目的：分析肥胖相关指标、血脂水平与原发性开角型青光眼(POAG)关系及探究其危险因素。

方法：选取2022年3月至2024年3月我院收治的POAG患者为POAG组，同时选取同期的健康体检者为非POAG组，比较两组患者的肥胖相关指标\〖体质量指数(BMI)、腰围、腰臀比\〗、血脂水平\〖血清总胆固醇(TC)、甘油三酯(TG)、高密度脂蛋白(HDL-C)、低密度脂蛋白(LDL-C)\〗以及一般临床资料，采用二元Logistic回归分析影响POAG发生的危险因素，建立ROC曲线分析各项变量预测POAG发生的效能。

结果：本研究纳入POAG组54例，非POAG组54例。POAG组年龄50.30±5.73岁，男29例，女25例。非POAG组年龄50.28±5.72岁，男28例，女26例。POAG组的BMI、眼压、腰围、腰臀比、TG均高于非POAG组，而HDL-C水平低于非POAG组(均P<0.001)。高BMI、高腰围、高腰臀比、高TG水平都是影响POAG发生的相关危险因素(均P<0.05)，而高HDL-C水平则为影响POAG发生的保护因素(P<0.05)。BMI、腰围、腰臀比、TG、HDL-C、联合预测的AUC分别为0.885、0.873、0.707、0.777、0.664、0.974，敏感度分别为0.815、0.833、0.730、0.637、0.663、0.944，特异度分别为0.852、0.759、0.711、0.789、0.752、0.889。表明联合多指标预测的效能最佳，且敏感度和特异度良好。

结论：肥胖相关指标(如高BMI、腰围、腰臀比)及血脂异常(高TG、低HDL-C)是影响POAG发生的相关危险因素。联合BMI、腰围、腰臀比、TG、HDL-C多指标预测模型在POAG风险评估中表现出最佳效能，兼具高敏感度和特异度，为临床早期筛查和干预提供了科学依据。

METHODS:Patients with POAG treated in the hospital from March 2022 to March 2024 were selected as the POAG group, and healthy people in the same period were selected as the non-POAG group. The obesity related indicators \〖body mass index(BMI), waist circumference, waist-to-hip ratio\〗, blood lipid levels \〖serum total cholesterol(TC), triglyceride(TG), high-density lipoprotein(HDL-C), low-density lipoprotein(LDL-C)\〗 and general clinical data of the two groups were compared. The binary Logistic regression analysis was used to analyze the risk factors affecting the occurrence of POAG, and the ROC curve was established to analyze the efficiency of various variables in predicting the occurrence of POAG.

RESULTS:A total of 54 POAG patients and 54 non-POAG subjects were included. The POAG group had a mean age of 50.30±5.73 y(29 males, 25 females), and the non-POAG group had a mean age of 50.28±5.72 y(28 males, 26 females).The BMI, intraocular pressure, waist circumference, waist-to-hip ratio, and TG levels in the POAG group were higher than those in the non-POAG group, while the HDL-C level was lower than that in the non-POAG group(all P<0.001). High BMI, high waist circumference, high waist-to-hip ratio, and high TG were identified as risk factors for POAG(all P<0.05), whereas high HDL-C was a protective factor(P<0.05).The AUCs for BMI, waist circumference, waist-to-hip ratio, TG, HDL-C, and the combined prediction were 0.885, 0.873, 0.707, 0.777, 0.664, and 0.974, respectively; the sensitivities were 0.815, 0.833, 0.730, 0.637, 0.663, and 0.944, respectively; and the specificities were 0.852, 0.759, 0.711, 0.789, 0.752, and 0.889, respectively. These results indicate that the combined multi-index prediction had the best performance, with good sensitivity and specificity.

CONCLUSION:Obesity-related indicators such as high BMI, high waist circumference, high waist-to-hip ratio, and dyslipidemia(high TG, low HDL-C)are associated risk factors for POAG. The combined multi-index prediction model including BMI, waist circumference, waist-to-hip ratio, TG, and HDL-C demonstrates the best performance in POAG risk assessment, with both high sensitivity and specificity, providing a scientific basis for early clinical screening and intervention.