Digital Integration of Biomarkers and Risk Prediction for Chronic Disease Prevention in Public Health

The field of digital public health is witnessing rapid transformation driven by the convergence of molecular biology, population epidemiology, and digital technology. Chronic diseases such as cardiovascular, metabolic, oncological, and neurodegenerative conditions continue to represent the largest global health burden, challenging healthcare systems and public health strategies worldwide. The proliferation of digital infrastructures, including electronic health records, mobile applications, wearable sensors, and connected devices, now allows continuous health monitoring and large-scale data generation. Parallel developments in omics fields, imaging, and clinical informatics provide an unprecedented opportunity to link biological markers with behavioural and environmental data. However, despite these advances, substantial gaps persist in effectively integrating multi-source digital data into public health decision-making, especially regarding the validation, interpretability, and equitable use of predictive biomarkers at the population level.This Research Topic aims to advance the digital transformation of chronic disease prevention and management through the biomarker integration with digital health analytics. It focuses on developing and translating predictive models that leverage biological, clinical, and digital signals for early risk detection, prevention, and precision intervention. Central goals include improving population-based risk stratification, enabling real-time disease surveillance, and informing health policy through robust data-driven methodologies. By uniting expertise from public health researchers, data scientists, clinicians, and digital health innovators, this initiative seeks to bridge research and practice, making biomarker-driven prevention actionable in real-world community and health system contexts.To gather further insights into the use of digital data for biomarker integration and precision public health, we welcome articles addressing, but not limited to, the following themes:- Integration of molecular, clinical, behavioural, and digital biomarkers for population-level risk assessment- AI-driven predictive analytics and modelling for digital disease surveillance and prevention- Applications of wearable technologies, remote monitoring, and mobile health for chronic disease risk evaluation- Leveraging electronic health records, registries, and public health databases for biomarker-based prevention strategies- Approaches for ensuring model fairness, interpretability, and public health utility in predictive algorithms- Implementation science and policy frameworks for digital biomarker integration in health systems- Real-world evidence on digital interventions and their impact on chronic disease outcomes and health equity Topic editors: Chunguang Guo, Zhe Xing, Tao Yu, Sijie Ji Source: https://www.frontiersin.org/research-topics/82895/digital-integration-of-biomarkers-and-risk-prediction-for-chronic-disease-prevention-in-public-health