ArticleInternational journal of obesity (2005)2026
Reassessing BMI thresholds for obesity: a population-based study in Turkish adults.
Article in International journal of obesity (2005), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe global prevalence of obesity has been steadily increasing, posing major public health challenges. Current universal BMI cut-off points may not accurately reflect obesity risk across different ethnicities, including the Turkish population.
objectiveThis study aimed to evaluate the relationship between body mass index (BMI) and body fat percentage (BF%) and determine appropriate BMI cut-off points for diagnosing obesity in Turkish adults.
methodsA cross-sectional study was conducted among Turkish individuals aged 18-65 in four major districts of Kayseri. Anthropometric measurements were obtained, including height, weight, waist circumference, and hip circumference. Body composition was assessed using bioelectrical impedance analysis (BIA). ROC (Receiver Operating Characteristic) curve analysis was performed to identify optimal BMI cut-off points for predicting obesity based on BF%. Pearson correlation analysis evaluated the relationship between BMI and BF%.
resultsThe participants had a mean age of 39.9 ± 13.3 years, and 51.6% were female. A strong positive correlation between BMI and BF% was found for both women (r = 0.86, p < 0.001) and men (r = 0.68, p < 0.001). The optimal BMI cut-off point for detecting obesity was 28.76 kg/m² for the total sample, 28.80 kg/m² for women, and 28.76 kg/m² for men. Using these new thresholds, the obesity prevalence increased from 27.7% to 33.6%. The AUC values indicated excellent discriminative power for women (AUC = 0.98) and good discriminative power for men (AUC = 0.91).
conclusionThe current universal BMI cut-off points (≥30 kg/m²) may underestimate obesity prevalence in the Turkish population. Establishing national and gender-specific BMI thresholds could improve the accuracy of obesity diagnosis and support more effective public health interventions targeting chronic non-communicable diseases.
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