Exploring the classification and treatment of osteoporosis from the perspectives of natural medicines, molecular targets, and symptom clusters

Osteoporosis (OP) is a metabolic bone disease characterized by reduced bone density and fragility, impairing quality of life. Traditional treatments often overlook symptoms like back and joint pain, increasing burden. This study aims to map relationships between natural medicines, targets, and symptom clusters, demonstrating their effectiveness in personalized OP treatment to enhance clinical strategies and self-assessment. We used compounds and targets, applying Summary data-based Mendelian Randomisation (SMR) analysis for biological process and molecular function enrichment. Additionally, we employed Phenome-Wide Association Studies (PheWAS) to select two natural drugs—Rhizoma Drynariae (RD) and Lycii Fructus (LF)—for case analysis. The study found that RD primarily improves symptoms such as indigestion, constipation, fatigue, polyuria, and depression, while LF significantly ameliorates symptoms related to the nervous and muscular systems, such as hoarseness, dizziness, vertigo, and fever symptoms. This analysis successfully differentiated two groups of symptoms and precisely constructed a logical chain among "natural Medicines-molecular tArGets-Illness-symptom Clusters" (MAGIC chain) achieving a refined classification of OP. The results of this study support the effectiveness of implementing personalized medical strategies in the treatment of OP, providing a scientific basis for the clinical application of natural medicines and patient self-management.