Showing papers by "Claude Bouchard published in 2020"
Boston University1, University of Michigan2, Washington University in St. Louis3, University of North Carolina at Chapel Hill4, Icahn School of Medicine at Mount Sinai5, University of Edinburgh6, University of Mississippi Medical Center7, Leiden University Medical Center8, University of Colorado Denver9, Veterans Health Administration10, Outcomes Research Consortium11, Brigham and Women's Hospital12, University of Iceland13, National Institutes of Health14, Erasmus University Medical Center15, University of Kentucky16, Broad Institute17, Harvard University18, Mashhad University of Medical Sciences19, Fred Hutchinson Cancer Research Center20, University of Aberdeen21, Pennington Biomedical Research Center22, Johns Hopkins University23, Beth Israel Deaconess Medical Center24, Los Angeles Biomedical Research Institute25, New York Academy of Medicine26, University of Dundee27, VA Boston Healthcare System28
TL;DR: The identification of loci exhibiting potential interaction with baseline smoking status provides evidence for genetic interactions with smoking exposure that may explain some of the heterogeneity in the association between smoking and T2D.
Abstract: Smoking is a potentially causal behavioral risk factor for type 2 diabetes (T2D), but not all smokers develop T2D. It is unknown whether genetic factors partially explain this variation. We performed genome-environment-wide interaction studies to identify loci exhibiting potential interaction with baseline smoking status (ever vs. never) on incident T2D and fasting glucose (FG). Analyses were performed in participants of European (EA) and African ancestry (AA) separately. Discovery analyses were conducted using genotype data from the 50,000-single-nucleotide polymorphism (SNP) ITMAT-Broad-CARe (IBC) array in 5 cohorts from from the Candidate Gene Association Resource Consortium (n = 23,189). Replication was performed in up to 16 studies from the Cohorts for Heart Aging Research in Genomic Epidemiology Consortium (n = 74,584). In meta-analysis of discovery and replication estimates, 5 SNPs met at least one criterion for potential interaction with smoking on incident T2D at p<1x10-7 (adjusted for multiple hypothesis-testing with the IBC array). Two SNPs had significant joint effects in the overall model and significant main effects only in one smoking stratum: rs140637 (FBN1) in AA individuals had a significant main effect only among smokers, and rs1444261 (closest gene C2orf63) in EA individuals had a significant main effect only among nonsmokers. Three additional SNPs were identified as having potential interaction by exhibiting a significant main effects only in smokers: rs1801232 (CUBN) in AA individuals, rs12243326 (TCF7L2) in EA individuals, and rs4132670 (TCF7L2) in EA individuals. No SNP met significance for potential interaction with smoking on baseline FG. The identification of these loci provides evidence for genetic interactions with smoking exposure that may explain some of the heterogeneity in the association between smoking and T2D.
43 citations
TL;DR: Adipose tissue and skeletal muscle morphology and metabolism are substantially altered by chronic overfeeding and there is some evidence for a contribution of a genetic component to this response variability.
Abstract: This systematic review has examined more than 300 original papers dealing with the biology of overfeeding. Studies have varied from 1 day to 6 months. Overfeeding produced weight gain in adolescents, adult men and women and in older men. In longer term studies, there was a clear and highly significant relationship between energy ingested and weight gain and fat storage with limited individual differences. There is some evidence for a contribution of a genetic component to this response variability. The response to overfeeding was affected by the baseline state of the groups being compared: those with insulin resistance versus insulin sensitivity; those prone to obesity versus those resistant to obesity; and those with metabolically abnormal obesity versus those with metabolically normal obesity. Dietary components, such as total fat, polyunsaturated fat and carbohydrate influenced the patterns of adipose tissue distribution as did the history of low or normal birth weight. Overfeeding affected the endocrine system with increased circulating concentrations of insulin and triiodothyronine frequently present. Growth hormone, in contrast, was rapidly suppressed. Changes in plasma lipids were influenced by diet, exercise and the magnitude of weight gain. Adipose tissue and skeletal muscle morphology and metabolism are substantially altered by chronic overfeeding.
40 citations
TL;DR: Cardiorespiratory fitness in young adulthood and a PRS are modestly associated with midlife BMI, although future BMI is associated with BMI inYoung adulthood, and models that included baseline BMI and surveillance of BMI over time were better in predicting BMI at year 25 compared with the PRS.
Abstract: Importance Obesity is a major determinant of disease burden worldwide. Polygenic risk scores (PRSs) have been posited as key predictors of obesity. How a PRS can be translated to the clinical encounter (especially in the context of fitness, activity, and parental history of overweight) remains unclear. Objective To quantify the relative importance of a PRS, fitness, activity, parental history of overweight, and body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) in young adulthood on BMI trends over 25 years. Design, Setting, and Participants This population-based prospective cohort study at 4 US centers included white individuals and black individuals with assessments of polygenic risk of obesity, fitness, activity, and BMI in young adulthood (in their 20s) and up to 25 years of follow-up. Data collected between March 1985 and August 2011 were analyzed from April 25, 2019, to September 29, 2019. Main Outcomes and Measures Body mass index at the initial visit and 25 years later. Results This study evaluated an obesity PRS from a recently reported study of 1608 white individuals (848 women [52.7%]) and 909 black individuals (548 women [60.3%]) across the United States. At baseline (year 0), mean (SD) overall BMI was 24.2 (4.5), which increased to 29.6 (6.9) at year 25. Among white individuals, the PRS (combined with age, sex, self-reported parental history of overweight, and principal components of ancestry) explained 11.9% (at year 0) and 13.6% (at year 25) of variation in BMI. Although the addition of fitness increased the explanatory capability of the model (24.0% variance at baseline and up to 18.1% variance in BMI at year 25), baseline BMI in young adulthood was the strongest factor, explaining 52.3% of BMI in midlife in combination with age, sex, and self-reported parental history of overweight. Accordingly, models that included baseline BMI (especially BMI surveillance over time) were better in predicting BMI at year 25 compared with the PRS. In fully adjusted models, the effect sizes for fitness and the PRS on BMI were comparable in opposing directions. The added explanatory capacity of the PRS among black individuals was lower than among white individuals. Among white individuals, addition of baseline BMI and surveillance of BMI over time was associated with improved precision of predicted BMI at year 25 (mean error in predicted BMI 0 kg/m2[95% CI, −11.4 to 11.4] to 0 kg/m2[95% CI, −8.5 to 8.5] for baseline BMI and mean error 0 kg/m2[95% CI, −5.3 to 5.3] for BMI surveillance). Conclusions and Relevance Cardiorespiratory fitness in young adulthood and a PRS are modestly associated with midlife BMI, although future BMI is associated with BMI in young adulthood. Fitness has a comparable association with future BMI as does the PRS. Caution should be exercised in the widespread use of polygenic risk for obesity prevention in adults, and close clinical surveillance and fitness may have prime roles in limiting the adverse consequences of elevated BMI on health.
18 citations
TL;DR: Findings provide specific sites across the mitochondrial genome that may be related to maximal oxygen uptake trainability in HR and LR individuals.
Abstract: Purpose We designed the study to determine whether mitochondrial DNA (mtDNA) haplogroup, sequence, and heteroplasmy differed between individuals previously characterized as low (LR) or high responders (HR) as defined by their maximal oxygen uptake response to a standardized aerobic exercise training program. Methods DNA was isolated from whole blood in subjects from the HERITAGE Family Study that were determined to be either HR (n = 15) or LR (n = 15). mtDNA was amplified by long-range polymerase chain reaction, then tagged with Nextera libraries and sequenced on a MiSeq instrument. Results Different mtDNA haplogroup subtypes were found in HR and LR individuals. Compared with HR subjects, significantly more LR subjects had variants in 13 sites, including 7 in hypervariable (HV) regions: HV2 (G185A: 0 vs 6, P = 0.02; G228A: 0 vs 5, P = 0.04; C295T: 0 vs 6; P = 0.04), HV3 (C462T: 0 vs 5, P = 0.04; T489C: 0 vs 5; P = 0.04), and HV1 (C16068T: 0 vs 6, P = 0.02; T16125C: 0 vs 6, P = 0.02). Remaining variants were in protein coding genes, mtND1 (1 vs 8, P = 0.02), mtND3 (A10397G: 0 vs 5, P = 0.04), mtND4 (A11250G: 1 vs 8, P = 0.02), mtND5 (G13707A: 0 vs 5, P = 0.04), and mtCYTB (T14797C: 0 vs 5, P = 0.04; C15451A: 1 vs 8, P = 0.02). Average total numbers of heteroplasmies (P = 0.83) and frequency of heteroplasmies (P = 0.05) were similar between the groups. Conclusions Our findings provide specific sites across the mitochondrial genome that may be related to maximal oxygen uptake trainability.
14 citations
01 Sep 2020
TL;DR: Individual differences in physical performance in the sedentary state and in response to exercise training have been observed in rodent and human studies as discussed by the authors, and the genomic variants underlying these genetic components are unknown.
Abstract: Individual differences in physical performance in the sedentary state and in response to exercise training have been observed in rodent and human studies. The genomic variants underlying these genetic components are unknown. Nonetheless, without a rich genetic endowment, world-class athletic performance is out of reach.
9 citations
Brigham and Women's Hospital1, Leiden University Medical Center2, Washington University in St. Louis3, University of Regensburg4, National Institutes of Health5, Harvard University6, University of Copenhagen7, University of Texas Health Science Center at Houston8, University of São Paulo9, University of Washington10, Erasmus University Rotterdam11, Boston University12, Tulane University13, Duke University14, Pennington Biomedical Research Center15, University of Mississippi Medical Center16, University of North Carolina at Chapel Hill17, University of Tartu18, Northwestern University19, University of Edinburgh20, University of Groningen21, Queen Mary University of London22, Wake Forest University23, Helmholtz Zentrum München24, Vanderbilt University Medical Center25, University of Adelaide26, University of Maryland, Baltimore27, Johns Hopkins University School of Medicine28, University of Texas Health Science Center at San Antonio29, University of Minnesota30, Kaiser Permanente31, University of Alabama at Birmingham32, Los Angeles Biomedical Research Institute33, Erasmus University Medical Center34, Jackson State University35, Columbia University36, UCLA Medical Center37, George Washington University38, University of Iowa39, University of Eastern Finland40, University of Mainz41, Ludwig Maximilian University of Munich42, Greifswald University Hospital43, Sir Charles Gairdner Hospital44, University of Iceland45, University of Tennessee Health Science Center46, Fred Hutchinson Cancer Research Center47, Case Western Reserve University48, University Medical Center Groningen49, Public Health Research Institute50, University of Southern California51
TL;DR: It is indicated that sleep and primary mechanisms regulating BP may interact to elevate BP level, suggesting novel insights into sleep-related BP regulation.
Abstract: Long and short sleep duration are associated with elevated blood pressure (BP), possibly through effects on molecular pathways that influence neuroendocrine and vascular systems. To gain new insights into the genetic basis of sleep-related BP variation, we performed genome-wide gene by short or long sleep duration interaction analyses on four BP traits (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure) across five ancestry groups using 1 degree of freedom (1df) interaction and 2df joint tests. Primary multi-ancestry analyses in 62,969 individuals in stage 1 identified 3 novel loci that were replicated in an additional 59,296 individuals in stage 2, including rs7955964 (FIGNL2/ANKRD33) showing significant 1df interactions with long sleep duration and rs73493041 (SNORA26/C9orf170) and rs10406644 (KCTD15/LSM14A) showing significant 1df interactions with short sleep duration. Secondary ancestry-specific two-stage analyses and combined stage 1 and 2 analyses additionally identified 23 novel loci that need external replication, including 3 and 5 loci showing significant 1df interactions with long and short sleep duration, respectively. Multiple genes mapped to our 26 novel loci have known functions in sleep-wake regulation, nervous and cardiometabolic systems. We also identified new gene by long sleep interactions near five known BP loci (≤1Mb) including NME7, FAM208A, MKLN1, CEP164, and RGL3/ELAVL3. This study indicates that sleep and primary mechanisms regulating BP may interact to elevate BP level, suggesting novel insights into sleep-related BP regulation.
5 citations
11 Aug 2020
TL;DR: In this paper, two kinds of genetic effects are considered: additive genetic effect, or the so-called heritability, and the second one is the genotype-environment interaction effect, which may lead to excess body weight and obesity.
Abstract: Reduced energy expenditure for a given energy intake level causes positive energy balance and eventually may lead to excess body weight and obesity. Two kinds of genetic effects are considered in this chapter. The first one is additive genetic effect, or the so-called heritability, and the second one is the genotype-environment interaction effect. Resting Metabolic Rate is a complex phenotype associated with the metabolic rates of all tissues and organs of the body measured in the basal state after an overnight fast. Although the mechanisms linking high fat consumption to increased body fat stores remain to be elucidated, inter-individual differences in substrate oxidation, particularly lipid oxidation, are likely to be involved. The thermic effect of food is the integrated increase of energy expenditure after food ingestion. Negative or positive energy balance sustained for a long period influences energy expenditure. The studies reviewed here suggest that inter-individual differences observed in various energy expenditure components are partly determined by the genotype.
3 citations
15 Sep 2020
TL;DR: The 9p21.3 locus was found to be responsible for 20% of myocardial infarctions (MIs) in several populations as discussed by the authors, and the risk of MIs appeared to act independen...
Abstract: Background: Sequence variation at chromosome 9p21.3 accounts for 20% of myocardial infarctions (MIs) in several populations. Whereas the risk conferred by the 9p21.3 locus appears to act independen...
2 citations
11 Aug 2020
TL;DR: A review of the literature about the influence of genes in energy intake and food preferences can be found in this article, where the authors reveal the presence of familial resemblance in energy consumption and food preference.
Abstract: This chapter reviews the literature about the influence of genes in energy intake and food preferences. The presence of familial aggregation in total energy intake and intake of macronutrients (carbohydrates, lipids, and proteins) and micronutrients is a well-documented phenomenon. Several twin studies have been undertaken to assess the role of heredity in energy intake and food preferences. Taste preferences represent a major determinant of food intake and food selection in humans and have already been linked with obesity and weight gain. Despite the recognition that eating behavior may play a role in the development of obesity in humans, very little is known about the role of genes in this behavior. The literature reviewed thus far indicates a rather moderate role of heredity in energy intake and food preferences. The results reviewed here reveal the presence of familial resemblance in energy intake and food preferences.
01 Jan 2020
TL;DR: In the SOS cohort, patients with the chromosome 9p21.3 rs1333049 risk allele together with high fasting insulin levels benefitted from bariatric surgery in terms of reduced incidence of MI.
Abstract: Background: Sequence variation at chromosome 9p21.3 accounts for 20% of myocardial infarctions (MIs) in several populations. Whereas the risk conferred by the 9p21.3 locus appears to act independen...