Prioritizing Osteoporosis-Associated Candidates Through Multi-Omics Mendelian Randomization, Transcriptomic Modeling, and in vitro Validation

Gut microbiota, immune traits, and circulating metabolites may contribute to skeletal biology. We integrated genetic association analyses, transcriptomic profiling, and cell experiments to prioritize osteoporosis-associated molecular candidates.

Two-sample Mendelian randomization (MR), gene prioritization, and three GEO datasets comprising 110 samples were integrated. MR estimates were recalculated while retaining outcome-associated instruments, followed by false-discovery-rate correction. Exploratory classifiers and a five-gene nomogram were evaluated internally. Protein expression and osteoclast formation were examined in control and RANKL-treated RAW264.7 cells in three independent experiments per group. Supplementary validation repeated normalization, screening and tuning within training folds.

Among 2,329 IVW tests, the metabolite exposure indexed by GCST90200621 was associated with osteoporosis after multiplicity adjustment (odds ratio 1.296, 95% confidence interval 1.155-1.454; within-domain adjusted P = 0.0135). No colocalization record had PP.H4 above 0.5. The transcriptomic workflow prioritized CCR1, TRMT2A, FADS2, FBXL14, and TTLL12. The original nine-gene RF analysis had an internal test-partition AUC of 0.805. Expression of all five proteins increased during RANKL-induced osteoclastogenesis. Additional nested validation yielded a mean AUC of 0.528; cross-study performance remained limited. Fixed-five-gene ridge sensitivity analysis yielded a mean internal AUC of 0.756, conditional on prior gene selection.

Integrating genetic and transcriptomic analyses with cell experiments prioritized five osteoporosis-associated candidates whose protein expression increased during osteoclast differentiation. These complementary findings provide a focused basis for biological follow-up; predictive and gene-specific functional applications require further validation.