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Øvretveit K, Ingeström EML, Spitieris M, Tragante V, Thomas LF, Steinsland I, Brumpton BM, Gudbjartsson DF, Holm H, Stefansson K, Wisløff U, Hveem K. Polygenic Interactions With Environmental Exposures in Blood Pressure Regulation: The HUNT Study. J Am Heart Assoc 2024:e034612. [PMID: 39291479 DOI: 10.1161/jaha.123.034612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 07/10/2024] [Indexed: 09/19/2024]
Abstract
BACKGROUND The essential hypertension phenotype results from an interplay between genetic and environmental factors. The influence of lifestyle exposures such as excess adiposity, alcohol consumption, tobacco use, diet, and activity patterns on blood pressure (BP) is well established. Additionally, polygenic risk scores for BP traits are associated with clinically significant phenotypic variation. However, interactions between genetic and environmental risk factors in hypertension morbidity and mortality are poorly characterized. METHODS AND RESULTS We used genotype and phenotype data from up to 49 234 participants from the HUNT (Trøndelag Health Study) to model gene-environment interactions between genome-wide polygenic risk scores for systolic BP and diastolic BP and 125 environmental exposures. Among the 125 environmental exposures assessed, 108 and 100 were independently associated with SBP and DBP, respectively. Of these, 12 interactions were identified for genome-wide PRSs for systolic BP and 4 for genome-wide polygenic risk scores for diastolic BP, 2 of which were overlapping (P < 2 × 10-4). We found evidence for gene-dependent influence of lifestyle factors such as cardiorespiratory fitness, dietary patterns, and tobacco exposure, as well as biomarkers such as serum cholesterol, creatinine, and alkaline phosphatase on BP. CONCLUSIONS Individuals that are genetically susceptible to high BP may be more vulnerable to common acquired risk factors for hypertension, but these effects appear to be modifiable. The gene-dependent influence of several common acquired risk factors indicates the potential of genetic data combined with lifestyle assessments in risk stratification, and gene-environment-informed risk modeling in the prevention and management of hypertension.
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Affiliation(s)
- Karsten Øvretveit
- HUNT Center for Molecular and Clinical Epidemiology (MCE), Department of Public Health and Nursing Norwegian University of Science and Technology (NTNU) Trondheim Norway
| | - Emma M L Ingeström
- Cardiac Exercise Research Group (CERG), Department of Circulation and Medical Imaging Norwegian University of Science and Technology (NTNU) Trondheim Norway
| | - Michail Spitieris
- HUNT Center for Molecular and Clinical Epidemiology (MCE), Department of Public Health and Nursing Norwegian University of Science and Technology (NTNU) Trondheim Norway
- Department of Mathematical Sciences Norwegian University of Science and Technology (NTNU) Trondheim Norway
| | | | - Laurent F Thomas
- HUNT Center for Molecular and Clinical Epidemiology (MCE), Department of Public Health and Nursing Norwegian University of Science and Technology (NTNU) Trondheim Norway
- Department of Clinical and Molecular Medicine Norwegian University of Science and Technology (NTNU) Trondheim Norway
| | - Ingelin Steinsland
- Department of Mathematical Sciences Norwegian University of Science and Technology (NTNU) Trondheim Norway
| | - Ben M Brumpton
- HUNT Center for Molecular and Clinical Epidemiology (MCE), Department of Public Health and Nursing Norwegian University of Science and Technology (NTNU) Trondheim Norway
- HUNT Research Centre, Department of Public Health and Nursing Norwegian University of Science and Technology (NTNU) Levanger Norway
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen Inc. Reykjavik Iceland
- School of Engineering and Natural Sciences University of Iceland Reykjavik Iceland
| | - Hilma Holm
- deCODE Genetics/Amgen Inc. Reykjavik Iceland
| | - Kari Stefansson
- deCODE Genetics/Amgen Inc. Reykjavik Iceland
- Faculty of Medicine University of Iceland Reykjavik Iceland
| | - Ulrik Wisløff
- Cardiac Exercise Research Group (CERG), Department of Circulation and Medical Imaging Norwegian University of Science and Technology (NTNU) Trondheim Norway
| | - Kristian Hveem
- HUNT Center for Molecular and Clinical Epidemiology (MCE), Department of Public Health and Nursing Norwegian University of Science and Technology (NTNU) Trondheim Norway
- Department of Innovation and Research, St. Olav's Hospital Trondheim Norway
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Frostdahl H, Ahmad N, Hammar U, Mora AM, Langner T, Fall T, Kullberg J, Ahlström H, Brooke HL, Ahmad S. The interaction of genetics and physical activity in the pathogenesis of metabolic dysfunction associated liver disease. Sci Rep 2024; 14:17817. [PMID: 39090170 PMCID: PMC11294342 DOI: 10.1038/s41598-024-68271-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 07/22/2024] [Indexed: 08/04/2024] Open
Abstract
Genetic variants associated with increased liver fat and volume have been reported, but whether physical activity (PA) can attenuate the impact of genetic susceptibility to these traits is poorly understood. We aimed to investigate whether higher PA modify genetic impact on liver-related traits in the UK Biobank cohort. PA was self-reported, while magnetic resonance images were used to estimate liver fat (n = 27,243) and liver volume (n = 24,752). Metabolic dysfunction-associated liver disease (MASLD) and chronic liver disease (CLD) were diagnosed using ICD-9 and ICD-10 codes. Ten liver fat and eleven liver volume-associated genetic variants were selected and unweighted genetic-risk scores for liver fat (GRSLF) and liver volume (GRSLV) were computed. Linear regression analyses were performed to explore interactions between GRSLF/ GRSLV and PA in relation to liver-related traits. Association between GRSLF and liver fat was not different among lower (β = 0.063, 95% CI 0.041-0.084) versus higher PA individuals (β = 0.065, 95% CI 0.054-0.077, pinteraction = 0.62). The association between the GRSLV and liver volume was not different across different PA groups (pinteraction = 0.71). Similarly, PA did not modify the effect of GRSLF and GRSLV on MASLD or CLD. Our findings show that physical activity and genetic susceptibility to liver-related phenotypes seem to act independently, benefiting all individuals regardless of genetic risk.
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Affiliation(s)
- Hanna Frostdahl
- Molecular Epidemiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Nouman Ahmad
- Radiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Ulf Hammar
- Molecular Epidemiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | | | - Taro Langner
- Radiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Antaros Medical AB, BioVenture Hub, Mölndal, Sweden
| | - Tove Fall
- Molecular Epidemiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Joel Kullberg
- Radiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Antaros Medical AB, BioVenture Hub, Mölndal, Sweden
| | - Håkan Ahlström
- Radiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Antaros Medical AB, BioVenture Hub, Mölndal, Sweden
| | - Hannah L Brooke
- Medical Epidemiology, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Shafqat Ahmad
- Molecular Epidemiology, Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
- Preventive Medicine Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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Cohen J, Kilmer SL, DiBernardo B, Jacob C, Okoro SA, Cho Y. A Novel Approach to Shaping the Lateral Abdomen: Simultaneous Application of High-Intensity Focused Electromagnetic (HIFEM) Therapy and Synchronized Radiofrequency at the Flanks: A Multicenter MRI Study. Aesthet Surg J 2024; 44:850-858. [PMID: 38470830 PMCID: PMC11247522 DOI: 10.1093/asj/sjae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND An accumulation of adipose tissue on the lateral abdomen (flanks) coupled with muscle deconditioning negatively affects core stability, muscular balance, and the intrinsic strength essential for maintaining optimal body mechanics and posture. This lateral fat accumulation and diminution of muscle result in an unfavorable abdominal profile and present challenges in finding appropriately fitting attire. OBJECTIVES The aim of this study was to explore the effectiveness and safety of the simultaneous application of high-intensity focused electromagnetic (HIFEM) therapy and synchronized radiofrequency for sculpting the lateral abdomen. METHODS All patients were scheduled to undergo four 30-minute treatments at approximately weekly intervals and then subsequent follow-up visits at 1 month and 3 months after the last treatment. The primary evaluation assessed changes in the oblique muscles, adipose tissue thickness, and cross-sectional area (CSA) by MRI performed at baseline and follow-ups. The secondary outcomes included digital photographs of the treated areas, a Subject Satisfaction Questionnaire, and a Therapy Comfort Questionnaire. Adverse events and side effects were monitored throughout the study duration. RESULTS The muscle tissue showed a substantial increase in thickness (+27.2%) and CSA (+29.0%). The adipose tissue measurements showed a decrease of -30.5% in CSA and -28.8% in thickness. As secondary outcomes, 81.8% of patients reported feeling more toned, and 84.9% of patients found the treatment comfortable and reported less than mild pain. CONCLUSIONS Based on the evaluation, the study suggests that the simultaneous application of HIFEM and synchronized radiofrequency is safe and effective for reducing adipose tissue and strengthening muscle in the area of the lateral abdomen.
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Ghosh S, Bouchard C. Considerations on efforts needed to improve our understanding of the genetics of obesity. Int J Obes (Lond) 2024:10.1038/s41366-024-01528-0. [PMID: 38849463 DOI: 10.1038/s41366-024-01528-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 04/18/2024] [Accepted: 04/24/2024] [Indexed: 06/09/2024]
Affiliation(s)
- Sujoy Ghosh
- Pennington Biomedical Research Center, Baton Rouge, LA, 70808, USA.
| | - Claude Bouchard
- Pennington Biomedical Research Center, Baton Rouge, LA, 70808, USA.
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Chermon D, Birk R. Deciphering the Interplay between Genetic Risk Scores and Lifestyle Factors on Individual Obesity Predisposition. Nutrients 2024; 16:1296. [PMID: 38732542 PMCID: PMC11085817 DOI: 10.3390/nu16091296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 04/24/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024] Open
Abstract
Obesity's variability is significantly influenced by the interplay between genetic and environmental factors. We aimed to integrate the combined impact of genetic risk score (GRSBMI) with physical activity (PA), sugar-sweetened beverages (SSB), wine intake, and eating habits score (EHS) on obesity predisposition risk. Adults' (n = 5824) data were analyzed for common obesity-related single nucleotide polymorphisms and lifestyle habits. The weighted GRSBMI was constructed and categorized into quartiles (Qs), and the adjusted multivariate logistic regression models examined the association of GRSBMI with obesity (BMI ≥ 30) and lifestyle factors. GRSBMI was significantly associated with obesity risk. Each GRSBMI unit was associated with an increase of 3.06 BMI units (p ≤ 0.0001). PA markedly reduced obesity risk across GRSBMI Qs. Inactive participants' (≥90 min/week) mean BMI was higher in GRSBMI Q3-Q4 compared to Q1 (p = 0.003 and p < 0.001, respectively). Scoring EHS ≥ median, SSBs (≥1 cup/day), and non-wine drinking were associated with higher BMI within all GRSBMI Qs compared to EHS < median, non-SSBs, and non-wine drinkers. Mean BMI was higher in GRSBMI Q4 compared to other quartiles (p < 0.0001) in non-wine drinkers and compared to Q1 for SSB's consumers (p = 0.07). A higher GRSBMI augmented the impact of lifestyle factors on obesity. The interplay between GRSBMI and modifiable lifestyle factors provides a tailored personalized prevention and treatment for obesity management.
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Affiliation(s)
| | - Ruth Birk
- Nutrition Department, Health Science Faculty, Ariel University, Ariel 40700, Israel;
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Brittain EL, Han L, Annis J, Master H, Hughes A, Roden DM, Harris PA, Ruderfer DM. Physical Activity and Incident Obesity Across the Spectrum of Genetic Risk for Obesity. JAMA Netw Open 2024; 7:e243821. [PMID: 38536175 PMCID: PMC10973894 DOI: 10.1001/jamanetworkopen.2024.3821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 01/30/2024] [Indexed: 06/16/2024] Open
Abstract
Importance Despite consistent public health recommendations, obesity rates in the US continue to increase. Physical activity recommendations do not account for individual genetic variability, increasing risk of obesity. Objective To use activity, clinical, and genetic data from the All of Us Research Program (AoURP) to explore the association of genetic risk of higher body mass index (BMI) with the level of physical activity needed to reduce incident obesity. Design, Setting, and Participants In this US population-based retrospective cohort study, participants were enrolled in the AoURP between May 1, 2018, and July 1, 2022. Enrollees in the AoURP who were of European ancestry, owned a personal activity tracking device, and did not have obesity up to 6 months into activity tracking were included in the analysis. Exposure Physical activity expressed as daily step counts and a polygenic risk score (PRS) for BMI, calculated as weight in kilograms divided by height in meters squared. Main Outcome and Measures Incident obesity (BMI ≥30). Results A total of 3124 participants met inclusion criteria. Among 3051 participants with available data, 2216 (73%) were women, and the median age was 52.7 (IQR, 36.4-62.8) years. The total cohort of 3124 participants walked a median of 8326 (IQR, 6499-10 389) steps/d over a median of 5.4 (IQR, 3.4-7.0) years of personal activity tracking. The incidence of obesity over the study period increased from 13% (101 of 781) to 43% (335 of 781) in the lowest and highest PRS quartiles, respectively (P = 1.0 × 10-20). The BMI PRS demonstrated an 81% increase in obesity risk (P = 3.57 × 10-20) while mean step count demonstrated a 43% reduction (P = 5.30 × 10-12) when comparing the 75th and 25th percentiles, respectively. Individuals with a PRS in the 75th percentile would need to walk a mean of 2280 (95% CI, 1680-3310) more steps per day (11 020 total) than those at the 50th percentile to have a comparable risk of obesity. To have a comparable risk of obesity to individuals at the 25th percentile of PRS, those at the 75th percentile with a baseline BMI of 22 would need to walk an additional 3460 steps/d; with a baseline BMI of 24, an additional 4430 steps/d; with a baseline BMI of 26, an additional 5380 steps/d; and with a baseline BMI of 28, an additional 6350 steps/d. Conclusions and Relevance In this cohort study, the association between daily step count and obesity risk across genetic background and baseline BMI were quantified. Population-based recommendations may underestimate physical activity needed to prevent obesity among those at high genetic risk.
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Affiliation(s)
- Evan L. Brittain
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Center for Digital Genomic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Lide Han
- Center for Digital Genomic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Division of Genetic Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jeffrey Annis
- Vanderbilt Institute of Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hiral Master
- Vanderbilt Institute of Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Andrew Hughes
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Dan M. Roden
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Paul A. Harris
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biomedical Engineering, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Douglas M. Ruderfer
- Center for Digital Genomic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Division of Genetic Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
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7
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Misra S, Aguilar-Salinas CA, Chikowore T, Konradsen F, Ma RCW, Mbau L, Mohan V, Morton RW, Nyirenda MJ, Tapela N, Franks PW. The case for precision medicine in the prevention, diagnosis, and treatment of cardiometabolic diseases in low-income and middle-income countries. Lancet Diabetes Endocrinol 2023; 11:836-847. [PMID: 37804857 DOI: 10.1016/s2213-8587(23)00164-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 05/08/2023] [Accepted: 06/01/2023] [Indexed: 10/09/2023]
Abstract
Cardiometabolic diseases are the leading preventable causes of death in most geographies. The causes, clinical presentations, and pathogenesis of cardiometabolic diseases vary greatly worldwide, as do the resources and strategies needed to prevent and treat them. Therefore, there is no single solution and health care should be optimised, if not to the individual (ie, personalised health care), then at least to population subgroups (ie, precision medicine). This optimisation should involve tailoring health care to individual disease characteristics according to ethnicity, biology, behaviour, environment, and subjective person-level characteristics. The capacity and availability of local resources and infrastructures should also be considered. Evidence needed for equitable precision medicine cannot be generated without adequate data from all target populations, and the idea that research done in high-income countries will transfer adequately to low-income and middle-income countries (LMICs) is problematic, as many migration studies and transethnic comparisons have shown. However, most data for precision medicine research are derived from people of European ancestry living in high-income countries. In this Series paper, we discuss the case for precision medicine for cardiometabolic diseases in LMICs, the barriers and enablers, and key considerations for implementation. We focus on three propositions: first, failure to explore and implement precision medicine for cardiometabolic disease in LMICs will enhance global health disparities. Second, some LMICs might already be placed to implement cardiometabolic precision medicine under appropriate circumstances, owing to progress made in treating infectious diseases. Third, improvements in population health from precision medicine are most probably asymptotic; the greatest gains are more likely to be obtained in countries where health-care systems are less developed. We outline key recommendations for implementation of precision medicine approaches in LMICs.
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Affiliation(s)
- Shivani Misra
- Division of Metabolism, Digestion and Reproduction, Imperial College London, London, UK; Department of Diabetes and Endocrinology, St Mary's Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - Carlos A Aguilar-Salinas
- Dirección de Nutricion, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico; Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, México
| | - Tinashe Chikowore
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Flemming Konradsen
- Novo Nordisk Foundation, Copenhagen, Denmark; Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Sciences, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | | | - Viswanathan Mohan
- Madras Diabetes Research Foundation, ICMR Centre for Advanced Research in Diabetes, Chennai, India; Dr Mohan's Diabetes Specialties Centre, IDF Centre of Excellence in Diabetes Care, Chennai, India
| | | | - Moffat J Nyirenda
- MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda; London School of Hygiene and Tropical Medicine, London, UK
| | - Neo Tapela
- Botswana Harvard AIDS Institute Partnership, Gaborone, Botswana; International Consortium for Health Outcomes Measurement, Oxford, UK
| | - Paul W Franks
- Novo Nordisk Foundation, Copenhagen, Denmark; Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Harvard T H Chan School of Public Health, Boston, MA, USA.
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Pledger SL, Ahmadizar F. Gene-environment interactions and the effect on obesity risk in low and middle-income countries: a scoping review. Front Endocrinol (Lausanne) 2023; 14:1230445. [PMID: 37664850 PMCID: PMC10474324 DOI: 10.3389/fendo.2023.1230445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 07/18/2023] [Indexed: 09/05/2023] Open
Abstract
Background Obesity represents a major and preventable global health challenge as a complex disease and a modifiable risk factor for developing other non-communicable diseases. In recent years, obesity prevalence has risen more rapidly in low- and middle-income countries (LMICs) compared to high-income countries (HICs). Obesity traits are shown to be modulated by an interplay of genetic and environmental factors such as unhealthy diet and physical inactivity in studies from HICs focused on populations of European descent; however, genetic heterogeneity and environmental differences prevent the generalisation of study results to LMICs. Primary research investigating gene-environment interactions (GxE) on obesity in LMICs is limited but expanding. Synthesis of current research would provide an overview of the interactions between genetic variants and environmental factors that underlie the obesity epidemic and identify knowledge gaps for future studies. Methods Three databases were searched systematically using a combination of keywords such as "genes", "obesity", "LMIC", "diet", and "physical activity" to find all relevant observational studies published before November 2022. Results Eighteen of the 1,373 articles met the inclusion criteria, of which one was a genome-wide association study (GWAS), thirteen used a candidate gene approach, and five were assigned as genetic risk score studies. Statistically significant findings were reported for 12 individual SNPs; however, most studies were small-scale and without replication. Conclusion Although the results suggest significant GxE interactions on obesity in LMICs, updated robust statistical techniques with more precise and standardised exposure and outcome measurements are necessary for translatable results. Future research should focus on improved quality replication efforts, emphasising large-scale and long-term longitudinal study designs using multi-ethnic GWAS.
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Affiliation(s)
- Sophia L. Pledger
- Department of Epidemiology and Global Health, Julius Global Health, University Medical Center Utrecht, Utrecht, Netherlands
| | - Fariba Ahmadizar
- Department of Data Science and Biostatistics, Julius Global Health, University Medical Center Utrecht, Utrecht, Netherlands
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Han L, Annis J, Master H, Hughes A, Roden D, Harris P, Ruderfer D, Brittain E. Quantifying physical activity needed to mitigate genetic risk for obesity. RESEARCH SQUARE 2023:rs.3.rs-2986582. [PMID: 37333237 PMCID: PMC10275053 DOI: 10.21203/rs.3.rs-2986582/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Despite consistent public health recommendations, obesity rates continue to increase. Physical activity (e.g. daily steps) is a well-established modifier of body weight. Genetic background is an important, but typically uncaptured, contributor to obesity risk. Leveraging physical activity, clinical, and genetic data from the All of Us Research Program, we measured the impact of genetic risk of obesity on the level of physical activity needed to reduce incident obesity. For example, we show that an additional 3,310 steps per day (11,910 steps total) would be needed to mitigate a 25% higher than average genetic risk of obesity. We quantify the number of daily steps needed to mitigate obesity risk across the spectrum of genetic risk. This work quantifies the relationship between physical activity and genetic risk showing significant independent effects and provides a first step towards personalized activity recommendations that incorporate genetic information to reduce incident obesity risk.
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Affiliation(s)
- Lide Han
- VANDERBILT UNIVERSITY MEDICAL CENTER
| | | | | | | | - Dan Roden
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
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10
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Chermon D, Birk R. Drinking Habits and Physical Activity Interact and Attenuate Obesity Predisposition of TMEM18 Polymorphisms Carriers. Nutrients 2023; 15:266. [PMID: 36678137 PMCID: PMC9860767 DOI: 10.3390/nu15020266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/02/2023] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
The transmembrane protein 18 (TMEM18) gene plays a central and peripheral role in weight regulation. TMEM18 genetic polymorphisms have been identified as an important risk factor for obesity, depending on ethnic population and age. This research aimed to study the association of common TMEM18 polymorphisms with obesity and their interactions with modifiable factors, namely drinking habits (sugar-sweetened beverages (SSBs), flavored water and wine) and physical activity (PA) in the Israeli population. Adults (n = 3089) were analyzed for common TMEM18 polymorphisms and lifestyle and nutrition habits were obtained from questionnaires using adjusted (age, sex) binary logistic regression models. TMEM18 rs939583 and rs1879523 were significantly associated with increased obesity risk (OR = 1.35, 95% CI (1.17−1.57) and OR = 1.66, 95% CI (1.29−2.15), respectively). TMEM18 rs939583 interacted with consumption of 1−3 weekly glasses of wine and PA to attenuate obesity risk (OR = 0.82 95% CI (0.74−0.9; p < 0.001) and OR = 0.74 95% CI (0.68−0.8), respectively), while physical inactivity, SSBs and flavored water consumption significantly enhanced obesity risk (OR = 1.54 95% CI (1.41−1.67), OR = 1.31 95% CI (1.14−1.51) and OR = 1.35 95% CI (1.13−1.62), respectively). PA duration was significantly associated with a lower BMI for rs939583 risk carriers, with a PA cutoff of >30 min/week (p = 0.005) and >90 min/week (p = 0.01). Common TMEM18 SNPs were significantly linked with adult obesity risk and interacted with modifiable lifestyle factors.
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Affiliation(s)
| | - Ruth Birk
- Nutrition Department, Health Science Faculty, Ariel University, Ariel 40700, Israel
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11
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Kang HS, Kim SY, Choi HG, Lim H, Kim JH, Kim JH, Cho SJ, Nam ES, Min KW, Park HY, Kim NY, Choi Y, Kwon MJ. Comparison of the Concordance of Cardiometabolic Diseases and Physical and Laboratory Examination Findings between Monozygotic and Dizygotic Korean Adult Twins: A Cross-Sectional Study Using KoGES HTS Data. Nutrients 2022; 14:nu14224834. [PMID: 36432523 PMCID: PMC9693823 DOI: 10.3390/nu14224834] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/31/2022] [Accepted: 11/12/2022] [Indexed: 11/17/2022] Open
Abstract
This study investigated the contribution of genetic and environmental factors to cardiometabolic diseases (CMDs) by comparing disease concordance in monozygotic and dizygotic twins. This cross-sectional study analyzed 1294 (1040 monozygotic and 254 dizygotic) twin pairs (>20 years) based on the Korean Genome and Epidemiology Study data (2005−2014). The odds ratios of disease concordance were calculated using binomial and multinomial logistic regression models. The occurrence of CMDs (hypertension, hyperlipidemia, type 2 diabetes, cerebral stroke, transient ischemic attack, and ischemic heart disease) and related physical and laboratory levels did not differ between the monozygotic and dizygotic twin groups. The odds for concordance of the presence/absence of CMDs and the likelihood of incident CMD within monozygotic twins were comparable to that of dizygotic twins. The absolute differences in hemoglobin A1c, insulin, low- and high-density lipoprotein cholesterol, total cholesterol, triglycerides, and systolic blood pressure were lower in monozygotic twins than in dizygotic twins. Absolute differences in fasting glucose and diastolic blood pressure did not differ between groups. Although baseline levels of several laboratory parameters related to CMD showed a strong likelihood of heritability in monozygotic twins, CMD phenotype appears to be largely affected by environmental factors.
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Affiliation(s)
- Ho Suk Kang
- Division of Gastroenterology, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
| | - So Young Kim
- Department of Otorhinolaryngology-Head & Neck Surgery, CHA Bundang Medical Center, CHA University College of Medicine, Seongnam 13488, Republic of Korea
| | - Hyo Geun Choi
- Department of Otorhinolaryngology-Head & Neck Surgery, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
| | - Hyun Lim
- Division of Gastroenterology, Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
| | - Joo-Hee Kim
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
| | - Ji Hee Kim
- Department of Neurosurgery, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
| | - Seong-Jin Cho
- Department of Pathology, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul 05355, Republic of Korea
| | - Eun Sook Nam
- Department of Pathology, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul 05355, Republic of Korea
| | - Kyueng-Whan Min
- Department of Pathology, Hanyang University Guri Hospital, Hanyang University College of Medicine, Guri 11923, Republic of Korea
| | - Ha Young Park
- Department of Pathology, Busan Paik Hospital, Inje University College of Medicine, Busan 47392, Republic of Korea
| | - Nan Young Kim
- Hallym Institute of Translational Genomics and Bioinformatics, Hallym University Medical Center, Anyang 14068, Republic of Korea
| | - Younghee Choi
- Department of Pathology, Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong 18450, Republic of Korea
- Research Insititute for Complementary & Alternative Medicine, Hallym University, Hwaseong 18450, Republic of Korea
| | - Mi Jung Kwon
- Department of Pathology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Republic of Korea
- Correspondence:
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Sørensen TIA, Metz S, Kilpeläinen TO. Do gene-environment interactions have implications for the precision prevention of type 2 diabetes? Diabetologia 2022; 65:1804-1813. [PMID: 34993570 DOI: 10.1007/s00125-021-05639-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 11/05/2021] [Indexed: 01/10/2023]
Abstract
The past decades have seen a rapid global rise in the incidence of type 2 diabetes. This surge has been driven by diabetogenic environmental changes that may act together with a genetic predisposition to type 2 diabetes. It is possible that there is a synergistic gene-environment interaction, where the effects of the diabetogenic environment depend on the genetic predisposition to type 2 diabetes. Randomised trials have shown that it is possible to delay, or even prevent the development of type 2 diabetes in individuals at elevated risk through behavioural modification, focusing on weight loss, physical activity and diet. There is wide heterogeneity between individuals regarding the effectiveness of these interventions, which could, in part, be due to genetic differences. However, the studies of gene-environment interactions performed thus far suggest that behavioural modifications appear equally effective in reducing the incidence of type 2 diabetes from the stage of impaired glucose tolerance, regardless of the known underlying genetic predisposition. Recent studies suggest that there may be several subtypes of type 2 diabetes, which give new opportunities for gaining insight into gene-environment interactions. At present, the role of gene-environment interactions in the development of type 2 diabetes remains unclear. With many puzzle pieces missing in the general picture of type 2 diabetes development, the available evidence of gene-environment interactions is not ready for translation to individualised type 2 diabetes prevention based on genetic profiling.
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Affiliation(s)
- Thorkild I A Sørensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Section of Epidemiology, Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sophia Metz
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tuomas O Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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13
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Miao J, Lin Y, Wu Y, Zheng B, Schmitz LL, Fletcher JM, Lu Q. A quantile integral linear model to quantify genetic effects on phenotypic variability. Proc Natl Acad Sci U S A 2022; 119:e2212959119. [PMID: 36122202 PMCID: PMC9522331 DOI: 10.1073/pnas.2212959119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/22/2022] [Indexed: 11/18/2022] Open
Abstract
Detecting genetic variants associated with the variance of complex traits, that is, variance quantitative trait loci (vQTLs), can provide crucial insights into the interplay between genes and environments and how they jointly shape human phenotypes in the population. We propose a quantile integral linear model (QUAIL) to estimate genetic effects on trait variability. Through extensive simulations and analyses of real data, we demonstrate that QUAIL provides computationally efficient and statistically powerful vQTL mapping that is robust to non-Gaussian phenotypes and confounding effects on phenotypic variability. Applied to UK Biobank (n = 375,791), QUAIL identified 11 vQTLs for body mass index (BMI) that have not been previously reported. Top vQTL findings showed substantial enrichment for interactions with physical activities and sedentary behavior. Furthermore, variance polygenic scores (vPGSs) based on QUAIL effect estimates showed superior predictive performance on both population-level and within-individual BMI variability compared to existing approaches. Overall, QUAIL is a unified framework to quantify genetic effects on the phenotypic variability at both single-variant and vPGS levels. It addresses critical limitations in existing approaches and may have broad applications in future gene-environment interaction studies.
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Affiliation(s)
- Jiacheng Miao
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, WI 53706
| | - Yupei Lin
- Baylor College of Medicine, Houston, TX 77030
| | - Yuchang Wu
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, WI 53706
| | - Boyan Zheng
- Department of Sociology, University of Wisconsin–Madison, Madison, WI 53706
| | - Lauren L. Schmitz
- Robert M. La Follette School of Public Affairs, University of Wisconsin–Madison, Madison, WI 53706
- Center for Demography of Health and Aging, University of Wisconsin–Madison, Madison, WI 53706
| | - Jason M. Fletcher
- Department of Sociology, University of Wisconsin–Madison, Madison, WI 53706
- Robert M. La Follette School of Public Affairs, University of Wisconsin–Madison, Madison, WI 53706
- Center for Demography of Health and Aging, University of Wisconsin–Madison, Madison, WI 53706
| | - Qiongshi Lu
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, WI 53706
- Center for Demography of Health and Aging, University of Wisconsin–Madison, Madison, WI 53706
- Department of Statistics, University of Wisconsin–Madison, Madison, WI 53706
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14
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Lin WY. The most effective exercise to prevent obesity: A longitudinal study of 33,731 Taiwan biobank participants. Front Nutr 2022; 9:944028. [PMID: 36211487 PMCID: PMC9539558 DOI: 10.3389/fnut.2022.944028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 09/08/2022] [Indexed: 11/13/2022] Open
Abstract
Regular physical exercise is recommended to reduce the risk of obesity. However, it remains unclear which activities are more effective in preventing obesity. In this study, five obesity indices and lifestyle factors of 33,731 Taiwan Biobank adults were measured/collected twice with a mean time interval of 4.06 years. A linear mixed effects model was fitted to assess the associations of exercises with obesity indices, in which a random intercept term was used to account for individual differences. The five obesity indices included body mass index (BMI), body fat percentage (BFP), waist circumference (WC), hip circumference (HC), and waist-hip ratio (WHR). Among 23 exercises, jogging and yoga were consistently the most effective choices across all five obesity indices. One more weekly hour to jog was associated with a 0.093 kg/m2 decrease in BMI (p = 4.2E-20), a 0.297% decrease in BFP (p = 3.8E-36), a 0.398 cm decrease in WC (p = 1.6E-21), and a 2.9E-3 decrease in WHR (p = 1.3E-17). One more weekly hour to perform yoga was associated with a 0.225 cm decrease in HC (p = 6.4E-14). Jogging is an exercise for the entire body. Arms swing, waist turn, legs and feet run, and shoulders and abdomen are also involved in this act. By contrast, many yoga poses use muscles around the hips and pelvis, and therefore yoga is the most effective exercise to reduce HC.
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Affiliation(s)
- Wan-Yu Lin
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- Master of Public Health Degree Program, College of Public Health, National Taiwan University, Taipei, Taiwan
- Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan
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15
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Specht IO, Heitmann BL, Larsen SC. Physical Activity and Subsequent Change in Body Weight, Composition and Shape: Effect Modification by Familial Overweight. Front Endocrinol (Lausanne) 2022; 13:787827. [PMID: 35242107 PMCID: PMC8886019 DOI: 10.3389/fendo.2022.787827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 01/24/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Physical activity (PA) has been shown to attenuate the genetic risk of obesity as measured using polygenic risk scores. However, familial obesity history might be an easier predictor. We examined associations between PA and subsequent changes in BMI, body fat percentage (BF%) and waist circumference (WC) among participants with and without adiposity and a familial overweight. METHODS In total, 1971 participants from the Danish MONICA cohort were included. Mean differences for 6-year changes in BMI, BF% and WC across PA levels were estimated. Association between walking and biking and subsequent change in adiposity were analysed. Effect modification by familial obesity was assessed by adding product terms to the models. RESULTS We observed weak associations between leisure PA level and changes in WC [participants with low PA: 3.4 cm (95%CI: 2.8;4.0), participants with high PA: 2.4 cm (95%CI: 1.8;3.0)], with no evidence of effect modification by familial obesity. We found effect modification in analyses on walking and biking in relation to changes in BMI (P-interaction<0.01) and BF% (P-interaction=0.04), suggesting lower gain with more hours of activity among participants with adiposity and familial overweight. CONCLUSIONS The results were modest but suggested that PA, especially walking and biking, may prevent future adiposity.
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Affiliation(s)
- Ina Olmer Specht
- The Parker Institute, Research Unit for Dietary Studies, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- *Correspondence: Ina Olmer Specht,
| | - Berit Lilienthal Heitmann
- The Parker Institute, Research Unit for Dietary Studies, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- The Boden Group, Faculty of Medicine and Health, Sydney University, Sydney, NSW, Australia
- The Department of Public Health, Section for General medicine, University of Copenhagen, Copenhagen, Denmark
| | - Sofus Christian Larsen
- The Parker Institute, Research Unit for Dietary Studies, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
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16
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Marderstein AR, Kulm S, Peng C, Tamimi R, Clark AG, Elemento O. A polygenic-score-based approach for identification of gene-drug interactions stratifying breast cancer risk. Am J Hum Genet 2021; 108:1752-1764. [PMID: 34363748 PMCID: PMC8456164 DOI: 10.1016/j.ajhg.2021.07.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 07/16/2021] [Indexed: 12/24/2022] Open
Abstract
An individual's genetics can dramatically influence breast cancer (BC) risk. Although clinical measures for prevention do exist, non-invasive personalized measures for reducing BC risk are limited. Commonly used medications are a promising set of modifiable factors, but no previous study has explored whether a range of widely taken approved drugs modulate BC genetics. In this study, we describe a quantitative framework for exploring the interaction between the genetic susceptibility of BC and medication usage among UK Biobank women. We computed BC polygenic scores (PGSs) that summarize BC genetic risk and find that the PGS explains nearly three-times greater variation in disease risk within corticosteroid users compared to non-users. We map 35 genes significantly interacting with corticosteroid use (FDR < 0.1), highlighting the transcription factor NRF2 as a common regulator of gene-corticosteroid interactions in BC. Finally, we discover a regulatory variant strongly stratifying BC risk according to corticosteroid use. Within risk allele carriers, 18.2% of women taking corticosteroids developed BC, compared to 5.1% of the non-users (with an HR = 3.41 per-allele within corticosteroid users). In comparison, there are no differences in BC risk within the reference allele homozygotes. Overall, this work highlights the clinical relevance of gene-drug interactions in disease risk and provides a roadmap for repurposing biobanks in drug repositioning and precision medicine.
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Affiliation(s)
- Andrew R Marderstein
- Tri-Institutional Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY 10021, USA; Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA; Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA
| | - Scott Kulm
- Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Cheng Peng
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Rulla Tamimi
- Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA
| | - Andrew G Clark
- Tri-Institutional Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY 10021, USA; Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA.
| | - Olivier Elemento
- Tri-Institutional Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY 10021, USA; Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
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17
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Cifuentes L, Hurtado A. MD, Eckel-Passow J, Acosta A. Precision Medicine for Obesity. DIGESTIVE DISEASE INTERVENTIONS 2021; 5:239-248. [PMID: 36203650 PMCID: PMC9534386 DOI: 10.1055/s-0041-1729945] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Obesity is a multifactorial disease with a variable and underwhelming weight loss response to current treatment approaches. Precision medicine proposes a new paradigm to improve disease classification based on the premise of human heterogeneity, with the ultimate goal of maximizing treatment effectiveness, tolerability, and safety. Recent advances in high-throughput biochemical assays have contributed to the partial characterization of obesity's pathophysiology, as well as to the understanding of the role that intrinsic and environmental factors, and their interaction, play in its development and progression. These data have led to the development of biological markers that either are being or will be incorporated into strategies to develop personalized lines of treatment for obesity. There are currently many ongoing initiatives aimed at this; however, much needs to be resolved before precision obesity medicine becomes common practice. This review aims to provide a perspective on the currently available data of high-throughput technologies to treat obesity.
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Affiliation(s)
- Lizeth Cifuentes
- Precision Medicine for Obesity Program, Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, Minnesota
| | - Maria Daniela Hurtado A.
- Precision Medicine for Obesity Program, Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, Minnesota
- Division of Endocrinology, Diabetes, Metabolism, and Nutrition, Department of Medicine, Mayo Clinic Health System La Crosse, Rochester, Minnesota
| | - Jeanette Eckel-Passow
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Andres Acosta
- Precision Medicine for Obesity Program, Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, Minnesota
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18
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Brito MDF, Torre C, Silva-Lima B. Scientific Advances in Diabetes: The Impact of the Innovative Medicines Initiative. Front Med (Lausanne) 2021; 8:688438. [PMID: 34295913 PMCID: PMC8290522 DOI: 10.3389/fmed.2021.688438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/02/2021] [Indexed: 12/16/2022] Open
Abstract
Diabetes Mellitus is one of the World Health Organization's priority diseases under research by the first and second programmes of Innovative Medicines Initiative, with the acronyms IMI1 and IMI2, respectively. Up to October of 2019, 13 projects were funded by IMI for Diabetes & Metabolic disorders, namely SUMMIT, IMIDIA, DIRECT, StemBANCC, EMIF, EBiSC, INNODIA, RHAPSODY, BEAT-DKD, LITMUS, Hypo-RESOLVE, IM2PACT, and CARDIATEAM. In general, a total of €447 249 438 was spent by IMI in the area of Diabetes. In order to prompt a better integration of achievements between the different projects, we perform a literature review and used three data sources, namely the official project's websites, the contact with the project's coordinators and co-coordinator, and the CORDIS database. From the 662 citations identified, 185 were included. The data collected were integrated into the objectives proposed for the four IMI2 program research axes: (1) target and biomarker identification, (2) innovative clinical trials paradigms, (3) innovative medicines, and (4) patient-tailored adherence programmes. The IMI funded projects identified new biomarkers, medical and research tools, determinants of inter-individual variability, relevant pathways, clinical trial designs, clinical endpoints, therapeutic targets and concepts, pharmacologic agents, large-scale production strategies, and patient-centered predictive models for diabetes and its complications. Taking into account the scientific data produced, we provided a joint vision with strategies for integrating personalized medicine into healthcare practice. The major limitations of this article were the large gap of data in the libraries on the official project websites and even the Cordis database was not complete and up to date.
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Affiliation(s)
| | - Carla Torre
- Faculty of Pharmacy, University of Lisbon, Lisbon, Portugal.,Laboratory of Systems Integration Pharmacology, Clinical & Regulatory Science-Research Institute for Medicines (iMED.ULisboa), Lisbon, Portugal
| | - Beatriz Silva-Lima
- Faculty of Pharmacy, University of Lisbon, Lisbon, Portugal.,Laboratory of Systems Integration Pharmacology, Clinical & Regulatory Science-Research Institute for Medicines (iMED.ULisboa), Lisbon, Portugal
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19
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Chauhdary Z, Rehman K, Akash MSH. The composite alliance of FTO locus with obesity-related genetic variants. Clin Exp Pharmacol Physiol 2021; 48:954-965. [PMID: 33735452 DOI: 10.1111/1440-1681.13498] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/05/2021] [Accepted: 03/15/2021] [Indexed: 12/14/2022]
Abstract
Obesity has become a genuine global pandemic due to lifestyle and environmental modifications, and is associated with chronic lethal comorbidities. Various environmental factors such as lack of physical activity due to modernization and higher intake of energy-rich diets are primary obesogenic factors in pathogenesis of obesity. Genome-wide association study has identified the crucial role of FTO (fat mass and obesity) in human obesity. A bunch of SNPs in the first intron of FTO has been identified and subsequently correlated to body mass index and body composition. Findings of in silico, in vitro, and in vivo studies have manifested the robust role of FTO in regulation of energy expenditure and food consumption. Numerous studies have highlighted the mechanistic pathways behind the concomitant functions of FTO in adipogenesis and body size. Current investigation has also revealed the link of FTO neighbouring genes i.e., RPGRIP1L, IRX3 and IRX5 and epigenetic factors with obesity phenotypes. The motive behind this review is to cite the consequences of FTO on obesity vulnerability.
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Affiliation(s)
- Zunera Chauhdary
- Department of Pharmaceutical Chemistry, Government College University, Faisalabad, Pakistan
| | - Kanwal Rehman
- Department of Pharmacy, University of Agriculture, Faisalabad, Pakistan
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20
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Martikainen P, Korhonen K, Jelenkovic A, Lahtinen H, Havulinna A, Ripatti S, Borodulin K, Salomaa V, Davey Smith G, Silventoinen K. Joint association between education and polygenic risk score for incident coronary heart disease events: a longitudinal population-based study of 26 203 men and women. J Epidemiol Community Health 2021; 75:651-657. [PMID: 33408166 DOI: 10.1136/jech-2020-214358] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 11/03/2020] [Accepted: 12/16/2020] [Indexed: 12/28/2022]
Abstract
BACKGROUND Genetic vulnerability to coronary heart disease (CHD) is well established, but little is known whether these effects are mediated or modified by equally well-established social determinants of CHD. We estimate the joint associations of the polygenetic risk score (PRS) for CHD and education on CHD events. METHODS The data are from the 1992, 1997, 2002, 2007 and 2012 surveys of the population-based FINRISK Study including measures of social, behavioural and metabolic factors and genome-wide genotypes (N=26 203). Follow-up of fatal and non-fatal incident CHD events (N=2063) was based on nationwide registers. RESULTS Allowing for age, sex, study year, region of residence, study batch and principal components, those in the highest quartile of PRS for CHD had strongly increased risk of CHD events compared with the lowest quartile (HR=2.26; 95% CI: 1.97 to 2.59); associations were also observed for low education (HR=1.58; 95% CI: 1.32 to 1.89). These effects were largely independent of each other. Adjustment for baseline smoking, alcohol use, body mass index, igh-density lipoprotein (HDL) and total cholesterol, blood pressure and diabetes attenuated the PRS associations by 10% and the education associations by 50%. We do not find strong evidence of interactions between PRS and education. CONCLUSIONS PRS and education predict CHD events, and these associations are independent of each other. Both can improve CHD prediction beyond behavioural risks. The results imply that observational studies that do not have information on genetic risk factors for CHD do not provide confounded estimates for the association between education and CHD.
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Affiliation(s)
- Pekka Martikainen
- Population Research Unit, University of Helsinki Faculty of Social Sciences, Helsinki, Finland
- Centre for Health Equity Studies, Stockholm University, Stockholm, Sweden
- Max Planck Institute for Demographic Research, Rostock, Germany
| | - Kaarina Korhonen
- Population Research Unit, University of Helsinki Faculty of Social Sciences, Helsinki, Finland
| | - Aline Jelenkovic
- Department of Physiology, University of the Basque Country, Bilbao, País Vasco, Spain
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Hannu Lahtinen
- Population Research Unit, University of Helsinki Faculty of Social Sciences, Helsinki, Finland
| | - Aki Havulinna
- Institute for Molecular Medicine Finland, Helsinki, Finland
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Uusimaa, Finland
| | - Samuli Ripatti
- Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Institute for Molecular Medicine Finland, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Katja Borodulin
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Uusimaa, Finland
- Age Institute, Helsinki, Finland
| | - Veikko Salomaa
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Uusimaa, Finland
| | - George Davey Smith
- Department of Social Medicine, University of Bristol, Bristol, Bristol, UK
| | - Karri Silventoinen
- Population Research Unit, University of Helsinki Faculty of Social Sciences, Helsinki, Finland
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21
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Rana S, Sultana A, Bhatti AA. Effect of interaction between obesity-promoting genetic variants and behavioral factors on the risk of obese phenotypes. Mol Genet Genomics 2021; 296:919-938. [PMID: 33966103 DOI: 10.1007/s00438-021-01793-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 04/22/2021] [Indexed: 01/28/2023]
Abstract
The studies investigating gene-gene and gene-environment (or gene-behavior) interactions provide valuable insight into the pathomechanisms underlying obese phenotypes. The Pakistani population due to its unique characteristics offers numerous advantages for conducting such studies. In this view, the current study was undertaken to examine the effects of gene-gene and gene-environment/behavior interactions on the risk of obesity in a sample of Pakistani population. A total of 578 adult participants including 290 overweight/obese cases and 288 normal-weight controls were involved. The five key obesity-associated genetic variants namely MC4R rs17782313, BDNF rs6265, FTO rs1421085, TMEM18 rs7561317, and NEGR1 rs2815752 were genotyped using the TaqMan allelic discrimination assays. The data related to behavioral factors, such as eating pattern, diet consciousness, the tendency toward fat-dense food (TFDF), sleep duration, sleep-wake cycle (SWC), shift work (SW), and physical activity levels were collected via a questionnaire. Gene-gene and gene-behavior interactions were analyzed by multifactor dimensionality reduction and linear regression, respectively. In our study, only TMEM18 rs7561317 was found to be significantly associated with anthropometric traits with no significant effect of gene-gene interactions were observed on obesity-related phenotypes. However, the genetic variants were found to interact with the behavioral factors to significantly influence various obesity-related anthropometric traits including BMI, waist circumference, hip circumference, waist-to-hip ratio, waist-to-height ratio, and percentage of body fat. In conclusion, the interaction between genetic architecture and behavior/environment determines the outcome of obesity-related anthropometric phenotypes. Thus, gene-environment/behavior interaction studies should be promoted to explore the risk of complex and multifactorial disorders, such as obesity.
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Affiliation(s)
- Sobia Rana
- Molecular Biology and Human Genetics Laboratory, Dr. Panjwani Center for Molecular Medicine and Drug Research (PCMD), International Center for Chemical and Biological Sciences (ICCBS), University of Karachi, Karachi, 75270, Pakistan.
| | - Ayesha Sultana
- Molecular Biology and Human Genetics Laboratory, Dr. Panjwani Center for Molecular Medicine and Drug Research (PCMD), International Center for Chemical and Biological Sciences (ICCBS), University of Karachi, Karachi, 75270, Pakistan
| | - Adil Anwar Bhatti
- Molecular Biology and Human Genetics Laboratory, Dr. Panjwani Center for Molecular Medicine and Drug Research (PCMD), International Center for Chemical and Biological Sciences (ICCBS), University of Karachi, Karachi, 75270, Pakistan
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22
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Sikdar S, Wyss AB, Lee MK, Hoang TT, Richards M, Beane Freeman LE, Parks C, Thorne PS, Hankinson JL, Umbach DM, Motsinger-Reif A, London SJ. Interaction between Genetic Risk Scores for reduced pulmonary function and smoking, asthma and endotoxin. Thorax 2021; 76:1219-1226. [PMID: 33963087 PMCID: PMC8572320 DOI: 10.1136/thoraxjnl-2020-215624] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 02/17/2021] [Accepted: 03/22/2021] [Indexed: 01/04/2023]
Abstract
Rationale Genome-wide association studies (GWASs) have identified numerous loci associated with lower pulmonary function. Pulmonary function is strongly related to smoking and has also been associated with asthma and dust endotoxin. At the individual SNP level, genome-wide analyses of pulmonary function have not identified appreciable evidence for gene by environment interactions. Genetic Risk Scores (GRSs) may enhance power to identify gene–environment interactions, but studies are few. Methods We analysed 2844 individuals of European ancestry with 1000 Genomes imputed GWAS data from a case–control study of adult asthma nested within a US agricultural cohort. Pulmonary function traits were FEV1, FVC and FEV1/FVC. Using data from a recent large meta-analysis of GWAS, we constructed a weighted GRS for each trait by combining the top (p value<5×10−9) genetic variants, after clumping based on distance (±250 kb) and linkage disequilibrium (r2=0.5). We used linear regression, adjusting for relevant covariates, to estimate associations of each trait with its GRS and to assess interactions. Results Each trait was highly significantly associated with its GRS (all three p values<8.9×10−8). The inverse association of the GRS with FEV1/FVC was stronger for current smokers (pinteraction=0.017) or former smokers (pinteraction=0.064) when compared with never smokers and among asthmatics compared with non-asthmatics (pinteraction=0.053). No significant interactions were observed between any GRS and house dust endotoxin. Conclusions Evaluation of interactions using GRSs supports a greater impact of increased genetic susceptibility on reduced pulmonary function in the presence of smoking or asthma.
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Affiliation(s)
- Sinjini Sikdar
- Department of Mathematics and Statistics, Old Dominion University, Norfolk, Virginia, USA.,Epidemiology Branch, National Institute of Environmental Health Sciences, Durham, North Carolina, USA
| | - Annah B Wyss
- Epidemiology Branch, National Institute of Environmental Health Sciences, Durham, North Carolina, USA
| | - Mi Kyeong Lee
- Epidemiology Branch, National Institute of Environmental Health Sciences, Durham, North Carolina, USA
| | - Thanh T Hoang
- Epidemiology Branch, National Institute of Environmental Health Sciences, Durham, North Carolina, USA
| | | | - Laura E Beane Freeman
- Occupational and Environmental Epidemiology Branch, National Cancer Institute, Bethesda, Maryland, USA
| | - Christine Parks
- Epidemiology Branch, National Institute of Environmental Health Sciences, Durham, North Carolina, USA
| | - Peter S Thorne
- Department of Occupational and Environmental Health, University of Iowa, Iowa City, Iowa, USA
| | | | - David M Umbach
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, North Carolina, USA
| | - Alison Motsinger-Reif
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, North Carolina, USA
| | - Stephanie J London
- Epidemiology Branch, National Institute of Environmental Health Sciences, Durham, North Carolina, USA
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23
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Gordon-Larsen P, French JE, Moustaid-Moussa N, Voruganti VS, Mayer-Davis EJ, Bizon CA, Cheng Z, Stewart DA, Easterbrook JW, Shaikh SR. Synergizing Mouse and Human Studies to Understand the Heterogeneity of Obesity. Adv Nutr 2021; 12:2023-2034. [PMID: 33885739 PMCID: PMC8483969 DOI: 10.1093/advances/nmab040] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/17/2021] [Accepted: 03/13/2021] [Indexed: 12/12/2022] Open
Abstract
Obesity is routinely considered as a single disease state, which drives a "one-size-fits-all" approach to treatment. We recently convened the first annual University of North Carolina Interdisciplinary Nutrition Sciences Symposium to discuss the heterogeneity of obesity and the need for translational science to advance understanding of this heterogeneity. The symposium aimed to advance scientific rigor in translational studies from animal to human models with the goal of identifying underlying mechanisms and treatments. In this review, we discuss fundamental gaps in knowledge of the heterogeneity of obesity ranging from cellular to population perspectives. We also advocate approaches to overcoming limitations in the field. Examples include the use of contemporary mouse genetic reference population models such as the Collaborative Cross and Diversity Outbred mice that effectively model human genetic diversity and the use of translational models that integrate -omics and computational approaches from pre-clinical to clinical models of obesity. Finally, we suggest best scientific practices to ensure strong rigor that will allow investigators to delineate the sources of heterogeneity in the population with obesity. Collectively, we propose that it is critical to think of obesity as a heterogeneous disease with complex mechanisms and etiologies, requiring unique prevention and treatment strategies tailored to the individual.
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Affiliation(s)
| | - John E French
- Department of Nutrition, Gillings School of Global Public Health and School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA,Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Naima Moustaid-Moussa
- Obesity Research Institute and Department of Nutritional Sciences, Texas Tech University, Lubbock, TX, USA
| | - Venkata S Voruganti
- Department of Nutrition, Gillings School of Global Public Health and School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA,Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Elizabeth J Mayer-Davis
- Department of Nutrition, Gillings School of Global Public Health and School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Christopher A Bizon
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, NC, USA
| | - Zhiyong Cheng
- Food Science and Human Nutrition Department, University of Florida, Gainesville, FL, USA
| | - Delisha A Stewart
- Department of Nutrition, Gillings School of Global Public Health and School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA,Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - John W Easterbrook
- Department of Nutrition, Gillings School of Global Public Health and School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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24
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Schnurr TM, Stallknecht BM, Sørensen TIA, Kilpeläinen TO, Hansen T. Evidence for shared genetics between physical activity, sedentary behaviour and adiposity-related traits. Obes Rev 2021; 22:e13182. [PMID: 33354910 DOI: 10.1111/obr.13182] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 11/23/2020] [Accepted: 11/25/2020] [Indexed: 12/20/2022]
Abstract
Observational, cross-sectional and longitudinal studies showed that physical activity and sedentary behaviour are associated with adiposity-related traits, apparently in a bidirectional manner. Physical activity is also suggested to suppress the genetic risk of adiposity. Since phenotypic associations with genetic variants are not subject to reverse causation or confounding, they may be used as tools to shed light on cause and effect in this complex interdependency. We review the evidence for shared genetics of physical activity and adiposity-related traits and for gene-by-physical activity interactions on adiposity-related traits in human studies. We outline limitations, challenges and opportunities in studying and understanding of these relationships. In summary, physical activity and sedentary behaviour are genetically correlated with body mass index and fat percentage but may not be correlated with lean body mass. Mendelian randomisation analyses show that physical activity and sedentary behaviour have bidirectional relationships with adiposity. Several studies suggest that physical activity suppresses genetic risk of adiposity. No studies have yet tested whether adiposity enhances genetic predisposition to sedentariness. The complexity of the comprehensive causal model makes the assessment of the single or combined components challenging. Substantial progress in this field may need long-term intervention studies.
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Affiliation(s)
- Theresia M Schnurr
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Bente M Stallknecht
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Thorkild I A Sørensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tuomas O Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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25
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Identification of genetic loci affecting body mass index through interaction with multiple environmental factors using structured linear mixed model. Sci Rep 2021; 11:5001. [PMID: 33654129 PMCID: PMC7925554 DOI: 10.1038/s41598-021-83684-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 02/05/2021] [Indexed: 11/08/2022] Open
Abstract
Multiple environmental factors could interact with a single genetic factor to affect disease phenotypes. We used Struct-LMM to identify genetic variants that interacted with environmental factors related to body mass index (BMI) using data from the Korea Association Resource. The following factors were investigated: alcohol consumption, education, physical activity metabolic equivalent of task (PAMET), income, total calorie intake, protein intake, carbohydrate intake, and smoking status. Initial analysis identified 7 potential single nucleotide polymorphisms (SNPs) that interacted with the environmental factors (P value < 5.00 × 10-6). Of the 8 environmental factors, PAMET score was excluded for further analysis since it had an average Bayes Factor (BF) value < 1 (BF = 0.88). Interaction analysis using 7 environmental factors identified 11 SNPs (P value < 5.00 × 10-6). Of these, rs2391331 had the most significant interaction (P value = 7.27 × 10-9) and was located within the intron of EFNB2 (Chr 13). In addition, the gene-based genome-wide association study verified EFNB2 gene significantly interacting with 7 environmental factors (P value = 5.03 × 10-10). BF analysis indicated that most environmental factors, except carbohydrate intake, contributed to the interaction of rs2391331 on BMI. Although the replication of the results in other cohorts is warranted, these findings proved the usefulness of Struct-LMM to identify the gene-environment interaction affecting disease.
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26
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Naureen Z, Miggiano GAD, Aquilanti B, Velluti V, Matera G, Gagliardi L, Zulian A, Romanelli R, Bertelli M. Genetic test for the prescription of diets in support of physical activity. ACTA BIO-MEDICA : ATENEI PARMENSIS 2020; 91:e2020011. [PMID: 33170161 PMCID: PMC8023120 DOI: 10.23750/abm.v91i13-s.10584] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 09/17/2020] [Indexed: 01/03/2023]
Abstract
Owing to the fields of nutrigenetics and nutrigenomics today we can think of devising approaches to optimize health, delay onset of diseases and reduce its severity according to our genetic blue print. However this requires a deep understanding of nutritional impact on expression of genes that may result in a specific phenotype. The extensive research and observational studies during last two decades reporting interactions between genes, diet and physical activity suggest a cross talk between various genetic and environmental factors and lifestyle interventions. Although considerable efforts have been made in unraveling the mechanisms of gene-diet interactions the scientific evidences behind developing commercial genetic tests for providing personalized nutrition recommendations are still scarce. In this scenario the current mini-review aims to provide useful insights into salient feature of nutrition based genetic research and its commercial application and the ethical issue and concerns related to its outcome.
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Affiliation(s)
- Zakira Naureen
- Department of Biological Sciences and Chemistry, College of Arts and Sciences, University of Nizwa, Nizwa, Oman.
| | | | - Barbara Aquilanti
- UOC Nutrizione Clinica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
| | - Valeria Velluti
- UOC Nutrizione Clinica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
| | - Giuseppina Matera
- UOC Nutrizione Clinica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
| | - Lucilla Gagliardi
- UOC Nutrizione Clinica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
| | | | | | - Matteo Bertelli
- MAGI'S LAB, Rovereto (TN), Italy; MAGI EUREGIO, Bolzano, Italy; EBTNA-LAB, Rovereto (TN), Italy.
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27
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Kerin M, Marchini J. Inferring Gene-by-Environment Interactions with a Bayesian Whole-Genome Regression Model. Am J Hum Genet 2020; 107:698-713. [PMID: 32888427 PMCID: PMC7536582 DOI: 10.1016/j.ajhg.2020.08.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 08/11/2020] [Indexed: 01/05/2023] Open
Abstract
The contribution of gene-by-environment (GxE) interactions for many human traits and diseases is poorly characterized. We propose a Bayesian whole-genome regression model for joint modeling of main genetic effects and GxE interactions in large-scale datasets, such as the UK Biobank, where many environmental variables have been measured. The method is called LEMMA (Linear Environment Mixed Model Analysis) and estimates a linear combination of environmental variables, called an environmental score (ES), that interacts with genetic markers throughout the genome. The ES provides a readily interpretable way to examine the combined effect of many environmental variables. The ES can be used both to estimate the proportion of phenotypic variance attributable to GxE effects and to test for GxE effects at genetic variants across the genome. GxE effects can induce heteroskedasticity in quantitative traits, and LEMMA accounts for this by using robust standard error estimates when testing for GxE effects. When applied to body mass index, systolic blood pressure, diastolic blood pressure, and pulse pressure in the UK Biobank, we estimate that 9.3%, 3.9%, 1.6%, and 12.5%, respectively, of phenotypic variance is explained by GxE interactions and that low-frequency variants explain most of this variance. We also identify three loci that interact with the estimated environmental scores (-log10p>7.3).
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Affiliation(s)
- Matthew Kerin
- Wellcome Trust Center for Human Genetics, Oxford, OX3 7BN, UK
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28
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Lin WY, Huang CC, Liu YL, Tsai SJ, Kuo PH. Polygenic approaches to detect gene-environment interactions when external information is unavailable. Brief Bioinform 2020; 20:2236-2252. [PMID: 30219835 PMCID: PMC6954453 DOI: 10.1093/bib/bby086] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 08/14/2018] [Accepted: 08/16/2018] [Indexed: 12/18/2022] Open
Abstract
The exploration of 'gene-environment interactions' (G × E) is important for disease prediction and prevention. The scientific community usually uses external information to construct a genetic risk score (GRS), and then tests the interaction between this GRS and an environmental factor (E). However, external genome-wide association studies (GWAS) are not always available, especially for non-Caucasian ethnicity. Although GRS is an analysis tool to detect G × E in GWAS, its performance remains unclear when there is no external information. Our 'adaptive combination of Bayes factors method' (ADABF) can aggregate G × E signals and test the significance of G × E by a polygenic test. We here explore a powerful polygenic approach for G × E when external information is unavailable, by comparing our ADABF with the GRS based on marginal effects of SNPs (GRS-M) and GRS based on SNP × E interactions (GRS-I). ADABF is the most powerful method in the absence of SNP main effects, whereas GRS-M is generally the best test when single-nucleotide polymorphisms main effects exist. GRS-I is the least powerful test due to its data-splitting strategy. Furthermore, we apply these methods to Taiwan Biobank data. ADABF and GRS-M identified gene × alcohol and gene × smoking interactions on blood pressure (BP). BP-increasing alleles elevate more BP in drinkers (smokers) than in nondrinkers (nonsmokers). This work provides guidance to choose a polygenic approach to detect G × E when external information is unavailable.
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Affiliation(s)
- Wan-Yu Lin
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.,Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Ching-Chieh Huang
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Yu-Li Liu
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli County, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, TaipeiVeterans General Hospital, Taipei, Taiwan.,Division of Psychiatry, National Yang-Ming University, Taipei, Taiwan
| | - Po-Hsiu Kuo
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.,Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan
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29
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Flores-Dorantes MT, Díaz-López YE, Gutiérrez-Aguilar R. Environment and Gene Association With Obesity and Their Impact on Neurodegenerative and Neurodevelopmental Diseases. Front Neurosci 2020; 14:863. [PMID: 32982666 PMCID: PMC7483585 DOI: 10.3389/fnins.2020.00863] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Accepted: 07/24/2020] [Indexed: 12/12/2022] Open
Abstract
Obesity is a multifactorial disease in which environmental conditions and several genes play an important role in the development of this disease. Obesity is associated with neurodegenerative diseases (Alzheimer, Parkinson, and Huntington diseases) and with neurodevelopmental diseases (autism disorder, schizophrenia, and fragile X syndrome). Some of the environmental conditions that lead to obesity are physical activity, alcohol consumption, socioeconomic status, parent feeding behavior, and diet. Interestingly, some of these environmental conditions are shared with neurodegenerative and neurodevelopmental diseases. Obesity impairs neurodevelopment abilities as memory and fine-motor skills. Moreover, maternal obesity affects the cognitive function and mental health of the offspring. The common biological mechanisms involved in obesity and neurodegenerative/neurodevelopmental diseases are insulin resistance, pro-inflammatory cytokines, and oxidative damage, among others, leading to impaired brain development or cell death. Obesogenic environmental conditions are not the only factors that influence neurodegenerative and neurodevelopmental diseases. In fact, several genes implicated in the leptin-melanocortin pathway (LEP, LEPR, POMC, BDNF, MC4R, PCSK1, SIM1, BDNF, TrkB, etc.) are associated with obesity and neurodegenerative and neurodevelopmental diseases. Moreover, in the last decades, the discovery of new genes associated with obesity (FTO, NRXN3, NPC1, NEGR1, MTCH2, GNPDA2, among others) and with neurodegenerative or neurodevelopmental diseases (APOE, CD38, SIRT1, TNFα, PAI-1, TREM2, SYT4, FMR1, TET3, among others) had opened new pathways to comprehend the common mechanisms involved in these diseases. In conclusion, the obesogenic environmental conditions, the genes, and the interaction gene-environment would lead to a better understanding of the etiology of these diseases.
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Affiliation(s)
- María Teresa Flores-Dorantes
- Laboratorio de Biología Molecular y Farmacogenómica, Centro de Investigación de Ciencia y Tecnología Aplicada de Tabasco, División Académica de Ciencias Básicas, Universidad Juárez Autónoma de Tabasco, Villahermosa, Mexico
| | - Yael Efren Díaz-López
- Laboratorio de Enfermedades Metabólicas: Obesidad y Diabetes, Hospital Infantil de México “Federico Gómez,”Mexico City, Mexico
- División de Investigación, Facultad de Medicina, Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico
| | - Ruth Gutiérrez-Aguilar
- Laboratorio de Enfermedades Metabólicas: Obesidad y Diabetes, Hospital Infantil de México “Federico Gómez,”Mexico City, Mexico
- División de Investigación, Facultad de Medicina, Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico
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30
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Gene-Environment Interplay Between Physical Exercise and Fitness and Depression Symptomatology. Behav Genet 2020; 50:346-362. [PMID: 32797342 PMCID: PMC7441057 DOI: 10.1007/s10519-020-10009-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 07/20/2020] [Indexed: 11/21/2022]
Abstract
Studies often report beneficial effects of physical exercise on depression symptomatology, both in clinical and community samples. In clinical samples, effects are observed using physical exercise as primary treatment and supplement to antidepressant medications and/or psychotherapies. Magnitudes vary with sample characteristics, exercise measure, and study rigor. Both propensity to exercise and vulnerability to depression show genetic influences, suggesting gene–environment interplay. We investigated this in a Danish Twin Registry-based community sample who completed a cycle fitness test and detailed assessments of depression symptomatology and regular exercise engagement that enabled estimates of typical total, intentional exercise-specific, and other metabolic equivalent (MET) expenditures. All exercise-related measures correlated negatively with depression symptomatology (− .07 to − .19). Genetic variance was lower at higher levels of cycle fitness, with genetic and shared environmental correlations of − .50 and 1.0, respectively. Nonshared environmental variance in depression was lower at higher levels of total MET, with no indications of genetic or environmental covariance. Being physically active and/or fit tended to prevent depression, apparently because fewer participants with higher levels of activity and fitness reported high depression symptomatology. This was driven by nonshared environmental influences on activity but genetic influences on physical fitness. Genetic correlation suggested people less genetically inclined toward physical fitness may also be genetically vulnerable to depression, possibly because inertia impedes activity but also possibly due to social pressures to be fit. Exercise programs for general well-being should emphasize participation, not performance level or fitness. We discuss possible interrelations between fitness aptitude and metabolism.
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31
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Quantile-dependent heritability of computed tomography, dual-energy x-ray absorptiometry, anthropometric, and bioelectrical measures of adiposity. Int J Obes (Lond) 2020; 44:2101-2112. [PMID: 32665611 PMCID: PMC7530941 DOI: 10.1038/s41366-020-0636-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 06/07/2020] [Accepted: 07/03/2020] [Indexed: 12/18/2022]
Abstract
Background/Objectives: Quantile-dependent expressivity occurs when a gene’s
phenotypic expression depends upon whether the trait (e.g., BMI) is high or
low relative to its distribution. We have previously shown that the obesity
effects of a genetic risk score (GRSBMI) increased significantly
with increasing quantiles of BMI. However, BMI is an inexact adiposity
measure and GRSBMI explains <3% of the BMI variance. The
purpose of this paper is to test BMI for quantile-dependent expressivity
using a more inclusive genetic measure
(h2, heritability in
the narrow sense), extend the result to other adiposity measures, and
demonstrate its consistency with purported gene-environment
interactions. Subjects/Methods: Quantile-specific offspring-parent regression slopes
(βOP) were obtained from quantile regression for
height (ht) and computed tomography (CT), dual-energy x-ray absorptiometry
(DXA), anthropometric, and bioelectrical impedance (BIA) adiposity measures.
Heritability was estimated by 2βOP/(1+rspouse)
in 6,227 offspring-parent pairs from the Framingham Heart Study, where
rspouse is the spouse correlation. Results: Compared to h2 at the
10th percentile, genetic heritability was significantly
greater at the 90th population percentile for BMI (3.14-fold
greater, P<10−15), waist girth/ht (3.27-fold,
P<10−15), hip girth/ht (3.12-fold,
P=6.3×10−14), waist-to-hip ratio (1.75-fold,
P=0.01), sagittal diameter/ht (3.89-fold,
P=3.7×10−7), DXA total fat/ht2
(3.62-fold, P=0.0002), DXA leg fat/ht2 (3.29-fold,
P=2.0×10−11), DXA arm fat/ht2
(4.02-fold, P=0.001), CT-visceral fat/ht2 (3.03-fold, P=0.002),
and CT-subcutaneous fat/ht2 (3.54-fold, P=0.0004). External
validity was suggested by the phenomenon’s consistency with numerous
published reports. Quantile-dependent expressivity potentially explains
precision medicine markers for weight gain from overfeeding or antipsychotic
medications, and the modifying effects of physical activity, sleep, diet,
polycystic ovary syndrome, socioeconomic status, and depression on gene-BMI
relationships. Conclusion: Genetic heritabilities of anthropometric, CT, and DXA adiposity
measures increase with increasing adiposity. Some gene-environment
interactions may arise from analyzing subjects by characteristics that
distinguish high vs. low adiposity rather than the effects of environmental
stimuli on transcriptional and epigenetic processes.
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32
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Lin WY, Lin YS, Chan CC, Liu YL, Tsai SJ, Kuo PH. Using Genetic Risk Score Approaches to Infer Whether an Environmental Factor Attenuates or Exacerbates the Adverse Influence of a Candidate Gene. Front Genet 2020; 11:331. [PMID: 32457790 PMCID: PMC7225361 DOI: 10.3389/fgene.2020.00331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Accepted: 03/20/2020] [Indexed: 11/18/2022] Open
Abstract
Some candidate genes have been robustly reported to be associated with complex traits, such as the fat mass and obesity-associated (FTO) gene on body mass index (BMI), and the fibroblast growth factor 5 (FGF5) gene on blood pressure levels. It is of interest to know whether an environmental factor (E) can attenuate or exacerbate the adverse influence of a candidate gene. To this end, we here evaluate the performance of “genetic risk score” (GRS) approaches to detect “gene-environment interactions” (G × E). In the first stage, a GRS is calculated according to the genotypes of variants in a candidate gene. In the second stage, we test whether E can significantly modify this GRS effect. This two-stage procedure can not only provide a p-value for a G × E test but also guide inferences on how E modifies the adverse effect of a gene. With systematic simulations, we compared several ways to construct a GRS. If E exacerbates the adverse influence of a gene, GRS formed by the elastic net (ENET) or the least absolute shrinkage and selection operator (LASSO) is recommended. However, the performance of ENET or LASSO will be compromised if E attenuates the adverse influence of a gene, and using the ridge regression (RIDGE) can be more powerful in this situation. Applying RIDGE to 18,424 subjects in the Taiwan Biobank, we showed that performing regular exercise can attenuate the adverse influence of the FTO gene on four obesity measures: BMI (p = 0.0009), body fat percentage (p = 0.0031), waist circumference (p = 0.0052), and hip circumference (p = 0.0001). As another example, we used RIDGE and found the FGF5 gene has a stronger effect on blood pressure in Han Chinese with a higher waist-to-hip ratio [p = 0.0013 for diastolic blood pressure (DBP) and p = 0.0027 for systolic blood pressure (SBP)]. This study provides an evaluation on the GRS approaches, which is important to infer whether E attenuates or exacerbates the adverse influence of a candidate gene.
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Affiliation(s)
- Wan-Yu Lin
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.,Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Yu-Shun Lin
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Chang-Chuan Chan
- Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.,Institute of Environmental and Occupational Health Sciences, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Yu-Li Liu
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.,Division of Psychiatry, National Yang-Ming University, Taipei, Taiwan.,Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan
| | - Po-Hsiu Kuo
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.,Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan
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Jackson SE, Llewellyn CH, Smith L. The obesity epidemic - Nature via nurture: A narrative review of high-income countries. SAGE Open Med 2020; 8:2050312120918265. [PMID: 32435480 PMCID: PMC7222649 DOI: 10.1177/2050312120918265] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 03/04/2020] [Indexed: 12/19/2022] Open
Abstract
Over the last three decades, the prevalence of obesity has increased rapidly in populations around the world. Despite a wealth of research, the relative contributions of the different mechanisms underlying this global epidemic are not fully understood. While there is growing consensus that the rapid rise in obesity prevalence has been driven by changes to the environment, it is evident that biology plays a central role in determining who develops obesity and who remains lean in the current obesogenic environment. This review summarises evidence on the extent to which genes and the environment influence energy intake and energy expenditure, and as a result, contribute to the ongoing global obesity epidemic. The concept of genetic susceptibility to the environment driving human variation in body weight is discussed.
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Affiliation(s)
- Sarah E Jackson
- Department of Behavioural Science and Health, University College London, London, UK
- Sarah E Jackson, Department of Behavioural Science and Health, University College London, 1-19 Torrington Place, London WC1E 6BT, UK.
| | - Clare H Llewellyn
- Department of Behavioural Science and Health, University College London, London, UK
| | - Lee Smith
- Cambridge Centre for Sport and Exercise Sciences, Anglia Ruskin University, Cambridge, UK
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LMX1B rs10733682 Polymorphism Interacts with Macronutrients, Dietary Patterns on the Risk of Obesity in Han Chinese Girls. Nutrients 2020; 12:nu12051227. [PMID: 32357537 PMCID: PMC7281971 DOI: 10.3390/nu12051227] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 04/22/2020] [Accepted: 04/24/2020] [Indexed: 02/06/2023] Open
Abstract
Previous studies have found that LMX1B rs10733682 polymorphism is associated with Body Mass Index (BMI) in European and American Indian adults. In this study, the association of rs10733682 polymorphism with obesity-related indicators, and its interaction with macronutrients and dietary patterns (DPs) were explored in Chinese children (n = 798). The rs10733682 polymorphism was genotyped by improved Multiple Ligase Detection Reaction (iMLDR). Four DPs were identified by factor analysis. The AA genotype had a higher incidence of overweight/obesity than GG+GA genotypes (P = 0.010) in girls (n = 398), but no difference in boys. The AA genotype in girls could interact with intake of energy, fat and carbohydrate, causing an increased triglyceride (TG), (P = 0.021, 0.003, 0.002, respectively), and also could interact with energy from protein, causing an elevated BMI (P = 0.023) and waist (P = 0.019). Girls inclining to the HED (high-energy density)-DP were associated with increased TG (P = 0.033), and girls inclining to the VEF (vegetables, eggs, and fishes based)-DP were associated with decreased total cholesterol (TC, P = 0.045) and decreased low density lipoprotein cholesterin (LDL, P = 0.016). The findings indicated that the AA genotype of rs10733682 and the HED-DP are potential risk factors of obesity in Chinese girls.
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Abstract
Stress and other negative emotions, such as depression and anxiety, can lead to both decreased and increased food intake. The term 'emotional eating' has been widely used to refer to the latter response: a tendency to eat in response to negative emotions with the chosen foods being primarily energy-dense and palatable ones. Emotional eating can be caused by various mechanisms, such as using eating to cope with negative emotions or confusing internal states of hunger and satiety with physiological changes related to emotions. An increasing number of prospective studies have shown that emotional eating predicts subsequent weight gain in adults. This review discusses particularly three lines of research on emotional eating and obesity in adults. First, studies implying that emotional eating may be one behavioural mechanism linking depression and development of obesity. Secondly, studies highlighting the relevance of night sleep duration by showing that adults with a combination of shorter sleep and higher emotional eating may be especially vulnerable to weight gain. Thirdly, an emerging literature suggesting that genes may influence body weight partly through emotional eating and other eating behaviour dimensions. The review concludes by discussing what kind of implications these three avenues of research offer for obesity prevention and treatment interventions.
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Leite JMRS, Soler JMP, Horimoto ARVR, Alvim RO, Pereira AC. Heritability and Sex-Specific Genetic Effects of Self-Reported Physical Activity in a Brazilian Highly Admixed Population. Hum Hered 2020; 84:151-158. [PMID: 32088709 PMCID: PMC7212208 DOI: 10.1159/000506007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 01/17/2020] [Indexed: 01/13/2023] Open
Abstract
INTRODUCTION The engagement in sports or habitual physical activity (PA) has shown an extensive protective role against multiple diseases such as cancer, obesity, and many others. Additionally, PA has also a significant impact on life quality, since it aids with managing stress, preserving cognitive function and memory, and preventing fractures in the elderly. OBJECTIVE Considering there has been multiple evidence showing that genetic variation underpins variation of PA-related traits, we aimed to estimate the heritability (h2) of these phenotypes in a sample from the Brazilian population and assess whether males and females differ in relation to those estimates. METHODS 2,027 participants from a highly admixed population from Baependi, MG, Brazil, had information regarding their PA and sedentary behavior (SB) phenotypes collected through a questionnaire (IPAQ-SF). After data cleaning and transformation procedures, we obtained four variables to be evaluated: total PA (TPA MET), walking time, (WK MET), moderate-vigorous PA (MVPA MET), and SB. A model selection procedure was performed using a single-step covariate inclusion approach. We tested for BMI, waist, hip and neck circumferences, smoking, and depression separately, and performed correlation tests among covariates. Linear mixed models, selection procedure, and the variance components approach to estimate h2 were implemented using SOLAR-Eclipse 8.3.1. RESULTS We obtained estimates of 0.221, 0.109, 0.226, and 0 for TPA MET, WK MET, MVPA MET, and SB, respectively. We found evidence for gene-sex interactions, with males having higher sex-specific heritabilities than females for TPA MET and MVPA MET. In addition, we found higher estimates of the genetic variance component in males than females for most phenotypes. DISCUSSION/CONCLUSION The heritability estimates presented in this work show a moderate heritable set of genetic factors affecting PA in a sample from the Brazilian population. The evaluation of the genetic variance component suggests segregating genetic factors in male individuals are more heterogeneous, which can explain why men globally tend to need to practice more intense PA than women to achieve similar health benefits. Hence, these findings have significant implications for the understanding of the genetic architecture of PA and might aid to promote health in the future.
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Affiliation(s)
| | | | - Andréa Roseli Vançan Russo Horimoto
- Laboratory of Genetics and Molecular Cardiology, Heart Institute, São Paulo, Brazil
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Rafael O Alvim
- Department of Physiological Sciences, Federal University of Amazonas, Manaus, Brazil
| | - Alexandre C Pereira
- Laboratory of Genetics and Molecular Cardiology, Heart Institute, São Paulo, Brazil
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Hüls A, Czamara D. Methodological challenges in constructing DNA methylation risk scores. Epigenetics 2020; 15:1-11. [PMID: 31318318 PMCID: PMC6961658 DOI: 10.1080/15592294.2019.1644879] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 06/28/2019] [Accepted: 07/09/2019] [Indexed: 12/23/2022] Open
Abstract
Polygenic approaches often access more variance of complex traits than is possible by single variant approaches. For genotype data, genetic risk scores (GRS) are widely used for risk prediction as well as in association and interaction studies. Recently, interest has been growing in transferring GRS approaches to DNA methylation data (methylation risk scores, MRS), which can be used 1) as biomarkers for environmental exposures, 2) in association analyses in which single CpG sites do not achieve significance, 3) as dimension reduction approach in interaction and mediation analyses, and 4) to predict individual risks of disease or treatment success. Most GRS approaches can directly be transferred to methylation data. However, since methylation data is more sensitive to confounding, e.g. by age and tissue, it is more complex to find appropriate external weights. In this review, we will outline the adaption of current GRS approaches to methylation data and highlight occurring challenges.
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Affiliation(s)
- Anke Hüls
- Department of Human Genetics, Emory University, Atlanta, GA, USA
- Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital Research Institute, and Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Darina Czamara
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
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Viljakainen H, Dahlström E, Figueiredo R, Sandholm N, Rounge TB, Weiderpass E. Genetic risk score predicts risk for overweight and obesity in Finnish preadolescents. Clin Obes 2019; 9:e12342. [PMID: 31595703 PMCID: PMC6900004 DOI: 10.1111/cob.12342] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 09/04/2019] [Indexed: 12/12/2022]
Abstract
Common genetic variants predispose to obesity with varying contribution by age. We incorporated known genetic variants into genetic risk scores (GRSs) and investigated their associations with overweight/obesity and central obesity in preadolescents. Furthermore, we compared GRSs with lifestyle factors, and tested if they predict the change in body size and shape in a 4-year follow-up. We utilized 1142 subjects from the Finnish Health in Teens (Fin-HIT) cohort. Overweight and obesity were defined with age- and gender-specific body mass index (BMI) z-score (BMIz), while central obesity by the waist-to-height ratio (WHtR). Background data on parental language, eating habits, leisure-time physical activity (LTPA) and sleep duration were included. Genotyping was performed with the Metabochip platform. Weighted, standardized GRSs were derived. Of the11-year-old children, 25.5% were at least overweight and 90.8% had Finnish speaking background. BMI-GRS was associated with higher risk for overweight with odds ratio (95% confidence interval) of 1.39 (1.20; 1.60) and obesity 1.41 (1.08; 1.83), but not with central obesity. BMI-GRS was weakly and inversely associated with the changes in BMIz and WHtR in the 4-year follow-up. Waist-to-hip ratio-GRS was not related to any obesity measures at baseline nor in the follow-up. The effect of BMI-GRS is similar to that of low LTPA on overweight. An interaction between parental language and BMI-GRS was noted (P = .019): BMI-GRS associated more strongly with overweight in Swedish than in Finnish speakers. We further identified two suggestive genetic variants near LOC101926977 and LOC105369677 associated with BMIz in preadolescents which were replicated in the adult population. In preadolescents, known genetic predisposing factors induce a risk for overweight comparable to low LTPA. However, the GRS was poor in predicting short-term changes in BMI or WHtR.
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Affiliation(s)
- Heli Viljakainen
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Food and Nutrition, University of Helsinki, Helsinki, Finland
| | - Emma Dahlström
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, Abdominal Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Rejane Figueiredo
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Niina Sandholm
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, Abdominal Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Trine B Rounge
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Research, Cancer Registry of Norway, Institute of Population-based Cancer Research, Oslo, Norway
| | - Elisabete Weiderpass
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Research, Cancer Registry of Norway, Institute of Population-based Cancer Research, Oslo, Norway
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
- Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, The Arctic University of Norway, Tromsø, Norway
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McCaffery JM. Precision behavioral medicine: Implications of genetic and genomic discoveries for behavioral weight loss treatment. ACTA ACUST UNITED AC 2019; 73:1045-1055. [PMID: 30394782 DOI: 10.1037/amp0000253] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
This article reviews the concept of precision behavioral medicine and the progress toward applying genetics and genomics as tools to optimize weight management intervention. We discuss genetic, epigenetic, and genomic markers, as well as interactions between genetics and the environment as they relate to obesity and behavioral weight loss to date. Recommendations for the conditions under which genetics and genomics could be incorporated to support clinical decision-making in behavioral weight loss are outlined and illustrative scenarios of how this approach could improve clinical outcomes are provided. It is concluded that there is not yet sufficient evidence to leverage genetics or genomics to aid the treatment of obesity but the foundations are being laid. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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Affiliation(s)
- Jeanne M McCaffery
- Weight Control and Diabetes Research Center, Department of Psychiatry and Human Behavior, The Miriam Hospital
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40
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Manco L, Pinho S, Albuquerque D, Machado‐Rodrigues AM, Padez C. Physical activity and the association between the
FTO
rs9939609 polymorphism and obesity in Portuguese children aged 3 to 11 years. Am J Hum Biol 2019; 31:e23312. [DOI: 10.1002/ajhb.23312] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 07/02/2019] [Accepted: 08/03/2019] [Indexed: 11/11/2022] Open
Affiliation(s)
- Licínio Manco
- Research Centre for Anthropology and Health (CIAS), Department of Life SciencesUniversity of Coimbra Coimbra Portugal
| | - Simão Pinho
- Research Centre for Anthropology and Health (CIAS), Department of Life SciencesUniversity of Coimbra Coimbra Portugal
| | - David Albuquerque
- Research Centre for Anthropology and Health (CIAS), Department of Life SciencesUniversity of Coimbra Coimbra Portugal
| | - Aristides M. Machado‐Rodrigues
- Research Centre for Anthropology and Health (CIAS), Department of Life SciencesUniversity of Coimbra Coimbra Portugal
- High School of EducationPolytechnic Institute of Viseu Viseu Portugal
| | - Cristina Padez
- Research Centre for Anthropology and Health (CIAS), Department of Life SciencesUniversity of Coimbra Coimbra Portugal
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Frank M, Dragano N, Arendt M, Forstner AJ, Nöthen MM, Moebus S, Erbel R, Jöckel KH, Schmidt B. A genetic sum score of risk alleles associated with body mass index interacts with socioeconomic position in the Heinz Nixdorf Recall Study. PLoS One 2019; 14:e0221252. [PMID: 31442235 PMCID: PMC6707579 DOI: 10.1371/journal.pone.0221252] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 08/04/2019] [Indexed: 01/01/2023] Open
Abstract
Body mass index (BMI) is influenced by genetic, behavioral and environmental factors, while interactions between genetic and socioeconomic factors have been suggested. Aim of the study was to investigate whether socioeconomic position (SEP) interacts with a BMI-related genetic sum score (GRSBMI) to affect BMI in a population-based cohort. SEP-related health behaviors and a GRS associated with educational attainment (GRSEdu) were included in the analysis to explore potential interactions underlying the GRSBMIxSEP effect. Baseline information on SEP indicators (education, income), BMI, smoking, physical activity, alcohol consumption and genetic risk factors were available for 4,493 participants of the Heinz Nixdorf Recall Study. Interaction analysis was based on linear regression as well as on stratified analyses. In SEP-stratified analyses, the highest genetic effects were observed in the lowest educational group with a 0.24 kg/m2 higher BMI (95%CI: 0.16; 0.31) and in the lowest income quartile with a 0.14 kg/m2 higher BMI (95%CI: 0.09; 0.18) per additional risk allele. Indication for a GRSBMIxSEP interaction was observed for education (ßGRSbmixeducation = -0.02 [95%CI:-0.03; -0.01]) and income (ßGRSbmixincome = -0.05 [95%CI: -0.08; -0.02]). When adjusting for interactions with the GRSEdu and SEP-related health behaviors, effect size estimates of the GRSBMIxSEP interaction remained virtually unchanged. Results gave indication for an interaction of BMI-related genetic risk factors with SEP indicators, showing substantially stronger genetic effects in low SEP groups. This supports the hypothesis that expression of genetic risks is higher in socioeconomically disadvantaged environments. No indication was observed that the GRSBMIxSEP interaction was affected by other SEP-related factors included in the analysis.
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Affiliation(s)
- Mirjam Frank
- Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Essen, Germany
| | - Nico Dragano
- Institute of Medical Sociology, Centre for Health and Society, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Marina Arendt
- Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Essen, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
- Centre for Human Genetics, University of Marburg, Marburg, Germany
- Department of Biomedicine, University of Basel, Basel, Switzerland
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Susanne Moebus
- Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Essen, Germany
| | - Raimund Erbel
- Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Essen, Germany
| | - Karl-Heinz Jöckel
- Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Essen, Germany
| | - Börge Schmidt
- Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Essen, Germany
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Salem ESB, Vonberg AD, Borra VJ, Gill RK, Nakamura T. RNAs and RNA-Binding Proteins in Immuno-Metabolic Homeostasis and Diseases. Front Cardiovasc Med 2019; 6:106. [PMID: 31482095 PMCID: PMC6710452 DOI: 10.3389/fcvm.2019.00106] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 07/17/2019] [Indexed: 12/16/2022] Open
Abstract
The increasing prevalence of worldwide obesity has emerged as a major risk factor for type 2 diabetes (T2D), hepatosteatosis, and cardiovascular disease. Accumulating evidence indicates that obesity has strong inflammatory underpinnings tightly linked to the development of metabolic diseases. However, the molecular mechanisms by which obesity induces aberrant inflammation associated with metabolic diseases are not yet clearly defined. Recently, RNAs have emerged as important regulators of stress responses and metabolism. RNAs are subject to changes in modification status, higher-order structure, and cellular localization; all of which could affect the affinity for RNA-binding proteins (RBPs) and thereby modify the RNA-RBP networks. Proper regulation and management of RNA characteristics are fundamental to cellular and organismal homeostasis, as well as paramount to health. Identification of multiple single nucleotide polymorphisms (SNPs) within loci of fat mass- and obesity-associated protein (FTO) gene, an RNA demethylase, through genome-wide association studies (GWAS) of T2D, and functional assessments of FTO in mice, support the concept that disruption in RNA modifications leads to the development of human diseases including obesity and metabolic disorder. In obesity, dynamic alterations in modification and localization of RNAs appear to modulate the RNA-RBP networks and activate proinflammatory RBPs, such as double-stranded RNA (dsRNA)-dependent protein kinase (PKR), Toll-like receptor (TLR) 3 and TLR7, and RNA silencing machinery. These changes induce aberrant inflammation and the development of metabolic diseases. This review will describe the current understanding of the underlying causes of these common and altered characteristics of RNA-RBP networks which will pave the way for developing novel approaches to tackle the pandemic issue of obesity.
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Affiliation(s)
- Esam S B Salem
- Division of Endocrinology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pharmacology and Systems Physiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Andrew D Vonberg
- Division of Endocrinology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Vishnupriya J Borra
- Division of Endocrinology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Rupinder K Gill
- Division of Endocrinology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Takahisa Nakamura
- Division of Endocrinology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States.,Department of Metabolic Bioregulation, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
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Dunn AR, O'Connell KMS, Kaczorowski CC. Gene-by-environment interactions in Alzheimer's disease and Parkinson's disease. Neurosci Biobehav Rev 2019; 103:73-80. [PMID: 31207254 PMCID: PMC6700747 DOI: 10.1016/j.neubiorev.2019.06.018] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 06/06/2019] [Accepted: 06/12/2019] [Indexed: 12/12/2022]
Abstract
Diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) arise from complex interactions of genetic and environmental factors, with genetic variants regulating individual responses to environmental exposures (i.e. gene-by-environment interactions). Identifying gene-by-environment interactions will be critical to fully understanding disease mechanisms and developing personalized therapeutics, though these interactions are still poorly understood and largely under-studied. Candidate gene approaches have shown that known disease risk variants often regulate response to environmental factors. However, recent improvements in exposome- and genome-wide association and interaction studies in humans and mice are enabling discovery of novel genetic variants and pathways that predict response to a variety of environmental factors. Here, we highlight recent approaches and ongoing developments in human and rodent studies to identify genetic modulators of environmental factors using AD and PD as exemplars. Identifying gene-by-environment interactions in disease will be critical to developing personalized intervention strategies and will pave the way for precision medicine.
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Affiliation(s)
- Amy R Dunn
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA.
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Lin WY, Chan CC, Liu YL, Yang AC, Tsai SJ, Kuo PH. Performing different kinds of physical exercise differentially attenuates the genetic effects on obesity measures: Evidence from 18,424 Taiwan Biobank participants. PLoS Genet 2019; 15:e1008277. [PMID: 31369549 PMCID: PMC6675047 DOI: 10.1371/journal.pgen.1008277] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 06/26/2019] [Indexed: 12/17/2022] Open
Abstract
Obesity is a worldwide health problem that is closely linked to many metabolic disorders. Regular physical exercise has been found to attenuate the genetic predisposition to obesity. However, it remains unknown what kinds of exercise can modify the genetic risk of obesity. This study included 18,424 unrelated Han Chinese adults aged 30–70 years who participated in the Taiwan Biobank (TWB). A total of 5 obesity measures were investigated here, including body mass index (BMI), body fat percentage (BFP), waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR). Because there have been no large genome-wide association studies on obesity for Han Chinese, we used the TWB internal weights to construct genetic risk scores (GRSs) for each obesity measure, and then test the significance of GRS-by-exercise interactions. The significance level throughout this work was set at 0.05/550 = 9.1x10-5 because a total of 550 tests were performed. Performing regular exercise was found to attenuate the genetic effects on 4 obesity measures, including BMI, BFP, WC, and HC. Among the 18 kinds of self-reported regular exercise, 6 mitigated the genetic effects on at least one obesity measure. Regular jogging blunted the genetic effects on BMI, BFP, and HC. Mountain climbing, walking, exercise walking, international standard dancing, and a longer practice of yoga also attenuated the genetic effects on BMI. Exercises such as cycling, stretching exercise, swimming, dance dance revolution, and qigong were not found to modify the genetic effects on any obesity measure. Across all 5 obesity measures, regular jogging consistently presented the most significant interactions with GRSs. Our findings show that the genetic effects on obesity measures can be decreased to various extents by performing different kinds of exercise. The benefits of regular physical exercise are more impactful in subjects who are more predisposed to obesity. The complex interplay of genetics and lifestyle makes obesity a challenging issue. Previous studies have found performing regular physical exercise could blunt the genetic effects on body mass index (BMI). However, BMI does not take into account lean body mass or identify central obesity. Moreover, it remains unclear what kinds of exercise could more effectively attenuate the genetic effects on obesity measures. With a sample of 18,424 unrelated Han Chinese adults, we comprehensively investigated gene-exercise interactions on 5 obesity measures: BMI, body fat percentage, waist circumference, hip circumference, and waist-to-hip ratio. Moreover, we tested whether the genetic effects on obesity measures could be modified by any of 18 kinds of self-reported regular exercise. Because no large genome-wide association studies on obesity have been done for Han Chinese, we constructed genetic risk scores with internal weights for analyses. Among these exercises, regular jogging consistently presented the strongest evidence to mitigate the genetic effects on all 5 obesity measures. Moreover, mountain climbing, walking, exercise walking, international standard dancing, and a longer practice of yoga attenuated the genetic effects on BMI. The benefits of regularly performing these 6 kinds of exercise are more impactful in subjects who are more predisposed to obesity.
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Affiliation(s)
- Wan-Yu Lin
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan
- * E-mail: (WYL); (PHK)
| | - Chang-Chuan Chan
- Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan
- Institute of Occupational Medicine and Industrial Hygiene, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Yu-Li Liu
- Center for Neuropsychiatric Research, National Health Research Institutes, Zhunan, Miaoli County, Taiwan
| | - Albert C. Yang
- Division of Psychiatry, National Yang-Ming University, Taipei, Taiwan
- Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, Massachusetts, United States of America
- Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan
| | - Shih-Jen Tsai
- Division of Psychiatry, National Yang-Ming University, Taipei, Taiwan
- Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan
- Department of Psychiatry, Taipei Veterans General Hospital, Beitou District, Taipei, Taiwan
| | - Po-Hsiu Kuo
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan
- * E-mail: (WYL); (PHK)
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Gene-Environment Interactions on Body Fat Distribution. Int J Mol Sci 2019; 20:ijms20153690. [PMID: 31357654 PMCID: PMC6696304 DOI: 10.3390/ijms20153690] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 07/18/2019] [Accepted: 07/19/2019] [Indexed: 02/08/2023] Open
Abstract
The prevalence of obesity has been increasing markedly in the U.S. and worldwide in the past decades; and notably, the obese populations are signified by not only the overall elevated adiposity but also particularly harmful accumulation of body fat in the central region of the body, namely, abdominal obesity. The profound shift from “traditional” to “obesogenic” environments, principally featured by the abundance of palatable, energy-dense diet, reduced physical activity, and prolonged sedentary time, promotes the obesity epidemics and detrimental body fat distribution. Recent advances in genomics studies shed light on the genetic basis of obesity and body fat distribution. In addition, growing evidence from investigations in large cohorts and clinical trials has lent support to interactions between genetic variations and environmental factors, e.g., diet and lifestyle factors, in relation to obesity and body fat distribution. This review summarizes the recent discoveries from observational studies and randomized clinical trials on the gene–environment interactions on obesity and body fat distribution.
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Deng WQ, Mao S, Kalnapenkis A, Esko T, Mägi R, Paré G, Sun L. Analytical strategies to include the X-chromosome in variance heterogeneity analyses: Evidence for trait-specific polygenic variance structure. Genet Epidemiol 2019; 43:815-830. [PMID: 31332826 DOI: 10.1002/gepi.22247] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 06/07/2019] [Accepted: 06/13/2019] [Indexed: 12/12/2022]
Abstract
Genotype-stratified variance of a quantitative trait could differ in the presence of gene-gene or gene-environment interactions. Genetic markers associated with phenotypic variance are thus considered promising candidates for follow-up interaction or joint location-scale analyses. However, as in studies of main effects, the X-chromosome is routinely excluded from "whole-genome" scans due to analytical challenges. Specifically, as males carry only one copy of the X-chromosome, the inherent sex-genotype dependency could bias the trait-genotype association, through sexual dimorphism in quantitative traits with sex-specific means or variances. Here we investigate phenotypic variance heterogeneity associated with X-chromosome single nucleotide polymorphisms (SNPs) and propose valid and powerful strategies. Among those, a generalized Levene's test has adequate power and remains robust to sexual dimorphism. An alternative approach is a sex-stratified analysis but at the cost of slightly reduced power and modeling flexibility. We applied both methods to an Estonian study of gene expression quantitative trait loci (eQTL; n = 841), and two complex trait studies of height, hip, and waist circumferences, and body mass index from Multi-Ethnic Study of Atherosclerosis (MESA; n = 2,073) and UK Biobank (UKB; n = 327,393). Consistent with previous eQTL findings on mean, we found some but no conclusive evidence for cis regulators being enriched for variance association. SNP rs2681646 is associated with variance of waist circumference (p = 9.5E-07) at X-chromosome-wide significance in UKB, with a suggestive female-specific effect in MESA (p = 0.048). Collectively, an enrichment analysis using permutated UKB (p < 0.1) and MESA (p < 0.01) datasets, suggests a possible polygenic structure for the variance of human height.
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Affiliation(s)
- Wei Q Deng
- Department of Statistical Sciences, Faculty of Arts and Science, University of Toronto, Toronto, Canada
| | - Shihong Mao
- Department of Pathology and Molecular Medicine, Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada
| | - Anette Kalnapenkis
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.,Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Guillaume Paré
- Department of Pathology and Molecular Medicine, Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Canada.,Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada
| | - Lei Sun
- Department of Statistical Sciences, Faculty of Arts and Science, University of Toronto, Toronto, Canada.,Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
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Osazuwa-Peters OL, Schwander K, Waken RJ, de Las Fuentes L, Kilpeläinen TO, Loos RJF, Racette SB, Sung YJ, Rao DC. The Promise of Selecting Individuals from the Extremes of Exposure in the Analysis of Gene-Physical Activity Interactions. Hum Hered 2019; 83:315-332. [PMID: 31167214 DOI: 10.1159/000499711] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 03/19/2019] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Dichotomization using the lower quartile as cutoff is commonly used for harmonizing heterogeneous physical activity (PA) measures across studies. However, this may create misclassification and hinder discovery of new loci. OBJECTIVES This study aimed to evaluate the performance of selecting individuals from the extremes of the exposure (SIEE) as an alternative approach to reduce such misclassification. METHOD For systolic and diastolic blood pressure in the Framingham Heart Study, we performed a genome-wide association study with gene-PA interaction analysis using three PA variables derived by SIEE and two other dichotomization approaches. We compared number of loci detected and overlap with loci found using a quantitative PA variable. In addition, we performed simulation studies to assess bias, false discovery rates (FDR), and power under synergistic/antagonistic genetic effects in exposure groups and in the presence/absence of measurement error. RESULTS In the empirical analysis, SIEE's performance was neither the best nor the worst. In most simulation scenarios, SIEE was consistently outperformed in terms of FDR and power. Particularly, in a scenario characterized by antagonistic effects and measurement error, SIEE had the least bias and highest power. CONCLUSION SIEE's promise appears limited to detecting loci with antagonistic effects. Further studies are needed to evaluate SIEE's full advantage.
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Affiliation(s)
| | - Karen Schwander
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - R J Waken
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Lisa de Las Fuentes
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA.,Cardiovascular Division, Department of Medicine, Washington University, St. Louis, Missouri, USA
| | - Tuomas O Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Environmental Medicine and Public Health, The Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ruth J F Loos
- Icahn School of Medicine at Mount Sinai, The Charles Bronfman Institute for Personalized Medicine, New York, New York, USA.,Icahn School of Medicine at Mount Sinai, The Mindich Child Health and Development Institute, New York, New York, USA
| | - Susan B Racette
- Program in Physical Therapy, Washington University School of Medicine, St. Louis, Missouri, USA.,Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Yun Ju Sung
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - D C Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, USA
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Liaw YC, Liaw YP, Lan TH. Physical Activity Might Reduce the Adverse Impacts of the FTO Gene Variant rs3751812 on the Body Mass Index of Adults in Taiwan. Genes (Basel) 2019; 10:genes10050354. [PMID: 31075924 PMCID: PMC6562480 DOI: 10.3390/genes10050354] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 05/03/2019] [Accepted: 05/06/2019] [Indexed: 12/22/2022] Open
Abstract
The fat mass and obesity-associated (FTO) gene is a significant genetic contributor to polygenic obesity. We investigated whether physical activity (PA) modulates the effect of FTO rs3751812 on body mass index (BMI) among Taiwanese adults. Analytic samples included 10,853 Taiwan biobank participants. Association of the single-nucleotide polymorphism (SNP) with BMI was assessed using linear regression models. Physical activity was defined as any kind of exercise lasting 30 min each session, at least three times a week. Participants with heterozygous (TG) and homozygous (TT) genotypes had higher BMI compared to those with wild-type (GG) genotypes. The β value was 0.381(p < 0.0001) for TG individuals and 0.684 (p = 0.0204) for TT individuals. There was a significant dose-response effect among carriers of different risk alleles (p trend <0.0001). Active individuals had lower BMI than their inactive counterparts (β = -0.389, p < 0.0001). Among the active individuals, significant associations were found only with the TG genotype (β = 0.360, p = 0.0032). Inactive individuals with TG and TT genotypes had increased levels of BMI compared to those with GG genotypes: Their β values were 0.381 (p = 0.0021) and 0.950 (p = 0.0188), respectively. There was an interaction between the three genotypes, physical inactivity, and BMI (p trend = 0.0002). Our data indicated that increased BMI owing to genetic susceptibility by FTO rs3751812 may be reduced by physical activity.
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Affiliation(s)
- Yi-Ching Liaw
- School of Nutrition and Health Sciences, Taipei Medical University, Taipei 11031, Taiwan.
- Institute of Clinical Medicine, National Yang-Ming University, Taipei 11221, Taiwan.
| | - Yung-Po Liaw
- Department of Public Health and Institute of Public Health, Chung Shan Medical University, No. 110, Sec. 1 Jianguo N. Road, Taichung 40201, Taiwan.
- Department of Family and Community Medicine, Chung Shan Medical University Hospital, Taichung 40201, Taiwan.
| | - Tsuo-Hung Lan
- Institute of Clinical Medicine, National Yang-Ming University, Taipei 11221, Taiwan.
- Department of Psychiatry, Taichung Veterans General Hospital, 1650 Taiwan Boulevard Sect. 4, Xitun District, Taichung 407, Taiwan.
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Celis-Morales CA, Lyall DM, Bailey MES, Petermann-Rocha F, Anderson J, Ward J, Mackay DF, Welsh P, Pell JP, Sattar N, Gill JMR, Gray SR. The Combination of Physical Activity and Sedentary Behaviors Modifies the Genetic Predisposition to Obesity. Obesity (Silver Spring) 2019; 27:653-661. [PMID: 30900409 DOI: 10.1002/oby.22417] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2018] [Accepted: 11/08/2018] [Indexed: 01/19/2023]
Abstract
OBJECTIVE This study aimed to investigate whether the association between a validated genetic profile risk score for BMI (GPRS-BMI) (based on 93 single-nucleotide polymorphisms) and phenotypic obesity (BMI) was modified by the combined categories of physical activity (PA) and sedentary behaviors in a large population-based study. METHODS This study included cross-sectional baseline data from 338,216 white European adult men and women aged 37 to 73 years. Interaction effects of GPRS-BMI with the combined categories of PA and sedentary behaviors on BMI were investigated. RESULTS There was a significant interaction between GPRS-BMI and the combined categories of objectively measured PA and total sedentary behavior (P[interaction] = 3.5 × 10-6 ); among physically inactive and highly sedentary individuals, BMI was higher by 0.60 kg/m2 per 1-SD increase in GPRS-obesity (P = 8.9 × 10-50 ), whereas the relevant BMI difference was 38% lower among physically active individuals and those with low sedentary time (β: 0.37 kg/m2 ; P = 2.3 × 10-51 ). A similar pattern was observed for the combined categories of objective PA and TV viewing (inactive/high TV viewing β: 0.60 vs. active/low TV viewing β: 0.40 kg/m2 ; P[interaction] = 2.9 × 10-6 ). CONCLUSIONS This study provides evidence that combined categories of PA and sedentary behaviors modify the extent to which genetic predisposition to obesity results in higher BMI.
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Affiliation(s)
- Carlos A Celis-Morales
- British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
- Centre for Research in Exercise Physiology (CIFE), Universidad Mayor, Santiago, Chile
| | - Donald M Lyall
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Mark E S Bailey
- School of Life Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Fanny Petermann-Rocha
- British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Jana Anderson
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Joey Ward
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Daniel F Mackay
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Paul Welsh
- British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Jill P Pell
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Naveed Sattar
- British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Jason M R Gill
- British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Stuart R Gray
- British Heart Foundation Glasgow Cardiovascular Research Centre, Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
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50
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Barroso I, McCarthy MI. The Genetic Basis of Metabolic Disease. Cell 2019; 177:146-161. [PMID: 30901536 PMCID: PMC6432945 DOI: 10.1016/j.cell.2019.02.024] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 02/11/2019] [Accepted: 02/14/2019] [Indexed: 02/06/2023]
Abstract
Recent developments in genetics and genomics are providing a detailed and systematic characterization of the genetic underpinnings of common metabolic diseases and traits, highlighting the inherent complexity within systems for homeostatic control and the many ways in which that control can fail. The genetic architecture underlying these common metabolic phenotypes is complex, with each trait influenced by hundreds of loci spanning a range of allele frequencies and effect sizes. Here, we review the growing appreciation of this complexity and how this has fostered the implementation of genome-scale approaches that deliver robust mechanistic inference and unveil new strategies for translational exploitation.
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Affiliation(s)
- Inês Barroso
- Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK; Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Churchill Hospital, Old Road, Headington, Oxford OX3 7LJ, UK; Oxford NIHR Biomedical Research Centre, Churchill Hospital, Old Road, Headington, Oxford OX3 7LJ, UK
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