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Hepatic de novo lipogenesis is influenced by the branched-chain α-keto acid dehydrogenase (BCKDH) kinase (BCKDK). Here, we aimed to determine whether circulating levels of the immediate substrates of BCKDH, the branched-chain α-keto acids (BCKAs), and hepatic BCKDK expression are associated with the presence and severity of nonalcoholic fatty liver disease (NAFLD). Eighty metabolites (3 BCKAs, 14 amino acids, 43 acylcarnitines, 20 ceramides) were quantified in plasma from 288 patients with bariatric surgery with severe obesity and scored liver biopsy samples. Metabolite principal component analysis factors, BCKAs, branched-chain amino acids (BCAAs), and the BCKA/BCAA ratio were tested for associations with steatosis grade and presence of nonalcoholic steatohepatitis (NASH). Of all analytes tested, only the Val-derived BCKA, α-keto-isovalerate, and the BCKA/BCAA ratio were associated with both steatosis grade and NASH. Gene expression analysis in liver samples from 2 independent bariatric surgery cohorts showed that hepatic BCKDK mRNA expression correlates with steatosis, ballooning, and levels of the lipogenic transcription factor SREBP1 . Experiments in AML12 hepatocytes showed that SREBP1 inhibition lowered BCKDK mRNA expression. These findings demonstrate that higher plasma levels of BCKA and hepatic expression of BCKDK are features of human NAFLD/NASH and identify SREBP1 as a transcriptional regulator of BCKDK .

We sought to determine if novel plasma biomarkers improve traditionally defined metabolic health (MH) in predicting risk of cardiovascular disease (CVD) events irrespective of weight. Poor MH was defined in CATHGEN biorepository participants (n > 9300), a follow-up cohort (> 5600 days) comprising participants undergoing evaluation for possible ischemic heart disease. Lipoprotein subparticles, lipoprotein-insulin resistance (LP-IR), and GlycA were measured using NMR spectroscopy (n = 8385), while acylcarnitines and amino acids were measured using flow-injection, tandem mass spectrometry (n = 3592). Multivariable Cox proportional hazards models determined association of poor MH and plasma biomarkers with time-to-all-cause mortality or incident myocardial infarction. Low-density lipoprotein particle size and high-density lipoprotein, small and medium particle size (HMSP), GlycA, LP-IR, short-chain dicarboxylacylcarnitines (SCDA), and branched-chain amino acid plasma biomarkers were independently associated with CVD events after adjustment for traditionally defined MH in the overall cohort ( p  = 3.3 × 10 −4 –3.6 × 10 −123 ), as well as within most of the individual BMI categories ( p  = 8.1 × 10 −3 –1.4 × 10 −49 ). LP-IR, GlycA, HMSP, and SCDA improved metrics of model fit analyses beyond that of traditionally defined MH. We found that LP-IR, GlycA, HMSP, and SCDA improve traditionally defined MH models in prediction of adverse CVD events irrespective of BMI.

Levels of certain circulating short-chain dicarboxylacylcarnitine (SCDA), long-chain dicarboxylacylcarnitine (LCDA) and medium chain acylcarnitine (MCA) metabolites are heritable and predict cardiovascular disease (CVD) events. Little is known about the biological pathways that influence levels of most of these metabolites. Here, we analyzed genetics, epigenetics, and transcriptomics with metabolomics in samples from a large CVD cohort to identify novel genetic markers for CVD and to better understand the role of metabolites in CVD pathogenesis. Using genomewide association in the CATHGEN cohort (N = 1490), we observed associations of several metabolites with genetic loci. Our strongest findings were for SCDA metabolite levels with variants in genes that regulate components of endoplasmic reticulum (ER) stress (USP3, HERC1, STIM1, SEL1L, FBXO25, SUGT1) These findings were validated in a second cohort of CATHGEN subjects (N = 2022, combined p = 8.4x10-6-2.3x10-10). Importantly, variants in these genes independently predicted CVD events. Association of genomewide methylation profiles with SCDA metabolites identified two ER stress genes as differentially methylated (BRSK2 and HOOK2). Expression quantitative trait loci (eQTL) pathway analyses driven by gene variants and SCDA metabolites corroborated perturbations in ER stress and highlighted the ubiquitin proteasome system (UPS) arm. Moreover, culture of human kidney cells in the presence of levels of fatty acids found in individuals with cardiometabolic disease, induced accumulation of SCDA metabolites in parallel with increases in the ER stress marker BiP. Thus, our integrative strategy implicates the UPS arm of the ER stress pathway in CVD pathogenesis, and identifies novel genetic loci associated with CVD event risk.

Angiopoietin-like 4 (ANGPTL4) is an endogenous inhibitor of lipoprotein lipase that modulates lipid levels, coronary atherosclerosis risk, and nutrient partitioning. We hypothesize that loss of ANGPTL4 function might improve glucose homeostasis and decrease risk of type 2 diabetes (T2D). We investigate protein-altering variants in ANGPTL4 among 58,124 participants in the DiscovEHR human genetics study, with follow-up studies in 82,766 T2D cases and 498,761 controls. Carriers of p.E40K, a variant that abolishes ANGPTL4 ability to inhibit lipoprotein lipase, have lower odds of T2D (odds ratio 0.89, 95% confidence interval 0.85–0.92, p  = 6.3 × 10 −10 ), lower fasting glucose, and greater insulin sensitivity. Predicted loss-of-function variants are associated with lower odds of T2D among 32,015 cases and 84,006 controls (odds ratio 0.71, 95% confidence interval 0.49–0.99, p  = 0.041). Functional studies in Angptl4 -deficient mice confirm improved insulin sensitivity and glucose homeostasis. In conclusion, genetic inactivation of ANGPTL4 is associated with improved glucose homeostasis and reduced risk of T2D. Genetic variation in ANGPTL4 is associated with lipid traits. Here, the authors find that predicted loss-of-function variants in ANGPTL4 are associated with glucose homeostasis and reduced risk of type 2 diabetes and that Angptl4 − / − mice on a high-fat diet show improved insulin sensitivity.

DNA methylation age (DNAm age) has become a widely utilized epigenetic biomarker for the aging process. The Horvath method for determining DNAm age is perhaps the most widely utilized and validated DNA methylation age assessment measure. Horvath DNAm age is calculated based on methylation measurements at 353 loci, present on Illumina's 450k and 27k DNA methylation microarrays. With increasing use of the more recently developed Illumina MethylationEPIC (850k) microarray, it is worth revisiting this aging measure to evaluate estimation differences due to array design. Of the requisite 353 loci, 17 are missing from the 850k microarray. Similarly, an alternate, 71 loci DNA methylation age assessment measure created by Hannum et al. is missing 6 requisite loci. Using 17 datasets with 27k, 450k, and/or 850k methylation data, we compared each sample's epigenetic age estimated from all 353 loci required by the Horvath DNAm age calculator, and using only the 336 loci available on the 850k array. In 450k/27k data, removing loci not on the 850k array resulted in underestimation of Horvath's DNAm age. Underestimation of Horvath DNAm age increased from ages 0 to ~20, remaining stable thereafter (mean deviation = -3.46 y, SD = 1.13 for individuals ≥20 years). Underestimation of Horvath's DNAm age by the reduced 450k/27k data was similar to the underestimation observed in the 850k data indicating it is driven by missing probes. In analogous examination of Hannum's DNAm age, the magnitude and direction of epigenetic age misestimation varied with chronological age. In conclusion, inter-array deviations in DNAm age estimations may be largely driven by missing probes between arrays, despite default probe imputation procedures. Though correlations and associations based on Horvath's DNAm age may be unaffected, researchers should exercise caution when interpreting results based on absolute differences in DNAm age or when mixing samples assayed on different arrays.

•The relation between lipoprotein(a) (Lp[a]) and coronary atherosclerosis is unclear.•In PROMISE, Lp(a) ≥50 mg/100 ml was associated with obstructive coronary disease.•This relation was independent of low-density lipoprotein cholesterol control.•However, Lp(a) was not associated with high-risk coronary plaque.•Further research is needed to understand the role of Lp(a) in coronary artery disease. The role of lipoprotein (a) (Lp[a]) in the development of obstructive coronary artery disease (CAD) and high-risk plaque (HRP) in primary prevention patients with stable chest pain is unknown. We sought to evaluate the relation of Lp(a), independent of low-density lipoprotein cholesterol (LDL-C), with the presence of obstructive CAD and HRP to improve understanding of the residual risk imparted by Lp(a) on CAD. We performed a secondary analysis in Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) Trial participants who had coronary computed tomographic angiography (CTA) performed and Lp(a) data available. Lp(a) concentration was analyzed as a binary variable, with elevated Lp(a) defined as ≥50 mg/100 ml. “Stenosis ≥50%” was defined as ≥50% coronary artery stenosis in any epicardial vessel, and “stenosis ≥70%” was defined as ≥70% coronary artery stenosis in any epicardial vessel and/or ≥50% left main coronary artery stenosis. HRP was defined as presence of plaque on CTA imaging with evidence of positive remodeling, low computed tomography attenuation, or napkin-ring sign. Multivariate logistic regression models were constructed to evaluate the association between Lp(a) and the outcomes of obstructive CAD and HRP stratified by LDL-C ≥100 versus <100 mg/100 ml. Of the 1,815 patients who underwent CTA and had Lp(a) data available, those with elevated Lp(a) were more commonly women and Black than those with lower Lp(a). Elevated Lp(a) was associated with stenosis ≥50% (odds ratio 1.57, 95% confidence interval 1.14 to 2.15, p = 0.005) and stenosis ≥70% (odds ratio 2.05, 95% confidence interval 1.34 to 3.11, p = 0.0008) in the multivariate models, and this relation was not modified by LDL-C ≥100 versus <100 mg/100 ml (interaction p >0.4). Elevated Lp(a) was not associated with HRP when adjusted for obstructive CAD. This study of patients without known CAD found that elevated Lp(a) ≥50 mg/100 ml was independently associated with the presence of obstructive CAD regardless of controlled versus uncontrolled LDL-C but was not independently associated with HRP when stenosis ≥50% or ≥70% was accounted for. Further research is warranted to delineate the role of Lp(a) in the residual risk for atherosclerotic cardiovascular disease that patients may have despite optimal LDL-C lowering.

Changes in platelet physiology are associated with simultaneous changes in microRNA concentrations, suggesting a role for microRNA in platelet regulation. Here we investigated potential associations between microRNA and platelet reactivity (PR), a marker of platelet function, in two cohorts following a non-ST elevation acute coronary syndrome (NSTE-ACS) event. First, non-targeted microRNA concentrations and PR were compared in a case (N = 77) control (N = 76) cohort within the larger TRILOGY-ACS trial. MicroRNA significant in this analysis plus CVD-associated microRNAs from the literature were then quantified by targeted rt-PCR in the complete TRILOGY-ACS cohort (N = 878) and compared with matched PR samples. Finally, microRNA significant in the non-targeted & targeted analyses were verified in an independent post NSTE-ACS cohort (N = 96). From the non-targeted analysis, 14 microRNAs were associated with PR (Fold Change: 0.91–1.27, p-value: 0.004–0.05). From the targeted analysis, five microRNAs were associated with PR (Beta: −0.09–0.22, p-value: 0.004–0.05). Of the 19 significant microRNAs, three, miR-15b-5p, miR-93 and miR-126, were consistently associated with PR in the TRILOGY-ACS and independent Singapore post-ACS cohorts, suggesting the measurement of circulating microRNA concentrations may report on dynamic changes in platelet biology following a cardiovascular ischemic event.

Clonal hematopoiesis of indeterminate potential (CHIP) is the age-related presence of expanded somatic clones secondary to leukemogenic driver mutations and is associated with cardiovascular (CV) disease and mortality. We sought to evaluate relationships between CHIP with cardiometabolic diseases and incident outcomes in high-risk individuals. CHIP genotyping was performed in 8469 individuals referred for cardiac catheterization at Duke University (CATHGEN study) to identify variants present at a variant allele fraction (VAF) ≥2%. Associations were tested among any CHIP variant, large CHIP clones (VAF > 10%) and individual CHIP genes with prevalent cardiometabolic traits. Cox proportional hazard models tested CHIP associations with time-to-overall mortality and Fine-Gray analyses tested CHIP associations with incident cardiovascular outcomes. We identified 463 CHIP variants in 427 individuals (5.0%) of which 268 (3.2%) harbored large CHIP clones. CHIP and large CHIP were associated with lower odds of obesity (OR 0.79 [95% CI 0.65-0.98], p = 0.03; OR 0.76 [95% CI 0.57-0.99], p = 0.04, respectively). CHIP was associated with prevalent heart failure (HF, OR 1.25 [95% CI 1.01-1.55], p = 0.04; especially for non-DNMT3A CHIP (OR 1.38 [95% CI 1.04-1.82], p = 0.02). CHIP was also associated with incident events: Non-DNMT3A CHIP was associated with increased risk of time-to-HF hospitalization (HR 1.29 [95% CI 1.02-1.63], p = 0.03). In high-risk individuals referred for cardiac catheterization, large CHIP and non-DNTM3A CHIP were associated with obesity, prevalent HF, incident CV events. These findings strengthen the importance of CHIP as a biomarker for CV disease and highlight the contributing risk of large CHIP clones and non-DNMT3A CHIP variants.

There is increasing evidence that HFpEF is a heterogeneous clinical entity and distinct molecular pathways may contribute to pathophysiology. Leveraging unbiased proteomics to identify novel biomarkers, this study seeks to understand the underlying molecular mechanisms of HFpEF. The discovery cohort consisted of HFpEF cases and non-HF controls from the CATHGEN study (N = 176); the validation cohort consisted of participants from the TECOS trial of patients with diabetes (N = 109). Proteins associated with HFpEF were included in a LASSO model to create a discriminative multi-protein model and assessed in the validation cohort. Survival models and meta-analysis were used to test the association of proteins with incident clinical outcomes, including HF hospitalization, mortality and HFpEF hospitalization in CATHGEN, TECOS and the Jackson Heart Study. In the derivation set, 190 proteins were associated with HFpEF in univariate analysis, of which 65 remained significant in the multivariate model. Twenty (30.8%) of these proteins validated in TECOS, including LCN2, U-PAR, IL-1ra, KIM1, CSTB and Gal-9 (OR 1.93–2.77, p  < 0.01). LASSO regression yielded a 13-protein model which, when added to a clinical model inclusive of NT-proBNP, improved the AUC from 0.82 to 0.92 ( p  = 1.5 × 10 –4 ). Five proteins were associated with incident HF hospitalization, four with HFpEF hospitalization and eleven with mortality ( p  < 0.05). We identified and validated multiple circulating biomarkers associated with HFpEF as well as HF outcomes. These biomarkers added incremental discriminative capabilities beyond clinical factors and NT-proBNP.

Obesity is an epidemic internationally. While weight loss interventions are efficacious, they are compounded by heterogeneity with regards to clinically relevant metabolic responses. Thus, we sought to identify metabolic biomarkers that are associated with beneficial metabolic changes to weight loss and which distinguish individuals with obesity who would most benefit from a given type of intervention. Liquid chromatography mass spectrometry-based profiling was used to measure 765 metabolites in baseline plasma from three different weight loss studies: WLM (behavioral intervention, N = 443), STRRIDE-PD (exercise intervention, N = 163), and CBD (surgical cohort, N = 125). The primary outcome was percent change in insulin resistance (as measured by the Homeostatic Model Assessment of Insulin Resistance [%ΔHOMA-IR]) over the intervention. Overall, 92 individual metabolites were associated with %ΔHOMA-IR after adjustment for multiple comparisons. Concordantly, the most significant metabolites were triacylglycerols (TAGs; p = 2.3e-5) and diacylglycerols (DAGs; p = 1.6e-4), with higher baseline TAG and DAG levels associated with a greater improvement in insulin resistance with weight loss. In tests of heterogeneity, 50 metabolites changed differently between weight loss interventions; we found amino acids, peptides, and their analogues to be most significant (4.7e-3) in this category. Our results highlight novel metabolic pathways associated with heterogeneity in response to weight loss interventions, and related biomarkers which could be used in future studies of personalized approaches to weight loss interventions.