Chen Yanrong, Zhang Yindi, Qin Si, Yu Fadong, Ni Yinxing, Zhong Jian
Department of Endocrinology, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Front Nutr. 2025 Mar 11;12:1525105. doi: 10.3389/fnut.2025.1525105. eCollection 2025.
Osteoporosis (OP) has emerged as one of the most rapidly escalating complications associated with diabetes mellitus. However, the potential risk factors contributing to OP in patients with type 2 diabetes mellitus (T2DM) remain controversial. The aim of this study was to explore the relationship between triglyceride glucose-body mass index (TyG-BMI), a marker of insulin resistance calculated as Ln [triglyceride (TG, mg/dL) × fasting plasma glucose (mg/dL)/2] × BMI, and the risk of OP in T2DM patients.
This retrospective cross-sectional study enrolled 386 inpatients with T2DM, comprising both male and postmenopausal female participants aged 40 years or older. Individuals with significant medical histories or medications known to influence bone mineral density were excluded. Machine learning algorithms were employed to rank factors affecting OP risk. Logistic regression analysis was performed to identify independent influencing factors for OP, while subgroup analysis was conducted to evaluate the impact of TyG-BMI on OP across different subgroups. Restricted cubic spline (RCS) analysis was used to explore the dose-response relationship between TyG-BMI and OP. Additionally, the receiver operating characteristic (ROC) curve was utilized to assess the predictive efficiency of TyG-BMI for OP.
Machine learning analysis identified TyG-BMI as the strongest predictor for type 2 diabetic osteoporosis in middle-aged and elderly patients. After adjusting for confounding factors, multivariate logistic regression analysis revealed that age, osteocalcin, and uric acid were independent influencing factors for OP. Notably, TyG-BMI also emerged as an independent risk factor for OP (95%CI 1.031-1.054, < 0.01). Subgroup analysis demonstrated a consistent increase in OP risk with higher TyG-BMI levels across all subgroups. RCS analysis indicated a threshold effect, with the risk of OP gradually increasing when TyG-BMI exceeded 191.52. Gender-specific analysis showed increasing the risk of OP when TyG-BMI surpassed 186.21 in males and 198.46 in females, with a more pronounced trend observed in females. ROC suggested that TyG-BMI index has significant discriminative power for type 2 diabetic osteoporosis.
TyG-BMI has been identified as a robust predictive biomarker for assessing OP risk in middle-aged and elderly populations with T2DM.
骨质疏松症（OP）已成为糖尿病最迅速升级的并发症之一。然而，2型糖尿病（T2DM）患者发生OP的潜在危险因素仍存在争议。本研究旨在探讨甘油三酯葡萄糖-体重指数（TyG-BMI）与T2DM患者OP风险之间的关系，TyG-BMI是一种胰岛素抵抗标志物，计算方法为Ln[甘油三酯（TG，mg/dL）×空腹血糖（mg/dL）/2]×BMI。
这项回顾性横断面研究纳入了386例T2DM住院患者，包括40岁及以上的男性和绝经后女性参与者。排除有重大病史或已知会影响骨密度的药物的个体。采用机器学习算法对影响OP风险的因素进行排序。进行逻辑回归分析以确定OP的独立影响因素，同时进行亚组分析以评估TyG-BMI对不同亚组OP的影响。采用限制立方样条（RCS）分析探讨TyG-BMI与OP之间的剂量反应关系。此外，利用受试者工作特征（ROC）曲线评估TyG-BMI对OP的预测效率。
机器学习分析确定TyG-BMI是中老年2型糖尿病骨质疏松症的最强预测因子。在调整混杂因素后，多因素逻辑回归分析显示年龄、骨钙素和尿酸是OP的独立影响因素。值得注意的是，TyG-BMI也成为OP的独立危险因素（95%CI 1.031-1.054，P<0.01）。亚组分析表明，在所有亚组中，随着TyG-BMI水平升高，OP风险持续增加。RCS分析显示存在阈值效应，当TyG-BMI超过191.52时，OP风险逐渐增加。性别特异性分析显示，当TyG-BMI超过186.21（男性）和198.46（女性）时，OP风险增加，女性的趋势更明显。ROC分析表明TyG-BMI指数对2型糖尿病骨质疏松症具有显著的判别能力。
TyG-BMI已被确定为评估中老年T2DM患者OP风险的可靠预测生物标志物。
