Abstract Background Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations. Methods We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested...

Additional file 2: Supplementary tables and figures. Figure S1. Pearson correlation between the 117 original metabolites. Figure S2. Sensitivity analyses of mutually adjusted ORs for the overall associations and cancer type-specific deviations. Figure S3. Sensitivity analysis of mutually adjusted ORs for the overall associations and cancer type-specific deviations with or without excluding hormone users. Figure S4. p-values of tests for departure from linearity and effect modification by BMI. Figure S5. ORs for the overall associations...

Type 2 diabetes (T2D) is a global public health challenge. Whilst the advent of genome-wide association studies has identified >400 genetic variants associated with T2D, our understanding of its biological mechanisms and translational insights is still limited. The EPIC-InterAct project, centred in 8 countries in the European Prospective Investigations into Cancer and Nutrition study, is one of the largest prospective studies of T2D. Established as a nested case-cohort study to investigate the interplay between genetic...

Additional file 1. Supplementary material regarding (i) the definition of cancer cases for HCC, GBC, Adv.PrC and Loc.PrC; (ii) the definition and implementation of the data-shared lasso; (iii) the models used to derive point estimates and confidence intervals from the model selected by the data-shared lasso; and (iv) the univariate analysis conducted for comparison.

Additional file 1. Supplementary material regarding (i) the definition of cancer cases for HCC, GBC, Adv.PrC and Loc.PrC; (ii) the definition and implementation of the data-shared lasso; (iii) the models used to derive point estimates and confidence intervals from the model selected by the data-shared lasso; and (iv) the univariate analysis conducted for comparison.

Abstract Background Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations. Methods We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested...

Additional file 2: Supplementary tables and figures. Figure S1. Pearson correlation between the 117 original metabolites. Figure S2. Sensitivity analyses of mutually adjusted ORs for the overall associations and cancer type-specific deviations. Figure S3. Sensitivity analysis of mutually adjusted ORs for the overall associations and cancer type-specific deviations with or without excluding hormone users. Figure S4. p-values of tests for departure from linearity and effect modification by BMI. Figure S5. ORs for the overall associations...