxena R, Fowler S, Shuldiner AR, Knowler WC, Altshuler D, Florez JC. Common variants in 40 genes assessed for diabetes incidence and response to metformin and lifestyle intervention in the diabetes prevention program. Diabetes 2010; 59:2672-81. [PMID: 20682687 PMCID: PMC3279522 DOI: 10.2337/db10-0543] [Citation(s) in RCA: 208] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Genome-wide association studies have begun to elucidate the genetic architecture of type 2 diabetes. We examined whether single nucleotide polymorphisms (SNPs) identified through targeted complementary approaches affect diabetes incidence in the at-risk population of the Diabetes Prevention Program (DPP) and whether they influence a response to preventive interventions. RESEARCH DESIGN AND METHODS We selected SNPs identified by prior genome-wide association studies for type 2 diabetes and related traits, or capturing common variation in 40 candidate genes previously associated with type 2 diabetes, implicated in monogenic diabetes, encoding type 2 diabetes drug targets or drug-metabolizing/transporting enzymes, or involved in relevant physiological processes. We analyzed 1,590 SNPs for association with incident diabetes and their interaction with response to metformin or lifestyle interventions in 2,994 DPP participants. We controlled for multiple hypothesis testing by assessing false discovery rates. RESULTS We replicated the association of variants in the metformin transporter gene SLC47A1 with metformin response and detected nominal interactions in the AMP kinase (AMPK) gene STK11, the AMPK subunit genes PRKAA1 and PRKAA2, and a missense SNP in SLC22A1, which encodes another metformin transporter. The most significant association with diabetes incidence occurred in the AMPK subunit gene PRKAG2 (hazard ratio 1.24, 95% CI 1.09-1.40, P = 7 × 10(-4)). Overall, there were nominal associations with diabetes incidence at 85 SNPs and nominal interactions with the metformin and lifestyle interventions at 91 and 69 mostly nonoverlapping SNPs, respectively. The lowest P values were consistent with experiment-wide 33% false discovery rates. CONCLUSIONS We have identified potential genetic determinants of metformin response. These results merit confirmation in independent samples.
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Affiliation(s)
- Kathleen A Jablonski
- The Biostatistics Center, George Washington University, Rockville, Maryland, USA.
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