Bone mineral density, bone metabolism-related factors, and microRNA-218 are correlated with disease activities in Chinese ankylosing spondylitis patients

Hounsfield units in assessing bone mineral density in ankylosing spondylitis patients with cervical fracture-dislocation

BACKGROUND

Cervical spine fracture-dislocations in patients with ankylosing spondylitis (AS) are mostly unstable and require surgery. However, osteoporosis, one of the comorbidities for AS, could lead to detrimental prognoses. There are few accurate assessments of bone mineral density in AS patients.

AIM

To analyze Hounsfield units (HUs) for assessing bone mineral density in AS patients with cervical fracture-dislocation.

METHODS

The HUs from C2 to C7 of 51 patients obtained from computed tomography (CT) scans and three-dimensional reconstruction of the cervical spine were independently assessed by two trained spinal surgeons and statistically analyzed. Inter-reader reliability and agreement were assessed by interclass correlation coefficient.

RESULTS

The HUs decreased gradually from C2 to C7. The mean values of the left and right levels were significantly higher than those in the middle. Among the 51 patients, 25 patients (49.02%) may be diagnosed with osteoporosis, and 16 patients (31.37%) may be diagnosed with osteopenia.

CONCLUSION

The HUs obtained by cervical spine CT are feasible for assessing bone mineral density with excellent agreement in AS patients with cervical fracture-dislocation.

Osteopontin Upregulation, Induced by the Continuous Mechanical Load in Adipose Tissue-Derived Mesenchymal Stem Cells, is Strongly Restricted in INF-γ/TNF-α/IL-22 Microenvironment

The osteogenic potential of mesenchymal stem cells (MSc) in axial spondyloarthritis (AxSpA) depends on the interplay of inflammation and multiple hormonal and local mechanical factors. In this study, MCs, derived from the adipose tissue of a healthy donor, were cultured under or without continuous mechanical load in the osteogenic differentiation medium with or without the addition of testosterone, cocktail of INF-γ/TNF-α/IL-22, or both. Real-time PCR for osteogenic transcription factors demonstrated that in the absence of INF-γ/TNF-α/IL-22, mechanical load causes significant upregulation of SPP1 (osteopontin), while the presence of the inflammatory cytokines almost completely abolishes this effect. In addition, exposure to INF-γ/TNF-α/IL-22 slightly upregulated BMP2, but suppressed the expression of ALPL, Col1A1, and SPP1, reinforcing the hypothesis that the inflammatory environment allows MSc to commit toward the IL-22-driven osteogenic differentiation but can restrict the later stages of osteogenesis. In summary, osteopontin can play a role in the pathogenesis of AxSpA, linking between mechanical load and pathological bone formation.

Exploring causal correlations between inflammatory cytokines and ankylosing spondylitis: a bidirectional mendelian-randomization study

Background

The impact of inflammatory factors on the development of Ankylosing Spondylitis (AS) is widely recognized, but the exact causal relationship remains unclear.

Methods

The bidirectional mendelian-randomization study utilized genetic data from a genome-wide association study (GWAS) of 186 AS cases and 456,162 controls of European ancestry. Inflammatory cytokines were obtained from a GWAS summary of 8,293 healthy participants. Causal associations were primarily investigated using the inverse variance-weighted method, supplemented by MR Egger, weighted median and weighted mode analyses. Heterogeneity in the results was assessed using the Cochrane Q test. Horizontal pleiotropy was evaluated through the MR-Egger intercept test and the MR pleiotropy residual sum and outliers (MR-PRESSO) test. Sensitivity analysis was conducted through leave-one-out analysis.

Results

The results suggest a genetically predicted potential association between beta-nerve growth factor (βNGF), Interleukin-1-beta (IL-1β), and TNF-related apoptosis inducing ligand (TRAIL) with the risk of AS (OR: 2.17, 95% CI: 1.13-4.16; OR: 0.41, 95% CI: 0.18-0.95,; OR: 1.47, 95% CI: 1.02-2.13).Additionally, Interleukin-12p70 (IL-12p70), Interleukin-17 (IL-17), Interleukin-6 (IL-6), Interleukin-4 (IL-4), Stromal-cell-derived factor 1 alpha (SDF-1α), Macrophage inflammatory protein 1β (MIP1β), Monocyte chemoattractant protein-3 (MCP-3), Platelet-derived growth factor bb (PDGFbb), Granulocyte-colony stimulating factor (GCSF), Fibroblast growth factor basic (bFGF), TNF-related apoptosis inducing ligand (TRAIL), and Interferon-gamma (IFN -γ) are suggested as consequences of AS in genetically prediction.No evidence of horizontal pleiotropy or heterogeneity between the genetic variants was found (P>0.05), and a leave-one-out test confirmed the stability and robustness of this association.

Conclusion

These findings suggest that βNGF, IL-1β, and TRAIL may play a crucial role in the pathogenesis of AS. Additionally, AS may impact the expression of cytokines such as IL-12p70, IL-17, IL-6, IL-4, SDF-1α, MIP1β, MCP-3, PDGFbb,GCSF, bFGF,TRAIL,and IFN-γ. Further investigations are warranted to determine whether these biomarkers can be utilized for the prevention or treatment of AS.

Serum DKK-1 level in ankylosing spondylitis: insights from meta-analysis and Mendelian randomization

Objective

The purpose of this study was to precisely evaluate the serum Dickkopf-1 (DKK-1) level in patients with ankylosing spondylitis (AS) relative to that in normal controls and to test the causal relationship between DKK-1 and the risk of AS.

Methods

Embase, PubMed, Web of Science, WANFANG DATA, VIP, and China National Knowledge Infrastructure (CNKI) were comprehensively searched until July 2022 for pertinent studies. The pooled standardized mean difference (SMD) with a 95% confidence interval (CI) was calculated by the fixed or random-effect model. In Mendelian randomization (MR) analysis on the causal relationship between serum DKK-1 level and AS risk, the inverse variance weighting method (IVW), MR-Egger regression, weighted median method, and weighted pattern method were applied. Sensitivity analyses, including the horizontal pleiotropy test, heterogeneity test, and leave-one-out test, were also performed.

Results

The meta-analysis of 40 studies containing 2,371 AS patients and 1,633 healthy controls showed that there was no significant difference in DKK-1 serum level between AS patients and normal controls (pooled SMD=0.207, 95% CI =-0.418-0.832, P=0.516). The subgroup analysis of the CRP ≤ 10 mg/L group showed that AS patients had higher serum DKK-1 concentration than the healthy controls (SMD=2.267, 95% CI = 0.102-4.432, P=0.040). Similarly, MR analysis also demonstrated no significant association between DKK-1 serum level and AS (IVW OR=0.999, 95% CI = 0.989-1.008, P=0.800). All sensitivity analyses revealed consistent results.

Conclusions

There was no significant change in serum DKK-1 concentration between AS patients and healthy controls. In addition, no causal relationship exists between serum DKK-1 levels and AS risk.

Immune mechanism of low bone mineral density caused by ankylosing spondylitis based on bioinformatics and machine learning

Background and Objective: This study aims to find the key immune genes and mechanisms of low bone mineral density (LBMD) in ankylosing spondylitis (AS) patients. Methods: AS and LBMD datasets were downloaded from the GEO database, and differential expression gene analysis was performed to obtain DEGs. Immune-related genes (IRGs) were obtained from ImmPort. Overlapping DEGs and IRGs got I-DEGs. Pearson coefficients were used to calculate DEGs and IRGs correlations in the AS and LBMD datasets. Louvain community discovery was used to cluster the co-expression network to get gene modules. The module most related to the immune module was defined as the key module. Metascape was used for enrichment analysis of key modules. Further, I-DEGs with the same trend in AS and LBMD were considered key I-DEGs. Multiple machine learning methods were used to construct diagnostic models based on key I-DEGs. IID database was used to find the context of I-DEGs, especially in the skeletal system. Gene-biological process and gene-pathway networks were constructed based on key I-DEGs. In addition, immune infiltration was analyzed on the AS dataset using the CIBERSORT algorithm. Results: A total of 19 genes were identified I-DEGs, of which IFNAR1, PIK3CG, PTGER2, TNF, and CCL3 were considered the key I-DEGs. These key I-DEGs had a good relationship with the hub genes of key modules. Multiple machine learning showed that key I-DEGs, as a signature, had an excellent diagnostic performance in both AS and LBMD, and the SVM model had the highest AUC value. Key I-DEGs were closely linked through bridge genes, especially in the skeletal system. Pathway analysis showed that PIK3CG, IFNAR1, CCL3, and TNF participated in NETs formation through pathways such as the MAPK signaling pathway. Immune infiltration analysis showed neutrophils had the most significant differences between case and control groups and a good correlation with key I-DEG. Conclusion: The key I-DEGs, TNF, CCL3, PIK3CG, PTGER2, and IFNAR1, can be utilized as biomarkers to determine the risk of LBMD in AS patients. They may affect neutrophil infiltration and NETs formation to influence the bone remodeling process in AS.

Associations between bone mineral density and subclinical peripheral arterial disease in elderly men with type 2 diabetes mellitus

The association between peripheral arterial disease (PAD) and osteoporosis in elderly men with type 2 diabetes mellitus (T2DM) remains unclear. We demonstrated the association between subclinical PAD and decreased total hip bone mineral density (BMD) in men aged ≥ 55 years with T2DM, providing clinical clues for the early detection of decreased bone density in total hip.

PURPOSE

To investigate the association between subclinical peripheral arterial disease (PAD) and bone mineral density (BMD) at total hip, femoral neck, and lumbar spine (L_1-4) in elderly men with type 2 diabetes mellitus (T2DM).

METHODS

We identified 2,466 patients with confirmed diabetes in this retrospective cross-sectional study. A total of 272 men aged ≥ 55 years with T2DM (50 with subclinical PAD and 222 without PAD) were analyzed. Partial correlation analysis was conducted to explore the associations between ankle-brachial index (ABI) and BMD. Multivariate logistic regression analysis was performed to analyze the contributor for low bone density (T-score <  - 1.0).

RESULTS

Patients with T2DM and subclinical PAD (ABI ≤ 0.9) had significantly lower total hip BMD and T-score (BMD, 0.87 ± 0.14 vs. 0.92 ± 0.15 g/cm², P = 0.014; T-score, -1.30 [-1.70 to -0.45] vs. -0.80 [-1.40 to 0.00], P = 0.009) than those in the control group. The partial correlation analyses indicated that ABI significantly correlated with the total hip T-score (adjusted r = 0.166, P = 0.009). The logistic regression analysis indicated that subclinical PAD was an independent risk factor for the risk of decreased bone density in total hip (adjusted odds ratio [95% CI]: 8.933 [1.075-74.222], P = 0.043).  CONCLUSION: Subclinical PAD (ABI ≤ 0.9) could be used as a risk factor for decreased total hip BMD in men aged ≥ 55 years with T2DM, which provides clinical clues for the early detection of low bone density in total hip in such populations.