Jin Ching, Zhu Zhen
Centre for Interdisciplinary Methodologies, University of Warwick, Coventry CV4 7AL, United Kingdom.
Kent Business School, University of Kent, Canterbury CT2 7FS, United Kingdom.
JAMIA Open. 2025 May 30;8(3):ooaf049. doi: 10.1093/jamiaopen/ooaf049. eCollection 2025 Jun.
This study aims to explore multimorbidity patterns associated with diabetes by analyzing user engagement in online diabetes support communities and their interactions with other disease-related communities. Additionally, it seeks to assess whether early signals of diabetes can be detected through online engagement data.
We collected Reddit data for 3 primary diabetes-related subreddits ("diabetes," "diabetes_t1," and "diabetes_t2") and 88 other disease-related subreddits from 2008 to 2024. A bipartite network was constructed linking users to subreddits, which was then transformed into a weighted multimorbidity network. Significant links were identified using a statistical threshold to ensure meaningful connections between subreddits. Additionally, we analyzed user engagement timelines to identify potential early signals of diabetes.
Diabetes is strongly linked to mental health conditions (such as depression, anxiety, and ADHD) and weight management discussions. Other notable associations include autoimmune diseases, chronic pain, gastrointestinal disorders, and reproductive health issues. Early signals of type 2 diabetes were detected in mental health, obesity, and pregnancy conditions, but no significant early indicators were found for type 1 diabetes.
This study is the first large-scale empirical analysis of multimorbidity patterns and early signals of diabetes in online communities. The findings reinforce the known multimorbidity of diabetes, particularly its ties to mental health and obesity. The presence of early signals suggests that social media data could help identify individuals at risk before diagnosis, offering opportunities for early intervention.
Our findings demonstrate that social media data can reveal both multimorbidity patterns and early signals of diabetes, offering insights beyond traditional health records. As digital health data continue to grow, effectively leveraging these resources will become increasingly important for advancing diabetes prevention and management.
本研究旨在通过分析在线糖尿病支持社区中的用户参与度及其与其他疾病相关社区的互动，探索与糖尿病相关的共病模式。此外，它还试图评估是否可以通过在线参与数据检测到糖尿病的早期信号。
我们收集了2008年至2024年期间Reddit上3个主要的糖尿病相关子版块（“糖尿病”、“1型糖尿病”和“2型糖尿病”）以及88个其他疾病相关子版块的数据。构建了一个将用户与子版块联系起来的二分网络，然后将其转化为加权共病网络。使用统计阈值确定显著链接，以确保子版块之间有有意义的联系。此外，我们分析了用户参与时间线，以识别糖尿病的潜在早期信号。
糖尿病与心理健康状况（如抑郁症、焦虑症和注意力缺陷多动障碍）以及体重管理讨论密切相关。其他值得注意的关联包括自身免疫性疾病、慢性疼痛、胃肠道疾病和生殖健康问题。在心理健康、肥胖和妊娠状况中检测到了2型糖尿病的早期信号，但未发现1型糖尿病的显著早期指标。
本研究是对在线社区中糖尿病共病模式和早期信号的首次大规模实证分析。研究结果强化了已知的糖尿病共病情况，特别是其与心理健康和肥胖的联系。早期信号的存在表明，社交媒体数据可以帮助在诊断前识别有风险的个体，为早期干预提供机会。
我们的研究结果表明，社交媒体数据可以揭示糖尿病的共病模式和早期信号，提供超越传统健康记录的见解。随着数字健康数据的不断增长，有效利用这些资源对于推进糖尿病预防和管理将变得越来越重要。
