TY - JOUR
T1 - Skeletal muscle mass and all-cause mortality
T2 - Findings from the CRONICAS cohort study
AU - Bernabe-Ortiz, Antonio
AU - Carrillo-Larco, Rodrigo M.
AU - Gilman, Robert H.
AU - Smeeth, Liam
AU - Checkley, William
AU - Miranda, J. Jaime
N1 - Funding Information:
The CRONICAS Cohort Study was supported by the National Heart, Lung and Blood Institute Global Health Initiative under the contract Global Health Activities in Developing Countries to Combat Non‐Communicable Chronic Diseases (Project Number 268200900033C‐1‐0‐1). Rodrigo M. Carrillo‐Larco is supported by a Wellcome Trust International Training Fellowship (214185/Z/18/Z).
Funding Information:
National Heart, Lung and Blood Institute Global Health Initiative, Grant/Award Number: 268200900033C‐1‐0‐1; Wellcome Trust International Training Fellowship, Grant/Award Number: 214185/Z/18/Z Funding information
Publisher Copyright:
© 2022 John Wiley & Sons Ltd.
PY - 2023/2
Y1 - 2023/2
N2 - 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.
AB - 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.
KW - all-cause mortality
KW - bio-impedance analysis
KW - cohort study
KW - skeletal muscle mass
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U2 - 10.1111/tmi.13844
DO - 10.1111/tmi.13844
M3 - Article
C2 - 36573344
AN - SCOPUS:85145365897
SN - 1360-2276
VL - 28
SP - 107
EP - 115
JO - Tropical Medicine and International Health
JF - Tropical Medicine and International Health
IS - 2
ER -