Aims. Despite the interest in the association of gut microbiota with bone health, limited population-based studies of gut microbiota and bone mineral density (BMD) have been made. Our aim is to explore the possible association between gut microbiota and BMD. Methods. A total of 3,321 independent loci of gut microbiota were used to calculate the individual polygenic risk score (PRS) for 114 gut microbiota-related traits. The individual genotype data were obtained from UK Biobank cohort. Linear regressions were then conducted to evaluate the possible association of gut microbiota with L1-L4 BMD (n = 4,070), total BMD (n = 4,056), and femur total BMD (n = 4,054), respectively. PLINK 2.0 was used to detect the single-nucleotide polymorphism (SNP) × gut microbiota interaction effect on the risks of L1-L4 BMD, total BMD, and femur total BMD, respectively. Results. We detected five, three, and seven candidate gut microbiota-related traits for L1-L4 BMD, total BMD, and femur BMD, respectively, such as genus Dialister (p = 0.004) for L1-L4 BMD, and genus Eisenbergiella (p = 0.046) for total BMD. We also detected two common gut microbiota-related traits shared by L1-L4 BMD, total BMD, and femur total BMD, including genus Escherichia Shigella and genus Lactococcus. Interaction analysis of BMD detected several genes that interacted with gut microbiota, such as phospholipase D1 (PLD1) and endomucin (EMCN) interacting with genus Dialister in total BMD, and COL12A1 and Discs Large MAGUK Scaffold Protein 2 (DLG2) interacting with genus Lactococcus in femur BMD. Conclusion. Our results suggest associations between gut microbiota and BMD, which will be helpful to further explore the regulation mechanism and intervention gut microbiota of BMD. Cite this article: Bone Joint Res 2021;10(11):734–741

Objectives. The objective of this study was to investigate the association of four single-nucleotide polymorphisms (SNPs) of the cannabinoid receptor 2 (CNR2) gene, gene-obesity interaction, and haplotype combination with osteoporosis (OP) susceptibility. Methods. Chinese patients with OP were recruited between March 2011 and December 2015 from our hospital. In this study, a total of 1267 post-menopausal female patients (631 OP patients and 636 control patients) were selected. The mean age of all subjects was 69.2 years (sd 15.8). A generalized multifactor dimensionality reduction (GMDR) model and logistic regression model were used to examine the interaction between SNP and obesity on OP. For OP patient-control haplotype analyses, the SHEsis online haplotype analysis software (. http://analysis.bio-x.cn/. ) was employed. Results. The logistic regression model revealed that the C allele of rs2501431 and the G allele of rs3003336 were associated with increased OP risk, compared with those with wild genotype. However, no significant correlations were found when analyzing the association of rs4237 and rs2229579 with OP risk. The GMDR analysis suggested that the interaction model composed of two factors, rs3003336 and abdominal obesity (AO), was the best model with statistical significance (p-value from sign test (P. sign. ) = 0.012), indicating a potential gene-environment interaction between rs3003336 and AO. Overall, the two-locus models had a cross-validation consistency of 10/10 and had a testing accuracy of 0.641. Abdominally obese subjects with the AG or GG genotype have the highest OP risk, compared with subjects with the AA genotype and normal waist circumference (WC) (odds ratio (OR) 2.23, 95% confidence interval (CI) 1.54 to 3.51). Haplotype analysis also indicated that the haplotype containing the rs3003336-G and rs2501431-C alleles was associated with a statistically increased OP risk. Conclusion. Our results suggested that the C allele of rs2501431 and the G allele of rs3003336 of the CNR2 gene, interaction between rs3003336 and AO, and the haplotype containing the rs3003336-G and rs2501431-C alleles were all associated with increased OP risk. Cite this article: Bone Joint Res 2019;8:544–549

Aims

We aimed to develop a gene signature that predicts the occurrence of postmenopausal osteoporosis (PMOP) by studying its genetic mechanism.