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Aim: Evidence linking dietary patterns and the risk of gastric cancer was limited, especially in Chinese populations. This study aimed to explore the association between dietary patterns and the risk of gastric cancer in residents of the Huaihe River Basin, China., Methods: The association between dietary patterns and the risk of gastric cancer was investigated through a case-control study. Dietary patterns were identified with factor analysis based on responses to a food frequency questionnaire (FFQ). Gastric cancer was diagnosed according to the International Classification of Diseases, 10th Revision (ICD 10). Odds ratios ( OR s) and 95% confidence intervals ( CI s) were calculated across the tertiles of dietary pattern scores using unconditional logistic regression models., Results: A total of 2,468 participants were included in this study. Six main dietary patterns were extracted, and those patterns explained 57.09% of the total variation in food intake. After adjusting for demographic characteristics, lifestyle factors, individual disease history, family history of cancer and Helicobacter. Pylori ( H. pylori ) infection, comparing the highest with the lowest tertiles of dietary pattern scores, the multivariable OR s (95% CI s) were 0.786 (0.488, 1.265; P trend < 0.001) for the flavors, garlic and protein pattern, 2.133 (1.299, 3.502; P trend < 0.001) for the fast food pattern, 1.050 (0.682, 1.617; P trend < 0.001) for the vegetable and fruit pattern, 0.919 (0.659, 1.282; P trend < 0.001) for the pickled food, processed meat products and soy products pattern, 1.149 (0.804, 1.642; P trend < 0.001) for the non-staple food pattern and 0.690 (0.481, 0.989; P trend < 0.001) for the coffee and dairy pattern., Conclusions: The specific dietary patterns were associated with the risk of gastric cancer. This study has implications for the prevention of gastric cancer., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wu, Zhang, Guo, Wang, Fan, Zhang, Zhang, Wang, Fang and Wu.)

Frequency matching is commonly used in epidemiological case control studies to balance the distributions of the matching factors between the case and control groups and to improve the efficiency of case-control designs. Applied researchers have held a common opinion that unconditional logistic regression should be used to analyze frequency matched designs and conditional logistic regression is unnecessary. However, the justification of this view is unclear. To compare the performances of ULR and CLR in terms of simplicity, unbiasedness, and efficiency in a more intuitive way, we viewed frequency matching from the perspective of weighted sampling and derived the outcome models describing how the exposure and matching factors are associated with the outcome in the matched data separately in two scenarios: (1) only categorical variables are used for matching; (2) continuous variables are categorized for matching. In either scenario the derived outcome model is a logit model with stratum-specific intercepts. Correctly specified unconditional logistic regression can be more efficient than conditional logistic regression, particularly when continuous matching factors are used, whereas conditional logistic regression is a more practical approach because it is less dependent on modeling choices., (© 2022 John Wiley & Sons Ltd.)

Background: The current research aimed to expound the genes and pathways that are involved in coronary artery disease (CAD) and ischaemic stroke (IS) and the related mechanisms., Methods: Two array CAD datasets of (GSE66360 and GSE97320) and an array IS dataset (GSE22255) were downloaded. Differentially expressed genes (DEGs) were identified using the limma package. The online tool Database for Annotation, Visualization and Integrated Discovery (DAVID) (version 6.8; david.abcc.ncifcrf.gov) was used to annotate the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses of the DEGs. A protein-protein interaction (PPI) network was constructed by Cytoscape software, and then Molecular Complex Detection (MCODE) analysis was used to screen for hub genes. The hub genes were also confirmed by RT-qPCR and unconditional logistic regression analysis in our CAD and IS patients., Results: A total of 20 common DEGs (all upregulated) were identified between the CAD/IS and control groups. Eleven molecular functions, 3 cellular components, and 49 biological processes were confirmed by GO enrichment analysis, and the 20 common upregulated DEGs were enriched in 21 KEGG pathways. A PPI network including 24 nodes and 68 edges was constructed with the STRING online tool. After MCODE analysis, the top 5 high degree genes, including Jun proto-oncogene (JUN, degree = 9), C-X-C motif chemokine ligand 8 (CXCL8, degree = 9), tumour necrosis factor (TNF, degree = 9), suppressor of cytokine signalling 3 (SOCS3, degree = 8) and TNF alpha induced protein 3 (TNFAIP3, degree = 8) were noted. RT-qPCR results demonstrated that the expression levels of CXCL8 were increased in IS patients than in normal participants and the expression levels of SOCS3, TNF and TNFAIP were higher in CAD/IS patients than in normal participants. Meanwhile, unconditional logistic regression analysis revealed that the incidence of CAD or IS was positively correlated with the CXCL8, SOCS3, TNF and TNFAIP3., Conclusions: The CXCL8, TNF, SOCS3 and TNFAIP3 associated with inflammation may serve as biomarkers for the diagnosis of CAD or IS. The possible mechanisms may involve the Toll-like receptor, TNF, NF-kappa B, cytokine-cytokine receptor interactions and the NOD-like receptor signalling pathways.

Objective: To investigate the effects of physical activity (PA) on dyslipidemia and elevated resting heart rate (RHR) in a large-scale cross-sectional study in China., Methods: We recruited community-based individuals who were 40-60 years old using a cluster sampling method. The PA levels of the participants were classified as low, moderate, or high, using the International Physical Activity Questionnaire. Dyslipidemia was defined as the detection of abnormalities in lipid indicators, and 4 lipid parameters were evaluated using fasting blood samples. Multivariate logistic regression analyses were used to evaluate the associations of PA with dyslipidemia and RHR., Results: A total of 10,321 participants (38.88% men) were included in this study. The percentages of individuals with high, moderate, and low PA levels were 46.5%, 43.9%, and 9.6%, respectively. In both men and women, high PA provided odds ratios of 0.88 [95% confidence interval (CI): 0.83, 0.94] for dyslipidemia and 0.82 (95% CI: 0.73, 0.92) for elevated RHR, compared to participants with low PA., Conclusion: Our data suggested that substantial health benefits (related to dyslipidemia and elevated RHR) occurred at higher intensity PA, with greater energy consumption, in middle-aged Chinese people, and particularly in men., (Copyright © 2015 The Editorial Board of Biomedical and Environmental Sciences. Published by China CDC. All rights reserved.)