Causal relationship of interleukin-6 and its receptor on sarcopenia traits using mendelian randomization.

BACKGROUND: Previous research has extensively examined the role of interleukin 6 (IL-6) in sarcopenia. However, the presence of a causal relationship between IL-6, its receptor (IL-6R), and sarcopenia remains unclear. METHOD: In this study, we utilized summary-level data from genome-wide association studies (GWAS) focused on appendicular lean mass (ALM), hand grip strength, and walking pace. Single nucleotide polymorphisms (SNPs) were employed as genetic instruments for IL-6 and IL-6R to estimate the causal effect of sarcopenia traits. We adopted the Mendelian randomization (MR) approach to investigate these associations using the inverse variance weighted (IVW) method as the primary analytical approach. Additionally, we performed sensitivity analyses to validate the reliability of the MR results. RESULT: This study revealed a significant negative association between main IL-6R and eQTL IL-6R on the left grip strength were - 0.013 (SE = 0.004, p < 0.001) and -0.029 (SE = 0.007, p < 0.001), respectively. While for the right grip strength, the estimates were - 0.011 (SE = 0.001, p < 0.001) and - 0.021 (SE = 0.008, p = 0.005). However, no evidence of an association for IL-6R with ALM and walking pace. In addition, IL-6 did not affect sarcopenia traits. CONCLUSION: Our study findings suggest a negative association between IL-6R and hand grip strength. Additionally, targeting IL-6R may hold potential value as a therapeutic approach for the treatment of hand grip-related issues.

Investigating the relationship between muscle mass and nasal Methicillin-Resistant Staphylococcus aureus (MRSA) colonization: Analysis of the National Health and Nutrition Examination Survey (NHANES).

BACKGROUND: Methicillin-resistant Staphylococcus aureus (MRSA) nasal colonization is associated with an increased risk of infection disease. Low muscle mass has been linked to higher levels of inflammatory markers and weakened immune response, which may impact the susceptibility to nasal MRSA colonization. The relationship between muscle function and immune response to pathogens may be bidirectional. This study investigates the association between muscle mass and nasal MRSA colonization in adults. METHODS: The present cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2001 and 2004. Appendicular skeletal muscle mass (ASM) adjusted by body mass index (BMI) (ASM/BMI) was used to evaluate muscle mass. Multivariate logistic regression, adjusted for demographic and infection factors, was used to analyze the association between muscle mass and nasal colonization by MRSA. A subgroup analysis based on age and gender was performed to assess the impact of muscle mass on nasal MRSA colonization. RESULTS: Nasal MRSA colonization was more prevalent in females, those with smaller household sizes, lower income, lower ASM/BMI, those who had stayed in healthcare facilities in the past 12 months, and individuals with diabetes and smoking habits. After adjusting for confounding factors, a dose-dependent association was found between decreasing quartiles of ASM/BMI and the risk of nasal MRSA colonization (p < 0.05). Additionally, per 1 unit increase in ASM/BMI was related to a 64% lower risk of nasal MRSA colonization. CONCLUSIONS: This study suggests a significant negative correlation between ASM/BMI and the risk of nasal MRSA colonization. However, more prospective studies are required to investigate the causal relationship between muscle mass and colonization.

Exploring the shared gene signatures of smoking-related osteoporosis and chronic obstructive pulmonary disease using machine learning algorithms.

Objectives: Cigarette smoking has been recognized as a predisposing factor for both osteoporosis (OP) and chronic obstructive pulmonary disease (COPD). This study aimed to investigate the shared gene signatures affected by cigarette smoking in OP and COPD through gene expression profiling. Materials and methods: Microarray datasets (GSE11784, GSE13850, GSE10006, and GSE103174) were obtained from Gene Expression Omnibus (GEO) and analyzed for differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA). Least absolute shrinkage and selection operator (LASSO) regression method and a random forest (RF) machine learning algorithm were used to identify candidate biomarkers. The diagnostic value of the method was assessed using logistic regression and receiver operating characteristic (ROC) curve analysis. Finally, immune cell infiltration was analyzed to identify dysregulated immune cells in cigarette smoking-induced COPD. Results: In the smoking-related OP and COPD datasets, 2858 and 280 DEGs were identified, respectively. WGCNA revealed 982 genes strongly correlated with smoking-related OP, of which 32 overlapped with the hub genes of COPD. Gene Ontology (GO) enrichment analysis showed that the overlapping genes were enriched in the immune system category. Using LASSO regression and RF machine learning, six candidate genes were identified, and a logistic regression model was constructed, which had high diagnostic values for both the training set and external validation datasets. The area under the curves (AUCs) were 0.83 and 0.99, respectively. Immune cell infiltration analysis revealed dysregulation in several immune cells, and six immune-associated genes were identified for smoking-related OP and COPD, namely, mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1), tissue-type plasminogen activator (PLAT), sodium channel 1 subunit alpha (SCNN1A), sine oculis homeobox 3 (SIX3), sperm-associated antigen 9 (SPAG9), and vacuolar protein sorting 35 (VPS35). Conclusion: The findings suggest that immune cell infiltration profiles play a significant role in the shared pathogenesis of smoking-related OP and COPD. The results could provide valuable insights for developing novel therapeutic strategies for managing these disorders, as well as shedding light on their pathogenesis.

The association between antibiotics and community-associated Staphylococcus aureus colonization in the United States population: Analysis of the National Health and Nutrition Examination Survey (NHANES).

Staphylococcus aureus nasal colonization is a seriously opportunistic infection. However, there is a lack of research of relationship between antibiotics and S aureus colonization in the general population. Through a cross-sectional investigation, this study intends to establish the parameters related to S aureus nasal colonization, specifically the function antibiotics play in colonization. The key information from 2001 to 2004 was abstracted from National Health and Nutrition Examination Survey (NHANES), including information on general demographics, health care status, antibiotic prescription, diabetes, alcohol consumption, and tobacco smoke exposure. The participants colonized with methicillin-susceptible S aureus (MSSA), or methicillin-resistant S aureus (MRSA) were defined as the case group, and the control group was subjects without positive S aureus colonization. Univariate and multivariate logistic regression models were used to identify the variables associated with MSSA and MRSA colonization. The records of 18,607 individuals were included, involving 13,205 cases without S aureus colonization, 5195 cases with MSSA, and 207 cases with MRSA. In the multivariate logistic regression analysis, the risk of MSSA colonization was significantly reduced with fluoroquinolone use (75% risk reduction, P = .02), sulfonamide use (98% risk reduction, P < .01), tetracycline use (81% risk reduction, P < .01) and antibiotic combination therapy (risk reduction 76%, P < .01). Female, race and total household size were strongly associated with MSSA carriage. On the other hand, regarding MRSA colonization, fluoroquinolone use, long-term care, and former smoker were positively associated with MRSA colonization, while high income was negatively associated with MRSA colonization. More proper use of broad-spectrum antibiotics contributes to reducing MSSA colonization. Former smokers should also practice better personal hygiene to limit the possibility of MRSA colonization.

Identification of a Potential MiRNA-mRNA Regulatory Network for Osteoporosis by Using Bioinformatics Methods: A Retrospective Study Based on the Gene Expression Omnibus Database.

Introduction: As a systemic skeletal dysfunction, osteoporosis (OP) is characterized by low bone mass, impairment of bone microstructure, and a high global morbidity rate. There is increasing evidence that microRNAs (miRNAs) are associated with the pathogenesis of OP. Weighted gene co-expression network analysis (WGCNA) is a systematic method for identifying clinically relevant genes involved in disease pathogenesis. However, the study of the miRNA-messenger RNA (mRNA) regulatory network in combination with WGCNA in OP is still lacking. Methods: The GSE93883 and GSE7158 microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DE-miRNAs) and differentially expressed genes (DEGs) were analyzed with the limma package. OP-related miRNAs from the most clinically relevant module were identified by the WGCNA method. The overlap of DE-miRNAs and OP-related miRNAs was identified as OP-related DE-miRNAs. Both upstream transcription factors and downstream targets of OP-related DE-miRNAs were predicted by FunRich. An intersection of predicted target genes and DEGs was confirmed as downstream target genes of OP-related DE-miRNAs. With the use of clusterProfiler in R, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed on target genes. Finally, both the protein-protein interaction (PPI) network and miRNA-mRNA network were constructed and analyzed. Results: A total of 79 OP-related DE-miRNAs were obtained, most of which were predicted to be regulated by specificity protein 1 (SP1). Subsequently, 197 downstream target genes were screened out. The target genes were enriched in multiple pathways, including signaling pathways closely related to the onset of OP, such as Ras, PI3K-Akt, and ErbB signaling pathways. Through the construction of the OP-related miRNA-mRNA regulatory network, a hub network that may play a prominent role in the formation of OP was documented. Conclusion: By using WGCNA, we constructed a potential OP-related miRNA-mRNA regulatory network, offering a novel perspective on miRNA regulatory mechanisms in OP.

Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning.

OBJECTIVES: Smoking is a significant independent risk factor for postmenopausal osteoporosis, leading to genome variations in postmenopausal smokers. This study investigates potential biomarkers and molecular mechanisms of smoking-related postmenopausal osteoporosis (SRPO). MATERIALS AND METHODS: The GSE13850 microarray dataset was downloaded from Gene Expression Omnibus (GEO). Gene modules associated with SRPO were identified using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and pathway and functional enrichment analyses. Feature genes were selected using two machine learning methods: support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF). The diagnostic efficiency of the selected genes was assessed by gene expression analysis and receiver operating characteristic curve. RESULTS: Eight highly conserved modules were detected in the WGCNA network, and the genes in the module that was strongly correlated with SRPO were used for constructing the PPI network. A total of 113 hub genes were identified in the core network using topological network analysis. Enrichment analysis results showed that hub genes were closely associated with the regulation of RNA transcription and translation, ATPase activity, and immune-related signaling. Six genes (HNRNPC, PFDN2, PSMC5, RPS16, TCEB2, and UBE2V2) were selected as genetic biomarkers for SRPO by integrating the feature selection of SVM-RFE and RF. CONCLUSION: The present study identified potential genetic biomarkers and provided a novel insight into the underlying molecular mechanism of SRPO.