Subclinical Heart Dysfunction in Relation to Metabolic and Inflammatory Markers: A Community-Based Study

BACKGROUND: Population studies investigating the contribution of immunometabolic disturbances to heart dysfunction remain scarce. We combined high-throughput biomarker profiling, multidimensional network analyses, and regression statistics to identify immunometabolic markers associated with subclinical heart dysfunction in the community. METHODS: In 1,236 individuals (mean age, 51.0 years; 51.5% women), we measured 39 immunometabolic markers and assessed echocardiographic indexes of left ventricular diastolic dysfunction (LVDD) and left atrial (LA) reservoir dysfunction. We used partial least squares (PLS) to filter the most relevant biomarkers related to the echocardiographic characteristics. Subsequently, we assessed the associations between the echocardiographic features and biomarkers selected in PLS while accounting for clinical confounders. RESULTS: Influential biomarkers in PLS of echocardiographic characteristics included blood sugar, γ-glutamyl transferase, d-dimer, ferritin, hemoglobin, interleukin (IL)-4, IL-6, and serum insulin and uric acid. In stepwise regression incorporating clinical confounders, higher d-dimer was independently associated with higher E/e' ratio and LA volume index (P ≤ 0.05 for all). In multivariable-adjusted analyses, the risk for LVDD increased with higher blood sugar and d-dimer (P ≤ 0.048). After full adjustment, higher serum insulin and serum uric acid were independently related to worse LA reservoir strain and higher risk for LA reservoir dysfunction (P ≤ 0.039 for all). The biomarker panels detected LVDD and LA reservoir dysfunction with 87% and 79% accuracy, respectively (P < 0.0001). CONCLUSIONS: Biomarkers of insulin resistance, hyperuricemia, and chronic low-grade inflammation were associated with cardiac dysfunction. These biomarkers might help to unravel cardiac pathology and improve the detection and management of cardiac dysfunction in clinical practice.