| Title: | Proteomics-driven discovery of intervention windows and risk subtypes in osteoporosis: A prospective cohort study |
| Journal: | Bone |
| Published: | 1 Jun 2026 |
| DOI: | https://doi.org/10.1016/j.bone.2026.117969 |
Publication 19810
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Abstract
Given the limited feasibility of population-wide bone mineral density screening and the infrequency of long-term monitoring in healthy individuals, identifying the window for early intervention and the populations to be prioritized for screening is critical. This study aimed to identify intervention windows for osteoporosis and to determine potential high-risk subtypes within the healthy population. Based on proteomic data from 41,408 healthy adults, we conducted the DE-SWAN method to identify change peaks in plasma protein during the pre-diagnostic osteoporosis phase, and employed finite Gaussian mixture model-based clustering to delineate high-risk subtypes of osteoporosis. We identified 122 protein biomarkers significantly associated with osteoporosis risk throughout the follow-up period. Importantly, we identified two critical peaks occurring approximately 10 and 6 years before diagnosis, with the former enriched in immune-related pathways and the latter prominently involving responses to retinoic acid and glucocorticoids. Furthermore, one high-risk subtype for osteoporosis was identified in both males and females, termed the Frailty and Obesity Subtype. This subtype is characterized by a high degree of frailty and obesity, accompanied by a significantly elevated risk of both osteoporosis and fractures. Finally, we developed a predictive model comprising 10 proteins for identifying high-risk subtypes of osteoporosis, which demonstrated better performance than the traditional risk factor model (AUC: 0.743 vs. 0.680). Our findings demonstrate that proteomic profiling can reveal early molecular changes and identify high-risk subtypes years before clinical onset, providing a foundation for screening and precision prevention of osteoporosis.</p>
9 Authors
- Yi Zheng
- Jincheng Li
- Yucan Li
- Jiacheng Wang
- Chen Suo
- Yanfeng Jiang
- Li Jin
- Kelin Xu
- Xingdong Chen
1 Application
| Application ID | Title |
|---|---|
| 92718 | Deciphering chronic disease - association studies of genome, exposome and phenome |
Enabling scientific discoveries that improve human health