Illicit drug use is associated with lower bone mineral density and bone strength.

Objectives: To evaluate the association of illicit drug use with bone mineral density (BMD) and hip geometric parameters at the narrow neck. Methods: This is a cross-sectional matched cohort study conducted in the Hong Kong Chinese population. Associations with illicit drug use were estimated using linear regression for BMD (lumbar spine and femoral neck) and hip geometrical parameters (cross-sectional area [CSA], cross-sectional moment of inertia [CSMI], section modulus [SM], average cortical thickness [ACT] and BMD at the narrow neck) after adjusting for age, body mass index (BMI), smoking status, drinking status, physical activity, and history of antipsychotic and antidepressant use. Mean difference and 95% confidence intervals (95% CI) were calculated between 108 illicit drug users and 108 controls using an adjusted linear model and cluster-robust standard errors after matching by age and sex. The false discovery rate was used to correct for multiple testing. Results: Illicit drug users had a significantly lower BMD (g/cm2) at the lumbar spine (mean difference: -0.062; 95% CI: -0.108 to -0.015), and femoral neck (mean difference: -0.058; 95% CI: -0.106 to -0.010) in the fully adjusted model. Illicit drug users also had a significantly lower CSA (mean difference: -0.238 cm2; 95% CI: -0.462 to -0.013), ACT (mean difference: -0.018 cm; 95% CI: -0.030 to -0.006) and BMD (mean difference: -0.070 g/cm2; 95% CI: -0.128 to -0.012) at the narrow neck. Conclusions: Illicit drug use is associated with lower BMD and bone strength. Future studies evaluating the risk of illicit drug use with fragility fracture are warranted.

Association of Bone Mineral Density and Bone Turnover Markers with the Risk of Diabetes: Hong Kong Osteoporosis Study and Mendelian Randomization.

Preclinical studies demonstrated that bone plays a central role in energy metabolism. However, how bone metabolism is related to the risk of diabetes in humans is unknown. We investigated the association of bone health (bone mineral density [BMD] and bone turnover markers) with incident type-2 diabetes mellitus (T2DM) based on the Hong Kong Osteoporosis Study (HKOS). A total of 993 and 7160 participants from the HKOS were studied for the cross-sectional and prospective analyses, respectively. The cross-sectional study evaluated the association of BMD and bone biomarkers with fasting glucose and glycated hemoglobin (HbA1c ) levels, whereas the prospective study examined the associations between BMD at study sites and the risk of T2DM by following subjects a median of 16.8 years. Body mass index (BMI) was adjusted in all full models. Mendelian randomization (MR) was conducted for causal inference. In the cross-sectional analysis, lower levels of circulating bone turnover markers and higher BMD were significantly associated with increased fasting glucose and HbA1c levels. In the prospective analysis, higher BMD (0.1 g/cm2 ) at the femoral neck and total hip was associated with increased risk of T2DM with hazard ratios (HRs) of 1.10 (95% confidence interval [CI], 1.03 to 1.18) and 1.14 (95% CI, 1.08 to 1.21), respectively. The presence of osteoporosis was associated with a 30% reduction in risk of T2DM compared to those with normal BMD (HR = 0.70; 95% CI, 0.55 to 0.90). The MR results indicate a robust genetic causal association of estimated BMD (eBMD) with 2-h glucose level after an oral glucose challenge test (estimate = 0.043; 95% CI, 0.007 to 0.079) and T2DM (odds ratio = 1.064; 95% CI, 1.036 to 1.093). Higher BMD and lower levels of circulating bone biomarkers were cross-sectionally associated with poor glycemic control. Moreover, higher BMD was associated with a higher risk of incident T2DM and the association is probably causal. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Metabolomics of Osteoporosis in Humans: A Systematic Review.

PURPOSE OF REVIEW: To systematically review recent studies investigating the association between metabolites and bone mineral density (BMD) in humans. METHODS: Using predefined keywords, we searched literature published from Jan 1, 2019 to Feb 20, 2022 in PubMed, Web of Science, Embase, and Scopus. Studies that met the predefined exclusion criteria were excluded. Among the included studies, we identified metabolites that were reported to be associated with BMD by at least three independent studies. RECENT FINDINGS: A total of 170 studies were retrieved from the databases. After excluding studies that did not meet our predefined inclusion criteria, 16 articles were used in this review. More than 400 unique metabolites in blood were shown to be significantly associated with BMD. Of these, three metabolites were reported by ≥ 3 studies, namely valine, leucine and glycine. Glycine was consistently shown to be inversely associated with BMD, while valine was consistently observed to be positively associated with BMD. Inconsistent associations with BMD was observed for leucine. With advances in metabolomics technology, an increasing number of metabolites associated with BMD have been identified. Two of these metabolites, namely valine and glycine, were consistently associated with BMD, highlighting their potential for clinical application in osteoporosis. International collaboration with a larger population to conduct clinical studies on these metabolites is warranted. On the other hand, given that metabolomics could be affected by genetics and environmental factors, whether the inconsistent association of the metabolites with BMD is due to the interaction between metabolites and genes and/or lifestyle warrants further study.

Transcriptome-wide summary data-based Mendelian randomization analysis reveals 38 novel genes associated with severe COVID-19.

Severe COVID-19 has a poor prognosis, while the genetic mechanism underlying severe COVID-19 remains largely unknown. We aimed to identify genes that are potentially causally associated with severe COVID-19. We conducted a summary data-based Mendelian randomization (SMR) analysis using expression quantitative trait loci (eQTL) data from 49 different tissues as the exposure and three COVID-19-phenotypes (very severe respiratory confirmed COVID-19 [severe COVID-19], hospitalized COVID-19, and SARS-CoV-2 infection) as the outcomes. SMR using multiple SNPs was used as a sensitivity analysis to reduce false positive rate. Multiple testing was corrected using the false discovery rate (FDR) q-value. We identified 309 significant gene-trait associations (FDR q value < 0.05) across 46 tissues for severe COVID-19, which mapped to 64 genes, of which 38 are novel. The top five most associated protein-coding genes were Interferon Alpha and Beta Receptor Subunit 2 (IFNAR2), 2'-5'-Oligoadenylate Synthetase 3 (OAS3), mucin 1 (MUC1), Interleukin 10 Receptor Subunit Beta (IL10RB), and Napsin A Aspartic Peptidase (NAPSA). The potential causal genes were enriched in biological processes related to type I interferons, interferon-gamma inducible protein 10 production, and chemokine (C-X-C motif) ligand 2 production. In addition, we further identified 23 genes and 5 biological processes which are unique to hospitalized COVID-19, as well as 13 genes that are unique to SARS-CoV-2 infection. We identified several genes that are potentially causally associated with severe COVID-19. These findings improve our limited understanding of the mechanism of COVID-19 and shed light on the development of therapeutic agents for treating severe COVID-19.

COVID-19 and platelet traits: A bidirectional Mendelian randomization study.

This study aimed to evaluate the host genetic liability of coronavirus disease 2019 (covid-19) with platelet traits using the Mendelian randomization (MR) approach. We conducted a bidirectional two-sample MR using summary statistics from the largest genome-wide association study of three variables, covid-19 severity (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] infection, covid-19 hospitalization, and severe covid-19, N = ~1 059 456-1 557 411) and four platelet traits (mean platelet volume [MPV], plateletcrit, platelet distribution width, and platelet count; N = 408 112). Inverse-variance weighted (IVW), median weighted, MR-Egger, and contamination mixture methods were used to estimate the causal association. Null and inconsistent associations in the IVW and sensitivity analyses were observed for SARS-CoV-2 infection and covid-19 hospitalization with platelet traits. For severe covid-19, significant associations with MPV and platelet count were observed in the IVW and sensitivity analyses, with the betaIVW of 0.01 (95% confidence interval [CI]: 0.005-0.016, p = 3.51 × 10-4 ) and -0.009 (95% CI: -0.015 to -0.002, p = 0.008) per doubling in odds of severe covid-19, respectively. Conversely, null associations were observed for platelet traits with covid-19 traits. In conclusion, host genetic liability to severe covid-19 was causally associated with increased MPV and reduced platelet count, which may provide insights into evaluating hypercoagulability and thromboembolic events in covid-19 patients.