BackgroundIndividuals’ 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.MethodsA 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.ResultsThe 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).ConclusionMetabolomic 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.

Osteoporosis is a highly prevalent chronic aging-related disease that frequently is only detected after fracture. We hypothesized that aminobutyric acids could serve as biomarkers for osteoporosis. We developed a quick, accurate, and sensitive screening method for aminobutyric acid isomers and enantiomers yielding correlations with bone mineral density (BMD) and osteoporotic fracture. In serum, γ-aminobutyric acid (GABA) and (R)-3-aminoisobutyric acid (D-BAIBA) have positive associations with physical activity in young lean women. D-BAIBA positively associated with hip BMD in older individuals without osteoporosis/osteopenia. Lower levels of GABA were observed in 60-80 year old women with osteoporotic fractures. Single nucleotide polymorphisms in seven genes related to these metabolites associated with BMD and osteoporosis. In peripheral blood monocytes, dihydropyrimidine dehydrogenase, an enzyme essential to D-BAIBA generation, exhibited positive association with physical activity and hip BMD. Along with their signaling roles, BAIBA and GABA might serve as biomarkers for diagnosis and treatments of osteoporosis.

Osteoporosis is characterized by low bone mineral density (BMD). The advancement of highthroughput 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-b, and WNT/b-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.

Bone mineral density (BMD) is a complex trait with high missing heritability. Numerous evidences have shown that BMD variation has a relationship with coronary artery disease (CAD). This relationship may come from a common genetic basis called pleiotropy. By leveraging the pleiotropy with CAD, we may be able to improve the detection power of genetic variants associated with BMD. Using a recently developed conditional false discovery rate (cFDR) method, we jointly analyzed summary statistics from two large independent genome wide association studies (GWAS) of lumbar spine (LS) BMD and CAD. Strong pleiotropic enrichment and 7 pleiotropic SNPs were found for the two traits. We identified 41 SNPs for LS BMD (cFDR<0.05), of which 20 were replications of previous GWASs and 21 were potential novel SNPs that were not reported before. Four genes encompassed by 9 cFDR-significant SNPs were partially validated in the gene expression assay. Further functional enrichment analysis showed that genes corresponding to the cFDR-significant LS BMD SNPs were enriched in GO terms and KEGG pathways that played crucial roles in bone metabolism (adjP < 0.05). In protein-protein interaction analysis, strong interactions were found between the proteins produced by the corresponding genes. Our study demonstrated the reliability and high-efficiency of the cFDR method on the detection of trait-associated genetic variants, the present findings shed novel insights into the genetic variability of BMD as well as the shared genetic basis underlying osteoporosis and CAD.

Both loss of muscle mass and strength are important sarcopenia-related traits. In this study, we investigated both specific and shared serum metabolites associated with these two traits in 136 Caucasian women using a liquid chromatography-mass spectrometry method. A joint analysis of multivariate traits was used to examine the associations of individual metabolites with muscle mass measured by the body mass index-adjusted appendicular lean mass (ALM/BMI) and muscle strength measured by hand grip strength (HGS). After adjusting for multiple testing, nine metabolites including two amino acids (aspartic acid and glutamic acid) and an amino acid derive (pipecolic acid), one peptide (phenylalanyl-threonine), one carbohydrate (methyl beta-D-glucopyranoside), and four lipids (12S-HETRE, arachidonic acid, 12S-HETE, and glycerophosphocholine) were significant in the joint analysis. Of them, the two amino acids (aspartic acid and glutamic acid) and two lipids (12S-HETRE and 12S-HETE) were associated with both ALM/BMI and HGS, and the other five were only associated with ALM/BMI. The pathway analysis showed the amino acid metabolism pathways (aspartic acid and glutamic acid) might play important roles in the regulation of muscle mass and strength. In conclusion, our study identified novel metabolites associated with sarcopenia-related traits, suggesting novel metabolic pathways for muscle regulation.

Context Though genome wide association studies (GWASs) have identified hundreds of genetic variants associated with osteoporosis related traits, such as bone mineral density (BMD) and fracture, it remains a challenge to interpret their biological functions and underlying biological mechanisms. Objective Integrate diverse expression quantitative trait loci (eQTLs) and splicing QTLs (sQTLs) data with several powerful GWASs datasets to identify novel candidate genes associated with osteoporosis. Design, Setting, and Participants Here, we conducted a transcriptome-wide association study (TWAS) for total body BMD (TB-BMD) (n = 66,628 for discovery and 7,697 for validation) and fracture (53,184 fracture cases and 373,611 controls for discovery and 37,857 cases and 227,116 controls for validation), respectively. We also conducted multi-SNP-based summarized mendelian randomization (SMR) analysis to further validate our findings. Results In total, we detected 88 genes significantly associated with TB-BMD or fracture through expression or RNA splicing. SMR analysis revealed that 78 of the significant genes may have potential causal effects on TB-BMD or fracture in at least one specific tissue. Among them, 64 genes have been reported in previous GWASs or TWASs for osteoporosis, such as ING3, CPED1 and WNT16, as well as 14 novel genes, such as DBF4B, GRN, TMUB2 and UNC93B1. Conclusions Overall, our findings provide novel insights into the pathogenesis mechanisms of osteoporosis and highlight the power of TWAS to identify and prioritize potential causal genes.

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-/post-menopausal 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 (PLS) 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. DA treatment differentially promoted osteoblast and osteoclast differentiation, especially for osteoclast differentiation at higher concentrations in vitro (e.g.,10, 100μM). 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.

Purpose Single-nucleotide polymorphism (SNP)-based genome-wide association studies (GWASs) have already identified about 100 loci associated with bone mineral density (BMD), but these loci only explain a small proportion of heritability to osteoporosis risk. In the present study, we performed a gene-based analysis of the largest GWASs in the bone field to identify additional BMD-associated genes. Methods BMD-associated genes were identified by combining the summary statistic P values of SNPs across individual genes in the two consecutive meta-analyses of GWASs from the Genetic Factors for Osteoporosis (GEFOS) studies. The potential functionality of these genes to bone was partially assessed by differential gene expression analysis. Additionally, the consistency of the identification of potential BMD-associated variants were evaluated by estimating the correlation of the P values of the same SNPs/genes between the two consecutive GEFOS studies with largely overlapping samples. Results Compared to the SNP-based analysis, the gene-based strategy identified additional BMD-associated genes with genome-wide significance and increased their mutual replication between the two GEFOS datasets. Among these BMD-associated genes, 3 novel genes (UBTF, AAAS, and C11orf58) were partially validated at the gene expression level. The correlation analysis presented a moderately high between-study consistency of potential BMD-associated variants. Conclusions Gene-based analysis as a supplementary strategy to SNP-based GWAS, when applied here, is shown that it helped identify some novel BMD-associated genes. In addition to its empirically increased statistical power, gene-based analysis also provides a higher testing stability for identification of BMD genes.