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Showing papers by "Claude Bouchard published in 2019"


Journal ArticleDOI
TL;DR: Large-scale aptamer-based scanning of plasma proteins coupled with machine learning demonstrates proof-of-concept and feasibility of an individualized health check using a single blood sample and is anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check.
Abstract: Proteins are effector molecules that mediate the functions of genes1,2 and modulate comorbidities3–10, behaviors and drug treatments11. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning12–16 coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check. Large-scale aptamer-based scanning of plasma proteins coupled with machine learning demonstrates proof-of-concept and feasibility of an individualized health check using a single blood sample.

230 citations


Journal ArticleDOI
TL;DR: This consensus statement on exercise response variability developed following a symposium dedicated to this topic discusses advantages and disadvantages of multiple methods of categorising exercise response levels and outlines approaches that may identify determinants and modifiers of CRF exercise response.
Abstract: There is evidence from human twin and family studies as well as mouse and rat selection experiments that there are considerable interindividual differences in the response of cardiorespiratory fitness (CRF) and other cardiometabolic traits to a given exercise programme dose. We developed this consensus statement on exercise response variability following a symposium dedicated to this topic. There is strong evidence from both animal and human studies that exercise training doses lead to variable responses. A genetic component contributes to exercise training response variability.In this consensus statement, we (1) briefly review the literature on exercise response variability and the various sources of variations in CRF response to an exercise programme, (2) introduce the key research designs and corresponding statistical models with an emphasis on randomised controlled designs with or without multiple pretests and post-tests, crossover designs and repeated measures designs, (3) discuss advantages and disadvantages of multiple methods of categorising exercise response levels-a topic that is of particular interest for personalised exercise medicine and (4) outline approaches that may identify determinants and modifiers of CRF exercise response. We also summarise gaps in knowledge and recommend future research to better understand exercise response variability.

143 citations


Journal ArticleDOI
Paul S. de Vries1, Michael R. Brown1, Amy R. Bentley2, Yun J. Sung3  +290 moreInstitutions (88)
TL;DR: In this paper, gene-alcohol interactions were incorporated into a multiancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density cholesterol, and triglycerides.
Abstract: A person's lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied. We therefore incorporated gene-alcohol interactions into a multiancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. We included 45 studies in stage 1 (genome-wide discovery) and 66 studies in stage 2 (focused follow-up), for a total of 394,584 individuals from 5 ancestry groups. Analyses covered the period July 2014-November 2017. Genetic main effects and interaction effects were jointly assessed by means of a 2-degrees-of-freedom (df) test, and a 1-df test was used to assess the interaction effects alone. Variants at 495 loci were at least suggestively associated (P < 1 × 10-6) with lipid levels in stage 1 and were evaluated in stage 2, followed by combined analyses of stage 1 and stage 2. In the combined analysis of stages 1 and 2, a total of 147 independent loci were associated with lipid levels at P < 5 × 10-8 using 2-df tests, of which 18 were novel. No genome-wide-significant associations were found testing the interaction effect alone. The novel loci included several genes (proprotein convertase subtilisin/kexin type 5 (PCSK5), vascular endothelial growth factor B (VEGFB), and apolipoprotein B mRNA editing enzyme, catalytic polypeptide 1 (APOBEC1) complementation factor (A1CF)) that have a putative role in lipid metabolism on the basis of existing evidence from cellular and experimental models.

79 citations


Journal ArticleDOI
David W. Clark1, Yukinori Okada2, Kristjan H. S. Moore3, Dan Mason  +493 moreInstitutions (142)
TL;DR: In this paper, the authors used genomic inbreeding coefficients (FROH) for >1.4 million individuals and found that FROH is significantly associated with apparently deleterious changes in 32 out of 100 traits analysed.
Abstract: In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of all environmental confounding.

74 citations


Journal ArticleDOI
Raymond Noordam1, Maxime M Bos2, Heming Wang3, Thomas W. Winkler4  +157 moreInstitutions (58)
TL;DR: The authors perform genome-wide gene-by-sleep interaction analysis and find 49 previously unreported lipid loci when considering short or long total sleep time, contributing to the understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.
Abstract: Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.

58 citations


Journal ArticleDOI
Jordi Merino1, Jordi Merino2, Hassan S. Dashti2, Hassan S. Dashti1, Sherly X. Li3, Chloé Sarnowski4, Anne E. Justice5, Anne E. Justice6, Misa Graff6, Constantina Papoutsakis7, Caren E. Smith8, George Dedoussis9, Rozenn N. Lemaitre10, Mary K. Wojczynski11, Satu Männistö12, Julius S. Ngwa4, Julius S. Ngwa13, Minjung Kho14, Tarunveer S. Ahluwalia15, Natalia Pervjakova, Denise K. Houston16, Claude Bouchard17, Tao Huang18, Marju Orho-Melander19, Alexis C. Frazier-Wood20, Dennis O. Mook-Kanamori21, Louis Pérusse22, Craig E. Pennell23, Paul S. de Vries24, Trudy Voortman25, Olivia Li26, Stavroula Kanoni27, Lynda M. Rose2, Terho Lehtimäki28, Jing Hua Zhao3, Mary F. Feitosa11, Jian'an Luan3, Nicola M. McKeown8, Jennifer A. Smith14, Torben Hansen15, Niina Eklund12, Mike A. Nalls29, Tuomo Rankinen17, Jinyan Huang, Dena G. Hernandez29, Christina-Alexandra Schulz19, Ani Manichaikul30, Ruifang Li-Gao21, Marie-Claude Vohl22, Carol A. Wang23, Frank J. A. van Rooij25, Jean Shin26, Ioanna P. Kalafati9, Felix R. Day3, Paul M. Ridker2, Mika Kähönen28, David S. Siscovick31, Claudia Langenberg3, Wei Zhao14, Arne Astrup15, Paul Knekt12, Melissa E. Garcia29, Dabeeru C. Rao11, Qibin Qi32, Luigi Ferrucci29, Ulrika Ericson19, John Blangero33, Albert Hofman2, Albert Hofman25, Zdenka Pausova26, Vera Mikkilä, Nicholas J. Wareham3, Sharon L.R. Kardia14, Oluf Pedersen15, Antti Jula12, Joanne E. Curran33, M. Carola Zillikens25, Jorma Viikari34, Nita G. Forouhi3, Jose M. Ordovas35, Jose M. Ordovas8, Jose M. Ordovas36, John C. Lieske37, Harri Rissanen12, André G. Uitterlinden25, Olli T. Raitakari34, Jessica C. Kiefte-de Jong25, Jessica C. Kiefte-de Jong21, Josée Dupuis4, Jerome I. Rotter38, Kari E. North6, Robert A. Scott3, Michael A. Province11, Markus Perola12, L. Adrienne Cupples4, L. Adrienne Cupples29, Stephen Turner37, Thorkild I. A. Sørensen15, Veikko Salomaa12, Yongmei Liu16, Yun J. Sung11, Lu Qi39, Stefania Bandinelli, Stephen S. Rich30, Renée de Mutsert21, Angelo Tremblay22, Wendy H. Oddy40, Wendy H. Oddy41, Oscar H. Franco25, Tomáš Paus42, Tomáš Paus26, Jose C. Florez1, Jose C. Florez2, Panos Deloukas27, Panos Deloukas43, Leo-Pekka Lyytikäinen28, Daniel I. Chasman2, Audrey Y. Chu2, Toshiko Tanaka29 
TL;DR: 12 suggestively significant loci are identified associated with intake of any macronutrient in 91,114 European ancestry participants, corroborating earlier FGF21 and FTO findings and providing new insight into biological functions related to macronsutrient intake.
Abstract: Macronutrient intake, the proportion of calories consumed from carbohydrate, fat, and protein, is an important risk factor for metabolic diseases with significant familial aggregation. Previous studies have identified two genetic loci for macronutrient intake, but incomplete coverage of genetic variation and modest sample sizes have hindered the discovery of additional loci. Here, we expanded the genetic landscape of macronutrient intake, identifying 12 suggestively significant loci (P < 1 × 10−6) associated with intake of any macronutrient in 91,114 European ancestry participants. Four loci replicated and reached genome-wide significance in a combined meta-analysis including 123,659 European descent participants, unraveling two novel loci; a common variant in RARB locus for carbohydrate intake and a rare variant in DRAM1 locus for protein intake, and corroborating earlier FGF21 and FTO findings. In additional analysis of 144,770 participants from the UK Biobank, all identified associations from the two-stage analysis were confirmed except for DRAM1. Identified loci might have implications in brain and adipose tissue biology and have clinical impact in obesity-related phenotypes. Our findings provide new insight into biological functions related to macronutrient intake.

46 citations


Journal ArticleDOI
TL;DR: Examination of 20 previously sedentary adults from the HERITAGE Family Study who completed 20 weeks of endurance exercise training provided further evidence of the effects of regular exercise on the circulating miRNA profile.
Abstract: The purpose of the present study was to examine the effects of regular exercise on the abundance of targeted circulating microRNAs (miRNAs). The present analysis examined 20 previously sedentary adults from the HERITAGE Family Study who completed 20 weeks of endurance exercise training. The expression of 53 miRNAs related to cardiovascular disease were measured in serum collected at baseline and post-training by performing RT-qPCR on the Human Cardiovascular Disease miRNA array (Qiagen, Germany). The effect of regular exercise on circulating miRNAs was assessed using paired t-tests of baseline and post-training expression levels. A false discovery rate threshold of 5% was used to determine significance. Regular exercise resulted in significantly decreased mean serum expression of nine miRNAs (miR-486-5p, let-7b-5p, miR-29c-3p, let-7e-5p, miR-93-5p, miR-7-5p, miR-25-3p, miR-92a-3p, and miR-29b-3p; fold change range: 0.64–83, p = 0.0002–0.01) and increased mean expression of five miRNAs (miR-142-3p, miR-221-3p, miR-126-3p, miR-146a-5p, and miR-27b-3p; fold change range: 1.41–3.60, p = 0.001–0.006). Enrichment analysis found that these 14 miRNAs target genes related to over 345 different biological pathways. These results provide further evidence of the effects of regular exercise on the circulating miRNA profile.

41 citations


Journal ArticleDOI
TL;DR: Low hepatic lipase activity may link high LDL triglycerides to increased cardiovascular risk, and two-sample Mendelian randomization analysis (HERITAGE and CARDIoGRAMplusC4D) using rs1800588 and rs10468017 as instrumental variable suggested that low hepatic lipid activity may cause increased cardiovascularrisk.

41 citations


Journal ArticleDOI
TL;DR: Dimethylguanidino valeric acid is an early marker of cardiometabolic dysfunction that is associated with attenuated improvements in lipid traits and insulin sensitivity after exercise training and may identify individuals who require additional therapies beyond guideline-directed exercise to improve their metabolic health.
Abstract: Importance Metabolic responses to exercise training are variable. Metabolite profiling may aid in the clinical assessment of an individual's responsiveness to exercise interventions. Objective To investigate the association between a novel circulating biomarker of hepatic fat, dimethylguanidino valeric acid (DMGV), and metabolic health traits before and after 20 weeks of endurance exercise training. Design, Setting, and Participants This study involved cross-sectional and longitudinal analyses of the Health, Risk Factors, Exercise Training, and Genetics (HERITAGE) Family Study, a 20-week, single-arm endurance exercise clinical trial performed in multiple centers between 1993 and 1997. White participants with sedentary lifestyles who were free of cardiometabolic disease were included. Metabolomic tests were performed using a liquid chromatography, tandem mass spectrometry method on plasma samples collected before and after exercise training in the HERITAGE study. Metabolomics and data analysis were performed from August 2017 to May 2018. Exposures Plasma DMGV levels. Main Outcome and Measures The association between DMGV levels and measures of body composition, plasma lipids, insulin, and glucose homeostasis before and after exercise training. Results Among the 439 participants included in analyses from HERITAGE, the mean (SD) age was 36 (15) years, 228 (51.9%) were female, and the median (interquartile range) body mass index was 25 (22-28). Baseline levels of DMGV were positively associated with body fat percentage, abdominal visceral fat, very low-density lipoprotein cholesterol, and triglycerides, and inversely associated with insulin sensitivity, low-density lipoprotein cholesterol, high-density lipoprotein size, and high-density lipoprotein cholesterol (range of β coefficients, 0.17-0.46 [SEs, 0.026-0.050]; all P < .001, after adjusting for age and sex). After adjusting for age, sex, and baseline traits, baseline DMGV levels were positively associated with changes in small high-density lipoprotein particles (β, 0.14 [95% CI, 0.05-0.23]) and inversely associated with changes in medium and total high-density lipoprotein particles (β, -0.15 [95% CI, -0.24 to -0.05] and -0.19 [95% CI, -0.28 to -0.10], respectively), apolipoprotein A1 (β, -0.14 [95% CI, -0.23 to -0.05]), and insulin sensitivity (β, -0.13; P = 3.0 × 10-3) after exercise training. Conclusions and Relevance Dimethylguanidino valeric acid is an early marker of cardiometabolic dysfunction that is associated with attenuated improvements in lipid traits and insulin sensitivity after exercise training. Levels of DMGV may identify individuals who require additional therapies beyond guideline-directed exercise to improve their metabolic health.

36 citations


Journal ArticleDOI
Yun Ju Sung1, Lisa de las Fuentes1, Thomas W. Winkler2, Daniel I. Chasman3  +314 moreInstitutions (101)
TL;DR: A genome-wide gene-smoking interaction study of mean arterial pressure (MAP) and pulse pressure (PP) in 129 913 individuals in stage 1 and follow-up analysis in 480 178 additional individuals in stages 2 identified 136 loci significantly associated with MAP and/or PP and identified nine new signals near known loci.
Abstract: Elevated blood pressure (BP), a leading cause of global morbidity and mortality, is influenced by both genetic and lifestyle factors. Cigarette smoking is one such lifestyle factor. Across five ancestries, we performed a genome-wide gene-smoking interaction study of mean arterial pressure (MAP) and pulse pressure (PP) in 129 913 individuals in stage 1 and follow-up analysis in 480 178 additional individuals in stage 2. We report here 136 loci significantly associated with MAP and/or PP. Of these, 61 were previously published through main-effect analysis of BP traits, 37 were recently reported by us for systolic BP and/or diastolic BP through gene-smoking interaction analysis and 38 were newly identified (P < 5 × 10-8, false discovery rate < 0.05). We also identified nine new signals near known loci. Of the 136 loci, 8 showed significant interaction with smoking status. They include CSMD1 previously reported for insulin resistance and BP in the spontaneously hypertensive rats. Many of the 38 new loci show biologic plausibility for a role in BP regulation. SLC26A7 encodes a chloride/bicarbonate exchanger expressed in the renal outer medullary collecting duct. AVPR1A is widely expressed, including in vascular smooth muscle cells, kidney, myocardium and brain. FHAD1 is a long non-coding RNA overexpressed in heart failure. TMEM51 was associated with contractile function in cardiomyocytes. CASP9 plays a central role in cardiomyocyte apoptosis. Identified only in African ancestry were 30 novel loci. Our findings highlight the value of multi-ancestry investigations, particularly in studies of interaction with lifestyle factors, where genomic and lifestyle differences may contribute to novel findings.

30 citations


Jordi Merino1, Jordi Merino2, Hassan S. Dashti1, Hassan S. Dashti2, Sherly X. Li3, Chloé Sarnowski4, Anne E. Justice5, Anne E. Justice6, Misa Graff5, Constantina Papoutsakis7, Caren E. Smith8, George Dedoussis9, Rozenn N. Lemaitre10, Mary K. Wojczynski11, Satu Männistö12, Julius S. Ngwa13, Julius S. Ngwa4, Minjung Kho14, Tarunveer S. Ahluwalia15, Natalia Pervjakova, Denise K. Houston16, Claude Bouchard17, Tao Huang18, Marju Orho-Melander19, Alexis C. Frazier-Wood20, Dennis O. Mook-Kanamori21, Louis Pérusse22, Craig E. Pennell23, Paul S. de Vries24, Trudy Voortman25, Olivia Li26, Stavroula Kanoni27, Lynda M. Rose2, Terho Lehtimäki28, Jing Hua Zhao3, Mary F. Feitosa11, Jian'an Luan3, Nicola M. McKeown8, Jennifer A. Smith14, Torben Hansen15, Niina Eklund12, Mike A. Nalls29, Tuomo Rankinen17, Jinyan Huang, Dena G. Hernandez29, Christina-Alexandra Schulz19, Ani Manichaikul30, Ruifang Li-Gao21, Marie-Claude Vohl22, Carol A. Wang23, Frank J. A. van Rooij25, Jean Shin26, Ioanna P. Kalafati9, Felix R. Day3, Paul M. Ridker2, Mika Kähönen28, David S. Siscovick31, Claudia Langenberg3, Wei Zhao14, Arne Astrup15, Paul Knekt12, Melissa E. Garcia29, Dabeeru C. Rao11, Qibin Qi32, Luigi Ferrucci29, Ulrika Ericson19, John Blangero33, Albert Hofman2, Albert Hofman25, Zdenka Pausova26, Vera Mikkilä, Nicholas J. Wareham3, Sharon L.R. Kardia14, Oluf Pedersen15, Antti Jula12, Joanne E. Curran33, M. Carola Zillikens25, Jorma Viikari34, Nita G. Forouhi3, Jose M. Ordovas35, Jose M. Ordovas36, Jose M. Ordovas8, John C. Lieske37, Harri Rissanen12, André G. Uitterlinden25, Olli T. Raitakari34, Jessica C. Kiefte-de Jong21, Jessica C. Kiefte-de Jong25, Josée Dupuis4, Jerome I. Rotter38, Kari E. North5, Robert A. Scott3, Michael A. Province11, Markus Perola12, L. Adrienne Cupples4, L. Adrienne Cupples29, Stephen Turner37, Thorkild I. A. Sørensen15, Veikko Salomaa12, Yongmei Liu16, Yun J. Sung11, Lu Qi39, Stefania Bandinelli, Stephen S. Rich30, Renée de Mutsert21, Angelo Tremblay22, Wendy H. Oddy40, Wendy H. Oddy41, Oscar H. Franco25, Tomáš Paus42, Tomáš Paus26, Jose C. Florez1, Jose C. Florez2, Panos Deloukas27, Panos Deloukas43, Leo-Pekka Lyytikäinen28, Daniel I. Chasman2, Audrey Y. Chu2, Toshiko Tanaka29 
01 Jan 2019
TL;DR: In this paper, the authors expanded the genetic landscape of macronutrient intake, identifying 12 suggestively significant loci (P < 1/1/×/10−6) associated with intake of any macro-nutrient in 91,114 European ancestry participants.
Abstract: Macronutrient intake, the proportion of calories consumed from carbohydrate, fat, and protein, is an important risk factor for metabolic diseases with significant familial aggregation. Previous studies have identified two genetic loci for macronutrient intake, but incomplete coverage of genetic variation and modest sample sizes have hindered the discovery of additional loci. Here, we expanded the genetic landscape of macronutrient intake, identifying 12 suggestively significant loci (P < 1 × 10−6) associated with intake of any macronutrient in 91,114 European ancestry participants. Four loci replicated and reached genome-wide significance in a combined meta-analysis including 123,659 European descent participants, unraveling two novel loci; a common variant in RARB locus for carbohydrate intake and a rare variant in DRAM1 locus for protein intake, and corroborating earlier FGF21 and FTO findings. In additional analysis of 144,770 participants from the UK Biobank, all identified associations from the two-stage analysis were confirmed except for DRAM1. Identified loci might have implications in brain and adipose tissue biology and have clinical impact in obesity-related phenotypes. Our findings provide new insight into biological functions related to macronutrient intake.

01 Jan 2019
TL;DR: In this article, the authors used genomic inbreeding coefficients (F ROH) for >1.4 million individuals and found that F ROH is significantly associated with apparently deleterious changes in 32 out of 100 traits analysed.
Abstract: In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (F ROH) for >1.4 million individuals, we show that F ROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: F ROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44–66%] in the odds of having children. Finally, the effects of F ROH are confirmed within full-sibling pairs, where the variation in F ROH is independent of all environmental confounding. Inbreeding depression has been observed in many different species, but in humans a systematic analysis has been difficult so far. Here, analysing more than 1.3 million individuals, the authors show that a genomic inbreeding coefficient (FROH) is associated with disadvantageous outcomes in 32 out of 100 traits tested.

Journal ArticleDOI
11 Feb 2019-PLOS ONE
TL;DR: It is suggested that MPO gains in both programs are potentially associated with metabolites indicative of baseline amino acid and translation processes with additional evidence for carbohydrate metabolism in ET.
Abstract: Background Recent studies have begun to identify the molecular determinants of inter-individual variability of cardiorespiratory fitness (CRF) in response to exercise training programs. However, we still have an incomplete picture of the molecular mechanisms underlying trainability in response to exercise training. Objective We investigated baseline serum and skeletal muscle metabolomics profile and its associations with maximal power output (MPO) gains in response to 8-week of continuous endurance training (ET) and high-intensity interval training (HIIT) programs matched for total units of exercise performed (the TIMES study). Methods Eighty healthy sedentary young adult males were randomized to one of three groups and 70 were defined as completers (> 90% of sessions): ET (n = 30), HIIT (n = 30) and control (CO, n = 10). For the CO, participants were asked to not exercise for 8 weeks. Serum and skeletal muscle samples were analyzed by 1H-NMR spectroscopy. The targeted screens yielded 43 serum and 70 muscle reproducible metabolites (intraclass > 0.75; coefficient of variation < 25%). Associations of baseline metabolites with MPO trainability were explored within each training program via three analytical strategies: (1) correlations with gains in MPO; (2) differences between high and low responders to ET and HIIT; and (3) metabolites contributions to the most significant pathways related to gains in MPO. The significance level was set at P < 0.01 or false discovery rate of 0.1. Results The exercise programs generated similar gains in MPO (ET = 21.4 ± 8.0%; HIIT = 24.3 ± 8.5%). MPO associated baseline metabolites supported by all three levels of evidence were: serum glycerol, muscle alanine, proline, threonine, creatinine, AMP and pyruvate for ET, and serum lysine, phenylalanine, creatine, and muscle glycolate for HIIT. The most common pathways suggested by the metabolite profiles were aminoacyl-tRNA biosynthesis, and carbohydrate and amino acid metabolism. Conclusion We suggest that MPO gains in both programs are potentially associated with metabolites indicative of baseline amino acid and translation processes with additional evidence for carbohydrate metabolism in ET.

Journal ArticleDOI
TL;DR: Higher protein intake was associated with a lower risk of pre-diabetes and diabetes, and associations were substantially attenuated after adjustments for BMI and waist circumference, which demonstrates a crucial role for adiposity and may account for previous conflicting findings.

01 Jan 2019
TL;DR: A person's lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied, so gene-alcohol interactions are incorporated into a multiancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density cholesterol, and triglycerides.
Abstract: A person's lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied. We therefore incorporated gene-alcohol interactions into a multiancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. We included 45 studies in stage 1 (genome-wide discovery) and 66 studies in stage 2 (focused follow-up), for a total of 394,584 individuals from 5 ancestry groups. Analyses covered the period July 2014-November 2017. Genetic main effects and interaction effects were jointly assessed by means of a 2-degrees-of-freedom (df) test, and a 1-df test was used to assess the interaction effects alone. Variants at 495 loci were at least suggestively associated (P < 1 × 10-6) with lipid levels in stage 1 and were evaluated in stage 2, followed by combined analyses of stage 1 and stage 2. In the combined analysis of stages 1 and 2, a total of 147 independent loci were associated with lipid levels at P < 5 × 10-8 using 2-df tests, of which 18 were novel. No genome-wide-significant associations were found testing the interaction effect alone. The novel loci included several genes (proprotein convertase subtilisin/kexin type 5 (PCSK5), vascular endothelial growth factor B (VEGFB), and apolipoprotein B mRNA editing enzyme, catalytic polypeptide 1 (APOBEC1) complementation factor (A1CF)) that have a putative role in lipid metabolism on the basis of existing evidence from cellular and experimental models.

01 Jan 2019
TL;DR: This paper performed a genome-wide gene-smoking interaction study of mean arterial pressure (MAP) and pulse pressure (PP) in 129, 913 individuals in stage 1 and follow-up analysis in 480,178 additional individuals in stages 2.
Abstract: Elevated blood pressure (BP), a leading cause of global morbidity and mortality, is influenced by both genetic and lifestyle factors. Cigarette smoking is one such lifestyle factor. Across five ancestries, we performed a genome-wide gene-smoking interaction study of mean arterial pressure (MAP) and pulse pressure (PP) in 129 913 individuals in stage 1 and follow-up analysis in 480 178 additional individuals in stage 2. We report here 136 loci significantly associated with MAP and/or PP. Of these, 61 were previously published through main-effect analysis of BP traits, 37 were recently reported by us for systolic BP and/or diastolic BP through gene-smoking interaction analysis and 38 were newly identified (P < 5 × 10-8, false discovery rate < 0.05). We also identified nine new signals near known loci. Of the 136 loci, 8 showed significant interaction with smoking status. They include CSMD1 previously reported for insulin resistance and BP in the spontaneously hypertensive rats. Many of the 38 new loci show biologic plausibility for a role in BP regulation. SLC26A7 encodes a chloride/bicarbonate exchanger expressed in the renal outer medullary collecting duct. AVPR1A is widely expressed, including in vascular smooth muscle cells, kidney, myocardium and brain. FHAD1 is a long non-coding RNA overexpressed in heart failure. TMEM51 was associated with contractile function in cardiomyocytes. CASP9 plays a central role in cardiomyocyte apoptosis. Identified only in African ancestry were 30 novel loci. Our findings highlight the value of multi-ancestry investigations, particularly in studies of interaction with lifestyle factors, where genomic and lifestyle differences may contribute to novel findings.

Journal ArticleDOI
TL;DR: This commentary focuses on endurance exercise performance and cardiorespiratory fitness, and three main topics are addressed to provide a brief summary of the current knowledge base.
Abstract: Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA IO N S Genetic differences contribute to human variability in exercise-related traits. In this commentary, the focus is on endurance exercise performance and cardiorespiratory fitness (CRF). The evidence pertaining to other relevant traits (e.g., muscle strength) will not be considered. Three main topics are addressed to provide a brief summary of our current knowledge base.

Journal ArticleDOI
TL;DR: An initial effort to define the underlying molecular substrates of intrinsic CRF warrants further studies based on appropriate cohorts and study designs, complemented by functional investigations.
Abstract: Intrinsic cardiorespiratory fitness (CRF) is measured in the sedentary state and is highly variable among sedentary adults. The physiology of variability in intrinsic cardiorespiratory fitness has ...

Posted ContentDOI
Raymond Noordam1, Maxime M. Bos1, Heming Wang2, Thomas W. Winkler3  +157 moreInstitutions (54)
25 Feb 2019-bioRxiv
TL;DR: A multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits and new gene-sleep interactions for known lipid loci such as LPL and PCSK9 contribute to the understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.
Abstract: Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To provide new insights in the biology of sleep-associated adverse lipid profile, we conducted multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identified 49 novel lipid loci, and 10 additional novel lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identified new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The novel gene-sleep interactions had a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explained 4.25% of the variance in triglyceride concentration. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.

Journal ArticleDOI
TL;DR: The results revealed that genetic background in the form of a 231 BMI-associated PRS has a significant impact on obesity, but a limited potential to accurately stratify it.
Abstract: Background and aims: Obesity is a major health problem worldwide. Given the heterogeneous obesity phenotype, an optimal obesity stratification would improve clinical management. Since obesity has a strong genetic component, we aimed to develop a polygenic risk score (PRS) to stratify obesity according to the genetic background of the individuals. Methods: A total of 231 single nucleotide polymorphisms (SNP) significantly associated to body mass index (BMI) from 21 genome-wide association studies were genotyped or imputed in 881 subjects from the Quebec Family Study (QFS). The population was randomly split into discovery (80%; n = 704) and validation (20%; n = 177) samples with similar obesity (BMI ≥ 30) prevalence (27.8% and 28.2%, respectively). Family-based associations with obesity were tested for every SNP in the discovery sample and a weighed and continuous PRS231 was constructed. Generalized linear mixed effects models were used to test the association of PRS231 with obesity in the QFS discovery sample and validated in the QFS replication sample. Furthermore, the Fatty Acid Sensor (FAS) Study (n = 141; 27.7% obesity prevalence) was used as an independent sample to replicate the results. Results: The linear trend test demonstrated a significant association of PRS231 with obesity in the QFS discovery sample (ORtrend = 1.19 [95% CI, 1.14-1.24]; P = 2.0x10-16). We also found that the obesity prevalence was significantly greater in the higher PRS231 quintiles compared to the lowest quintile. Significant and consistent results were obtained in the QFS validation sample for both the linear trend test (ORtrend = 1.16 [95% CI, 1.07-1.26]; P = 6.7x10-4), and obesity prevalence across quintiles. These results were partially replicated in the FAS sample (ORtrend = 1.12 [95% CI, 1.02-1.24]; P = 2.2x10-2). PRS231 explained 7.5%, 3.2%, and 1.2% of BMI variance in QFS discovery, QFS validation, and FAS samples, respectively. Conclusions: These results revealed that genetic background in the form of a 231 BMI-associated PRS has a significant impact on obesity, but a limited potential to accurately stratify it. Further studies are encouraged on larger populations.