Research Questions:
What is the longitudinal association between plasma protein levels and osteoporosis risk in diabetic patients?
Which proteins are causally linked to bone mineral density (BMD) changes in diabetes, and what are their functional roles?
Can a predictive model integrating proteomic data improve osteoporosis risk stratification in diabetes compared to traditional clinical factors?
Objectives:
Identify plasma proteins associated with BMD changes in diabetic individuals using UK Biobank data.
Explore causal relationships via Mendelian Randomization (MR) leveraging GWAS and pQTLs.
Develop a machine learning model to predict osteoporosis risk based on proteomic signatures.
Investigate biological pathways of candidate proteins through network and enrichment analyses.
Scientific Rationale:
Diabetes mellitus (types 1 and 2) is linked to altered bone metabolism, but the underlying mechanisms remain unclear. Plasma proteomics offers a systemic view of protein biomarkers influencing bone health. This study leverages the UK Biobank’s large-scale proteomic (Olink) and DXA-derived BMD data to:
Address gaps by longitudinally assessing protein-BMD associations while adjusting for confounders (e.g., age, BMI).
Elucidate causality using MR to mitigate confounding biases inherent in observational studies.
Translate findings into clinical tools (e.g., predictive models) for early osteoporosis intervention in diabetes.