An Integrated Inflammation-Metabolism Model for Stratifying Severity of Peripheral Artery Disease.
TL;DR
Inflammation-metabolism interactions are associated with PAD severity and CLTI status and the model should be interpreted as a CLTI risk stratification tool rather than proof of longitudinal disease prediction.
Abstract
Background
AND
Objective
Peripheral artery disease (PAD) encompasses a spectrum ranging from asymptomatic disease and intermittent claudication to chronic limb-threatening ischemia (CLTI). Inflammation and metabolic disturbance are increasingly recognized as interconnected features of atherosclerotic disease, but their combined relationship with CLTI status in PAD remains incompletely defined. This study investigated inflammation-metabolism interactions across molecular, population-level, and clinical datasets and developed a clinically applicable model for CLTI risk stratification among patients with PAD.
Methods
Public gene expression profiles were used to compare intermittent claudication with CLTI and healthy controls with PAD, focusing on inflammation- and metabolism-related pathways. These molecular signals were then mapped to clinically measurable indices. NHANES data were used to evaluate associations between these indices and ABI-defined PAD at the population level; NHANES did not permit reliable subclassification of PAD severity. A separate single-center clinical cohort was used to compare controls, PAD without CLTI, and CLTI and to build a CLTI stratification model. Variables were selected by least absolute shrinkage and selection operator regression and reintroduced into multivariable logistic regression. Model performance was assessed using the area under the receiver operating characteristic curve, calibration, and decision curve analysis.
Results
Inflammatory and metabolic pathways showed coordinated activation across disease-stage comparisons. In NHANES, the triglyceride-glucose index (TyG), neutrophil-to-lymphocyte ratio (NLR), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI) were associated with ABI-defined PAD in univariate analyses. In the clinical cohort, NLR, systemic immune-inflammation index (SII), TyG, and AISI increased from controls to PAD without CLTI and CLTI (all P<0.001), while qRT-PCR confirmed stepwise upregulation of key genes. Multivariable analysis identified high-sensitivity C-reactive protein (hs-CRP), TyG, and AISI for PAD without CLTI, and BMI, smoking, hypertension, hs-CRP, and TyG for CLTI. The CLTI stratification model demonstrated good discrimination (AUC = 0.856).
Conclusion
Inflammation-metabolism interactions are associated with PAD severity and CLTI status. Because the available data are cross-sectional, the model should be interpreted as a CLTI risk stratification tool rather than proof of longitudinal disease prediction. External prospective validation is required before clinical implementation.