Research population description.

Objective
This study aims to explore the correlation between Osteoporosis and stroke risk, and find potential common key genes and drugs for intervention through bioinformatics methods.
Methods
This study used clinical data to assess the relationship between Osteoporosis and stroke risk through univariate and multivariate logistic regression analyses. Additionally, blood sequencing data from patients with Osteoporosis and stroke were obtained from the GEO database, and common key genes were identified using differential analysis, LASSO regression, and ROC curve methods. Potential interventional drugs were predicted using the DSigDB database.
Results
In the initial model, Osteoporosis was significantly associated with stroke risk (OR=1.78, 95% CI: 1.14–2.78, p < 0.01). This association was still significant after adjusting for factors such as age, gender, race, and BMI (OR=1.84, 95% CI: 1.18–2.89, p = 0.007). Bioinformatics analysis identified LILRA5, HNRNPL and AGBL3 as common key genes for Osteoporosis and stroke, and these genes were highly effective in diagnosing both diseases. The DSigDB database predicted that Cyclopenthiazide, Neostigmine bromide, and R-atenolol could potentially intervene with these three genes.
Conclusion
There is a significant positive correlation between Osteoporosis and stroke risk. LILRA5, HNRNPL and AGBL3 could be key genes common to both diseases, and Cyclopenthiazide, Neostigmine bromide, and R-atenolol could be potential drugs for intervention.