Higher Fitness Predicts 58% Lower Odds of Developing Multiple Cardiometabolic Diseases
A 12–15 year study of 3,326 older adults finds estimated cardiorespiratory fitness strongly and inversely predicts cardiometabolic multimorbidity risk.
Summary
Using data from 3,326 older English adults followed for 12–15 years, researchers found that higher estimated cardiorespiratory fitness (eCRF) — derived from nonexercise prediction equations — was consistently associated with lower odds of developing cardiometabolic multimorbidity (CMM), defined as two or more of hypertension, cardiovascular disease, diabetes, or stroke. Each 1-MET increase in eCRF was associated with 22–27% lower odds of CMM. Participants in the highest fitness tertile had roughly 58–64% lower odds compared to those in the lowest tertile. All three eCRF equations tested produced similar results, and adding eCRF to conventional risk models modestly improved prediction. The findings suggest eCRF is a practical, no-exercise-test-needed tool for identifying older adults at elevated CMM risk.
Detailed Summary
Cardiometabolic multimorbidity — the simultaneous presence of two or more conditions such as hypertension, type 2 diabetes, cardiovascular disease, and stroke — is a growing burden in aging populations and is associated with markedly worse health outcomes than any single condition alone. While physical activity is a known protective factor, cardiorespiratory fitness (CRF) captures a broader, more objective physiological signal. This study asked whether estimated CRF, derived without exercise testing, could predict who goes on to develop CMM over more than a decade.
Researchers analyzed data from the English Longitudinal Study of Aging (ELSA), drawing on wave 4 (2008–2009) as baseline and following 3,326 adults (mean age 63 years; 54.7% women) through wave 10 (2021–2023). All participants were free of hypertension, CVD, diabetes, and stroke at baseline. eCRF was calculated using three validated nonexercise prediction equations: a heart rate-based Jackson equation, a BMI-based Jackson equation, and the sex-specific HUNT (Nord-Trøndelag Health Study) equation developed by Nes et al. These equations incorporate easily obtainable inputs — age, sex, resting heart rate, BMI, waist circumference, physical activity level, and smoking status — making them scalable for population-level research and clinical screening.
Over the follow-up period, 197 participants (5.9%) developed CMM. Each 1-MET increment in heart rate-based eCRF was associated with a 22% lower odds of CMM (OR=0.78; 95% CI, 0.70–0.87); the BMI-based model yielded an identical per-MET OR of 0.78 (95% CI, 0.68–0.89), and the HUNT-based model showed a 27% reduction per MET (OR=0.73; 95% CI, 0.65–0.82). Comparing the highest versus lowest tertile of eCRF, the heart rate- and BMI-based models each showed 58% lower odds of CMM (OR≈0.42), while the HUNT-based model showed 64% lower odds (OR=0.36). Natural cubic spline analyses confirmed predominantly linear inverse dose-response relationships across the eCRF distribution. Importantly, individuals who went on to develop CMM had lower baseline eCRF values across all three metrics, as well as higher BMI, larger waist circumference, faster resting heart rate, worse lipid profiles, and higher blood pressure.
When each eCRF measure was incorporated into a conventional risk prediction model — already including age, sex, alcohol, blood pressure, cholesterol, triglycerides, and handgrip strength — discrimination improved modestly, suggesting eCRF provides incremental but not transformative predictive value beyond standard clinical factors. The consistency across all three equations, each using slightly different inputs, strengthens confidence in the underlying physiological signal rather than equation-specific artifact.
The study's findings are clinically meaningful for older adult populations and primary care settings where exercise testing is rarely feasible. Because eCRF can be estimated from routine clinical variables, it could be integrated into existing electronic health records or risk calculators to flag older adults at elevated CMM risk who might benefit from targeted fitness-enhancing interventions. The authors note, however, that eCRF is an estimate subject to prediction error and cannot replace directly measured VO2max when precision is required.
Key Findings
- Each 1-MET increase in eCRF reduced CMM odds by 22–27%, consistent across all three prediction equations.
- Highest vs lowest eCRF tertile was associated with 58–64% lower odds of developing cardiometabolic multimorbidity.
- 5.9% of initially healthy older adults developed CMM over 12–15 years of follow-up.
- Dose-response relationships were predominantly linear, suggesting no safe lower threshold for fitness.
- Adding eCRF to conventional risk models modestly but consistently improved cardiometabolic multimorbidity prediction.
Methodology
Prospective cohort study using ELSA data (n=3,326; mean age 63 years); baseline wave 4 (2008–2009) through wave 10 (2021–2023). eCRF derived from three validated nonexercise equations; CMM defined as ≥2 of hypertension, CVD, diabetes, or stroke. Multivariable logistic regression and natural cubic splines used; complete-case analytic approach applied.
Study Limitations
eCRF is an estimate subject to prediction error and does not replace directly measured VO2max; the study used a complete-case approach that may introduce selection bias. CMM and comorbidities were based on self-reported physician diagnoses, which may undercount conditions. Participants were restricted to community-dwelling English adults aged ≥50, limiting generalizability to other ethnicities and settings.
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