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.

Background Musculoskeletal disorders pose major public health challenges, and accelerated biological aging may increase their risk. This study investigates the association between biological aging and musculoskeletal disorders, with a focus on sex-related differences. Methods We analyzed data from 172,332 UK Biobank participants (mean age of 56.03 ± 8.10 years). Biological age was calculated using the KDM-BA and PhenoAge algorithms based on blood biomarkers. Musculoskeletal disorders were diagnosed using the ICD-10 criteria, with sample sizes ranging from 1,182 to 23,668. Logistic regression assessed cross-sectional associations between age acceleration (AA) metrics and musculoskeletal disorders. Accelerated Failure Time (AFT) model was used for survival analysis to evaluate the relationships between AAs and musculoskeletal disorders onset. Models were adjusted for demographic, lifestyle, and socio-economic covariates. The threshold of P-values were set by the Holm-Bonferroni correction. Results Cross-sectional analyses reveal significant associations between AAs and fourteen musculoskeletal disorders. Survival analyses indicate that AAs significantly accelerate the onset of nine musculoskeletal disorders, including inflammatory polyarthropathies (RT_KDM-BA = 0.993; RT_PhenoAge = 0.983), systemic connective tissue disorders (RT_KDM-BA = 0.987; RT_PhenoAge = 0.980), spondylopathies (RT_PhenoAge= 0.994), disorders of bone density and structure (RT_PhenoAge= 0.991), gout (RT_PhenoAge= 0.968), arthritis (RT_PhenoAge= 0.991), pain in joint (RT_PhenoAge= 0.989), low back pain (RT_PhenoAge= 0.986), and osteoporosis (RT_PhenoAge= 0.994). Sensitivity analyses are consistent with the primary findings. Sex-specific variations are observed, with AAs accelerating spondylopathies, arthritis, and low back pain in females, while osteoporosis is accelerated in males. Conclusion Accelerated biological aging is significantly associated with the incidence of several musculoskeletal disorders. These insights highlight the importance of biological age assessments in gauging musculoskeletal disorder risk, aiding early detection, prevention, and management.

Introduction: Observational studies highlight associations of common diseases with individual schizophrenia symptoms. However, it is unclear whether these diseases are associated with individual treatment-resistant schizophrenia (TRS). We aimed to explore the genetic associations between common immune diseases, metabolic diseases, psychiatric disorders, gut microbiota and TRS. Methods: Genome-wide association study (GWAS) summary data of European participants (n = ∼456,327) included TRS, 11 psychiatric disorders, 23 immune and metabolic diseases, body mass index, height, and 211 gut microbiota. In this genetic correlation and two-sample Mendelian randomization (MR) study, linkage disequilibrium score (LDSC) regression was applied to infer genetic correlation estimates. Two-sample MR tested potential causal associations of genetic variants associated with common immune diseases, metabolic diseases, psychiatric disorders, and gut microbiota with TRS. Results: LDSC revealed candidate associations between attention deficit/hyperactivity disorder (ADHD), schizophrenia, intestinal infectious diseases, obesity and TRS (genetic correlation range, 0.230–0.702; p < 0.05). Two-sample MR analyses suggested that ADHD was positively associated with TRS (estimate [SE] = 0.204 [0.073], p = 0.005), a finding that remained stable across statistical models. Besides, schizophrenia and genus Barnesiella levels were causally associated with TRS but not consistent across MR approaches. Conclusion: This study reports genetic correlations between ADHD, schizophrenia, intestinal infectious diseases, obesity and TRS. The study also found that genus Barnesiella was associated with TRS. These findings may have clinical implications, highlighting the possible strategy for TRS prevention.

This study investigates the cellular origin and tissue heterogeneity in bipolar disorder (BD) by integrating multiomics data. Four distinct datasets were employed, including single-cell RNA sequencing (scRNA-seq) data (embryonic and fetal brain, n = 8, 1,266 cells), BD Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data (adult brain, n = 210), BD bulk RNA-seq data (adult brain, n = 314), and BD genome-wide association study (GWAS) summary data (n = 413,466). The integration of scRNA-seq data with multiomics data relevant to BD was accomplished using the single-cell disease relevance score (scDRS) algorithm. We have identified a novel brain cell cluster named ADCY1, which exhibits distinct genetic characteristics. From a high-resolution genetic perspective, glial cells emerge as the primary cytopathology associated with BD. Specifically, astrocytes were significantly related to BD at the RNA-seq level, while microglia showed a strong association with BD across multiple panels, including the transcriptome-wide association study (TWAS), ATAC-seq, and RNA-seq. Additionally, oligodendrocyte precursor cells displayed a significant association with BD in both ATAC-seq and RNA-seq panel. Notably, our investigation of brain regions affected by BD revealed significant associations between BD and all three types of glial cells in the dorsolateral prefrontal cortex (DLPFC). Through comprehensive analyses, we identified several BD-associated genes, including CRMP1, SYT4, UCHL1, and ZBTB18. In conclusion, our findings suggest that glial cells, particularly in specific brain regions such as the DLPFC, may play a significant role in the pathogenesis of BD. The integration of multiomics data has provided valuable insights into the etiology of BD, shedding light on potential mechanisms underlying this complex psychiatric disorder.

Previous studies showed that diet and gut microbiota had a correlation with sleep. However, the potential interaction effects of diet and gut microbiota on sleep are still unclear. The phenotypic data of insomnia (including 374,505 subjects) and sleep duration (including 372,805 subjects) were obtained from the UK Biobank cohort. The Single Nucleotide Polymorphisms (SNPs) associated with 114 gut microbiota, 84 dietary habits, and 4 dietary compositions were derived from the published Genome-wide Association Study (GWAS). We used Linkage Disequilibrium Score Regression (LDSC) to estimate the genetic correlation and colocalization analysis to assess whether dietary habits and insomnia/sleep duration shared a causal variant in a region of the genome. Using UK Biobank genotype data, the polygenetic risk score of gut microbiota, dietary habits, and dietary compositions were calculated for each subject. Logistic regression and linear regression models were used to assess the potential effects of diet-gut microbiota interactions on sleep phenotypes, including insomnia and sleep duration. Insomnia and sleep duration were used as dependent variables, and sex, age, the Townsend Deprivation Index scores, and smoking and drinking habits were selected as covariates in the regression analysis. All statistical analyses were conducted using R-3.5.1 software. Significant genetic correlations were discovered between insomnia/sleep duration and dietary habits. Further, we found several significant dietary compositions-gut microbiota interactions associated with sleep, such as fat × G_Collinsella_RNT (p = 1.843 × 10⁻²) and protein × G_Collinsella_HB (p = 7.11 × 10⁻³). Besides, multiple dietary habits-gut microbiota interactions were identified for sleep, such as overall beef intake × G_Desulfovibrio_RNT (p = 3.26 × 10⁻⁴), cups of coffee per day × G_Escherichia_Shigella_RNT (p = 1.14 × 10⁻³), and pieces of dried fruit per day × G_Bifidobacterium_RNT (p = 5.80 × 10⁻³). This study reported multiple diet-gut microbiota interactions associated with sleep, which may provide insights into the biological mechanisms of diet and gut microbiota affecting sleep.