Matthew A Lee

Adiposity distribution and risks of 12 obesity-related cancers: a Mendelian randomization analysis

Abstract Introduction There is convincing evidence that overall adiposity increases the risks of several cancers. Whether the distribution of adiposity plays a similar role is unclear. Methods We used 2-sample Mendelian randomization (MR) to examine causal relationships of 5 adiposity distribution traits (abdominal subcutaneous adipose tissue (ASAT); visceral adipose tissue (VAT); gluteofemoral adipose tissue (GFAT); liver fat; and pancreas fat) with the risks of 12 obesity-related cancers (endometrial, ovarian, breast, colorectal, pancreas, multiple myeloma, liver, kidney (renal cell), thyroid, gallbladder, esophageal adenocarcinoma, and meningioma). Results Sample size across all genome-wide association studies (GWAS) ranged from 8407 to 728 896 (median: 57 249). We found evidence that higher genetically predicted ASAT increased the risks of endometrial cancer, liver cancer, and esophageal adenocarcinoma (odds ratios (OR) and 95% confidence intervals (CI) per standard deviation (SD) higher ASAT = 1.79 (1.18 to 2.71), 3.83 (1.39 to 10.53), and 2.34 (1.15 to 4.78), respectively). Conversely, we found evidence that higher genetically predicted GFAT decreased the risks of breast cancer and meningioma (ORs and 95% CIs per SD higher genetically predicted GFAT = 0.77 (0.62 to 0.97) and 0.53 (0.32 to 0.90), respectively). We also found evidence for an effect of higher genetically predicted VAT and liver fat on increased liver cancer risk (ORs and 95% CIs per SD higher genetically predicted adiposity trait = 4.29 (1.41 to 13.07) and 4.09 (2.29 to 7.28), respectively). Discussion Our analyses provide novel insights into the relationship between adiposity distribution and cancer risk. These insights highlight the potential importance of adipose tissue distribution alongside maintaining a healthy weight for cancer prevention.

The Effect of Circulating Proteins and Their Role in Mediating Adiposity’s Effect on Endometrial Cancer Risk: Mendelian Randomization and Colocalization Analyses

Abstract Background: Proteomics could enhance our understanding of endometrial carcinogenesis. However, addressing confounding in traditional observational studies remains challenging, especially given the strong impact of adiposity on the plasma proteome and endometrial cancer risk. Methods: Using Mendelian randomization (MR) and colocalization analyses, we examined the causal association between 2,751 unique proteins from the UK Biobank (N proteins = 2,031; N = 52,363) and deCODE (N proteins = 1,667; N = 35,559) with endometrial cancer risk [overall (N cases = 12,270; N controls = 46,126), endometrioid (N cases = 8,758), and nonendometrioid (N cases = 1,230)]. We performed enrichment analyses to explore pathways overrepresented among plasma proteins in endometrioid and nonendometrioid cancer subtypes. We assessed whether circulating proteins mediated the effect of body mass index on endometrial cancer risk using uni- and multivariable MR. Results: Twenty proteins were associated with endometrial cancer risk in MR and colocalization analyses. GSTO1-1 and SKAP1 were positively and MMP10 was negatively associated with overall and endometrioid endometrial cancer; DTYMK and ABO were positively and TSSC4 was negatively associated with overall endometrial cancer; IGF2R was positively associated with endometrioid cancer; and MAPK9 was positively and DNAJB14, IFI16, LCN2, and SCT were negatively associated with nonendometrioid endometrial cancer. Distinct pathways were overrepresented in endometrioid (e.g., platelet-derived growth factor signaling and PTEN gene regulation) and nonendometrioid (e.g., noncanonical NF-κB signaling) cancer subtypes. There was weak evidence of associated proteins mediating the relationship between body mass index and endometrial cancer risk. Conclusions: We identified distinct plasma proteins and pathways associated with endometrioid and nonendometrioid endometrial cancer risk. Impact: Prioritized proteins may support noninvasive methods to differentiate endometrial cancer subtypes.