Showing papers by "Claude Bouchard published in 2022"

A saturated map of common genetic variants associated with human height

Abstract: Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention

Abstract: Although physical activity and sedentary behavior are moderately heritable, little is known about the mechanisms that influence these traits. Combining data for up to 703,901 individuals from 51 studies in a multi-ancestry meta-analysis of genome-wide association studies yields 99 loci that associate with self-reported moderate-to-vigorous intensity physical activity during leisure time (MVPA), leisure screen time (LST) and/or sedentary behavior at work. Loci associated with LST are enriched for genes whose expression in skeletal muscle is altered by resistance training. A missense variant in ACTN3 makes the alpha-actinin-3 filaments more flexible, resulting in lower maximal force in isolated type IIA muscle fibers, and possibly protection from exercise-induced muscle damage. Finally, Mendelian randomization analyses show that beneficial effects of lower LST and higher MVPA on several risk factors and diseases are mediated or confounded by body mass index (BMI). Our results provide insights into physical activity mechanisms and its role in disease prevention.

Proteomic profiling platforms head to head: Leveraging genetics and clinical traits to compare aptamer- and antibody-based methods

Abstract: High-throughput proteomic profiling using antibody or aptamer-based affinity reagents is used increasingly in human studies. However, direct analyses to address the relative strengths and weaknesses of these platforms are lacking. We assessed findings from the SomaScan1.3K (N = 1301 reagents), the SomaScan5K platform (N = 4979 reagents), and the Olink Explore (N = 1472 reagents) profiling techniques in 568 adults from the Jackson Heart Study and 219 participants in the HERITAGE Family Study across four performance domains: precision, accuracy, analytic breadth, and phenotypic associations leveraging detailed clinical phenotyping and genetic data. Across these studies, we show evidence supporting more reliable protein target specificity and a higher number of phenotypic associations for the Olink platform, while the Soma platforms benefit from greater measurement precision and analytic breadth across the proteome.

The HERITAGE Family Study: A Review of the Effects of Exercise Training on Cardiometabolic Health, with Insights into Molecular Transducers

Abstract: ABSTRACT The aim of the HERITAGE Family Study was to investigate individual differences in response to a standardized endurance exercise program, the role of familial aggregation, and the genetics of response levels of cardiorespiratory fitness and cardiovascular disease and diabetes risk factors. Here we summarize the findings and their potential implications for cardiometabolic health and cardiorespiratory fitness. It begins with overviews of background and planning, recruitment, testing and exercise program protocol, quality control measures, and other relevant organizational issues. A summary of findings is then provided on cardiorespiratory fitness, exercise hemodynamics, insulin and glucose metabolism, lipid and lipoprotein profiles, adiposity and abdominal visceral fat, blood levels of steroids and other hormones, markers of oxidative stress, skeletal muscle morphology and metabolic indicators, and resting metabolic rate. These summaries document the extent of the individual differences in response to a standardized and fully monitored endurance exercise program and document the importance of familial aggregation and heritability level for exercise response traits. Findings from genomic markers, muscle gene expression studies, and proteomic and metabolomics explorations are reviewed, along with lessons learned from a bioinformatics-driven analysis pipeline. The new opportunities being pursued in integrative -omics and physiology have extended considerably the expected life of HERITAGE and are being discussed in relation to the original conceptual model of the study.

Whole Genome Association Study of the Plasma Metabolome Identifies Metabolites Linked to Cardiometabolic Disease in Black Individuals

Abstract: Integrating genetic information with metabolomics has provided new insights into genes affecting human metabolism. However, gene-metabolite integration has been primarily studied in individuals of European Ancestry, limiting the opportunity to leverage genomic diversity for discovery. In addition, these analyses have principally involved known metabolites, with the majority of the profiled peaks left unannotated. Here, we perform a whole genome association study of 2,291 metabolite peaks (known and unknown features) in 2,466 Black individuals from the Jackson Heart Study. We identify 519 locus-metabolite associations for 427 metabolite peaks and validate our findings in two multi-ethnic cohorts. A significant proportion of these associations are in ancestry specific alleles including findings in APOE, TTR and CD36. We leverage tandem mass spectrometry to annotate unknown metabolites, providing new insight into hereditary diseases including transthyretin amyloidosis and sickle cell disease. Our integrative omics approach leverages genomic diversity to provide novel insights into diverse cardiometabolic diseases.

Effect of individual characteristics and aerobic training on the %HRR-%V˙O2R relationship.

Abstract: This study aimed to assess if, during incremental exercise, considering individual characteristics can make the relationship between the percentages of heart rate (HRR) and oxygen uptake (VO2R) reserve either 1:1 or more accurate. Cycle ergometer data of the maximal incremental exercise tests performed by 450 healthy and sedentary participants (17-66 years) of the HERITAGE Family Study, grouped for sex, ethnicity, age, body fat, resting HR, and VO2max, were used to calculate the individual linear regressions between %HRR and %VO2R. The mean slope and intercept of the individual linear regressions of each subgroup were compared with 1 and 0 (identity line), respectively, using Hotelling tests followed by post-hoc one-sample t-tests. Two multiple linear regressions were also performed, using either the slopes or intercepts of the individual linear regressions as dependent variables and sex, age, resting HR, and VO2max as independent variables. The mean %HRR-%VO2R relationships of all subgroups differed from the identity line. Moreover, individual linear regression intercepts (8.9±16.0) and slopes (0.971±0.190) changed (p<0.001) after 20 weeks of aerobic training (13.1±11.1 and 0.891±0.122). The multiple linear regressions could explain only 3.8% and 1.3% of the variance in the intercepts and slopes, whose variability remained high (standard error of estimate of 15.8 and 0.189). In conclusion, the %HRR-%VO2R relationship differs from the identity line regardless of individual characteristics and their difference increased after aerobic training. Moreover, due to the high interindividual variability, using a single equation for the whole population seems not suitable for representing the %HRR-%VO2R relationship of a given subject, even when several individual characteristics are considered.

Exercise training slows down proteomic age acceleration in middle-aged to older adults- heritage family study

Abstract: PURPOSE: Identification of a robust molecular signature is a major goal in aging research. Recent studies have identified proteins that resemble a proteomic clock and can predict accelerated biological aging. Exercise is well known to mitigate physiological and molecular changes. However, it is unknown whether regular exercise affects the predicted protein age. Our goal was to understand the effects of exercise training on the proteomic aging clock. METHODS: We measured over 5,000 circulating proteins using an aptamer-affinity based platform (SomaScan) before and after 20 weeks of endurance exercise training in 647 Black (n = 230) and White (n = 417) adults from the HERITAGE Family Study. Proteomic age score was calculated by summing the weighted expression values across 360 proteins validated in previous proteomic age score studies. Delta age (or proteomic age acceleration) was quantified as the difference between predicted and chronological age. Change in delta age was calculated by subtracting baseline delta age from post-training delta age. RESULTS: The proteomic age score was very strongly correlated with chronological age (r = 0.94, p < 0.0001). Proteomic age acceleration was associated with ethnicity, generation (parent vs offspring), and their interaction, but not sex. Specifically, baseline delta age (mean (SD)) was significantly lower in parents (5.2 (4.1) yrs) compared to offspring (10.6 (4.2) yrs) and in Blacks (8.0 (4.7) yrs) compared to Whites (9.2 (5.0) yrs). Exercise training resulted in a decrease in delta age in parents only (i.e., training attenuated proteomic age acceleration), with the decrease larger in White (-13.7 (8.0) yrs) compared to Black (-7.4 (9.0) yrs) parents. Conversely, offspring of both ethnic groups showed mean increases (+6.1 (4.1) yrs) in delta age with training (i.e., proteomic age acceleration increased). CONCLUSIONS: These results indicate that an established proteomic signature of age is sensitive to exercise training, but the magnitude of response differs by subgroups of age and ethnicity thereby limiting its potential clinical utility. Further studies are needed to examine whether reduced proteomic age acceleration with exercise training is associated with concomitant improvements in cardiometabolic traits related to healthy aging.

Abstract 13514: Associations Between Plasma Metabolites With Body Composition Traits and Their Responses to Exercise Training

Abstract: INTRODUCTION: Although exercise training can improve body composition, the molecular biomarkers and mechanisms related to these changes have not been fully elucidated. HYPOTHESIS: We hypothesized that distinct metabolite signatures exist for both baseline levels and exercise responsiveness of body composition traits, with substantial overlap across traits. METHODS: Measurements were taken before and after 20 weeks of endurance training in self-identified Black and White adults of the HERITAGE Family Study (n=652). 300 targeted plasma metabolites were measured using LC-MS. Underwater weighing, CT scans, and anthropometric measurements were used to derive the 11 body composition traits included in this study: BMI, body surface area, fat mass, fat free mass, %fat, waist circumference, waist-to-hip ratio, body weight, and abdominal visceral, subcutaneous, and total fat. Linear mixed models were used to test the association between plasma metabolites and each body composition trait at baseline and in response to training with full covariate adjustment. Significance was set to FDR<0.05. Results: On average, subjects were [mean and (SD)] 35 (14) years old, 33% Black, 54% female, and had BMI of 26.2 (5.2) kg/m 2 . The number of metabolites significantly associated with body composition traits at baseline ranged from 57-141 ( Table 1 ). DMGV was among the top 3 associated metabolites at baseline for all 11 traits, while SM(d18:1/16:1) was associated with 8 of 11 baseline traits. Few if any significant associations were found between change in metabolites and change in body composition measures in response to exercise training (range: 0-10) ( Table 1 ). CONCLUSIONS: Although numerous metabolites were associated with body composition traits at baseline, few associations were observed with trait responses to training. These results suggest that intrinsic body composition and its response to exercise training may have differing underlying metabolic signatures.

Abstract 15316: Associations Between Plasma Proteins With Body Composition Traits and Their Responses to Exercise Training

Abstract: Introduction: Although exercise training is known to alter body composition, the molecular biomarkers and mechanisms related to these changes have not been fully elucidated. The purpose of this study was to examine the associations between plasma proteins and body composition traits before and after exercise training. Methods: Measurements were taken before and after 20 weeks of standardized, endurance training in self-identified Black and White adults of the HERITAGE Family Study (n=647). Plasma proteins were measured using an affinity-based platform (n=4979 aptamers). 11 body composition traits were measured using underwater weighing, CT scans, and anthropometry: BMI, body surface area, fat mass, fat free mass, %fat, waist circumference, waist-to-hip ratio, body weight, and abdominal visceral, subcutaneous, and total fat. Linear mixed models were used to test the association between plasma proteins and each trait at baseline and in response to training with full covariate adjustment ( Table 1 ). Significance was set to FDR<0.05. Results: On average, subjects were [mean and (SD)] 35 (14) years old, 33% Black, 54% female, and had BMI 26.2 (5.2) kg/m 2 . The number of significant associations between proteins and traits at baseline and in response to exercise training ranged from 645-1714 and 0-42, respectively ( Table 1 ). While leptin was among the top associated proteins at both time points for almost all traits, our analysis revealed many novel associations between changes in proteins with changes in body composition. Changes in DLK-1 significantly associated with changes in 9 of 11 traits but was not associated with baseline measures. Conclusions: Numerous plasma proteins were associated with body composition traits at baseline, with fewer associated with trait responses to training. Notably, we identified a subset of proteins uniquely associated with exercise-induced changes in body composition traits.

Abstract 15399: Proteomic Landscape of Plasma Lipoprotein Responses to Regular Exercise

Abstract: Introduction: Lipoproteins are among the strongest predictors of CVD and are altered through regular exercise. However, the molecular changes underlying the potential benefits of exercise on plasma lipoproteins are unclear. Methods: Proteins (n=4979) were quantified from the plasma of 647 Black and White adults (56% women, mean age = 34.9 yrs) before and after 20-weeks of endurance exercise training using SomaScan. All subjects had complete data for 7 lipoprotein traits that improved with training: HDL-C, TG, large TG rich lipoprotein particles (LTRLP), small LDL particles (SLDLP), large HDL particles (LHDLP), TRLP size, and LDLP size. Fully adjusted linear mixed models were used to test the association of 1) baseline proteins with baseline lipoproteins and 2) delta proteins with delta lipoproteins (delta = post training - baseline value). Significance was determined as FDR<0.05. Results: We identified numerous proteins associated with baseline levels of at least one of the seven lipoprotein traits (range: 48-1099) and whose changes in response to regular exercise were associated with concomitant changes in lipoproteins (range: 4-95) ( Table 1 ). Substantial overlap was found between proteins associated with lipoproteins across timepoints, with 16 proteins associated with 4 or more lipoprotein traits at both timepoints. Plasma abundance of 12/16 proteins significantly changed with training (FDR <0.05). Additionally, for these proteins plasma abundance changed in concordance with the beneficial direction of the associated lipoprotein trait (e.g., proteins associated with atheroprotective lipoprotein traits increased with training and proteins associated with atherogenic traits decreased). Conclusions: Beneficial alterations to plasma lipoproteins with regular exercise are reflected in changes to the plasma proteome. Proteins identified here may represent novel markers of the benefits of regular exercise and of systemic lipoprotein metabolism.

Abstract 13353: Association of Plasma Metabolites With Inflammatory Markers CRP and GlycA and Their Responses to Exercise Training

Abstract: Introduction. C-reactive protein (CRP) and GlycA are established biomarkers of inflammation. Regular exercise tends to decrease CRP and GlycA levels. However, the spectrum of molecules associated with the anti-inflammatory effects of regular exercise are less well understood. Hypothesis. We hypothesized that distinct metabolite signatures exist for both baseline levels and exercise responsiveness of CRP and GlycA. Methods. Measures were performed before and after 20 weeks of endurance exercise training in 652 Black and White adults from the HERITAGE Family Study. A total of 300 targeted plasma metabolites were measured using LC-MS. High-sensitivity CRP and GlycA were measured using automated assays and NMR spectroscopy (LabCorp), respectively. Linear mixed models were used to test: 1) Association of baseline metabolites with baseline hsCRP and GlycA and 2) Association of changes in metabolite with changes in hsCRP and GlycA. Models were adjusted for age, sex, race, BMI, with family membership as a random variable, with change models also adjusting for baseline trait value. Significance was determined as FDR<0.05. Results. Baseline levels and changes of hsCRP and GlycA were moderately correlated (r=0.51 and 0.31, p<0.0001 for both, respectively). At baseline, 40 and 94 metabolites were associated with hsCRP and GlycA, respectively, with 30 metabolites associated with both phenotypes. The top baseline associations for both traits included multiple species of lysophosphatidylcholine (LPC) and phosphatidylethanolamine (PE), while cortisol, biliverdin, and bilirubin were among the metabolites associated with GlycA only. The changes in only one metabolite were associated with concomitant changes in CRP, while no associations were found for change in GlycA. Conclusions. Plasma metabolite associations with baseline hsCRP overlapped with those associated with baseline GlycA levels. Several unique metabolite associations with baseline GlycA were identified, including molecules in established inflammatory pathways. Metabolite changes with exercise were not associated with changes in either measure. These findings have implications for the use of metabolites as signatures of systemic inflammation vs as targets of lifestyle interventions.

Exercise training-induced changes in lipid traits are associated with changes in circulating proteins and metabolites

Abstract: PURPOSE: To examine the molecular mechanisms underlying beneficial effects of regular exercise on lipid metabolism. METHODS: Circulating proteins (n = 4979), metabolites (n = 300), and lipids/lipoproteins were measured in 647 Black and White adults from the HERITAGE Family Study at baseline and after 20 weeks of supervised endurance training. The current analysis focused on 7 lipid/lipoprotein traits that significantly changed with training: HDL- cholesterol (HDL-C), triglycerides (TG), large TG-rich lipoprotein particles (LTRLP), large HDL particles (LHDLP), small LDL particles (SLDLP), and mean TRLP (TRLPz) and LDLP size (LDLPz). The relationship between exercise-induced fold changes in circulating molecules and changes in lipid traits was examined using sparse canonical correlation analysis, which tests the joint associations between two sets of variables by creating composite canonical variates. All variables were corrected for age, sex, race, BMI, baseline value, and family membership via linear mixed models. RESULTS: We identified 3 canonical variate pairs of exercise-induced changes in lipid traits and circulating molecules. Molecular variate 1 was positively correlated with changes in TG, LTRLP, SLDLP, and TRLPz (r = 0.29-0.57, p < 0.0001). Conversely, molecular variate 3 was negatively correlated with changes in TG, LTRLP, SLDLP, and TRLPz (r = -0.30 to -0.39, p < 0.0001), and positively correlated with changes in LDLPz (r = 0.38, p < 0.0001). Molecular variate 2 was negatively correlated with HDL-C (r = -0.46, p < 0.0001) and LHDLP (r = -0.22, p < 0.0001) changes with training. Molecular loadings on the respective variates were largely distinct (Table 1). CONCLUSIONS: We identified groups of circulating molecules whose changes in response to exercise training are associated with changes in the plasma lipid and lipoprotein profile and may provide insights into the mechanisms underlying exercise-induced changes in lipid metabolism.

Abstract 237: Association Between The HDL-sized And Circulating Plasma Proteomes

Abstract: Background: Mass spectrometry (MS) profiling has identified over 250 proteins associated with HDL that are thought to underlie the diverse atheroprotective properties of HDL particles and thus may be important biomarkers of cardiovascular disease (CVD) risk. Likewise, recent studies have identified circulating plasma proteins as biomarkers of CVD risk factors and health outcomes. However, few studies have compared the HDL proteome with the circulating plasma proteome. Purpose: The purpose of this analysis was to examine the relationship between the abundance of individual proteins measured in whole plasma and the HDL-sized plasma fraction. Methods: We examined the HDL-sized and circulating plasma proteomes in 156 Black (30%) and White men and women (61%) from the HERITAGE Family Study. HDL was isolated from plasma via gel filtration chromatography and untargeted MS analysis was performed via nano-HPLC-MS/MS. The whole plasma proteome was measured using a modified aptamer (SOMAscan) assay. The correlations between protein abundances in HDL and whole plasma were examined for 101 HDL-associated proteins present in at least 40% of the sample. Results: The abundance of 56 proteins in HDL-sized and whole plasma were significantly (5% FDR) correlated with the strongest correlation for Haptoglobin levels (r=0.83, p=1.1x10 -40 ) ( Table 1 ). The remaining significant correlations ranged from weak to moderate (r= 0.18-0.64) and were found among a mix of frequently and occasionally observed HDL proteins. Discussion: We found that protein abundance measured in HDL-sized and whole plasma were moderately to strongly correlated for several proteins, whereas 45% of protein levels showed no association between HDL-sized and whole plasma fractions. Given the inherit differences in measurement techniques and sources of proteins, it appears that the plasma HDL-sized proteome is mostly distinct from the circulating whole plasma proteome as measured by the SOMAscan assay.

Abstract 13308: Complementary Investigations of Plasma Proteomics Identify Novel Protein Associations With Incident Diabetes and Glucose Metabolism

Abstract: Introduction: Type 2 diabetes (T2D) is a cardiovascular disease risk equivalent and likely results from broad metabolic changes, which high throughput proteomics have helped to unravel. Prior studies are limited by proteomic coverage, cross sectional design, and lack of physiologic phenotyping. Hypothesis: Complementary proteomic studies of incident T2D and physiologic responses to an intravenous glucose tolerance test (IVGTT) will identify novel proteins with roles in glucose homeostasis and future risk of T2D. Methods: Cardiovascular Health Study (CHS) and HERITAGE study participants without diabetes underwent SomaScan ® profiling of 4,776 plasma proteins. HERITAGE participants underwent IVGTT, from which insulin sensitivity index (S I ), acute insulin response to glucose (AIR G ), and glucose effectiveness (S G ) were derived. We used Cox regression to test protein associations with 18-year incident T2D in CHS, and multivariable linear regression to test protein associations with IVGTT measures in HERITAGE. Results: In CHS (N = 2631, 74 ± 5 years, 62% female, 14% Black), 57 proteins were significantly associated with incident T2D after comprehensive covariate and multiple testing adjustment. Of these, 44, 9, and 8 were associated with S I , AIR G , and S G respectively in HERITAGE (N = 752, 35 ± 14 years, 55% female, 38% Black) (Figure). Notable findings include beta-glucuronidase, which associated with increased T2D risk (HR 1.46 per SD increase in log 2 protein level) and lower S G , suggesting a role in insulin-independent glucose disposal, and thrombospondin-2, which associated with increased T2D risk (HR 1.26 per SD), lower AIR G , and not with S I , indicating that it may be a marker of pancreatic dysfunction. Conclusions: By integrating proteomics from two complementary prospective cohorts using different but related outcomes, we identified 34 novel protein-T2D associations, and characterized their relationship with physiologic axes of glucose metabolism.

Abstract P204: Association Of Plasma Proteome With Inflammatory Markers Crp And Glyca And Their Responses To Exercise Training

Abstract: Introduction: C-reactive protein (CRP) and GlycA are established biomarkers of inflammation. Regular exercise tends to decrease CRP and GlycA levels. However, the molecules underlying CRP and GlycA and their responses to exercise training are less known. Hypothesis: We hypothesized that distinct protein signatures exist for both baseline levels and exercise responsiveness of CRP and GlycA, however with some overlap of proteins across signatures. Methods: Measures were performed before and after 20 weeks of exercise training in 652 Black and White adults from the HERITAGE Family Study. 4,979 circulating proteins were measured using SomaScan. High-sensitivity CRP was measured using automated assays. GlycA was quantified by NMR spectroscopy (LabCorp). Linear mixed models were used to test: 1) Association of baseline proteins with baseline hsCRP and GlycA and 2) Association of change in protein with changes in hsCRP and GlycA. Models were adjusted for age, sex, race, BMI, with family membership as a random variable, with change models also adjusting for baseline trait value. Significance was determined as FDR<0.05. Results: Baseline levels (r=0.51, p<0.0001) and change (r=0.31, p<0.0001) of hsCRP and GlycA were moderately correlated. hsCRP did not change with training, while GlycA trended towards decreasing (Table 1). Across time points, 388 and 1746 unique proteins were associated with hsCRP and GlycA, respectively, with substantial overlap across traits and time points (Table 1). The CRP protein was the top association in 3 of 4 models (range: 1.4x10 -40 < p < 1.2x10 -08 ), with serum amyloid A-2 and A-1 (SAA2, SAA) among the top 6 hits for 3 of 4 models (Table 1). Conclusions: More unique proteins were associated with GlycA levels at both timepoints, with most hsCRP proteins overlapping with GlycA proteins. Overall, a substantial number of proteins are associated with CRP and GlycA levels regardless of when they are measured, which has potential implications for using proteins as signatures of these inflammatory biomarkers.

Genetic control of body weight by the human brain proteome

Abstract: Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with body weight but the biological relevance of most remains unexplored. Given the critical role of the brain in multiple biological processes associated with body weight regulation, we set out to determine whether genetic variants linked with body mass index (BMI) could be mapped to brain proteins. Using genetic colocalization, we mapped 23 loci from the largest BMI GWAS (n=806,834) to brain proteins (obtained from a dataset of >7000 dorsolateral prefrontal cortex proteins measured by mass spectrometry in >400 individuals). We also performed a proteome-wide Mendelian randomization analysis followed by genetic colocalization, which allowed us to identify an additional 48 brain proteins linked with BMI. Multi-trait colocalization suggested that more than 75% of the protein quantitative trait loci (pQTL)-BMI associations are mediated via protein expression and not via RNA expression. Single-cell sequencing from the human brain cortex revealed that the genes expressing the proteins associated with BMI may be predominantly expressed in oligodendrocytes. In the Quebec Family Study, a genetic risk score (GRS) including these brain pQTLs was associated with higher dietary carbohydrate intake and lower lipid intake whereas a GRS including the 67 variants most strongly associated with BMI was not associated with dietary intake. In conclusions, we identified 71 proteins expressed in the prefrontal cortex that may be critical regulators of body weight and possibly dietary intake in humans.

Associations Between Changes In Plasma Proteins And Body Composition Traits In Response To Endurance Training

Abstract: Although exercise training is known to improve body composition, the molecular biomarkers and mechanisms related to these changes have not been fully elucidated. PURPOSE: The purpose of this study was to examine the associations between change in plasma proteins and change in body composition traits in response to endurance training. METHODS: Measurements were taken before and after 20 weeks of standardized, endurance training in Black and White adults of the HERITAGE Family Study (n = 652). Over 5,000 plasma proteins were measured using an aptamer-affinity based platform (SomaScan). Underwater weighing, CT scans, and anthropometric measurements were used to derive the 11 body composition traits included in this study: BMI, body surface area, fat mass, fat free mass , %fat, waist circumference, waist-to-hip ratio (WHR), body weight, and abdominal visceral, subcutaneous, and total fat. Linear mixed models were used to test the association between change in plasma proteins and change in each body composition trait adjusted for age, sex, race, baseline BMI, and baseline trait value with family membership as a random variable. Significance was set to FDR < 0.05. RESULTS: On average, subjects were 35% Black, 56% female, 35 years old, and overweight at baseline (mean BMI 26.4 (SD 5.3) kg/m2), with %fat of 27.5 (10.4). All 11 traits significantly improved in response to training. Significant associations between changes in proteins and body composition were found for all traits except WHR, with 57 unique proteins identified. Leptin was the top association (range: 0.023 < FDR p-value<4.2x10-12) for all 9 body composition traits it associated with (Table 1). CONCLUSIONS: Although dozens of proteins were associated with changes in body composition traits, 6 proteins were associated with ≥8 traits. Globally, these proteins are involved in pathways such as adipogenesis, energy balance, and cell growth, which may potentially influence body composition and fat distribution traits.

Thursday , September 8 FLRC Day 1 Continued Session II : A Celebration of Innovation : New Technologies , New Insights

Abstract: The micronutrient iron is a risk factor for type II diabetes (T2D). The effects of iron are, in part, mediated by the iron-sensing hypoxia inducible factor (HIF) pathway. The interactions of iron and hypoxia in the progression of T2D are not fully understood. We therefore fed mice a fast-food (FF) diet supplemented with either normal iron (35 mg/kg) or high iron (2000 mg/kg) under normoxic (21% O2) and hypoxic (12% O2) conditions. There was an interactive effect of iron and hypoxia on fasting glucose and glucose tolerance (p=0.016, p=0.41 respectively) in which the combination of hypoxia and normal iron yielded a significant improvement as compared to all other groups. Direct measurement of insulin sensitivity showed no additional change with hypoxia, suggesting non-insulin mediated glucose uptake mechanisms. Transcriptional profile of mouse eWAT shows significant upregulation of classic HIF target genes involved in glycolysis, mitochondrial metabolism, and intracellular iron homeostasis in hypoxic mice and interestingly also in normoxic high iron mice. Lastly, protein expression of HIF regulators FIH1 and PHD2 were significantly reduced (20-30% reduction, p<0.05 for FIH and p<0.01 for PHD2) under hypoxia, while GLUT1 protein expression was significantly increased under hypoxia with a trend of higher GLUT1 expression in the normal iron hypoxic group. We conclude that both iron and hypoxia affect glucose tolerance in mice on a FF diet, and their synergistic effects may be driven by the two regulatory arms of the hypoxia inducible factor (HIF) pathway through non-insulin mediated glucose uptake mechanisms.

Abstract P206: Proteomic And Metabolomic Signatures Of Plasma Triglyceride-related Trait Responses To Regular Exercise

Abstract: Introduction: Elevated plasma triglycerides (TG) are associated with risk of cardiovascular disease and are modifiable through lifestyle interventions such as regular exercise. However, TG responses to regular exercise are characterized by significant inter-individual differences. Hypothesis: We hypothesized that baseline levels of circulating proteins and metabolites are associated with TG response to exercise and can predict exercise-induced changes in plasma TG traits. Methods: We measured circulating proteins (n=4979 proteins) and metabolites (n=300) in 650 Black and White adults of the HERITAGE Family Study who completed 20 weeks of exercise training and had complete data on TG traits. We investigated two TG-related traits that significantly improved with training: fasting TG and large TG-rich lipoprotein particle concentration (LTRLP). The association between baseline analyte values and exercise-induced changes in TG traits were examined using linear mixed models adjusted for age, sex, race, BMI, baseline trait value, and the random effect of family membership. Significance was determined as FDR<0.05. Molecular signatures of TG trait responses were generated via elastic net regression tuned using leave-one-out cross validation. Results: Regular exercise significantly reduced plasma TG (-1.03±1.3 mmol/L, p=0.01) and LTRLP (-0.24±2.2 nmol/L, p= 0.007), with their training-induced changes moderately correlated (r=0.4, p<0.0001). We identified largely distinct panels of baseline analytes associated with changes in TG and LTRLP ( Table 1 ). Similarly, elastic net regression yielded distinct models of 99 analytes for TG and 315 analytes for LTRLP responses to exercise with accuracy of RMSE=1.28 mmol/L for TG and 2.16 nmol/L for the LTRLP model. Conclusions: We identified panels of proteins and metabolites associated with exercise-induced changes in TG traits and demonstrate, within our data, circulating analytes hold promise for predicting the exercise responsiveness of plasma TG-related traits.

Genetically-estimated Telomere Length Weakly Associates With Body Composition And Metabolic Profiles But Not Cardiorespiratory Fitness

Abstract: Telomere length is associated with many age-related diseases. However, its association with fitness-related traits and exercise responses are less understood. PURPOSE: To investigate the association of genetically estimated telomere length (gTL) with cardiorespiratory fitness (CRF) traits and metabolic traits before and after endurance exercise training in the HERITAGE Family Study. METHODS: gTL was calculated in Black and White adults from the HERITAGE Family Study (n=708) using 9 established telomere length GWAS SNPs. Race-stratified associations between gTL and baseline measures and changes in CRF traits, body composition, and the components of metabolic syndrome were determined using partial correlations controlling for age. RESULTS: Mean (±SE) gTL significantly (p<0.001) differed between Black (754.5±8.2 bp) and White subjects (603.1±5.98 bp). There were no associations between gTL and CRF traits at baseline or in response to training in Black or White subjects. Furthermore, there were no associations between gTL and body composition traits at baseline or in response to training in Black subjects. In White subjects, gTL was negatively, weakly correlated with baseline body composition measures including: BMI (r=-0.14), waist circumference (r=-0.12), visceral fat (r=-0.11), fat mass (r=-0.13), percent body fat (r=-0.11); all p<0.05. Only change in waist circumference was correlated with gTL in Whites subjects (r=-0.12, p=0.02). At baseline, Black (n=55) and White (n=97) subjects with metabolic syndrome had significantly shorter mean gTL compared to those without (p<0.05). Furthermore, gTL was negatively correlated with number of baseline metabolic syndrome components in Whites (r=-0.12, p=0.02), but not Blacks. In response to training, gTL was negatively correlated with change in number of metabolic syndrome components in Whites (r=-0.10, p=0.044), but positively correlated in Blacks (r=0.15, p=0.047). CONCLUSIONS: Baseline CRF traits and their response to training were not associated with gTL. Increased central adiposity and metabolic syndrome components are associated with shorter gTL in Whites. The gTL estimate used may not be optimized in Blacks and perhaps not sensitive enough to fully capture hypothesized associations with fitness traits or metabolic health.