Composite metabolic biomarkers for cardiovascular risk assessment in CKM syndrome

Graphical abstract

Matters arising

The recent recognition of cardiovascular-kidney-metabolic (CKM) syndrome has fundamentally shifted cardiovascular prevention from the treatment of isolated risk factors toward integrated assessment of metabolic, renal, and vascular health [1]. Within this framework, early identification of individuals progressing toward overt cardiovascular disease before the onset of irreversible vascular injury has become a major research priority. Consequently, there is increasing interest in pragmatic biomarkers capable of capturing the complex interplay between metabolic dysfunction and cardiovascular risk [2].

In this issue, Lu and colleagues extend this concept by evaluating the triglyceride-glucose-visceral lipid ratio (TyG-VLR) [3], a composite biomarker integrating insulin resistance, visceral adiposity, and atherogenic lipid burden [4], in more than 6,000 participants from the China Health and Retirement Longitudinal Study. They demonstrate that higher baseline, cumulative, and persistently elevated TyG-VLR are independently associated with incident cardiovascular disease among individuals with CKM stages 0–3, with particularly strong associations observed for stroke. Importantly, cumulative exposure and longitudinal trajectories provided greater prognostic information than a single baseline measurement, reinforcing the concept that chronic metabolic burden, rather than isolated measurements, drives progressive vascular injury [5].

The appeal of TyG-VLR lies in its simplicity and accessibility. Unlike advanced imaging techniques or specialized circulating biomarkers, it can be derived entirely from routinely available clinical measurements, making it an attractive candidate for large-scale implementation across diverse healthcare settings [6]. As healthcare systems increasingly move toward preventive and precision medicine, inexpensive biomarkers capable of repeated longitudinal assessment are likely to become increasingly valuable. The findings from Lu and colleagues also reinforce an emerging principle in cardiometabolic medicine—that repeated assessment of metabolic health may be more informative than static measurements, analogous to cumulative exposure to LDL cholesterol, blood pressure, or glycaemia over time [7].

Nevertheless, several considerations warrant discussion. Although TyG-VLR was independently associated with cardiovascular events, improvements in risk discrimination were relatively modest, suggesting that the biomarker is more likely to complement existing risk assessment strategies than replace them. Whether TyG-VLR provides incremental prognostic information beyond established CKM staging, conventional cardiovascular risk scores, or imaging-based measures of subclinical atherosclerosis remains uncertain. Furthermore, because TyG-VLR incorporates several interrelated metabolic variables, future mechanistic studies may help determine which individual components contribute most strongly to its predictive performance and whether the composite index offers advantages over its constituent measures alone.

The study was performed in a nationally representative Chinese cohort using the Chinese Visceral Adiposity Index [8], providing important insights into risk assessment within this population. Future validation in other ethnic groups and healthcare settings will help determine the broader applicability of these findings. As with all observational studies, the reported associations should be interpreted as hypothesis-generating, and whether interventions that modify TyG-VLR translate into improved cardiovascular outcomes remains to be established.

This study adds to the growing body of evidence supporting the use of longitudinal metabolic markers for cardiovascular risk assessment within the CKM framework. Further studies will determine whether TyG-VLR can improve existing risk prediction strategies and help identify patients who may benefit from earlier preventive interventions.

Author contributions

S.K.: Conceptualization, Investigation, Writing–original draft, Visualization, Writing–review & editing. F.J.H.: Writing–review & editing, Supervision. N.N.: Conceptualization, Supervision, Writing–review & editing. All authors read and approved the final manuscript.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Conflict of interests

The authors declare no competing interests.