ArticleFrontiers in endocrinology2026
SHBG and APOA1 as serum biomarkers for low bone mineral density in Mexican postmenopausal women: a random forest-based analysis.
Article in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Osteoporosis is a major health issue that has implications worldwide, both clinically and socioeconomically. Therefore, searching for informative bone health biomarkers that provide early information on bone changes and assess treatment efficacy is essential. Previously, our group used label-free proteomics and bioinformatic approaches to analyze the serum proteomic profile of postmenopausal women with normal bone mineral density (BMD), osteopenia, and osteoporosis. Based on their role in bone metabolism and literature review, a panel of 12 candidate serum biomarkers for low BMD was proposed. Objective: To validate a set of 6 proteins as potential minimally invasive serum biomarkers, Gelsolin (GSN), Sex Hormone-Binding Globulin (SHBG), Apolipoprotein A1 (APOA1), Apolipoprotein A2 (APOA2), Ryanodine Receptor 1 (RYR1), and Ryanodine Receptor 3 (RYR3), for diagnosing low BMD in postmenopausal women. We also investigated whether a combination of these proteins can provide more informative insights into the risk of having low BMD compared to individual biomarkers. Methods: The enzyme-linked immunosorbent assay was carried out to measure the concentrations of these proteins in 456 serum samples from postmenopausal Mexican women with normal or low BMD. Results: A random forest classifier demonstrated that SHBG and APOA1 are the most effective biomarkers for discriminating between women with normal or low BMD, with notable high sensitivity (86%), specificity (75%), precision (92%), and overall performance (89.32%). Conclusions: The combination of APOA1 and SHBG correlates better with the risk of bone loss than the performance of single biomarkers in postmenopausal women. Further validation in a larger cohort is necessary to confirm these results.
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