Author:
Yang Yuqi,Li Qian,Qiu Wanting,Zhang Helin,Qiu Yuyang,Yuan Jing,Zha Yan
Abstract
AbstractAlthough decreasing body mass index (BMI) is associated with higher mortality risk in patients undergoing hemodialysis (HD), BMI neither differentiates muscle and fat mass nor provides information about the variations of fat distribution. It remains unclear whether changes over time in fat and muscle mass are associated with mortality. We examined the prognostic significance of trajectory in the triceps skinfold (TSF) thickness and mid-upper arm circumference (MUAC). In this multicenter prospective cohort study, 972 outpatients (mean age, 54.5 years; 55.3% men) undergoing maintenance HD at 22 treatment centers were included. We calculated the relative change in TSF and MUAC over a 1-year period. The outcome was all-cause mortality. Kaplan–Meier, Cox proportional hazard analyses, restricted cubic splines, and Fine and Gray sub-distribution hazards models were performed to examine whether TSF and MUAC trajectories were associated with all-cause mortality. During follow-up (median, 48.0 months), 206 (21.2%) HD patients died. Compared with the lowest trajectory group, the highest trajectories of TSF and MUAC were independently associated with lower risk for all-cause mortality (HR = 0.405, 95% CI 0.257–0.640; HR = 0.537; 95% CI 0.345–0.837; respectively), even adjusting for BMI trajectory. Increasing TSF and MUAC over time, measured as continuous variables and expressed per 1-standard deviation decrease, were associated with a 55.7% (HR = 0.443, 95% CI 0.302–0.649), and 97.8% (HR = 0.022, 95% CI 0.005–0.102) decreased risk of all-cause mortality. Reduction of TSF and MUAC are independently associated with lower all-cause mortality, independent of change in BMI. Our study revealed that the trajectory of TSF thickness and MUAC provides additional prognostic information to the BMI trajectory in HD patients.
Funder
Guizhou Science and Technology Project
Guizhou High-level Innovative Talents Program
Guizhou Clinical Research Center for Kidney Disease
Publisher
Springer Science and Business Media LLC
Reference41 articles.
1. Collins, A. J. et al. United States renal data system 2011 annual data report: Atlas of chronic kidney disease & end-stage renal disease in the United States. Am. J. Kidney Dis. 59(1 Suppl 1), A7-e420 (2012).
2. Bello, A. K. et al. Epidemiology of haemodialysis outcomes. Nat Rev Nephrol. 18(6), 378–395 (2022).
3. Zhang, L. et al. China Kidney Disease Network (CK-NET) 2016 annual data report. Kidney Int. Suppl. (2011) 10(2), e97–e185 (2020).
4. Saran, R., et al. US Renal Data System 2016 annual data report: Epidemiology of kidney disease in the United States [published correction appears in Am J Kidney Dis. 2017 May;69(5):712]. Am. J. Kidney Dis. 2017;69(3 Suppl 1):A7-A8.
5. Oreopoulos, A. et al. The obesity paradox in the elderly: potential mechanisms and clinical implications. Clin. Geriatr. Med. 25(4), 643–viii (2009).