{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T13:27:38Z","timestamp":1768570058988,"version":"3.49.0"},"posted":{"date-parts":[[2025,5,13]]},"group-title":"Epidemiology","reference-count":49,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T00:00:00Z","timestamp":1747094400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Post-harmonization, inter-study variability in BMI was significantly reduced (\u03a9\u00b2 = 0.0028), and BMD associations with covariates remained consistent across datasets. Harmonized models showed improved predictive performance, with explained variance in BMD increasing (R\u00b2 = 0.14). PCA confirmed the effective alignment of genetic data, reducing batch effects and improving cross-study compatibility.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Mini-Abstract<\/jats:title>\n                  <jats:p>Harmonizing osteoporosis datasets improves BMD prediction accuracy and enhances risk assessment. This study developed a harmonization framework integrating phenotypic and genomic data across three major datasets. After harmonization, predictive model performance improved, enabling better osteoporosis risk stratification and advancing precision medicine for fracture prevention.<\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2025.05.12.25327471","type":"posted-content","created":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T18:15:13Z","timestamp":1747160113000},"source":"Crossref","is-referenced-by-count":0,"title":["Integrative Harmonization of Phenotypic and Genomic Data Improves Bone Mineral Density Prediction in Multi-Study Osteoporosis Research"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6807-3693","authenticated-orcid":false,"given":"Anqi","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4679-8903","authenticated-orcid":false,"given":"Qing","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025051506450654000_2025.05.12.25327471v1.1","doi-asserted-by":"publisher","DOI":"10.5152\/eurjrheum.2016.048"},{"key":"2025051506450654000_2025.05.12.25327471v1.2","doi-asserted-by":"publisher","DOI":"10.1007\/s00198-018-4704-5"},{"key":"2025051506450654000_2025.05.12.25327471v1.3","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1080\/03007995.2022.2141483","article-title":"Closing the gap in osteoporosis management: the critical role of primary care in bone health","volume":"39","year":"2023","journal-title":"Curr Med Res Opin"},{"key":"2025051506450654000_2025.05.12.25327471v1.4","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1016\/S0140-6736(02)08657-9","article-title":"Epidemiology and outcomes of osteoporotic fractures","volume":"359","year":"2002","journal-title":"The Lancet"},{"key":"2025051506450654000_2025.05.12.25327471v1.5","doi-asserted-by":"crossref","first-page":"288","DOI":"10.3389\/fgene.2019.00288","article-title":"Advanced Genetic Approaches in Discovery and Characterization of Genes Involved With Osteoporosis in Mouse and Human","volume":"10","year":"2019","journal-title":"Front Genet"},{"key":"2025051506450654000_2025.05.12.25327471v1.6","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2249"},{"key":"2025051506450654000_2025.05.12.25327471v1.7","doi-asserted-by":"publisher","DOI":"10.1038\/nature14878"},{"key":"2025051506450654000_2025.05.12.25327471v1.8","doi-asserted-by":"publisher","DOI":"10.1038\/nrg3932"},{"key":"2025051506450654000_2025.05.12.25327471v1.9","doi-asserted-by":"publisher","DOI":"10.9778\/cmajo.20180062"},{"key":"2025051506450654000_2025.05.12.25327471v1.10","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.3389\/fgene.2019.01227","article-title":"Data Integration Methods for Phenotype Harmonization in Multi-Cohort Genome-Wide Association Studies With Behavioral Outcomes","volume":"10","year":"2019","journal-title":"Front Genet"},{"key":"2025051506450654000_2025.05.12.25327471v1.11","doi-asserted-by":"publisher","DOI":"10.1093\/molbev\/msq148"},{"key":"2025051506450654000_2025.05.12.25327471v1.12","doi-asserted-by":"crossref","unstructured":"Hinrichs AL , Culverhouse RC , Suarez BK . 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