Integration of the Human Gut Microbiome and Serum Metabolome Reveals Novel Biological Factors Involved in the Regulation of Bone Mineral Density

Gut microbiota alterations in postmenopausal women with osteoporosis and osteopenia from Shanghai, China

Background

The importance of the gut microbiota in maintaining bone homeostasis has been increasingly emphasized by recent research. This study aimed to identify whether and how the gut microbiome of postmenopausal women with osteoporosis and osteopenia may differ from that of healthy individuals.

Methods

Fecal samples were collected from 27 individuals with osteoporosis (OP), 44 individuals with osteopenia (ON), and 23 normal controls (NC). The composition of the gut microbial community was analyzed by 16S rRNA gene sequencing.

Results

No significant difference was found in the microbial composition between the three groups according to alpha and beta diversity. At the phylum level, Proteobacteria and Fusobacteriota were significantly higher and Synergistota was significantly lower in the ON group than in the NC group. At the genus level, Roseburia, Clostridia_UCG.014, Agathobacter, Dialister and Lactobacillus differed between the OP and NC groups as well as between the ON and NC groups (p < 0.05). Linear discriminant effect size (LEfSe) analysis results showed that one phylum community and eighteen genus communities were enriched in the NC, ON and OP groups, respectively. Spearman correlation analysis showed that the abundance of the Dialister genus was positively correlated with BMD and T score at the lumbar spine (p < 0.05). Functional predictions revealed that pathways relevant to amino acid biosynthesis, vitamin biosynthesis, and nucleotide metabolism were enriched in the NC group. On the other hand, pathways relevant to metabolites degradation and carbohydrate metabolism were mainly enriched in the ON and OP groups respectively.

Conclusions

Our findings provide new epidemiologic evidence regarding the relationship between the gut microbiota and postmenopausal bone loss, laying a foundation for further exploration of therapeutic targets for the prevention and treatment of postmenopausal osteoporosis (PMO).

Multi-modal molecular determinants of clinically relevant osteoporosis subtypes

Due to a rapidly aging global population, osteoporosis and the associated risk of bone fractures have become a wide-spread public health problem. However, osteoporosis is very heterogeneous, and the existing standard diagnostic measure is not sufficient to accurately identify all patients at risk of osteoporotic fractures and to guide therapy. Here, we constructed the first prospective multi-omics atlas of the largest osteoporosis cohort to date (longitudinal data from 366 participants at three time points), and also implemented an explainable data-intensive analysis framework (DLSF: Deep Latent Space Fusion) for an omnigenic model based on a multi-modal approach that can capture the multi-modal molecular signatures (M3S) as explicit functional representations of hidden genotypes. Accordingly, through DLSF, we identified two subtypes of the osteoporosis population in Chinese individuals with corresponding molecular phenotypes, i.e., clinical intervention relevant subtypes (CISs), in which bone mineral density benefits response to calcium supplements in 2-year follow-up samples. Many snpGenes associated with these molecular phenotypes reveal diverse candidate biological mechanisms underlying osteoporosis, with xQTL preferences of osteoporosis and its subtypes indicating an omnigenic effect on different biological domains. Finally, these two subtypes were found to have different relevance to prior fracture and different fracture risk according to 4-year follow-up data. Thus, in clinical application, M3S could help us further develop improved diagnostic and treatment strategies for osteoporosis and identify a new composite index for fracture prediction, which were remarkably validated in an independent cohort (166 participants).

Application and prospect of metabolomics in the early diagnosis of osteoporosis: a narrative review

This paper reviews the application of metabolomics in the early diagnosis of osteoporosis in recent years. The authors searched electronic databases for the keywords "metabolomics", "osteoporosis" and "biomarkers", then analyzed the relationship between functional markers and osteoporosis using categorical summarization. Lipid metabolism, amino acid metabolism and energy metabolism are closely related to osteoporosis development and can become early diagnostic markers of the condition. However, the existing studies in metabolomics suffer from varying application methods, difficulty in identifying isomers, small study cohorts and insufficient research on metabolic mechanisms. Consequently, it is important for future research to focus on broadening and standardizing the scope of the application of metabolomics. High-quality studies on a large scale should also be conducted while promoting the early diagnosis of osteoporosis in a more precise, comprehensive and sensitive manner.

Racial and Ethnic Differences in Studies of the Gut Microbiome and Osteoporosis

PURPOSE OF REVIEW

The purpose of this review is to summarize the scientific evidence published in the past 5 years examining the epidemiology of bone health as it relates to the gut microbiome, across race and ethnicity groups.

RECENT FINDINGS

The link between the gut microbiome and bone health is well established and is supported by numerous biological mechanisms. However, human study research in this field is dominated by studies of older adults residing in Asian countries. A limited number of epidemiological and randomized controlled trials have been conducted with individuals in other countries; however, they are marked by their racial and ethnic homogeneity, use varied measures of the gut microbiome, and different interventions (where applicable), making comparisons across race and ethnic groups difficult. As the global prevalence of osteoporosis increases, the need for lifestyle interventions is critical. Existing data suggest that racial and ethnic differences in gut microbiome exist. Studies examining the relation between bone health and gut microbial structure and function across diverse racial and ethnic groups are needed to determine appropriate microbiome-based interventions.

A Prediction Model for Osteoporosis Risk Using a Machine-Learning Approach and Its Validation in a Large Cohort

BACKGROUND

Osteoporosis develops in the elderly due to decreased bone mineral density (BMD), potentially increasing bone fracture risk. However, the BMD is not regularly measured in a clinical setting. This study aimed to develop a good prediction model for the osteoporosis risk using a machine learning (ML) approach in adults over 40 years in the Ansan/Anseong cohort and the association of predicted osteoporosis risk with a fracture in the Health Examinees (HEXA) cohort.

METHODS

The 109 demographic, anthropometric, biochemical, genetic, nutrient, and lifestyle variables of 8,842 participants were manually selected in an Ansan/Anseong cohort and included in the ML algorithm. The polygenic risk score (PRS) of osteoporosis was generated with a genome-wide association study and added for the genetic impact of osteoporosis. Osteoporosis was defined with < -2.5 T scores of the tibia or radius compared to people in their 20s-30s. They were divided randomly into the training (n = 7,074) and test (n = 1,768) sets-Pearson's correlation between the predicted osteoporosis risk and fracture in the HEXA cohort.

RESULTS

XGBoost, deep neural network, and random forest generated the prediction model with a high area under the curve (AUC, 0.86) of the receiver operating characteristic (ROC) with 10, 15, and 20 features; the prediction model by XGBoost had the highest AUC of ROC, high accuracy and k-fold values (> 0.85) in 15 features among seven ML approaches. The model included the genetic factor, genders, number of children and breastfed children, age, residence area, education, seasons to measure, height, smoking status, hormone replacement therapy, serum albumin, hip circumferences, vitamin B6 intake, and body weight. The prediction models for women alone were similar to those for both genders, with lower accuracy. When the prediction model was applied to the HEXA study, the correlation between the fracture incidence and predicted osteoporosis risk was significant but weak (r = 0.173, P < 0.001).

CONCLUSION

The prediction model for osteoporosis risk generated by XGBoost can be applied to estimate osteoporosis risk. The biomarkers can be considered for enhancing the prevention, detection, and early therapy of osteoporosis risk in Asians.

Enteric nervous system damage caused by abnormal intestinal butyrate metabolism may lead to functional constipation

Functional constipation (FC) is a high morbidity gastrointestinal disease for which dysfunction in the enteric nervous system is a major pathogenesis mechanism. To enhance our understanding of the involvement of intestinal microbiota and its metabolites in the pathogenesis of FC, we conducted a shotgun metagenomic sequencing analysis of gut microbiota and serum short-chain fatty acids (SCFAs) analysis in 460 Chinese women with different defecation frequencies. We observed that the abundance ofFusobacterium_varium, a butyric acid-producing bacterium, was positively correlated (P = 0.0096) with the frequency of defecation; however, the concentrations of serum butyric acid was negatively correlated (P = 3.51E-05) with defecation frequency. These results were verified in an independent cohort (6 patients with FC and 6 controls). To further study the effects of butyric acid on intestinal nerve cells, we treated mouse intestinal neurons in vitro with various concentrations of butyrate (0.1, 0.5, 1, and 2.5 mM). We found that intestinal neurons treated with 0.5 mM butyrate proliferated better than those in the other treatment groups, with significant differences in cell cycle and oxidative phosphorylation signal pathways. We suggest that the decreased butyrate production resulting from the reduced abundance of Fusobacterium in gut microbiota affects the proliferation of intestinal neurons and the energy supply of intestinal cells. However, with FC disease advancing, the consumption and excretion of butyric acid reduce, leading to its accumulation in the intestine. Moreover, the accumulation of an excessively high amount of butyric acid inhibits the proliferation of nerve cells and subsequently exacerbates the disease.

Discovery of potential biomarkers for osteoporosis diagnosis by individual omics and multi-omics technologies

INTRODUCTION

Global aging has made osteoporosis an increasingly serious public health problem. Osteoporotic fractures seriously affect the quality of life of patients and increase disability and mortality rates. Early diagnosis is important for timely intervention. The continuous development of individual- and multi-omics methods is helpful for the exploration and discovery of biomarkers for the diagnosis of osteoporosis.

AREAS COVERED

In this review, we first introduce the epidemiological status of osteoporosis and then describe the pathogenesis of osteoporosis. Furthermore, the latest progress in individual- and multi-omics technologies for exploring biomarkers for osteoporosis diagnosis is summarized. Moreover, we clarify the advantages and disadvantages of the application of osteoporosis biomarkers obtained using the omics method. Finally, we put forward valuable views on the future research direction of diagnostic biomarkers of osteoporosis.

EXPERT OPINION

Omics methods undoubtedly provide greatly contribute to the exploration of diagnostic biomarkers of osteoporosis; however, in the future, the clinical validity and clinical utility of the obtained potential biomarkers should be thoroughly examined. In addition, the improvement and optimization of the detection methods for different types of biomarkers and standardization of the detection process guarantee the reliability and accuracy of the detection results.

Identifying microbial signatures for patients with postmenopausal osteoporosis using gut microbiota analyses and feature selection approaches

Osteoporosis (OP) is a metabolic bone disorder characterized by low bone mass and deterioration of micro-architectural bone tissue. The most common type of OP is postmenopausal osteoporosis (PMOP), with fragility fractures becoming a global burden for women. Recently, the gut microbiota has been connected to bone metabolism. The aim of this study was to characterize the gut microbiota signatures in PMOP patients and controls. Fecal samples from 21 PMOP patients and 37 controls were collected and analyzed using amplicon sequencing of the V3-V4 regions of the 16S rRNA gene. The bone mineral density (BMD) measurement and laboratory biochemical test were performed on all participants. Two feature selection algorithms, maximal information coefficient (MIC) and XGBoost, were employed to identify the PMOP-related microbial features. Results showed that the composition of gut microbiota changed in PMOP patients, and microbial abundances were more correlated with total hip BMD/T-score than lumbar spine BMD/T-score. Using the MIC and XGBoost methods, we identified a set of PMOP-related microbes; a logistic regression model revealed that two microbial markers (Fusobacteria and Lactobacillaceae) had significant abilities in disease classification between the PMOP and control groups. Taken together, the findings of this study provide new insights into the etiology of OP/PMOP, as well as modulating gut microbiota as a therapeutic target in the diseases. We also highlight the application of feature selection approaches in biological data mining and data analysis, which may improve the research in medical and life sciences.

The role of melatonin in the development of postmenopausal osteoporosis

Melatonin is an important endogenous hormone that modulates homeostasis in the microenvironment. Recent studies have indicated that serum melatonin levels are closely associated with the occurrence and development of osteoporosis in postmenopausal women. Exogenous melatonin could also improve bone mass and increase skeletal strength. To determine the underlying mechanisms of melatonin in the prevention and treatment of postmenopausal osteoporosis, we performed this review to analyze the role of melatonin in bone metabolism according to its physiological functions. Serum melatonin is related to bone mass, the measurement of which is a potential method for the diagnosis of osteoporosis. Melatonin has a direct effect on bone remodeling by promoting osteogenesis and suppressing osteoclastogenesis. Melatonin also regulates the biological rhythm of bone tissue, which benefits its osteogenic effect. Additionally, melatonin participates in the modulation of the bone microenvironment. Melatonin attenuates the damage induced by oxidative stress and inflammation on osteoblasts and prevents osteolysis from reactive oxygen species and inflammatory factors. As an alternative drug for osteoporosis, melatonin can improve the gut ecology, remodel microbiota composition, regulate substance absorption and maintain metabolic balance, all of which are beneficial to the health of bone structure. In conclusion, our review systematically demonstrates the effects of melatonin on bone metabolism. Based on the evidence in this review, melatonin will play a more important role in the diagnosis, prevention and treatment of postmenopausal osteoporosis.

Application of metabolomics in osteoporosis research

Osteoporosis (OP) is a systemic disease characterized by bone metabolism imbalance and bone microstructure destruction, which causes serious social and economic burden. At present, the diagnosis and treatment of OP mainly rely on imaging combined with drugs. However, the existing pathogenic mechanisms, diagnosis and treatment strategies for OP are not clear and effective enough, and the disease progression that cannot reflect OP further restricts its effective treatment. The application of metabolomics has facilitated the study of OP, further exploring the mechanism and behavior of bone cells, prevention, and treatment of the disease from various metabolic perspectives, finally realizing the possibility of a holistic approach. In this review, we focus on the application of metabolomics in OP research, especially the newer systematic application of metabolomics and treatment with herbal medicine and their extracts. In addition, the prospects of clinical transformation in related fields are also discussed. The aim of this study is to highlight the use of metabolomics in OP research, especially in exploring the pathogenesis of OP and the therapeutic mechanisms of natural herbal medicine, for the benefit of interdisciplinary researchers including clinicians, biologists, and materials engineers.