Metabolomic Association and Risk Prediction With Heart Failure in Older Adults

Abstract

Background: Older adults have markedly increased risks of heart failure (HF), specifically HF with preserved ejection fraction (HFpEF). Identifying novel biomarkers can help in understanding HF pathogenesis and improve at-risk population identification. This study aimed to identify metabolites associated with incident HF, HFpEF, and HF with reduced ejection fraction and examine risk prediction in older adults.

Methods: Untargeted metabolomic profiling was performed in Black and White adults from the ARIC study (Atherosclerosis Risk in Communities) visit 5 (n=3719; mean age, 75 years). We applied Cox regressions to identify metabolites associated with incident HF and its subtypes. The metabolite risk score (MRS) was constructed and examined for associations with HF, echocardiographic measures, and HF risk prediction. Independent samples from visit 3 (n=1929; mean age, 58 years) were used for replication.

Results: Sixty metabolites (hazard ratios range, 0.79-1.49; false discovery rate, <0.05) were associated with incident HF after adjusting for clinical risk factors, eGFR, and NT-proBNP (N-terminal pro-B-type natriuretic peptide). Mannonate, a hydroxy acid, was replicated (hazard ratio, 1.36 [95% CI, 1.19-1.56]) with full adjustments. MRS was associated with an 80% increased risk of HF per SD increment, and the highest MRS quartile had 8.7× the risk of developing HFpEF than the lowest quartile. High MRS was also associated with unfavorable values of cardiac structure and function. Adding MRS over clinical risk factors and NT-proBNP improved 5-year HF risk prediction C statistics from 0.817 to 0.850 (∆C, 0.033 [95% CI, 0.017-0.047]). The association between MRS and incident HF was replicated after accounting for clinical risk factors (P<0.05).

Conclusions: Novel metabolites associated with HF risk were identified, elucidating disease pathways, specifically HFpEF. An MRS was associated with HF risk and improved 5-year risk prediction in older adults, which may assist at at-risk population identification.

Keywords: blood pressure; echocardiography; heart failure; hypertension; stroke.

Figure 1.

Study flowchart for sample selection, metabolites selection, and analysis process. Samples were extracted from the ARIC study (Atherosclerosis Risk in Communities Visit) 5 (n=6538) and visit 3 (n=12 887). After applying the exclusion criteria, the final analysis sample for visit 5 was 3719 with 308 heart failure (HF) cases and 1929 for visit 3 with 316 HF cases. In the metabolite selection section, Cox regression was applied to 790 metabolites at visit 5 and 254 metabolites significantly associated with incident HF at false discovery rate (FDR) adjusted P<0.05 level was retained for later analysis. At visit 3240 out of 254 metabolites were available for replication.

Figure 2.

The 60 metabolites associated with incident heart failure (HF), HF with preserved ejection fraction (HFpEF), and HF with reduced (HFrEF) among ARIC study (Atherosclerosis Risk in Communities) visit 5 participants (n=3719). Hazard ratios were calculated adjusting for age, sex, race, study center, heart rate, current smoking status (current, former, and never), prevalent diabetes, prevalent hypertension, body mass index, systolic blood pressure, prevalent coronary heart disease, prevalent atrial fibrillation, estimated glomerular filtration rate (eGFR), and NT-proBNP (N-terminal pro-B-type natriuretic peptide). False discovery rate (FDR) using Benjamini-Hochberg procedure. Solid dots indicate metabolites are significantly associated with respect outcome after FDR adjustment, while hollow dots indicate metabolites are not significantly associated. Gray bar under each dot shows the 95% CIs for 1 SD increase in metabolite level. GPC indicates glycerophosphocholines; and GPE, glycerophosphocholines.

Figure 3.

Metabolite modules and their association with heart failure. A, Chord diagram of 254 heart failure related-metabolites modules. The upper semicircle shows the 9 constructed modules (from left to right: grey, turquoise, red, black, blue, brown, green, yellow, and pink) using the weighted gene correlation network approach. The bottom semicircle shows the 254 heart failure related metabolites categorized by super pathways. The links between super pathway grouped metabolites and modules are colored by super pathways, which indicate that a particular metabolite from a super pathway was clustered into a corresponding module. B, Forest plots for metabolite color modules and heart failure association among ARIC study (Atherosclerosis Risk in Communities) visit 5 participants (n=3719). In model 1, adjustments included age, sex, race, study center, heart rate, current smoking status (current, former, and never), prevalent diabetes, prevalent hypertension, body mass index, systolic blood pressure, prevalent coronary heart disease, prevalent atrial fibrillation. Models 2 and 3 were future adjusted for estimated glomerular filtration rate (eGFR) and NT-proBNP (N-terminal pro-B-type natriuretic peptide). Model 2 for Module turquoise was not shown due to collinearity between Module turquoise eigenvector and eGFR. Hazard ratios were calculated per 1 SD increment of the standardized module eigenvector, calculated from the first component of module matrix. Short bars represent the 95% CIs for 1 SD increase in module eigenvector. A indicates amino acid; C&V, cofactors and vitamins; CA, carbohydrate; E, energy; HF, heart failure; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction; HR, hazard ratio; L, lipid; N, nucleotide; PCM, partially characterized molecules; PEP, peptide; PFOA, perfluorooctanoic acid; PFOS, perfluorooctane sulfonic acid; TCA, tricarboxylic acid; and X, xenobiotics.

Figure 4.

Receiver operating characteristic (ROC) curve for 5-year heart failure risk prediction models among older adults (aged 75 years+) from the ARIC study (Atherosclerosis Risk in Communities). Clinical risk factors (CRF) including age, sex, race, center, heart rate, current smoking status, prevalent diabetes, prevalent hypertension, body mass index, systolic blood pressure, prevalent coronary heart disease, and prevalent atrial fibrillation. MRS indicates metabolite risk score; and NT-proBNP, N-terminal pro-B-type natriuretic peptide.