October 11, 2025Open Access

Hierarchical random forest model, inflammation and oxidative stress as predictors of the atherogenic index of plasma and diabetes progression

HJHerbert JelinekKhalifa University of Science and Technology IMIssam MuteirMonash Health HAHayder Al-AubaidyLa Trobe University

Key Points

Distinct biomarker profiles linked to atherogenic index were identified across diabetes stages—indicating dynamic risk factors.
Oxidative stress and inflammation were significant predictors, especially as diabetes progressed from prediabetes to established diabetes.
Hierarchical random forest methods enhanced model stability and predictive accuracy, leveraging diverse clinical and biomarker data.
This approach supports targeted interventions based on evolving biomarker associations, facilitating better diabetes management.

Abstract

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease that increases the risk of cardiovascular complications. The atherogenic index of plasma (AIP) is a risk marker for T2DM and cardiovascular disease on the basis of lipid profiles. T2DM and CVD risk are also associated with nonlipid biomarkers, including oxidative stress, inflammation, and mitochondrial dysfunction, and are linked to diabetes progression. This study applies hierarchical random forest (HRF) machine learning to identify stage-specific predictors of AIP in normoglycemic, prediabetic, and diabetic individuals. Participants were divided into normal (< 5.7%), prediabetic (5.7–6.4%), and diabetic (≥ 6.5%) groups based on their HbA1c values. Clinical, oxidative, inflammatory, and mitochondrial biomarkers were included in the study. Lipid measures directly contributing to the AIP calculation were excluded to minimize collinearity. Predictive models were developed via random forest (RF) and hierarchical random forest (HRF) approaches. HRF incorporates repeated threefold cross-validation to improve stability and feature importance across subgroups. Model performance was evaluated via the coefficient of determination (R²) and mean squared error (MSE). HRF models revealed distinct biomarker profiles associated with AIP and diabetes progression associated with inflammation, oxidative stress, and mitochondrial function variables. Waist-to-height ratio was the main contributing variable in the stratified dataset. For the stratified data, mitochondrial redox markers (p66Shc, humanin) were among the top predictors in the normoglycemia group. In individuals with prediabetes, the importance of these cytokines decreased, whereas oxidative stress-associated biomarkers (GSH, 8-OHdG) provided more accurate classifications. In the diabetes group, 8-OHdG remained moderately predictive, whereas the mitochondrial peptide MOTSc and inflammatory markers (IL-1β) were key features. These results indicate that the progression from mitochondrial-associated changes in the early stages of diabetes to immunometabolic dysfunction in individuals with established diabetes is correlated with AIP. Hierarchical random forest machine learning combined with glycemic stratification reveals evolving biomarker associations with the atherogenic index of plasma linked with diabetes progression. Mitochondrial and immune markers contribute differently across disease stages, supporting their potential use in stage-specific risk stratification and targeted intervention in T2DM management.

AI에게 질문

Bookmark

View Full Paper