Zhang Zixi, Li Chenyang, Xiao Yichao, Liu Chan, Luo Xiaoqin, Wang Cancan, Dai Yongguo, Lin Qiuzhen, Zhang Zeying, Zheng Cheng, Lin Jiafeng, Tu Tao, Liu Qiming
Department of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, People's Republic of China.
Department of Cardiology, The Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang Province, People's Republic of China.
Ann Med. 2025 Dec;57(1):2536204. doi: 10.1080/07853890.2025.2536204. Epub 2025 Jul 25.
Chronic systemic inflammation is a key contributor to cardiometabolic complications in diabetes mellitus (DM) and prediabetes (PreDM). Composite inflammatory indices-including neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), platelet-to-hemoglobin ratio (PHR), and aggregate inflammation systemic index (AISI)-have shown prognostic value for mortality. However, their integrated assessment using machine learning and quantification at the population level remain limited.
In this retrospective cohort study, 11,304 adults with DM or PreDM from the National Health and Nutrition Examination Survey (NHANES, 2005-2018) were analyzed. The primary outcomes were all-cause and cardiovascular mortality. Associations between inflammatory indices and mortality were evaluated using Cox proportional hazards models. Predictive performance was assessed via Extreme Gradient Boosting (XGBoost), and population attributable fractions (PAFs) estimated the mortality burden related to systemic inflammation.
NLR, MLR, SIRI, SII, and AISI were independently associated with all-cause and cardiovascular mortality. MLR showed the strongest association (HR: 2.948 and 3.717 for all-cause and CVD mortality, respectively). XGBoost identified SIRI, SII, AISI, MLR, and NLR as key predictors, with SIRI ranked highest for cardiovascular mortality. Inclusion of inflammatory indices improved model discrimination and calibration. PAF analysis suggested that 10-20% of mortality reduction could be attributed to improved inflammatory profiles.
Systemic inflammatory indices are independent predictors of mortality in individuals with DM or PreDM. Their integration into machine learning models enhances risk prediction and may inform population-level strategies for cardiometabolic risk stratification.
慢性全身性炎症是糖尿病（DM）和糖尿病前期（PreDM）中心血管代谢并发症的关键促成因素。包括中性粒细胞与淋巴细胞比值（NLR）、单核细胞与淋巴细胞比值（MLR）、全身炎症反应指数（SIRI）、全身免疫炎症指数（SII）、血小板与血红蛋白比值（PHR）以及综合炎症全身指数（AISI）在内的综合炎症指标已显示出对死亡率的预后价值。然而，在人群水平上使用机器学习对其进行综合评估和量化仍然有限。
在这项回顾性队列研究中，分析了来自美国国家健康与营养检查调查（NHANES，2005 - 2018年）的11304名患有DM或PreDM的成年人。主要结局是全因死亡率和心血管死亡率。使用Cox比例风险模型评估炎症指标与死亡率之间的关联。通过极端梯度提升（XGBoost）评估预测性能，人群归因分数（PAF）估计与全身炎症相关的死亡负担。
NLR、MLR、SIRI、SII和AISI与全因死亡率和心血管死亡率独立相关。MLR显示出最强的关联（全因死亡率和心血管疾病死亡率的HR分别为2.948和3.717）。XGBoost将SIRI、SII、AISI、MLR和NLR确定为关键预测因子，其中SIRI在心血管死亡率方面排名最高。纳入炎症指标可改善模型的辨别力和校准。PAF分析表明，10 - 20%的死亡率降低可归因于炎症指标的改善。
全身炎症指标是DM或PreDM患者死亡率的独立预测因子。将它们纳入机器学习模型可增强风险预测，并可能为心血管代谢风险分层的人群水平策略提供参考。
