Zheng Quan Ri Bao·2025-09-03 12:39Core Insights - JD Health Exploration Institute (JDHXLab) presented two chronic disease prediction studies at the European Society of Cardiology Congress, highlighting the potential of e-commerce behavior data and disease trajectory analysis in predicting chronic disease risks [2][5] Group 1: E-commerce Behavior Data Study - The first study linked e-commerce purchasing behavior with health conditions, creating a "category index" covering 13,354 product categories to predict risks for eight common chronic diseases [3] - The model indicated that individuals identified as high-risk had a significantly higher actual disease incidence, with an average risk ratio (RR) of 4.07, suggesting that high-risk individuals are over four times more likely to develop diseases compared to low-risk individuals [3] - Key behavioral features beyond traditional demographic risk factors were identified, such as the purchase of "plus-size clothing," "alcohol," and "smoking accessories," which were significantly associated with obesity risk [3] Group 2: Disease Trajectory Analysis Study - The second study analyzed 207 million medical records from 37.38 million patients in Northern China from 2016 to 2024, constructing a "temporal comorbidity network" involving 351 chronic diseases and 8,672 associated pathways [4] - Cardiovascular-kidney-metabolic (CKM) diseases were found to be central in disease progression, with over half of the common comorbidities for hypertension patients being CKM diseases, including chronic ischemic heart disease (47.79%) and stroke (43.34%) [4] - The research utilized Monte Carlo simulation techniques to predict potential future chronic disease pathways for individuals, aiding in early identification and intervention for high-risk patients [4] Group 3: Industry Implications - These studies provide new methodologies for chronic disease risk prediction, potentially leading to more accurate personal health risk assessments and improved public health management [5] - JD Health aims to deepen research in chronic disease prediction and management, contributing to global health initiatives and encouraging societal focus on chronic disease prevention [5]