For millions of middle-aged and older adults, the path to poor health rarely runs through a single disease. High blood pressure blends into diabetes, kidney function quietly declines, and within a few years a patient is managing several cardiometabolic conditions at once. Clinicians have long tried to predict who will slide into this state of cardiometabolic multimorbidity, and the newest framework, known as cardiovascular-kidney-metabolic or CKM assessment, groups risks across the heart, kidneys and metabolism into a unified picture. But a new multi-cohort study published in BMC Endocrine Disorders argues that something fundamental is missing from that picture: the body’s own functional reserve, the physical capacity a person retains to withstand physiological stress.
The research, led by Cheng Shen and Bohao Xue of the Department of General Practice at Shanghai Tenth People’s Hospital, Tongji University School of Medicine, together with Jin Li of the hospital’s Department of Geriatrics, set out to test whether objective measurements of functional reserve could add a complementary layer to existing CKM risk stratification. Their reasoning was straightforward. Two people can carry identical cardiovascular, kidney and metabolic risk profiles yet experience dramatically different health trajectories, and the authors suspected that differences in underlying physical capacity might help explain that heterogeneity. Rather than treating risk as a simple tally of conditions, they wanted to know whether the intersection of high organ-system burden and low functional reserve marks out a genuinely vulnerable subgroup.
To answer the question, the team assembled an unusually broad evidence base spanning four large longitudinal aging studies from three continents. The China Health and Retirement Longitudinal Study, or CHARLS, served as the development cohort where the main analytical strategy was built and refined. The Health and Retirement Study venous blood sample, HRS VBS, from the United States acted as the primary supportive replication cohort. The English Longitudinal Study of Ageing, ELSA, provided a simplified transportability test with fewer available measures, and wave six of the Survey of Health, Ageing and Retirement in Europe, SHARE, offered a grip-strength-only robustness check across a very large sample. In total, the descriptive samples comprised 3,238 participants in CHARLS, 1,627 in HRS VBS, 1,927 in ELSA and 12,641 in SHARE, with 284, 117, 73 and 701 incident cases of cardiometabolic multimorbidity respectively.
The analytical design was deliberately transparent. The researchers constructed equal-weight cohort-specific counts of CKM domains and crossed them with an age- and sex-standardized measure of objective functional reserve. Cardiometabolic multimorbidity was defined conservatively as the first follow-up observation in which a participant reported at least two cardiometabolic conditions. Cox proportional hazards models formed the statistical backbone, but the authors went far beyond a single headline estimate, supplementing their analysis with discrete-time models, common-domain and common-threshold variants, three-domain analyses, grip-only analyses, interaction testing, combined CMM-or-death endpoints, selection-weighted analyses, landmark analyses, subgroup analyses and five-year risk stratification. Where mortality data permitted, they added Fine-Gray and cause-specific competing-risk models, recognizing that in older populations death often competes with disease onset as an outcome.
The central finding was consistent across all four cohorts, spanning very different health systems and cultures. Participants who combined a high CKM burden with low functional reserve experienced substantially higher rates of incident cardiometabolic multimorbidity than the rest of the population. In CHARLS, the hazard ratio reached 3.52, with a 95 percent confidence interval of 2.47 to 5.00, meaning that these participants developed multimorbidity at more than three times the rate of their peers. HRS VBS yielded a hazard ratio of 1.71 (1.03 to 2.84), ELSA a striking 3.02 (1.62 to 5.63), and SHARE 1.67 (1.35 to 2.06). When the team applied Fine-Gray subdistribution models to account for the competing risk of death, the estimates held steady at 1.68 in both HRS VBS and SHARE. A phenotype that replicates across China, the United States, England and continental Europe commands attention, particularly one built from such simple, widely available measurements.
Yet the study is equally notable for what it did not find. The researchers tested formally for statistical interaction between CKM burden and functional reserve, on both the multiplicative scale and an approximate additive scale, and could not demonstrate it. In practical terms, low reserve appears to raise risk in a way that is broadly consistent across levels of CKM burden, rather than acting as a multiplier that specifically amplifies the danger of existing organ-system damage. This distinction matters for how the concept might eventually be used. A main-effects association supports reserve as a useful complementary marker, but the absence of demonstrated interaction means the two layers of information may be largely additive rather than synergistic, a more modest but still meaningful relationship.
The authors were also candid about the limits of predictive performance. When functional reserve measures were added to models predicting five-year multimorbidity risk, the gains in discrimination were small, with apparent changes in the area under the receiver operating characteristic curve ranging from just 0.0011 to 0.0150. In their conclusions, the team states plainly that the analyses did not establish a clinically deployable prediction tool. For readers hoping for a simple test that will tell them their multimorbidity fate, the message is one of tempered expectations. What the study delivers instead is a phenotyping concept: a way of identifying a subgroup of high-burden, low-reserve individuals whose reported disease incidence is elevated, and for whom targeted prevention trials might be designed in the future.
Several methodological caveats shape how the findings should be interpreted. Disease outcomes were primarily self-reported, which introduces the possibility of misclassification even in well-validated cohorts. Mortality ascertainment varied across the four studies and did not support a uniform competing-risk analysis in every cohort, which is why the Fine-Gray models were applied only where the timing of deaths was adequate. In the five-year analyses, deaths occurring before multimorbidity onset were counted separately rather than classified as controls, a decision that avoids inflating the apparent risk but also complicates direct comparison with simpler analyses. The team used 1,000 bootstrap resamples to gauge the stability of their risk stratification estimates, and their reporting followed both STROBE and TRIPOD guidelines, reflecting an unusually thorough commitment to transparent epidemiological practice.
The implications for aging populations are difficult to ignore. As societies worldwide grow older, the prevalence of cardiometabolic multimorbidity is climbing, straining health systems that are still largely organized around single-disease specialties. If a low-cost measurement such as grip strength, or a simple battery of physical performance tests, can flag the patients most likely to accumulate multiple cardiometabolic conditions, then prevention could be concentrated where it matters most. The SHARE grip-only cohort is particularly encouraging on this front, because grip strength is cheap, quick and feasible in almost any clinical setting, and the association held even when the reserve measure was reduced to that single robust indicator across more than twelve thousand European participants.
The road from phenotype to practice remains long. The authors themselves frame their conclusions as supporting further evaluation of the phenotyping concept rather than immediate clinical adoption, and they note that the study received funding from the Shanghai Municipal Health Commission while analyzing de-identified secondary data that required no additional participant contact. Future work will need to test whether reserve-informed stratification improves outcomes when used prospectively, whether it works with clinically measured rather than survey-based variables, and whether interventions aimed at preserving functional reserve can genuinely delay the onset of multimorbidity in high-risk individuals. Until then, this multi-cohort study stands as a careful, honestly reported demonstration that the body’s remaining functional capacity deserves a seat at the table when cardiovascular, kidney and metabolic risks are weighed together.
Subject of Research: Reserve-informed cardiovascular-kidney-metabolic risk stratification for predicting cardiometabolic multimorbidity in aging populations
Article Title: Reserve-informed cardiovascular-kidney-metabolic risk stratification for cardiometabolic multimorbidity progression in middle-aged and older adults: a multi-cohort study
Article References: Shen, C., Xue, B., & Li, J. (2026). Reserve-informed cardiovascular-kidney-metabolic risk stratification for cardiometabolic multimorbidity progression in middle-aged and older adults: a multi-cohort study. BMC Endocrine Disorders. https://doi.org/10.1186/s12902-026-02514-5
Image Credits: AI Generated
DOI: 10.1186/s12902-026-02514-5
Keywords: cardiovascular-kidney-metabolic, functional reserve, cardiometabolic multimorbidity, risk stratification, cohort study, aging, grip strength, CHARLS, HRS, ELSA, SHARE, epidemiology
