Lipoprotein subclasses are associated with Hepatic steatosis: insights from the prospective multicenter imaging study for the evaluation of chest pain (PROMISE) clinical trial.

Objectives: To determine the relationship between lipoprotein particle size/number with hepatic steatosis (HS), given its association with traditional lipoproteins and coronary atherosclerosis. Methods: Individuals with available CT data and blood samples enrolled in the PROMISE trial were studied. HS was defined based on CT attenuation. Lipoprotein particle size/number were measured by nuclear magnetic resonance spectroscopy. Principal components analysis (PCA) was used for dimensionality reduction. The association of PCA factors and individual lipoprotein particle size/number with HS were assessed in multivariable regression models. Associations were validated in an independent cohort of 59 individuals with histopathology defined HS. Results: Individuals with HS (n=410/1,509) vs those without (n=1,099/1,509), were younger (59±8 vs 61±8 years) and less often females (47.6 % vs 55.9 %). All PCA factors were associated with HS: factor 1 (OR:1.36, 95 %CI:1.21-1.53), factor 3 (OR:1.75, 95 %CI:1.53-2.02) and factor 4 (OR:1.49; 95 %CI:1.32-1.68) were weighted heavily with small low density lipoprotein (LDL) and triglyceride-rich (TRL) particles, while factor 2 (OR:0.86, 95 %CI:0.77-0.97) and factor 5 (OR:0.74, 95 %CI:0.65-0.84) were heavily loaded with high density lipoprotein (HDL) and larger LDL particles. These observations were confirmed with the analysis of individual lipoprotein particles in PROMISE. In the validation cohort, association between HS and large TRL (OR: 8.16, 95 %CI:1.82-61.98), and mean sizes of TRL- (OR: 2.82, 95 %CI:1.14-9.29) and HDL (OR:0.35, 95 %CI:0.13-0.72) were confirmed. Conclusions: Large TRL, mean sizes of TRL-, and HDL were associated with radiographic and histopathologic HS. The use of lipoprotein particle size/number could improve cardiovascular risk assessment in HS.

Proteomics discovery in children and young adults with HIV identifies fibrosis, inflammatory, and immune biomarkers associated with myocardial impairment.

People with HIV are at increased risk of cardiac dysfunction; however, limited tools are available to identify patients at highest risk for future cardiac disease. We performed proteomic profiling using plasma samples from children and young adults with perinatally acquired HIV without clinical cardiac disease, comparing samples from participants with and without an abnormal myocardial performance index (MPI). We identified four proteins independently associated with subclinical cardiac dysfunction: ST2, CA1, EN-RAGE, and VSIG2.

Mitochondrial metabolites predict adverse cardiovascular events in individuals with diabetes.

Metabolic mechanisms underlying the heterogeneity of major adverse cardiovascular (CV) event (MACE) risk in individuals with type 2 diabetes mellitus (T2D) remain unclear. We hypothesized that circulating metabolites reflecting mitochondrial dysfunction predict incident MACE in T2D. Targeted mass-spectrometry profiling of 60 metabolites was performed on baseline plasma samples from the Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS; discovery cohort) and Exenatide Study of Cardiovascular Event Lowering (EXSCEL; validation cohort) biomarker substudy cohorts. A principal components analysis metabolite factor comprising medium-chain acylcarnitines (MCACs) was associated with MACE in TECOS and validated in EXSCEL, with higher levels associated with higher MACE risk. Meta-analysis showed that long-chain acylcarnitines (LCACs) and dicarboxylacylcarnitines were also associated with MACE. Metabolites remained associated with MACE in multivariate models and favorably changed with exenatide therapy. A third cohort (Cardiac Catheterization Genetics [CATHGEN]) with T2D was assessed to determine whether these metabolites improved discriminative capability of multivariate models for MACE. Nine metabolites (MCACs and LCACs and 1 dicarboxylacylcarnitine) were associated with time to MACE in the CATHGEN cohort. Addition of these metabolites to clinical models minimally improved the discriminative capability for MACE but did significantly down reclassify risk. Thus, metabolites reporting on dysregulated mitochondrial fatty acid oxidation are present in higher levels in individuals with T2D who experience subsequent MACE. These biomarkers may improve CV risk prediction models, be therapy responsive, and highlight emerging risk mechanisms.

Proteomic Pathways across Ejection Fraction Spectrum in Heart Failure: an EXSCEL Substudy.

Background: Ejection fraction (EF) is a key component of heart failure (HF) classification, including the increasingly codified HF with mildly reduced EF (HFmrEF) category. However, the biologic basis of HFmrEF as an entity distinct from HF with preserved EF (HFpEF) and reduced EF (HFrEF) has not been well characterized. Methods: The EXSCEL trial randomized participants with type 2 diabetes (T2DM) to once-weekly exenatide (EQW) vs. placebo. For this study, profiling of ∼5000 proteins using the SomaLogic SomaScan platform was performed in baseline and 12-month serum samples from N=1199 participants with prevalent HF at baseline. Principal component analysis (PCA) and ANOVA (FDR p<0.1) were used to determine differences in proteins between three EF groups, as previously curated in EXSCEL (EF>55% [HFpEF], EF 40-55% [HFmrEF], EF<40% [HFrEF]). Cox proportional hazards was used to assess association between baseline levels of significant proteins, and changes in protein level between baseline and 12-month, with time-to-HF hospitalization. Mixed models were used to assess whether significant proteins changed differentially with exenatide vs. placebo therapy. Results: Of N=1199 EXSCEL participants with prevalent HF, 284 (24%), 704 (59%) and 211 (18%) had HFpEF, HFmrEF and HFrEF, respectively. Eight PCA protein factors and 221 individual proteins within these factors differed significantly across the three EF groups. Levels of the majority of proteins (83%) demonstrated concordance between HFmrEF and HFpEF, but higher levels in HFrEF, predominated by the domain of extracellular matrix regulation, e.g. COL28A1 and tenascin C [TNC]; p<0.0001. Concordance between HFmrEF and HFrEF was observed in a minority of proteins (1%) including MMP-9 (p<0.0001). Biologic pathways of epithelial mesenchymal transition, ECM receptor interaction, complement and coagulation cascades, and cytokine receptor interaction demonstrated enrichment among proteins with the dominant pattern, i.e. HFmrEF-HFpEF concordance. Baseline levels of 208 (94%) of the 221 proteins were associated with time-to-incident HF hospitalization including domains of extracellular matrix (COL28A1, TNC), angiogenesis (ANG2, VEGFa, VEGFd), myocyte stretch (NT-proBNP), and renal function (cystatin-C). Change in levels of 10 of the 221 proteins from baseline to 12 months (including increase in TNC) predicted incident HF hospitalization (p<0.05). Levels of 30 of the 221 significant proteins (including TNC, NT-proBNP, ANG2) were reduced differentially by EQW compared with placebo (interaction p<0.0001). Conclusions: In this HF substudy of a large clinical trial of people with T2DM, we found that serum levels of most proteins across multiple biologic domains were similar between HFmrEF and HFpEF. HFmrEF may be more biologically similar to HFpEF than HFrEF, and specific related biomarkers may offer unique data on prognosis and pharmacotherapy modification with variability by EF.

Protein biomarkers of cardiac remodeling and inflammation associated with HFpEF and incident events.

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.

Urine tricarboxylic acid cycle signatures of early-stage diabetic kidney disease.

INTRODUCTION: Urine tricarboxylic acid (TCA) cycle organic anions (OAs) are elevated in diabetes and may be biomarkers for diabetic kidney disease (DKD) progression. OBJECTIVES: We assessed associations of 10 urine TCA cycle OAs with estimated glomerular filtration rate (eGFR) and eGFR slope. METHODS: This study is ancillary to the Simultaneous Risk Factor Control Using Telehealth to SlOw Progression of Diabetic Kidney Disease (STOP-DKD) Trial-a randomized trial of pharmacist-led medication and behavior management in 281 patients with early to moderate DKD at Duke from 2014 to 2015. We used linear mixed models to assess associations of urine TCA cycle OAs with outcomes and modelled TCA cycle OAs as: (1) the average of z-scores for each OA; and (2) principal component (PC) scores derived by principal component analysis (PCA). Untargeted urine metabolomics were added for additional discovery. RESULTS: Among 132 participants with 24 h urine samples (50% men; 58% Black; mean age 64 years [SD 9]; mean eGFR 74 ml/min/1.73m2 [SD 21] and median urine albumin-to-creatinine [UACR] 20 mg/g [IQR 8-95]), PCA identified 3 OA metabolite PCs. Malate, fumarate, pyruvate, α-ketoglutarate, lactate, succinate and citrate/isocitrate loaded positively on PC1; methylsuccinate, ethylmalonate and succinate loaded positively on PC2; and methylmalonate, ethylmalonate and citrate/isocitrate loaded negatively on PC3. Over a median follow-up of 1.8 years (IQR, 1.2 to 2.2), higher average OA z-score was strongly associated with higher eGFR after covariate adjustment (p = 0.01), but not with eGFR slope (p = 0.9). Higher PC3, but not other PCs, was associated with lower eGFR (p < 0.001). Conditional random forests and smooth clipped absolute deviation models confirmed methylmalonate, citrate/isocitrate, and ethylmalonate, and added lactate as top ranked metabolites in models of baseline eGFR (R-squared 0.32 and 0.33, respectively). Untargeted urine metabolites confirmed association of urine TCA cycle OAs with kidney function. CONCLUSION: Thus, lower urine TCA cycle OAs, most notably lower methylmalonate, ethylmalonate and citrate/isocitrate, are potential indicators of kidney impairment in early stage DKD.