Integrated machine learning and population attributable fraction analysis of systemic inflammatory indices for mortality risk prediction in diabetes and prediabetes - Zixi Zhang; Chenyang Li; Yichao Xiao; Chan Liu; Xiaoqin Luo; Cancan Wang; Yongguo Dai; Qiuzhen Lin; Zeying Zhang; Cheng Zheng; Jiafeng Lin; Tao Tu; et al. (Annals of Medicine)
Background 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.Methods 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.Results 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.Conclusion 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.