Jing-Jing Wang,1,2,* Jie Hu,2,3,* Yi-fan Xu,2 Wu Dai,1,2 Jun-Cang Wu,2,3 Yong-Hong Cao1,2 

1Department of Endocrinology, Hefei Hospital Affiliated to Anhui Medical University (The Second People’s Hospital of Hefei), Hefei, Anhui, 230011, People’s Republic of China; 2The Fifth Clinical School of Medicine, Anhui Medical University, Hefei, Anhui, 230032, People’s Republic of China; 3Department of Neurology, Hefei Hospital Affiliated to Anhui Medical University (The Second People’s Hospital of Hefei), Hefei, Anhui, 230011, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Jun-Cang Wu, Department of Neurology, Hefei Hospital Affiliated to Anhui Medical University (The Second People’s Hospital of Hefei), Hefei, Anhui, 230011, People’s Republic of China, Email wujincang126@126.com Yong-Hong Cao, Department of Endocrinology, Hefei Hospital Affiliated to Anhui Medical University (The Second People’s Hospital of Hefei), Hefei, Anhui, 230011, People’s Republic of China, Email fish1982cao@126.com

Aim: To investigate the correlation between blood biomarkers and blood glucose fluctuations with the risk of osteoporosis (OP) in postmenopausal women with type 2 diabetes mellitus (T2DM), and to construct a predictive nomogram for OP.
Methods: Based on bone mineral density (BMD) results from dual-energy X-ray absorptiometry (DXA), participants were divided into OP (BMD T-value ≤ − 2.5 SD) and Non-OP (BMD T-value > − 2.5 SD) groups. Logistic analysis were used to explore the potential risk factors, following by the construction of a nomogram to predict the risk of OP. The discrimination and calibration of the nomogram were evaluated using concordance index (C-index), area under curve (AUC), and calibration curves.
Results: We finally included 381 participants, with 147 in the OP group. Correlation analysis revealed a significant positive correlation between age and SII, and a negative correlation between BMI and CV. SII and CV demonstrated a positive dose-response relationship with OP, while FT3 exhibited a negative relationship. Multivariate logistic analysis showed that age (OR=1.088, 95% CI 1.052– 1.125, P< 0.001), BMI (OR=0.772, 95% CI 0.702– 0.848, P< 0.001), SII (OR=1.004, 95% CI 1.003– 1.005, P< 0.001), FT3 (OR=0.529, 95% CI 0.280– 0.998, P=0.049), and CV (OR=1.051, 95% CI 1.007– 1.097, P=0.022) were independent risk factors. The subgroup analysis showed the correlation between SII and OP occurred primarily in individuals aged ≥ 60 years. A predictive nomogram model was constructed based on age, BMI, SII, FT3, and CV, with a C-index of 0.842 (range 0.801– 0.883). Decision Curve Analysis (DCA) demonstrated good clinical fit of the model.
Conclusion: SII can predict the OP occurrence in women aged ≥ 60 years, while FT3 is applicable for predicting OP in women aged ≥ 70 years and those with a BMI < 24 kg/m². The predictive nomogram demonstrated great predictive value in postmenopausal women with T2DM.

Keywords: type 2 diabetes mellitus, osteoporosis, postmenopausal women, systemic immune-inflammation index, free triiodothyronine, nomogram