Anthropometry, body fat composition and reproductive factors and risk of oesophageal and gastric cancer by subtype and subsite in the UK Biobank cohort

Abstract

Background Obesity has been positively associated with upper gastrointestinal cancers, but prospective data by subtype/subsite are limited. Obesity influences hormonal factors, which may play a role in these cancers. We examined anthropometry, body fat and reproductive factors in relation to oesophageal and gastric cancer by subtype/subsite in the UK Biobank cohort. Methods Among 458,713 UK Biobank participants, 339 oesophageal adenocarcinomas, 124 oesophageal squamous cell carcinomas, 137 gastric cardia and 92 gastric non-cardia cancers were diagnosed during a mean of 6.5 years follow-up. Cox models estimated multivariable hazard ratios (HRs) and 95% confidence intervals (CIs). Results Body mass index (BMI), hip circumference, waist circumference, waist-to-hip ratio, waist-toheight ratio, total body fat and trunk fat were positively associated with oesophageal adenocarcinoma (highest vs lowest category:

A Body Shape Index (ABSI), hip index, and risk of cancer in the UK Biobank cohort

“…The major differences between our findings and previous reports concern associations between body shape indices and cancers associated strongly with BMI 3 . While published associations of WC and HC unadjusted for BMI resemble associations with BMI, that is, strongly positive for obesity‐related cancers of the endometrium, 18 post‐menopausal breast 19 or pancreas 20 but inverse for esophageal SCC in women 21 or prostate cancer in men, 22 we did not find corresponding associations with ABSI or HI, independent of BMI. As regional body dimensions are strongly correlated with overall body size, risk estimates based on traditional body shape indices examined individually are influenced by associations with BMI.…”

“…The main compatibilities between our findings and previous reports concern cancers associated more strongly with waist size. Thus, positive associations based on WC or WHR, with or without adjustment for BMI and in some studies for HC, have previously been reported for cancers of the head and neck, 23 , 24 , 25 esophagus and gastric cardia (adenocarcinoma), 21 , 24 , 26 colon, 24 , 27 , 28 liver, 24 , 29 lung overall, 30 and bladder (men). 24 , 31 , 32 Nevertheless, while we observed comparable associations with ABSI and WHI, previous studies have often reported positive associations with WC but not with WHR adjusted for BMI.…”

Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study

“…Encompassing non-traditional health data, including anthropometric measurements and lifestyle insights, allows for the assessment of commonly overlooked, yet easily collectable, variables to supplement the already-known clinical factors. The ability to capture a deeper phenotype of the individual prior to infection has proved integral to the model’s performance, in line with other disease-specific prediction models developed on the UKB 27–29 . Notably, we identified baseline waist circumference, height, weight, and hip circumference to be valuable independent of BMI and obesity, accounting for four of the top-seven RF-ranked features ( Supplementary Figure 3 ).…”

Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study

“…Encompassing non-traditional health data, including anthropometric measurements and lifestyle insights, allows for the assessment of commonly overlooked, yet easily collectable, variables to supplement the already-known clinical factors. The ability to capture a deeper phenotype of the individual prior to infection has proved integral to the model's performance, in line with other disease-specific prediction models developed on the UKB [31][32][33] . Notably, we identified baseline waist circumference, height, weight, and hip circumference to be valuable independent of BMI and obesity, accounting for four of the top-seven RF-ranked features (Supplementary Fig.…”