Feng Jing, Teng Zhenjie, Chen Shuchun
Department of Endocrinology, Hebei Medical University, Shijiazhuang, China.
Department of Endocrinology, Hebei General Hospital, Shijiazhuang, China.
PeerJ. 2025 May 13;13:e19442. doi: 10.7717/peerj.19442. eCollection 2025.
To investigate the relation between obesity-related indices and mild cognitive impairment (MCI) in elderly patients with type 2 diabetes (T2D).
A total of 597 eligible elderly patients with T2D were included in this retrospective study. All patients were divided into MCI group and normal cognitive group based on neuropsychological assessment. Twelve obesity-related indices were calculated, including body mass index (BMI), waist-hip ratio (WHR), waist-to-height ratio (WHtR), lipid accumulation product (LAP), body roundness index (BRI), conicity index (CI), visceral adiposity index (VAI), body adiposity index (BAI), abdominal volume index (AVI), a body shape index (ABSI), triglyceride glucose (TyG) index and cardiometabolic index (CMI). Multivariate logistic regression analysis, tests for trend and restricted cubic splines were used to assess the relationships between the tests for trend and MCI in elderly patients with T2D. Receiver operating characteristic (ROC) curves and areas under the curves (AUC) were used to assess the performance and predictive ability of the obesity-related indices for identifying MCI in elderly patients with T2D.
Multivariate logistic regression showed that elevated BMI, WHR, WHtR, LAP, BRI, CI, VAI, AVI, TyG index, and CMI were associated with an increased risk of MCI in elderly T2D patients after adjusting for potential confounders (all < 0.05). In addition, TyG index, LAP, CMI, VAI, AVI, WHR, WHtR, BRI, and CI had negative correlations with Montreal Cognitive Assessment (MoCA) scores (all < 0.05). There was a significant linear trend between the levels of BMI ( for trend = 0.004, for non-linearity = 0.637), WHR ( for trend = 0.006, for non-linearity = 0.430), WHtR ( for trend < 0.001, for non-linearity = 0.452), BRI ( for trend < 0.001, for non-linearity = 0.252), AVI ( for trend < 0.001, for non-linearity = 0.944), and TyG index ( for trend < 0.001, for non-linearity = 0.514) and risk of MCI in elderly patients with T2D after adjusting for potential confounders. There was a nonlinear association between LAP, VAI or CMI and risk of MCI in elderly patients with T2D (all for non-linearity < 0.001). CMI had the greatest AUC (AUC = 0.682), followed by VAI (AUC = 0.679), TyG index (AUC = 0.673), LAP (AUC = 0.669), AVI (AUC = 0.580), WHtR and BRI (AUC = 0.575), BMI (AUC = 0.560), CI (AUC = 0.556), WHR (AUC = 0.554), BAI (AUC = 0.547), and ABSI (AUC = 0.536).
Elevated obesity-related indices, particularly CMI, VAI, TyG index and LAP, which displayed the higher predictive power, were instrumental in forecasting and evaluating MCI in elderly T2D patients. These findings may provide clues for future studies exploring early diagnostic biomarkers and treatment of MCI in elderly T2D patients.
探讨老年2型糖尿病（T2D）患者肥胖相关指标与轻度认知障碍（MCI）之间的关系。
本回顾性研究共纳入597例符合条件的老年T2D患者。根据神经心理学评估将所有患者分为MCI组和正常认知组。计算12项肥胖相关指标，包括体重指数（BMI）、腰臀比（WHR）、腰高比（WHtR）、脂质蓄积产物（LAP）、体圆度指数（BRI）、锥度指数（CI）、内脏脂肪指数（VAI）、身体脂肪指数（BAI）、腹部容积指数（AVI）、体型指数（ABSI）、甘油三酯葡萄糖（TyG）指数和心脏代谢指数（CMI）。采用多因素logistic回归分析、趋势检验和限制性立方样条法评估老年T2D患者趋势检验与MCI之间的关系。采用受试者工作特征（ROC）曲线和曲线下面积（AUC）评估肥胖相关指标在识别老年T2D患者MCI中的性能和预测能力。
多因素logistic回归显示，在调整潜在混杂因素后，BMI、WHR、WHtR、LAP、BRI、CI、VAI、AVI、TyG指数和CMI升高与老年T2D患者MCI风险增加相关（均P<0.05）。此外，TyG指数、LAP、CMI VAI、AVI、WHR、WHtR、BRI和CI与蒙特利尔认知评估（MoCA）评分呈负相关（均P<0.05）。在调整潜在混杂因素后老年T2D患者中，BMI水平（趋势P=0.004，非线性P=0.637）、WHR（趋势P==0.006，非线性P=0.430）、WHtR（趋势P<0.001，非线性P=0.452）、BRI（趋势P<0.001，非线性P=0.252）、AVI（趋势P<0.001，非线性P=0.944）和TyG指数（趋势P<0.001，非线性P=0.514）与MCI风险之间存在显著线性趋势。LAP、VAI或CMI与老年T2D患者MCI风险之间存在非线性关联（所有非线性P<0.001）。CMI的AUC最大（AUC=0.682），其次是VAI（AUC=0.679）、TyG指数（AUC=0.673）、LAP（AUC=0.669）、AVI（AUC=0.580）、WHtR和BRI（AUC=0.）575）、BMI（AUC=0.560）、CI（AUC=0.556）、WHR（AUC=0.554）、BAI（AUC=0.547）和ABSI（AUC=0.536）。
肥胖相关指标升高，尤其是CMI、VAI、TyG指数和LAP，具有较高的预测能力，有助于预测和评估老年T2D患者的MCI。这些发现可能为未来探索老年T2D患者MCI的早期诊断生物标志物和治疗的研究提供线索。
