FreshRSS

🔒
❌ Acerca de FreshRSS
Hay nuevos artículos disponibles. Pincha para refrescar la página.
Hoy — Marzo 4th 2026Tus fuentes RSS

Associations between indices of body composition and metabolic status in normal-weight adults: a cross-sectional study of the Tehran Lipid and Glucose Study

Por: Maleki · S. · Hosseinpanah · F. · Mahdavi · M. · Momenan · A. A. · Ebadi · S. A. · Rahmani · F. · Azizi · F. · Valizadeh · M.
Objective

To investigate associations between body composition indices and metabolic status among normal-weight adults.

Design

Cross-sectional study using data from the Tehran Lipid and Glucose Study (phaseVII: 2019–2021).

Setting

Primary care and community health services in an urban Tehran population.

Participants

1298 adults (40.5% men, 59.5% women), aged 18–80years, body mass index (BMI) 18.5–24.9 kg/m². Exclusions: known diabetes, cardiovascular disease, kidney failure, malignancy, pregnancy or lactation, diuretic or glucocorticoid use. Participants were classified as metabolically healthy normal weight (MHNW) or unhealthy (MUHNW).

Primary and secondary outcome measures

The primary outcome was the association between body composition and anthropometric indices with metabolic status. The secondary outcome was identification of the strongest predictors of MUHNW. Body composition was assessed by bioelectrical impedance analysis to obtain fat mass (FM), body fat percentage (BFP), skeletal muscle mass percentage (SMM%), fat mass index (FMI), fat-free mass index, skeletal muscle indices and the fat-to-muscle mass ratio (FMR). Anthropometric measures included waist circumference (WC) and waist-to-hip ratio (WHR). Associations were examined using logistic regression adjusted for age, smoking and physical activity.

Results

Mean age: 37.5±12.8 y; MUHNW participants were older than MHNW (44.5±13.2 vs 35.8±12.1 years, p

Conclusions

BMI, WC, WHR and body fat indices were positively associated with metabolically unhealthy status among normal-weight adults of both sexes. WHR was the strongest predictor, highlighting its value for identifying at-risk individuals where advanced body composition tools are unavailable.

❌