Background: Proteomic approaches allow measurement of thousands of proteins in a single specimen, which can accelerate biomarker discovery. However, applying these technologies to massive biobanks is not currently feasible because of the practical barriers and costs of implementing such assays at scale. To overcome these challenges, we used a “virtual proteomic” approach, linking genetically predicted protein levels to clinical diagnoses in >40 000 individuals. Methods: We used genome-wide association data from the Framingham Heart Study (n=759) to construct genetic predictors for 1129 plasma protein levels. We validated the genetic predictors for 268 proteins and used them to compute predicted protein levels in 41 288 genotyped individuals in the Electronic Medical Records and Genomics (eMERGE) cohort. We tested associations for each predicted protein with 1128 clinical phenotypes. Lead associations were validated with directly measured protein levels and either low-density lipoprotein cholesterol or subclinical atherosclerosis in the MDCS (Malmö Diet and Cancer Study; n=651). Results: In the virtual proteomic analysis in eMERGE, 55 proteins were associated with 89 distinct diagnoses at a false discovery rate q<0.1. Among these, 13 associations involved lipid (n=7) or atherosclerosis (n=6) phenotypes. We tested each association for validation in MDCS using directly measured protein levels. At Bonferroni-adjusted significance thresholds, levels of apolipoprotein E isoforms were associated with hyperlipidemia, and circulating C-type lectin domain family 1 member B and platelet-derived growth factor receptor-β predicted subclinical atherosclerosis. Odds ratios for carotid atherosclerosis were 1.31 (95% CI, 1.08–1.58; P =0.006) per 1-SD increment in C-type lectin domain family 1 member B and 0.79 (0.66–0.94; P =0.008) per 1-SD increment in platelet-derived growth factor receptor-β. Conclusions: We demonstrate a biomarker discovery paradigm to identify candidate biomarkers of cardiovascular and other diseases.

Purpose High throughput profiling of metabolic status (metabolomics) allows for the assessment of small-molecule metabolites that may participate in exercise-induced biochemical pathways and corresponding cardiometabolic risk modification. We sought to describe the changes in a diverse set of plasma metabolite profiles in patients undergoing chronic exercise training and assess the relationship between metabolites and cardiometabolic response to exercise. Methods secondary analysis was performed in 216 middle-aged abdominally obese men and women ([mean (SD)], 52.4 (8.0) years) randomized into one of four groups varying in exercise amount and intensity for 6 months duration: high amount high intensity, high amount low intensity, low amount low intensity, and control. 147 metabolites were profiled by liquid chromatography-tandem mass spectrometry. Results No significant differences in metabolite changes between specific exercise groups were observed; therefore, subsequent analyses were collapsed across exercise groups. There were no significant differences in metabolite changes between the exercise and control groups after 24 weeks at a Bonferroni-adjusted statistical significance (p < 3.0 × 10-4). Seven metabolites changed in the exercise group compared to the control group at p < 0.05. Changes in several metabolites from distinct metabolic pathways were associated with change in cardiometabolic risk traits, and three baseline metabolite levels predicted changes in cardiometabolic risk traits. Conclusion Metabolomic profiling revealed no significant plasma metabolite changes between exercise compared to control after 24-weeks at Bonferroni significance. However, we identified circulating biomarkers that were predictive or reflective of improvements in cardiometabolic traits in the exercise group.

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) FamilyStudy, 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 MEASURESThe 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.

Background: Increased left ventricular (LV) mass is associated with future adverse cardiovascular events including heart failure (HF). Both increased LV mass and HF disproportionately affect black individuals. To understand the mechanisms that drive disease, particularly in black individuals, we undertook a proteomic screen in a black cohort and compared it to a white cohort. Methods: We measured 1305 plasma proteins using an aptamer-based proteomic platform (SOMAscan™) in 1772 black participants in the Jackson Heart Study (JHS) with available baseline LV mass as assessed by 2D echocardiography, as well as 1600 free of HF with follow-up assessment of incident cases. Mean follow-up time was 11 years; 152 cases of incident HF hospitalization were identified. Models were adjusted for age, sex, body mass index, estimated glomerular filtration rate (as calculated by CKD-EPI equation), systolic blood pressure, hypertension treatment, presence of diabetes, total/HDL cholesterol, prevalent coronary disease, and current smoking status. Incident HF models were also adjusted for incident coronary heart disease. We then compared protein associations in JHS to those observed in whites from the Framingham Heart Study (FHS) to examine significant differences. Results: In JHS, there were 112 proteins associated with LV mass and 10 proteins associated with incident HF hospitalization with FDR <5%. Several proteins showed expected associations with both LV mass and HF, including N-terminal pro-BNP (β = 0.04 [0.02, 0.05], p = 1.0 x 10 -8 , HR = 1.46 [1.20, 1.79], p = 0.0002). The strongest association with LV mass was more novel: leukotriene A4 hydrolase (LKHA4) (β = 0.05 [0.04, 0.06], p = 2.6 x 10 -15 ). Conversely, Fractalkine/CX3CL1 showed a novel association with incident HF (HR = 1.32 [1.14, 1.54], p = 0.0003). While proteins like Cystatin C and N-terminal pro-BNP showed consistent effects in FHS, LKHA4 and Fractalkine were significantly different. Conclusions: We identify several novel biological pathways specific to black individuals hypothesized to contribute to the pathophysiologic cascade of LV hypertrophy and incident HF including LKHA4 and Fractalkine. Further studies are needed to validate these results and elucidate the detailed underlying mechanisms.

Background - Increased left ventricular (LV) mass is associated with adverse cardiovascular events including heart failure (HF). Both increased LV mass and HF disproportionately affect Black individuals. To understand underlying mechanisms, we undertook a proteomic screen in a Black cohort and compared the findings to results from a white cohort. Methods - We measured 1305 plasma proteins using the SomaScan® platform in 1772 Black participants (mean age 56 years, 62% women) in the Jackson Heart Study (JHS) with LV mass assessed by 2D echocardiography. Incident HF was assessed in 1600 participants. We then compared protein associations in JHS to those observed in white participants from the Framingham Heart Study (FHS, mean age 54 years, 56% women). Results - In JHS, there were 110 proteins associated with LV mass and 13 proteins associated with incident HF hospitalization with false discovery rate <5% after multivariable adjustment. Several proteins showed expected associations with both LV mass and HF, including N-terminal pro-BNP (β = 0.04, p = 2 × 10 -8 ; HR = 1.48, p = 0.0001). The strongest association with LV mass was novel: Leukotriene A-4 hydrolase (LKHA4) (β = 0.05, p = 5 × 10 -15 ). This association was confirmed on an alternate proteomics platform and further supported by related metabolomic data. Fractalkine/CX3CL1 showed a novel association with incident HF (HR = 1.32, p = 0.0002). While established biomarkers such as cystatin C and N-terminal pro-BNP showed consistent associations in Black and white individuals, LKHA4 and fractalkine were significantly different between the two groups. Conclusions - We identified several novel biological pathways specific to Black adults hypothesized to contribute to the pathophysiologic cascade of LV hypertrophy and incident HF including LKHA4 and fractalkine.