A multiethnic whole genome sequencing study to identify novel loci for bone mineral density.

At present, there have only been a few DNA sequencing-based studies to explore the genetic determinants of bone mineral density (BMD). We carried out the largest whole genome sequencing analysis to date for femoral neck and spine BMD (n = 4981), with one of the highest average sequencing depths implemented thus far at 22×, in a multiethnic sample (58% Caucasian and 42% African American) from the Louisiana Osteoporosis Study (LOS). The LOS samples were combined with summary statistics from the GEFOS consortium and several independent samples of various ethnicities to perform GWAS meta-analysis (n = 44 506). We identified 31 and 30 genomic risk loci for femoral neck and spine BMD, respectively. The findings substantiate many previously reported susceptibility loci (e.g. WNT16 and ESR1) and reveal several others that are either novel or have not been widely replicated in GWAS for BMD, including two for femoral neck (IGF2 and ZNF423) and one for spine (SIPA1). Although we were not able to uncover ethnicity specific differences in the genetic determinants of BMD, we did identify several loci which demonstrated sex-specific associations, including two for women (PDE4D and PIGN) and three for men (TRAF3IP2, NFIB and LYSMD4). Gene-based rare variant association testing detected MAML2, a regulator of the Notch signaling pathway, which has not previously been suggested, for association with spine BMD. The findings provide novel insights into the pathophysiological mechanisms of osteoporosis.

Identification of the serum metabolites associated with cow milk consumption in Chinese Peri-/Postmenopausal women.

Cow milk consumption (CMC) and downstream alterations of serum metabolites are commonly considered important factors regulating human health status. Foods may lead to metabolic changes directly or indirectly through remodelling gut microbiota (GM). We sought to identify the metabolic alterations in Chinese Peri-/Postmenopausal women with habitual CMC and explore if the GM mediates the CMC-metabolite associations. 346 Chinese Peri-/Postmenopausal women participants were recruited in this study. Fixed effects regression and partial least squares discriminant analysis (PLS-DA) were applied to reveal alterations of serum metabolic features in different CMC groups. Spearman correlation coefficient was computed to detect metabolome-metagenome association. 36 CMC-associated metabolites including palmitic acid (FA(16:0)), 7alpha-hydroxy-4-cholesterin-3-one (7alphaC4), citrulline were identified by both fixed effects regression (FDR < 0.05) and PLS-DA (VIP score > 2). Some significant metabolite-GM associations were observed, including FA(16:0) with gut species Bacteroides ovatus, Bacteroides sp.D2. These findings would further prompt our understanding of the effect of cow milk on human health.

Identification of potential genetic causal variants for obesity-related traits using statistical fine mapping.

BACKGROUND: Obesity is highly influenced by heritability and variant effects. While previous genome-wide association studies (GWASs) have successfully identified numerous genetic loci associated with obesity-related traits [body mass index (BMI) and waist-to-hip ratio (WHR)], most causal variants remain unidentified. The high degree of linkage disequilibrium (LD) throughout the genome makes it extremely difficult to distinguish the GWAS-associated SNPs that exert a true biological effect. OBJECTIVE: This study was to identify the potential causal variants having a biological effect on obesity-related traits. METHODS: We used Probabilistic Annotation INTegratOR, a Bayesian fine-mapping method, which incorporated genetic association data (GWAS summary statistics), LD structure, and functional annotations to calculate a posterior probability of causality for SNPs across all loci of interest. Moreover, we performed gene expression analysis using the available public transcriptomic data to validate the corresponding genes of the potential causal SNPs partially. RESULTS: We identified 96 SNPs for BMI and 43 SNPs for WHR with a high posterior probability of causality (> 99%), including 49 BMI SNPs and 24 WHR SNPs which did not reach genome-wide significance in the original GWAS. Finally, we partially validated some genes corresponding to the potential causal SNPs. CONCLUSION: Using a statistical fine-mapping approach, we identified a set of potential causal variants to be prioritized for future functional validation and also detected some novel trait-associated variants. These results provided novel insight into our understanding of the genetics of obesity and also demonstrated that fine mapping may improve upon the results identified by the original GWASs.

Novel Prognostic Model for Gastric Cancer using 13 Co-Expression Long Non-Coding RNAs (LncRNAs).

BACKGROUND The established clinical criteria for gastric cancer prognosis are insufficient due to molecular heterogeneity. Therefore, constructing a robust prognostic model is essential to predict gastric cancer patient survival. MATERIAL AND METHODS A comprehensive method, which combined weighted gene co-expression network analysis (WGCNA) with elastic-net Cox regression, was utilized to identify prognostic long non-coding RNAs (lncRNAs) from Gene Expression Omnibus database for overall survival (OS) prediction. Methods using WGCNA or elastic-net Cox regression alone were treated as "contrast" methods. The univariate and multivariate Cox regression was used to identify independent prognostic clinical factors. We performed 3-year and 5-year area under the curve (AUC) of the time-dependent receiver operating characteristic comparison of 3 different methods in gene and clinical-gene models to explore the prediction ability of the comprehensive method. The optimal model identified in the training set were validated in the validation set. Biological information analysis for the optimal model was also explored. RESULTS The clinical-gene model containing 13 co-expression lncRNAs identified by the comprehensive method and 3 clinical factors including molecular subtype, recurrence status and operation type, was the found to be the optimal model in the study, with 0.832 and 0.830 for the 3-year and 5-year AUC in the training set, and 0.764 and 0.778 in the validation set, respectively. Biological information analysis suggested that lipid metabolism played an important role in the occurrence and development of gastric cancer. CONCLUSIONS We constructed a novel prognostic model containing 13 co-expression lncRNAs and 3 clinical factors for gastric cancer patients.

Enhanced Identification of Potential Pleiotropic Genetic Variants for Bone Mineral Density and Breast Cancer.

Epidemiological and clinical evidences have shown that bone mineral density (BMD) has a close relationship with breast cancer (BC). They might potentially have a shared genetic basis. By incorporating information about these pleiotropic effects, we may be able to explore more of the traits' total heritability. We applied a recently developed conditional false discovery rate (cFDR) method to the summary statistics from two independent GWASs to identify the potential pleiotropic genetic variants for BMD and BC. By jointly analyzing two large independent GWASs of BMD and BC, we found strong pleiotropic enrichment between them and identified 102 single-nucleotide polymorphisms (SNPs) in BMD and 192 SNPs in BC with cFDR < 0.05, including 230 SNPs that might have been overlooked by the standard GWAS analysis. cFDR-significant genes were enriched in GO terms and KEGG pathways which were crucial to bone metabolism and/or BC pathology (adjP < 0.05). Some cFDR-significant genes were partially validated in the gene expressional validation assay. Strong interactions were found between proteins produced by cFDR-significant genes in the context of biological mechanism of bone metabolism and/or BC etiology. Totally, we identified 7 pleiotropic SNPs that were associated with both BMD and BC (conjunction cFDR < 0.05); CCDC170, ESR1, RANKL, CPED1, and MEOX1 might play important roles in the pleiotropy of BMD and BC. Our study highlighted the significant pleiotropy between BMD and BC and shed novel insight into trait-specific as well as the potentially shared genetic architecture for both BMD and BC.

Somatomotor-visual resting state functional connectivity increases after 2 years in the UK Biobank longitudinal cohort.

Purpose: Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, in which high connectivity among all brain regions changes to a more modular structure with maturation. We examine FC changes in older adults after 2 years of aging in the UK Biobank (UKB) longitudinal cohort. Approach: We process fMRI connectivity data using the Power264 atlas and then test whether the average internetwork FC changes in the 2722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t-test. We also compare the ability of Power264 and UKB-provided, independent component analysis (ICA)-based FC to determine which of a longitudinal scan pair is older. Finally, we investigate cross-sectional FC changes as well as differences due to differing scanner tasks in the UKB, Philadelphia Neurodevelopmental Cohort, and Alzheimer's Disease Neuroimaging Initiative datasets. Results: We find a 6.8% average increase in somatomotor network (SMT)-visual network (VIS) connectivity from younger to older scans (corrected p<10-15) that occurs in male, female, older subject (>65 years old), and younger subject (<55 years old) groups. Among all internetwork connections, the average SMT-VIS connectivity is the best predictor of relative scan age. Using the full FC and a training set of 2000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC. Conclusions: We conclude that SMT-VIS connectivity increases with age in the UKB longitudinal cohort and that resting state FC increases with age in the UKB cross-sectional cohort.

Unveiling the link between physical activity levels and dementia risk: Insights from the UK Biobank study.

BACKGROUND: There is limited information on the mixture effect and weights of light physical activity (LPA), moderate physical activity (MPA), and vigorous physical activity (VPA) on dementia risk. METHODS: A prospective cohort study was conducted based on the UK Biobank dataset. We included participants aged at least 45 years old without dementia at baseline between 2006-2010. The weighted quantile sum regression was used to explore the mixture effect and weights of three types of physical activity on dementia risk. RESULTS: This study includes 354,123 participants, with a mean baseline age of 58.0-year-old and 52.4 % of female participants. During a median follow-up time of 12.5 years, 5,136 cases of dementia were observed. The mixture effect of LPA, MPA, and VPA on dementia was statistically significant (ß: -0.0924, 95 % Confidence Interval (CI): (-0.1402, -0.0446), P < 0.001), with VPA (weight: 0.7922) contributing most to a lower dementia risk, followed by MPA (0.1939). For Alzheimer's disease, MPA contributed the most (0.8555); for vascular dementia, VPA contributed the most (0.6271). CONCLUSION: For Alzheimer's disease, MPA was identified as the most influential factor, while VPA stood out as the most impactful for vascular dementia.

omicsMIC: a comprehensive benchmarking platform for robust comparison of imputation methods in mass spectrometry-based omics data.

Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in mass spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. omicsMIC is freely available at https://github.com/WQLin8/omicsMIC.

Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics.

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-view 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.

A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture.

Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. We propose a novel staged model that combines advanced imaging and clinical data to improve predictive performance. By using convolutional neural networks (CNNs) to extract features from hip DXA images, along with clinical variables, shape measurements, and texture features, our method provides a comprehensive framework for assessing fracture risk. The study cohort included 547 patients, with 94 experiencing hip fracture. A staged machine learning-based model was developed using two ensemble models: Ensemble 1 (clinical variables only) and Ensemble 2 (clinical variables and DXA imaging features). This staged approach used uncertainty quantification from Ensemble 1 to decide if DXA features are necessary for further prediction. Ensemble 2 exhibited the highest performance, achieving an Area Under the Curve (AUC) of 0.9541, an accuracy of 0.9195, a sensitivity of 0.8078, and a specificity of 0.9427. The staged model also performed well, with an AUC of 0.8486, an accuracy of 0.8611, a sensitivity of 0.5578, and a specificity of 0.9249, outperforming Ensemble 1, which had an AUC of 0.5549, an accuracy of 0.7239, a sensitivity of 0.1956, and a specificity of 0.8343. Furthermore, the staged model suggested that 54.49% of patients did not require DXA scanning. It effectively balanced accuracy and specificity, offering a robust solution when DXA data acquisition is not always feasible. Statistical tests confirmed significant differences between the models, highlighting the advantages of the advanced modeling strategies. Our staged approach offers a cost-effective holistic view of patients' health. It could identify individuals at risk with a high accuracy but reduce the unnecessary DXA scanning. Our approach has great promise to guide interventions to prevent hip fractures with reduced cost and radiation.

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.

Somatomotor-Visual Resting State Functional Connectivity Increases After Two Years in the UK Biobank Longitudinal Cohort.

Purpose: Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, where high connectivity among all brain regions changes to a more modular structure with maturation. In this work, we examine changes in FC in older adults after two years of aging in the UK Biobank longitudinal cohort. Approach: We process data using the Power264 atlas, then test whether FC changes in the 2,722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t-test. We also compare the ability of Power264 and UKB-provided, ICA-based FC to determine which of a longitudinal scan pair is older. Results: We find a 6.8% average increase in SMT-VIS connectivity from younger to older scan (from ρ = 0.39 to ρ = 0.42) that occurs in male, female, older subject (> 65 years old), and younger subject (< 55 years old) groups. Among all inter-network connections, this average SMT-VIS connectivity is the best predictor of relative scan age, accurately predicting which scan is older 57% of the time. Using the full FC and a training set of 2,000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC. Conclusions: We conclude that SMT-VIS connectivity increases in the longitudinal cohort, while resting state FC increases generally with age in the cross-sectional cohort. However, we consider the possibility of a change in resting state scanner task between UKB longitudinal data acquisitions.

Somatomotor-Visual Resting State Functional Connectivity Increases After Two Years in the UK Biobank Longitudinal Cohort.

Purpose: Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, where high connectivity among all brain regions changes to a more modular structure with maturation. In this work, we examine changes in FC in older adults after two years of aging in the UK Biobank longitudinal cohort. Approach: We process data using the Power264 atlas, then test whether FC changes in the 2,722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t-test. We also compare the ability of Power264 and UKB-provided, ICA-based FC to determine which of a longitudinal scan pair is older. Results: We find a 6.8% average increase in SMT-VIS connectivity from younger to older scan (from ρ=0.39 to ρ=0.42) that occurs in male, female, older subject (> 65 years old), and younger subject (< 55 years old) groups. Among all inter-network connections, this average SMT-VIS connectivity is the best predictor of relative scan age, accurately predicting which scan is older 57% of the time. Using the full FC and a training set of 2,000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC. Conclusions: We conclude that SMT-VIS connectivity increases in the longitudinal cohort, while resting state FC increases generally with age in the cross-sectional cohort. However, we consider the possibility of a change in resting state scanner task between UKB longitudinal data acquisitions.

Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results.

Functional magnetic resonance (fMRI) is an invaluable tool in studying cognitive processes in vivo. Many recent studies use functional connectivity (FC), partial correlation connectivity (PC), or fMRI-derived brain networks to predict phenotypes with results that sometimes cannot be replicated. At the same time, FC can be used to identify the same subject from different scans with great accuracy. In this paper, we show a method by which one can unknowingly inflate classification results from 61% accuracy to 86% accuracy by treating longitudinal or contemporaneous scans of the same subject as independent data points. Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10,000 training subjects without double-dipping. We replicate this effect in four different datasets: the UK Biobank (UKB), the Philadelphia Neurodevelopmental Cohort (PNC), the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP), and an OpenNeuro Fibromyalgia dataset (Fibro). The unintentional improvement ranges between 7% and 25% in the four datasets. Additionally, we find that by using dynamic functional connectivity (dFC), one can apply this method even when one is limited to a single scan per subject. One major problem is that features such as ROIs or connectivities that are reported alongside inflated results may confuse future work. This article hopes to shed light on how even minor pipeline anomalies may lead to unexpectedly superb results.

omicsMIC: a Comprehensive Benchmarking Platform for Robust Comparison of Imputation Methods in Mass Spectrometry-based Omics Data.

Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics, and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive and systematic comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to simulate and evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. OmicsMIC is freely available at https://github.com/WQLin8/omicsMIC.

Systematic metabolomic studies identified adult adiposity biomarkers with acetylglycine associated with fat loss in vivo.

Obesity is associated with various adverse health outcomes. Body fat (BF) distribution is recognized as an important factor of negative health consequences of obesity. Although metabolomics studies, mainly focused on body mass index (BMI) and waist circumference, have explored the biological mechanisms involved in the development of obesity, these proxy composite measures are not accurate and cannot reflect BF distribution, and thus may hinder accurate assessment of metabolic alterations and differential risk of metabolic disorders among individuals presenting adiposity differently throughout the body. Thus, the exact relations between metabolites and BF remain to be elucidated. Here, we aim to examine the associations of metabolites and metabolic pathways with BF traits which reflect BF distribution. We performed systematic untargeted serum metabolite profiling and dual-energy X-ray absorptiometry (DXA) whole body fat scan for 517 Chinese women. We jointly analyzed DXA-derived four BF phenotypes to detect cross-phenotype metabolite associations and to prioritize important metabolomic factors. Topology-based pathway analysis was used to identify important BF-related biological processes. Finally, we explored the relationships of the identified BF-related candidate metabolites with BF traits in different sex and ethnicity through two independent cohorts. Acetylglycine, the top distinguished finding, was validated for its obesity resistance effect through in vivo studies of various diet-induced obese (DIO) mice. Eighteen metabolites and fourteen pathways were discovered to be associated with BF phenotypes. Six of the metabolites were validated in varying sex and ethnicity. The obesity-resistant effects of acetylglycine were observed to be highly robust and generalizable in both human and DIO mice. These findings demonstrate the importance of metabolites associated with BF distribution patterns and several biological pathways that may contribute to obesity and obesity-related disease etiology, prevention, and intervention. Acetylglycine is highlighted as a potential therapeutic candidate for preventing excessive adiposity in future studies.

Multi-view information fusion using multi-view variational autoencoder to predict proximal femoral fracture load.

Background: Hip fracture occurs when an applied force exceeds the force that the proximal femur can support (the fracture load or "strength") and can have devastating consequences with poor functional outcomes. Proximal femoral strengths for specific loading conditions can be computed by subject-specific finite element analysis (FEA) using quantitative computerized tomography (QCT) images. However, the radiation and availability of QCT limit its clinical usability. Alternative low-dose and widely available measurements, such as dual energy X-ray absorptiometry (DXA) and genetic factors, would be preferable for bone strength assessment. The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion. Results: We developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively. Conclusion: The proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for predicting FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation exposure from QCT.

Metabolomic profiles associated with physical activity in White and African American adult men.

BACKGROUND: Physical activity (PA) is associated with various health benefits, especially in improving chronic health conditions. However, the metabolic changes in host metabolism in response to PA remain unclear, especially in racially/ethnically diverse populations. OBJECTIVE: This study is to assess the metabolic profiles associated with the frequency of PA in White and African American (AA) men. METHODS: Using the untargeted metabolomics data collected from 698 White and AA participants (mean age: 38.0±8.0, age range: 20-50) from the Louisiana Osteoporosis Study (LOS), we conducted linear regression models to examine metabolites that are associated with PA levels (assessed by self-reported regular exercise frequency levels: 0, 1-2, and ≥3 times per week) in White and AA men, respectively, as well as in the pooled sample. Covariates considered for statistical adjustments included race (only for the pooled sample), age, BMI, waist circumstance, smoking status, and alcohol drinking. RESULTS: Of the 1133 untargeted compounds, we identified 7 metabolites associated with PA levels in the pooled sample after covariate adjustment with a false discovery rate of 0.15. Specifically, compared to participants who did not exercise, those who exercised at a frequency ≥3 times/week showed higher abundances in uracil, orotate, 1-(1-enyl-palmitoyl)-2-oleoyl-GPE (P-16:0/18:1) (GPE), threonate, and glycerate, but lower abundances in salicyluric glucuronide and adenine in the pooled sample. However, in Whites, salicyluric glucuronide and orotate were not significant. Adenine, GPE, and threonate were not significant in AAs. In addition, the seven metabolites were not significantly different between participants who exercised ≥3 times/week and 1-2 times/week, nor significantly different between participants with 1-2 times/week and 0/week in the pooled sample and respective White and AA groups. CONCLUSIONS: Metabolite responses to PA are dose sensitive and may differ between White and AA populations. The identified metabolites may help advance our knowledge of guiding precision PA interventions. Studies with rigorous study designs are warranted to elucidate the relationship between PA and metabolites.

Gut microbiota impacts bone via Bacteroides vulgatus-valeric acid-related pathways.

Although the gut microbiota has been reported to influence osteoporosis risk, the individual species involved, and underlying mechanisms, remain largely unknown. We performed integrative analyses in a Chinese cohort of peri-/post-menopausal women with metagenomics/targeted metabolomics/whole-genome sequencing to identify novel microbiome-related biomarkers for bone health. Bacteroides vulgatus was found to be negatively associated with bone mineral density (BMD), which was validated in US white people. Serum valeric acid (VA), a microbiota derived metabolite, was positively associated with BMD and causally downregulated by B. vulgatus. Ovariectomized mice fed B. vulgatus demonstrated increased bone resorption and poorer bone micro-structure, while those fed VA demonstrated reduced bone resorption and better bone micro-structure. VA suppressed RELA protein production (pro-inflammatory), and enhanced IL10 mRNA expression (anti-inflammatory), leading to suppressed maturation of osteoclast-like cells and enhanced maturation of osteoblasts in vitro. The findings suggest that B. vulgatus and VA may represent promising targets for osteoporosis prevention/treatment.

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.