About this Research Topic
Diabetes has garnered significant attention as a global health issue, especially in the modern era marked by lifestyle changes and dietary shifts, which have contributed to its escalating prevalence. With the advancement of big data technologies, there is an opportunity to delve deeper into the relationship between diabetes and risk model predictions. Human routines and dietary patterns play a pivotal role in diabetes risk, and the wealth of publicly available databases provides an opportunity for in-depth exploration.
Through this Research Topic, we aspire to delve deeper into the forefront issues surrounding diabetes and novel biomarkers, offering new perspectives and methodologies for more precise diabetes risk assessment and prevention. We look forward to your contributions!
Our Research Topic aims to gather the latest research on clinical indicators and novel biomarkers regarding diabetes prevention. We invite contributions from researchers who can share their findings, innovative methods, and insights into utilizing big data for improved diabetes risk prediction. Studies can incorporate various designs, including large-scale survey designed as cross-sectional, case-control, and cohort studies. We strongly encourage the use of advanced causal inference methods, such as difference-in-differences models, instrumental variables, propensity score matching, and inverse probability weighting, within the framework of causal inference.
Original research covering the identification of novel biomarkers and clinical indicators for diabetes or related endocrine diseases are welcome. Potential sub-topics include but are not limited to:
• Newly identified novel biomarkers or clinical indicators for diabetes, along with endocrine diseases.
• Causality inference of risk factors for diabetes-related diseases.
• Novel manners to enhance the generality of research using public databases.
Through this Research Topic, we aspire to delve deeper into the forefront issues surrounding diabetes and novel biomarkers, offering new perspectives and methodologies for more precise diabetes risk assessment and prevention. We look forward to your contributions!
Our Research Topic aims to gather the latest research on clinical indicators and novel biomarkers regarding diabetes prevention. We invite contributions from researchers who can share their findings, innovative methods, and insights into utilizing big data for improved diabetes risk prediction. Studies can incorporate various designs, including large-scale survey designed as cross-sectional, case-control, and cohort studies. We strongly encourage the use of advanced causal inference methods, such as difference-in-differences models, instrumental variables, propensity score matching, and inverse probability weighting, within the framework of causal inference.
Original research covering the identification of novel biomarkers and clinical indicators for diabetes or related endocrine diseases are welcome. Potential sub-topics include but are not limited to:
• Newly identified novel biomarkers or clinical indicators for diabetes, along with endocrine diseases.
• Causality inference of risk factors for diabetes-related diseases.
• Novel manners to enhance the generality of research using public databases.
Keywords: Diabetes, Biomarkers, Real-World Research, Data mining, Public health
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
