Association between Lower-to-Upper Ratio of Appendicular Skeletal Muscle and Metabolic Syndrome.
Hyun Eui MoonTae Sic LeeTae-Ha ChungPublished in: Journal of clinical medicine (2022)
(1) Background: Metabolic syndrome (MetS) is a cluster-based disorder comprising several pre-disease or pre-clinical statuses for diabetes, hypertension, dyslipidemia, cardiovascular risk, and mortality. Appendicular skeletal muscle (ASM), or lean mass, is considered the main site of insulin-mediated glucose utilization. Therefore, we aimed to reveal the association between lower appendicular skeletal muscle mass to upper appendicular skeletal muscle mass ratio (LUR) and risk for MetS. (2) Methods: We analyzed the 2008-2011 Korean National Health Examination and Nutrition Survey (KNHANES) data. Quintiles of lower ASM to upper ASM ratio (LUR) were categorized as follows: Q1: ≤2.65, Q2: 2.66-2.80, Q3: 2.81-2.94, Q4: 2.95-3.11, and Q5: ≥3.12 in men and Q1: ≤3.00, Q2: 3.01-3.18, Q3: 3.19-3.36, Q4: 3.37-3.60, and Q5: ≥3.61 in women. Multivariate logistic regression models were used after setting MetS and the LUR quintiles as the independent and dependent variables and adjusting for covariates. (3) Result: In men, MetS in accordance with the LUR quintiles exhibits a reverse J-curve. All groups from Q2 to Q5 had a lower odds ratio (OR) (95% CI) for MetS compared to the Q1 group. The lowest OR (95% CI) of 0.85 (0.80-0.91) was observed in Q4. However, in women, the figure shows a sine curve. Compared to the Q1 group, the Q2 and Q3 groups had a higher OR, while the Q4 and Q5 groups presented a lower OR. Among them, the OR (95% CI) in the Q4 group was lowest, at 0.83 (0.76-0.91). (4) Conclusions: While total appendicular skeletal muscle mass is important to prevent MetS, it is necessary to maintain an optimal ratio of muscle mass between the upper and lower appendicular skeletal muscle mass.
Keyphrases
- skeletal muscle
- metabolic syndrome
- insulin resistance
- type diabetes
- polycystic ovary syndrome
- blood pressure
- physical activity
- cardiovascular disease
- coronary artery disease
- machine learning
- cardiovascular events
- dna methylation
- middle aged
- risk factors
- adipose tissue
- pregnant women
- big data
- cross sectional
- genome wide
- artificial intelligence
- postmenopausal women