Advancing Type 2 Diabetes Prevention Through Genomic Risk Prediction And Lifestyle Interventions

Abstract

Background: Type 2 diabetes (T2D) is a major global health challenge, with prevalence rising sharply due to genetic susceptibility and lifestyle factors. While lifestyle modification remains the foundation of prevention, recent advances in genomic medicine provide an opportunity to identify high-risk individuals earlier and tailor prevention strategies more effectively. This systematic review evaluates the role of genomic risk prediction combined with lifestyle interventions in advancing T2D prevention.

Methods: A comprehensive search was conducted across PubMed, Scopus, Web of Science, and Cochrane Library from January 2005 to May 2025. Studies were included if they evaluated the use of genomic risk prediction (e.g., polygenic risk scores, candidate gene profiling) in relation to lifestyle interventions for T2D prevention. The selection process followed PRISMA guidelines. From 340 initially retrieved studies, 35 met the inclusion criteria after screening and eligibility assessment. Data were synthesized narratively, and findings were categorized into genomic risk prediction utility, impact on lifestyle intervention effectiveness, and clinical translation.

Results: The included studies demonstrated that genomic risk prediction improves identification of high-risk individuals beyond traditional risk factors. Polygenic risk scores (PRS) were shown to stratify populations more effectively, allowing earlier preventive action. When coupled with lifestyle interventions, particularly those targeting diet, physical activity, and weight management, genomic-informed strategies enhanced patient motivation and adherence. Several randomized controlled trials revealed that participants informed of elevated genetic risk were more likely to maintain long-term behavioral changes compared to those without genetic feedback. However, heterogeneity in PRS construction, limited diversity in genetic datasets, and short follow-up periods limited generalizability. Few studies examined cost-effectiveness or real-world implementation, and ethical concerns around genetic data use were frequently highlighted.

Conclusion: Integrating genomic risk prediction with lifestyle interventions represents a promising precision prevention strategy for type 2 diabetes. While current evidence supports its potential to improve risk stratification and adherence, further longitudinal studies, standardized genomic tools, and equitable implementation frameworks are needed to translate this approach into clinical practice.