Of logistic and linear regression models, respectively, adjusted for age, sexOf logistic and linear regression

Of logistic and linear regression models, respectively, adjusted for age, sex
Of logistic and linear regression models, respectively, adjusted for age, sex, body weight, lipid-lowering medication (overall, statin, fibrates), purchase Tenapanor antihypertensive medication, antidiabetic medication as well as systemic corticoid intake.Pathway enrichment analysesannotations were considered. In case multiple probes mapped to one gene, the probe exhibiting the largest module membership was considered for downstream analyses. Pathway analyses were performed with IPA’s Core Analysis module. Both GO and IPA enrichment analyses are based on Fisher’s exact test.Construction of a multi-omics networkTo formally investigate whether the identified metabolite modules were enriched for specific biological pathways (super- and sub-pathways as described above, Table S1 in Additional file 2), weighted enrichment analyses were performed as applied before in different contexts [33,34]. Briefly, for each pathway c and module m, the enrichment statistics Scm was defined as the sum of module membership measures across all metabolites assigned to the respective pathway, whereas metabolites from other modules were assigned zero weight. Pathway assignment of all metabolites was randomly permuted 100,000 times, and enrichment statistics Scm(perm) were computed. Permutation P-values were then defined as the number of enrichment statistics Scm(perm) larger than the original Scm. For the gene expression modules, we explored enrichment for gene ontology (GO) terms using the R packages GO.db, version 2.9.0, AnnotationDbi, version 1.22.6, and org.Hs.eg.db, version 2.9.0. Furthermore, the commercial software Ingenuity Pathway Analysis (IPA) was applied to identify enriched canonical pathways (IPA build version 312825 M, content version 18841524, release date: 24 June 2014; analysis date: 4 July 2014 [35]). The reference set was restricted to genes represented on the IlluminaHT-12 v3 BeadChip, and only humanA partial correlation network was constructed from the modules significantly associated with BW as described in detail [36]. For each pair of modules, the partial correlation coefficient of the respective MEs was calculated as the Pearson’s correlation coefficient of the residuals of the two MEs with regard to all other MEs, as well as sex, age, body weight and BW. Since strong interrelationships among the modules might result in spurious negative partial correlations, pairwise marginal correlation (that is, the Pearson’s correlation, uncorrected for any other variables) was taken as a prerequisite. Effects on inter- and intra-module connectivity within the multi-omics network were studied as follows: interand intra-module connectivity was defined as the correlation of the MEs between two modules, and the average module membership strength (definition see above) across the module members of a certain module, respectively. PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/27532042 Significance of the difference in inter-/intra-module connectivity between the groups of weight gain and weight loss was determined through permutation testing (where weight change status was randomly shuffled). All statistical analyses were performed in R, version 3.0.1 [25].Results and discussion Using data from the population-based KORA S4/F4 cohort, we characterized the multi-omic signature associated with body weight change over a seven-year follow-up period. Two-platform serum metabolomics PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/28854080 and whole blood transcriptomics measurements were available from the follow-up examination F4 for 1,631 and 689 participants, respectively (Table 1). Clus.