The variables identified by LASSO regression.

BackgroundType 2 diabetes (T2D) is increasingly recognized as a significant global health challenge, with a rising prevalence of hyperlipidemia among diabetic patients. Effectively predicting and reducing the risk of hyperlipidemia in T2D patients to mitigate their cardiovascular risk remains an urgent issue.ObjectivesThe research sought to determine early clinical indicators that could predict the onset of hyperlipidemia in patients with T2D and to establish a predictive model that integrates clinical and laboratory indicators.MethodsA cohort of T2D patients, excluding those with pre-existing hyperlipidemia or confounding factors, was analyzed. Clinical and laboratory data were used in a LASSO regression model to select key predictive variables. A nomogram was then constructed and evaluated using receiver operating characteristic (ROC) analysis and calibration.ResultsAmong 269 participants, PCSK9 levels were significantly elevated in T2D patients with hyperlipidemia and exhibited a positive correlation with several lipid markers. LASSO regression identified six predictors: BMI, TG, TC, LDL-C, HbA1c, and PCSK9. The nomogram model exhibited robust predictive performance (AUC, 0.89 (95% CI: 0.802–0.977)) and showed good calibration.ConclusionsThis method effectively predicts the risk of hyperlipidemia in patients with T2D and provides a valuable tool for early intervention. PCSK9, as a key predictor, highlights its potential role in the pathogenesis of diabetes with hyperlipidemia and offers new avenues for targeted therapy.背景：2型糖尿病（Type 2 Diabetes, T2D）已被日益公认为一项严峻的全球性公共卫生挑战，糖尿病患者合并高脂血症的患病率亦逐年攀升。有效预测并降低2型糖尿病患者的高脂血症发病风险，以减轻其心血管疾病负担，仍是亟待解决的临床议题。 研究目的：本研究旨在筛选可预测2型糖尿病患者新发高脂血症的早期临床指标，并构建整合临床与实验室检测指标的预测模型。 研究方法：本研究纳入既往无高脂血症病史及其他混杂因素的2型糖尿病患者队列开展分析。采用套索回归（Least Absolute Shrinkage and Selection Operator, LASSO）筛选关键预测变量，随后构建列线图（nomogram），并通过受试者工作特征曲线（Receiver Operating Characteristic, ROC）分析与校准曲线对模型进行评价。 研究结果：在269名研究对象中，合并高脂血症的2型糖尿病患者的前蛋白转化酶枯草溶菌素9（Proprotein Convertase Subtilisin/Kexin type 9, PCSK9）水平显著升高，且与多项脂质标志物呈正相关。经套索回归筛选出6个预测变量：体质量指数（Body Mass Index, BMI）、甘油三酯（Triglyceride, TG）、总胆固醇（Total Cholesterol, TC）、低密度脂蛋白胆固醇（Low-Density Lipoprotein Cholesterol, LDL-C）、糖化血红蛋白（Glycated Hemoglobin A1c, HbA1c）以及PCSK9。本研究所构建的列线图模型展现出优异的预测性能（曲线下面积AUC=0.89，95%置信区间CI: 0.802–0.977），且校准度良好。 研究结论：本研究方法可有效预测2型糖尿病患者的高脂血症发病风险，为早期临床干预提供了实用工具。作为关键预测指标之一的PCSK9，其在糖尿病合并高脂血症的发病机制中或发挥重要作用，同时也为靶向治疗提供了新的研究方向。