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Past patterns of cancer disease and future changes in the demographic structure have a major influence on the projected incidences of human malignancies. In Germany, nearly a quarter of men and 20% of women die of cancer, and it is estimated that in Germany around 51% men and 43% women will develop cancer during lifetime. Here, we project the cancer incidence case number as well as the number of deaths for the most common cancers in the German population for the years 2020 and 2030. By 2030, prostate cancer will be the most common malignancy, surpassing breast cancer. Lung cancer will rank third most frequent cancer and will remain the most common cause of cancer‐related mortality. Additionally, our projections show a marked increase in liver cancer cases with a continuous rise in liver cancer‐related deaths. Finally, we project a constant increase in the incidence of pancreatic cancer. Based on our projections, pancreatic cancer will surpass colorectal and breast cancer to rank as the second most common cause of cancer‐related deaths in Germany by 2030.

The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co‐segregation, family cancer history profile, co‐occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case‐control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene‐specific calibration of evidence types used for variant classification.

OBJECTIVEMetformin is used as a first-line oral treatment for type 2 diabetes (T2D). However, the underlying mechanism is not fully understood. Here, we aimed to comprehensively investigate the pleiotropic effects of metformin. RESEARCH DESIGN AND METHODSWe analyzed both metabolomic and genomic data of the population-based KORA cohort. To evaluate the effect of metformin treatment on metabolite concentrations, we quantified 131 metabolites in fasting serum samples and used multivariable linear regression models in three independent cross-sectional studies (n = 151 patients with T2D treated with metformin [mt-T2D]). Additionally, we used linear mixed-effect models to study the longitudinal KORA samples (n = 912) and performed mediation analyses to investigate the effects of metformin intake on blood lipid profiles. We combined genotyping data with the identified metforminassociated metabolites in KORA individuals (n = 1,809) and explored the underlying pathways. RESULTSWe found significantly lower (P < 5.0E-06) concentrations of three metabolites (acyl-alkyl phosphatidylcholines [PCs]) when comparing mt-T2D with four control groups who were not using glucose-lowering oral medication. These findings were controlled for conventional risk factors of T2D and replicated in two independent studies. Furthermore, we observed that the levels of these metabolites decreased significantly in patients after they started metformin treatment during 7 years' follow-up. The reduction of these metabolites was also associated with a lowered blood level of LDL cholesterol (LDL-C). Variations of these three metabolites were significantly associated with 17 genes (including FADS1 and FADS2) and controlled by AMPK, a metformin target. CONCLUSIONSOur results indicate that metformin intake activates AMPK and consequently suppresses FADS, which leads to reduced levels of the three acyl-alkyl PCs and LDL-C. Our findings suggest potential beneficial effects of metformin in the prevention of cardiovascular disease.Type 2 diabetes (T2D) is a chronic disease with diminished response to insulin and relative insulin deficiency (1). Patients with T2D mostly take metformin as first-line oral treatment to lower their glucose levels and to improve insulin sensitivity (2). Despite metformin's use as an antihyperglycemic agent for more than 50 years, its