Integrative Harmonization of Phenotypic and Genomic Data Improves Bone Mineral Density Prediction in Multi-Study Osteoporosis Research.

PURPOSE: Harmonizing osteoporosis-related data across multiple datasets is essential for improving the accuracy and generalizability of bone mineral density (BMD) assessments. This study developed a harmonization framework to standardize phenotypic and genomic variables across three major U.S. osteoporosis datasets: GDBF, GWAS, and NHANES.

METHODS

We standardized key phenotypic variables (BMD, body mass index (BMI), age, sex, and race/ethnicity) using cohort-specific data dictionaries and applied multiple imputations by chained equations (MICE) to manage missing data. Genomic data were harmonized using principal component analysis (PCA)-based batch effect corrections. Residual regression methods were applied to standardize BMD values. The effectiveness of harmonization on BMD prediction was evaluated using generalized estimating equations (GEE) and mixed-effects models.

RESULTS

Post-harmonization, inter-study variability in BMI was significantly reduced (Ω2 = 0.0028), and BMD associations with covariates remained consistent across datasets. Harmonized models showed improved predictive performance, with explained variance in BMD increasing (R2 = 0.14). PCA confirmed the effective alignment of genetic data, reducing batch effects and improving cross-study compatibility.

CONCLUSION

This study demonstrates the feasibility and effectiveness of harmonizing phenotypic and genomic data for osteoporosis research. The harmonization framework enhances BMD prediction accuracy, supports more inclusive osteoporosis risk assessment, and improves the integration of multi-cohort datasets for future research. These findings highlight the potential of data harmonization in advancing precision medicine for osteoporosis prevention and management.