Osteoporosis (OP) is characterized by decreased bone mineral density and an increased fracture risk, often due to declining sex hormone levels that weaken bone strength. This study aims to explore estrogen related markers in OP to improve diagnosis and treatment.

Differentially expressed estrogen synthesis and metabolism-related genes (DE-ERGs) were identified through differential expression analysis and intersected with estrogen synthesis and metabolism-related genes (ERGs). Machine learning techniques and expression analysis were used to identify markers, followed by the construction of nomogram. Mendelian Randomization (MR) analysis was then employed to investigate the causal relationship between markers and OP. Finally, the expression of markers was validated by quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and western blot analyses.

CCND1, SCGB1A1, and DDIT4 were identified as markers for OP, with DDIT4 (Odds Ratio (OR) = 1.267; 95% Confidence Interval (CI): 1.054–1.523; P = 0.0012) serving as a risk factor. A nomogram was developed with a calibration curve slope near 1, and an area under the curve (AUC) value of 0.9, indicating strong predictive ability. The qRT-PCR and western blot analyses confirmed that DDIT4 was up-regulated and CCND1 was down-regulated in OP group.

This study highlighted CCND1, DDIT4, and SCGB1A1 as markers for OP, supporting their involvement in disease progression and offering a foundation for targeted therapeutic strategies.