Open AccessExploring the link between metabolic syndrome and risk of dysmobility syndrome in elderly population
Author(s) -
YuanYuei Chen,
TungWei Kao,
ChungChing Wang,
Ying-Jen Chen,
ChenJung Wu,
WeiLiang Chen
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0207608
Subject(s) - metabolic syndrome , insulin resistance , medicine , national health and nutrition examination survey , population , osteoporosis , body mass index , homeostatic model assessment , physiology , obesity , endocrinology , environmental health
Dysmobility syndrome (DMS) was considered as a comprehensive approach to evaluate the condition of musculoskeletal system and adverse health problems in older population. The objective of our study was to examine the association between metabolic syndrome (MetS) and DMS in a U.S. adult population. 1760 eligible participants from the National Health and Nutrition Examination Survey (NHANES) 1999–2002 were enrolled in the study. The criteria of DMS consisted of six domains including increased body fat, declined muscle mass, reduced muscle strength, osteoporosis, slow gait speed, and balance problem. A multivariate regression analysis was investigated to clarify the relationship among MetS and its components and DMS. A positive association between increased number of MetS components and the presence of DMS achieved significance (β = 0.142, 95%CI = 0.035, 0.249, p = 0.009). Among the components of MetS, hyperglycemia had a central place in the DMS after adjustment of clinical variables (β = 0.083, 95%CI = 0.030, 0.136, p = 0.002). Notably, insulin resistance assessed by homeostatic model assessment (HOMA-IR) was correlated to increased body fat (r = 0.092, p<0.05), osteoporosis (r = -0.105, p<0.05) and balance (r = 0.105, p<0.05) among these participants with MetS. Our study demonstrated a strong relationship between DMS and the presence of MetS and its components in elderly population, highlighting a possible mechanism through insulin resistance.