Apolipoprotein B100 predicts cardiovascular and limb events in type 2 diabetes patients with chronic limb-threatening ischemia

Background: Patients with type 2 diabetes mellitus (T2DM) and chronic limb-threatening ischemia (CLTI) undergoing lower extremity revascularization (LER) face high risks of major adverse cardiovascular events (MACE) and major adverse limb events (MALE), despite guideline-directed lipid-lowering therapy. Apolipoprotein B100 (ApoB100), reflecting total atherogenic particle number, may identify residual risk beyond LDL-cholesterol (LDL-C). Therefore, we investigated whether baseline ApoB100 levels independently predict MACE and MALE beyond conventional risk factors in this high-risk population. Methods: In this prospective cohort study, 167 T2DM patients with CLTI undergoing LER were followed for 12 months with visits at 1, 3, 6, and 12 months post-procedure. We measured baseline ApoB100 and assessed its ability to predict MACE, MALE, and composite endpoints, adjusting for clinical covariates. Results: Composite events occurred in 49.1% of patients, MACE in 24%, and MALE in 35.3%. ApoB100 levels were significantly higher in event groups (composite: 62.1 vs 38.0 mg/dL, p < 0.01; MACE: 66.1 vs 44.4 mg/dL, p < 0.01; MALE: 61.0 vs 42.4 mg/dL, p < 0.01). Multivariable analyses confirmed ApoB100 as an independent predictor (composite OR 1.14 per mg/dL, 95% CI 1.08–1.20, p < 0.01; MACE OR 1.10, p < 0.01; MALE OR 1.05, p < 0.01). ROC analysis demonstrated excellent predictive accuracy for ApoB100 (AUC 0.86, 95% CI 0.80–0.91), with optimal ROC-derived cut-off of 56.6 mg/dL. Adding baseline ApoB100 to conventional risk factors significantly improved model discrimination (AUC gains 0.08–0.15, all p < 0.01), while Kaplan–Meier curves by cut-off effectively stratified early events (log-rank p < 0.001). Conclusions: Elevated baseline ApoB100 independently predicted MACE, MALE, and composite events post-LER in T2DM-CLTI patients, substantially improving clinical risk models. Integrating ApoB100 into post-LER management algorithms could refine individualized therapeutic strategies, especially in patients with residual atherogenic risk despite optimal LDL-C control.