Abstract
BACKGROUND: To investigate the prevalence and characteristics of normal weight obesity (NWO) and its coexistence with sarcopenia among Chinese women, and to evaluate the diagnostic utility of common anthropometric and body composition indicators-including body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), and body fat percentage (BF%) assessed via bioelectrical impedance analysis (BIA). METHODS: A total of 1,530 female participants undergoing routine health check-ups at Zhongshan Boai Hospital between January 2022 and December 2023 were enrolled. Anthropometric data including height, weight, WC, and hip circumference were recorded. BIA was used to measure BF% and skeletal muscle index (SMI), along with assessments of handgrip strength and 6-meter gait speed to evaluate muscle function. Participants were classified as having obesity according to the Chinese Guidelines for the Diagnosis and Treatment of Obesity (2024 edition) and the WHO Asia-Pacific criteria. Sarcopenia was defined according to the Asian Working Group for Sarcopenia (AWGS) 2019 guidelines. The UpSet plot was used to visualize diagnostic overlaps among obesity-related indicators, highlighting high-risk phenotypes potentially missed by relying on BMI alone. RESULTS: Among the 1,530 participants, only 145 (9.48%) met the BMI criterion for obesity (BMI ≥ 28 kg/m²), while 1,072 (70.07%) had elevated body fat percentage (BF% >30%), indicating a higher prevalence of hidden obesity. A total of 483 (31.57%) were classified as NWO, including 390 (25.50%) with pre-sarcopenia and 57 (3.73%) with sarcopenic NWO. The UpSet plot revealed substantial diagnostic overlap among BF%, WHR, WHtR, and WC, while BMI had the lowest detection concordance. These findings indicate that combined use of multiple indicators greatly improves the identification of hidden obesity, while BMI alone may lead to significant underdiagnosis. CONCLUSION: NWO and sarcopenic NWO are prevalent among Chinese women. Reliance on BMI alone underestimates the actual burden of obesity. Clinical assessments should incorporate both body composition and fat distribution indicators to better identify high-risk individuals and improve the precision of metabolic disease prevention and intervention strategies.