发现特定呼气挥发物可直接影响血糖，助力无创糖尿病早期筛查。
Can Breath Biomarkers Causally Influence Blood Glucose? Investigating VOC-Mediated Modulation in Diabetes
- 用因果推断分析呼气中丙酮等挥发物对血糖的影响。
- 机器学习模型准确区分糖尿病人与健康人，风险分层效果显著。
- 适合关注无创糖尿病检测与个性化健康管理的研究者。
糖尿病是全球重大健康负担，早期检测对及时干预至关重要。本研究提出一种非侵入性、数据驱动的框架，利用挥发性有机化合物（VOCs）和生活方式变量识别糖尿病高风险人群。通过因果推断技术评估丙酮、异丙醇、异戊二烯和乙醇等VOCs对血糖水平的影响。同时设计分类器，基于非侵入性指标区分糖尿病患者与健康个体。构建了针对“灰色区域”人群的风险评分体系，并采用高斯混合模型识别群体中的自然聚类。结果表明，特定VOCs对血糖具有显著因果影响，且机器学习模型能可靠分类与分层高风险个体。该集成因果-可解释分析为开发无创糖尿病早期筛查工具提供了支持。
原文摘要 · Abstract (English)
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.
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