Assessing the Associations of Blood Metabolites With Osteoporosis: A Mendelian Randomization Study

Gut microbiota, plasma metabolites, and osteoporosis: unraveling links via Mendelian randomization

Objective

Osteoporosis, characterized by reduced bone density and heightened fracture risk, is influenced by genetic and environmental factors. This study investigates the interplay between gut microbiota, plasma metabolomics, and osteoporosis, identifying potential causal relationships mediated by plasma metabolites.

Methods

Utilizing aggregated genome-wide association studies (GWAS) data, a comprehensive two-sample Mendelian Randomization (MR) analysis was performed involving 196 gut microbiota taxa, 1,400 plasma metabolites, and osteoporosis indicators. Causal relationships between gut microbiota, plasma metabolites, and osteoporosis were explored.

Results

The MR analyses revealed ten gut microbiota taxa associated with osteoporosis, with five taxa positively linked to increased risk and five negatively associated. Additionally, 96 plasma metabolites exhibited potential causal relationships with osteoporosis, with 49 showing positive associations and 47 displaying negative associations. Mediation analyses identified six causal pathways connecting gut microbiota to osteoporosis through ten mediating relationships involving seven distinct plasma metabolites, two of which demonstrated suppression effects.

Conclusion

This study provides suggestive evidence of genetic correlations and causal links between gut microbiota, plasma metabolites, and osteoporosis. The findings underscore the complex, multifactorial nature of osteoporosis and suggest the potential of gut microbiota and plasma metabolite profiles as biomarkers or therapeutic targets in the management of osteoporosis.

Function-oriented mechanism discovery of coumarins from Psoralea corylifolia L. in the treatment of ovariectomy-induced osteoporosis based on multi-omics analysis

ETHNOPHARMACOLOGICAL RELEVANCE

Psoraleae Fructus (Bu Gu Zhi) is the fruit of Psoralea corylifolia L. (PCL) and has been used for centuries in traditional Chinese medicine formulas to treat osteoporosis (OP). A new drug called "BX" has been developed from PCL, but its mechanism for treating OP is not yet fully understood.

AIM OF THE STUDY

To explore the mechanism of action of BX in the treatment of ovariectomy-induced OP based function-oriented multi-omics analysis of gut microbiota (GM) and metabolites.

MATERIALS AND METHODS

C57BL/6 mice were bilaterally ovariectomized to replicate the OP model. The therapeutic efficacy of BX was evaluated by bone parameters (BMD, BV/TV, Tb.N, Tb.Sp), hematoxylin and eosin (H&E) staining results, and determination of bone formation markers procollagen type Ⅰ amino-terminal peptide (PⅠNP) and bone-specific alkaline phosphatase (BALP). Serum and fecal metabolomics and high-throughput 16S rDNA sequencing were performed to evaluate effects on endogenous metabolites and GM. In addition, an enzyme-based functional correlation algorithm (EBFC) algorithm was used to investigate functional correlations between GM and metabolites.

RESULTS

BX improved OP in OVX mice by increasing BMD, BV/TV, serum PⅠNP, BALP, and improving Tb.N and Tb.Sp. A total of 59 differential metabolites were identified, and 9 metabolic pathways, including arachidonic acid metabolism, glycerophospholipid metabolism, purine metabolism, and tryptophan metabolism, were found to be involved in the progression of OP. EBFC analysis results revealed that the enzymes related to purine and tryptophan metabolism, which are from Lachnospiraceae_NK4A136_group, Blautia, Rs-E47_termite_group, UCG-009, and Clostridia_UCG-014, were identified as the intrinsic link between GM and metabolites.

CONCLUSIONS

The regulation of GM and restoration of metabolic disorders may be the mechanisms of action of BX in alleviating OP. This research provides insights into the function-oriented mechanism discovery of traditional Chinese medicine in the treatment of OP.

GC-MS based comparative metabolomic analysis of human cancellous bone reveals the critical role of linoleic acid metabolism in femur head necrosis

INTRODUCTION

Femur head necrosis (FHN) is a challenging clinical disease with unclear underlying mechanism, which pathologically is associated with disordered metabolism. However, the disordered metabolism in cancellous bone of FHN was never analyzed by gas chromatography-mass spectrometry (GC-MS).

OBJECTIVES

To elucidate altered metabolism pathways in FHN and identify putative biomarkers for the detection of FHN.

METHODS

We recruited 26 patients with femur head necrosis and 22 patients with femur neck fracture in this study. Cancellous bone tissues from the femoral heads were collected after the surgery and were analyzed by GC-MS based untargeted metabolomics approach. The resulting data were analyzed via uni- and multivariate statistical approaches. The changed metabolites were used for the pathway analysis and potential biomarker identification.

RESULTS

Thirty-seven metabolites distinctly changed in FHN group were identified. Among them, 32 metabolites were upregulated and 5 were downregulated in FHN. The pathway analysis showed that linoleic acid metabolism were the most relevant to FHN pathology. On the basis of metabolites network, L-lysine, L-glutamine and L-serine were deemed as the junctions of the whole metabolites. Finally, 9,12-octadecadienoic acid, inosine, L-proline and octadecanoic acid were considered as the potential biomarkers of FHN.

CONCLUSION

This study provides a new insight into the pathogenesis of FHN and confirms linoleic acid metabolism as the core.

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.

Metabolome-Wide Mendelian Randomization Assessing the Causal Relationship Between Blood Metabolites and Bone Mineral Density

Mounting evidence has supported osteoporosis (OP) as a metabolic disorder. Recent metabolomics studies have discovered numerous metabolites related to bone mineral density (BMD). However, the causal effects of metabolites on BMD at distinct sites remained underexplored. Leveraging genome-wide association datasets, we conducted two-sample Mendelian randomization (MR) analyses to investigate the causal relationship between 486 blood metabolites and bone mineral density at five skeletal sites including heel (H), total body (TB), lumbar spine (LS), femoral neck (FN), and ultra-distal forearm (FA). Sensitivity analyses were performed to test the presence of the heterogeneity and the pleiotropy. To exclude the influences of reverse causation, genetic correlation, and linkage disequilibrium (LD), we further performed reverse MR, linkage disequilibrium regression score (LDSC), and colocalization analyses. In the primary MR analyses, 22, 10, 3, 7, and 2 metabolite associations were established respectively for H-BMD, TB-BMD, LS-BMD, FN-BMD, and FA-BMD at the nominal significance level (IVW, P < 0.05) and passing sensitivity analyses. Among these, one metabolite, androsterone sulfate showed a strong effect on four out of five BMD phenotypes (Odds ratio [OR] for H-BMD = 1.045 [1.020, 1.071]; Odds ratio [OR] for TB-BMD = 1.061 [1.017, 1.107]; Odds ratio [OR] for LS-BMD = 1.088 [1.023, 1.159]; Odds ratio [OR] for FN-BMD = 1.114 [1.054, 1.177]). Reverse MR analysis provided no evidence for the causal effects of BMD measurements on these metabolites. Colocalization analysis have found that several metabolite associations might be driven by shared genetic variants such as mannose for TB-BMD. This study identified some metabolites causally related to BMD at distinct sites and several key metabolic pathways, which shed light on predictive biomarkers and drug targets for OP.

Pulmonary embolism and 529 human blood metabolites: genetic correlation and two-sample Mendelian randomization study

BACKGROUND

The incidence of pulmonary embolism complications in the literature ranges from 10 to 50%, with a 0.5-10% risk of fatal pulmonary embolism. However, the biological cause of pulmonary embolism is unknown.

METHODS

This study used data from the Genome-Wide Association Study (GWAS) of Pulmonary Embolism and Human Blood Metabolites from the UK Biobank, and the data from subjects of European ancestry were analyzed. We explored the relationship between pulmonary embolism and blood metabolites in three ways. We first analyzed the genetic correlation between pulmonary embolism and human blood metabolites using the linkage disequilibrium score regression (LDSC) and then analyzed the causal relationship between pulmonary embolism and meaningful blood metabolites obtained from the LDSC, a procedure for which we used Mendelian randomization analysis. Finally, we obtained transcriptome sequencing data for patients with a pulmonary embolism from the GEO database, analyzed differentially expressed genes (DEGs) in patients with pulmonary embolism versus healthy populations, and compared the DEGs with the resulting blood metabolite genes to further validate the relationship between pulmonary embolism and blood metabolites.

RESULT

We found six human blood metabolites genetically associated with pulmonary embolism, stearic acid glycerol phosphate ethanolamine (correlation coefficient = 0.2582, P = 0.0493), hydroxytryptophan (correlation coefficient = 0.2894, P = 0.0435), and N1-methyladenosine (correlation coefficient = 0.0439, P = 0.3728), and a significant causal relationship was discovered between hydroxytryptophan and pulmonary embolism. After screening microarray data from the GEO database, we performed differential gene analysis on the GSE19151 dataset and screened a total of 22,216 genes with P values less than 0.05, including 17,361 upregulated genes and 4854 downregulated genes. By comparing the resulting differentially expressed genes with six genes encoding blood metabolites, LIPC and NAT2 were found to be differentially expressed in association with pulmonary embolism.

Systematic evaluation for the causal effects of blood metabolites on osteoporosis: Genetic risk score and Mendelian randomization

Purpose

Osteoporosis is associated with metabolic alterations, but the causal roles of serum metabolites on osteoporosis have not been identified.

Methods

Based on the large individual-level datasets from UK Biobank as well as GWAS summary datasets, we first constructed genetic risk scores (GRSs) for 308 of 486 human serum metabolites and evaluated the effect of each GRS on 2 major osteoporosis phenotypes, i.e., estimated bone miner density (eBMD) and fracture, respectively. Then, two-sample Mendelian Randomization (MR) was performed to validate the casual metabolites on osteoporosis. Multivariable MR analysis tested whether the effects of metabolites on osteoporosis are independent of possible confounders. Finally, we conducted metabolic pathway analysis for the metabolites involved in bone metabolism.

Results

We identified causal effects of 18 metabolites on eBMD and 1 metabolite on fracture with the GRS method after adjusting for multiple tests. Then, 9 of them were further validated with MR as replication, where comprehensive sensitive analyses proved robust of the causal associations. Although not identified in GRS, 3 metabolites were associated with at least three osteoporosis traits in MR results. Multivariable MR analysis determined the independent causal effect of several metabolites on osteoporosis. Besides, 23 bone metabolic pathways were detected, such as valine, leucine, isoleucine biosynthesis (p = 0.053), and Aminoacyl-tRNA biosynthesis (p = 0.076), and D-glutamine and D-glutamate metabolism (p = 0.004).

Conclusions

The systematic causal analyses strongly suggested that blood metabolites have causal effects on osteoporosis risk.

A genetic correlation scan identifies blood proteins associated with bone mineral density

Background

Osteoporosis is a common metabolic bone disease that is characterized by low bone mass. However, limited efforts have been made to explore the functional relevance of the blood proteome to bone mineral density across different life stages.

Methods

Using genome-wide association study summary data of the blood proteome and two independent studies of bone mineral density, we conducted a genetic correlation scan of bone mineral density and the blood proteome. Linkage disequilibrium score regression analysis was conducted to assess genetic correlations between each of the 3283 plasma proteins and bone mineral density.

Results

Linkage disequilibrium score regression identified 18 plasma proteins showing genetic correlation signals with bone mineral density in the TB-BMD cohort, such as MYOM2 (coefficient = 0.3755, P value = 0.0328) among subjects aged 0 ~ 15, POSTN (coefficient = − 0.5694, P value = 0.0192) among subjects aged 30 ~ 45 and PARK7 (coefficient = − 0.3613, P value = 0.0052) among subjects aged over 60.

Conclusions

Our results identified multiple plasma proteins associated with bone mineral density and provided novel clues for revealing the functional relevance of plasma proteins to bone mineral density.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05453-z.

Metabolomics-driven of relationships among kidney, bone marrow and bone of rats with postmenopausal osteoporosis

As a global public health problem, postmenopausal osteoporosis (PMOP) poses a great threat to old women's health. Bone is the target organ of PMOP, and the dynamic changes of bone marrow could affect the bone status. Kidney is the main organ regulating calcium and phosphorus homeostasis. Kidney, bone marrow and bone play crucial roles in PMOP, but the relationships of the three tissues in the disease have not been completely described. Here, metabolomics was employed to investigate the disease mechanism of PMOP from the perspectives of kidney, bone marrow and bone, and the relationships among the three tissues were also discussed. Six-month-old female Sprague-Dawley (SD) rats were randomly divided into ovariectomized (OVX) group (with bilateral ovariectomy) and sham group (with sham surgery). 13 weeks after surgery, gas chromatography-mass spectrometry (GC-MS) was performed to analyze the metabolic profiling of two groups. Multivariate statistical analysis revealed that the number of differential metabolites in kidney, bone marrow and bone between the two groups were 37, 16 and 17, respectively. The common differential metabolites of the three tissues were N-methyl-L-alanine. Kidney and bone marrow had common differential metabolites, including N-methyl-L-alanine, 2-hydroxybutyric acid, (R)-3-hydroxybutyric acid (β-hydroxybutyric acid, βHBA), urea and dodecanoic acid. There were three common differential metabolites between kidney and bone, including N-methyl-L-alanine, α-tocopherol and isofucostanol. The common differential metabolite of bone marrow and bone was N-methyl-L-alanine. Some common metabolic pathways were disturbed in multiple tissues of OVX rats, such as glycine, serine and threonine metabolism, purine metabolism, tryptophan metabolism, ubiquinone and other terpenoid-quinone biosynthesis and fatty acid biosynthesis. In conclusion, our study demonstrated that profound metabolic changes have taken place in the kidney, bone marrow and bone, involving common differential metabolites and metabolic pathways. The evaluation of differential metabolites strengthened the understanding of the kidney-bone axis and the metabolic relationships among the three tissues of OVX rats.

Metabolomics Coupled with Pathway Analysis Provides Insights into Sarco-Osteoporosis Metabolic Alterations and Estrogen Therapeutic Effects in Mice

Postmenopausal osteoporosis (PMOP) and sarcopenia are common diseases that predominantly affect postmenopausal women. In the occurrence and development of these two diseases, they are potentially pathologically connected with each other at various molecular levels. However, the application of metabolomics in sarco-osteoporosis and the metabolic rewiring happening throughout the estrogen loss-replenish process have not been reported. To investigate the metabolic alteration of sarco-osteoporosis and the possible therapeutical effects of estradiol, 24 mice were randomly divided into sham surgery, ovariectomy (OVX), and estradiol-treated groups. Three-dimensional reconstructions and histopathology examination showed significant bone loss after ovariectomy. Estrogen can well protect against OVX-induced bone loss deterioration. UHPLC-Q-TOF/MS was preformed to profile semi- polar metabolites of skeletal muscle samples from all groups. Metabolomics analysis revealed metabolic rewiring occurred in OVX group, most of which can be reversed by estrogen supplementation. In total, 65 differential metabolites were identified, and pathway analysis revealed that sarco-osteoporosis was related to the alterations in purine metabolism, glycerophospholipid metabolism, arginine biosynthesis, tryptophan metabolism, histidine metabolism, oxidative phosphorylation, and thermogenesis, which provided possible explanations for the metabolic mechanism of sarco-osteoporosis. This study indicates that an UHPLC-Q-TOF/MS-based metabolomics approach can elucidate the metabolic reprogramming mechanisms of sarco-osteoporosis and provide biological evidence of the therapeutical effects of estrogen on sarco-osteoporosis.

Metabolomic biomarkers of low BMD: a systematic review

Due to the metabolic nature of osteoporosis, this study was conducted to identify metabolomic studies investigating the metabolic profile of low bone mineral density (BMD) and osteoporosis. A comprehensive systematic literature search was conducted through PubMed, Web of Science, Scopus, and Embase databases up to April 08, 2020, to identify observational studies with cross-sectional or case-control designs investigating the metabolic profile of low BMD in adults using biofluid specimen via metabolomic platform. The quality assessment panel specified for the "omics"-based diagnostic research (QUADOMICS) tool was used to estimate the methodologic quality of the included studies. Ten untargeted and one targeted approach metabolomic studies investigating biomarkers in different biofluids through mass spectrometry or nuclear magnetic resonance platforms were included in the systematic review. Some metabolite panels, rather than individual metabolites, showed promising results in differentiating low BMD from normal. Candidate metabolites were of different categories including amino acids, followed by lipids and carbohydrates. Besides, certain pathways were suggested by some of the studies to be involved. This systematic review suggested that metabolic profiling could improve the diagnosis of low BMD. Despite valuable findings attained from each of these studies, there was great heterogeneity regarding the ethnicity and age of participants, samples, and the metabolomic platform. Further longitudinal studies are needed to validate the results and confirm the predictive role of metabolic profile on low BMD and fracture. It is also mandatory to address and minimize the heterogeneity in future studies by using reliable quantitative methods. Summary: Due to the metabolic nature of osteoporosis, researchers have considered metabolomic studies recently. This systematic review showed that metabolic profiling including different categories of metabolites could improve the diagnosis of low BMD. However, great heterogeneity was observed and it is mandatory to address and minimize the heterogeneity in future studies.

Perspective of the GEMSTONE Consortium on Current and Future Approaches to Functional Validation for Skeletal Genetic Disease Using Cellular, Molecular and Animal-Modeling Techniques

The availability of large human datasets for genome-wide association studies (GWAS) and the advancement of sequencing technologies have boosted the identification of genetic variants in complex and rare diseases in the skeletal field. Yet, interpreting results from human association studies remains a challenge. To bridge the gap between genetic association and causality, a systematic functional investigation is necessary. Multiple unknowns exist for putative causal genes, including cellular localization of the molecular function. Intermediate traits ("endophenotypes"), e.g. molecular quantitative trait loci (molQTLs), are needed to identify mechanisms of underlying associations. Furthermore, index variants often reside in non-coding regions of the genome, therefore challenging for interpretation. Knowledge of non-coding variance (e.g. ncRNAs), repetitive sequences, and regulatory interactions between enhancers and their target genes is central for understanding causal genes in skeletal conditions. Animal models with deep skeletal phenotyping and cell culture models have already facilitated fine mapping of some association signals, elucidated gene mechanisms, and revealed disease-relevant biology. However, to accelerate research towards bridging the current gap between association and causality in skeletal diseases, alternative in vivo platforms need to be used and developed in parallel with the current -omics and traditional in vivo resources. Therefore, we argue that as a field we need to establish resource-sharing standards to collectively address complex research questions. These standards will promote data integration from various -omics technologies and functional dissection of human complex traits. In this mission statement, we review the current available resources and as a group propose a consensus to facilitate resource sharing using existing and future resources. Such coordination efforts will maximize the acquisition of knowledge from different approaches and thus reduce redundancy and duplication of resources. These measures will help to understand the pathogenesis of osteoporosis and other skeletal diseases towards defining new and more efficient therapeutic targets.

Sex specific effect of gut microbiota on the risk of psychiatric disorders: A Mendelian randomisation study and PRS analysis using UK Biobank cohort

OBJECTIVE

The relationships between gut microbiota and brain-related diseases/traits remains not fully understood.

METHOD

A two-stage study was performed to investigate the relationships between gut microbiota and brain-related diseases/traits, and evaluate the potential sex specific effects of gut microbiota. In discovery stage, we systematically scanned the relationships between 515 brain-related diseases/traits and gut microbiota through two-sample Mendelian randomisation analysis. Using ∼500,000 individuals derived from the UK Biobank, polygenetic risk scoring (PRS) analysis was performed to validate the associations detected in discovery stage. To evaluate the potential sex-specific effect of gut microbiota on brain-related disorders, PRS analysis was conducted in female and male, respectively.

RESULTS

After systematically scanning diseases or traits, 41 of the 515 brain-related diseases/traits were identified to be associated with gut microbiota, such as Neuroticism score (P_2-MR = 0.0018), worrier/anxious feelings (P_2-MR = 0.0013), Suffer from 'nerves' (P_2-MR = 0.0062) and Nervous feelings (P_2-MR = 0.0158). 5 of 41 brain-related diseases or traits were successfully validated in UK Biobank, such as Neuroticism score (P_UK = 0.0024, P_UK-female = 0.0063, P_UK-male = 0.1142), Nervous feelings (P_UK = 0.0043, P_UK-female = 0.0115, P_UK-male = 0.1670) and Worrier/anxious feelings (P_UK = 0.0166, P_UK-female = 0.0196, P_UK-male = 0.2930).

CONCLUSION

Our results suggest that gut microbiota contributed more to brain-related diseases or traits in females than in males.Key pointsA two-stage study was performed to investigate the relationships between gut microbiota and brain-related diseases/traits.Using the individuals derived from the UK Biobank, polygenetic risk scoring analysis was performed to validate the associations detected in the discovery stage.Our results suggest that gut microbiota contributed more to brain-related diseases or traits in females than in males.

Identification and Functional Characterization of Metabolites for Bone Mass in Peri- and Postmenopausal Chinese Women

CONTEXT

Although metabolic profiles appear to play an important role in menopausal bone loss, the functional mechanisms by which metabolites influence bone mineral density (BMD) during menopause are largely unknown.

OBJECTIVE

We aimed to systematically identify metabolites associated with BMD variation and their potential functional mechanisms in peri- and postmenopausal women.

DESIGN AND METHODS

We performed serum metabolomic profiling and whole-genome sequencing for 517 perimenopausal (16%) and early postmenopausal (84%) women aged 41 to 64 years in this cross-sectional study. Partial least squares regression and general linear regression analysis were applied to identify BMD-associated metabolites, and weighted gene co-expression network analysis was performed to construct co-functional metabolite modules. Furthermore, we performed Mendelian randomization analysis to identify causal relationships between BMD-associated metabolites and BMD variation. Finally, we explored the effects of a novel prominent BMD-associated metabolite on bone metabolism through both in vivo/in vitro experiments.

RESULTS

Twenty metabolites and a co-functional metabolite module (consisting of fatty acids) were significantly associated with BMD variation. We found dodecanoic acid (DA), within the identified module causally decreased total hip BMD. Subsequently, the in vivo experiments might support that dietary supplementation with DA could promote bone loss, as well as increase the osteoblast and osteoclast numbers in normal/ovariectomized mice. Dodecanoic acid treatment differentially promoted osteoblast and osteoclast differentiation, especially for osteoclast differentiation at higher concentrations in vitro (eg,10, 100 μM).

CONCLUSIONS

This study sheds light on metabolomic profiles associated with postmenopausal osteoporosis risk, highlighting the potential importance of fatty acids, as exemplified by DA, in regulating BMD.

Metabolic Alterations in Older Women With Low Bone Mineral Density Supplemented With Lactobacillus reuteri

Osteoporosis and its associated fractures are highly prevalent in older women. Recent studies have shown that gut microbiota play important roles in regulating bone metabolism. A previous randomized controlled trial (RCT) found that supplementation with Lactobacillus reuteri ATCC PTA 6475 (L.reuteri) led to substantially reduced bone loss in older women with low BMD. However, the total metabolic effects of L. reuteri supplementation on older women are still not clear. In this study, a post hoc analysis (not predefined) of serum metabolomic profiles of older women from the previous RCT was performed to investigate the metabolic dynamics over 1 year and to evaluate the effects of L. reuteri supplementation on human metabolism. Distinct segregation of the L. reuteri and placebo groups in response to the treatment was revealed by partial least squares‐discriminant analysis. Although no individual metabolite was differentially and significantly associated with treatment after correction for multiple testing, 97 metabolites responded differentially at any one time point between L. reuteri and placebo groups (variable importance in projection score >1 and p value <0.05). These metabolites were involved in multiple processes, including amino acid, peptide, and lipid metabolism. Butyrylcarnitine was particularly increased at all investigated time points in the L. reuteri group compared with placebo, indicating that the effects of L. reuteri on bone loss are mediated through butyrate signaling. Furthermore, the metabolomic profiles in a case (low BMD) and control population (high BMD) of elderly women were analyzed to confirm the associations between BMD and the identified metabolites regulated by L. reuteri supplementation. The amino acids, especially branched‐chain amino acids, showed association with L. reuteri treatment and with low BMD in older women, and may serve as potential therapeutic targets. © 2021 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

A road map for understanding molecular and genetic determinants of osteoporosis

Osteoporosis is a highly prevalent disorder characterized by low bone mineral density and an increased risk of fracture, termed osteoporotic fracture. Notably, bone mineral density, osteoporosis and osteoporotic fracture are highly heritable; however, determining the genetic architecture, and especially the underlying genomic and molecular mechanisms, of osteoporosis in vivo in humans is still challenging. In addition to susceptibility loci identified in genome-wide association studies, advances in various omics technologies, including genomics, transcriptomics, epigenomics, proteomics and metabolomics, have all been applied to dissect the pathogenesis of osteoporosis. However, each technology individually cannot capture the entire view of the disease pathology and thus fails to comprehensively identify the underlying pathological molecular mechanisms, especially the regulatory and signalling mechanisms. A change to the status quo calls for integrative multi-omics and inter-omics analyses with approaches in 'systems genetics and genomics'. In this Review, we highlight findings from genome-wide association studies and studies using various omics technologies individually to identify mechanisms of osteoporosis. Furthermore, we summarize current studies of data integration to understand, diagnose and inform the treatment of osteoporosis. The integration of multiple technologies will provide a road map to illuminate the complex pathogenesis of osteoporosis, especially from molecular functional aspects, in vivo in humans.

Use of Mendelian Randomization to Examine Causal Inference in Osteoporosis

Epidemiological studies have identified many risk factors for osteoporosis, however it is unclear whether these observational associations reflect true causal effects, or the effects of latent confounding or reverse causality. Mendelian randomization (MR) enables causal relationships to be evaluated, by examining the relationship between genetic susceptibility to the risk factor in question, and the disease outcome of interest. This has been facilitated by the development of two-sample MR analysis, where the exposure and outcome are measured in different studies, and by exploiting summary result statistics from large well-powered genome-wide association studies that are available for thousands of traits. Though MR has several inherent limitations, the field is rapidly evolving and at least 14 methodological extensions have been developed to overcome these. The present paper aims to discuss some of the limitations in the MR analytical framework, and how this method has been applied to the osteoporosis field, helping to reinforce conclusions about causality, and discovering potential new regulatory pathways, exemplified by our recent MR study of sclerostin.

Integrative genomic analysis predicts novel functional enhancer-SNPs for bone mineral density

Osteoporosis is a skeletal disorder characterized by low bone mineral density (BMD) and deterioration of bone microarchitecture. To identify novel genetic loci underlying osteoporosis, an effective strategy is to focus on scanning of variants with high potential functional impacts. Enhancers play a crucial role in regulating cell-type-specific transcription. Therefore, single-nucleotide polymorphisms (SNPs) located in enhancers (enhancer-SNPs) may represent strong candidate functional variants. Here, we performed a targeted analysis for potential functional enhancer-SNPs that may affect gene expression and biological processes in bone-related cells, specifically, osteoblasts, and peripheral blood monocytes (PBMs), using five independent cohorts (n = 5905) and the genetics factors for osteoporosis summary statistics, followed by comprehensive integrative genomic analyses of chromatin states, transcription, and metabolites. We identified 15 novel enhancer-SNPs associated with femoral neck and lumbar spine BMD, including 5 SNPs mapped to novel genes (e.g., rs10840343 and rs10770081 in IGF2 gene) and 10 novel SNPs mapped to known BMD-associated genes (e.g., rs2941742 in ESR1 gene, and rs10249092 and rs4342522 in SHFM1 gene). Interestingly, enhancer-SNPs rs10249092 and rs4342522 in SHFM1 were tightly linked, but annotated to different enhancers in PBMs and osteoblasts, respectively, suggesting that even tightly linked SNPs may regulate the same target gene and contribute to the phenotype variation in cell-type-specific manners. Importantly, ten enhancer-SNPs may also regulate BMD variation by affecting the serum metabolite levels. Our findings revealed novel susceptibility loci that may regulate BMD variation and provided intriguing insights into the genetic mechanisms of osteoporosis.

Metabolomic profiles associated with bone mineral density in US Caucasian women

Background

Individuals’ peak bone mineral density (BMD) achieved and maintained at ages 20–40 years is the most powerful predictor of low bone mass and osteoporotic fractures later in life. The aim of this study was to identify metabolomic factors associated with peak BMD variation in US Caucasian women.

Methods

A total of 136 women aged 20–40 years, including 65 subjects with low and 71 with high hip BMD, were enrolled. The serum metabolites were assessed using a liquid chromatography-mass spectrometry (LC-MS) method. The partial least-squares discriminant analysis (PLS-DA) method and logistic regression models were used, respectively, to examine the associations of metabolomic profiles and individual metabolites with BMD.

Results

The low and high BMD groups could be differentiated by the detected serum metabolites using PLS-DA (P_permutation = 0.008). A total of 14 metabolites, including seven amino acids and amino acid derivatives, five lipids (including three bile acids), and two organic acids, were significantly associated with the risk for low BMD. Most of these metabolites are novel in that they have never been linked with BMD in humans earlier. The prediction model including the newly identified metabolites significantly improved the classification of the groups with low and high BMD. The area under the receiver operating characteristic curve without and with metabolites were 0.88 (95% CI: 0.83–0.94) and 0.97 (95% CI: 0.94–0.99), respectively (P for the difference = 0.0004).

Conclusion

Metabolomic profiling may improve the risk prediction of osteoporosis among Caucasian women. Our findings also suggest the potential importance of the metabolism of amino acids and bile acids in bone health.