As the number of individuals with diabetes who are also overweight continues to rise, the risks associated with abnormal bone mineral density (BMD) and osteoporosis are gradually emerging. Existing studies mostly rely on single obesity indicators to assess BMD, which fails to reflect the comprehensive impact of differences in fat distribution on bone metabolism and lacks stratified analysis methods for different obesity types. Therefore, this study, based on the construction of BMD risk stratification and determination rules, combined with multidimensional obesity indicators to systematically analyze the relationship between different types of obesity and BMD in patients with type 2 diabetes. A BMD risk label and differentiation determination rule that mapped ongoing BMD indicators to stratified risk levels were developed based on BMD measurements findings. Waist circumference, width-to-hip ratio, and body fat % were all adversely connected with BMD (r=−0.17 to −0.29, all P<0.05), although BMI had a significant correlation with BMD (r=0.28–0.31). Obesity at the abdomen (OR=1.64, 95%CI: 1.08–2.50) and mixed obesity (OR=2.12, 95%CI: 1.36–3.29) were found to be distinct risk variables for abnormal density of bone by a multivariate analysis.

Ya Su. Application of Machine Learning Algorithms in Bone Mineral Density Risk Stratification in Patients with Type 2 Diabetes and Obesity. Frontiers in Medical Science Research (2026), Vol. 8, Issue 1: 94-103. https://doi.org/10.25236/FMSR.2026.080111.

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