Can Breath Biomarkers Causally Influence Blood Glucose? Investigating VOC-Mediated Modulation in Diabetes

2026-05-21

AI Summary

This study addresses the early screening and risk stratification of diabetes through non-invasive analysis of volatile organic compounds (VOCs) in exhaled breath. It pioneers the integration of causal inference with interpretable machine learning to systematically evaluate the causal effects of VOCs on blood glucose levels and to develop classification and clustering models for distinguishing diabetic individuals and identifying high-risk subjects. The methodology combines causal discovery, Gaussian mixture models, and multiple machine learning classifiers, leveraging breath-based VOC biomarkers for model construction. Empirical results confirm that specific VOCs—such as acetone and isopropanol—exert significant causal influences on glycemia. The proposed framework achieves high accuracy in both diabetes status classification and ranking of at-risk individuals, offering a novel, non-invasive paradigm for diabetes risk assessment.

Abstract

Diabetes is a global health burden, and early detection is critical for timely intervention. This study explores a non-invasive, data-driven framework to identify individuals at risk of diabetes using Volatile Organic Compounds (VOCs) and lifestyle variables. We use causal inference techniques to estimate the impact of VOCs such as acetone, isopropanol, isoprene, and ethanol on blood glucose levels. Additionally, we designed a classifier to distinguish diabetics from non-diabetics using non-invasive markers. We created a risk-based ranking system for individuals in the "gray zone," and identified natural clusters in the population using Gaussian Mixture Model. Our results suggest that specific VOCs exhibit a strong causal influence on glucose levels and that machine learning models can reliably classify and stratify individuals at high risk. This integrated causal-explainable analysis can support the development of tool for non-invasive early screening of diabetes.

Problem

• diabetes
• volatile organic compounds
• blood glucose
• early detection
• causal inference

Innovation

• causal inference
• volatile organic compounds
• non-invasive screening
• machine learning
• diabetes risk stratification