Dysregulated Tricarboxylic Acid Cycle Metabolism Is Associated With Right Ventricular Maladaptation in Pulmonary Vascular Disease

Abstract

Background: Right ventricular (RV) maladaptation to elevated pulmonary afterload is the primary determinant of outcomes in pulmonary artery (PA) hypertension; however, the pathobiological mechanisms underlying RV decompensation remain poorly understood.

Methods: We performed global untargeted metabolomics on plasma from 55 patients who underwent gold-standard RV-PA coupling measurements using multibeat pressure volume loop assessment in a single-center cohort and from 1027 patients with coupling surrogate measurements in a larger multicenter cohort, the PVDOMICS (Pulmonary Vascular Disease Phenomics) study. Age and sex-adjusted linear regression was performed to identify associations between metabolites and coupling metrics. Additionally, we performed a metabolic flux analysis using gene expression data from RV tissue in an independent cohort of 32 patients. Partial least squares-discriminant analysis was used to identify metabolites and reactions characteristic of the decompensated RV.

Results: RV-PA coupling was negatively associated with tricarboxylic acid (TCA) cycle intermediate levels. Specifically, plasma α-ketoglutarate and fumarate were significantly associated with all coupling metrics in both cohorts. Metabolic flux analysis indicated that decompensated RVs exhibited aberrant TCA cycle activity, including reduced acetyl coenzyme A entry and increased lactate elimination, suggesting a shift from the TCA cycle toward glycolysis at the RV tissue level.

Conclusions: We identify an association between circulating TCA cycle intermediate levels and RV-PA uncoupling in 2 independent cohorts, and dysregulated TCA cycle metabolism in decompensated PA hypertension RVs, suggesting that aberrant TCA cycle metabolism could represent a hallmark of RV maladaptation in PA hypertension. Further study of this pathway is warranted to develop novel biomarkers of RV function or RV-targeted therapies.

Keywords: global untargeted metabolomics; right ventricular maladaptation; right ventricular–pulmonary artery coupling; tricarboxylic acid cycle.

Figure 1. Results from age‐ and sex‐ adjusted linear regression between metabolites and multibeat E _es/E _a in the discovery cohort.

A, β‐coefficients with 95% CI for metabolites with P value <0.05. B, Manhattan plot illustrating –log (P value) for metabolites separated by metabolic pathway. E _es/E _a indicates end‐systolic elastance/arterial elastance; and TCA, tricarboxylic acid.

Figure 2. Results from age‐ and sex‐adjusted linear regression between metabolites and SV/ESV in the validation cohort.

A, β‐coefficients with 95% CI for TCA cycle metabolites with q value <0.05. B, Manhattan plot illustrating –log (q value) separated by metabolic pathway, with TCA cycle metabolites in red. SV/ESV indicates stroke volume/end‐systolic volume; and TCA, tricarboxylic acid.

Figure 3. Results from age‐ and sex‐adjusted linear regression between metabolites and TAPSE/PASP in validation cohort.

A, β‐coefficients with 95% CI for TCA cycle metabolites with q value <0.05. B, Manhattan plot illustrating –log (q value) separated by metabolic pathway, with TCA cycle metabolites in red. TAPSE/PASP indicates tricuspid annular plane systolic excursion/pulmonary artery systolic pressure; and TCA, tricarboxylic acid.

Figure 4. Venn diagram illustrating overlap in metabolites associated with E _es/E _a, SV/ESV, and TAPSE/PASP in linear regression.

Fifteen metabolites significantly associated with all 3 coupling metrics are listed. E _es/E _a indicates end‐systolic elastance/arterial elastance; SV/ESV, stroke volume/end‐systolic volume; and TAPSE/PASP, tricuspid annular plane systolic excursion/pulmonary artery systolic pressure.

Figure 5. Results from PLS‐DA of inferred metabolic flux from RV transcriptomics (N=34).

A, Model discrimination of clinically defined RV functional states. Colored point clouds represent different RV functional states. B, VIP plot illustrating relative importance of each metabolic reaction contributing to class separation by component 1, with higher values reflecting greater contribution. Colored boxes to the right represent the relative metabolic flux of each reaction across the 3 RV functional states. PLS‐DA indicates partial least squares discriminant analysis; RV, right ventricle; and VIP, variable importance in projection.

Figure 6. Results from PLS‐DA of inferred metabolite balance from RV transcriptomics (N=34).

A, Model discrimination of clinically defined RV functional states. Colored point clouds represent different RV functional states. B, VIP plot illustrating relative importance of each metabolite contributing to class separation by component 1, with higher values reflecting greater contribution. Colored boxes to the right represent the relative balance of each metabolite across the 3 RV functional states. PLS‐DA indicates partial least squares discriminant analysis; RV, right ventricular; and VIP, variable importance in projection.