Metabolomic Pathways to Osteoporosis in Middle-Aged Women: A Genome-Metabolome-Wide Mendelian Randomization Study

Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data

BACKGROUND

Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies.

METHOD

In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information.

RESULTS

We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites.

CONCLUSION

The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.

Causal relationship between Interleukin-27 expression levels and osteoporosis: a bidirectional mendelian randomization study

BACKGROUND

This study aimed to evaluate the causal relationship between Interleukin-27 (IL-27) and osteoporosis by bidirectional Mendelian randomization (MR) analysis.

METHODS

Firstly, the genome-wide association study summary data of osteoporosis (finn-b-M13_OSTEOPOROSIS) and IL-27 levels (ebi-a-GCST90012017) were picked out from the Integrative Epidemiology Unit (IEU) OpenGWAS database. After filtrating instrumental variables (IVs), the bidirectional MR analysis between IL-27 levels and osteoporosis was performed by MR-Egger, Weighted median, Simple mode, Weighted mode, and Inverse variance weighted (IVW). Subsequently, the sensitivity analysis was adopted to evaluate the reliability of the MR results via the Heterogeneity, Horizontal pleiotropy test and Leave-One-Out (LOO) analysis. Finally, the enrichment analysis of genes corresponding to SNPs related to IL-27 levels derived from eQTLGen database was executed to explore in depth the biological function and regulatory mechanism of these genes on osteoporosis occurrence.

RESULTS

The bidirectional MR results based on IVW method revealed that IL-27 level as a risk factor was causally related to osteoporosis (P = 0.004, odds ratio (OR) = 1.123, 95% confidence interval (CI) = 1.037-1.217), whereas osteoporosis was not in significant connection with IL-27 levels (P > 0.05). In regard to the sensitivity analysis for forward MR results, there was no heterogeneity and horizontal pleiotropy, and no SNPs relevant to IL-27 levels existed severe bias, suggesting the reliability of forward MR analysis. Furthermore, a total of 74 genes corresponding to 26 SNPs of IL-27 levels were obtained and were mainly involved in immune and inflammatory pathways including MyD88-dependent toll-like receptor signaling pathway, Toll-like receptor signaling pathway, cytosolic DNA-sensing pathway and so forth.

CONCLUSIONS

This study supported that IL-27 level as a risk factor was causally connected with osteoporosis and might regulate the disease occurrence and progression by means of immune and inflammatory mechanisms, which could provide important reference and evidence for further exploring the role of IL-27 in the development of osteoporosis.

Causal Association between Circulating Metabolites and Dementia: A Mendelian Randomization Study

The causal association of circulating metabolites with dementia remains uncertain. We assessed the causal association of circulating metabolites with dementia utilizing Mendelian randomization (MR) methods. We performed univariable MR analysis to evaluate the associations of 486 metabolites with dementia, Alzheimer's disease (AD), and vascular dementia (VaD) risk. For secondary validation, we replicated the analyses using an additional dataset with 123 metabolites. We observed 118 metabolites relevant to the risk of dementia, 59 of which were lipids, supporting the crucial role of lipids in dementia pathogenesis. After Bonferroni adjustment, we identified nine traits of HDL particles as potential causal mediators of dementia. Regarding dementia subtypes, protective effects were observed for epiandrosterone sulfate on AD (OR = 0.60, 95% CI: 0.48-0.75) and glycoproteins on VaD (OR = 0.89, 95% CI: 0.83-0.95). Bayesian model averaging MR (MR-BMA) analysis was further conducted to prioritize the predominant metabolites for dementia risk, which highlighted the mean diameter of HDL particles and the concentration of very large HDL particles as the predominant protective factors against dementia. Moreover, pathway analysis identified 17 significant and 2 shared metabolic pathways. These findings provide support for the identification of promising predictive biomarkers and therapeutic targets for dementia.

Serum metabolomics analyses reveal biomarkers of osteoporosis and the mechanism of Quanduzhong capsules

With the aging of the population, the prevalence of osteoporosis (OP) is rising rapidly, making it an important public health concern. Early screening and effective treatment of OP are the primary challenges facing the management of OP today. Quanduzhong capsule (QDZ) is a single preparation composed of Eucommia ulmoides Oliv., which is included in the Pharmacopoeia of the People's Republic of China. It is used to treat OP in clinical practice, but its mechanisms are unclear. This study involved 30 patients with OP, 30 healthy controls (HC), and 28 OP patients treated with QDZ to identify potential biomarkers for the early diagnosis of OP and to investigate the potential mechanism of QDZ in treating OP. The serum samples were analyzed using targeted amino acid metabolomics. Significant differences in amino acid metabolism were identified between the OP cohort and the HC group, as well as between OP patients before and after QDZ treatment. Compared with HC, the serum levels of 14 amino acids in OP patients changed significantly. Kynurenine, arginine, citrulline, methionine, and their combinations are expected to be potential biomarkers for OP diagnosis. Notably, QDZ reversed the changes in levels of 10 amino acids in the serum of OP patients and significantly impacted numerous metabolic pathways during the treatment of OP. This study focuses on screening potential biomarkers for the early detection of OP, which offers a new insight into the mechanism study of QDZ in treating OP.

Genetically predicted elevated circulating 3,4-dihydroxybutyrate levels mediate the association between family Christensenellaceae and osteoporosis risk: a Mendelian randomization study

Objective

To investigate the impact of gut microbiota on osteoporosis and identify the mediating role of blood metabolites in this process.

Methods

This two-sample Mendelian randomization (MR) study utilized summary level data from genome-wide association studies (GWAS). Gut microbiota GWAS data were obtained from the MiBio-Gen consortium meta-analysis (n=13,266), while osteoporosis summary statistics were sourced from the FinnGen consortium R9 release data (7300 cases and 358,014 controls). Metabolite data, including 1400 metabolites or metabolite ratios, were derived from a study involving 8,299 unrelated individuals. The primary MR method employed was the inverse variance weighted (IVW) method. Reverse MR analysis was conducted on bacteria causally associated with osteoporosis in forward MR. The gut microbiota with the smallest p-value was selected as the top influencing factor for subsequent mediation analysis. A two-step MR approach quantified the proportion of the blood metabolite effect on gut microbiota influencing osteoporosis. IVW and Egger methods were used to assess heterogeneity and horizontal pleiotropy.

Results

IVW estimates indicated a suggestive effect of family Christensenellaceae on osteoporosis (odds ratio(OR) = 1.292, 95% confidence interval(CI): 1.110-1.503, P =9.198 × 10-4). Reverse MR analysis revealed no significant causal effect of osteoporosis on family Christensenellaceae (OR = 0.947, 95% CI: 0.836-1.072, P =0.386). The proportion of the effect of family Christensenellaceae on osteoporosis mediated by circulating levels of 3,4-dihydroxybutyrate was 9.727%. No significant heterogeneity or horizontal pleiotropy was detected in the instrumental variables used for MR analysis.

Conclusion

This study establishes a causal link between family Christensenellaceae and osteoporosis, with a minor proportion of the effect mediated by elevated circulating levels of 3,4-dihydroxybutyrate. Further randomized controlled trials (RCTs) are warranted to validate this conclusion.

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.

Unraveling the Evolutionary Diet Mismatch and Its Contribution to the Deterioration of Body Composition

Over the millennia, patterns of food consumption have changed; however, foods were always whole foods. Ultra-processed foods (UPFs) have been a very recent development and have become the primary food source for many people. The purpose of this review is to propose the hypothesis that, forsaking the evolutionary dietary environment, and its complex milieu of compounds resulting in an extensive metabolome, contributes to chronic disease in modern humans. This evolutionary metabolome may have contributed to the success of early hominins. This hypothesis is based on the following assumptions: (1) whole foods promote health, (2) essential nutrients cannot explain all the benefits of whole foods, (3) UPFs are much lower in phytonutrients and other compounds compared to whole foods, and (4) evolutionary diets contributed to a more diverse metabolome. Evidence will be presented to support this hypothesis. Nutrition is a matter of systems biology, and investigating the evolutionary metabolome, as compared to the metabolome of modern humans, will help elucidate the hidden connections between diet and health. The effect of the diet on the metabolome may also help shape future dietary guidelines, and help define healthy foods.

Assessing the association between a sedentary lifestyle and prevalence of primary osteoporosis: a community-based cross-sectional study among Chinese population

OBJECTIVES

To reveal the association between a sedentary lifestyle and the prevalence of primary osteoporosis (POP).

DESIGN

A community-based cross-sectional study was conducted.

SETTING

This study was conducted in communities in Hefei city, Anhui province, China.

PARTICIPANTS

A total of 1346 residents aged 40 and above underwent POP screening via calcaneus ultrasound bone mineral density (BMD) testing and completed a questionnaire survey.

OUTCOME MEASURES

The average daily sitting time was included in the study variable and used to assess sedentary behaviour. The 15 control variables included general information, dietary information and life behaviour information. Logistic regression was used to analyse the association between the POP prevalence and study or control variables in different models.

RESULTS

1346 participants were finally included in the study. According to the 15 control variables, the crude model and 4 models were established. The analysis revealed that the average daily sitting time showed a significant correlation with the prevalence of POP in the crude model (OR=2.02, 95% CI=1.74 to 2.36, p<0.001), Model 1 (OR=2.65, 95% CI=2.21 to 3.17, p<0.001), Model 2 (OR=2.63, 95% CI=2.19 to 3.15, p<0.001), Model 3 (OR=2.62, 95% CI=2.18 to 3.15, p<0.001) and Model 4 (OR=2.58, 95% CI=2.14 to 3.11, p<0.001). Besides, gender, age and body mass index showed a significant correlation with the POP prevalence in all models.

CONCLUSIONS

This study suggests a potential association between a sedentary lifestyle and the prevalence of POP within the Chinese population. Modifying sedentary behaviours could contribute to a reduction in POP risk. However, longitudinal cohort studies are necessary to confirm this hypothesis in the future.

Early changes of bone metabolites and lymphocyte subsets may participate in osteoporosis onset: a preliminary study of a postmenopausal osteoporosis mouse model

Purpose

Metabolic and immune changes in the early stages of osteoporosis are not well understood. This study aimed to explore the changes in bone metabolites and bone marrow lymphocyte subsets and their relationship during the osteoporosis onset.

Methods

We established OVX and Sham mouse models. After 5, 15, and 40 days, five mice in each group were sacrificed. Humeri were analyzed by microCT. The bone marrow cells of the left femur and tibia were collected for flow cytometry analysis. The right femur and tibia were analyzed by LC-MS/MS for metabolomics analysis.

Results

Bone microarchitecture was significantly deteriorated 15 days after OVX surgery. Analysis of bone metabolomics showed that obvious metabolite changes had happened since 5 days after surgery. Lipid metabolism was significant at the early stage of the osteoporosis. The proportion of immature B cells was increased, whereas the proportion of mature B cells was decreased in the OVX group. Metabolites were significantly correlated with the proportion of lymphocyte subsets at the early stage of the osteoporosis.

Conclusion

Lipid metabolism was significant at the early stage of the osteoporosis. Bone metabolites may influence bone formation by interfering with bone marrow lymphocyte subsets.

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.

Low Circulating Valine Associate With High Risk of Hip Fractures

CONTEXT

Hip fractures constitute a major health concern. An adequate supply of amino acids is crucial to ensure optimal acquisition and remodeling of bone. Circulating amino acid levels have been proposed as markers of bone mineral density, but data on their ability to predict incident fractures are scarce.

OBJECTIVES

To investigate the associations between circulating amino acids and incident fractures.

METHODS

We used UK Biobank (n = 111 257; 901 hip fracture cases) as a discovery cohort and the Umeå Fracture and Osteoporosis (UFO) hip fracture study (hip fracture cases n = 2225; controls n = 2225) for replication. Associations with bone microstructure parameters were tested in a subsample of Osteoporotic Fractures in Men Sweden (n = 449).

RESULTS

Circulating valine was robustly associated with hip fractures in the UK Biobank (HR per SD increase 0.79, 95% CI 0.73-0.84), and this finding was replicated in the UFO study (combined meta-analysis including 3126 incident hip fracture cases, odds ratio per SD increase 0.84, 95% CI 0.80-0.88). Detailed bone microstructure analyses showed that high circulating valine was associated with high cortical bone area and trabecular thickness.

CONCLUSION

Low circulating valine is a robust predictor of incident hip fractures. We propose that circulating valine may add information for hip fracture prediction. Future studies are warranted to determine whether low valine is causally associated with hip fractures.

Mendelian Randomization Study of Lipid Metabolites Reveals Causal Associations with Heel Bone Mineral Density

BACKGROUND

Osteoporosis, which is a bone disease, is characterized by low bone mineral density and an increased risk of fractures. The heel bone mineral density is often used as a representative measure of overall bone mineral density. Lipid metabolism, which includes processes such as fatty acid metabolism, glycerol metabolism, inositol metabolism, bile acid metabolism, carnitine metabolism, ketone body metabolism, sterol and steroid metabolism, etc., may have an impact on changes in bone mineral density. While some studies have reported correlations between lipid metabolism and heel bone mineral density, the overall causal relationship between metabolites and heel bone mineral density remains unclear.

OBJECTIVE

to investigate the causal relationship between lipid metabolites and heel bone mineral density using two-sample Mendelian randomization analysis.

METHODS

Summary-level data from large-scale genome-wide association studies were extracted to identify genetic variants linked to lipid metabolite levels. These genetic variants were subsequently employed as instrumental variables in Mendelian randomization analysis to estimate the causal effects of each lipid metabolite on heel bone mineral density. Furthermore, metabolites that could potentially be influenced by causal relationships with bone mineral density were extracted from the KEGG and WikiPathways databases. The causal associations between these downstream metabolites and heel bone mineral density were then examined. Lastly, a sensitivity analysis was conducted to evaluate the robustness of the results and address potential sources of bias.

RESULTS

A total of 130 lipid metabolites were analyzed, and it was found that acetylcarnitine, propionylcarnitine, hexadecanedioate, tetradecanedioate, myo-inositol, 1-arachidonoylglycerophosphorine, 1-linoleoylglycerophoethanolamine, and epiandrosterone sulfate had a causal relationship with heel bone mineral density (p < 0.05). Furthermore, our findings also indicate an absence of causal association between the downstream metabolites associated with the aforementioned metabolites identified in the KEGG and WikiPathways databases and heel bone mineral density.

CONCLUSION

This work supports the hypothesis that lipid metabolites have an impact on bone health through demonstrating a causal relationship between specific lipid metabolites and heel bone mineral density. This study has significant implications for the development of new strategies to osteoporosis prevention and treatment.

Characterisation of Fasting and Postprandial NMR Metabolites: Insights from the ZOE PREDICT 1 Study

BACKGROUND

Postprandial metabolomic profiles and their inter-individual variability are not well characterised. Here, we describe postprandial metabolite changes, their correlations with fasting values and their inter- and intra-individual variability, following a standardised meal in the ZOE PREDICT 1 cohort.

METHODS

In the ZOE PREDICT 1 study (n = 1002 (NCT03479866)), 250 metabolites, mainly lipids, were measured by a Nightingale NMR panel in fasting and postprandial (4 and 6 h after a 3.7 MJ mixed nutrient meal, with a second 2.2 MJ mixed nutrient meal at 4 h) serum samples. For each metabolite, inter- and intra-individual variability over time was evaluated using linear mixed modelling and intraclass correlation coefficients (ICC) were calculated.

RESULTS

Postprandially, 85% (of 250 metabolites) significantly changed from fasting at 6 h (47% increased, 53% decreased; Kruskal-Wallis), with 37 measures increasing by >25% and 14 increasing by >50%. The largest changes were observed in very large lipoprotein particles and ketone bodies. Seventy-one percent of circulating metabolites were strongly correlated (Spearman's rho >0.80) between fasting and postprandial timepoints, and 5% were weakly correlated (rho <0.50). The median ICC of the 250 metabolites was 0.91 (range 0.08-0.99). The lowest ICCs (ICC <0.40, 4% of measures) were found for glucose, pyruvate, ketone bodies (β-hydroxybutyrate, acetoacetate, acetate) and lactate.

CONCLUSIONS

In this large-scale postprandial metabolomic study, circulating metabolites were highly variable between individuals following sequential mixed meals. Findings suggest that a meal challenge may yield postprandial responses divergent from fasting measures, specifically for glycolysis, essential amino acid, ketone body and lipoprotein size metabolites.

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 therapeutic biomarkers of zoledronic acid by metabolomics

Zoledronic acid (ZOL) is a potent antiresorptive agent that increases bone mineral density (BMD) and reduces fracture risk in postmenopausal osteoporosis (PMOP). The anti-osteoporotic effect of ZOL is determined by annual BMD measurement. In most cases, bone turnover markers function as early indicators of therapeutic response, but they fail to reflect long-term effects. We used untargeted metabolomics to characterize time-dependent metabolic shifts in response to ZOL and to screen potential therapeutic markers. In addition, bone marrow RNA-seq was performed to support plasma metabolic profiling. Sixty rats were assigned to sham-operated group (SHAM, n = 21) and ovariectomy group (OVX, n = 39) and received sham operation or bilateral ovariectomy, respectively. After modeling and verification, rats in the OVX group were further divided into normal saline group (NS, n = 15) and ZOL group (ZA, n = 18). Three doses of 100 μg/kg ZOL were administrated to the ZA group every 2 weeks to simulate 3-year ZOL therapy in PMOP. An equal volume of saline was administered to the SHAM and NS groups. Plasma samples were collected at five time points for metabolic profiling. At the end of the study, selected rats were euthanatized for bone marrow RNA-seq. A total number of 163 compound were identified as differential metabolites between the ZA and NS groups, including mevalonate, a critical molecule in target pathway of ZOL. In addition, prolyl hydroxyproline (PHP), leucyl hydroxyproline (LHP), 4-vinylphenol sulfate (4-VPS) were identified as differential metabolites throughout the study. Moreover, 4-VPS negatively correlated with increased vertebral BMD after ZOL administration as time-series analysis revealed. Bone marrow RNA-seq showed that the PI3K-AKT signaling pathway was significantly associated with ZOL-mediated changes in expression (adjusted-p = 0.018). In conclusion, mevalonate, PHP, LHP, and 4-VPS are candidate therapeutic markers of ZOL. The pharmacological effect of ZOL likely occurs through inhibition of the PI3K-AKT signaling pathway.

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.

UPLC/Q-TOF-MS-based Metabolomics Study of the Antiosteoporosis Effects of Vaccarin in Ovariectomized Mice

Osteoporosis is a systemic and metabolic bone disease that usually occurs in postmenopausal women, which mainly manifests as bone loss and increased bone fragility that both facilitate fracture. However, few drugs for osteoporosis have shown good efficacy and limited side effects. Vaccarin has demonstrated its antiosteoporosis effects by inhibiting the formation and osteolytic activities of osteoclasts in our previous investigation. In this study, multivariate statistical analysis and ultrahigh-performance liquid chromatography and quadrupole time-of-flight tandem mass spectrometry were used to analyze the serum metabolites of ovariectomized mice treated with or without vaccarin. As a result, 9 serum metabolites were identified as biomarkers. The metabolic levels of 3 crucial biomarkers, namely, lysophosphatidylcholine [22 : 6, (4Z, 7Z, 10Z, 13Z, 16Z, 19Z)], 1-linoleoylglycerophosphocholine and 1-palmitoyl-Sn-glycero-3-phosphocholine, that were correlated with glycerophospholipid metabolism increased and then decreased significantly after vaccarin treatment. Molecular docking analysis and osteoclasts differentiation experiment further revealed that vaccarin may bind with phospholipase A2 and downregulated its activity to reduce the osteoclastogenesis. Therefore, the occurrence of osteoporosis is closely related with glycerophospholipid metabolism disorders, and vaccarin exerts antiosteoporosis effects by reducing the levels of glycerophospholipid metabolites.

Gut microbiota and plasma metabolites associated with bone mineral density in women with or at risk of HIV infection

OBJECTIVE

To evaluate gut microbiota (GMB) alterations and metabolite profile perturbations associated with bone mineral density (BMD) in the context of HIV infection.

DESIGN

Cross-sectional studies of 58 women with chronic HIV infection receiving antiretroviral therapy and 33 women without HIV infection.

METHODS

We examined associations of GMB and metabolites with BMD among 91 women. BMD was measured by dual-energy X-ray absorptiometry (DXA), and T -scores of lumbar spine or total hip less than -1 defined low BMD. GMB was measured by 16S rRNA V4 region sequencing on fecal samples, and plasma metabolites were measured by liquid chromatography-tandem mass spectrometry. Associations of GMB with plasma metabolites were assessed in a larger sample (418 women; 280 HIV+ and 138 HIV-).

RESULTS

Relative abundances of five predominant bacterial genera ( Dorea , Megasphaera , unclassified Lachnospiraceae, Ruminococcus , and Mitsuokella ) were higher in women with low BMD compared with those with normal BMD (all linear discriminant analysis (LDA) scores >2.0). A distinct plasma metabolite profile was identified in women with low BMD, featuring lower levels of several metabolites belonging to amino acids, carnitines, caffeine, fatty acids, pyridines, and retinoids, compared with those with normal BMD. BMD-associated bacterial genera, especially Megasphaera , were inversely associated with several BMD-related metabolites (e.g. 4-pyridoxic acid, C4 carnitine, creatinine, and dimethylglycine). The inverse association of Megasphaera with dimethylglycine was more pronounced in women with HIV infection compared with those without HIV infection ( P for interaction = 0.016).

CONCLUSION

Among women with and at risk of HIV infection, we identified altered GMB and plasma metabolite profiles associated with low BMD.

Osteometabolism: Metabolic Alterations in Bone Pathologies

Renewing interest in the study of intermediate metabolism and cellular bioenergetics is brought on by the global increase in the prevalence of metabolic illnesses. Understanding of the mechanisms that integrate energy metabolism in the entire organism has significantly improved with the application of contemporary biochemical tools for quantifying the fuel substrate metabolism with cutting-edge mouse genetic procedures. Several unexpected findings in genetically altered mice have prompted research into the direction of intermediate metabolism of skeletal cells. These findings point to the possibility of novel endocrine connections through which bone cells can convey their energy status to other metabolic control centers. Understanding the expanded function of skeleton system has in turn inspired new lines of research aimed at characterizing the energy needs and bioenergetic characteristics of these bone cells. Bone-forming osteoblast and bone-resorbing osteoclast cells require a constant and large supply of energy substrates such as glucose, fatty acids, glutamine, etc., for their differentiation and functional activity. According to latest research, important developmental signaling pathways in bone cells are connected to bioenergetic programs, which may accommodate variations in energy requirements during their life cycle. The present review article provides a unique perspective of the past and present research in the metabolic characteristics of bone cells along with mechanisms governing energy substrate utilization and bioenergetics. In addition, we discussed the therapeutic inventions which are currently being utilized for the treatment and management of bone-related diseases such as osteoporosis, rheumatoid arthritis (RA), osteogenesis imperfecta (OIM), etc., by modulating the energetics of bone cells. We further emphasized on the role of GUT-associated metabolites (GAMs) such as short-chain fatty acids (SCFAs), medium-chain fatty acids (MCFAs), indole derivates, bile acids, etc., in regulating the energetics of bone cells and their plausible role in maintaining bone health. Emphasis is importantly placed on highlighting knowledge gaps in this novel field of skeletal biology, i.e., "Osteometabolism" (proposed by our group) that need to be further explored to characterize the physiological importance of skeletal cell bioenergetics in the context of human health and bone related metabolic diseases.

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.

Association of amino acid metabolites with osteoporosis, a metabolomic approach: Bushehr elderly health program

INTRODUCTION AND OBJECTIVES

Amino acids are the most frequently reported metabolites associated with low bone mineral density (BMD) in metabolomics studies. We aimed to evaluate the association between amino acid metabolic profile and bone indices in the elderly population.

METHODS

400 individuals were randomly selected from 2384 elderly men and women over 60 years participating in the second stage of the Bushehr elderly health (BEH) program, a population-based prospective cohort study that is being conducted in Bushehr, a southern province of Iran. Frozen plasma samples were used to measure 29 amino acid and derivatives metabolites using the UPLC-MS/MS-based targeted metabolomics platform. We conducted Elastic net regression analysis to detect the metabolites associated with BMD of different sites and lumbar spine trabecular bone score, and also to examine the ability of the measured metabolites to differentiate osteoporosis.

RESULTS

We adjusted the analysis for possible confounders (age, BMI, diabetes, smoking, physical activity, vitamin D level, and sex). Valine, leucine, isoleucine, and alanine in women and tryptophan in men were the most important amino acids inversely associated with osteoporosis (OR range from 0.77 to 0.89). Sarcosine, followed by tyrosine, asparagine, alpha aminobutyric acid, and ADMA in women and glutamine in men and when both women and men were considered together were the most discriminating amino acids detected in individuals with osteoporosis (OR range from 1.15 to 1.31).

CONCLUSION

We found several amino acid metabolites associated with possible bone status in elderly individuals. Further studies are required to evaluate the utility of these metabolites as clinical biomarkers for osteoporosis prediction and their effect on bone health as dietary supplements.

Fluctuations in Metabolites and Bone Markers Across the Menstrual Cycle in Eumenorrheic Women and Oral Contraceptive Users

CONTEXT

Little is known about changes in circulating metabolites during the menstrual cycle and how use of oral contraceptives (OCs) affects these changes.

OBJECTIVES

To study fluctuations in circulating metabolite and bone marker levels during the menstrual/pill cycle in eumenorrheic women and OC users.

METHODS

Plasma samples were collected from 28 eumenorrheic women and 10 OC users at 7 to 9 time points across a menstrual/pill cycle. Longitudinal and cross-sectional analyses were performed to examine the cycle- and OC-induced variations in the plasma metabolite and bone turnover marker levels.

RESULTS

In eumenorrheic women, plasma levels of alanine, glutamine, threonine, and tyrosine varied significantly across the menstrual cycle, and all dropped to the lowest level around day 21 of the menstrual cycle. These amino acid concentrations were negatively correlated with fluctuations in progesterone and/or estrogen levels. A between-group analysis showed that plasma levels of alanine, glutamine, glycine, proline, and tyrosine were lower in OC users than in nonusers. Concomitantly, plasma C-terminal telopeptide of type I collagen (CTX) and N-terminal propeptide of type I procollagen (PINP) levels were lower in OC users. Intriguingly, when all data were pooled, variations in CTX and PINP levels were positively correlated with fluctuations in proline and glycine concentrations (r > 0.5 or 0.3 < r < 0.5, P < 0.05).

CONCLUSIONS

The menstrual cycle and the use of OCs alter plasma levels of metabolites and bone turnover markers in young women. While the impact of these findings remains to be established, the lower glycine level among OC users and the accompanying lower CTX level supports that the use of OCs lowers collagen turnover in young women and may thereby have long-term implications for bone health among OC users.

From pharmacogenetics to pharmaco-omics: Milestones and future directions

The origins of pharmacogenetics date back to the 1950s, when it was established that inter-individual differences in drug response are partially determined by genetic factors. Since then, pharmacogenetics has grown into its own field, motivated by the translation of identified gene-drug interactions into therapeutic applications. Despite numerous challenges ahead, our understanding of the human pharmacogenetic landscape has greatly improved thanks to the integration of tools originating from disciplines as diverse as biochemistry, molecular biology, statistics, and computer sciences. In this review, we discuss past, present, and future developments of pharmacogenetics methodology, focusing on three milestones: how early research established the genetic basis of drug responses, how technological progress made it possible to assess the full extent of pharmacological variants, and how multi-dimensional omics datasets can improve the identification, functional validation, and mechanistic understanding of the interplay between genes and drugs. We outline novel strategies to repurpose and integrate molecular and clinical data originating from biobanks to gain insights analogous to those obtained from randomized controlled trials. Emphasizing the importance of increased diversity, we envision future directions for the field that should pave the way to the clinical implementation of pharmacogenetics.

Integrative Pathway Analysis of SNP and Metabolite Data Using a Hierarchical Structural Component Model

Integrative multi-omics analysis has become a useful tool to understand molecular mechanisms and drug discovery for treatment. Especially, the couplings of genetics to metabolomics have been performed to identify the associations between SNP and metabolite. However, while the importance of integrative pathway analysis is increasing, there are few approaches to utilize pathway information to analyze phenotypes using SNP and metabolite. We propose an integrative pathway analysis of SNP and metabolite data using a hierarchical structural component model considering the structural relationships of SNPs, metabolites, pathways, and phenotypes. The proposed method utilizes genome-wide association studies on metabolites and constructs the genetic risk scores for metabolites referred to as genetic metabolomic scores. It is based on the hierarchical model using the genetic metabolomic scores and pathways. Furthermore, this method adopts a ridge penalty to consider the correlations between genetic metabolomic scores and between pathways. We apply our method to the SNP and metabolite data from the Korean population to identify pathways associated with type 2 diabetes (T2D). Through this application, we identified well-known pathways associated with T2D, demonstrating that this method adds biological insights into disease-related pathways using genetic predispositions of metabolites.

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

While the gut microbiome has been reported to play a role in bone metabolism, the individual species and underlying functional mechanisms have not yet been characterized. We conducted a systematic multi-omics analysis using paired metagenomic and untargeted serum metabolomic profiles from a large sample of 499 peri- and early post-menopausal women to identify the potential crosstalk between these biological factors which may be involved in the regulation of bone mineral density (BMD). Single omics association analyses identified 22 bacteria species and 17 serum metabolites for putative association with BMD. Among the identified bacteria, Bacteroidetes and Fusobacteria were negatively associated, while Firmicutes were positively associated. Several of the identified serum metabolites including 3-phenylpropanoic acid, mainly derived from dietary polyphenols, and glycolithocholic acid, a secondary bile acid, are metabolic byproducts of the microbiota. We further conducted a supervised integrative feature selection with respect to BMD and constructed the inter-omics partial correlation network. Although still requiring replication and validation in future studies, the findings from this exploratory analysis provide novel insights into the interrelationships between the gut microbiome and serum metabolome that may potentially play a role in skeletal remodeling processes.

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.

Association Between HDL-C and Bone Mineral Density: An Cross-Sectional Analysis

Background

Dyslipidemia has been found to contribute to increased risk of osteoporosis and its association with bone mineral density (BMD) remains controversial. We determined whether blood lipid levels are linked with change of BMD.

Methods

In a large sample from the MIDUS II study, we sought to evaluate the relationship between blood lipid levels and BMD. Multivariate linear regression models and smooth curve analysis were constructed by controlling a great range of confounding factors.

Results

The median age of them was 52.5 years, and the number of males was 176 (40%). Univariate analysis showed that blood high-density lipoprotein-cholesterol (HDL-C) level was negatively related to lunar total femur (r = −0.266, P < 0.001), lunar radius ultradistal (UD) (r = −0.297, P < 0.001), lunar radius 1/3 (r = −0.307, P = 0.001) and femoral neck (r = −0.172, P = 0.001). In multivariate linear analysis, except for blood triglyceride, total cholesterol and low-density lipoprotein cholesterol (LDL-C), we found that blood HDL-C level was still negatively related to lunar total femur [B = −0.002, B 95% CI (−0.002, −0.001), P < 0.001], lunar radius UD [B = −0.001, 95% CI (−0.001, 0), P = 0.002], lunar radius 1/3 [B = −0.001, 95% CI (−0.001, 0), P = 0.003] and femoral neck [B = −0.001, 95% CI (−0.002, 0), P = 0.039] after adjustments of demographic characteristics, lifestyle, disease history were made. Furthermore, we found that age, sex, and body mass index (BMI) had modifying effects on this negative association.

Conclusion

This study confirmed the negative association between HDL-C and BMD in the observational analysis from (MIDUS) study and provides high-quality evidence that age, sex and BMI had modifying effects on this negative association.

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.

Serum Glycine Levels Are Associated With Cortical Bone Properties and Fracture Risk in Men

CONTEXT

In a recent study a pattern of 27 metabolites, including serum glycine, associated with bone mineral density (BMD).

OBJECTIVE

To investigate associations for serum and urinary glycine levels with BMD, bone microstructure, and fracture risk in men.

METHODS

In the population-based Osteoporotic Fractures in Men (MrOS) Sweden study (men, 69-81 years) serum glycine and BMD were measured at baseline (n = 965) and 5-year follow-up (n = 546). Cortical and trabecular bone parameters of the distal tibia were measured at follow-up using high-resolution peripheral quantitative computed tomography. Urinary (n = 2682) glycine was analyzed at baseline. X-ray-validated fractures (n = 594) were ascertained during a median follow-up of 9.6 years. Associations were evaluated using linear regression (bone parameters) or Cox regression (fractures).

RESULTS

Circulating glycine levels were inversely associated with femoral neck (FN)-BMD. A meta-analysis (n = 7543) combining MrOS Sweden data with data from 3 other cohorts confirmed a robust inverse association between serum glycine levels and FN-BMD (P = 7.7 × 10-9). Serum glycine was inversely associated with the bone strength parameter failure load in the distal tibia (P = 0.002), mainly as a consequence of an inverse association with cortical cross-sectional area and a direct association with cortical porosity. Both serum and urinary glycine levels predicted major osteoporotic fractures (serum: hazard ratio [HR] per SD increase = 1.22, 95% CI, 1.05-1.43; urine: HR = 1.13, 95% CI, 1.02-1.24). These fracture associations were only marginally reduced in models adjusted by FRAX with BMD.

CONCLUSIONS

Serum and urinary glycine are indirectly associated with FN-BMD and cortical bone strength, and directly associated with fracture risk in men.

A Distinctive Human Metabolomics Alteration Associated with Osteopenic and Osteoporotic Patients

Osteoporosis is a common progressive metabolic bone disease resulting in decreased bone mineral density (BMD) and a subsequent increase in fracture risk. The known bone markers are not sensitive and specific enough to reflect the balance in the bone metabolism. Finding a metabolomics-based biomarker specific for bone desorption or lack of bone formation is crucial for predicting bone health earlier. This study aimed to investigate patients' metabolomic profiles with low BMD (LBMD), including those with osteopenia (ON) and osteoporosis (OP), compared to healthy controls. An untargeted mass spectrometry (MS)-based metabolomics approach was used to analyze serum samples. Results showed a clear separation between patients with LBMD and control (Q² = 0.986, R² = 0.994), reflecting a significant difference in the dynamic of metabolic processes between the study groups. A total of 116 putatively identified metabolites were significantly associated with LBMD. Ninety-four metabolites were dysregulated, with 52 up- and 42 downregulated in patients with LBMD compared to controls. Histidine metabolism, aminoacyl-tRNA biosynthesis, glyoxylate, dicarboxylate metabolism, and biosynthesis of unsaturated fatty acids were the most common metabolic pathways dysregulated in LBMD. Furthermore, 35 metabolites were significantly dysregulated between ON and OP groups, with 11 up- and 24 downregulated in ON compared to OP. Among the upregulated metabolites were 3-carboxy-4-methyl-5-propyl-2-2furanopropionic acid (CMPF) and carnitine derivatives (i.e., 3-hydroxy-11-octadecenoylcarnitine, and l-acetylcarnitine), whereas phosphatidylcholine (PC), sphingomyelin (SM), and palmitic acid (PA) were among the downregulated metabolites in ON compared to OP. This study would add a layer to understanding the possible metabolic alterations associated with ON and OP. Additionally, this identified metabolic panel would help develop a prediction model for bone health and OP progression.

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.

Icariin protects against cage layer osteoporosis by intervening in steroid biosynthesis and glycerophospholipid metabolism

Cage layer osteoporosis (CLO) is a common bone metabolism disease in the breeding industry of China. However, effective prevention for CLO has not been developed. Icariin (ICA), the main bioactive component of the Chinese herb Epimedium, has been shown to have good therapeutic effects on bone-related diseases. In this study, the effects of ICA were further evaluated in a low-calcium diet-induced CLO, and a serum metabolomics assay was performed to understand the underlying mechanisms. A total of 144 31-wk-old Lohmann pink-shell laying hens were randomly allocated to 4 groups with 6 replicates of 6 hens per replicate. The 4 dietary treatment groups consisted of a basal diet (3.5% calcium), a low-calcium diet (2.0% calcium), and a low-calcium diet supplemented with 0.5 or 2.0 g/kg ICA. The results showed that ICA exerted good osteoprotective effects on low-calcium diet-induced CLO. ICA significantly increased femur bone mineral density, improved bone microstructure, decreased bone metabolic level, and upregulated mRNA expression of bone formation genes in femoral bone tissue. Serum untargeted metabolomics analysis showed that 8 metabolite levels were significantly changed after ICA treatment, including increased contents of 7-dehydrocholesterol, 7-oxocholesterol, desmosterol, PC (18:1(9Z)/18:1(9Z)), PS (18:0/18:1(9Z)), N,N-dimethylaniline and 2-hydroxy-butanoic acid and decreased N2,N2-dimethylguanosine. Metabolic pathway analysis based on the above 8 metabolites indicated that ICA mainly perturbed steroid biosynthesis and glycerophospholipid metabolism. These findings suggest that ICA can effectively prevent bone loss in low-calcium diet-induced CLO by mediating steroid biosynthesis and glycerophospholipid metabolism and provide new information for the regulation of bone metabolic diseases.

Metabolomics Insights into Osteoporosis Through Association With Bone Mineral Density

Osteoporosis, a disease characterized by low bone mineral density (BMD), increases the risk for fractures. Conventional risk factors alone do not completely explain measured BMD or osteoporotic fracture risk. Metabolomics may provide additional information. We aim to identify BMD-associated metabolomic markers that are predictive of fracture risk. We assessed 209 plasma metabolites by liquid chromatography with tandem mass spectrometry (LC-MS/MS) in 1552 Framingham Offspring Study participants, and measured femoral neck (FN) and lumbar spine (LS) BMD 2 to 10 years later using dual-energy X-ray absorptiometry. We assessed osteoporotic fractures up to 27-year follow-up after metabolomic profiling. We identified 27 metabolites associated with FN-BMD or LS-BMD by LASSO regression with internal validation. Incorporating selected metabolites significantly improved the prediction and the classification of osteoporotic fracture risk beyond conventional risk factors (area under the curve [AUC] = 0.74 for the model with identified metabolites and risk factors versus AUC = 0.70 with risk factors alone, p = .001; net reclassification index = 0.07, p = .03). We replicated significant improvement in fracture prediction by incorporating selected metabolites in 634 participants from the Hong Kong Osteoporosis Study (HKOS). The glycine, serine, and threonine metabolism pathway (including four identified metabolites: creatine, dimethylglycine, glycine, and serine) was significantly enriched (false discovery rate [FDR] p value = .028). Furthermore, three causally related metabolites (glycine, phosphatidylcholine [PC], and triacylglycerol [TAG]) were negatively associated with FN-BMD, whereas PC and TAG were negatively associated with LS-BMD through Mendelian randomization analysis. In summary, metabolites associated with BMD are helpful in osteoporotic fracture risk prediction. Potential causal mechanisms explaining the three metabolites on BMD are worthy of further experimental validation. Our findings may provide novel insights into the pathogenesis of osteoporosis. © 2021 American Society for Bone and Mineral Research (ASBMR).

Diet-derived fruit and vegetable metabolites show sex-specific inverse relationships to osteoporosis status

BACKGROUND

The impact of nutrition on the metabolic profile of osteoporosis (OS) is unknown.

OBJECTIVE

Identify biochemical factors driving the association of fruit and vegetable (FV) intakes with OS prevalence using an untargeted metabolomics approach.

DESIGN

Cross-sectional dietary, anthropometric and plasma metabolite data were examined from the Boston Puerto Rican Osteoporosis Study, n = 600 (46-79 yr).

METHODS

Bone mineral density was assessed by DXA. OS was defined by clinical standards. A culturally adapted FFQ assessed usual dietary intake. Principal components analysis (PCA) of 42 FV items created 6 factors. Metabolomic profiles derived from plasma samples were assessed on a commercial platform. Differences in levels of 525 plasma metabolites between disease groups (OS vs no-OS) were compared using logistic regression; and associations with FV intakes by multivariable linear regression, adjusted for covariates. Metabolites significantly associated with OS status or with total FV intake were analyzed for enrichment in various biological pathways using Mbrole 2.0, MetaboAnalyst, and Reactome, using FDR correction of P-values. Correlation coefficients were calculated as Spearman's rho rank correlations, followed by hierarchical clustering of the resulting correlation coefficients using PCA FV factors and sex-specific sets of OS-associated metabolites.

RESULTS

High FV intake was inversely related to OS prevalence (Odds Ratio = 0.73; 95% CI = 0.57, 0.94; P = 0.01). Several biological processes affiliated with the FV-associating metabolites, including caffeine metabolism, carnitines and fatty acids, and glycerophospholipids. Important processes identified with OS-associated metabolites were steroid hormone biosynthesis in women and branched-chain amino acid metabolism in men. Factors derived from PCA were correlated with the OS-associated metabolites, with high intake of dark leafy greens and berries/melons appearing protective in both sexes.

CONCLUSIONS

These data warrant investigation into whether increasing intakes of dark leafy greens, berries and melons causally affect bone turnover and BMD among middle-aged and older adults at risk for osteoporosis via sex-specific metabolic pathways, and how gene-diet interactions alter these sex-specific metabolomic-osteoporosis links. ClinicalTrials.gov Identifier: NCT01231958.

Metabolite Profiling of Osteoporosis and Atherosclerosis in Postmenopausal Women: A Cross-Sectional Study

Purpose

Atherosclerosis (AS) and osteoporosis (OP) are common causes of morbidity and mortality in postmenopausal women and are connected via an unknown mechanistic link. Metabolite profiling of blood samples may allow the identification of new biomarkers and pathways for this enigmatic association.

Patients and Methods

We studied the difference in 148 metabolite levels from serum samples in postmenopausal women with AS and OP compared with those in healthy participants in this cross-sectional study. Quantitative AS was assessed by carotid artery intima-media thickness (cIMT) and carotid artery calcifications (CACs) by ultrasound, as well as OP by femoral neck (FN) bone mineral density (BMD) and 148 metabolic measures with high-throughput proton (¹H) nuclear magnetic resonance (NMR) in serum samples from 280 postmenopausal (PM) women. Subjects were a randomly selected subsample from the population-based Kuopio Osteoporosis Risk Factor and Prevention (OSTPRE) study. The final study population included the following groups: OP with CAC (n=16, group I), non-OP with no CAC (n=59, group II), high cIMT tertile with OP (n=11, group III) and low cIMT tertile without OP (n=48, group IV).

Results

There were differences in several metabolite levels between groups I and II. The acetate level was lower in group I compared to that in group II (group I mean ± SD: 0.033 ± 0.0070; group II: 0.041 ± 0.014, CI95%: 0.018‒0.15, p=0.014). The result was similar with diacylglycerol (p=0.002), leucine (p=0.031), valine (p=0.022) and several very low-density lipoprotein (VLDL) metabolite levels, which were lower in group I compared to those in group II. However, no associations were found in adjusted analyses with total body (TB) fat mass (FM), age and statin use (p>0.05).

Conclusion

Our novel study found differences in the metabolite profiling of altered amino acid and lipoprotein metabolism in participants with OP and AS compared with those in healthy women. The causative mechanisms remain unknown and further studies are needed.

Association between the metabolome and bone mineral density in a Chinese population

BACKGROUND

Osteoporosis is a common metabolic bone disease, which always leads to osteoporotic fractures. Biomarkers of bone mineral density (BMD) are helpful for prevention and early diagnosis of osteoporosis. This study aims to identify metabolomic biomarkers of low BMD.

METHODS

We included 701 participants who had BMD measures by dual-energy X-ray absorptiometry scans and donated fasting plasma samples from three clinical centres as a discovery set and another 278 participants from the fourth centre as an independent replication set. We used a liquid chromatography-mass spectrometry-based metabolomics approach to profile the global metabolites of fasting plasma.

FINDINGS

Among the 265 named metabolites identified in our study, six were associated with low BMD (FDR-adjusted P<0.05) in the discovery set and were successfully validated in the independent replication set. The circulating levels of five metabolites, i.e., inosine, hypoxanthine, PC (O-18:0/22:6), SM (d18:1/21:0) and isoleucyl-proline were associated with decreased odds of low BMD, and PC (16:0/18:3) level was associated with increased odds of low BMD. Per 1-SD increase in a composite metabolite score of these six metabolites was associated with about half decreased odds of low BMD (odds ratio 0.59, 95% confidence interval: 0.52-0.68). Furthermore, introduction of a panel of metabolites selected by elastic net regression to a prediction model of classical risk factors and plasma biomarker of bone resorption substantially improved the prediction performance for low BMD (AUCs: 0.782 vs. 0.698, P=0.002).

INTERPRETATION

Metabolomics profiling may help identify novel biomarkers of low BMD and be helpful for early diagnosis of osteoporosis beyond the current clinical index.

FUNDING

This study was supported by the National Key R&D Program of China [2018YFC2001500 to J.S.], Shanghai Municipal Science and Technology Major Project [2017SHZDZX01], the National Natural Science Foundation of China [Key Program, 91749204 to J.S.], the National Natural Science Foundation of China [General Program, 81771491 to J.S.], the Project of Shanghai Subject Chief Scientist [2017BR011 to J.S.], Grants from the TCM Supported Project [18431902300 to J.S.] from the Science and Technology Commission of Shanghai Municipality, and the National Natural Science Foundation of China [General Program, 81972089 to Z.X.]. Y.Z. was supported by the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, and the National Natural Science Foundation of China [81973032].

Serum Metabolome of Coffee Consumption and its Association With Bone Mineral Density: The Hong Kong Osteoporosis Study

BACKGROUND

Inconsistent associations between coffee consumption and bone mineral density (BMD) have been observed in epidemiological studies. Moreover, the relationship of bioactive components in coffee with BMD has not been studied. The aim of the current study is to identify coffee-associated metabolites and evaluate their association with BMD.

METHODS

Two independent cohorts totaling 564 healthy community-dwelling adults from the Hong Kong Osteoporosis Study (HKOS) who visited in 2001-2010 (N = 329) and 2015-2016 (N = 235) were included. Coffee consumption was self-reported in an food frequency questionnaire. Untargeted metabolomic profiling on fasting serum samples was performed using liquid chromatography-mass spectrometry platforms. BMD at lumbar spine and femoral neck was measured by dual-energy X-ray absorptiometry. Multivariable linear regression and robust regression were used for the association analyses.

RESULTS

12 serum metabolites were positively correlated with coffee consumption after Bonferroni correction for multiple testing (P < 4.87 × 10-5), with quinate, 3-hydroxypyridine sulfate, and trigonelline (N'-methylnicotinate) showing the strongest association. Among these metabolites, 11 known metabolites were previously identified to be associated with coffee intake and 6 of them were related to caffeine metabolism. Habitual coffee intake was positively and significantly associated with BMD at the lumbar spine and femoral neck. The metabolite 5-acetylamino-6-formylamino-3-methyluracil (AFMU) (β = 0.012, SE = 0.005; P = 0.013) was significantly associated with BMD at the lumbar spine, whereas 3-hydroxyhippurate (β = 0.007, SE = 0.003, P = 0.027) and trigonelline (β = 0.007, SE = 0.004; P = 0.043) were significantly associated with BMD at the femoral neck.

CONCLUSIONS

12 metabolites were significantly associated with coffee intake, including 6 caffeine metabolites. Three of them (AFMU, 3-hydroxyhippurate, and trigonelline) were further associated with BMD. These metabolites could be potential biomarkers of coffee consumption and affect bone health.

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.

Multi-omics Data Integration for Identifying Osteoporosis Biomarkers and Their Biological Interaction and Causal Mechanisms

Osteoporosis is characterized by low bone mineral density (BMD). The advancement of high-throughput technologies and integrative approaches provided an opportunity for deciphering the mechanisms underlying osteoporosis. Here, we generated genomic, transcriptomic, methylomic, and metabolomic datasets from 119 subjects with high (n = 61) and low (n = 58) BMDs. By adopting sparse multiple discriminative canonical correlation analysis, we identified an optimal multi-omics biomarker panel with 74 differentially expressed genes (DEGs), 75 differentially methylated CpG sites (DMCs), and 23 differential metabolic products (DMPs). By linking genetic data, we identified 199 targeted BMD-associated expression/methylation/metabolite quantitative trait loci (eQTLs/meQTLs/metaQTLs). The reconstructed networks/pathways showed extensive biomarker interactions, and a substantial proportion of these biomarkers were enriched in RANK/RANKL, MAPK/TGF-β, and WNT/β-catenin pathways and G-protein-coupled receptor, GTP-binding/GTPase, telomere/mitochondrial activities that are essential for bone metabolism. Five biomarkers (FADS2, ADRA2A, FMN1, RABL2A, SPRY1) revealed causal effects on BMD variation. Our study provided an innovative framework and insights into the pathogenesis of osteoporosis.

Novel GC-MS/MS Technique Reveals a Complex Steroid Fingerprint of Subclinical Hypercortisolism in Adrenal Incidentalomas

CONTEXT

Improvement of imaging methods has led to more incidental adrenal tumor findings, especially adenomas. Routine hormonal evaluation uses only a few steroids to evaluate possible hormonal hypersecretion of these adenomas, but a wide spectrum of serum steroid hormone changes has not been published.

OBJECTIVE

To measure the serum levels of 83 steroids from patients with unilateral and bilateral adrenal incidentalomas to uncover full steroid profile changes in patients with subclinical hypercortisolism (SH).

DESIGN

Cross-sectional study.

SETTING

The study was conducted at a tertiary inpatient clinic.

PATIENTS

Fifty-two patients with adrenal incidentalomas (unilateral, n = 29; bilateral, n = 23), including nonfunctioning (n = 11) vs SH (n = 41), and 26 age- and sex-matched controls from the general population were included.

MAIN OUTCOME MEASURES

Eighty-three serum steroids were measured by gas chromatography-tandem mass spectrometry (GC-MS/MS) before and after 1 mg dexamethasone, ACTH, midnight serum cortisol, and urinary free cortisol/24 hour.

RESULTS

Of 83 measured steroids, 10 were significantly decreased in patients with SH, including dehydroepiandrosterone sulfate (DHEAS), androsterone sulfate, epiandrosterone sulfate, androstenediol sulfate, conjugated 5α-androstane-3β,17β-diol, and conjugated 5α-androstane-3α,17β-diol. This finding was observed even when unilateral, bilateral, male, and female subgroups were analyzed separately. When we compared routine clinical methods and GC-MS/MS‒measured steroids, the most discriminatory was DHEAS followed by midnight serum cortisol, epiandrosterone sulfate, androsterone sulfate, ACTH, and 16α-hydroxypregnenolone.

CONCLUSIONS

SH was associated with decreased levels of adrenal androgens, their metabolites, and pregnenolone metabolite. GC-MS/MS is a powerful tool for measuring serum levels of these undescribed changes in steroid metabolism, which are characteristic of SH in adrenal incidentalomas.

A Metabolic Screen in Adolescents Reveals an Association Between Circulating Citrate and Cortical Bone Mineral Density

Observations that insulin and adiponectin levels are related to cortical bone size in adolescents, independently of body composition, suggest factors related to fat metabolism directly influence skeletal development. To explore this question, we examined associations between a metabolic screen focusing on fat metabolism, and peripheral quantitative computed tomography (pQCT) measures of the mid-tibia, in 15-year-olds from the Avon Longitudinal Study of Parents and Children. Metabolic profiles were generated by proton nuclear magnetic resonance spectroscopy, from blood samples obtained at the same time as pQCT scans. Ordinary least squares linear regression was used to investigate relationships between metabolic measures and periosteal circumference (PC), cortical thickness (CT), and cortical bone mineral density (BMDC ). Metabolic profiles yielded 22 independent components following principal component analysis (PCA), giving a Bonferroni-adjusted threshold for statistical significance of p = 0.002. Data were available in 1121 subjects (487 males), mean age 15 years. Several metabolites related to lipid and cholesterol metabolism were associated with PC, CT, and BMDC after adjustment for age, sex, and Tanner stage. After additional adjustment for height, fat, and lean mass, only the association between citrate and BMDC remained below the Bonferroni-significant threshold (β = -0.14 [-0.18, -0.09]) (β represents a standardized coefficient). Citrate also showed evidence of association with PC (β = 0.06 [0.03, 0.10]) and strength strain index (SSI; β = 0.04 [0.01, 0.08]). Subsequently, we investigated whether these relationships were explained by increased bone resorption. Citrate was strongly related to serum β-C-telopeptides of type I collagen (β-CTX) (β = 0.20 [0.16, 0.23]). After additional adjustment for β-CTX the above associations between citrate and BMDC (β = -0.04 [-0.08, 0.01]), PC (β = 0.03 [-0.01, 0.07]) and SSI (β = 0.03 [-0.01, 0.07]) were no longer observed. We conclude that in adolescents, circulating levels of citrate are inversely related to BMDC and positively related to PC, reflecting associations with higher bone turnover. Further studies are justified to elucidate possible contributions of citrate, a constituent of bone matrix, to bone resorption and cortical density. © 2019 American Society for Bone and Mineral Research.

The analysis of causal relationships between blood lipid levels and BMD

Bone mineral density (BMD) and lipid levels are two of the most extensively studied risk factors for common diseases of aging, such as cardiovascular disease (CVD) and osteoporosis (OP). These two risk factors are also correlated with each other, but little is known about the molecular mechanisms behind this correlation. Recent studies revealed that circulating levels of several metabolites involved in the biosynthesis of androsterone correlate significantly with BMD and have the capacity to affect cholesterol and lipids levels. A main aim of the present study was to investigate the hypothesis that androsterone-related metabolites could provide a link between CVD and OP, as a common cause of lipid levels and BMD. The present study employed data from the NIHR BRC TwinsUK BioResource, comprising 1909 and 1994 monozygotic and dizygotic twin pairs, respectively, to address the causal relationships among BMD and lipids, and their associated metabolites, using reciprocal causation twin modelling, as well as Mendelian randomization (MR) using large publicly-available GWAS datasets on lipids and BMD, in conjunction with TwinsUK metabolite data. While results involving the twin modelling and MR analyses with metabolites were unable to establish a causal link between metabolite levels and either lipids or BMD, MR analyses of BMD and lipids suggest that lipid levels have a causal impact on BMD, which is consistent with findings from clinical trials of lipid-lowering drugs, which have also increased BMD.

Metabolomics-based profiles predictive of low bone mass in menopausal women

Osteoporosis is a skeletal disorder characterized by compromised bone strength and increased risk of fracture. Low bone mass and/or pre-existing bone fragility fractures serve as diagnostic criteria in deciding when to start medication for osteoporosis. Although osteoporosis is a metabolic disorder, metabolic markers to predict reduced bone mass are unknown. Here, we show serum metabolomics profiles of women grouped as pre-menopausal with normal bone mineral density (BMD) (normal estrogen and normal BMD; NN), post-menopausal with normal BMD (low estrogen and normal BMD; LN) or post-menopausal with low BMD (low estrogen and low BMD; LL) using comprehensive metabolomics analysis. To do so, we enrolled healthy volunteer and osteoporosis patient female subjects, surveyed them with a questionnaire, measured their BMD, and then undertook a comprehensive metabolomics analysis of sera of the three groups named above. We identified 24 metabolites whose levels differed significantly between NN/LN and NN/LL groups, as well as 18 or 10 metabolites whose levels differed significantly between NN/LN and LN/LL, or LN/LL and NN/LN groups, respectively. Our data shows metabolomics changes represent useful markers to predict estrogen deficiency and/or bone loss.

LncRNA AK077216 promotes RANKL-induced osteoclastogenesis and bone resorption via NFATc1 by inhibition of NIP45

Osteoclasts derived from the monocyte/macrophage hematopoietic lineage regulate bone resorption, a process balanced by bone formation in the continual renewal of the skeletal system. As dysfunctions of these cells result in bone metabolic diseases such as osteoporosis and osteopetrosis, the exploration of the mechanisms regulating their differentiation is a priority. A potential mechanism may involve long noncoding RNAs (lncRNAs), which are known to regulate various cell biology activities, including proliferation, differentiation, and apoptosis. The expression of the lncRNA AK077216 (Lnc-AK077216) is significantly upregulated during osteoclastogenesis identified by microarray and verified by qPCR. Up- and downregulation of Lnc-AK077216, respectively promotes and inhibits osteoclast differentiation, bone resorption, and the expression of related genes on the basis of tartrate-resistant acid phosphatase staining, qPCR, and western blot results. In addition, Lnc-AK077216 suppresses NIP45 expression and promotes the expression of NFATc1, an essential transcription factor during osteoclastogenesis. Besides, it was found that the expression of Lnc-AK077216 and Nfatc1 is upregulated, whereas Nip45 expression is downregulated in bone marrow and spleen tissues of ovariectomized mice. The results suggest that Lnc-AK077216 regulates NFATc1 expression and promotes osteoclast formation and function, providing a novel mechanism of osteoclastogenesis and a potential biomarker or a new drug target for 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.

Genetic effects on bone health

PURPOSE OF REVIEW

In recent years, the lower costs of arrays and sequencing technologies, and the better availability of data from genome-wide association studies (GWASs) have led to more reports on genetic factors that are associated with bone health. However, there remains the need for a summary of the newly identified genetic targets that are associated with bone metabolism, and the status of their functional characterization.

RECENT FINDINGS

GWASs revealed dozens of novel genetic loci that are associated with bone mineral density (BMD). Some of these targets have been functionally characterized, although the vast majority have not. Glypican 6, a membrane surface proteoglycan involved in cellular growth control and differentiation, was identified as a novel determinant of BMD and represents a possible drug target for treatment of osteoporosis. Pathway analysis also showed that cell-growth pathways and the SMAD proteins associated with low BMD.

SUMMARY

Hits that were significantly associated with BMD in different studies represent likely candidates (e.g. SOST, WNT16, ESR1 and RANKL) for functional characterization and development of osteoporosis treatments. Indeed, currently available treatment for osteoporosis (antibody against RANKL) appeared a significant target in four recent GWAS studies indicating their applicability and importance for future treatment development.

Metabolomics-A Promising Approach to Pituitary Adenomas

Background: Metabolomics-the novel science that evaluates the multitude of low-molecular-weight metabolites in a biological system, provides new data on pathogenic mechanisms of diseases, including endocrine tumors. Although development of metabolomic profiling in pituitary disorders is at an early stage, it seems to be a promising approach in the near future in identifying specific disease biomarkers and understanding cellular signaling networks. Objectives: To review the metabolomic profile and the contributions of metabolomics in pituitary adenomas (PA). Methods: A systematic review was conducted via PubMed, Web of Science Core Collection and Scopus databases, summarizing studies that have described metabolomic aspects of PA. Results: Liquid chromatography tandem mass spectrometry (LC-MS/MS) and nuclear magnetic resonance (NMR) spectrometry, which are traditional techniques employed in metabolomics, suggest amino acids metabolism appears to be primarily altered in PA. N-acetyl aspartate, choline-containing compounds and creatine appear as highly effective in differentiating PA from healthy tissue. Deoxycholic and 4-pyridoxic acids, 3-methyladipate, short chain fatty acids and glucose-6-phosphate unveil metabolite biomarkers in patients with Cushing's disease. Phosphoethanolamine, N-acetyl aspartate and myo-inositol are down regulated in prolactinoma, whereas aspartate, glutamate and glutamine are up regulated. Phosphoethanolamine, taurine, alanine, choline-containing compounds, homocysteine, and methionine were up regulated in unclassified PA across studies. Intraoperative use of ultra high mass resolution matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), which allows localization and delineation between functional PA and healthy pituitary tissue, may contribute to achievement of complete tumor resection in addition to preservation of pituitary cell lines and vasopressin secretory cells, thus avoiding postoperative diabetes insipidus. Conclusion: Implementation of ultra high performance metabolomics analysis techniques in the study of PA will significantly improve diagnosis and, potentially, the therapeutic approach, by identifying highly specific disease biomarkers in addition to novel molecular pathogenic mechanisms. Ultra high mass resolution MALDI-MSI emerges as a helpful clinical tool in the neurosurgical treatment of pituitary tumors. Therefore, metabolomics appears to be a science with a promising prospect in the sphere of PA, and a starting point in pituitary care.