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IMPORTANCE: Blood pressure response during acute exercise (exercise blood pressure [EBP]) is associated with the future risk of hypertension and cardiovascular disease (CVD). Biochemical characterization of EBP could inform disease biology and identify novel biomarkers of future hypertension. OBJECTIVE: To identify protein markers associated with EBP and test their association with incident hypertension. DESIGN, SETTING, AND PARTICIPANTS: This study assayed 4977 plasma proteins in 681 healthy participants (from 763 assessed) of the Health, Risk Factors, Exercise Training and Genetics (HERITAGE; data collection from January 1993 to December 1997 and plasma proteomics from January 2019 to January 2020) Family Study at rest who underwent 2 cardiopulmonary exercise tests. Individuals were free of CVD at the time of recruitment. Individuals with resting SBP ≥160 mm Hg or DBP ≥100 mm Hg or taking antihypertensive drug therapy were excluded from the study. The association between resting plasma protein levels to both resting BP and EBP was evaluated. Proteins associated with EBP were analyzed for their association with incident hypertension in the Framingham Heart Study (FHS; n = 1177) and validated in the Jackson Heart Study (JHS; n = 772) and Multi-Ethnic Study of Atherosclerosis (MESA; n = 1367). Proteins associated with incident hypertension were tested for putative causal links in approximately 700 000 individuals using cis-protein quantitative loci mendelian randomization (cis-MR). Data were analyzed from January 2023 to January 2024. EXPOSURES: Plasma proteins. MAIN OUTCOMES AND MEASURES: EBP was defined as the BP response during a fixed workload (50 W) on a cycle ergometer. Hypertension was defined as BP ≥140/90 mm Hg or taking antihypertensive medication. RESULTS: Among the 681 participants in the HERITAGE Family Study, the mean (SD) age was 34 (13) years; 366 participants (54%) were female; 238 (35%) were self-reported Black and 443 (65%) were self-reported White. Proteomic profiling of EBP revealed 34 proteins that would not have otherwise been identified through profiling of resting BP alone. Transforming growth factor β receptor 3 (TGFBR3) and prostaglandin D2 synthase (PTGDS) had the strongest association with exercise systolic BP (SBP) and diastolic BP (DBP), respectively (TGFBR3: exercise SBP, β estimate, −3.39; 95% CI, −4.79 to −2.00; P = 2.33 × 10−6; PTGDS: exercise DBP β estimate, −2.50; 95% CI, −3.29 to −1.70; P = 1.18 × 10−9). In fully adjusted models, TGFBR3 was inversely associated with incident hypertension in FHS, JHS, and MESA (hazard ratio [HR]: FHS, 0.86; 95% CI, 0.75-0.97; P = .01; JHS, 0.87; 95% CI, 0.77-0.97; P = .02; MESA, 0.84; 95% CI, 0.71-0.98; P = .03; pooled cohort, 0.86; 95% CI, 0.79-0.92; P = 6 × 10−5). Using cis-MR, genetically predicted levels of TGFBR3 were associated with SBP, hypertension, and CVD events (SBP: β, −0.38; 95% CI, −0.64 to −0.11; P = .006; hypertension: odds ratio [OR], 0.99; 95% CI, 0.98-0.99; P < .001; heart failure with hypertension: OR, 0.86; 95% CI, 0.77-0.97; P = .01; CVD: OR, 0.84; 95% CI, 0.77-0.92; P = 8 × 10−5; cerebrovascular events: OR, 0.77; 95% CI, 0.70-0.85; P = 5 × 10−7). CONCLUSIONS AND RELEVANCE: Plasma proteomic profiling of EBP identified a novel protein, TGFBR3, which may protect against elevated BP and long-term CVD outcomes.

Despite the wide effects of cardiorespiratory fitness (CRF) on metabolic, cardiovascular, pulmonary and neurological health, challenges in the feasibility and reproducibility of CRF measurements have impeded its use for clinical decision-making. Here we link proteomic profiles to CRF in 14,145 individuals across four international cohorts with diverse CRF ascertainment methods to establish, validate and characterize a proteomic CRF score. In a cohort of around 22,000 individuals in the UK Biobank, a proteomic CRF score was associated with a reduced risk of all-cause mortality (unadjusted hazard ratio 0.50 (95% confidence interval 0.48–0.52) per 1 s.d. increase). The proteomic CRF score was also associated with multisystem disease risk and provided risk reclassification and discrimination beyond clinical risk factors, as well as modulating high polygenic risk of certain diseases. Finally, we observed dynamicity of the proteomic CRF score in individuals who undertook a 20-week exercise training program and an association of the score with the degree of the effect of training on CRF, suggesting potential use of the score for personalization of exercise recommendations. These results indicate that population-based proteomics provides biologically relevant molecular readouts of CRF that are additive to genetic risk, potentially modifiable and clinically translatable.

Elevated BCAA levels are strongly associated with diabetes, but how diabetes affects BCAA, branched-chain ketoacids (BCKAs), and the broader metabolome after a meal is not well known.

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n= 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n= 3,175; Olink, n= 4,822), plasma metabolomics (Metabolon HD4, n= 8,153), serum metabolomics (Nightingale, n= 37,359) and whole-blood Illumina RNA sequencing (n= 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK–STAT signalling and coronary atherosclerosis. Finally, we develop a portal (https://www.omicspred.org/) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

To identify distinct biological pathways of glucose metabolism, we conducted a systematic evaluation of biochemical changes after an oral glucose tolerance test (OGTT) in a community-based population. Metabolic profiling was performed on 377 nondiabetic Framingham Offspring cohort participants (mean age 57 years, 42% women, BMI 30 kg/[m.sup.2]) before and after OGTT. Changes in metabolite levels were evaluated with paired Student t tests, cluster-based analyses, and multivariable linear regression to examine differences associated with insulin resistance. Of 110 metabolites tested, 91 significantly changed with OGTT (P ≤ 0.0005 for all). Amino acids, β-hydroxybutyrate, and tricarboxylic acid cycle intermediates decreased after OGTT, and glycolysis products increased, consistent with physiological insulin actions. Other pathways affected by OGTT included decreases in serotonin derivatives, urea cycle metabolites, and B vitamins. We also observed an increase in conjugated, and a decrease in unconjugated, bile acids. Changes in β-hydroxybutyrate, isoleucine, lactate, and pyridoxate were blunted in those with insulin resistance. Our findings demonstrate changes in 91 metabolites representing distinct biological pathways that are perturbed in response to an OGTE We also identify metabolite responses that distinguish individuals with and without insulin resistance. These findings suggest that unique metabolic phenotypes can be unmasked by OGTT in the prediabetic state., Diabetes affects >1 in 10 adults 20 years of age or older in the U.S., and more than one-third of all adults have prediabetes (1). Changes in traditional measures of [...]

The health benefits of exercise are well-recognized and are observed across multiple organ systems. These beneficial effects enhance overall resilience, healthspan and longevity. The molecular mechanisms that underlie the beneficial effects of exercise, however, remain poorly understood. Since the discovery in 2000 that muscle contraction releases IL-6, the number of exercise-associated signalling molecules that have been identified has multiplied. Exerkines are defined as signalling moieties released in response to acute and/or chronic exercise, which exert their effects through endocrine, paracrine and/or autocrine pathways. A multitude of organs, cells and tissues release these factors, including skeletal muscle (myokines), the heart (cardiokines), liver (hepatokines), white adipose tissue (adipokines), brown adipose tissue (baptokines) and neurons (neurokines). Exerkines have potential roles in improving cardiovascular, metabolic, immune and neurological health. As such, exerkines have potential for the treatment of cardiovascular disease, type 2 diabetes mellitus and obesity, and possibly in the facilitation of healthy ageing. This Review summarizes the importance and current state of exerkine research, prevailing challenges and future directions.

IMPORTANCE: African American individuals have disproportionate rates of coronary heart disease (CHD) but lower levels of coronary artery calcium (CAC), a marker of subclinical CHD, than non-Hispanic White individuals. African American individuals may have distinct metabolite profiles associated with incident CHD risk compared with non-Hispanic White individuals, and examination of these differences could highlight important processes that differ between them. OBJECTIVES: To identify novel biomarkers of incident CHD and CAC among African American individuals and to replicate incident CHD findings in a multiethnic cohort. DESIGN, SETTING, AND PARTICIPANTS: This analysis targeted plasma metabolomic profiling of 2346 participants in the Jackson Heart Study (JHS), a prospective population-based cohort study that included 5306 African American participants who were examined at baseline (2000-2004) and 2 follow-up visits. Replication of CHD-associated metabolites was sought among 1588 multiethnic participants from the Women’s Health Initiative (WHI), a prospective population-based multiethnic cohort study of 161 808 postmenopausal women who were examined at baseline (1991-1995) and ongoing follow-up visits. Regression analyses were performed for each metabolite to examine the associations with incident CHD and CAC scores. Data were collected from the WHI between 1994 and 2009 and from the JHS between 2000 and 2015. All data were analyzed from November 2020 to August 2021. EXPOSURES: Plasma metabolites. MAIN OUTCOMES AND MEASURES: Incident CHD was defined as definite or probable myocardial infarction or definite fatal CHD in both the JHS and WHI cohorts. In the JHS cohort, silent myocardial infarction between examinations (as determined by electrocardiography) and coronary revascularization were included in the incident CHD analysis. Coronary artery calcium was measured using a 16-channel computed tomographic system and reported as an Agatston score. RESULTS: Among 2346 African American individuals in the JHS cohort, the mean (SD) age was 56 (13) years, and 1468 individuals (62.6%) were female. Among 1588 postmenopausal women in the WHI cohort, the mean (SD) age was 67 (7) years; 217 individuals (13.7%) self-identified as African American, 1219 (76.8%) as non-Hispanic White, and 152 (9.6%) as other races or ethnicities. In the fully adjusted model including 1876 individuals, 46 of 303 targeted metabolites were associated with incident CHD (false discovery rate q <0.100). Data for 32 of the 46 metabolites were available in the WHI cohort, and 13 incident CHD–associated metabolites from the JHS cohort were replicated in the WHI cohort. A total of 1439 participants from the JHS cohort with available CAC scores received metabolomic profiling. Nine metabolites were associated with CAC scores. Minimal overlap was found between the results from the incident CHD and CAC analyses, with only 3 metabolites shared between the 2 analyses. CONCLUSIONS AND RELEVANCE: This cohort study identified metabolites that were associated with incident CHD among African American individuals, including 13 incident CHD–associated metabolites that were replicated in a multiethnic population and 9 novel metabolites that included N-acylamides, leucine, and lipid species. These findings may help to elucidate common and distinct metabolic processes that may be associated with CHD among individuals with different self-identified race.

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ObjectiveTryptophan can be catabolised to various metabolites through host kynurenine and microbial indole pathways. We aimed to examine relationships of host and microbial tryptophan metabolites with incident type 2 diabetes (T2D), host genetics, diet and gut microbiota.MethodWe analysed associations between circulating levels of 11 tryptophan metabolites and incident T2D in 9180 participants of diverse racial/ethnic backgrounds from five cohorts. We examined host genome-wide variants, dietary intake and gut microbiome associated with these metabolites.ResultsTryptophan, four kynurenine-pathway metabolites (kynurenine, kynurenate, xanthurenate and quinolinate) and indolelactate were positively associated with T2D risk, while indolepropionate was inversely associated with T2D risk. We identified multiple host genetic variants, dietary factors, gut bacteria and their potential interplay associated with these T2D-relaetd metabolites. Intakes of fibre-rich foods, but not protein/tryptophan-rich foods, were the dietary factors most strongly associated with tryptophan metabolites. The fibre-indolepropionate association was partially explained by indolepropionate-associated gut bacteria, mostly fibre-using Firmicutes. We identified a novel association between a host functional LCTvariant (determining lactase persistence) and serum indolepropionate, which might be related to a host gene-diet interaction on gut Bifidobacterium, a probiotic bacterium significantly associated with indolepropionate independent of other fibre-related bacteria. Higher milk intake was associated with higher levels of gut Bifidobacteriumand serum indolepropionate only among genetically lactase non-persistent individuals.ConclusionHigher milk intake among lactase non-persistent individuals, and higher fibre intake were associated with a favourable profile of circulating tryptophan metabolites for T2D, potentially through the host–microbial cross-talk shifting tryptophan metabolism toward gut microbial indolepropionate production.

ObjectivesWe investigated whether high responsiveness or low responsiveness to exercise training aggregates in the same individuals across seven cardiometabolic traits.MethodsA total of 564 adults (29.2% black, 53.7% female) from the HERITAGE family study completed a 20-week endurance training programme (at 55%–75% of participants’ maximal oxygen uptake (VO2max)) with VO2max, per cent body fat, visceral adipose tissue, fasting levels of insulin, high-density lipoprotein cholesterol, small low-density lipoprotein particles and inflammatory marker GlycA measured before and after training. For each exercise response trait, we created ethnicity-specific, sex-specific and generation-specific quintiles. High responses were defined as those within the 20th percentile representing the favourable end of the response trait distribution, low responses were defined as the 20th percentile from the least favourable end, and the remaining were labelled as average responses.ResultsOnly one individual had universally high or low responses for all seven cardiometabolic traits. Almost half (49%) of the cohort had at least one high response and one low response across the seven traits. About 24% had at least one high response but no low responses, 24% had one or more low responses but no high responses, and 2.5% had average responses across all traits.ConclusionsInterindividual variation in exercise responses was evident in all the traits we investigated, and responsiveness did not aggregate consistently in the same individuals. While adherence to an exercise prescription is known to produce health benefits, targeted risk factors may not improve.

While conventional nutrition research has yielded biomarkers such as doubly labeled water for energy metabolism and 24-h urinary nitrogen for protein intake, a critical need exists for additional, equally robust biomarkers that allow for objective assessment of specific food intake and dietary exposure. Recent advances in high-throughput MS combined with improved metabolomics techniques and bioinformatic tools provide new opportunities for dietary biomarker development. In September 2018, the NIH organized a 2-d workshop to engage nutrition and omics researchers and explore the potential of multiomics approaches in nutritional biomarker research. The current Perspective summarizes key gaps and challenges identified, as well as the recommendations from the workshop that could serve as a guide for scientists interested in dietary biomarkers research. Topics addressed included study designs for biomarker development, analytical and bioinformatic considerations, and integration of dietary biomarkers with other omics techniques. Several clear needs were identified, including larger controlled feeding studies, testing a variety of foods and dietary patterns across diverse populations, improved reporting standards to support study replication, more chemical standards covering a broader range of food constituents and human metabolites, standardized approaches for biomarker validation, comprehensive and accessible food composition databases, a common ontology for dietary biomarker literature, and methodologic work on statistical procedures for intake biomarker discovery. Multidisciplinary research teams with appropriate expertise are critical to moving forward the field of dietary biomarkers and producing robust, reproducible biomarkers that can be used in public health and clinical research.

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.

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Betaine improves insulin sensitivity during glucose tolerance testing but has little other metabolic effect. Dimethylglycine dehydrogenase limits betaine metabolism in prediabetes.

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IMPORTANCE: Clinical practice guidelines currently endorse a reliance on clinical symptoms of overt left ventricular (LV) failure to time aortic valve replacement for severe aortic stenosis; however, delayed aortic valve replacement can result in irreversible LV injury and adverse outcomes. Blood metabolomic signatures possess prognostic value in heart failure; this study assesses whether they are informative in aortic stenosis. OBJECTIVE: To evaluate the value of metabolomic signatures in reflecting the extent of maladaptive LV remodeling in patients with end-stage aortic stenosis undergoing transcatheter aortic valve replacement, and to assess whether this procedure reverses metabolomic aberrations. DESIGN, SETTING, AND PARTICIPANTS: This study of 44 patients with symptomatic severe aortic stenosis who underwent transfemoral transcatheter aortic valve replacement at a single-center tertiary care hospital. Liquid chromatography–mass spectrometry-based metabolomic profiling was performed on blood samples collected before and 24 hours after the procedure, and analyses were conducted to identify metabolites related to the measures of LV remodeling. MAIN OUTCOMES AND MEASURES: We evaluated LV ejection fraction, LV mass index, and relative wall thickness, as well as levels of the acylcarnitines C16, C18:1, C18:2, C18, C26, choline, and kynurenine. RESULTS: We enrolled 44 patients with severe aortic stenosis with a mean (SD) age of 81.9 (8.5) years, of whom 23 (52%) were women. The mean (SD) LV ejection fraction was 56.7% (18.2%), mean (SD) LV mass index was 117.3 (41.4) g/m2, and relative wall thickness was 0.53 (0.14). The mean β values of acylcarnitines C16, C18:1, C18:2, C18, and C26 were independently associated with LV mass index (C16: mean, 19.24; 95% CI, 5.48-33.01; P = .008; C18:1: mean, 26.18; 95% CI, 14.04-38.32; P < 1.0 × 10-4; C18:2: mean, 17.42; 95% CI, 3.40-31.43; P = .02; C18: mean, 25.25; 95% CI, 10.91-39.58; P = .001; C26: mean, 19.93; 95% CI, 4.41-35.45; P = .01), even after adjustments for age, sex, diabetes status, renal function, and B-type natriuretic peptide (BNP). Circulating levels of C18:2 acylcarnitine were associated with LV ejection fraction before and after multivariable adjustment (mean, −6.11; 95% CI, −10.88 to 1.34; P = .01). Blood metabolite levels did not independently relate to relative wall thickness. Within 24 hours of transcatheter aortic valve replacement, circulating levels of C16 decreased by 30.2% (P = 7.3 × 10-6), C18:1 by 42.7% (P = 3.7 × 10-8), C18:2 by 37.3% (P = 5.1 × 10-6), and C18 by 38.3% (P = 3.4 × 10-5). CONCLUSIONS AND RELEVANCE: In symptomatic patients with severe aortic stenosis undergoing transcatheter aortic valve replacement, circulating levels of long-chain acylcarnitines were independently associated with measures of maladaptive LV remodeling, and metabolic perturbations lessened after procedure completion. Further efforts are needed to determine the clinical applicability of these novel biomarkers.

We assessed the association between urinary metabolites, genetic variants, and incident chronic kidney disease (CKD) in the Framingham Offspring cohort. Among the participants, 193 individuals developed CKD (estimated glomerular filtration rate under 60 ml/min/1.73m2) between cohort examinations 6 (1995-1998) and 8 (2005-2008, mean follow-up 9.7 years). They were age- and sex-matched to 193 control individuals free of CKD. A total of 154 urinary metabolites were measured using mass spectrometry, and the association between metabolites and CKD was examined using logistic regression. Next, we tested the genetic associations of each metabolite with an Illumina exome chip. Urinary glycine and histidine were associated with a lower risk of incident CKD with an odds ratio of 0.59 (95% confidence interval [CI] 0.43-0.80) and 0.65 (0.50-0.85) respectively, per one standard deviation increase in metabolite concentration. Follow-up in the Atherosclerosis Risk in Communities cohort confirmed the association of urinary glycine with CKD. In exome chip analyses, 36 single nucleotide polymorphisms at 30 loci were significantly associated with 31 metabolites. We surveyed exome chip findings for associations with known renal function loci such as rs8101881 in SLC7A9coding for an amino acid transporter, which has been associated with a lower risk of CKD. We found this polymorphism was significantly associated with higher levels of lysine and NG-monomethyl-L-arginine (NMMA). Increased urinary lysine and NMMA were associated with a lower risk of CKD (0.73 [0.50-0.90] and 0.66 [0.53-0.83], respectively) in the univariate model. Thus, low urinary glycine and histidine are associated with incident CKD. Furthermore, genomic association of urinary metabolomics identified lysine and NMMA as being linked with CKD and provided additional evidence for the association of SLC7A9with kidney disease.

Plasma biomarkers that reflect molecular states of the cardiovascular system are central for clinical decision making. Routinely used plasma biomarkers include troponins, natriuretic peptides, and lipoprotein particles, yet interrogate only a modest subset of pathways relevant to cardiovascular disease. Systematic profiling of a larger portion of circulating plasma proteins (the plasma proteome) will provide opportunities for unbiased discovery of novel markers to improve diagnostic or predictive accuracy. In addition, proteomic profiling may inform pathophysiological understanding and point to novel therapeutic targets. Obstacles for comprehensive proteomic profiling include the immense size and structural heterogeneity of the proteome, and the broad range of abundance levels, as well. Proteome-wide, untargeted profiling can be performed in tissues and cells with tandem mass spectrometry. However, applications to plasma are limited by the need for complex preanalytical sample preparation stages limiting sample throughput. Multiplexing of targeted methods based on capture and detection of specific proteins are therefore receiving increasing attention in plasma proteomics. Immunoaffinity assays are the workhorse for measuring individual proteins but have been limited for proteomic applications by long development times, cross-reactivity preventing multiplexing, specificity issues, and incomplete sensitivity to detect proteins in the lower range of the abundance spectrum (below picograms per milliliter). Emerging technologies to address these issues include nucleotide-labeled immunoassays and aptamer reagents that can be automated for efficient multiplexing of thousands of proteins at high sample throughput, coupling of affinity capture methods to mass spectrometry for improved specificity, and ultrasensitive detection systems to measure low-abundance proteins. In addition, proteomics can now be integrated with modern genomics tools to comprehensively relate proteomic profiles to genetic variants, which may both influence binding of affinity reagents and serve to validate the target specificity of affinity assays. The application of deep quantitative proteomic profiling to large cohorts has thus become increasingly feasible with emerging affinity methods. The aims of this article are to provide the broad readership of Circulationwith a timely overview of emerging methods for affinity proteomics and recent progress in cardiovascular medicine based on such methods.

AMP-activated protein kinase (AMPK) is a metabolic enzyme that can be activated by nutrient stress or genetic mutations. Missense mutations in the regulatory subunit, PRKAG2, activate AMPK and cause left ventricular hypertrophy, glycogen accumulation, and ventricular pre-excitation. Using human iPS cell models combined with three-dimensional cardiac microtissues, we show that activating PRKAG2 mutations increase microtissue twitch force by enhancing myocyte survival. Integrating RNA sequencing with metabolomics, PRKAG2 mutations that activate AMPK remodeled global metabolism by regulating RNA transcripts to favor glycogen storage and oxidative metabolism instead of glycolysis. As in patients with PRKAG2 cardiomyopathy, iPS cell and mouse models are protected from cardiac fibrosis, and we define a crosstalk between AMPK and post-transcriptional regulation of TGFβ isoform signaling that has implications in fibrotic forms of cardiomyopathy. Our results establish critical connections among metabolic sensing, myocyte survival, and TGFβ signaling.

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Cardiometabolic diseases are the leading cause of death worldwide and are strongly linked to both genetic and nutritional factors. The field of nutrigenomics encompasses multiple approaches aimed at understanding the effects of diet on health or disease development, including nutrigenetic studies investigating the relationship between genetic variants and diet in modulating cardiometabolic risk, as well as the effects of dietary components on multiple “omic” measures, including transcriptomics, metabolomics, proteomics, lipidomics, epigenetic modifications, and the microbiome. Here, we describe the current state of the field of nutrigenomics with respect to cardiometabolic disease research and outline a direction for the integration of multiple omics techniques in future nutrigenomic studies aimed at understanding mechanisms and developing new therapeutic options for cardiometabolic disease treatment and prevention.

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Introduction:Angiopoietin like protein (ANGPTL) complexes 3/8 and 4/8 are established inhibitors of lipoprotein lipase and modifiable by regular exercise. However, the molecular underpinning of these novel biomarkers has not been fully elucidated. The purpose of this study was to examine the associations between plasma metabolites and ANGPTL3/8 and 4/8 before and after exercise training.Methods:Measurements were taken before and after 20 weeks of exercise training in 630 adults (36% Black, 56% women, mean age 35 yrs) of the HERITAGE Family Study. Meso Scale Discovery immunoassays were used to measure ANGPTL3/8 and 4/8 in serum. Plasma metabolites (n=300 named) were measured using LC-MS and the HILIC-pos method. Linear mixed models tested the association between metabolites and each trait at baseline and post-training with full covariate adjustment. Significance was set to FDR<0.05.Results:A total of 111 and 93 metabolites were significantly associated with ANGPTL3/8 at baseline or post-training, respectively (Figure 1A). A total of 69 metabolites were associated with ANGPTL3/8 at both time points, including ketone bodies, acylcarnitines, and DMGV, while 24 metabolites were only associated at post-training including citric acid, methionine, and glycine. A total of 89 and 40 metabolites were significantly associated with ANGPTL4/8 at baseline or post-training, respectively (Figure 1B). A total of 13 metabolites were associated with ANGPTL4/8 at both time points including glycine and lysophospholipids, whereas 27 metabolites were associated only at post-training including hydroxyproline, N-Lignoceroyl Taurine, and methylguanidine.Conclusions:We identified several plasma metabolites associated with ANGPTL3/8 and 4/8 before and after exercise training. Our findings indicate potential metabolic pathways related to the exercise responsiveness of ANGPTL3/8 and 4/8, including glucose-alanine cycle, urea cycle, and glycine and methionine metabolism.

Introduction:Angiopoietin like protein (ANGPTL) complexes 3/8 and 4/8 are established inhibitors of lipoprotein lipase and modifiable by regular exercise. However, the molecular biomarkers related to their exercise responses have not been fully elucidated. The purpose of this study was to examine the associations between plasma proteins and ANGPTL3/8 and 4/8 before and after exercise training.Methods:Measurements were taken before and after 20 weeks of exercise training in 630 adults (36% Black, 56% women, mean age 35 yrs) of the HERITAGE Family Study. Meso Scale Discovery immunoassays were used to measure ANGPTL3/8 and ANGPTL4/8 complexes in serum. Plasma proteins (n=4979 aptamers) were quantified using SomaScan. Linear mixed models tested the association between plasma proteins and each trait at baseline and post- training with full covariate adjustment. Significance was set to FDR<0.05.Results:A total of 862 and 126 proteins were significantly associated with ANGPTL3/8 at baseline or post-training, respectively, with 80 proteins associated at both time points, including PCSK9 and PLTP (Figure 1A). Conversely, 46 proteins were only associated with ANGPTL3/8 at post-training, 23 of which whose levels significantly changed with training including NRP1, ghrelin, HHIP, and APOA1. A total of 433 and 71 proteins were significantly associated with ANGPTL4/8 at baseline or post-training, respectively, with 46 proteins associated with both time points (Figure 1B). A total of 25 proteins were only associated with ANGPTL4/8 at post-training, 12 of which whose levels significantly changed with training including ghrelin, COL6A2, and MMP16.Conclusions:We identified a subset of proteins uniquely associated with exercise-induced changes in ANGPTL3/8 and 4/8. Thus, the responsiveness of ANGPTL3/8 and 4/8 to regular exercise may be related to cholesterol esterification, lipoprotein remodeling, inflammatory response, and regulation of fibrinolysis and coagulation among other biological pathways.

Background:Identification of novel biomarkers of cerebral small vessel disease is critical to elucidate pathophysiology and guide therapeutic development for prevention. We evaluated plasma proteomic associations of clinically asymptomatic, covert brain infarction (cBI) and white matter hyperintensity (WMH) burden on cranial magnetic resonance imaging (MRI) in the population-based Cardiovascular Health Study (CHS).Methods:Eligible CHS participants underwent one or two cranial MRI scans (~5 years apart) and aptamer-based SOMAScan platform measurement of up to 7288 plasma proteins concurrent with the initial MRI. cBI was defined by MRI signal characteristics and size ≥ 3 mm, and WMH burden was graded (WMG) 0 (least) to 9 (most severe). The relationship between 1-standard deviation increase in each log2-normalized plasma protein fluorescence was modeled separately with incident cBI and WMG worsening (≥1 grade increase) using multivariable relative risk regression, adjusting for demographics, estimated glomerular filtration rate (eGFR), and stroke risk factors. In secondary analyses, multivariable linear regression evaluated the cross-sectional relationship between each plasma protein with number of cBI and WMG on initial MRI. Bonferroni correction, based on the number of principal components that explained 99% of the protein fluorescence variance in CHS participants, accounted for multiple comparisons (p<2.1x10-5(0.05/2377)).Results:For eligible participants (n=2615), mean age was 74.4 ± 4.9 years, 61.7% were women, and 16.4% were Black. After adjustment for demographics, eGFR, and stroke risk factors, multiple circulating proteins were associated with cBI and WMG (Table).Conclusion:Multiple plasma proteins - implicated in systemic inflammation, fatty acid metabolism, neuronal adhesion/migration, and cellular cytoskeletal integrity/remodeling - were independently associated with cBI and WMG in a cohort of older adults.

Regular exercise has many favorable effects on human health, which may be mediated in part by the release of circulating bioactive factors during each bout of exercise. Limited data exist regarding the kinetic responses of plasma proteins during and after acute exercise. Proteomic profiling of 4163 proteins was performed using a large-scale, affinity-based platform in 75 middle-aged adults who were referred for treadmill exercise stress testing. Plasma proteins were quantified at baseline, peak exercise, and 1-h postexercise, and those with significant changes at both exercise timepoints were further examined for their associations with cardiometabolic traits and change with aerobic exercise training in the HEalth, RIsk factors, exercise Training And GEnetics Family Study, a 20-week exercise intervention study. A total of 765 proteins changed (false discovery rate < 0.05) at peak exercise compared to baseline, and 128 proteins changed (false discovery rate < 0.05) at 1-h postexercise. The 56 proteins that changed at both timepoints included midkine, brain-derived neurotrophic factor, metalloproteinase inhibitor 4, and coiled-coil domain-containing protein 126 and were enriched for secreted proteins. The majority had concordant direction of change at both timepoints. Across all proteins assayed, gene set enrichment analysis showed increased abundance of coagulation-related proteins at 1-h postexercise. Forty-five proteins were associated with at least one measure of adiposity, lipids, glucose homeostasis, or cardiorespiratory fitness in HEalth, RIsk factors, exercise Training And GEnetics, and 20 proteins changed with aerobic exercise training. We identified hundreds of novel proteins that change during acute exercise, most of which resolved by 1 h into recovery. Proteins with sustained changes during exercise and recovery may be of particular interest as circulating biomarkers and pathways for further investigation in cardiometabolic diseases. These data will contribute to a biochemical roadmap of acute exercise that will be publicly available for the entire scientific community.

Individuals homozygous for the “Z” mutation in alpha-1 antitrypsin deficiency are known to be at increased risk for liver disease. It has also become clear that some degree of risk is similarly conferred by the heterozygous state. A lack of model systems that recapitulate heterozygosity in human hepatocytes has limited the ability to study the impact of a single Z alpha-1 antitrypsin (ZAAT) allele on hepatocyte biology. Here, we describe the derivation of syngeneic induced pluripotent stem cells (iPSCs) engineered to determine the effects of ZAAT heterozygosity in iPSC-hepatocytes (iHeps). We find that heterozygous MZ iHeps exhibit an intermediate disease phenotype and share with ZZ iHeps alterations in AAT protein processing and downstream perturbations including altered endoplasmic reticulum (ER) and mitochondrial morphology, reduced mitochondrial respiration, and branch-specific activation of the unfolded protein response in cell subpopulations. Our model of MZ heterozygosity thus provides evidence that a single Z allele is sufficient to disrupt hepatocyte homeostatic function.

We have developed a novel plasma protein analysis platform with optimized sample preparation, chromatography, and MS analysis protocols. The workflow, which utilizes chemical isobaric mass tag labeling for relative quantification of plasma proteins, achieves far greater depth of proteome detection and quantification while simultaneously having increased sample throughput than prior methods. We applied the new workflow to a time series of plasma samples from patients undergoing a therapeutic, “planned” myocardial infarction for hypertrophic cardiomyopathy, a unique human model in which each person serves as their own biologic control. Over 5300 proteins were confidently identified in our experiments with an average of 4600 proteins identified per sample (with two or more distinct peptides identified per protein) using iTRAQ four-plex labeling. Nearly 3400 proteins were quantified in common across all 16 patient samples. Compared with a previously published label-free approach, the new method quantified almost fivefold more proteins/sample and provided a six- to nine-fold increase in sample analysis throughput. Moreover, this study provides the largest high-confidence plasma proteome dataset available to date. The reliability of relative quantification was also greatly improved relative to the label-free approach, with measured iTRAQ ratios and temporal trends correlating well with results from a 23-plex immunoMRM (iMRM) assay containing a subset of the candidate proteins applied to the same patient samples. The functional importance of improved detection and quantification was reflected in a markedly expanded list of significantly regulated proteins that provided many new candidate biomarker proteins. Preliminary evaluation of plasma sample labeling with TMT six-plex and ten-plex reagents suggests that even further increases in multiplexing of plasma analysis are practically achievable without significant losses in depth of detection relative to iTRAQ four-plex. These results obtained with our novel platform provide clear demonstration of the value of using isobaric mass tag reagents in plasma-based biomarker discovery experiments.

Supplemental Digital Content is available in the text.

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/m2. 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.

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 baselinemetabolites with baselinehsCRP and GlycA and 2) Association of changesin metabolite with changesin 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.

Introduction:The blood pressure (BP) response during acute exercise (acute exercise blood pressure (AEBP)) is related to future risk of hypertension (HTN) and cardiovascular disease (CVD). Biochemical characterization of AEBP could identify novel biomarkers of CVD risk and inform understanding of its biology.Methods:We applied an aptamer-based proteomics platform to assay ~5000 proteins at rest in 681 healthy individuals of the HERITAGE Family Study (mean age 34y, 54% women) who underwent an acute exercise perturbation. AEBP was measured during submaximal exercise on a cycle ergometer. We then performed a proteomic association study of resting BP and AEBP. Top protein associations with acute exercise SBP and DBP were analyzed for their association with incident HTN and CVD in the Framingham Heart Study (FHS). Putative causal effects of AEBP-related proteins were investigated by Mendelian randomization (MR), using cis-protein quantitative loci genetic instruments.Results:We identified new markers of AEBP, many of which are secreted proteins that span biological processes relevant to vascular function including transforming-growth factor beta-receptor 3 (β = -3.39, p = 2.33e-6), prostaglandin D2-synthase (β = -2.50, p = 1.18e-10-9), and angiopoietin-related protein 4 (β = 2.44, p = 1.05e-4). The addition of 12 AEBP-associated proteins to clinical information informed future risk of HTN and CVD among normotensive community-dwelling individuals (clinical model, clinical proteomic model AUCs: incident HTN 0.69, 0.73; coronary heart disease 0.73, 0.76; heart failure 0.83, 0.87). MR analysis of AEBP-associated proteins supported a potential causal relationship between 5 proteins and BP regulation (TGFBR3, ANGPTL4, CGA FSHB, PTGDS, KLK8).Conclusions:We identified new blood biomarkers of BP regulation that were elicited only by an acute exercise perturbation and highlight novel potential causal pathways.

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/m2. 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.

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.

Introduction:Branched chain amino acids (BCAA) are strongly associated with incident diabetes and CVD, and drugs that target their metabolism, especially in the context of a meal, are emerging treatments for diabetes given supportive genetic data. However, little is known about how BCAA, their breakdown metabolites—branched chain ketoacids (BCKA), and other metabolites change after a meal.Hypothesis:BCAA and BCKA levels are higher in individuals with diabetes after a mixed meal tolerance test (MMTT) than in those without diabetes, and a MMTT will uncover additional metabolites involved in dysregulated metabolic pathways secondary to diabetes.Methods:We administered a MMTT to 13 adults with diabetes treated by metformin alone and 11 age and sex matched controls. We measured BCKA, BCAA, and 194 metabolites at 8 timepoints across 6 hours. We used a mixed model for repeated measurements to compare between group differences at each timepoint with adjustment for baseline levels.Results:Across the participants (57 ± 8 years, 71% female, BMI 28 ± 4 kg/m2), adjusted BCAA levels were similar at all timepoints between groups, but adjusted BCKA levels were higher in those with diabetes at 120 minutes (Figure). An additional 9 metabolites had 4 or more timepoints with significant between group differences (Figure). Acylcarnitine, palmitic acid, and linoleic acid showed slower rates of decrease and recovery in those with diabetes, altogether supportive of impaired fatty acid uptake and oxidation. Glycocholic acid showed no increase in those with diabetes, a finding that requires further study given growing recognition of bile acids’ paracrine effects on the liver.Conclusions:In contrast to BCAA, BCKA remained elevated after a MMTT in those with diabetes, suggesting that BCKA catabolism may be a key dysregulated process in the link between BCAA and diabetes. Other metabolites with different kinetics after a MMTT may be markers of dysmetabolism and impaired nutrient handling.

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 (SI), acute insulin response to glucose (AIRG), and glucose effectiveness (SG) 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 SI, AIRG, and SGrespectively 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 log2protein level) and lower SG, suggesting a role in insulin-independent glucose disposal, and thrombospondin-2, which associated with increased T2D risk (HR 1.26 per SD), lower AIRG, and not with SI, 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.

Background:High-throughput metabolite profiling allows for the assessment of thousands of metabolites that may participate in exercise-related pathways. Here, we characterize plasma metabolite changes in healthy individuals undergoing endurance exercise training (ET) to identify biochemical features of VO2max and/or those responsive to ET.Methods:We measured 444 known metabolites and 3221 non-targeted metabolite peaks using liquid chromatography tandem mass spectrometry and VO2max using CPET before and after 20 weeks of supervised, endurance ET in 654 sedentary participants (mean age=34y; 39% self-identified Black) from the HERITAGE Study. Multivariate linear regression assessed the relation between metabolites and baseline VO2max (ml·kg–1·min–1), adjusting for age, sex, and race. Changes after ET were determined using paired ttests. We used a false discovery rate (q) <0.05 to determine statistical significance.Results:494 metabolites were associated with VO2max, the majority (375/494) of which were non-targeted (unknown) metabolites with greater effect sizes than targeted peaks [β range: -29.66 - 17.80 and -22.76 - 12.87 for unknown and known metabolites]. We recapitulated known metabolite associations with VO2max including DMGV, branched chain amino acids, and uric acid (Figure 1). 110 of 560 metabolites that changed after ET were associated with VO2max, including non-targeted peaks that concordantly changed after ET (e.g. metabolite peaks positively associated with VO2max that increased with ET; m/z385.3056 and 695.5086; β for VO2max = 9.82 and -19.33, q= 5.56e-7 and 2.61e-17, and log fold-change = 0.03 and -0.01, q=9.58e-13 and 1.93e-4, respectively).Conclusions:We identified novel metabolite peaks associated with VO2max that concordantly changed after ET. Non-targeted metabolomic profiling reveals new markers related to VO2max and exercise response, motivating ongoing work to unambiguously identify these compounds.

Introduction:Clonal hematopoiesis of indeterminate potential (CHIP) is a phenomenon where hematopoietic stem cells acquire leukemogenic mutations without blood cancer. CHIP is a causal risk factor for coronary artery disease (CAD), yet underlying mechanisms remain unclear. The plasma proteome may provide novel mechanistic insights into the links between CHIP and CAD.Methods:We studied the associations of CHIP with the plasma proteins and CAD risk in four Trans-Omics for Precision Medicine (TOPMed) longitudinal cohorts: ARIC (N=9,084), CHS (N=1,727), JHS (N=1,858), and MESA (N=978). CHIP (variant allele fraction >2%) was identified from whole genome sequences of blood DNA and modeled both as a composite of the most common drivers (DNMT3A, TET2, ASXL1, and JAK2) and separately. Levels of ~1,300 serum proteins measured by SomaScan were log-transformed and residualized on study-specific covariates. The association between CHIP and protein levels was estimated across all participants within each study, as well as by sex, and meta-analyzed. For proteins significantly (FDR<0.05) associated with CHIP, pathway analysis and functional validation in Tet2-/-mice were conducted.Results:Across all studies (mean age: 59.8 y; 43.6% male), 720 (5.6%) individuals were identified with CHIP. There were 143 significant CHIP-protein pairs, with carbonic anhydrase 1, lysozyme C, and properdin being the most associated proteins. TET2had the largest number of, and strongest, associations among examined driver genes. In sex-stratified analysis, follicle-stimulating hormone was inversely associated in females. Pathway analysis implicated the inflammation-related STAT3 and IL-17 signaling pathways. We observed significantly greater expression of Cfpand Lyz(encoding properdin and lysosome C, respectively) in the monocytes of Tet2-/- female mice compared to wild-type female mice and Tet2-/- male mice. A number of proteins (including PCSK9) were significantly associated with both CHIP and CAD, suggesting both shared and nonshared mechanisms.Conclusions:CHIP, particularly TET2, is broadly associated with plasma proteins, as validated by evidence in Tet2-/- mice. Further studies are needed to understand the biological mechanisms linking CHIP to CAD.

Adipose tissue secretions are depot-specific and vary based on anatomical location. Considerable attention has been focused on visceral (VAT) and subcutaneous (SAT) adipose tissue with regard to metabolic disease, yet our knowledge of the secretome from these depots is incomplete. We conducted a comprehensive analysis of VAT and SAT secretomes in the context of metabolic function. Conditioned media generated using SAT and VAT explants from individuals with obesity were analyzed using proteomics, mass spectrometry, and multiplex assays. Conditioned media were administered in vitro to rat hepatocytes and myotubes to assess the functional impact of adipose tissue signaling on insulin responsiveness. VAT secreted more cytokines (IL-12p70, IL-13, TNF-α, IL-6, and IL-8), adipokines (matrix metalloproteinase-1, PAI-1), and prostanoids (TBX2, PGE2) compared with SAT. Secretome proteomics revealed differences in immune/inflammatory response and extracellular matrix components. In vitro, VAT-conditioned media decreased hepatocyte and myotube insulin sensitivity, hepatocyte glucose handling, and increased basal activation of inflammatory signaling in myotubes compared with SAT. Depot-specific differences in adipose tissue secretome composition alter paracrine and endocrine signaling. The unique secretome of VAT has distinct and negative impact on hepatocyte and muscle insulin action.

Virtually all organisms seek to maximize fitness by matching fuel availability with energy expenditure. In vertebrates, glucose homeostasis is central to this process, with glucose levels finely tuned to match changing energy requirements. To discover new pathways regulating glucose levels in vivo, we performed a large-scale chemical screen in live zebrafish and identified the small molecule alexidine as a potent glucose-lowering agent. We found that alexidine inhibits the PTEN-like mitochondrial phosphatase PTPMT1 and that other pharmacological and genetic means of inactivating PTPMT1 also decrease glucose levels in zebrafish. Mutation of ptpmt1eliminates the effect of alexidine, further confirming it as the glucose-lowering target of alexidine. We then identified succinate dehydrogenase (SDH) as a substrate of PTPMT1. Inactivation of PTPMT1 causes hyperphosphorylation and activation of SDH, providing a possible mechanism by which PTPMT1 coordinates glucose homeostasis. Therefore, PTPMT1 appears to be an important regulator of SDH phosphorylation status and glucose concentration.