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

TL;DR

This study investigates whether specific breath VOCs causally influence blood glucose levels and develops non-invasive methods for early diabetes risk detection using causal inference and machine learning.

Contribution

It introduces a data-driven framework combining causal inference and classification models to identify and stratify diabetes risk based on breath biomarkers.

Findings

VOCs like acetone and isopropanol have a strong causal impact on glucose levels.

Machine learning models can accurately classify diabetics and non-diabetics using breath VOCs.

Natural population clusters related to diabetes risk were identified using Gaussian Mixture Models.

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.