Construction and validation of a nomogram for the diagnosis of postmenopausal osteoporosis

Osteoporosis (OP) is an increasing global public health concern and practical tools for predicting and detecting postmenopausal osteoporosis (PMOP) at an early stage are still needed. The present study aimed to develop and validate an individualized diagnostic prediction model for PMOP to facilitate risk assessment and guide therapeutic strategies for high‑risk patients. RNA sequencing data and corresponding clinical features were obtained from the Gene Expression Omnibus database (training group, n=90) and The Fourth Affiliated Hospital of School of Medicine and International School of Medicine, Zhejiang University, Zhejiang China (validation group, n=18). Gene Set Enrichment Analysis (GSEA) and Protein‑Protein Interaction (PPI) network analyses were used to identify hub genes associated with OP. Univariate, multivariate, least absolute shrinkage and selection operator logistic regression analyses were performed to identify the factors and generate a dynamic nomogram. Validation was performed using the receiver operating characteristic curve and decision curve analysis (DCA) to evaluate the effectiveness and net benefit of the clinical prediction nomogram. PPI and GSEA identified 13 estrogen‑response‑related hub genes (CD44, FDFT1, SLC29A1, P2RY2, RHOBTB3, TNNC1, RPS6KA2, TSKU, CYP26B1, SEMA3B, SYT12, IL17RB and ELF3) that were incorporated into a risk score model. A nomogram combining this risk score with clinical parameters demonstrated a promising preliminary diagnostic performance for OP. DCA indicated that the combined risk score and clinical‑factor model provided the highest net benefit. The clinical prediction nomogram exhibited a promising preliminary performance in both the training and validation groups. The present study developed a novel diagnostic nomogram for predicting PMOP. This exploratory tool showed potential clinical utility for risk stratification, although prospective validation in larger cohorts is required before clinical application.