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BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a complex, heterogeneous disease with multiple extrapulmonary manifestations, among which vitamin D deficiency and insufficiency are very common in COPD and are associated with the health status and clinical outcomes of COPD patients. OBJECTIVES: This paper aims to analyze the impact of leisure-time physical activity (LTPA) and daily sitting time (DST) and their interactions on serum vitamin D in patients with COPD. MATERIAL AND METHODS: Participants aged ≥40 years from the National Health and Nutrition Examination Survey (NHANES) in the USA from 2007 to 2012 who had undergone pulmonary function tests and vitamin D tests were selected as the study participants. Participants' LTPA and DST were assessed using the General Practice Assessment Questionnaire (GPAQ). Multivariate logistic regression analysis was used to analyze the relationship between serum vitamin D, LTPA, DSA and the combination of the 2 in patients with COPD, and the results were expressed as odds ratio (OR) and 95% confidence interval (95% CI). RESULTS: This study included 1,448 samples. The mean vitamin D concentration of the samples was (68.27 ±26.78) nmol/L; 360 participants (24.86%) had vitamin D deficiency and 539 participants (37.22%) had vitamin D insufficiency. Vitamin D and 25(OH)D3 expression levels differed across the 4 groups (150 min/week and DST > 8 h revealed the highest vitamin D expression levels, while LTPA 8 h showed the lowest. Vitamin D was weakly correlated with FEV1, FVC, BMI, age, and LTPA (p < 0.01), but not with DST. Body mass index (BMI) was weakly positively correlated with DST (r = 0.142, p < 0.01). CONCLUSIONS: Serum physical activity and DST independently affect vitamin D levels in COPD patients; therefore, increasing physical activity and minimizing DST may help improve vitamin D levels and prevent vitamin D deficiency.
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The continued spread of SARS-CoV-2 has presented unprecedented obstacles to the worldwide public health system. Especially, individuals with chronic obstructive pulmonary disease (COPD) are at a heightened risk of contracting SARS-CoV-2 infection due to their pre-existing respiratory symptoms that are not well-managed. However, the viral mechanism of affecting the expression of host genes, COPD progression, and prognosis is not clear yet.This study integrated the differential expression information of COPD patients and then calculated the correlation between mRNAs and miRNAs to construct a COPD-specific ceRNA network. The DEGs of individuals with SARS-CoV-2 infection and anticipated miRNAs and their targets were analyzed in 9 SARS-CoV-2 sequences from different geographic locations. Furthermore, combining the experimentally validated miRNAs and genes, the regulatory miRNA-mRNA relationships were identified. All the regulatory relationships were integrated into the COPD-specific network and the network modules were explored to get insight into the functional mechanism of SARS-CoV-2 infection in COPD patients.A higher proportion of DEGs compete with the same miRNA suggesting a higher expression of genes in the COPD-specific ceRNA network. Hsa-miR-21-3p is the largest connected point in the network, but the proportion of genes upregulated by hsa-miR-21-3p is low (P = 0.1406). This indicates that the regulatory relationship of competitive inhibition has little effect on has-miR-21, and the high expression pattern is a poor prognostic factor in COPD. Hsa-miR-15a-5p is the most significant miRNA with the highest proportion of DEGs. And ANXA2P3 is the only gene in the COPD ceRNA network that interferes with hsa-miR-15a-5p. In addition, we found that has-miR-1184- and has-miR-99-cored modules were significant, and genes ZDHHC18, PCGF3, and KIAA0319L interacting with them were all associated with COPD prognosis, and high expression of these genes could lead to poor prognosis in COPD.The key regulators such as miR-21, miR-15a, ANXA2P3, ZDHHC18, PCGF3, and KIAA0319L can be used as prognostic biomarkers for early intervention in COPD with SARS-CoV-2 infection.
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BACKGROUND: Parkinson's disease (PD) ranks as the second most frequently occurring neurodegenerative disease. The precise pathogenic mechanism of this disease remains unknown. The aim of the present study was to identify the biomarkers in PD and classify the primary differentially expressed genes (DEGs). METHODS: The present study searched for and downloaded mRNA expression data from the Gene Expression Omnibus database to identify differences in mRNA expression in the substantia nigra (SN) and blood of patients with PD and healthy controls. In addition, in order to investigate the biological functions of the classified dysregulated genes, the present study utilized Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), reverse transcription-quantitative PCR (RT-qPCR), gene co-expression network analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. A receiver operating characteristic (ROC) curve was applied to assay TMEM243 as a diagnostic marker. RESULTS: Between PD and controls in GSE20292, the present study identified 1862 DEGs. Using the weighted gene co-expression network analysis, the present study identified 15 modules in PD. The module preservation analysis revealed that the tan, blue and green-yellow modules were the most stable. KEGG pathway analysis revealed that five DEGs in the black module were significantly enriched in the ubiquitin-mediated proteolysis pathway, nucleotide excision repair pathway, mismatch repair pathway. The present study selected 303 genes with high connectivity in blue, green-yellow and tan modules as hub genes, where 58 were differentially expressed in both the GSE20292 and GSE54536 datasets. In the SN and blood, 11 genes exhibited the same trend of expression. Furthermore, in the blood samples of patients with PD, the results displayed a significant upregulation of TMEM243. The expression levels of CCR4, CAMK1D, ACTR1B and SPSB3 increased, while both the levels of INA and PSMD4 decreased. These findings are consistent with the bioinformatics analysis results but are not statistically significant. TMEM243 can be considered as a diagnostic biomarker (area under the curveâ¯=â¯0.694; sensitivity, 80 %; specificity, 56 %; Pâ¯<â¯0.018). CONCLUSION: TMEM243 was distinctly upregulated in the blood samples of patients with PD, as validated via RT-qPCR, and was highly sensitive, revealing its potential as a biomarker for the future diagnosis of PD.
