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Hayden K, Mielke M, Evans J, Neiberg R, Molina-Henry D, Culkin M, Marcovina S, Johnson K, Carmichael O, Rapp S, Sachs B, Ding J, Shappell H, Wagenknecht L, Luchsinger J, Espeland M. Erratum to: Association between Modifiable Risk Factors and Levels of Blood-Based Biomarkers of Alzheimer's and Related Dementias in the Look AHEAD Cohort. JAR Life 2024; 13:29. [PMID: 38533271 PMCID: PMC10964847 DOI: 10.14283/jarlife.2024.3] [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] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
[This corrects the article DOI: 10.14283/jarlife.2024.1.].
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Affiliation(s)
- K.M. Hayden
- Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - M.M. Mielke
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - J.K. Evans
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - R. Neiberg
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - D. Molina-Henry
- Winston-Salem State University, Winston-Salem, NC, USA
- Alzheimer’s Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, CA, USA
| | - M. Culkin
- Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - S. Marcovina
- Medpace Reference Laboratories, Cincinnati, OH, USA
| | - K.C. Johnson
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - O.T. Carmichael
- Biomedical Imaging Center, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - S.R. Rapp
- Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston-Salem, NC, USA
- Department of Psychiatry & Behavioral Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - B.C. Sachs
- Department of Neurology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
- Sticht Division of Gerontology and Geriatric Medicine Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - J. Ding
- Sticht Division of Gerontology and Geriatric Medicine Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - H. Shappell
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - L. Wagenknecht
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - J.A. Luchsinger
- Departments of Medicine and Epidemiology, Columbia University Irving Medical Center, New York, NY, USA
| | - M.A. Espeland
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
- Sticht Division of Gerontology and Geriatric Medicine Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA
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Kost RG, Cheng A, Andrews J, Chatterjee R, Dozier A, Ford D, Schlesinger N, Dykes C, Kelly-Pumarol I, Kennedy N, Lewis-Land C, Lindo S, Martinez L, Musty M, Roberts J, Vaughan R, Wagenknecht L, Carey S, Coffran C, Goodrich J, Panjala P, Cheema S, Qureshi A, Thomas E, O’Neill L, Bascompte-Moragas E, Harris P. Empowering the Participant Voice (EPV): Design and implementation of collaborative infrastructure to collect research participant experience feedback at scale. J Clin Transl Sci 2024; 8:e40. [PMID: 38476242 PMCID: PMC10928700 DOI: 10.1017/cts.2024.19] [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: 09/28/2023] [Revised: 01/15/2024] [Accepted: 01/25/2024] [Indexed: 03/14/2024] Open
Abstract
Empowering the Participant Voice (EPV) is an NCATS-funded six-CTSA collaboration to develop, demonstrate, and disseminate a low-cost infrastructure for collecting timely feedback from research participants, fostering trust, and providing data for improving clinical translational research. EPV leverages the validated Research Participant Perception Survey (RPPS) and the popular REDCap electronic data-capture platform. This report describes the development of infrastructure designed to overcome identified institutional barriers to routinely collecting participant feedback using RPPS and demonstration use cases. Sites engaged local stakeholders iteratively, incorporating feedback about anticipated value and potential concerns into project design. The team defined common standards and operations, developed software, and produced a detailed planning and implementation Guide. By May 2023, 2,575 participants diverse in age, race, ethnicity, and sex had responded to approximately 13,850 survey invitations (18.6%); 29% of responses included free-text comments. EPV infrastructure enabled sites to routinely access local and multi-site research participant experience data on an interactive analytics dashboard. The EPV learning collaborative continues to test initiatives to improve survey reach and optimize infrastructure and process. Broad uptake of EPV will expand the evidence base, enable hypothesis generation, and drive research-on-research locally and nationally to enhance the clinical research enterprise.
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Affiliation(s)
- Rhonda G. Kost
- The Rockefeller University Center for Clinical and Translational
Science, New York, NY, USA
| | - Alex Cheng
- Department of Biomedical Informatics, Vanderbilt
University, Nashville, TN,
USA
| | - Joseph Andrews
- Wake Forest School of Medicine, Clinical and Translational
Science Institute, Winston-Salem, NC,
USA
| | - Ranee Chatterjee
- Department of Medicine, Duke University School of
Medicine, Duke Clinical Translational Science Institute,
Durham, NC, USA
| | - Ann Dozier
- Department of Public Health Sciences, School of Medicine and Dentistry,
University of Rochester, Rochester,
NY, USA
| | - Daniel Ford
- Johns Hopkins University Institute for Clinical and Translational
Research, Baltimore, MD, USA
| | - Natalie Schlesinger
- The Rockefeller University Center for Clinical and Translational
Science, New York, NY, USA
| | - Carrie Dykes
- Clinical and Translational Science Institute, University of
Rochester, Rochester, NY, USA
| | - Issis Kelly-Pumarol
- Wake Forest School of Medicine, Clinical and Translational
Science Institute, Winston-Salem, NC,
USA
| | - Nan Kennedy
- Vanderbilt Institute for Clinical and Translational Research,
Vanderbilt University Medical Center, Nashville,
TN, USA
| | - Cassie Lewis-Land
- Johns Hopkins University Institute for Clinical and Translational
Research, Baltimore, MD, USA
| | - Sierra Lindo
- Duke Clinical Translational Science Institute,
Durham, NC, USA
| | - Liz Martinez
- Johns Hopkins University Institute for Clinical and Translational
Research, Baltimore, MD, USA
| | - Michael Musty
- Duke Clinical Translational Science Institute,
Durham, NC, USA
| | | | - Roger Vaughan
- The Rockefeller University Center for Clinical and Translational
Science, New York, NY, USA
| | - Lynne Wagenknecht
- Wake Forest School of Medicine, Clinical and Translational
Science Institute, Winston-Salem, NC,
USA
| | - Scott Carey
- Johns Hopkins University Institute for Clinical and Translational
Research, Baltimore, MD, USA
| | - Cameron Coffran
- The Rockefeller University Center for Clinical and Translational
Science, New York, NY, USA
| | - James Goodrich
- Duke University School of Medicine, Duke Office of Clinical
Research, Durham, NC, USA
| | - Pavithra Panjala
- Clinical and Translational Science Institute, University of
Rochester, Rochester, NY, USA
| | - Sameer Cheema
- Duke University School of Medicine, Duke Office of Clinical
Research, Durham, NC, USA
| | - Adam Qureshi
- The Rockefeller University Center for Clinical and Translational
Science, New York, NY, USA
| | - Ellis Thomas
- Department of Biomedical Informatics, Vanderbilt
University, Nashville, TN,
USA
| | - Lindsay O’Neill
- Department of Biomedical Informatics, Vanderbilt
University, Nashville, TN,
USA
| | | | - Paul Harris
- Department of Biomedical Informatics, Vanderbilt
University, Nashville, TN,
USA
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3
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Hayden K, Mielke M, Evans J, Neiberg R, Molina-Henry D, Culkin M, Marcovina S, Johnson K, Carmichael O, Rapp S, Sachs B, Ding J, Shappell H, Wagenknecht L, Luchsinger J, Espeland M. Association between Modifiable Risk Factors and Levels of Blood-Based Biomarkers of Alzheimer's and Related Dementias in the Look AHEAD Cohort. JAR Life 2024; 13:1-21. [PMID: 38204926 PMCID: PMC10775955 DOI: 10.14283/jarlife.2024.1] [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] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 12/04/2023] [Indexed: 01/12/2024]
Abstract
Background Emerging evidence suggests that a number of factors can influence blood-based biomarker levels for Alzheimer's disease (AD) and Alzheimer's related dementias (ADRD). We examined the associations that demographic and clinical characteristics have with AD/ADRD blood-based biomarker levels in an observational continuation of a clinical trial cohort of older individuals with type 2 diabetes and overweight or obesity. Methods Participants aged 45-76 years were randomized to a 10-year Intensive Lifestyle Intervention (ILI) or a diabetes support and education (DSE) condition. Stored baseline and end of intervention (8-13 years later) plasma samples were analyzed with the Quanterix Simoa HD-X Analyzer. Changes in Aβ42, Aβ40, Aβ42/Aβ40, ptau181, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) were evaluated in relation to randomization status, demographic, and clinical characteristics. Results In a sample of 779 participants from the Look AHEAD cohort, we found significant associations between blood-based biomarkers for AD/ADRD and 15 of 18 demographic (age, gender, race and ethnicity, education) and clinical characteristics (APOE, depression, alcohol use, smoking, body mass index, HbA1c, diabetes duration, diabetes treatment, estimated glomerular filtration rate, hypertension, and history of cardiovascular disease) . Conclusions Blood-based biomarkers of AD/ADRD are influenced by common demographic and clinical characteristics. These factors should be considered carefully when interpreting these AD/ADRD blood biomarker values for clinical or research purposes.
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Affiliation(s)
- K.M. Hayden
- Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - M.M. Mielke
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - J.K. Evans
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - R. Neiberg
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - D. Molina-Henry
- Winston-Salem State University, Winston-Salem, NC, USA
- Alzheimer’s Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, CA, USA
| | - M. Culkin
- Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - S. Marcovina
- Medpace Reference Laboratories, Cincinnati, OH, USA
| | - K.C. Johnson
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - O.T. Carmichael
- Biomedical Imaging Center, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - S.R. Rapp
- Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston-Salem, NC, USA
- Department of Psychiatry & Behavioral Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - B.C. Sachs
- Department of Neurology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
- Sticht Division of Gerontology and Geriatric Medicine Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - J. Ding
- Sticht Division of Gerontology and Geriatric Medicine Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - H. Shappell
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - L. Wagenknecht
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - J.A. Luchsinger
- Departments of Medicine and Epidemiology, Columbia University Irving Medical Center, New York, NY, USA
| | - M.A. Espeland
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
- Sticht Division of Gerontology and Geriatric Medicine Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA
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Kaptoge S, Seshasai SRK, Sun L, Walker M, Bolton T, Spackman S, Ataklte F, Willeit P, Bell S, Burgess S, Pennells L, Altay S, Assmann G, Ben-Shlomo Y, Best LG, Björkelund C, Blazer DG, Brenner H, Brunner EJ, Dagenais GR, Cooper JA, Cooper C, Crespo CJ, Cushman M, D'Agostino RB, Daimon M, Daniels LB, Danker R, Davidson KW, de Jongh RT, Donfrancesco C, Ducimetiere P, Elders PJM, Engström G, Ford I, Gallacher I, Bakker SJL, Goldbourt U, de La Cámara G, Grimsgaard S, Gudnason V, Hansson PO, Imano H, Jukema JW, Kabrhel C, Kauhanen J, Kavousi M, Kiechl S, Knuiman MW, Kromhout D, Krumholz HM, Kuller LH, Laatikainen T, Lowler DA, Meyer HE, Mukamal K, Nietert PJ, Ninomiya T, Nitsch D, Nordestgaard BG, Palmieri L, Price JF, Ridker PM, Sun Q, Rosengren A, Roussel R, Sakurai M, Salomaa V, Schöttker B, Shaw JE, Strandberg TE, Sundström J, Tolonen H, Tverdal A, Verschuren WMM, Völzke H, Wagenknecht L, Wallace RB, Wannamethee SG, Wareham NJ, Wassertheil-Smoller S, Yamagishi K, Yeap BB, Harrison S, Inouye M, Griffin S, Butterworth AS, Wood AM, Thompson SG, Sattar N, Danesh J, Di Angelantonio E, Tipping RW, Russell S, Johansen M, Bancks MP, Mongraw-Chaffin M, Magliano D, Barr ELM, Zimmet PZ, Knuiman MW, Whincup PH, Willeit J, Willeit P, Leitner C, Lawlor DA, Ben-Shlomo Y, Elwood P, Sutherland SE, Hunt KJ, Cushman M, Selmer RM, Haheim LL, Ariansen I, Tybjaer-Hansen A, Frikkle-Schmidt R, Langsted A, Donfrancesco C, Lo Noce C, Balkau B, Bonnet F, Fumeron F, Pablos DL, Ferro CR, Morales TG, Mclachlan S, Guralnik J, Khaw KT, Brenner H, Holleczek B, Stocker H, Nissinen A, Palmieri L, Vartiainen E, Jousilahti P, Harald K, Massaro JM, Pencina M, Lyass A, Susa S, Oizumi T, Kayama T, Chetrit A, Roth J, Orenstein L, Welin L, Svärdsudd K, Lissner L, Hange D, Mehlig K, Salomaa V, Tilvis RS, Dennison E, Cooper C, Westbury L, Norman PE, Almeida OP, Hankey GJ, Hata J, Shibata M, Furuta Y, Bom MT, Rutters F, Muilwijk M, Kraft P, Lindstrom S, Turman C, Kiyama M, Kitamura A, Yamagishi K, Gerber Y, Laatikainen T, Salonen JT, van Schoor LN, van Zutphen EM, Verschuren WMM, Engström G, Melander O, Psaty BM, Blaha M, de Boer IH, Kronmal RA, Sattar N, Rosengren A, Nitsch D, Grandits G, Tverdal A, Shin HC, Albertorio JR, Gillum RF, Hu FB, Cooper JA, Humphries S, Hill- Briggs F, Vrany E, Butler M, Schwartz JE, Kiyama M, Kitamura A, Iso H, Amouyel P, Arveiler D, Ferrieres J, Gansevoort RT, de Boer R, Kieneker L, Crespo CJ, Assmann G, Trompet S, Kearney P, Cantin B, Després JP, Lamarche B, Laughlin G, McEvoy L, Aspelund T, Thorsson B, Sigurdsson G, Tilly M, Ikram MA, Dorr M, Schipf S, Völzke H, Fretts AM, Umans JG, Ali T, Shara N, Davey-Smith G, Can G, Yüksel H, Özkan U, Nakagawa H, Morikawa Y, Ishizaki M, Njølstad I, Wilsgaard T, Mathiesen E, Sundström J, Buring J, Cook N, Arndt V, Rothenbacher D, Manson J, Tinker L, Shipley M, Tabak AG, Kivimaki M, Packard C, Robertson M, Feskens E, Geleijnse M, Kromhout D. Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries: 23 million person-years of observation. Lancet Diabetes Endocrinol 2023; 11:731-742. [PMID: 37708900 PMCID: PMC7615299 DOI: 10.1016/s2213-8587(23)00223-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 07/14/2023] [Accepted: 07/14/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND The prevalence of type 2 diabetes is increasing rapidly, particularly among younger age groups. Estimates suggest that people with diabetes die, on average, 6 years earlier than people without diabetes. We aimed to provide reliable estimates of the associations between age at diagnosis of diabetes and all-cause mortality, cause-specific mortality, and reductions in life expectancy. METHODS For this observational study, we conducted a combined analysis of individual-participant data from 19 high-income countries using two large-scale data sources: the Emerging Risk Factors Collaboration (96 cohorts, median baseline years 1961-2007, median latest follow-up years 1980-2013) and the UK Biobank (median baseline year 2006, median latest follow-up year 2020). We calculated age-adjusted and sex-adjusted hazard ratios (HRs) for all-cause mortality according to age at diagnosis of diabetes using data from 1 515 718 participants, in whom deaths were recorded during 23·1 million person-years of follow-up. We estimated cumulative survival by applying age-specific HRs to age-specific death rates from 2015 for the USA and the EU. FINDINGS For participants with diabetes, we observed a linear dose-response association between earlier age at diagnosis and higher risk of all-cause mortality compared with participants without diabetes. HRs were 2·69 (95% CI 2·43-2·97) when diagnosed at 30-39 years, 2·26 (2·08-2·45) at 40-49 years, 1·84 (1·72-1·97) at 50-59 years, 1·57 (1·47-1·67) at 60-69 years, and 1·39 (1·29-1·51) at 70 years and older. HRs per decade of earlier diagnosis were similar for men and women. Using death rates from the USA, a 50-year-old individual with diabetes died on average 14 years earlier when diagnosed aged 30 years, 10 years earlier when diagnosed aged 40 years, or 6 years earlier when diagnosed aged 50 years than an individual without diabetes. Using EU death rates, the corresponding estimates were 13, 9, or 5 years earlier. INTERPRETATION Every decade of earlier diagnosis of diabetes was associated with about 3-4 years of lower life expectancy, highlighting the need to develop and implement interventions that prevent or delay the onset of diabetes and to intensify the treatment of risk factors among young adults diagnosed with diabetes. FUNDING British Heart Foundation, Medical Research Council, National Institute for Health and Care Research, and Health Data Research UK.
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Wang FM, Cainzos-Achirica M, Ballew SH, Coresh J, Folsom AR, Howard CM, Post WS, Wagenknecht L, Budoff MJ, Blaha MJ, Matsushita K. Defining Demographic-specific Coronary Artery Calcium Percentiles in the Population Aged ≥75: The ARIC Study and MESA. Circ Cardiovasc Imaging 2023; 16:e015145. [PMID: 37655462 PMCID: PMC10721116 DOI: 10.1161/circimaging.122.015145] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 08/11/2023] [Indexed: 09/02/2023]
Abstract
BACKGROUND Current clinical guidelines recommend a coronary artery calcium (CAC) score of 100 Agatston Units or demographic-specific 75th percentile as high-risk thresholds for guiding atherosclerotic cardiovascular disease preventive therapy. Meanwhile, low CAC can help derisk individuals who may safely defer statin therapy. However, limited data from the early 2000s, including just 208 older Black individuals, inform CAC percentiles for adults aged 75 to 85 years, and none have been established in adults aged ≥85 years. This study aims to characterize the distribution of CAC and establish demographic-specific CAC percentiles in the population aged ≥75 years. METHODS We assessed 2886 participants aged ≥75 years without clinical coronary heart disease from the ARIC study (Atherosclerosis Risk in Communities) visit 7 (2018-2019; n=2217) and the MESA (Multi-Ethnic Study of Atherosclerosis) visit 5 (2010-2011; n=669). Prevalence of any CAC >0 and sex- and race-specific CAC percentiles across age were estimated nonparametrically with locally weighted regression models and pooled residual ranking. RESULTS The median age was 80 (interquartile interval, 77-83) years, and 60% were female. The prevalence of zero CAC was lowest in White males (4%), followed by Black males (13%), White females (14%), and highest in Black females (18%). Regardless of sex and race, most participants had CAC>100 (62.5%). CAC scores increased with age, with CAC identified in ≈95% of participants aged ≥90 years across sex-race subgroups. The 75th percentile corresponded to higher CAC scores for Black older adults (n=741), especially females, than currently used thresholds. CONCLUSIONS In community-dwelling adults aged ≥75 years free of clinical coronary heart disease, the prevalence of zero CAC was 11%, and CAC >100 as a threshold for high ASCVD risk would categorize most of this older population as high risk. Demographic-specific CAC percentiles from this study are a valuable tool for interpreting CAC in the population aged ≥75 years.
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Affiliation(s)
- Frances M. Wang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Miguel Cainzos-Achirica
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Division of Cardiovascular Prevention and Wellness, Houston Methodist, Houston, TX
| | - Shoshana H. Ballew
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Aaron R. Folsom
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN
| | - Candace M. Howard
- Department of Radiology, University of Mississippi Medical Center, Jackson, MS
| | - Wendy S. Post
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Lynne Wagenknecht
- Wake Forest University Population Health Sciences, Winston Salem, NC
| | - Matthew J. Budoff
- Department of Medicine, Lundquist Institute at Harbor-UCLA, Torrance CA
| | - Michael J. Blaha
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Kunihiro Matsushita
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
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6
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Mok Y, Wang F, Ballew SH, Menez S, Butler KR, Wagenknecht L, Sedaghat S, Lutsey PL, Coresh J, Blaha MJ, Matsushita K. Kidney function, bone-mineral metabolism markers, and calcification of coronary arteries, aorta, and cardiac valves in older adults. Atherosclerosis 2023; 368:35-43. [PMID: 36754659 PMCID: PMC9992265 DOI: 10.1016/j.atherosclerosis.2023.01.007] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/10/2023] [Indexed: 01/15/2023]
Abstract
BACKGROUND AND AIMS The contribution of kidney dysfunction, especially at mild-to-moderate stages, and bone-mineral metabolism (BMM) markers to vascular calcification remains controversial or unclear. We comprehensively evaluated the association of kidney and BMM markers with coronary artery calcification (CAC) and extra-coronary calcification (ECC). METHODS In 1931 ARIC participants (age 73-95 years) without coronary heart disease at visit 7 (2018-19), we investigated the associations of estimated glomerular filtration rate (eGFR) (with creatinine, cystatin C, and both) and five serum BMM markers (calcium, fibroblast growth factor 23, magnesium, parathyroid hormone, and phosphorus) with high CAC and ECC (sex-race specific ≥75th vs. <75th percentile Agatston score) or any vs. zero CAC and ECC using multivariable logistic regression. For eGFR and BMM markers, we took their weighted cumulative averages from visit 1 (1987-89) to visit 5 (2011-13). RESULTS Lower eGFR, regardless of equations used, was not robustly associated with high CAC or ECC. Among BMM markers, only higher phosphorus levels, even within the normal range, showed robust associations with high CAC (only when modeled continuously) and ECC, independently of kidney function (e.g., odds ratio 1.94 [95%CI 1.38-2.73] for high aortic valve calcification, in the highest vs. lowest quartile). Results were generally consistent when analyzing any CAC or ECC, although cystatin C-based eGFR <60 mL/min/1.73 m2 became significantly associated with mitral valve calcification (odds ratio 1.69 [1.10-2.60]). CONCLUSIONS Among kidney and BMM measures tested, only serum phosphorus demonstrated robust associations with both CAC and ECC, supporting a key role of phosphorus in the pathophysiology of vascular calcification.
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Affiliation(s)
- Yejin Mok
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Frances Wang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Shoshana H Ballew
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Steve Menez
- Division of Nephrology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Kenneth R Butler
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Lynne Wagenknecht
- Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Sanaz Sedaghat
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Pamela L Lutsey
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Michael J Blaha
- Division of Cardiology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Kunihiro Matsushita
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Division of Cardiology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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7
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Mok Y, Ballew S, Schrack J, Howard-Claudio CM, Butler KR, Wagenknecht L, Coresh J, Budoff M, Tanaka H, Blaha MJ, Matsushita K. Abstract P438: Physical Activity and Calcification of Coronary Arteries, Aorta, and Cardiac Valves: The Atherosclerosis Risk in Communities (ARIC) Study. Circulation 2023. [DOI: 10.1161/circ.147.suppl_1.p438] [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] [Indexed: 03/15/2023]
Abstract
Background:
Regular physical activity (PA) has been associated with reduced risk of coronary heart disease, but conflicting results have been reported regarding its association with coronary artery calcification (CAC), one of the strongest predictors of cardiovascular disease. Moreover, different domains of PA (e.g., sports vs. work) and extra coronary calcification (ECC) have not been extensively studied in this context.
Methods:
We investigated 2,025 ARIC participants (age 73-95 years) who had no coronary heart disease and underwent a non-contrast cardiac-gated computed tomography scan at visit 7 (2018-19). PA was based on a modified Baecke questionnaire, which provides total and domain-specific (sport, non-sport leisure, and work) PA scores. We modeled the cumulative PA scores in middle-age at visit 1 (1987-89) and visit 3 (1993-95). We explored continuous CAC and ECC (log-transformed [Agatston score+1]) or any CAC and ECC (Agatston score >0 vs. 0) as dependent variables using linear regression and multivariable logistic models, as appropriate.
Results:
Higher total and domain-specific PA scores showed no association with lower CAC. Among ECC, descending aorta calcification demonstrated the strongest association with total PA scores (coefficient -0.10 [95% CI -0.21, 0.01], p=0.075) (
Figure
). When specific domains were explored, higher sport and work PA scores were significantly associated with lower descending aorta calcification. When we analyzed any CAC and ECC as the outcome variable, the results were generally consistent.
Conclusions:
Higher PA in midlife was associated with lower calcification of descending aorta in late life. Our results further support the health benefits of PA and unique pathological process across different vascular beds.
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Affiliation(s)
- Yejin Mok
- Johns Hopkins Bloomberg Sch of Public Health, Baltimore, MD
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8
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Casanova R, Anderson A, Barnard R, Walker K, Hughes T, Kritchevsky S, Wagenknecht L. ACCELERATED BRAIN AGING IS ASSOCIATED WITH MORTALITY ACROSS RACE. Innov Aging 2022. [PMCID: PMC9766967 DOI: 10.1093/geroni/igac059.2834] [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] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
There is an increasing interest in using machine learning and artificial intelligence to estimate chronological age using neuroimaging data. The gap between chronological age and estimated brain age (brain age gap, BAG) is used as a measure of accelerated/resilient brain aging. Accelerated brain aging has been associated with increased mortality risk. However, these reports are based on cohorts mostly composed by white individuals. Here we capitalized on the racially diverse nature of the Atherosclerosis Risk in Communities Study (ARIC) cohort to investigate associations of brain across race. We used brain MRI scans from 1172 cognitively normal ARIC participants that were collected at ARIC Visit 5. Of those 772 were White and 366 were African Americans. We used Cox regression models to investigate BAG values associations with mortality. There were 163 deaths (dw = 124 and daa = 39) over 8 years of follow-up. Participants were stratified by tertiles according to BAG values. We found that, compared to those individuals with BAG scores in the highest tertile (>=1.15), those who scored in the lowest tertile (<= -1.3 years) to be associated with significantly lower mortality among the White (HR=0.41, 95% CI, [0.26–0.66], p < 0.001) and Black (HR=0.43, 95% CI, [0.20–0.92], p = 0.03) participants after adjusting for age, race-center, sex, education, diabetes, smoking and hypertension. Our analyses show that our approach to estimate chronological age using high-dimensional elastic net regression, produces BAG values which are associated with mortality not only in White individuals but also in African Americans.
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Affiliation(s)
- Ramon Casanova
- Wake Forest School of Medicine, Winston-Salem, North Carolina, United States
| | - Andrea Anderson
- Wake Forest School of Medicine, Winston-Salem, North Carolina, United States
| | - Ryan Barnard
- Wake Forest School of Medicine, Winston-Salem, North Carolina, United States
| | - Keenan Walker
- National Institute of Health, Baltimore, Maryland, United States
| | - Timothy Hughes
- Wake Forest School of Medicine, Winston-Salem, North Carolina, United States
| | - Stephen Kritchevsky
- Wake Forest School of Medicine, Winston-Salem, North Carolina, United States
| | - Lynne Wagenknecht
- Wake Forest School of Medicine, Winston-Salem, North Carolina, United States
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9
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Fernández-Rhodes L, Graff M, Buchanan VL, Justice AE, Highland HM, Guo X, Zhu W, Chen HH, Young KL, Adhikari K, Palmer ND, Below JE, Bradfield J, Pereira AC, Glover L, Kim D, Lilly AG, Shrestha P, Thomas AG, Zhang X, Chen M, Chiang CW, Pulit S, Horimoto A, Krieger JE, Guindo-Martínez M, Preuss M, Schumann C, Smit RA, Torres-Mejía G, Acuña-Alonzo V, Bedoya G, Bortolini MC, Canizales-Quinteros S, Gallo C, González-José R, Poletti G, Rothhammer F, Hakonarson H, Igo R, Adler SG, Iyengar SK, Nicholas SB, Gogarten SM, Isasi CR, Papnicolaou G, Stilp AM, Qi Q, Kho M, Smith JA, Langefeld CD, Wagenknecht L, Mckean-Cowdin R, Gao XR, Nousome D, Conti DV, Feng Y, Allison MA, Arzumanyan Z, Buchanan TA, Chen YDI, Genter PM, Goodarzi MO, Hai Y, Hsueh W, Ipp E, Kandeel FR, Lam K, Li X, Nadler JL, Raffel LJ, Roll K, Sandow K, Tan J, Taylor KD, Xiang AH, Yao J, Audirac-Chalifour A, Peralta Romero JDJ, Hartwig F, Horta B, Blangero J, Curran JE, Duggirala R, Lehman DE, Puppala S, Fejerman L, John EM, Aguilar-Salinas C, Burtt NP, Florez JC, García-Ortíz H, González-Villalpando C, Mercader J, Orozco L, Tusié-Luna T, Blanco E, Gahagan S, Cox NJ, Hanis C, Butte NF, Cole SA, Comuzzie AG, Voruganti VS, Rohde R, Wang Y, Sofer T, Ziv E, Grant SF, Ruiz-Linares A, Rotter JI, Haiman CA, Parra EJ, Cruz M, Loos RJ, North KE. Erratum: Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits-The Hispanic/Latino Anthropometry Consortium. HGG Adv 2022; 4:100149. [PMID: 36268164 PMCID: PMC9576563 DOI: 10.1016/j.xhgg.2022.100149] [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] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
[This corrects the article DOI: 10.1016/j.xhgg.2022.100099.].
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10
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Boakye E, Dardari Z, Obisesan OH, Osei AD, Wang FM, Honda Y, Dzaye O, Osuji N, Carr JJ, Howard-Claudio CM, Wagenknecht L, Konety S, Coresh J, Matsushita K, Blaha MJ, Whelton SP. Sex-and race-specific burden of aortic valve calcification among older adults without overt coronary heart disease: The Atherosclerosis Risk in Communities Study. Atherosclerosis 2022; 355:68-75. [PMID: 35718559 DOI: 10.1016/j.atherosclerosis.2022.06.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/20/2022] [Accepted: 06/03/2022] [Indexed: 01/22/2023]
Abstract
BACKGROUND AND AIMS The prevalence of aortic valve calcification (AVC) increases with age. However, the sex-and race-specific burden of AVC and associated cardiovascular risk factors among adults ≥75 years are not well studied. METHODS We calculated the sex-and race-specific burden of AVC among 2283 older Black and White adults (mean age:80.5 [SD:4.3] years) without overt coronary heart disease from the Atherosclerosis Risk in Communities Study who underwent non-contrast cardiac-gated CT-imaging at visit 7 (2018-2019). Using Poisson regression with robust variance, we calculated the adjusted prevalence ratios (aPR) of the association of AVC with cardiovascular risk factors. RESULTS The overall AVC prevalence was 44.8%, with White males having the highest prevalence at 58.2%. The prevalence was similar for Black males (40.5%), White females (38.9%), and Black females (36.8%). AVC prevalence increased significantly with age among all race-sex groups. The probability of any AVC at age 80 years was 55.4%, 40.0%, 37.3%, and 36.2% for White males, Black males, White females, and Black females, respectively. Among persons with prevalent AVC, White males had the highest median AVC score (100.9 Agatston Units [AU]), followed by Black males (68.5AU), White females (52.3AU), and Black females (46.5AU). After adjusting for cardiovascular risk factors, Black males (aPR:0.53; 95%CI:0.33-0.83), White females (aPR:0.68; 95%CI:0.61-0.77), and Black females (aPR:0.49; 95%CI:0.31-0.77) had lower AVC prevalence compared to White males. In addition, systolic blood pressure, non-HDL-cholesterol, and lipoprotein (a) were independently associated with AVC, with no significant race/sex interactions. CONCLUSIONS AVC, although highly prevalent, was not universally present in this cohort of older adults. White males had ∼50-60% higher prevalence than other race-sex groups. Moreover, cardiovascular risk factors measured in older age showed significant association with AVC.
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Affiliation(s)
- Ellen Boakye
- Johns Hopkins Ciccarone Centre for the Prevention of Cardiovascular Diseases, Baltimore, MD, USA
| | - Zeina Dardari
- Johns Hopkins Ciccarone Centre for the Prevention of Cardiovascular Diseases, Baltimore, MD, USA
| | | | - Albert D Osei
- Department of Medicine, MedStar Union Memorial Hospital, Baltimore, MD, USA
| | - Frances M Wang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Yasuyuki Honda
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Omar Dzaye
- Johns Hopkins Ciccarone Centre for the Prevention of Cardiovascular Diseases, Baltimore, MD, USA
| | - Ngozi Osuji
- Johns Hopkins Ciccarone Centre for the Prevention of Cardiovascular Diseases, Baltimore, MD, USA
| | - John Jeffery Carr
- Department of Radiology, Vanderbilt University Medical Centre, Nashville, TN, USA
| | | | - Lynne Wagenknecht
- Department of Epidemiology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Suma Konety
- Division of Cardiology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kunihiro Matsushita
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Michael J Blaha
- Johns Hopkins Ciccarone Centre for the Prevention of Cardiovascular Diseases, Baltimore, MD, USA.
| | - Seamus P Whelton
- Johns Hopkins Ciccarone Centre for the Prevention of Cardiovascular Diseases, Baltimore, MD, USA
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11
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Oram RA, Sharp SA, Pihoker C, Ferrat L, Imperatore G, Williams A, Redondo MJ, Wagenknecht L, Dolan LM, Lawrence JM, Weedon MN, D’Agostino R, Hagopian WA, Divers J, Dabelea D. Utility of Diabetes Type-Specific Genetic Risk Scores for the Classification of Diabetes Type Among Multiethnic Youth. Diabetes Care 2022; 45:1124-1131. [PMID: 35312757 PMCID: PMC9174964 DOI: 10.2337/dc20-2872] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [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: 11/24/2020] [Accepted: 01/30/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Genetic risk scores (GRS) aid classification of diabetes type in White European adult populations. We aimed to assess the utility of GRS in the classification of diabetes type among racially/ethnically diverse youth in the U.S. RESEARCH DESIGN AND METHODS We generated type 1 diabetes (T1D)- and type 2 diabetes (T2D)-specific GRS in 2,045 individuals from the SEARCH for Diabetes in Youth study. We assessed the distribution of genetic risk stratified by diabetes autoantibody positive or negative (DAA+/-) and insulin sensitivity (IS) or insulin resistance (IR) and self-reported race/ethnicity (White, Black, Hispanic, and other). RESULTS T1D and T2D GRS were strong independent predictors of etiologic type. The T1D GRS was highest in the DAA+/IS group and lowest in the DAA-/IR group, with the inverse relationship observed with the T2D GRS. Discrimination was similar across all racial/ethnic groups but showed differences in score distribution. Clustering by combined genetic risk showed DAA+/IR and DAA-/IS individuals had a greater probability of T1D than T2D. In DAA- individuals, genetic probability of T1D identified individuals most likely to progress to absolute insulin deficiency. CONCLUSIONS Diabetes type-specific GRS are consistent predictors of diabetes type across racial/ethnic groups in a U.S. youth cohort, but future work needs to account for differences in GRS distribution by ancestry. T1D and T2D GRS may have particular utility for classification of DAA- children.
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Affiliation(s)
- Richard A. Oram
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, and The Academic Kidney Unit, Royal Devon and Exeter NHS Foundation Trust, Exeter, U.K
| | - Seth A. Sharp
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, and The Academic Kidney Unit, Royal Devon and Exeter NHS Foundation Trust, Exeter, U.K
| | | | - Lauric Ferrat
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, and The Academic Kidney Unit, Royal Devon and Exeter NHS Foundation Trust, Exeter, U.K
| | - Giuseppina Imperatore
- Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, GA
| | - Adrienne Williams
- Biostatistics Shared Resource, Wake Forest School of Medicine, Winston-Salem, NC
| | - Maria J. Redondo
- Section of Diabetes and Endocrinology, Texas Children’s Hospital, Baylor College of Medicine, Houston, TX
| | - Lynne Wagenknecht
- Biostatistics Shared Resource, Wake Forest School of Medicine, Winston-Salem, NC
| | - Lawrence M. Dolan
- Division of Endocrinology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Jean M. Lawrence
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA
| | - Michael N. Weedon
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, and The Academic Kidney Unit, Royal Devon and Exeter NHS Foundation Trust, Exeter, U.K
| | - Ralph D’Agostino
- Biostatistics Shared Resource, Wake Forest School of Medicine, Winston-Salem, NC
| | | | - Jasmin Divers
- Division of Health Services Research, Foundation of Medicine, NYU Long Island School of Medicine, Mineola, NY
| | - Dana Dabelea
- Departments of Pediatrics and Epidemiology, University of Colorado School of Medicine, Aurora, CO
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12
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Fernández-Rhodes L, Graff M, Buchanan VL, Justice AE, Highland HM, Guo X, Zhu W, Chen HH, Young KL, Adhikari K, Palmer ND, Below JE, Bradfield J, Pereira AC, Glover L, Kim D, Lilly AG, Shrestha P, Thomas AG, Zhang X, Chen M, Chiang CW, Pulit S, Horimoto A, Krieger JE, Guindo-Martínez M, Preuss M, Schumann C, Smit RA, Torres-Mejía G, Acuña-Alonzo V, Bedoya G, Bortolini MC, Canizales-Quinteros S, Gallo C, González-José R, Poletti G, Rothhammer F, Hakonarson H, Igo R, Adler SG, Iyengar SK, Nicholas SB, Gogarten SM, Isasi CR, Papnicolaou G, Stilp AM, Qi Q, Kho M, Smith JA, Langefeld CD, Wagenknecht L, Mckean-Cowdin R, Gao XR, Nousome D, Conti DV, Feng Y, Allison MA, Arzumanyan Z, Buchanan TA, Ida Chen YD, Genter PM, Goodarzi MO, Hai Y, Hsueh W, Ipp E, Kandeel FR, Lam K, Li X, Nadler JL, Raffel LJ, Roll K, Sandow K, Tan J, Taylor KD, Xiang AH, Yao J, Audirac-Chalifour A, de Jesus Peralta Romero J, Hartwig F, Horta B, Blangero J, Curran JE, Duggirala R, Lehman DE, Puppala S, Fejerman L, John EM, Aguilar-Salinas C, Burtt NP, Florez JC, García-Ortíz H, González-Villalpando C, Mercader J, Orozco L, Tusié-Luna T, Blanco E, Gahagan S, Cox NJ, Hanis C, Butte NF, Cole SA, Comuzzie AG, Voruganti VS, Rohde R, Wang Y, Sofer T, Ziv E, Grant SF, Ruiz-Linares A, Rotter JI, Haiman CA, Parra EJ, Cruz M, Loos RJ, North KE. Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits-The Hispanic/Latino Anthropometry Consortium. HGG Adv 2022; 3:100099. [PMID: 35399580 PMCID: PMC8990175 DOI: 10.1016/j.xhgg.2022.100099] [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] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 03/06/2022] [Indexed: 02/05/2023] Open
Abstract
Hispanic/Latinos have been underrepresented in genome-wide association studies (GWAS) for anthropometric traits despite their notable anthropometric variability, ancestry proportions, and high burden of growth stunting and overweight/obesity. To address this knowledge gap, we analyzed densely imputed genetic data in a sample of Hispanic/Latino adults to identify and fine-map genetic variants associated with body mass index (BMI), height, and BMI-adjusted waist-to-hip ratio (WHRadjBMI). We conducted a GWAS of 18 studies/consortia as part of the Hispanic/Latino Anthropometry (HISLA) Consortium (stage 1, n = 59,771) and generalized our findings in 9 additional studies (stage 2, n = 10,538). We conducted a trans-ancestral GWAS with summary statistics from HISLA stage 1 and existing consortia of European and African ancestries. In our HISLA stage 1 + 2 analyses, we discovered one BMI locus, as well as two BMI signals and another height signal each within established anthropometric loci. In our trans-ancestral meta-analysis, we discovered three BMI loci, one height locus, and one WHRadjBMI locus. We also identified 3 secondary signals for BMI, 28 for height, and 2 for WHRadjBMI in established loci. We show that 336 known BMI, 1,177 known height, and 143 known WHRadjBMI (combined) SNPs demonstrated suggestive transferability (nominal significance and effect estimate directional consistency) in Hispanic/Latino adults. Of these, 36 BMI, 124 height, and 11 WHRadjBMI SNPs were significant after trait-specific Bonferroni correction. Trans-ancestral meta-analysis of the three ancestries showed a small-to-moderate impact of uncorrected population stratification on the resulting effect size estimates. Our findings demonstrate that future studies may also benefit from leveraging diverse ancestries and differences in linkage disequilibrium patterns to discover novel loci and additional signals with less residual population stratification.
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Affiliation(s)
- Lindsay Fernández-Rhodes
- Department of Biobehavioral Health, Pennsylvania State University, 219 Biobehavioral Health Building, University Park, PA 16802, USA
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Victoria L. Buchanan
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Anne E. Justice
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biomedical and Translational Informatics, Geisinger Health System, Danville, PA 17822, USA
| | - Heather M. Highland
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Wanying Zhu
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Hung-Hsin Chen
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Kristin L. Young
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Kaustubh Adhikari
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, MK7 6AA Milton Keynes, UK
| | - Nicholette D. Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| | - Jennifer E. Below
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Jonathan Bradfield
- Center for Applied Genomics, Division of Human Genetics, Department of Pediatrics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Alexandre C. Pereira
- Laboratory of Genetics and Molecular Cardiology, Heart Institute, University of São Paulo, São Paulo 05508-220, Brazil
| | - LáShauntá Glover
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Daeeun Kim
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Adam G. Lilly
- Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Poojan Shrestha
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Alvin G. Thomas
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Xinruo Zhang
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Minhui Chen
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Charleston W.K. Chiang
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90007, USA
| | - Sara Pulit
- Vertex Pharmaceuticals, W2 6BD Oxford, UK
| | - Andrea Horimoto
- Laboratory of Genetics and Molecular Cardiology, Heart Institute, University of São Paulo, São Paulo 05508-220, Brazil
| | - Jose E. Krieger
- Laboratory of Genetics and Molecular Cardiology, Heart Institute, University of São Paulo, São Paulo 05508-220, Brazil
| | - Marta Guindo-Martínez
- The Charles Bronfman Institutes for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- The Novo Nordisk Center for Basic Metabolic Research, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Michael Preuss
- The Charles Bronfman Institutes for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Claudia Schumann
- Hasso Plattner Institute, University of Potsdam, Digital Health Center, 14482 Potsdam, Germany
| | - Roelof A.J. Smit
- The Charles Bronfman Institutes for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Gabriela Torres-Mejía
- Department of Research in Cardiovascular Diseases, Diabetes Mellitus, and Cancer, Population Health Research Center, National Institute of Public Health, Cuernavaca, Morelos 62100, Mexico
| | | | - Gabriel Bedoya
- Molecular Genetics Investigation Group, University of Antioquia, Medellín 1226, Colombia
| | - Maria-Cátira Bortolini
- Department of Genetics, Federal University of Rio Grande do Sul, Porto Alegre 90040-060, Brazil
| | - Samuel Canizales-Quinteros
- Population Genomics Applied to Health Unit, The National Institute of Genomic Medicine and the Faculty of Chemistry at the National Autonomous University of Mexico, Mexico City 04510, Mexico
| | - Carla Gallo
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
| | - Rolando González-José
- Patagonian Institute of the Social and Human Sciences, Patagonian National Center, Puerto Madryn U9120, Argentina
| | - Giovanni Poletti
- Laboratorios de Investigación y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
| | | | - Hakon Hakonarson
- Center for Applied Genomics, Division of Human Genetics, Department of Pediatrics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Robert Igo
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Sharon G. Adler
- Division of Nephrology and Hypertension, Harbor-University of California Los Angeles Medical Center, Torrance, CA 90502, USA
| | - Sudha K. Iyengar
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Susanne B. Nicholas
- Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
| | | | - Carmen R. Isasi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | | | - Adrienne M. Stilp
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Minjung Kho
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jennifer A. Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Carl D. Langefeld
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| | - Lynne Wagenknecht
- Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| | - Roberta Mckean-Cowdin
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA
| | - Xiaoyi Raymond Gao
- Department of Ophthalmology and Visual Sciences, Department of Biomedical Informatics, Division of Human Genetics, The Ohio State University, Columbus, OH 43210, USA
| | - Darryl Nousome
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA
| | - David V. Conti
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Ye Feng
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA
| | - Matthew A. Allison
- Department of Family Medicine, University of California, San Diego, CA 92161, USA
| | - Zorayr Arzumanyan
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Thomas A. Buchanan
- Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Pauline M. Genter
- Department of Medicine, Division of Endocrinology, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Mark O. Goodarzi
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Yang Hai
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Willa Hsueh
- Department of Internal Medicine, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Eli Ipp
- Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, CA 90095, USA
- Department of Medicine, Division of Endocrinology, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Fouad R. Kandeel
- Department of Translational Research & Cellular Therapeutics, Beckman Research Institute of City of Hope, Duarte, CA 91010, USA
| | - Kelvin Lam
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Xiaohui Li
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Jerry L. Nadler
- Department of Pharmacology at New York Medical College School of Medicine, Valhalla, NY 10595, USA
| | - Leslie J. Raffel
- Division of Genetic and Genomic Medicine, Department of Pediatrics, University of California, Irvine, CA 92697, USA
| | - Kathryn Roll
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Kevin Sandow
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Jingyi Tan
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Anny H. Xiang
- Research and Evaluation Branch, Kaiser Permanente of Southern California, Pasadena, CA 91101, USA
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Astride Audirac-Chalifour
- Medical Research Unit in Biochemistry, Specialty Hospital, National Medical Center of the Twenty-First Century, Mexican Institute of Social Security, Mexico City 06725, Mexico
| | - Jose de Jesus Peralta Romero
- Medical Research Unit in Biochemistry, Specialty Hospital, National Medical Center of the Twenty-First Century, Mexican Institute of Social Security, Mexico City 06725, Mexico
| | - Fernando Hartwig
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas 96010-610, Brazil
| | - Bernando Horta
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas 96010-610, Brazil
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville and Edinburg, TX 78520 and 78539, USA
| | - Joanne E. Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville and Edinburg, TX 78520 and 78539, USA
| | - Ravindranath Duggirala
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville and Edinburg, TX 78520 and 78539, USA
| | - Donna E. Lehman
- Department of Medicine, School of Medicine, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Sobha Puppala
- Department of Internal Medicine, Section of Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27109, USA
| | - Laura Fejerman
- Department of Public Health Sciences, School of Medicine, and the Comprehensive Cancer Center, University of California Davis, Davis, CA 95616, USA
| | - Esther M. John
- Departments of Epidemiology & Population Health and Medicine-Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Carlos Aguilar-Salinas
- Division of Nutrition, Salvador Zubirán National Institute of Health Sciences and Nutrition, Mexico City 14080, Mexico
| | - Noël P. Burtt
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of the Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Jose C. Florez
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of the Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Humberto García-Ortíz
- Laboratory of Immunogenomics and Metabolic Diseases, National Institute of Genomic Medicine, Mexico City 14610, Mexico
| | - Clicerio González-Villalpando
- Center for Diabetes Studies, Research Unit for Diabetes and Cardiovascular Risk, Center for Population Health Studies, National Institute of Public Health, Mexico City 14080, Mexico
| | - Josep Mercader
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of the Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Lorena Orozco
- Laboratory of Immunogenomics and Metabolic Diseases, National Institute of Genomic Medicine, Mexico City 14610, Mexico
| | - Teresa Tusié-Luna
- Molecular Biology and Medical Genomics Unity, Institute of Biomedical Research, The National Autonomous University of Mexico and the Salvador Zubirán National Institute of Health Sciences and Nutrition, Mexico City 14080, Mexico
| | - Estela Blanco
- Center for Community Health, Division of Academic General Pediatrics, University of California at San Diego, San Diego, CA 92093, USA
| | - Sheila Gahagan
- Center for Community Health, Division of Academic General Pediatrics, University of California at San Diego, San Diego, CA 92093, USA
| | - Nancy J. Cox
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Craig Hanis
- University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Nancy F. Butte
- United States Department of Agriculture, Agricultural Research Service, The Children’s Nutrition Research Center, and the Department Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Shelley A. Cole
- Population Health Program, Texas Biomedical Research Institute, San Antonio, TX 78227, USA
| | | | - V. Saroja Voruganti
- Department of Nutrition and Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC 28081, USA
| | - Rebecca Rohde
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yujie Wang
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Tamar Sofer
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Elad Ziv
- Division of General Internal Medicine, Department of Medicine, Helen Diller Family Comprehensive Cancer Center, Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94115, USA
| | - Struan F.A. Grant
- Center for Applied Genomics, Division of Human Genetics, Department of Pediatrics, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Andres Ruiz-Linares
- Ministry of Education Key Laboratory of Contemporary Anthropology and Collaborative Innovation Center of Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai 200438, China
- Department of Genetics, Evolution and Environment, and Genetics Institute of the University College London, London WC1E 6BT, UK
- Laboratory of Biocultural Anthropology, Law, Ethics, and Health, Aix-Marseille University, Marseille 13385, France
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502 USA
| | - Christopher A. Haiman
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Esteban J. Parra
- Department of Anthropology, University of Toronto- Mississauga, Mississauga, ON L5L 1C6, Canada
| | - Miguel Cruz
- Medical Research Unit in Biochemistry, Specialty Hospital, National Medical Center of the Twenty-First Century, Mexican Institute of Social Security, Mexico City 06725, Mexico
| | - Ruth J.F. Loos
- The Charles Bronfman Institutes for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kari E. North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
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13
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Boakye E, Obisesan OH, Osei AD, Dardari Z, Dzaye O, Osuji N, Wang FM, Honda Y, Carr JJ, Howard-Claudio C, Wagenknecht L, Konety SH, Coresh J, Matsushita K, Blaha M, Whelton SP. SEX- AND RACE-SPECIFIC BURDEN OF AORTIC VALVE CALCIFICATION AMONG ADULTS ≥75 YEARS - THE ATHEROSCLEROSIS RISK IN COMMUNITIES STUDY. J Am Coll Cardiol 2022. [DOI: 10.1016/s0735-1097(22)02710-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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14
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Kucharska-Newton AM, Loop MS, Bullo M, Moore C, Haas SW, Wagenknecht L, Whitsel EA, Heiss G. Use of troponins in the classification of myocardial infarction from electronic health records. The Atherosclerosis Risk in Communities (ARIC) Study. Int J Cardiol 2022; 348:152-156. [PMID: 34921902 PMCID: PMC8775766 DOI: 10.1016/j.ijcard.2021.12.022] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 11/12/2021] [Accepted: 12/13/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Electronic health record (EHR) data are underutilized for abstracting classification criteria for heart disease. We compared extraction of EHR data on troponin I and T levels with human abstraction. METHODS Using EHR for hospitalizations identified through the Atherosclerosis Risk in Communities (ARIC) Study in four US hospitals, we compared blood levels of troponins I and T extracted from EHR structured data elements with levels obtained through data abstraction by human abstractors to 3 decimal places. Observations were divided randomly 50/50 into training and validation sets. Bayesian multilevel logistic regression models were used to estimate agreement by hospital in first and maximum troponin levels, troponin assessment date, troponin upper limit of normal (ULN), and classification of troponin levels as normal (< ULN), equivocal (1-2× ULN), abnormal (>2× ULN), or missing. RESULTS Estimated overall agreement in first measured troponin level in the validation data was 88.2% (95% credible interval: 65.0%-97.5%) and 95.5% (91.2-98.2%) for the maximum troponin level observed during hospitalization. The largest variation in probability of agreement was for first troponin measured, which ranged from 66.4% to 95.8% among hospitals. CONCLUSION Extraction of maximum troponin values during a hospitalization from EHR structured data is feasible and accurate.
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Affiliation(s)
- Anna M Kucharska-Newton
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY, United States of America.
| | - Matthew Shane Loop
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | | | - Carlton Moore
- Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Stephanie W Haas
- School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Lynne Wagenknecht
- Wake Forest University Population Health Sciences, Winston-Salem, NC, United States of America
| | - Eric A Whitsel
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America; Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
| | - Gerardo Heiss
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
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15
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Casanova R, Anderson A, Justice J, Windham G, Gottesman R, Mosley T, Wagenknecht L, Kritchevsky S. Can a Data-Driven Measure of Neuroanatomic Dementia Risk be Considered a Measure of Brain Aging? Innov Aging 2021. [PMCID: PMC8681922 DOI: 10.1093/geroni/igab046.3470] [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] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
There is an increasing interest in identifying aging-related factors which may be permissive of Alzheimer’s Disease (AD) emergence. We previously used machine learning to derive an index of neuroanatomic risk of dementia called AD pattern similarity (AD-PS) score using MRIs obtained in the Atherosclerosis Risk in Communities (ARIC) study. Here, we investigate the potential of the AD-PS scores as a brain-focused measure of biologic age. Among 1970 ARIC participants with MRI collected at ARIC Visit 5, we related AD-PS scores to three measures of aging: mortality (n=356) over 8 years of follow-up; an a priori panel of 32 proteins related to aging (N=1647); and a deficit accumulation index (DAI) based on 38 health-related measures. We found lower AD-PS scores associated with significantly lower mortality (HR=0.58, CI-95%, [0.45 - 0.75], p < 0.001) after adjusting for age, race, smoking and hypertension. Among the 32 proteins, nine were significantly associated to AD-PS scores (p < 0.05) with 4 remaining significant adjusting for multiple comparisons (Growth/differentiation factor 15, Tumor necrosis factor receptor superfamily member 1A and 1B and Collagen alpha-1(XVIII) chain). Finally, in a linear regression model after adjusting for age, race, sex, hypertension and smoking, AD-PS scores were associated with the DAI (p < 0.001). The consistent patterns of associations suggest that a data-driven measure of AD neuroanatomic risk may be capturing aspects of biologic age in older adults.
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Affiliation(s)
- Ramon Casanova
- Wake Forest School of Medicine, Winston-Salem, North Carolina, United States
| | - Andrea Anderson
- Wake Forest School of Medicine, Wake Forest School of Medicine, North Carolina, United States
| | - Jamie Justice
- Wake Forest School of Medicine, Wake Forest School of Medicine, North Carolina, United States
| | - Gwen Windham
- University of Mississippi Medical Center, Jackson, Mississippi, United States
| | | | - Thomas Mosley
- The University of Mississippi Medical Center, Jackson, Mississippi, United States
| | - Lynne Wagenknecht
- Wake Forest School of Medicine, Winston Salem, North Carolina, United States
| | - Stephen Kritchevsky
- Wake Forest School of Medicine, Wake Forest School of Medicine, North Carolina, United States
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16
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Kucharska-Newton A, Matsushita K, Mok Y, Minotti M, Oelsner EC, Ring K, Wagenknecht L, Hughes TM, Mosley T, Palta P, Lutsey PL, Coresh J. Loneliness and its predictors among older adults prior to and during the COVID-19 pandemic: cross-sectional and longitudinal survey findings from participants of the Atherosclerosis Risk in Communities (ARIC) Study cohort in the USA. BMJ Open 2021; 11:e053542. [PMID: 34857573 PMCID: PMC8640201 DOI: 10.1136/bmjopen-2021-053542] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 10/27/2021] [Indexed: 11/26/2022] Open
Abstract
OBJECTIVES We aimed to ascertain the prevalence of perceived loneliness among older adults following the onset of the COVID-19 pandemic and to examine factors contributing to the perception of loneliness. DESIGN Cross-sectional and longitudinal data from the Atherosclerosis Risk in Communities (ARIC) Study cohort. SETTING The ARIC Study cohort, a prospective cohort that recruited (1987-1989) participants from four distinct communities in the USA. PARTICIPANTS 2984 ARIC cohort members. PRIMARY AND SECONDARY OUTCOMES Perceived loneliness assessed using the University of California at Los Angeles (UCLA) UCLA three-item Loneliness Scale telephone interviews conducted May-October 2020 and prior to March 2020. RESULTS Of the total 5037 participants alive in 2020, 2984 (56.2%) responded to the UCLA three-item questionnaire (mean age 82.6 (SD 4.6) years, 586 (19.6%) black participants, 1081 (36.2%) men), of which 66 (2.2%) reported having had a COVID-19 infection during the observation period. The proportion of participants reporting feeling lonely was 56.3% (n=1680). Among participants with repeat measures of loneliness (n=516), 35.2% (n=182) reported feeling more lonely following pandemic onset. Self-rated health and emotional resilience were strongly associated with self-perceived loneliness. The burden of COVID-19 infections, concern about the pandemic and decreased self-reported physical activity were greater among black as compared with white participants and among those with an educational attainment of less than high school as compared with high school or more. CONCLUSION Findings from this study document the increase in perceived loneliness among older adults during the COVID-19 pandemic in the USA.
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Affiliation(s)
- Anna Kucharska-Newton
- Department of Epidemiology, University of North Carolina at Chapel Hill Gillings School of Global Public Health, Chapel Hill, North Carolina, USA
- Department of Epidemiology, University of Kentucky, Lexington, Kentucky, USA
| | - Kunihiro Matsushita
- Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yejin Mok
- Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Melissa Minotti
- Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA
| | - Elizabeth C Oelsner
- Department of Medicine, Columbia University Medical Center, New York, New York, USA
| | - Kim Ring
- Department of Biostatistics, The Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lynne Wagenknecht
- Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Timothy M Hughes
- Department of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Thomas Mosley
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Priya Palta
- Department of Medicine, Columbia University, New York, New York, USA
| | - Pamela L Lutsey
- Division of Epidemiology & Community Health, University of Minnesota School of Public Health, Minneapolis, Minnesota, USA
| | - Joe Coresh
- Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA
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17
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Lawrence JM, Reynolds K, Saydah SH, Mottl A, Pihoker C, Dabelea D, Dolan L, Henkin L, Liese AD, Isom S, Divers J, Wagenknecht L. Demographic Correlates of Short-Term Mortality Among Youth and Young Adults With Youth-Onset Diabetes Diagnosed From 2002 to 2015: The SEARCH for Diabetes in Youth Study. Diabetes Care 2021; 44:2691-2698. [PMID: 34607833 PMCID: PMC8669529 DOI: 10.2337/dc21-0728] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [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: 04/02/2021] [Accepted: 09/03/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To examine short-term mortality and cause of death among youth and young adults (YYAs) with youth-onset diabetes. RESEARCH DESIGN AND METHODS We included 19,717 YYAs newly diagnosed with diabetes before 20 years of age from 1 January 2002 to 31 December 2015 enrolled in the SEARCH for Diabetes in Youth Study. Of these, 14,721 had type 1; 4,141 type 2; and 551 secondary and 304 other/unknown diabetes type. Cases were linked with the National Death Index through 31 December 2017. We calculated standardized mortality ratios (SMRs) and 95% CIs based on age, sex, and race/ethnicity for state and county population areas and examined underlying causes of death. RESULTS During 170,148 person-years (PY) (median follow-up 8.5 years), 283 individuals died: 133 with type 1 (103.0/100,000 PY), 55 with type 2 (161.5/100,000 PY), 87 with secondary (1,952/100,000 PY), and 8 with other/unknown diabetes type (312.3/100,000 PY). SMRs (95% CI) for the first three groups were 1.5 (1.2-1.8), 2.3 (1.7-3.0), and 28.0 (22.4-34.6), respectively. Diabetes was the underlying cause of death for 42.1%, 9.1%, and 4.6% of deaths, respectively. The SMR was greater for type 2 than for type 1 diabetes (P < 0.001). SMRs were significantly higher for individuals with type 1 diabetes who were <20 years of age, non-Hispanic White and Hispanic, and female and for individuals with type 2 diabetes who were <25 years of age, from all race/ethnic minority groups, and from both sexes. CONCLUSIONS Excess mortality was observed among YYAs for each type of diabetes with differences in risk associated with diabetes type, age, race/ethnicity, and sex. The root causes of excess mortality among YYAs with diabetes merit further study.
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Affiliation(s)
- Jean M Lawrence
- Division of Epidemiologic Research, Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA
- Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD
| | - Kristi Reynolds
- Division of Epidemiologic Research, Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA
| | - Sharon H Saydah
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Hyattsville, MD
| | - Amy Mottl
- Division of Nephrology and Hypertension, University of North Carolina School of Medicine, Chapel Hill, NC
| | | | - Dana Dabelea
- Lifecourse Epidemiology of Adiposity & Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO
- Department of Epidemiology, University of Colorado School of Public Health, Aurora, CO
| | - Lawrence Dolan
- Division of Endocrinology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Leora Henkin
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC
| | - Angela D Liese
- Department of Epidemiology and Biostatistics, University of South Carolina Arnold School of Public Health, Columbia, SC
| | - Scott Isom
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC
| | - Jasmin Divers
- Division of Health Services Research, Department of Foundations of Medicine, New York University Long Island School of Medicine, Mineola, NY
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18
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Shah AS, Isom S, Dabelea D, D'Agostino R, Dolan LM, Wagenknecht L, Imperatore G, Saydah S, Liese AD, Lawrence JM, Pihoker C, Urbina EM. A cross sectional study to compare cardiac structure and diastolic function in adolescents and young adults with youth-onset type 1 and type 2 diabetes: The SEARCH for Diabetes in Youth Study. Cardiovasc Diabetol 2021; 20:136. [PMID: 34233679 PMCID: PMC8265135 DOI: 10.1186/s12933-021-01328-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 06/29/2021] [Indexed: 01/19/2023] Open
Abstract
AIMS To compare left ventricular structure (LV) and diastolic function in young adults with youth- onset diabetes by type, determine the prevalence of abnormal diastolic function by diabetes type using published values from age similar healthy controls, and examine the risk factors associated with diastolic function. METHODS In a cross sectional analysis we compared LV structure and diastolic function from two dimensional echocardiogram in participants with type 1 (T1D) and type 2 diabetes (T2D) who participated in the SEARCH for Diabetes in Youth Study. Linear models were used to examine the risk factors associated with worse diastolic function. RESULTS Of 479 participants studied, 258 had T1D (mean age 21.2 ± 5.2 years, 60.5% non-Hispanic white, 53.9% female) and 221 had T2D (mean age 24.8 ± 4.3 years, 24.4% non-Hispanic white, 73.8% female). Median diabetes duration was 11.6 years. Participants with T2D had greater LV mass index and worse diastolic function that persisted after adjustment for differences in risk factors compared with participants with T1D (all p < 0.05). Abnormal diastolic function, quantified using healthy controls, was pronounced in both groups but greater in those with T2D than T1D (T2D: 57.7% vs T1D: 47.2%, respectively), p < 0.05. Risk factors associated with worse diastolic function included older age at diabetes diagnosis, female sex, higher BP, heart rate and HbA1c and longer diabetes duration. CONCLUSIONS LV structure and diastolic function is worse in individuals with T2D compared to T1D. However, abnormal diastolic function in seen in both groups compared to published values from age similar healthy controls.
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MESH Headings
- Adolescent
- Adult
- Age of Onset
- Case-Control Studies
- Cross-Sectional Studies
- Diabetes Mellitus, Type 1/diagnosis
- Diabetes Mellitus, Type 1/epidemiology
- Diabetes Mellitus, Type 2/diagnosis
- Diabetes Mellitus, Type 2/epidemiology
- Diastole
- Echocardiography
- Female
- Humans
- Hypertrophy, Left Ventricular/diagnostic imaging
- Hypertrophy, Left Ventricular/epidemiology
- Hypertrophy, Left Ventricular/physiopathology
- Male
- Predictive Value of Tests
- Prevalence
- Risk Assessment
- Risk Factors
- United States/epidemiology
- Ventricular Dysfunction, Left/diagnostic imaging
- Ventricular Dysfunction, Left/epidemiology
- Ventricular Dysfunction, Left/physiopathology
- Ventricular Function, Left
- Ventricular Remodeling
- Young Adult
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Affiliation(s)
- Amy S Shah
- Department of Pediatrics, Division of Endocrinology, Cincinnati Children's Hospital Medical Center and The University of Cincinnati, 3333 Burnet Ave ML 7012, Cincinnati, OH, 45229, USA.
| | - Scott Isom
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, USA
| | - Dana Dabelea
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus (CU-Anschutz), Aurora, USA
| | - Ralph D'Agostino
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, USA
| | - Lawrence M Dolan
- Department of Pediatrics, Division of Endocrinology, Cincinnati Children's Hospital Medical Center and The University of Cincinnati, 3333 Burnet Ave ML 7012, Cincinnati, OH, 45229, USA
| | - Lynne Wagenknecht
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, USA
| | - Giuseppina Imperatore
- Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, USA
| | - Sharon Saydah
- Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, USA
| | - Angela D Liese
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, USA
| | - Jean M Lawrence
- Department of Research & Evaluation, Kaiser Permanente Southern California, Los Angeles, USA
| | - Cate Pihoker
- Department of Pediatrics, The University of Washington, Washington, USA
| | - Elaine M Urbina
- Department of Pediatrics, Division of Endocrinology, Cincinnati Children's Hospital Medical Center and The University of Cincinnati, 3333 Burnet Ave ML 7012, Cincinnati, OH, 45229, USA
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19
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Yu Z, Grams ME, Ndumele CE, Wagenknecht L, Boerwinkle E, North KE, Rebholz CM, Giovannucci EL, Coresh J. Association Between Midlife Obesity and Kidney Function Trajectories: The Atherosclerosis Risk in Communities (ARIC) Study. Am J Kidney Dis 2021; 77:376-385. [PMID: 32979415 PMCID: PMC7904650 DOI: 10.1053/j.ajkd.2020.07.025] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [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: 11/19/2019] [Accepted: 07/21/2020] [Indexed: 01/13/2023]
Abstract
RATIONALE & OBJECTIVE Obesity has been related to risk for chronic kidney disease. However, the associations of different measures of midlife obesity with long-term kidney function trajectories and whether they differ by sex and race are unknown. STUDY DESIGN Observational study. SETTING & PARTICIPANTS 13,496 participants from the Atherosclerosis Risk in Communities (ARIC) Study. PREDICTORS Midlife obesity status as measured by body mass index (BMI), waist-to-hip ratio, and predicted percent fat at baseline. OUTCOMES Estimated glomerular filtration rate (eGFR) calculated using serum creatinine level measured at 5 study visits, and incident kidney failure with replacement therapy (KFRT). ANALYTICAL APPROACH Mixed models with random intercepts and random slopes for eGFR. Cox proportional hazards models for KFRT. RESULTS Baseline mean age was 54 years, median eGFR was 103mL/min/1.73m2, and median BMI was 27kg/m2. Over 30 years of follow-up, midlife obesity measures were associated with eGFR decline in White and Black women but not consistently in men. Adjusted for age, center, smoking, and coronary heart disease, the differences in eGFR slope per 1-SD higher BMI, waist-to-hip ratio, and predicted percent fat were 0.09 (95% CI, -0.18 to 0.36), -0.25 (95% CI, -0.50 to 0.01), and-0.14 (95% CI, -0.41 to 0.13) mL/min/1.73m2 per decade for White men; -0.91 (95% CI, -1.15 to-0.67), -0.82 (95% CI, -1.06 to-0.58), and-1.02 (95% CI, -1.26 to-0.78) mL/min/1.73m2 per decade for White women; -0.70 (95% CI, -1.54 to 0.14), -1.60 (95% CI, -2.42 to-0.78), and-1.24 (95% CI, -2.08 to-0.40) mL/min/1.73m2 per decade for Black men; and-1.24 (95% CI, -2.08 to-0.40), -1.50 (95% CI, -2.05 to-0.95), and-1.43 (95% CI, -2.00 to-0.86) mL/min/1.73m2 per decade for Black women. Obesity indicators were independently associated with risk for KFRT for all sex-race groups except White men. LIMITATIONS Loss to follow-up during 3 decades of follow-up with 5 eGFR assessments. CONCLUSIONS Obesity status is a risk factor for future decline in kidney function and development of KFRT in Black and White women, with less consistent associations among men.
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Affiliation(s)
- Zhi Yu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD
| | - Morgan E Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD; Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD
| | - Chiadi E Ndumele
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD
| | - Lynne Wagenknecht
- Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Casey M Rebholz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD
| | - Edward L Giovannucci
- Department of Epidemiology, Harvard T. H. Chan School of Public Health; Department of Nutrition, Harvard T. H. Chan School of Public Health; Channing Division of Network Medicine, Brigham and Women's Hospital
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD; Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD.
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Dabelea D, Sauder KA, Jensen ET, Mottl AK, Huang A, Pihoker C, Hamman RF, Lawrence J, Dolan LM, Agostino RD, Wagenknecht L, Mayer-Davis EJ, Marcovina SM. Twenty years of pediatric diabetes surveillance: what do we know and why it matters. Ann N Y Acad Sci 2021; 1495:99-120. [PMID: 33543783 DOI: 10.1111/nyas.14573] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [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: 11/30/2020] [Revised: 01/14/2021] [Accepted: 01/20/2021] [Indexed: 12/23/2022]
Abstract
SEARCH for Diabetes in Youth (SEARCH) was initiated in 2000 as a multicenter study to address major gaps in the understanding of childhood diabetes in the United States. An active registry of youth diagnosed with diabetes at age <20 years since 2002 assessed prevalence, annual incidence, and trends by age, race/ethnicity, sex, and diabetes type. An observational cohort nested within the population-based registry was established to assess the natural history and risk factors for acute and chronic diabetes-related complications, as well as the quality of care and quality of life of children and adolescents with diabetes from diagnosis into young adulthood. SEARCH findings have contributed to a better understanding of the complex and heterogeneous nature of youth-onset diabetes. Continued surveillance of the burden and risk of type 1 and type 2 diabetes is important to track and monitor incidence and prevalence within the population. SEARCH reported evidence of early diabetes complications highlighting that continuing the long-term follow-up of youth with diabetes is necessary to further our understanding of its natural history and to develop the most appropriate approaches to primary, secondary, and tertiary prevention of diabetes and its complications. This review summarizes two decades of research and suggests avenues for further work.
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Affiliation(s)
- Dana Dabelea
- Lifecourse Epidemiology of Adiposity and Diabetes Center, Department of Epidemiology and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Katherine A Sauder
- Lifecourse Epidemiology of Adiposity and Diabetes Center, Department of Epidemiology and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Elizabeth T Jensen
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Amy K Mottl
- Division of Nephrology and Hypertension, University of North Carolina School of Medicine, Chapel Hill, North Carolina
| | - Alyssa Huang
- Department of Pediatrics, University of Washington, Seattle, Washington
| | - Catherine Pihoker
- Department of Pediatrics, University of Washington, Seattle, Washington
| | - Richard F Hamman
- Lifecourse Epidemiology of Adiposity and Diabetes Center, Department of Epidemiology and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Jean Lawrence
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California
| | - Lawrence M Dolan
- Division of Endocrinology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Ralph D' Agostino
- Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Lynne Wagenknecht
- Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Elizabeth J Mayer-Davis
- Department of Nutrition and Medicine, University of North Carolina, Chapel Hill, North Carolina
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21
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Gottesman RF, Wu A, Coresh J, Jack CR, Knopman DS, Rahmim A, Sharrett AR, Wagenknecht L, Walker K, Wong D, Mosley TH. Abstract WP479: Associations Between Vascular Risk, Amyloid Burden and Incident Dementia: The ARIC-PET Study. Stroke 2020. [DOI: 10.1161/str.51.suppl_1.wp479] [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] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background:
Midlife vascular risk factors (MVRF) are associated with incident dementia. Similarly, amyloid β(Aβ) and neurodegeneration (e.g.brain volumes), as parts of the Alzheimer’s Disease (AD) ATN framework, are associated with cognition. Whether vascular and AD-associated factors contribute to dementia independently or interact synergistically to reduce cognitive ability is not well understood.
Methods:
Recruited from 3 U.S. communities, ARIC-PET participants were followed from 1987-89 (45-64 yo) through 2016-17 (74-94 yo). Cognition was evaluated in 2011-13 (ages 69-88), and twice more, every 2-3 years. In 2011-13, nondemented ARIC-PET participants had a brain MRI, with measurement of white matter hyperintensities (WMH) and brain volumes, with florbetapir (Aβ) PET scans in 2012-14; global cortical standardized uptake value ratio (SUVR) was log-transformed and standardized. Dementia was classified by expert review, as well as phone and medical record surveillance. The relative contributions of vascular risk (MVRF, WMH volume) and AD pathology (elevated Aβ SUVR, smaller AD signature region volumes) to incident dementia were evaluated with Cox proportional hazards regression.
Results:
In 298 individuals, 36 developed dementia. In models with key MVRF, demographics, and Aβ SUVR, hypertension and Aβ each independently predicted dementia risk (per SD of Aβ SUVR: HR 2.57, 95% CI 1.72-3.84; hypertension: HR 2.57, 95% CI 1.16-5.67), but didn’t interact on dementia risk. WMH (per SD: HR 1.51, 95% CI 1.03-2.20) and Aβ SUVR (per SD: HR 2.52, 95% CI 1.83-3.47) each contributed to incident dementia but WMH lost significance when MVRF were added to the model. Smaller AD signature regions were associated with incident dementia, independent of Aβ SUVR, and remained significant after adjustment for MVRF (HR per SD 2.18, 95% CI 1.18-4.01).
Conclusions:
Midlife hypertension and late-life Aβ independently contribute to dementia risk, but don’t synergize on a multiplicative scale. Neurodegeneration (e.g.smaller AD signature region volume) is also associated with incident dementia, independent of Aβ and MVRF. Multiple pathways leading to dementia should be considered when evaluating risk factors and interventions to reduce the burden of dementia.
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Affiliation(s)
| | - Aozhou Wu
- Epidemiology, Johns Hopkins Univ, Baltimore, MD
| | | | | | | | | | | | | | | | - Dean Wong
- Radiology, Johns Hopkins Univ, Baltimore, MD
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22
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Jakicic JM, Horton ES, Curtis JM, Killean TM, Bray GA, Cheskin LJ, Johnson KC, Middelbeek RJW, Pi-Sunyer FX, Regensteiner JG, Ribisl PM, Wagenknecht L, Espeland MA. Abnormal Exercise Test or CVD History on Weight Loss or Fitness: the Look AHEAD Trial. Transl J Am Coll Sports Med 2020; 5:e000134. [PMID: 34017914 PMCID: PMC8130141 DOI: 10.1249/tjx.0000000000000134] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
PURPOSE Obesity and type 2 diabetes are associated with an increased risk of cardiovascular disease (CVD) and the combination of weight loss and increased physical exercise are commonly recommended to reduce CVD. This study examined whether people with obesity and type 2 diabetes with an abnormal graded exercise tolerance test (GXT) or a history of CVD would have less success in achieving weight loss and improved fitness, compared to adults without these conditions. METHODS The Look AHEAD Study examined whether an intensive lifestyle intervention (ILI) compared with diabetes support and education (DSE) reduced cardiovascular events in adults with overweight/obesity and type 2 diabetes. Participants underwent a baseline maximal GXT and provided medical history data. Weight loss and fitness change were examined in 5011 participants over four years in those with or without an abnormal baseline GXT and/or history of CVD. RESULTS After four years, weight loss in both ILI and DSE were significantly greater in those without a prior history of CVD than in those with a CVD history (6.69% vs 5.98%, p=0.02, in ILI and 0.73 vs -.07% (weight gain), p=0.01, in DSE). Likewise, those without a prior history of CVD experienced greater improvements in fitness in both ILI and DSE relative to those with a history of CVD. Having an abnormal GXT at baseline did not affect weight loss or fitness. CONCLUSIONS A history of CVD at baseline modestly lessened weight loss and fitness changes at 4 years, whereas having any abnormality on the baseline GXT did not affect these outcomes. Thus, weight loss and improved fitness are achievable in adults with a history of CVD or ECG abnormalities.
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Affiliation(s)
| | | | - Jeffrey M. Curtis
- NIDDK, Phoenix, AZ
- St. Joseph’s Hospital and Medical Center, Phoenix, AZ
| | - Tina M. Killean
- NIDDK, Phoenix, AZ
- Northern Navajo Medical Center, Shiprock, NM
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23
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Fretts AM, Imamura F, Marklund M, Micha R, Wu JHY, Murphy RA, Chien KL, McKnight B, Tintle N, Forouhi NG, Qureshi WT, Virtanen JK, Wong K, Wood AC, Lankinen M, Rajaobelina K, Harris TB, Djoussé L, Harris B, Wareham NJ, Steffen LM, Laakso M, Veenstra J, Samieri C, Brouwer IA, Yu CI, Koulman A, Steffen BT, Helmer C, Sotoodehnia N, Siscovick D, Gudnason V, Wagenknecht L, Voutilainen S, Tsai MY, Uusitupa M, Kalsbeek A, Berr C, Mozaffarian D, Lemaitre RN. Associations of circulating very-long-chain saturated fatty acids and incident type 2 diabetes: a pooled analysis of prospective cohort studies. Am J Clin Nutr 2019; 109:1216-1223. [PMID: 30982858 PMCID: PMC6500926 DOI: 10.1093/ajcn/nqz005] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [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: 10/16/2018] [Accepted: 01/07/2019] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Saturated fatty acids (SFAs) of different chain lengths have unique metabolic and biological effects, and a small number of recent studies suggest that higher circulating concentrations of the very-long-chain SFAs (VLSFAs) arachidic acid (20:0), behenic acid (22:0), and lignoceric acid (24:0) are associated with a lower risk of diabetes. Confirmation of these findings in a large and diverse population is needed. OBJECTIVE We investigated the associations of circulating VLSFAs 20:0, 22:0, and 24:0 with incident type 2 diabetes in prospective studies. METHODS Twelve studies that are part of the Fatty Acids and Outcomes Research Consortium participated in the analysis. Using Cox or logistic regression within studies and an inverse-variance-weighted meta-analysis across studies, we examined the associations of VLSFAs 20:0, 22:0, and 24:0 with incident diabetes among 51,431 participants. RESULTS There were 14,276 cases of incident diabetes across participating studies. Higher circulating concentrations of 20:0, 22:0, and 24:0 were each associated with a lower risk of incident diabetes. Pooling across cohorts, the RR (95% CI) for incident diabetes comparing the 90th percentile to the 10th percentile was 0.78 (0.70, 0.87) for 20:0, 0.84 (0.77, 0.91) for 22:0, and 0.75 (0.69, 0.83) for 24:0 after adjustment for demographic, lifestyle, adiposity, and other health factors. Results were fully attenuated in exploratory models that adjusted for circulating 16:0 and triglycerides. CONCLUSIONS Results from this pooled analysis indicate that higher concentrations of circulating VLSFAs 20:0, 22:0, and 24:0 are each associated with a lower risk of diabetes.
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Affiliation(s)
- Amanda M Fretts
- Department of Epidemiology
- Cardiovascular Health Research Unit
| | - Fumiaki Imamura
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Matti Marklund
- Clinical Nutrition and Metabolism, Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Renata Micha
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA
| | - Jason H Y Wu
- The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Rachel A Murphy
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Kuo-Liong Chien
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | | | | | - Nita G Forouhi
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | | | | | - Kerry Wong
- Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Alexis C Wood
- USDA / Agricultural Research Service Children's Nutrition Research Center, Baylor College of Medicine, Houston, TX
| | | | - Kalina Rajaobelina
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Bordeaux, France
| | | | - Luc Djoussé
- Divisions of Aging, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Bill Harris
- OmegaQuant Analytics, Sioux Falls, SD
- Department of Internal Medicine, Sanford School of Medicine, University of South Dakota, Vermillion, SD
| | - Nick J Wareham
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Lyn M Steffen
- Division of Epidemiology and Community Health, School of Public Health
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | | | - Cécilia Samieri
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Bordeaux, France
| | - Ingeborg A Brouwer
- Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | | | - Albert Koulman
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health Research Biomedical Research Centres Core Nutritional Biomarker Laboratory
- National Institute for Health Research Biomedical Research Centres Core Metabolomics and Lipidomics Laboratory, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
- Medical Research Council Elsie Widdowson Laboratory, Cambridge, United Kingdom
| | - Brian T Steffen
- Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN
| | - Catherine Helmer
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Bordeaux, France
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit
- Department of Medicine, University of Washington, Seattle, WA
| | | | - Vilmundur Gudnason
- Icelandic Heart Association, Kópavogur, Iceland
- Faculty of Medicine, University of Iceland, Reyjavik, Iceland
| | | | - Lynne Wagenknecht
- Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC
| | | | - Michael Y Tsai
- Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN
| | | | - Anya Kalsbeek
- Department of Biology, Dordt College, Sioux Center, IA
| | - Claudine Berr
- Inserm, University of Montpellier, Neuropsychiatry: Epidemiological and Clinical Research, Montpellier, France
- Memory Research and Resources Center, Department of Neurology, Montpellier University Hospital, Montpellier, France
| | - Dariush Mozaffarian
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA
| | - Rozenn N Lemaitre
- Cardiovascular Health Research Unit
- Department of Medicine, University of Washington, Seattle, WA
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24
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Espeland MA, Carmichael O, Hayden K, Neiberg RH, Newman AB, Keller JN, Wadden TA, Rapp SR, Hill JO, Horton ES, Johnson KC, Wagenknecht L, Wing RR. Long-term Impact of Weight Loss Intervention on Changes in Cognitive Function: Exploratory Analyses from the Action for Health in Diabetes Randomized Controlled Clinical Trial. J Gerontol A Biol Sci Med Sci 2018; 73:484-491. [PMID: 28958022 PMCID: PMC5861893 DOI: 10.1093/gerona/glx165] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [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: 12/19/2016] [Accepted: 08/23/2017] [Indexed: 11/13/2022] Open
Abstract
Background Diabetes adversely impacts cognition. Lifestyle change can improve diabetes control and potentially improve cognition. We examined whether weight loss through reduced caloric intake and increased physical activity was associated with slower cognitive aging in older adults with type 2 diabetes mellitus. Methods The Look AHEAD randomized controlled clinical trial delivered 10 years of intensive lifestyle intervention (ILI) that yielded long-term weight losses. During 5 years spanning the end of intervention and postintervention follow-up, repeated cognitive assessments were obtained in 1,091 individuals who had been assigned to ILI or a control condition of diabetes support and education (DSE). We compared the means and slopes of scores on cognitive testing over these repeated assessments. Results Compared with DSE, assignment to ILI was associated with a -0.082 SD deficit in mean global cognitive function across repeated assessments (p = .010). However, overweight (body mass index [BMI] < 30 kg/m2) ILI participants had 0.099 (95% confidence interval [CI]: -0.006, 0.259) better mean global cognitive function compared with overweight DSE participants, while obese (BMI ≥ 30 kg/m2) ILI participants had -0.117 (-0.185, -0.049) SD worse mean composite cognitive function scores (interaction p = .014) compared to obese DSE participants. For both overweight and obese participants, cognitive decline was marginally (-0.014 SD/y overall) steeper for ILI participants (p = .068), with 95% CI for differences in slopes excluding 0 for measures of attention and memory. Conclusions The behavioral weight loss intervention was associated with small relative deficits in cognitive function among individuals who were obese and marginally greater cognitive decline overall compared to control. ClinicalTrials.gov Identifier: NCT00017953.
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Affiliation(s)
- Mark A Espeland
- Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC
| | - Owen Carmichael
- Brain and Metabolism Imaging in Chronic Disease Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA
| | - Kathleen Hayden
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC
| | - Rebecca H Neiberg
- Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC
| | - Anne B Newman
- Healthy Aging Research Program, University of Pittsburgh, PA
| | - Jeffery N Keller
- Institute for Dementia Research and Prevention, Pennington Biomedical Research Center, Baton Rouge, LA
| | - Thomas A Wadden
- Center for Weight and Eating Disorders, University of Pennsylvania, Philadelphia
| | - Stephen R Rapp
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, NC
| | - James O Hill
- Center for Human Nutrition, University of Colorado Anschutz Medical Campus, Denver
| | | | - Karen C Johnson
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis
| | - Lynne Wagenknecht
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC
| | - Rena R Wing
- Weight Control and Diabetes Research Center, Miriam Hospital, Providence, RI
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Saeed A, Nambi V, Sun W, Virani S, Deswal A, Hoogeveen R, Wagenknecht L, Taffet G, Matsushita K, Selvin E, Coresh J, de Lemos J, Ballantyne C. IMPACT OF BIOMARKERS ON SHORT-TERM RISK PREDICTION OF GLOBAL CARDIOVASCULAR EVENTS IN OLDER ADULTS: ATHEROSCLEROSIS RISK IN COMMUNITIES STUDY. J Am Coll Cardiol 2018. [DOI: 10.1016/s0735-1097(18)33209-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Wu JHY, Marklund M, Imamura F, Tintle N, Ardisson Korat AV, de Goede J, Zhou X, Yang WS, de Oliveira Otto MC, Kröger J, Qureshi W, Virtanen JK, Bassett JK, Frazier-Wood AC, Lankinen M, Murphy RA, Rajaobelina K, Del Gobbo LC, Forouhi NG, Luben R, Khaw KT, Wareham N, Kalsbeek A, Veenstra J, Luo J, Hu FB, Lin HJ, Siscovick DS, Boeing H, Chen TA, Steffen B, Steffen LM, Hodge A, Eriksdottir G, Smith AV, Gudnason V, Harris TB, Brouwer IA, Berr C, Helmer C, Samieri C, Laakso M, Tsai MY, Giles GG, Nurmi T, Wagenknecht L, Schulze MB, Lemaitre RN, Chien KL, Soedamah-Muthu SS, Geleijnse JM, Sun Q, Harris WS, Lind L, Ärnlöv J, Riserus U, Micha R, Mozaffarian D. Omega-6 fatty acid biomarkers and incident type 2 diabetes: pooled analysis of individual-level data for 39 740 adults from 20 prospective cohort studies. Lancet Diabetes Endocrinol 2017; 5:965-974. [PMID: 29032079 PMCID: PMC6029721 DOI: 10.1016/s2213-8587(17)30307-8] [Citation(s) in RCA: 196] [Impact Index Per Article: 28.0] [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: 06/07/2017] [Revised: 08/01/2017] [Accepted: 08/04/2017] [Indexed: 12/23/2022]
Abstract
BACKGROUND The metabolic effects of omega-6 polyunsaturated fatty acids (PUFAs) remain contentious, and little evidence is available regarding their potential role in primary prevention of type 2 diabetes. We aimed to assess the associations of linoleic acid and arachidonic acid biomarkers with incident type 2 diabetes. METHODS We did a pooled analysis of new, harmonised, individual-level analyses for the biomarkers linoleic acid and its metabolite arachidonic acid and incident type 2 diabetes. We analysed data from 20 prospective cohort studies from ten countries (Iceland, the Netherlands, the USA, Taiwan, the UK, Germany, Finland, Australia, Sweden, and France), with biomarkers sampled between 1970 and 2010. Participants included in the analyses were aged 18 years or older and had data available for linoleic acid and arachidonic acid biomarkers at baseline. We excluded participants with type 2 diabetes at baseline. The main outcome was the association between omega-6 PUFA biomarkers and incident type 2 diabetes. We assessed the relative risk of type 2 diabetes prospectively for each cohort and lipid compartment separately using a prespecified analytic plan for exposures, covariates, effect modifiers, and analysis, and the findings were then pooled using inverse-variance weighted meta-analysis. FINDINGS Participants were 39 740 adults, aged (range of cohort means) 49-76 years with a BMI (range of cohort means) of 23·3-28·4 kg/m2, who did not have type 2 diabetes at baseline. During a follow-up of 366 073 person-years, we identified 4347 cases of incident type 2 diabetes. In multivariable-adjusted pooled analyses, higher proportions of linoleic acid biomarkers as percentages of total fatty acid were associated with a lower risk of type 2 diabetes overall (risk ratio [RR] per interquintile range 0·65, 95% CI 0·60-0·72, p<0·0001; I2=53·9%, pheterogeneity=0·002). The associations between linoleic acid biomarkers and type 2 diabetes were generally similar in different lipid compartments, including phospholipids, plasma, cholesterol esters, and adipose tissue. Levels of arachidonic acid biomarker were not significantly associated with type 2 diabetes risk overall (RR per interquintile range 0·96, 95% CI 0·88-1·05; p=0·38; I2=63·0%, pheterogeneity<0·0001). The associations between linoleic acid and arachidonic acid biomarkers and the risk of type 2 diabetes were not significantly modified by any prespecified potential sources of heterogeneity (ie, age, BMI, sex, race, aspirin use, omega-3 PUFA levels, or variants of the FADS gene; all pheterogeneity≥0·13). INTERPRETATION Findings suggest that linoleic acid has long-term benefits for the prevention of type 2 diabetes and that arachidonic acid is not harmful. FUNDING Funders are shown in the appendix.
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Affiliation(s)
- Jason H Y Wu
- The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
| | - Matti Marklund
- Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Uppsala, Sweden
| | - Fumiaki Imamura
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Nathan Tintle
- Department of Mathematics and Statistics, Dordt College, Sioux Center, IA, USA
| | - Andres V Ardisson Korat
- Department of Nutrition and Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Janette de Goede
- Division of Human Nutrition, Wageningen University, Wageningen, Netherlands
| | - Xia Zhou
- School of Public Health, Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | - Wei-Sin Yang
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Marcia C de Oliveira Otto
- Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center, School of Public Health, Houston, TX, USA
| | - Janine Kröger
- German Institute of Human Nutrition, Potsdam, Germany
| | | | - Jyrki K Virtanen
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | | | - Alexis C Frazier-Wood
- US Department of Agriculture/Agricultural Research Service, Children's Nutrition Research Center, Houston, TX, USA
| | - Maria Lankinen
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | | | - Kalina Rajaobelina
- University of Bordeaux, INSERM, Bordeaux Population Health Research Centre, UMR 1219, Bordeaux, France
| | - Liana C Del Gobbo
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nita G Forouhi
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Robert Luben
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Kay-Tee Khaw
- Department of Public Health and Primary Care, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Department of Mathematics and Statistics, Dordt College, Sioux Center, IA, USA
| | - Nick Wareham
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Anya Kalsbeek
- Department of Mathematics and Statistics, Dordt College, Sioux Center, IA, USA; Department of Biology, Dordt College, Sioux Center, IA, USA
| | - Jenna Veenstra
- Department of Mathematics and Statistics, Dordt College, Sioux Center, IA, USA; Department of Biology, Dordt College, Sioux Center, IA, USA
| | - Juhua Luo
- Department of Epidemiology and Biostatistics, Indiana University, Bloomington, IN, USA
| | - Frank B Hu
- Department of Nutrition and Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Hung-Ju Lin
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | | | - Heiner Boeing
- German Institute of Human Nutrition, Potsdam, Germany
| | - Tzu-An Chen
- US Department of Agriculture/Agricultural Research Service, Children's Nutrition Research Center, Houston, TX, USA
| | - Brian Steffen
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Lyn M Steffen
- School of Public Health, Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
| | | | | | | | | | | | | | - Claudine Berr
- INSERM U1061, Neuropsychiatry: Epidemiological and Clinical Research, and Montpellier University Hospital, Montpellier University, Montpellier, France
| | - Catherine Helmer
- University of Bordeaux, INSERM, Bordeaux Population Health Research Centre, UMR 1219, Bordeaux, France
| | - Cecilia Samieri
- University of Bordeaux, INSERM, Bordeaux Population Health Research Centre, UMR 1219, Bordeaux, France
| | - Markku Laakso
- Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Michael Y Tsai
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | | | - Tarja Nurmi
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | | | | | - Rozenn N Lemaitre
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Kuo-Liong Chien
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | | | | | - Qi Sun
- Department of Nutrition and Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - William S Harris
- Department of Internal Medicine, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA; OmegaQuant Analytics, Sioux Falls, SD, USA
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Johan Ärnlöv
- Department of Neurobiology, Care Sciences and Society, Division of Family Medicine, Karolinska Institute, Stockholm, Sweden; School of Health and Social Studies, Dalarna University, Falun, Sweden
| | - Ulf Riserus
- Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Uppsala, Sweden
| | - Renata Micha
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Dariush Mozaffarian
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
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Johnson KC, Lewis CE, Womack C, Garcia KR, Wagenknecht L, Pownall HJ, Horton ES, Pi-Sunyer X, Gregg EW, Schwartz AV. The Effect of Intentional Weight Loss on Fracture Risk in Persons With Diabetes: Results From the Look AHEAD Randomized Clinical Trial. J Bone Miner Res 2017; 32:2278-2287. [PMID: 28678345 PMCID: PMC5685890 DOI: 10.1002/jbmr.3214] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [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: 03/06/2017] [Revised: 06/29/2017] [Accepted: 07/03/2017] [Indexed: 12/24/2022]
Abstract
Intentional weight loss is an important treatment option for overweight persons with type 2 diabetes mellitus (DM), but the effects on long-term fracture risk are not known. The purpose of this Look AHEAD analysis was to evaluate whether long-term intentional weight loss would increase fracture risk in overweight or obese persons with DM. Look AHEAD is a multicenter, randomized clinical trial. Recruitment began in August 2001 and follow-up continued for a median of 11.3 years at 16 academic centers. A total of 5145 persons aged 45 to 76 years with DM were randomized to either an intensive lifestyle intervention (ILI) with reduced calorie consumption and increased physical activity designed to achieve and maintain ≥7% weight loss or to diabetes support and education intervention (DSE). Incident fractures were ascertained every 6 months by self-report and confirmed with central adjudication of medical records. The baseline mean age of participants was 59 years, 60% were women, 63% were white, and the mean BMI was 36 kg/m2 . Weight loss over the intervention period (median 9.6 years) was 6.0% in ILI and 3.5% in DSE. A total of 731 participants had a confirmed incident fracture (358 in DSE versus 373 in ILI). There were no statistically significant differences in incident total or hip fracture rates between the ILI and DSE groups. However, compared to the DSE group, the ILI group had a statistically significant 39% increased risk of a frailty fracture (HR 1.39; 95% CI, 1.02 to 1.89). An intensive lifestyle intervention resulting in long-term weight loss in overweight/obese adults with DM was not associated with an overall increased risk of incident fracture but may be associated with an increased risk of frailty fracture. When intentional weight loss is planned, consideration of bone preservation and fracture prevention is warranted. © 2017 American Society for Bone and Mineral Research.
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Affiliation(s)
| | - Karen C. Johnson
- Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN
| | - Cora E. Lewis
- University of Alabama at Birmingham, Birmingham, ALA
| | - Catherine Womack
- Department of Preventive Medicine and Medicine, University of Tennessee Health Science Center, Memphis, TN
| | - Katelyn R. Garcia
- Wake Forest School of Medicine, Wake Forest University, Winston-Salem, NC
| | - Lynne Wagenknecht
- Wake Forest School of Medicine, Wake Forest University, Winston-Salem, NC
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Lee CMY, Woodward M, Pandeya N, Adams R, Barrett-Connor E, Boyko EJ, Eliasson M, Franco LJ, Fujimoto WY, Gonzalez C, Howard BV, Jacobs DR, Keinanen-Kiukaanniemi S, Magliano D, Schreiner P, Shaw JE, Stevens J, Taylor A, Tuomilehto J, Wagenknecht L, Huxley RR. Comparison of relationships between four common anthropometric measures and incident diabetes. Diabetes Res Clin Pract 2017; 132:36-44. [PMID: 28783531 PMCID: PMC5728360 DOI: 10.1016/j.diabres.2017.07.022] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [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: 08/29/2016] [Revised: 04/19/2017] [Accepted: 07/17/2017] [Indexed: 11/21/2022]
Abstract
AIMS First, to conduct a detailed exploration of the prospective relations between four commonly used anthropometric measures with incident diabetes and to examine their consistency across different population subgroups. Second, to compare the ability of each of the measures to predict five-year risk of diabetes. METHODS We conducted a meta-analysis of individual participant data on body mass index (BMI), waist circumference (WC), waist-hip and waist-height ratio (WHtR) from the Obesity, Diabetes and Cardiovascular Disease Collaboration. Cox proportional hazard models were used to estimate the association between a one standard deviation increment in each anthropometric measure and incident diabetes. Harrell's concordance statistic was used to test the predictive accuracy of each measure for diabetes risk at five years. RESULTS Twenty-one studies with 154,998 participants and 9342 cases of incident diabetes were available. Each of the measures had a positive association with incident diabetes. A one standard deviation increment in each of the measures was associated with 64-80% higher diabetes risk. WC and WHtR more strongly associated with risk than BMI (ratio of hazard ratios: 0.95 [0.92,0.99] - 0.97 [0.95,0.98]) but there was no appreciable difference between the four measures in the predictive accuracy for diabetes at five years. CONCLUSIONS Despite suggestions that abdominal measures of obesity have stronger associations with incident diabetes and better predictive accuracy than BMI, we found no overall advantage in any one measure at discriminating the risk of developing diabetes. Any of these measures would suffice to assist in primary diabetes prevention efforts.
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Affiliation(s)
- Crystal Man Ying Lee
- School of Public Health, Curtin University, Australia
- The Boden Institute of Obesity, Nutrition, Exercise & Eating Disorders, University of Sydney, Australia
| | | | - Nirmala Pandeya
- School of Public Health, University of Queensland, Australia
| | - Robert Adams
- The Health Observatory, Discipline of Medicine, University of Adelaide, Australia
| | | | | | - Mats Eliasson
- Department of Public Health and Clinical Medicine, Umea University, Sweden
| | | | - Wilfred Y. Fujimoto
- Division of Metabolism, Endocrinology and Nutrition, University of Washington, USA
| | - Clicerio Gonzalez
- Unidad de Investigación en Diabetes y Riesgo Cardiovascular, Instituto Nacional de Salud Publica, Mexico
| | - Barbara V. Howard
- MedStar Health Research Institute, Georgetown University Hospital, USA
| | | | | | | | - Pamela Schreiner
- Center for Life Course Epidemiology and Systems Medicine, University of Oulu, Finland
| | | | - June Stevens
- Departments of Nutrition and Epidemiology, University of North Carolina at Chapel Hill, USA
| | - Anne Taylor
- Population Research & Outcomes Studies, Discipline of Medicine, University of Adelaide, Australia
| | - Jaakko Tuomilehto
- Dasman Diabetes Institute, Kuwait
- Department of Neurosciences and Preventive Medicine, Danube-University Krems, Austria
- Chronic Disease Prevention Unit, National Institute for Health and Welfare, Finland
- Saudi Diabetes Research Group, King Abdulaziz University, Saudi Arabia
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Windham BG, Griswold ME, Wang W, Kucharska-Newton A, Demerath EW, Gabriel KP, Pompeii LA, Butler K, Wagenknecht L, Kritchevsky S, Mosley TH. The Importance of Mid-to-Late-Life Body Mass Index Trajectories on Late-Life Gait Speed. J Gerontol A Biol Sci Med Sci 2017; 72:1130-1136. [PMID: 27811156 DOI: 10.1093/gerona/glw200] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [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: 04/14/2016] [Accepted: 09/21/2016] [Indexed: 11/13/2022] Open
Abstract
Background Prior studies suggest being overweight may be protective against poor functional outcomes in older adults. Methods Body mass index (BMI, kg/m2) was measured over 25 years across five visits (1987-2011) among Atherosclerosis Risk in Communities Study participants (baseline Visit 1 n = 15,720, aged 45-64 years). Gait speed was measured at Visit 5 ("late-life", aged ≥65 years, n = 6,229). BMI trajectories were examined using clinical cutpoints and continuous mixed models to estimate effects of patterns of BMI change on gait speed, adjusting for demographics and comorbidities. Results Mid-life BMI (baseline visit; 55% women; 27% black) was associated with late-life gait speed 25 years later; gait speeds were 94.3, 89.6, and 82.1 cm/s for participants with baseline normal BMI (<25), overweight (25 ≤ BMI < 30), and obese (BMI ≥ 30) (p < .001). In longitudinal analyses, late-life gait speeds were 96.9, 88.8, and 81.3 cm/s for participants who maintained normal, overweight, and obese weight status, respectively, across 25 years (p < .01). Increasing BMI over 25 years was associated with poorer late-life gait speeds; a 1%/year BMI increase for a participant with a baseline BMI of 22.5 (final BMI 28.5) was associated with a 4.6-cm/s (95% confidence interval: -7.0, -1.8) slower late-life gait speed than a participant who maintained a baseline BMI of 22.5. Conclusion Being overweight in older age was not protective of mobility function. Maintaining a normal BMI in mid- and late-life may help preserve late-life mobility.
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Affiliation(s)
| | - Michael E Griswold
- Department of Data Science, University of Mississippi Medical Center, Jackson
| | - Wanmei Wang
- Department of Data Science, University of Mississippi Medical Center, Jackson
| | | | - Ellen W Demerath
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis
| | - Kelley Pettee Gabriel
- University of Texas School of Public Health in Austin, Department of Epidemiology, Human Genetics, and Environmental Sciences
| | - Lisa A Pompeii
- University of Texas School of Public Health, Department of Epidemiology, Human Genetics, and Environmental Sciences
| | | | - Lynne Wagenknecht
- Center on Diabetes, Obesity, and Metabolism; Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Stephen Kritchevsky
- Sticht Center on Aging; Wake Forest School of Medicine, Winston-Salem, North Carolina
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30
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Mayer-Davis EJ, Lawrence JM, Dabelea D, Divers J, Isom S, Dolan L, Imperatore G, Linder B, Marcovina S, Pettitt DJ, Pihoker C, Saydah S, Wagenknecht L. Incidence Trends of Type 1 and Type 2 Diabetes among Youths, 2002-2012. N Engl J Med 2017; 376:1419-1429. [PMID: 28402773 PMCID: PMC5592722 DOI: 10.1056/nejmoa1610187] [Citation(s) in RCA: 930] [Impact Index Per Article: 132.9] [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: 11/19/2022]
Abstract
BACKGROUND Diagnoses of type 1 and type 2 diabetes in youths present a substantial clinical and public health burden. The prevalence of these diseases increased in the 2001-2009 period, but data on recent incidence trends are lacking. METHODS We ascertained cases of type 1 and type 2 diabetes mellitus at five study centers in the United States. Denominators (4.9 million youths annually) were obtained from the U.S. Census or health-plan member counts. After the calculation of annual incidence rates for the 2002-2012 period, we analyzed trends using generalized autoregressive moving-average models with 2-year moving averages. RESULTS A total of 11,245 youths with type 1 diabetes (0 to 19 years of age) and 2846 with type 2 diabetes (10 to 19 years of age) were identified. Overall unadjusted estimated incidence rates of type 1 diabetes increased by 1.4% annually (from 19.5 cases per 100,000 youths per year in 2002-2003 to 21.7 cases per 100,000 youths per year in 2011-2012, P=0.03). In adjusted pairwise comparisons, the annual rate of increase was greater among Hispanics than among non-Hispanic whites (4.2% vs. 1.2%, P<0.001). Overall unadjusted incidence rates of type 2 diabetes increased by 7.1% annually (from 9.0 cases per 100,000 youths per year in 2002-2003 to 12.5 cases per 100,000 youths per year in 2011-2012, P<0.001 for trend across race or ethnic group, sex, and age subgroups). Adjusted pairwise comparisons showed that the relative annual increase in the incidence of type 2 diabetes among non-Hispanic whites (0.6%) was lower than that among non-Hispanic blacks, Asians or Pacific Islanders, and Native Americans (P<0.05 for all comparisons) and that the annual rate of increase among Hispanics differed significantly from that among Native Americans (3.1% vs. 8.9%, P=0.01). After adjustment for age, sex, and race or ethnic group, the relative annual increase in the incidence of type 1 diabetes was 1.8% (P<0.001) and that of type 2 diabetes was 4.8% (P<0.001). CONCLUSIONS The incidences of both type 1 and type 2 diabetes among youths increased significantly in the 2002-2012 period, particularly among youths of minority racial and ethnic groups. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases and the Centers for Disease Control and Prevention.).
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Affiliation(s)
- Elizabeth J Mayer-Davis
- From the Departments of Nutrition and Medicine, University of North Carolina, Chapel Hill (E.J.M.-D.), and the Department of Biostatistical Sciences (J.D., S.I.) and the Division of Public Health Sciences (L.W.), Wake Forest School of Medicine, Winston-Salem - both in North Carolina; the Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena (J.M.L.), and Santa Barbara (D.J.P.) - both in California; the Department of Epidemiology, Colorado School of Public Health, Aurora (D.D.); the Department of Endocrinology, Children's Hospital Medical Center, Cincinnati (L.D.); the Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta (G.I., S.S.); the Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD (B.L.); and the Northwest Lipid Research Laboratory (S.M.) and the Department of Pediatrics, University of Washington (C.P.) - both in Seattle
| | - Jean M Lawrence
- From the Departments of Nutrition and Medicine, University of North Carolina, Chapel Hill (E.J.M.-D.), and the Department of Biostatistical Sciences (J.D., S.I.) and the Division of Public Health Sciences (L.W.), Wake Forest School of Medicine, Winston-Salem - both in North Carolina; the Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena (J.M.L.), and Santa Barbara (D.J.P.) - both in California; the Department of Epidemiology, Colorado School of Public Health, Aurora (D.D.); the Department of Endocrinology, Children's Hospital Medical Center, Cincinnati (L.D.); the Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta (G.I., S.S.); the Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD (B.L.); and the Northwest Lipid Research Laboratory (S.M.) and the Department of Pediatrics, University of Washington (C.P.) - both in Seattle
| | - Dana Dabelea
- From the Departments of Nutrition and Medicine, University of North Carolina, Chapel Hill (E.J.M.-D.), and the Department of Biostatistical Sciences (J.D., S.I.) and the Division of Public Health Sciences (L.W.), Wake Forest School of Medicine, Winston-Salem - both in North Carolina; the Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena (J.M.L.), and Santa Barbara (D.J.P.) - both in California; the Department of Epidemiology, Colorado School of Public Health, Aurora (D.D.); the Department of Endocrinology, Children's Hospital Medical Center, Cincinnati (L.D.); the Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta (G.I., S.S.); the Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD (B.L.); and the Northwest Lipid Research Laboratory (S.M.) and the Department of Pediatrics, University of Washington (C.P.) - both in Seattle
| | - Jasmin Divers
- From the Departments of Nutrition and Medicine, University of North Carolina, Chapel Hill (E.J.M.-D.), and the Department of Biostatistical Sciences (J.D., S.I.) and the Division of Public Health Sciences (L.W.), Wake Forest School of Medicine, Winston-Salem - both in North Carolina; the Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena (J.M.L.), and Santa Barbara (D.J.P.) - both in California; the Department of Epidemiology, Colorado School of Public Health, Aurora (D.D.); the Department of Endocrinology, Children's Hospital Medical Center, Cincinnati (L.D.); the Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta (G.I., S.S.); the Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD (B.L.); and the Northwest Lipid Research Laboratory (S.M.) and the Department of Pediatrics, University of Washington (C.P.) - both in Seattle
| | - Scott Isom
- From the Departments of Nutrition and Medicine, University of North Carolina, Chapel Hill (E.J.M.-D.), and the Department of Biostatistical Sciences (J.D., S.I.) and the Division of Public Health Sciences (L.W.), Wake Forest School of Medicine, Winston-Salem - both in North Carolina; the Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena (J.M.L.), and Santa Barbara (D.J.P.) - both in California; the Department of Epidemiology, Colorado School of Public Health, Aurora (D.D.); the Department of Endocrinology, Children's Hospital Medical Center, Cincinnati (L.D.); the Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta (G.I., S.S.); the Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD (B.L.); and the Northwest Lipid Research Laboratory (S.M.) and the Department of Pediatrics, University of Washington (C.P.) - both in Seattle
| | - Lawrence Dolan
- From the Departments of Nutrition and Medicine, University of North Carolina, Chapel Hill (E.J.M.-D.), and the Department of Biostatistical Sciences (J.D., S.I.) and the Division of Public Health Sciences (L.W.), Wake Forest School of Medicine, Winston-Salem - both in North Carolina; the Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena (J.M.L.), and Santa Barbara (D.J.P.) - both in California; the Department of Epidemiology, Colorado School of Public Health, Aurora (D.D.); the Department of Endocrinology, Children's Hospital Medical Center, Cincinnati (L.D.); the Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta (G.I., S.S.); the Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD (B.L.); and the Northwest Lipid Research Laboratory (S.M.) and the Department of Pediatrics, University of Washington (C.P.) - both in Seattle
| | - Giuseppina Imperatore
- From the Departments of Nutrition and Medicine, University of North Carolina, Chapel Hill (E.J.M.-D.), and the Department of Biostatistical Sciences (J.D., S.I.) and the Division of Public Health Sciences (L.W.), Wake Forest School of Medicine, Winston-Salem - both in North Carolina; the Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena (J.M.L.), and Santa Barbara (D.J.P.) - both in California; the Department of Epidemiology, Colorado School of Public Health, Aurora (D.D.); the Department of Endocrinology, Children's Hospital Medical Center, Cincinnati (L.D.); the Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta (G.I., S.S.); the Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD (B.L.); and the Northwest Lipid Research Laboratory (S.M.) and the Department of Pediatrics, University of Washington (C.P.) - both in Seattle
| | - Barbara Linder
- From the Departments of Nutrition and Medicine, University of North Carolina, Chapel Hill (E.J.M.-D.), and the Department of Biostatistical Sciences (J.D., S.I.) and the Division of Public Health Sciences (L.W.), Wake Forest School of Medicine, Winston-Salem - both in North Carolina; the Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena (J.M.L.), and Santa Barbara (D.J.P.) - both in California; the Department of Epidemiology, Colorado School of Public Health, Aurora (D.D.); the Department of Endocrinology, Children's Hospital Medical Center, Cincinnati (L.D.); the Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta (G.I., S.S.); the Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD (B.L.); and the Northwest Lipid Research Laboratory (S.M.) and the Department of Pediatrics, University of Washington (C.P.) - both in Seattle
| | - Santica Marcovina
- From the Departments of Nutrition and Medicine, University of North Carolina, Chapel Hill (E.J.M.-D.), and the Department of Biostatistical Sciences (J.D., S.I.) and the Division of Public Health Sciences (L.W.), Wake Forest School of Medicine, Winston-Salem - both in North Carolina; the Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena (J.M.L.), and Santa Barbara (D.J.P.) - both in California; the Department of Epidemiology, Colorado School of Public Health, Aurora (D.D.); the Department of Endocrinology, Children's Hospital Medical Center, Cincinnati (L.D.); the Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta (G.I., S.S.); the Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD (B.L.); and the Northwest Lipid Research Laboratory (S.M.) and the Department of Pediatrics, University of Washington (C.P.) - both in Seattle
| | - David J Pettitt
- From the Departments of Nutrition and Medicine, University of North Carolina, Chapel Hill (E.J.M.-D.), and the Department of Biostatistical Sciences (J.D., S.I.) and the Division of Public Health Sciences (L.W.), Wake Forest School of Medicine, Winston-Salem - both in North Carolina; the Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena (J.M.L.), and Santa Barbara (D.J.P.) - both in California; the Department of Epidemiology, Colorado School of Public Health, Aurora (D.D.); the Department of Endocrinology, Children's Hospital Medical Center, Cincinnati (L.D.); the Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta (G.I., S.S.); the Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD (B.L.); and the Northwest Lipid Research Laboratory (S.M.) and the Department of Pediatrics, University of Washington (C.P.) - both in Seattle
| | - Catherine Pihoker
- From the Departments of Nutrition and Medicine, University of North Carolina, Chapel Hill (E.J.M.-D.), and the Department of Biostatistical Sciences (J.D., S.I.) and the Division of Public Health Sciences (L.W.), Wake Forest School of Medicine, Winston-Salem - both in North Carolina; the Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena (J.M.L.), and Santa Barbara (D.J.P.) - both in California; the Department of Epidemiology, Colorado School of Public Health, Aurora (D.D.); the Department of Endocrinology, Children's Hospital Medical Center, Cincinnati (L.D.); the Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta (G.I., S.S.); the Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD (B.L.); and the Northwest Lipid Research Laboratory (S.M.) and the Department of Pediatrics, University of Washington (C.P.) - both in Seattle
| | - Sharon Saydah
- From the Departments of Nutrition and Medicine, University of North Carolina, Chapel Hill (E.J.M.-D.), and the Department of Biostatistical Sciences (J.D., S.I.) and the Division of Public Health Sciences (L.W.), Wake Forest School of Medicine, Winston-Salem - both in North Carolina; the Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena (J.M.L.), and Santa Barbara (D.J.P.) - both in California; the Department of Epidemiology, Colorado School of Public Health, Aurora (D.D.); the Department of Endocrinology, Children's Hospital Medical Center, Cincinnati (L.D.); the Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta (G.I., S.S.); the Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD (B.L.); and the Northwest Lipid Research Laboratory (S.M.) and the Department of Pediatrics, University of Washington (C.P.) - both in Seattle
| | - Lynne Wagenknecht
- From the Departments of Nutrition and Medicine, University of North Carolina, Chapel Hill (E.J.M.-D.), and the Department of Biostatistical Sciences (J.D., S.I.) and the Division of Public Health Sciences (L.W.), Wake Forest School of Medicine, Winston-Salem - both in North Carolina; the Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena (J.M.L.), and Santa Barbara (D.J.P.) - both in California; the Department of Epidemiology, Colorado School of Public Health, Aurora (D.D.); the Department of Endocrinology, Children's Hospital Medical Center, Cincinnati (L.D.); the Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta (G.I., S.S.); the Division of Diabetes, Endocrinology, and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD (B.L.); and the Northwest Lipid Research Laboratory (S.M.) and the Department of Pediatrics, University of Washington (C.P.) - both in Seattle
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Kucharska-Newton A, Griswold M, Yao ZH, Foraker R, Rose K, Rosamond W, Wagenknecht L, Koton S, Pompeii L, Windham BG. Cardiovascular Disease and Patterns of Change in Functional Status Over 15 Years: Findings From the Atherosclerosis Risk in Communities (ARIC) Study. J Am Heart Assoc 2017; 6:e004144. [PMID: 28249844 PMCID: PMC5523991 DOI: 10.1161/jaha.116.004144] [Citation(s) in RCA: 10] [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] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Accepted: 01/17/2017] [Indexed: 12/12/2022]
Abstract
BACKGROUND Cardiovascular disease (CVD) is the leading cause of premature disability, yet few prospective studies have examined functional status (FS) among persons with CVD. Our aim was to examine patterns of change in FS prior to and after hospitalization for nonfatal myocardial infarction, stroke, and heart failure among members of the Atherosclerosis Risk in Communities (ARIC) study cohort. METHODS AND RESULTS FS was assessed using a modified Rosow-Breslau questionnaire administered during routine annual telephone interviews conducted from 1993 through 2007 among 15 277 ARIC study participants. An FS score was constructed as a summary measure of responses to questions about participants' ability to perform selected tasks of daily living (eg, walking half a mile, climbing stairs). Incidence of CVD was assessed through ARIC surveillance of hospitalized events. Rate of change in FS over time prior to and following a CVD event was examined using generalized estimating equations. A decline in FS was observed on average 2 years prior to a myocardial infarction hospitalization and on average 3 years prior to a stroke or heart failure hospitalization. FS post-myocardial infarction declined relative to pre-event levels but improved to close to pre-myocardial infarction levels within 3 years. Decline in FS following incident heart failure and stroke remained over time. Observed patterns of change in FS did not differ appreciably by race or sex. CONCLUSIONS This study documents that a decline in FS precedes incidence of CVD-related hospitalization by at least 2 years, providing a strong argument for routine preventative assessment of FS among older adults.
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Rodriguez CJ, Still CH, Garcia KR, Wagenknecht L, White S, Bates JT, Del Cid MV, Lioudis M, Lopez Barrera N, Moreyra A, Punzi H, Ringer RJ, Cushman WC, Contreras G, Servilla K, Rocco M. Baseline blood pressure control in Hispanics: characteristics of Hispanics in the Systolic Blood Pressure Intervention Trial. J Clin Hypertens (Greenwich) 2016; 19:116-125. [PMID: 27862904 DOI: 10.1111/jch.12942] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2016] [Revised: 08/19/2016] [Accepted: 08/22/2016] [Indexed: 11/30/2022]
Abstract
The Systolic Blood Pressure Intervention Trial (SPRINT) tested whether a systolic blood pressure (SBP) value <120 mm Hg reduces adverse clinical outcomes compared with the goal of <140 mm Hg. Here the authors describe the baseline characteristics of Hispanic participants in SPRINT. Nondiabetic hypertensive patients 50 years and older with SBP 130-180 mm Hg taking zero to four blood pressure (BP) medications were enrolled from the mainland United States and Puerto Rico. Cross-sectional, bivariate analysis was employed comparing sociodemographic and clinical factors in Hispanics vs non-Hispanics. Multivariable logistic regression models restricted to Hispanics were used to identify factors associated with achieved BP control (SBP <140 mm Hg and diastolic BP <90 mm Hg) at baseline. Eleven percent (n=984) of SPRINT participants were Hispanic; 56% (n=549) of Hispanics were living in Puerto Rico and the remainder were living on the US mainland. Hispanics overall were younger, more often female, less likely to live alone, and more likely to have lower education and be uninsured, although just as likely to be employed compared with non-Hispanics. BP control was not different between Hispanics vs non-Hispanics at baseline. However, a significantly higher percentage of Hispanics on the US mainland (compared with Hispanics in Puerto Rico) had controlled BP. BP control was independently associated with cardiovascular disease history and functional status among Hispanics, specifically those living in Puerto Rico, whereas functional status was the only independent predictor of BP control identified among mainland Hispanics. These findings highlight the diversity of the SPRINT population. It remains to be seen whether factors identified among Hispanics impact intervention goals and subsequent clinical outcomes.
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Affiliation(s)
- Carlos J Rodriguez
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC, USA.,Department of Internal Medicine - Cardiovascular Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Carolyn H Still
- Division of Nephrology and Hypertension, Clinical Hypertension Program, University Hospitals Case Medical Center, Cleveland, OH, USA
| | - Katelyn R Garcia
- Department of Biostatistics, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Lynne Wagenknecht
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Suzanne White
- Department of Internal Medicine, NEON Health Centers, Cleveland, OH, USA
| | - Jeffrey T Bates
- Department of Internal Medicine, Veterans Affairs Medical Center and Baylor College of Medicine, Houston, TX, USA
| | | | - Michael Lioudis
- Department of Nephrology and Hypertension, Cleveland Clinic, Cleveland, OH, USA
| | | | - Abel Moreyra
- Division of Cardiovascular Disease, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Henry Punzi
- Department of Medicine, Punzi Medical Center, Carrollton, TX, USA
| | - Robert J Ringer
- Department of Pharmacology, Veterans Affairs Medical Center, Albuquerque, NM, USA
| | | | - Gabriel Contreras
- Department of Nephrology - Internal Medicine, University of Miami, Miami, FL, USA
| | - Karen Servilla
- Department of Medicine - Nephrology, Veterans Affairs Medical Center, Albuquerque, NM, USA
| | - Michael Rocco
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC, USA.,Department of Internal Medicine - Nephrology, Wake Forest School of Medicine, Winston-Salem, NC, USA
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Wing RR, Espeland MA, Clark JM, Hazuda HP, Knowler WC, Pownall HJ, Unick J, Wadden T, Wagenknecht L. Association of Weight Loss Maintenance and Weight Regain on 4-Year Changes in CVD Risk Factors: the Action for Health in Diabetes (Look AHEAD) Clinical Trial. Diabetes Care 2016; 39:1345-55. [PMID: 27271190 PMCID: PMC4955927 DOI: 10.2337/dc16-0509] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Accepted: 05/05/2016] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Short-term weight loss improves cardiovascular disease (CVD) risk factors. We sought to determine the longer-term effects of maintaining weight loss or, conversely, regaining weight. RESEARCH DESIGN AND METHODS We used data from Action for Health in Diabetes (Look AHEAD), a randomized trial of intensive lifestyle intervention (ILI) compared to a control condition in overweight/obese individuals with type 2 diabetes. ILI participants were grouped according to weight change patterns, as follows: 1) no weight loss (±3% at years 1 and 4); 2) moderate weight loss (3-8% at years 1 and 4); 3) large weight loss (8-20% at years 1 and 4); 4) moderate loss/full regain (3-8% at year 1/±3% at year 4); 5) large loss/full regain (8-20% at year 1/± 3% year 4); and 6) large loss/partial regain (8-20% at year 1/3-8% at year 4) and changes in CVD risk factors were compared. RESULTS Adjusting for baseline differences and medication use, larger weight losses produced greater improvements in HbA1c, systolic blood pressure, HDL cholesterol, and triglycerides at years 1 and 4 (all P ≤ 0.02). Despite maintenance of weight loss, HbA1c levels worsened between years 1 and 4, and remained below baseline only in those with large weight losses. We found no negative associations of losing and regaining weight relative to not having lost weight. Moreover, those who had large initial weight loss but full regain of weight had greater improvements in HbA1c levels at year 4 than those with smaller or no initial weight loss. CONCLUSIONS Larger initial weight loss should be encouraged in individuals with type 2 diabetes, despite the possibility of regain.
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Pitts R, Garcia K, Ribisl P, Vitolins M, Cheskin L, Glasser S, Balasubramanyam A, Pownall HJ, Wagenknecht L, Eckel R. TRIGLYCERIDE LEVELS AND ITS RELATIONSHIP WITH HEMOGLOBIN A1C IN PATIENTS WITH DIABETES. J Am Coll Cardiol 2016. [DOI: 10.1016/s0735-1097(16)32021-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Liu CT, Young KL, Brody JA, Olden M, Wojczynski MK, Heard-Costa N, Li G, Morrison AC, Muzny D, Gibbs RA, Reid JG, Shao Y, Zhou Y, Boerwinkle E, Heiss G, Wagenknecht L, McKnight B, Borecki IB, Fox CS, North KE, Cupples LA. Sequence variation in TMEM18 in association with body mass index: Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study. ACTA ACUST UNITED AC 2015; 7:344-9. [PMID: 24951660 DOI: 10.1161/circgenetics.13.000067] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
BACKGROUND Genome-wide association studies for body mass index (BMI) previously identified a locus near TMEM18. We conducted targeted sequencing of this region to investigate the role of common, low-frequency, and rare variants influencing BMI. METHODS AND RESULTS We sequenced TMEM18 and regions downstream of TMEM18 on chromosome 2 in 3976 individuals of European ancestry from 3 community-based cohorts (Atherosclerosis Risk in Communities, Cardiovascular Health Study, and Framingham Heart Study), including 200 adults selected for high BMI. We examined the association between BMI and variants identified in the region from nucleotide position 586 432 to 677 539 (hg18). Rare variants (minor allele frequency, <1%) were analyzed using a burden test and the sequence kernel association test. Results from the 3 cohort studies were meta-analyzed. We estimate that mean BMI is 0.43 kg/m(2) higher for each copy of the G allele of single-nucleotide polymorphism rs7596758 (minor allele frequency, 29%; P=3.46×10(-4)) using a Bonferroni threshold of P<4.6×10(-4). Analyses conditional on previous genome-wide association study single-nucleotide polymorphisms associated with BMI in the region led to attenuation of this signal and uncovered another independent (r(2)<0.2), statistically significant association, rs186019316 (P=2.11×10(-4)). Both rs186019316 and rs7596758 or proxies are located in transcription factor binding regions. No significant association with rare variants was found in either the exons of TMEM18 or the 3' genome-wide association study region. CONCLUSIONS Targeted sequencing around TMEM18 identified 2 novel BMI variants with possible regulatory function.
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Ng MCY, Shriner D, Chen BH, Li J, Chen WM, Guo X, Liu J, Bielinski SJ, Yanek LR, Nalls MA, Comeau ME, Rasmussen-Torvik LJ, Jensen RA, Evans DS, Sun YV, An P, Patel SR, Lu Y, Long J, Armstrong LL, Wagenknecht L, Yang L, Snively BM, Palmer ND, Mudgal P, Langefeld CD, Keene KL, Freedman BI, Mychaleckyj JC, Nayak U, Raffel LJ, Goodarzi MO, Chen YDI, Taylor HA, Correa A, Sims M, Couper D, Pankow JS, Boerwinkle E, Adeyemo A, Doumatey A, Chen G, Mathias RA, Vaidya D, Singleton AB, Zonderman AB, Igo RP, Sedor JR, Kabagambe EK, Siscovick DS, McKnight B, Rice K, Liu Y, Hsueh WC, Zhao W, Bielak LF, Kraja A, Province MA, Bottinger EP, Gottesman O, Cai Q, Zheng W, Blot WJ, Lowe WL, Pacheco JA, Crawford DC, Grundberg E, Rich SS, Hayes MG, Shu XO, Loos RJF, Borecki IB, Peyser PA, Cummings SR, Psaty BM, Fornage M, Iyengar SK, Evans MK, Becker DM, Kao WHL, Wilson JG, Rotter JI, Sale MM, Liu S, Rotimi CN, Bowden DW. Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes. PLoS Genet 2014; 10:e1004517. [PMID: 25102180 PMCID: PMC4125087 DOI: 10.1371/journal.pgen.1004517] [Citation(s) in RCA: 150] [Impact Index Per Article: 15.0] [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: 01/21/2014] [Accepted: 06/05/2014] [Indexed: 12/11/2022] Open
Abstract
Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15×10−94<P<5×10−8, odds ratio (OR) = 1.09 to 1.36). Fine-mapping revealed that 88 of 158 previously identified T2D or glucose homeostasis loci demonstrated nominal to highly significant association (2.2×10−23 < locus-wide P<0.05). These novel and previously identified loci yielded a sibling relative risk of 1.19, explaining 17.5% of the phenotypic variance of T2D on the liability scale in African Americans. Overall, this study identified two novel susceptibility loci for T2D in African Americans. A substantial number of previously reported loci are transferable to African Americans after accounting for linkage disequilibrium, enabling fine mapping of causal variants in trans-ethnic meta-analysis studies. Despite the higher prevalence of type 2 diabetes (T2D) in African Americans than in Europeans, recent genome-wide association studies (GWAS) were examined primarily in individuals of European ancestry. In this study, we performed meta-analysis of 17 GWAS in 8,284 cases and 15,543 controls to explore the genetic architecture of T2D in African Americans. Following replication in additional 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry, we identified two novel and three previous reported T2D loci reaching genome-wide significance. We also examined 158 loci previously reported to be associated with T2D or regulating glucose homeostasis. While 56% of these loci were shared between African Americans and the other populations, the strongest associations in African Americans are often found in nearby single nucleotide polymorphisms (SNPs) instead of the original SNPs reported in other populations due to differential genetic architecture across populations. Our results highlight the importance of performing genetic studies in non-European populations to fine map the causal genetic variants.
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Affiliation(s)
- Maggie C. Y. Ng
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Daniel Shriner
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - Brian H. Chen
- Program on Genomics and Nutrition, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America
- Center for Metabolic Disease Prevention, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America
| | - Jiang Li
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Wei-Min Chen
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Jiankang Liu
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Suzette J. Bielinski
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Lisa R. Yanek
- The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Michael A. Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Mary E. Comeau
- Center for Public Health Genomics, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
- Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Laura J. Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Richard A. Jensen
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
| | - Daniel S. Evans
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, California, United States of America
| | - Yan V. Sun
- Department of Epidemiology and Biomedical Informatics, Emory University, Atlanta, Georgia, United States of America
| | - Ping An
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Sanjay R. Patel
- Division of Sleep Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Yingchang Lu
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Loren L. Armstrong
- Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Lynne Wagenknecht
- Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Lingyao Yang
- Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Beverly M. Snively
- Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Nicholette D. Palmer
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Poorva Mudgal
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Carl D. Langefeld
- Center for Public Health Genomics, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
- Department of Biostatistical Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Keith L. Keene
- Department of Biology, Center for Health Disparities, East Carolina University, Greenville, North Carolina, United States of America
| | - Barry I. Freedman
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Josyf C. Mychaleckyj
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
| | - Uma Nayak
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
| | - Leslie J. Raffel
- Medical Genetics Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
| | - Mark O. Goodarzi
- Medical Genetics Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
| | - Y-D Ida Chen
- Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Herman A. Taylor
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
- Jackson State University, Tougaloo College, Jackson, Mississippi, United States of America
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Mario Sims
- Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - David Couper
- Collaborative Studies Coordinating Center, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - James S. Pankow
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - Ayo Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - Rasika A. Mathias
- The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Division of Allergy and Clinical Immunology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Dhananjay Vaidya
- The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Andrew B. Singleton
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Alan B. Zonderman
- Laboratory of Personality and Cognition, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America
| | - Robert P. Igo
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - John R. Sedor
- Department of Medicine, Case Western Reserve University, MetroHealth System campus, Cleveland, Ohio, United States of America
- Department of Physiology and Biophysics, Case Western Reserve University, Cleveland, Ohio, United States of America
| | | | - Edmond K. Kabagambe
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - David S. Siscovick
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
| | - Barbara McKnight
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Kenneth Rice
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Wen-Chi Hsueh
- Department of Medicine, University of California, San Francisco, California, United States of America
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Lawrence F. Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Aldi Kraja
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Michael A. Province
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Erwin P. Bottinger
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Omri Gottesman
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - William J. Blot
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee; International Epidemiology Institute, Rockville, Maryland, United States of America
| | - William L. Lowe
- Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Jennifer A. Pacheco
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Dana C. Crawford
- Center for Human Genetics Research and Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
| | | | | | - Elin Grundberg
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | | | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
| | - M. Geoffrey Hayes
- Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Ingrid B. Borecki
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, Missouri, United States of America
| | - Patricia A. Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Steven R. Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, California, United States of America
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, University of Washington, Seattle, Washington, United States of America
- Department of Medicine, University of Washington, Seattle, Washington, United States of America
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
- Department of Health Services, University of Washington, Seattle, Washington, United States of America
| | - Myriam Fornage
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Sudha K. Iyengar
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Michele K. Evans
- Health Disparities Unit, National Institute on Aging, National Institutes of Health, Baltimore Maryland, United States of America
| | - Diane M. Becker
- The GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - W. H. Linda Kao
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - James G. Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, Mississippi, United States of America
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles BioMedical Research Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - Michèle M. Sale
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Medicine, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
| | - Simin Liu
- Program on Genomics and Nutrition, School of Public Health, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Epidemiology, University of California Los Angeles, Los Angeles, California, United States of America
- Departments of Epidemiology and Medicine, Brown University, Providence, Rhode Island, United States of America
- * E-mail: (SL); (CNR); (DWB)
| | - Charles N. Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
- * E-mail: (SL); (CNR); (DWB)
| | - Donald W. Bowden
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
- * E-mail: (SL); (CNR); (DWB)
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Bushnell C, Snively BM, Worrall BB, Colton C, Hoogeveen R, Mosley T, Gottesman R, Folsom A, Wagenknecht L. Abstract W P143: Race-specific Effects of Inflammatory and Vascular Remodeling Biomarkers in Ischemic Stroke. Stroke 2014. [DOI: 10.1161/str.45.suppl_1.wp143] [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] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background:
Blacks have a higher risk of stroke than whites; inflammatory and vascular remodeling (VR) biomarkers may mediate this risk. We aimed to determine associations between inflammatory and VR biomarkers and ischemic stroke in a bi-racial sample of the Atherosclerosis Risk In Communities (ARIC) cohort.
Methods:
In a case-control pilot study, 133 participants from Forsyth and Jackson Field Centers were identified: 32 blacks and 33 whites with first ischemic stroke after visit 2, and 33 black and 35 white stroke-free controls. We assayed IL-8, IL-10, IL-1 receptor antagonist (IL-1ra), IL-1β, matrix metalloproteinase-9 (MMP-9), and tissue inhibitor of metalloproteinase-1 (TIMP-1) with Quantikine ELISA. We calculated IL-1β/IL-1ra ratio, representing the IL-1 pathway. Biomarkers were covariates in race-specific logistic regression models; if significant, biomarker-by-race interactions were explored.
Results:
Cases were older (mean age 60y vs 56y; p<0.0001); more had hypertension (52% vs 35%; p=0.043), and diabetes (35% vs 13%; p=0.0034). There were no differences in biomarkers between blacks and whites (cases and controls). In blacks, there was a trend toward a reduced risk of stroke with increasing levels of IL-1ra after adjustment (p=0.074). In whites, stroke was associated with higher levels of TIMP-1 (p=0.011; interaction p=0.079) and with higher levels of IL-8 (p=0.011; interaction p=0.11) in the unadjusted, but not adjusted model (Table).
Conclusion:
We identified race-specific associations between biomarkers and incident stroke. VR biomarkers (TIMP-1) may be associated with stroke in whites, and there was a trend towards lower levels of IL-1ra (counter-inflammatory) and risk of stroke in blacks. Larger studies are warranted to confirm these findings and determine if these markers will explain differences in stroke risk in blacks vs whites.
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Affiliation(s)
| | - Beverly M Snively
- Public Health Sciences, Wake Forest Sch of Medicine, Winston Salem, NC
| | | | | | - Ronald Hoogeveen
- Atherosclerosis and Vascular Medicine, Baylor College of Medicine, Houston, TX
| | - Thomas Mosley
- Dept of Medicine, Univ of Mississippi Med Cntr, Jackson, MS
| | | | - Aaron Folsom
- Sch of Public Health, Univ of Minnesota, Minneapolis, MN
| | - Lynne Wagenknecht
- Public Health Sciences, Wake Forest Sch of Medicine, Winston Salem, NC
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Lee C, Liese A, Wagenknecht L, Lorenzo C, Haffner S, Hanley A. Fish consumption, insulin sensitivity and beta-cell function in the Insulin Resistance Atherosclerosis Study (IRAS). Nutr Metab Cardiovasc Dis 2013; 23:829-835. [PMID: 22835984 PMCID: PMC3485446 DOI: 10.1016/j.numecd.2012.06.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [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: 03/23/2012] [Revised: 05/10/2012] [Accepted: 06/06/2012] [Indexed: 10/28/2022]
Abstract
BACKGROUND AND AIMS Previous research on the association between fish consumption and incident type 2 diabetes has been inconclusive. In addition, few studies have investigated how fish consumption may be related to the metabolic abnormalities underlying diabetes. Therefore, we examined the association of fish consumption with measures of insulin sensitivity and beta-cell function in a multi-ethnic population. METHODS AND RESULTS We examined the cross-sectional association between fish consumption and measures of insulin sensitivity and secretion in 951 non-diabetic participants in the Insulin Resistance Atherosclerosis Study (IRAS). Fish consumption, categorized as <2 vs. ≥2 portions/week, was measured using a validated food frequency questionnaire. Insulin sensitivity (S(I)) and acute insulin response (AIR) were determined from frequently sampled intravenous glucose tolerance tests. Higher fish consumption was independently associated with lower S(I)-adjusted AIR (β = -0.13 [-0.25, -0.016], p = 0.03, comparing ≥2 vs. <2 portions/week). Fish consumption was positively associated with intact and split proinsulin/C-peptide ratios, however, these associations were confounded by ethnicity (multivariable-adjusted β = 0.073 [-0.014, 0.16] for intact proinsulin/C-peptide ratio, β = 0.031 [-0.065, 0.13] for split proinsulin/C-peptide ratio). We also observed a significant positive association between fish consumption and fasting blood glucose (multivariable-adjusted β = 2.27 [0.68, 3.86], p = 0.005). We found no association between fish consumption and S(I) (multivariable-adjusted β = -0.015 [-0.083, 0.053]) or fasting insulin (multivariable-adjusted β = 0.016 [-0.066, 0.10]). CONCLUSIONS Fish consumption was not associated with measures of insulin sensitivity in the multi-ethnic IRAS cohort. However, higher fish consumption may be associated with pancreatic beta-cell dysfunction.
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Affiliation(s)
- C. Lee
- Department of Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada
| | - A. Liese
- Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA
| | - L. Wagenknecht
- Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - C. Lorenzo
- Division of Clinical Epidemiology, University of Texas Health Science Centre, San Antonio, TX, USA
| | - S. Haffner
- Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - A. Hanley
- Department of Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada
- Department of Medicine and Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Leadership Sinai Centre for Diabetes, Mount Sinai Hospital, Toronto, Ontario, Canada
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Rosamond WD, Chambless LE, Heiss G, Mosley TH, Coresh J, Whitsel E, Wagenknecht L, Ni H, Folsom AR. Twenty-two-year trends in incidence of myocardial infarction, coronary heart disease mortality, and case fatality in 4 US communities, 1987-2008. Circulation 2012; 125:1848-57. [PMID: 22420957 DOI: 10.1161/circulationaha.111.047480] [Citation(s) in RCA: 269] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Knowledge of trends in the incidence of and survival after myocardial infarction (MI) in a community setting is important to understanding trends in coronary heart disease (CHD) mortality rates. METHODS AND RESULTS We estimated race- and gender-specific trends in the incidence of hospitalized MI, case fatality, and CHD mortality from community-wide surveillance and validation of hospital discharges and of in- and out-of-hospital deaths among 35- to 74-year-old residents of 4 communities in the Atherosclerosis Risk in Communities (ARIC) Study. Biomarker adjustment accounted for change from reliance on cardiac enzymes to widespread use of troponin measurements over time. During 1987-2008, a total of 30 985 fatal or nonfatal hospitalized acute MI events occurred. Rates of CHD death among persons without a history of MI fell an average 4.7%/y among men and 4.3%/y among women. Rates of both in- and out-of-hospital CHD death declined significantly throughout the period. Age- and biomarker-adjusted average annual rate of incident MI decreased 4.3% among white men, 3.8% among white women, 3.4% among black women, and 1.5% among black men. Declines in CHD mortality and MI incidence were greater in the second decade (1997-2008). Failure to account for biomarker shift would have masked declines in incidence, particularly among blacks. Age-adjusted 28-day case fatality after hospitalized MI declined 3.5%/y among white men, 3.6%/y among black men, 3.0%/y among white women, and 2.6%/y among black women. CONCLUSIONS Although these findings from 4 communities may not be directly generalizable to blacks and whites in the entire United States, we observed significant declines in MI incidence, primarily as a result of downward trends in rates between 1997 and 2008.
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Affiliation(s)
- Wayne D Rosamond
- Departments of Epidemiology, School of Medicine, University of North Carolina, Chapel Hill, USA.
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Wing RR, Lang W, Wadden TA, Safford M, Knowler WC, Bertoni AG, Hill JO, Brancati FL, Peters A, Wagenknecht L. Benefits of modest weight loss in improving cardiovascular risk factors in overweight and obese individuals with type 2 diabetes. Diabetes Care 2011; 34:1481-6. [PMID: 21593294 PMCID: PMC3120182 DOI: 10.2337/dc10-2415] [Citation(s) in RCA: 1116] [Impact Index Per Article: 85.8] [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: 02/07/2023]
Abstract
OBJECTIVE Overweight and obese individuals are encouraged to lose 5-10% of their body weight to improve cardiovascular disease (CVD) risk, but data supporting this recommendation are limited, particularly for individuals with type 2 diabetes. RESEARCH DESIGN AND METHODS We conducted an observational analysis of participants in the Look AHEAD (Action For Health in Diabetes) study (n=5,145, 40.5% male, 37% from ethnic/racial minorities) and examined the association between the magnitude of weight loss and changes in CVD risk factors at 1 year and the odds of meeting predefined criteria for clinically significant improvements in risk factors in individuals with type 2 diabetes. RESULTS The magnitude of weight loss at 1 year was strongly (P<0.0001) associated with improvements in glycemia, blood pressure, triglycerides, and HDL cholesterol but not with LDL cholesterol (P=0.79). Compared with weight-stable participants, those who lost 5 to <10% ([means±SD] 7.25±2.1 kg) of their body weight had increased odds of achieving a 0.5% point reduction in HbA1c (odds ratio 3.52 [95% CI 2.81-4.40]), a 5-mmHg decrease in diastolic blood pressure (1.48 [1.20-1.82]), a 5-mmHg decrease in systolic blood pressure (1.56 [1.27-1.91]), a 5 mg/dL increase in HDL cholesterol (1.69 [1.37-2.07]), and a 40 mg/dL decrease in triglycerides (2.20 [1.71-2.83]). The odds of clinically significant improvements in most risk factors were even greater in those who lost 10-15% of their body weight. CONCLUSIONS Modest weight losses of 5 to <10% were associated with significant improvements in CVD risk factors at 1 year, but larger weight losses had greater benefits.
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Affiliation(s)
- Rena R Wing
- Department of Psychiatry and Human Behavior, Brown Medical School, The Miriam Hospital, Providence, Rhode Island, USA.
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Wing RR, Lang W, Wadden TA, Safford M, Knowler WC, Bertoni AG, Hill JO, Brancati FL, Peters A, Wagenknecht L. Benefits of modest weight loss in improving cardiovascular risk factors in overweight and obese individuals with type 2 diabetes. Diabetes Care 2011. [PMID: 21593294 DOI: 10.2337/dc10-2415%jdiabetescare] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
OBJECTIVE Overweight and obese individuals are encouraged to lose 5-10% of their body weight to improve cardiovascular disease (CVD) risk, but data supporting this recommendation are limited, particularly for individuals with type 2 diabetes. RESEARCH DESIGN AND METHODS We conducted an observational analysis of participants in the Look AHEAD (Action For Health in Diabetes) study (n=5,145, 40.5% male, 37% from ethnic/racial minorities) and examined the association between the magnitude of weight loss and changes in CVD risk factors at 1 year and the odds of meeting predefined criteria for clinically significant improvements in risk factors in individuals with type 2 diabetes. RESULTS The magnitude of weight loss at 1 year was strongly (P<0.0001) associated with improvements in glycemia, blood pressure, triglycerides, and HDL cholesterol but not with LDL cholesterol (P=0.79). Compared with weight-stable participants, those who lost 5 to <10% ([means±SD] 7.25±2.1 kg) of their body weight had increased odds of achieving a 0.5% point reduction in HbA1c (odds ratio 3.52 [95% CI 2.81-4.40]), a 5-mmHg decrease in diastolic blood pressure (1.48 [1.20-1.82]), a 5-mmHg decrease in systolic blood pressure (1.56 [1.27-1.91]), a 5 mg/dL increase in HDL cholesterol (1.69 [1.37-2.07]), and a 40 mg/dL decrease in triglycerides (2.20 [1.71-2.83]). The odds of clinically significant improvements in most risk factors were even greater in those who lost 10-15% of their body weight. CONCLUSIONS Modest weight losses of 5 to <10% were associated with significant improvements in CVD risk factors at 1 year, but larger weight losses had greater benefits.
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Affiliation(s)
- Rena R Wing
- Department of Psychiatry and Human Behavior, Brown Medical School, The Miriam Hospital, Providence, Rhode Island, USA.
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Selvin E, Steffes MW, Zhu H, Matsushita K, Wagenknecht L, Pankow J, Coresh J, Brancati FL. Glycated hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults. N Engl J Med 2010; 362:800-11. [PMID: 20200384 PMCID: PMC2872990 DOI: 10.1056/nejmoa0908359] [Citation(s) in RCA: 1043] [Impact Index Per Article: 74.5] [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
BACKGROUND Fasting glucose is the standard measure used to diagnose diabetes in the United States. Recently, glycated hemoglobin was also recommended for this purpose. METHODS We compared the prognostic value of glycated hemoglobin and fasting glucose for identifying adults at risk for diabetes or cardiovascular disease. We measured glycated hemoglobin in whole-blood samples from 11,092 black or white adults who did not have a history of diabetes or cardiovascular disease and who attended the second visit (occurring in the 1990-1992 period) of the Atherosclerosis Risk in Communities (ARIC) study. RESULTS The glycated hemoglobin value at baseline was associated with newly diagnosed diabetes and cardiovascular outcomes. For glycated hemoglobin values of less than 5.0%, 5.0 to less than 5.5%, 5.5 to less than 6.0%, 6.0 to less than 6.5%, and 6.5% or greater, the multivariable-adjusted hazard ratios (with 95% confidence intervals) for diagnosed diabetes were 0.52 (0.40 to 0.69), 1.00 (reference), 1.86 (1.67 to 2.08), 4.48 (3.92 to 5.13), and 16.47 (14.22 to 19.08), respectively. For coronary heart disease, the hazard ratios were 0.96 (0.74 to 1.24), 1.00 (reference), 1.23 (1.07 to 1.41), 1.78 (1.48 to 2.15), and 1.95 (1.53 to 2.48), respectively. The hazard ratios for stroke were similar. In contrast, glycated hemoglobin and death from any cause were found to have a J-shaped association curve. All these associations remained significant after adjustment for the baseline fasting glucose level. The association between the fasting glucose levels and the risk of cardiovascular disease or death from any cause was not significant in models with adjustment for all covariates as well as glycated hemoglobin. For coronary heart disease, measures of risk discrimination showed significant improvement when glycated hemoglobin was added to models including fasting glucose. CONCLUSIONS In this community-based population of nondiabetic adults, glycated hemoglobin was similarly associated with a risk of diabetes and more strongly associated with risks of cardiovascular disease and death from any cause as compared with fasting glucose. These data add to the evidence supporting the use of glycated hemoglobin as a diagnostic test for diabetes.
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Affiliation(s)
- Elizabeth Selvin
- Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21287, USA.
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Avery CL, Mills KT, Chambless LE, Chang PP, Folsom AR, Mosley TH, Ni H, Rosamond WD, Wagenknecht L, Wood J, Heiss G. Long-term association between self-reported signs and symptoms and heart failure hospitalizations: the Atherosclerosis Risk In Communities (ARIC) Study. Eur J Heart Fail 2010; 12:232-8. [PMID: 20097681 DOI: 10.1093/eurjhf/hfp203] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.0] [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] [Indexed: 11/14/2022] Open
Abstract
AIMS Although studies of the accuracy of heart failure (HF) classification scoring systems are available, few have examined their performance when restricted to self-reported items. METHODS AND RESULTS We evaluated the association between a simplified version of the Gothenburg score, a validated HF score comprised of cardiac and pulmonary signs and symptoms and medication use, and incident HF hospitalizations in 15,430 Atherosclerosis Risk in Communities (ARIC) Study participants. Gothenburg scores (range: 0-3) were constructed using self-reported items obtained at study baseline (1987-89). Incident HF hospitalization over 14.7 years of follow-up was defined as the first identified hospitalization with an ICD-9 discharge code of 428 (n = 1,668). Self-reported Gothenburg scores demonstrated very high agreement with the original metric comprised of self-reported and clinical measures and were directly associated with incident HF hospitalizations: [score = 1: hazard rate ratio (HRR) = 1.23 (1.07-1.42); score = 2: HRR = 2.17 (1.92-2.43); score = 3: HRR = 3.98 (3.37-4.70)]. CONCLUSION In a population-based cohort, self-reported Gothenburg criteria items were associated with hospitalized HF over a prolonged follow-up time. The association was also consistent across groups defined by sex and race, suggesting that this simple score deserves further study as a screening tool for the identification of individuals at high risk of HF in resource-limited settings.
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Affiliation(s)
- Christy L Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Bank of America Center, 137 E. Franklin St, Suite 306, Chapel Hill, NC 27514, USA.
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Wagenknecht L, Wasserman B, Chambless L, Coresh J, Folsom A, Mosley T, Ballantyne C, Sharrett R, Boerwinkle E. Correlates of carotid plaque presence and composition as measured by MRI: the Atherosclerosis Risk in Communities Study. Circ Cardiovasc Imaging 2009; 2:314-22. [PMID: 19808612 PMCID: PMC2747117 DOI: 10.1161/circimaging.108.823922] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.5] [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: 11/16/2022]
Abstract
BACKGROUND The composition of atherosclerotic plaque affects the likelihood of an atherothrombotic event, but prospective studies relating risk factors to carotid wall and plaque characteristics measured by MRI are lacking. We hypothesized that traditional risk factors are predictors of carotid wall and plaque characteristics measured 2 decades later. METHODS AND RESULTS A high-resolution contrast-enhanced MRI examination of the carotid artery was performed in 1769 participants. Measures of carotid wall volume and maximum thickness; lipid core presence, volume and maximum area; and fibrous cap thickness were performed centrally. The sample was, on average, 70 years of age, 57% female, 81% white, and 19% black. Greater age, total and low-density lipoprotein cholesterol, male sex, white race, diabetes, hypertension, and smoking as measured at baseline were all significant predictors of increased wall volume and maximum wall thickness 18 years later. An analysis of lipid core was restricted to the 1180 participants with maximum wall thickness >/=1.5 mm. Lipid core was observed in 569 individuals (weighted percentage, 42%). Baseline age and total and low-density lipoprotein cholesterol were predictors of presence of lipid core 18 years later; however, these relationships were attenuated after adjustment for wall thickness. Concurrently measured low-density lipoprotein cholesterol was associated with greater lipid core volume, independent of wall thickness. Concurrently measured glucose and body mass index were inversely associated fibrous cap thickness. CONCLUSIONS Traditional atherosclerosis risk factors are related to increased wall volume and wall thickness 2 decades later, but they do not discriminate characteristics of plaque composition (core and cap) independent of wall size.
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Affiliation(s)
- Lynne Wagenknecht
- Wake Forest University School of Medicine, Division of Public Health Sciences, Winston-Salem, NC 27157, USA.
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Kraja AT, Province MA, Arnett D, Wagenknecht L, Tang W, Hopkins PN, Djoussé L, Borecki IB. Do inflammation and procoagulation biomarkers contribute to the metabolic syndrome cluster? Nutr Metab (Lond) 2007; 4:28. [PMID: 18154661 PMCID: PMC2254623 DOI: 10.1186/1743-7075-4-28] [Citation(s) in RCA: 47] [Impact Index Per Article: 2.8] [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: 08/13/2007] [Accepted: 12/21/2007] [Indexed: 11/10/2022] Open
Abstract
Context The metabolic syndrome (MetS), in addition to its lipid, metabolic, and anthropomorphic characteristics, is associated with a prothrombotic and the proinflammatory state. However, the relationship of inflammatory biomarkers to MetS is not clear. Objective To study the association between a group of thrombotic and inflammatory biomarkers and the MetS. Methods Ten conventional MetS risk variables and ten biomarkers were analyzed. Correlations, factor analysis, hexagonal binning, and regression of each biomarker with the National Cholesterol Education Program (NCEP) MetS categories were performed in the Family Heart Study (n = 2,762). Results Subjects in the top 75% quartile for plasminogen activator inhibitor-1 (PAI1) had a 6.9 CI95 [4.2–11.2] greater odds (p < 0.0001) of being classified with the NCEP MetS. Significant associations of the corresponding top 75% quartile to MetS were identified for monocyte chemotactic protein 1 (MCP1, OR = 2.19), C-reactive protein (CRP, OR = 1.89), interleukin-6 (IL6, OR = 2.11), sICAM1 (OR = 1.61), and fibrinogen (OR = 1.86). PAI1 correlated significantly with all obesity and dyslipidemia variables. CRP had a high correlation with serum amyloid A (0.6) and IL6 (0.51), and a significant correlation with fibrinogen (0.46). Ten conventional quantitative risk factors were utilized to perform multivariate factor analysis. Individual inclusion, in this analysis of each biomarker, showed that, PAI1, CRP, IL6, and fibrinogen were the most important biomarkers that clustered with the MetS latent factors. Conclusion PAI1 is an important risk factor for MetS. It correlates significantly with most of the variables studied, clusters in two latent factors related to obesity and lipids, and demonstrates the greatest relative odds of the 10 biomarkers studied with respect to the MetS. Three other biomarkers, CRP, IL6, and fibrinogen associate also importantly with the MetS cluster. These 4 biomarkers can contribute in the MetS risk assessment.
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Affiliation(s)
- Aldi T Kraja
- Division of Statistical Genomics, Washington University School of Medicine, Saint Louis, MO, USA.
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Pi-Sunyer X, Blackburn G, Brancati FL, Bray GA, Bright R, Clark JM, Curtis JM, Espeland MA, Foreyt JP, Graves K, Haffner SM, Harrison B, Hill JO, Horton ES, Jakicic J, Jeffery RW, Johnson KC, Kahn S, Kelley DE, Kitabchi AE, Knowler WC, Lewis CE, Maschak-Carey BJ, Montgomery B, Nathan DM, Patricio J, Peters A, Redmon JB, Reeves RS, Ryan DH, Safford M, Van Dorsten B, Wadden TA, Wagenknecht L, Wesche-Thobaben J, Wing RR, Yanovski SZ. Reduction in weight and cardiovascular disease risk factors in individuals with type 2 diabetes: one-year results of the look AHEAD trial. Diabetes Care 2007; 30:1374-83. [PMID: 17363746 PMCID: PMC2665929 DOI: 10.2337/dc07-0048] [Citation(s) in RCA: 1019] [Impact Index Per Article: 59.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE The effectiveness of intentional weight loss in reducing cardiovascular disease (CVD) events in type 2 diabetes is unknown. This report describes 1-year changes in CVD risk factors in a trial designed to examine the long-term effects of an intensive lifestyle intervention on the incidence of major CVD events. RESEARCH DESIGN AND METHODS This study consisted of a multicentered, randomized, controlled trial of 5,145 individuals with type 2 diabetes, aged 45-74 years, with BMI >25 kg/m2 (>27 kg/m2 if taking insulin). An intensive lifestyle intervention (ILI) involving group and individual meetings to achieve and maintain weight loss through decreased caloric intake and increased physical activity was compared with a diabetes support and education (DSE) condition. RESULTS Participants assigned to ILI lost an average 8.6% of their initial weight vs. 0.7% in DSE group (P < 0.001). Mean fitness increased in ILI by 20.9 vs. 5.8% in DSE (P < 0.001). A greater proportion of ILI participants had reductions in diabetes, hypertension, and lipid-lowering medicines. Mean A1C dropped from 7.3 to 6.6% in ILI (P < 0.001) vs. from 7.3 to 7.2% in DSE. Systolic and diastolic pressure, triglycerides, HDL cholesterol, and urine albumin-to-creatinine ratio improved significantly more in ILI than DSE participants (all P < 0.01). CONCLUSIONS At 1 year, ILI resulted in clinically significant weight loss in people with type 2 diabetes. This was associated with improved diabetes control and CVD risk factors and reduced medicine use in ILI versus DSE. Continued intervention and follow-up will determine whether these changes are maintained and will reduce CVD risk.
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Tang W, Arnett DK, Province MA, Lewis CE, North K, Carr JJ, Pankow JS, Hopkins PN, Devereux RB, Wilk JB, Wagenknecht L. Racial differences in the association of coronary calcified plaque with left ventricular hypertrophy: the National Heart, Lung, and Blood Institute Family Heart Study and Hypertension Genetic Epidemiology Network. Am J Cardiol 2006; 97:1441-8. [PMID: 16679080 DOI: 10.1016/j.amjcard.2005.11.076] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.8] [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: 08/05/2005] [Revised: 11/28/2005] [Accepted: 11/28/2005] [Indexed: 11/16/2022]
Abstract
Studies have reported a lower burden of calcified atherosclerotic plaque in coronary arteries in African-Americans than in whites. Findings from autopsy studies of sudden cardiac death have suggested a link between left ventricular hypertrophy and severity of coronary atherosclerosis. Echocardiograms and cardiac computed tomograms were analyzed in 334 African-American (84% hypertensive) and 196 white (66% hypertensive) adults with no history of coronary heart disease or revascularization procedures at study entry. The relation of coronary artery calcium (CAC) score to left ventricular mass and left ventricular mass indexed to body surface area was assessed by Spearman's correlations and mixed linear models. Covariates included age, gender, field center, weight, height, systolic blood pressure, number of antihypertensive medications, diabetes, total and high-density lipoprotein cholesterol levels, and current smoking and alcohol consumption. In African-Americans, a significant and independent association between CAC score and left ventricular mass or left ventricular mass indexed to body surface area was present with the 2 analytic strategies. Spearman's correlation coefficients for CAC score with left ventricular mass and left ventricular mass indexed to body surface area were 0.14 (p = 0.015) and 0.13 (p = 0.025), respectively, after multivariable adjustment. In whites, the associations of CAC score with measurements of left ventricular mass were weaker and only marginally significant in mixed linear models. In conclusion, these findings suggest that CAC reflects a different risk burden between African-Americans and whites, and future studies examining the prognostic implications of CAC in African-Americans should consider the potential association between CAC and left ventricular hypertrophy.
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Affiliation(s)
- Weihong Tang
- Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, USA.
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Palaniappan L, Carnethon MR, Wang Y, Hanley AJG, Fortmann SP, Haffner SM, Wagenknecht L. Predictors of the incident metabolic syndrome in adults: the Insulin Resistance Atherosclerosis Study. Diabetes Care 2004; 27:788-93. [PMID: 14988303 DOI: 10.2337/diacare.27.3.788] [Citation(s) in RCA: 221] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To prospectively investigate predictors of the incident metabolic syndrome in nondiabetic adults. RESEARCH DESIGN AND METHODS This analysis included 714 white, black, and Hispanic participants in the Insulin Resistance Atherosclerosis Study (IRAS) who were free of the metabolic syndrome at baseline; 139 of these developed the metabolic syndrome in the subsequent 5 years. We examined measures of glucose (fasting and 2 h), insulin (fasting and 2 h, acute insulin response, insulin sensitivity [Si], and proinsulin), lipids (HDL and triglycerides), blood pressure (systolic and diastolic), waist circumference, and baseline physical activity (total energy expenditure) as predictors of the metabolic syndrome. Logistic regression models were adjusted for age, sex, study site, ethnicity, and impaired glucose tolerance. Signal detection analysis was used to identify the characteristics of the highest risk group. RESULTS The best predictors of incident metabolic syndrome were waist circumference (odds ratio [OR] 1.7 [1.3-2.0] per 11 cm), HDL cholesterol (0.6 [0.4-0.7] per 15 mg/dl), and proinsulin (1.7 [1.4-2.0] per 3.3 pmol/l). Signal detection analysis identified waist circumference (>89 cm in women, >102 cm in men) as the optimal predictor. CONCLUSIONS These findings suggest that obesity may precede the development of other metabolic syndrome components. Interventions that address obesity and reduce waist circumference may reduce the incidence of the metabolic syndrome in nondiabetic adults.
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Affiliation(s)
- Latha Palaniappan
- Stanford University Medical Center, Stanford, California 94305-5705, USA.
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Henkin L, Zaccaro D, Haffner S, Karter A, Rewers M, Sholinsky P, Wagenknecht L. Cigarette smoking, environmental tobacco smoke exposure and insulin sensitivity: the Insulin Resistance Atherosclerosis Study. Ann Epidemiol 1999; 9:290-6. [PMID: 10976855 DOI: 10.1016/s1047-2797(99)00003-4] [Citation(s) in RCA: 56] [Impact Index Per Article: 2.2] [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] [Indexed: 10/18/2022]
Abstract
PURPOSE To investigate whether active smoking and/or exposure to environmental tobacco smoke (ETS) is associated with insulin sensitivity. METHODS Insulin sensitivity and tobacco use history were measured in 1481 participants in the Insulin Resistance Atherosclerosis Study (IRAS). IRAS is a large mulitcenter epidemiologic study designed to explore the cross-sectional relationships among insulin resistance, cardiovascular disease risk factors and behaviors, and disease in African-American, Hispanic, and non-Hispanic white men and women, aged 40-69 years, selected to represent a broad range of glucose tolerance. Multiple linear regression models and linear contrasts were employed to describe the association between smoking history, as assessed via structured interview, and insulin sensitivity, as assessed by an insulin modified frequently sampled intravenous glucose tolerance test (FSIGT) with minimal model analysis. RESULTS Active smoking was not associated with insulin sensitivity. Exposure to ETS was associated with lower insulin sensitivity. Specifically, for all participants combined, levels of SI were lower, indicating reduced insulin sensitivity, for those exposed to ETS when compared to those who were not exposed (p = 0.019). This association persisted for female participants (p = 0.013) and exhibited the same trend for males but failed to achieve statistical significance (p = 0.264). CONCLUSIONS Our study did not reveal an association between active smoking and insulin sensitivity, as has been shown previously. The association between ETS exposure and insulin sensitivity is a puzzling finding which deserves further investigation in the longitudinal data from IRAS as well as in other populations.
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Affiliation(s)
- L Henkin
- Department of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC 27157-1063, USA
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D'Agostino RB, Burke G, O'Leary D, Rewers M, Selby J, Savage PJ, Saad MF, Bergman RN, Howard G, Wagenknecht L, Haffner SM. Ethnic differences in carotid wall thickness. The Insulin Resistance Atherosclerosis Study. Stroke 1996; 27:1744-9. [PMID: 8841322 DOI: 10.1161/01.str.27.10.1744] [Citation(s) in RCA: 87] [Impact Index Per Article: 3.1] [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] [Indexed: 02/02/2023]
Abstract
BACKGROUND AND PURPOSE Ethnic differences in cardiovascular disease (CVD) morbidity and mortality have been observed in US adults. However, little data exist on differences in indices of preclinical atherosclerosis such as carotid wall intima-media thickness (IMT) for US non-Hispanic whites, Hispanics, and blacks. This study was undertaken to determine whether there were ethnic differences in carotid wall IMT. METHODS Internal carotid artery (ICA) IMT and common carotid artery (CCA) IMT, indices of atherosclerosis, were assessed with the use of B-mode ultrasound in 1020 nondiabetic participants in the Insulin Resistance Atherosclerosis Study, a multicenter study designed to examine the association between insulin resistance and carotid atherosclerosis. The study included 281 blacks, 329 Hispanics, and 410 non-Hispanic whites aged 40 to 69 years. RESULTS Blacks had significantly greater CCA IMT than non-Hispanic whites (865 versus 808 microns); this remained significant after adjustment for major CVD risk factors and insulin sensitivity (864 versus 823 microns). There were no significant differences in ICA IMT between blacks and non-Hispanic whites. Hispanics had significantly lesser CCA IMT than non-Hispanic whites (749 versus 776 microns), and these differences remained significant after adjustment for traditional cardiovascular risk factors and insulin sensitivity (750 versus 778 microns). There were no significant differences in ICA IMT between non-Hispanic whites and Hispanics. CONCLUSIONS We conclude that ethnic differences exist in CCA but not in ICA IMT in nondiabetic subjects. These differences in IMT, which are indicators of atherosclerosis, are a non-invasive measure that is consistent with some of the data on clinical end points. These differences may be associated with the observed differences in CVD morbidity and mortality among major ethnic groups in the United States.
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Affiliation(s)
- R B D'Agostino
- Department of Public Health Sciences, Bowman Gray School of Medicine, Winston-Salem, NC 27157-1063, USA.
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