Myocardial strain imaging unmasks subclinical heart failure risk across the cardiovascular–kidney–metabolic syndrome continuum

Abstract

The cardiovascular-kidney-metabolic (CKM) syndrome framework identifies a systemic progression toward cardiovascular adverse outcome and heart failure (HF). However, the extent of subclinical myocardial involvement, particularly assessed by sensitive deformation measures such as left ventricular (LV) global longitudinal strain (GLS), and its prognostic utility in HF-free populations within the early stages of this continuum (Stage 0–2) remain largely unexplored. To evaluate the incremental prognostic value of LV GLS over CKM staging for predicting incident HF and mortality in a primary prevention cohort. We analyzed 4,980 participants (mean age 52.7 years, 59.6% male) from a prospective cohort who were free of overt cardiovascular disease at baseline (CKM Stages 0–2). LV GLS was quantified by speckle-tracking echocardiography from standard apical views. The primary endpoint was a composite of HF hospitalization and all-cause death. Prognostic performance was evaluated using Cox proportional hazards models, time-dependent ROC curves, and Net Reclassification Improvement (NRI). LV GLS progressively worsened across CKM stages (Stage 0: -21.2 ± 2.1%; Stage 1: -20.2 ± 1.9%; Stage 2: -19.3 ± 2.3%, p for trend < 0.001). Over a median follow-up of 7.6 years, 261 composite events occurred. After multi-variable adjustment for clinical risk factors, LV mass index, and diastolic function, each 1% absolute decrease (more positive) in LV GLS was associated with a 13% higher risk of the primary outcome (Adjusted HR 1.13; 95% CI 1.07–1.18). Adding LV GLS to a baseline model of CKM staging significantly improved discrimination (5-year AUC 0.791 vs. 0.671; ΔAUC=0.120; 95% CI 0.095–0.146) and yielded significant risk reclassification (NRI 0.057; 95% CI 0.001–0.118). LV strain imaging reveals “hidden” cardiovascular risk that standard CKM staging may overlook. Integrating GLS into the CKM framework significantly enhances HF risk stratification, supporting its use in identifying high-risk individuals for intensive precision prevention.Figure 1Figure 2