Skeletal muscle mass and all-cause mortality: Findings from the CRONICAS cohort study
- ,
- Rodrigo M. Carrillo-Larco,
- Robert H. Gilman,
- Liam Smeeth,
- William Checkley,
- J. Jaime Miranda
- ,
- Universidad Peruana Cayetano Heredia,
- Imperial College London,
- Johns Hopkins University,
- London School of Hygiene and Tropical Medicine,
- Johns Hopkins University School of Medicine
Open access
Publication Information
Output type
Original language
EnglishPages from-to (Number of pages)
Pages 107-115 (9 pages)Journal (Volume, Issue Number)
Tropical Medicine and International Health (Volume 28, Issue 2)Publication milestones
- Accepted/In press - 2022
- Published - 02/2023
Publication status
ISSN
1360-2276Publication IDs
- Scopus: 85145365897
- PubMed: 36573344
Abstract
Objective: We aimed (1) to evaluate the agreement between two methods (equation and bio-impedance analysis [BIA]) to estimate skeletal muscle mass (SMM), and (2) to assess if SMM was associated with all-cause mortality risk in individuals across different geographical sites in Peru. Methods: We used data from the CRONICAS Cohort Study (2010–2018), a population-based longitudinal study in Peru to assess cardiopulmonary risk factors from different geographical settings. SMM was computed as a function of weight, height, sex and age (Lee equation) and by BIA. All-cause mortality was retrieved from national vital records. Cox proportional-hazard models were developed and results presented as hazard ratios (HRs) with 95% confidence intervals (95% CIs). Results: At baseline, 3216 subjects, 51.5% women, mean age 55.7 years, were analysed. The mean SMM was 23.1 kg (standard deviation [SD]: 6.0) by Lee equation, and 22.7 (SD: 5.6) by BIA. Correlation between SMM estimations was strong (Pearson's ρ coefficient = 0.89, p < 0.001); whereas Bland–Altman analysis showed a small mean difference. Mean follow-up was 7.0 (SD: 1.0) years, and there were 172 deaths. In the multivariable model, each additional kg in SMM was associated with a 19% reduction in mortality risk (HR = 0.81; 95% CI: 0.75–0.88) using the Lee equation, but such estimate was not significant when using BIA (HR = 0.98; 95% CI: 0.94–1.03). Compared to the lowest tertile, subjects at the highest SMM tertile had a 56% reduction in risk of mortality using the Lee equation, but there was no such association when using BIA estimations. Conclusion: There is a strong correlation and agreement between SMM estimates obtained by the Lee equation and BIA. However, an association between SMM and all-cause mortality exists only when the Lee equation is used. Our findings call for appropriate use of approaches to estimate SMM, and there should be a focus on muscle mass in promoting healthier ageing.
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