Meta-Analysis Reveals Compositional and Functional Microbial Changes Associated with Osteoporosis.

Over the past decade, the role of the gut microbiota in many disease states has gained a great deal of attention. Mounting evidence from case-control and observational studies has linked changes in the gut microbiota to the pathophysiology of osteoporosis (OP). Nonetheless, the results of these studies contain discrepancies, leaving the literature without a consensus on osteoporosis-associated microbial signatures. Here, we conducted a comprehensive meta-analysis combining and reexamining five publicly available 16S rRNA partial sequence data sets to identify gut bacteria consistently associated with osteoporosis across different cohorts. After adjusting for the batch effect associated with technical variation and heterogeneity of studies, we observed a significant shift in the microbiota composition in the osteoporosis group. An increase in the relative abundance of opportunistic pathogens , , and was observed in the OP group. Moreover, short-chain-fatty-acid (SCFA) producers, including members of the genera , , , , XIV, and , were depleted in the OP group relative to the healthy control (HC) group. Lactic acid-producing bacteria, including , were significantly increased in the OP group. The random forest algorithm further confirmed that these bacteria differentiate the two groups. Furthermore, functional prediction revealed depletion of the SCFA biosynthesis pathway (glycolysis, tricarboxylic acid [TCA] cycle, and Wood-Ljungdahl pathway) and amino acid biosynthesis pathway (methionine, histidine, and arginine) in the OP group relative to the HC group. This study uncovered OP-associated compositional and functional microbial alterations, providing robust insight into OP pathogenesis and aiding the possible development of a therapeutic intervention to manage the disease. Osteoporosis is the most common metabolic bone disease associated with aging. Mounting evidence has linked changes in the gut microbiota to the pathophysiology of osteoporosis. However, which microbes are associated with dysbiosis and their impact on bone density and inflammation remain largely unknown due to inconsistent results in the literature. Here, we present a meta-analysis with a standard workflow, robust statistical approaches, and machine learning algorithms to identify notable microbial compositional changes influencing osteoporosis.