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Identifying metabolite-disease associations is of paramount significance. With the advancement of research, computational methods have surpassed traditional experiments in efficiency. Nevertheless, current computational methods often overlook the integration of multiomics data, and the performance of the predictive models used is limited. To address these limitations, we propose the SVMBN algorithm for predicting metabolite-disease associations. The proposed approach involves the following steps: First, six similarity calculation methods are employed to construct the metabolite similarity network and the disease similarity network separately. Second, the metabolite and disease similarity networks are combined to obtain the original link features. Third, nonnegative Matrix Factorization (NMF) is applied to extract effective features from the original features, thereby reducing noise. Finally, Support Vector Machine (SVM) is utilized to predict potential associations between metabolites and diseases. Experimental results demonstrate that the SVMBN algorithm achieves an average AUC of 0.98 in 5-fold cross-validation, indicating its superiority over other methods. Furthermore, case studies prove that the SVMBN algorithm can accurately forecast the relationships between metabolites and diseases. [ABSTRACT FROM AUTHOR]

Background: Former research has emphasized a correlation between lung cancer (LC) and sepsis, but the causative link remains unclear., Method: This study used univariate Mendelian Randomization (MR) to explore the causal relationship between LC, its subtypes, and sepsis. Linkage Disequilibrium Score (LDSC) regression was used to calculate genetic correlations. Multivariate MR was applied to investigate the role of seven confounding factors. The primary method utilized was inverse-variance-weighted (IVW), supplemented by sensitivity analyses to assess directionality, heterogeneity, and result robustness., Results: LDSC analysis revealed a significant genetic correlation between LC and sepsis (genetic correlation = 0.325, p = 0.014). Following false discovery rate (FDR) correction, strong evidence suggested that genetically predicted LC (OR = 1.172, 95% CI 1.083-1.269, p = 8.29 × 10 -5 , P fdr = 2.49 × 10 -4 ), squamous cell lung carcinoma (OR = 1.098, 95% CI 1.021-1.181, p = 0.012, P fdr = 0.012), and lung adenocarcinoma (OR = 1.098, 95% CI 1.024-1.178, p = 0.009, P fdr = 0.012) are linked to an increased incidence of sepsis. Suggestive evidence was also found for small cell lung carcinoma (Wald ratio: OR = 1.156, 95% CI 1.047-1.277, p = 0.004) in relation to sepsis. The multivariate MR suggested that the partial impact of all LC subtypes on sepsis might be mediated through body mass index. Reverse analysis did not find a causal relationship ( p > 0.05 and P fdr > 0.05)., Conclusion: The study suggests a causative link between LC and increased sepsis risk, underscoring the need for integrated sepsis management in LC patients., 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 © 2024 Zhou, Zhang, Yang, Zhang, Li, Li, Peng and Chen.)

Background: Several studies have found that erectile dysfunction (ED) is associated with interstitial lung disease. However, the causal relationship between idiopathic pulmonary fibrosis (IPF) and ED risk remains unclear. The present two-sample Mendelian randomization (MR) study aimed to reveal the causal effect of IPF on ED risk., Methods: This study included two GWAS summary statistics of IPF (1,028 cases and 196,986 controls) and ED (6,175 cases and 217,630 controls) of European ancestry. The inverse-variance weighted (IVW) was applied as the primary method, and MR-Egger, weighted median, weighted mode, and simple mode were applied as complementary methods to estimate the causal impact of IPF on ED risk. The MR-PRESSO global test and MR-Egger regression were applied to evaluate the pleiotropy. The Cochran's Q test was applied to examine heterogeneity. The leave-one-out analysis ensured the robustness and reliability of the results., Results: Twenty-one genetic variants were obtained as IPF instrumental variables without pleiotropy and heterogeneity. MR analysis using the IVW showed a potential causal relationship between IPF and increased ED risk (OR IVW  = 1.046, 95% CI: 1.020-1.073, p  = 0.001), and consistent results were obtained with MR-Egger, weighted median, and weighted mode. The leave-one-out analysis showed that no instrumental variables unduly influenced the results., Conclusion: This study suggested that IPF may increase the ED risk of the European population., 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 Zhang, Zhou, Li and Chen.)

GABA, a prominent inhibitory neurotransmitter, is best known to regulate neuronal functions in the nervous system. However, much less is known about the role of GABA signaling in other physiological processes. Interestingly, recent work showed that GABA signaling can regulate life span via a metabotropic GABA B receptor in Caenorhabditis elegans . However, the role of other types of GABA receptors in life span has not been clearly defined. It is also unclear whether GABA signaling regulates health span. Here, using C. elegans as a model, we systematically interrogated the role of various GABA receptors in both life span and health span. We find that mutations in four different GABA receptors extend health span by promoting resistance to stress and pathogen infection and that two such receptor mutants also show extended life span. Different GABA receptors engage distinct transcriptional factors to regulate life span and health span, and even the same receptor regulates life span and health span via different transcription factors. Our results uncover a novel, profound role of GABA signaling in aging in C. elegans , which is mediated by different GABA receptors coupled to distinct downstream effectors.