Metabolic Syndrome and Body Composition Among People Aged 50 Years and Over: Results from The Neyshabur Longitudinal Study on Ageing (NeLSA).
Mohsen Azimi-NezhadNayyereh AminisaniAhmad GhasemiAzam Rezaei FarimaniFatemeh KhorashadizadehSeyed Reza MirhafezMartin HydeSeyed Morteza ShamshirgaranPublished in: Indian journal of clinical biochemistry : IJCB (2021)
There are few studies regarding body composition and metabolic syndrome (MetS) association in older adults . To evaluate the association between MetS and body composition indices in a large-scale population of subjects with an age of 50 and up . This study was based on the data from Neyshabur Longitudinal Study on Ageing (NeLSA) in a total of 7462 people of Neyshabur city in IRAN. The best cut-off scores and AUC value of body composition variables for having association with likelihood of MetS were determined by using a receiver operating curve analysis. Each unit increase in the Waist/Hip ratio, the odds of having MetS increase 3-6 times (OR: 4.937, 95%CI: 3.930, 6.203 in men; OR: 3.322, 95%CI: 2.259, 4.884 in women). In addition, in the case of BMI (OR: 1.256, 95% Cl: 1.226, 1.286 in men; OR: 1.104, 95% Cl: 1.086, 1.121 in women) and BFM (OR: 1.119, 95% Cl: 1.105, 1.133 in men; OR: 1.050, 95% Cl: 1.041, 1.060 in women), the chance of having MetS increases with increasing these variables. Totally, BMI and BFM showed the best AUC values. The optimal cut-off values for BMI in men was 26.45 and in women was 27.35 and for BFM in men was 23.35 and in women was 26.85. These results suggest that adiposity measures such as BMI and BFM are associated with likelihood of having MetS in subjects with an age of 50 and up, and that avoiding high adiposity is important to prevent MetS incidence.
Keyphrases
- body composition
- polycystic ovary syndrome
- resistance training
- metabolic syndrome
- bone mineral density
- body mass index
- insulin resistance
- pregnancy outcomes
- weight gain
- middle aged
- cervical cancer screening
- breast cancer risk
- type diabetes
- physical activity
- pregnant women
- risk factors
- machine learning
- cardiovascular disease
- skeletal muscle
- big data
- cardiovascular risk factors
- weight loss
- postmenopausal women