The study, led by Yi Zhang and AnGe Liu of Capital Medical University’s Beijing Chao-Yang Hospital together with colleagues, drew on data from the China Health and Retirement Longitudinal Study, known as CHARLS. This nationally representative cohort follows adults aged 45 and older across China, and the research team focused on 4,219 participants classified within stages 0 to 3 of the cardiovascular-kidney-metabolic, or CKM, syndrome. This staging framework, promoted by the American Heart Association, recognizes that obesity, metabolic dysfunction, chronic kidney disease, and cardiovascular disease form an interconnected continuum rather than separate conditions. Stage 0 represents no detectable risk factors, while stage 3 involves subclinical cardiovascular disease or high-risk metabolic states, meaning the study population had not yet reached overt clinical cardiovascular events.

To quantify frailty, the investigators constructed a 32-item frailty index, a tool rooted in the cumulative deficit model of aging developed by geriatricians Kenneth Rockwood and Arnold Mitnitski. Rather than defining frailty through a handful of physical measures such as grip strength or walking speed, the frailty index aggregates deficits across many domains, including symptoms, chronic conditions, functional limitations, and cognitive measures. Each participant’s index score represents the proportion of deficits present, so a score of 0.25 means roughly a quarter of the measured health items are impaired. Participants were classified at baseline as robust, pre-frail, or frail depending on their index values.

The central innovation of the study, however, was its longitudinal design. Using frailty index measurements from four waves spanning 2011 to 2018, the researchers applied latent class growth modeling, a statistical technique that identifies hidden subgroups of people who share similar trajectories over time. Three distinct patterns emerged: a low-stable trajectory in which frailty remained minimal, a moderate-gradual increase trajectory showing steady deficit accumulation, and a high-rapid rise trajectory characterized by both elevated starting frailty and accelerating deterioration. This approach captures something a single snapshot cannot: the velocity of biological decline.

During follow-up, 541 participants developed cardiovascular disease. After adjusting for a comprehensive set of confounders, the differences between trajectory groups were striking. Compared with the low-stable group, those on the moderate-gradual increase trajectory had 87 percent higher odds of cardiovascular disease, with an odds ratio of 1.87 and a 95 percent confidence interval of 1.51 to 2.32. The high-rapid rise group fared far worse, with odds ratios reaching 3.52, or a confidence interval of 2.55 to 4.87, both statistically significant at P less than 0.001. In other words, people whose frailty was climbing quickly faced more than three and a half times the odds of a cardiovascular event compared with those whose deficit burden stayed low and flat.

The association was even more dramatic for stroke. Among participants on the high-rapid rise trajectory, the odds of stroke occurrence reached an odds ratio of 4.98, with a confidence interval of 3.01 to 8.24. This near fivefold elevation suggests that accelerating frailty is particularly informative about cerebrovascular risk, a finding consistent with growing evidence linking frailty to cerebrovascular disease mechanisms. The frailty index captures not only vascular risk factors but also inflammation, muscle wasting, and declining physiological reserve, all of which may converge to make the brain’s blood supply especially vulnerable.

Because observational studies of trajectories can be vulnerable to reverse causation, meaning that subclinical disease might itself drive frailty upward, the team performed two sensitivity analyses designed to strengthen causal interpretation. In a wave 4 fixed-interval landmark analysis, they examined events recorded at wave 5 among people still event-free at wave 4. In a wave 3 landmark Cox analysis, they tracked incident events at waves 4 and 5, and crucially, additionally adjusted for baseline frailty index. Both landmark analyses reproduced the primary association pattern, and in the wave 3 Cox analysis the links with total cardiovascular disease and stroke persisted even after accounting for starting frailty levels. This indicates that the trajectory itself, the rate and pattern of change, carries prognostic information beyond a single baseline measurement.

The study also confirmed that baseline frailty status matters on its own. In multivariable-adjusted Cox models, pre-frail participants had a 61 percent higher hazard of incident cardiovascular disease compared with robust participants, with a hazard ratio of 1.61 and a confidence interval of 1.33 to 1.95, while frail participants showed a hazard ratio of 1.63, with a confidence interval of 1.23 to 2.17. Interestingly, the hazard for pre-frail and frail groups was nearly identical, hinting that even early deficit accumulation, well before overt frailty, already elevates cardiovascular risk. This challenges the common clinical habit of waiting until frailty is unmistakable before acting.

What might explain the biology behind these numbers? Frailty is increasingly understood as a state of diminished resilience driven by chronic low-grade inflammation, a phenomenon sometimes called inflammaging. Elevated circulating inflammatory markers such as interleukin-6 and C-reactive protein promote atherosclerosis, endothelial dysfunction, and thrombosis while simultaneously eroding muscle mass and cognitive function. Within the cardiovascular-kidney-metabolic framework, these processes are amplified: metabolic syndrome accelerates vascular damage, declining kidney function worsens fluid and mineral balance, and the resulting cardiac stress feeds back into further functional decline. A rapidly rising frailty index may therefore be an integrated readout of this vicious cycle, capturing the cumulative toll across organ systems that individual biomarkers miss.

The practical implications are considerable. Cardiovascular disease remains the leading cause of death globally, and risk prediction models that incorporate cardiovascular-kidney-metabolic health have been endorsed by the American Heart Association, yet they rely largely on conventional measures such as blood pressure, cholesterol, and glucose. The new findings suggest that serial frailty index assessment, which can be computed from routine clinical and self-reported data, could complement these models by flagging people whose health is deteriorating faster than their risk factors alone would predict. For clinicians managing the vast population of adults in early CKM stages, the message is that a single frailty assessment is a starting point, not a verdict. Repeated measurement can identify persistent or accelerating deficit accumulation that may warrant closer longitudinal monitoring, earlier intervention, and potentially targeted prevention before the first heart attack or stroke occurs. As populations age worldwide, tracking the speed of biological aging may prove as important as tracking the numbers on a lipid panel.

Subject of Research: Association between longitudinal frailty index trajectories and cardiovascular disease risk in adults with early cardiovascular-kidney-metabolic syndrome

Article Title: Frailty trajectories and cardiovascular disease in adults with CKM stages 0–3

Article References: Zhang, Y., Liu, A., Shi, C., Xu, Y., Yang, R., & An, Z. (2026). Frailty trajectories and cardiovascular disease in adults with CKM stages 0–3. GeroScience. https://doi.org/10.1007/s11357-026-02559-3

Image Credits: AI Generated

DOI: 10.1007/s11357-026-02559-3

Keywords: frailty, frailty index, cardiovascular disease, cardiovascular-kidney-metabolic syndrome, stroke, CHARLS, longitudinal trajectories, latent class growth modeling, aging, risk prediction, GeroScience, inflammaging