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16S sequencing data of Asian female population with BMD reduction

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Feb 02, 2024 version files 19.87 GB

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Abstract

Background: Osteopenia and osteoporosis are both common bone diseases in postmenopausal women, but their clinical manifestations tend to be unnoticeable until bone loss-induced fractures occur which could endanger personal safety and lead to other injuries. This phenomenon makes it difficult to identify the population with osteoporosis, leading to failure to receive treatment timely. As a significant public health concern, the early diagnosis of osteopenia and osteoporosis has become an urgent clinical topic.

Methods: We conducted metabolomic analysis on plasma samples and 16S sequencing on fecal samples from humans. While 142 women participated in metabolomic analysis, only 118 of them joined the transcriptomics cohort. By statistical methods, we managed to identify relevant biomarkers of osteopenia and osteoporosis consisting of metabolites and genera and establish early diagnosis models, including clinical features, classical indexes and selected biomarkers. The efficiency of these models was also validated.

Results: Metabolites included homo-L-arginine, 3-methylglutarylcarnitine, linoleyl carnitine, and estrone glucuronide, and gut microbes included Coprococcus, Dialister, Erysipelatoclostridium, Marvinbryantia and Stenotrophomonas were identified as biomarkers of osteoporosis while the metabolite dihomo-gamma-linolenoylethanolamide and genera including Lachnospiraceae_NC2004_group, Lachnospiraceae_NK4A136_group and Roseburia were identified as as biomarkers of osteopenia. The area under the curve (AUC) of early diagnosis models for distinguishing osteoporosis from normal, osteopenia from normal and osteoporosis from osteopenia were 0.95, 0.77, and 0.92 respectively.

Conclusion: We identified biomarkers of both osteopenia and osteoporosis consisting of metabolites and gut microbes and investigated the mechanisms underlying their influence on the reduction of BMD. High-reliability prediction models were built through artificial neural networks and random forest and these models can help early diagnosis of osteopenia and osteoporosis to realize early intervention and early treatment.