Margarita Cariolou

Summary: Background: Sugar sweetened beverages (SSBs), artificially sweetened beverages (ASBs), and fruit and vegetable juices are consumed worldwide, yet their associations with cancer remain unclear. Methods: Within World Cancer Research Fund International’s Global Cancer Update Programme (CUP Global), we conducted a systematic review by searching PubMed and Embase until September 2024 for cohort studies of SSBs, ASBs, and juices and cancer risk. Meta-analyses were conducted to calculate the relative risks (RRs) and 95% CIs per 1 serving/day (355 mL for SSBs/ASBs; 177 mL for juices). Evidence was graded by the CUP Global Expert Panel. The CUP Global standard protocol was registered at: https://osf.io/7utbm/ . Findings: We identified 158 publications from 51 cohorts. There was evidence of a probable causal positive association for SSBs, including carbonated SSBs, with pancreatic cancer incidence (RR 1.09 [95% CI 1.01–1.16]; I ² =8%, n=18 studies), and for SSBs with colorectal cancer incidence (RR 1.07 [95% CI 1.00–1.14]; I ² =41%, n=13). Limited suggestive evidence supported positive associations of SSBs with ovarian (RR 1.61 [95%CI 1.03–2.53]; I ² =0%, n=2), endometrial (RR 1.21 [95%CI 1.03-1.42]; I ² =0%, n=3), and postmenopausal breast cancer incidence (RR 1.05 [95%CI 1.00-1.10] ; I ² =0%, n=6), and of carbonated ASBs with leukaemia incidence (RR 1.29 [95%CI 1.01-1.64]; I ² =0%, n=2). There was evidence of a probable causal positive association for orange juice with different types of skin cancer including melanoma (RR 1.21 [95%CI 1.08-1.34]; I ² =0%, n=4), and basal (RR 1.12 [95%CI 1.06-1.17]; I ² =46%, n=2) and squamous cell carcinoma (RR 1.13 [95%CI 1.04-1.24]; I ² =0%, n=2). An interactive evidence platform is available at: https://teacup.cc.ic.ac.uk/soft-drinks-cancer.html . Interpretation: This review provides evidence supporting probable causal associations of SSBs with pancreatic and colorectal cancers, and of orange juice with skin cancers, with additional suggestive evidence for SSBs with other obesity-related cancers, extending concerns about sugary drink consumption beyond cardiometabolic health to cancer risk. Funding: World Cancer Research Fund network of charities (American Institute for Cancer Research; World Cancer Research Fund; Wereld Kanker Onderzoek Fonds).

Summary: Background: High levels of sedentary behaviour are an emerging global public health concern, but its impact on cancer risk remains unclear. Methods: Within the Global Cancer Update Programme (CUP Global), we systematically searched the literature in PubMed and Embase until September 2024 for observational cohort studies on sedentary behaviour and adult cancer risk. Using dose-response meta-analyses, we investigated sedentary behaviour domains (total, occupational, recreational, transportation, and/or other) and dimensions (duration, frequency), and additionally pooled across domains. The quality of evidence was graded by the CUP Global Expert Panel (protocol registration: https://osf.io/7utbm/ ). Findings: We identified 62 publications from 27 cohorts comprising 162,902 incident cancer cases across 19 anatomical sites. There was evidence for a probable causal positive association between sedentary time (mixed definitions) and breast (RR _{per 2 hours/day} =1.03; 95%CI=1.02–1.05; I ² =10%; n=11 studies) and colon (RR=1.05; 95%CI=1.03–1.07; I ² =0%; n=9) cancer risk, and between television watching time and colon cancer risk (RR _{per 2 hours/day} =1.08; 95%CI=1.05–1.11; I ² =0%; n=6). Limited suggestive evidence supported positive associations between sedentary time (mixed definitions) and lung (RR=1.04; 95%CI=1.00–1.09; I ² =69%; n=7), ovarian (RR=1.06; 95%CI=1.01–1.10; I ² =0%; n=7), premenopausal (RR=1.03; 95%CI=0.99–1.08; I ² =20%; n=7) and postmenopausal breast (RR=1.02; 95%CI=1.00–1.04; I ² =7%; n=11), and colorectal (RR=1.02; 95%CI=1.00–1.04; I ² =47%; n=11) cancers, and between occupational sitting time and breast (RR _{per 2 hours/day} =1.05; 95%CI=1.01–1.09; I ² =0%; n=4) and colon (RR=1.08; 95%CI=1.02–1.15; I ² =28%; n=2) cancers. An interactive evidence platform is available at: https://teacup.cc.ic.ac.uk/sedentary-behaviour-cancer.html . Interpretation: Evidence supports that prolonged sedentary behaviour is probably a cause of breast and colon cancers, while limited suggestive evidence supports positive associations for several other exposure–cancer pairs, including lung and ovarian cancers. This evidence should lead to revised cancer prevention recommendations. Future research should focus on device-based exposure assessments, repeated measurements, exposure substitution models, inclusion of diverse populations and investigation of biological mechanisms. Funding: World Cancer Research Fund network of charities (American Institute for Cancer Research; World Cancer Research Fund; Wereld Kanker Onderzoek Fonds).

Background: The 2018 World Cancer Research Fund (WCRF)/American Institute for Cancer Research Third Expert Report (TER) on diet, adiposity, physical activity and risk of 19 cancers could be enhanced with new data. A framework is needed to prioritize future systematic reviews. Methods: We searched PubMed (January 2019-February 2024) for meta-analyses, pooled analyses, randomized controlled trials (RCTs), Mendelian randomization (MR) studies, and large (>100,000 participants) cohort studies. We assessed TER findings using conditional power (CP) and fail-safe number (FSN) statistics. We developed an exposure-based prioritization score (PS) by awarding or subtracting points considering the quantity, statistical significance, direction, and novelty of associations. Results: We compared 366 meta-analyses, 121 pooled analyses, 19 RCTs, 174 MR studies, and 391 cohort studies covering 151 exposures and 28 cancers with 1,371 TER meta-analyses. Based on CP, non-significant TER associations likely to become significant with additional evidence included folate and colorectal, waist circumference and lung, total fat and ovarian, tea and ovarian, and red meat and kidney cancers. The FSN indicated that most significant TER associations are unlikely to change with additional evidence. The median PS was 6 (range: −15 to 163), with top scores observed for anthropometric measurements (PS _height =40 to PS _BMI =163), physical activity (PS=100), sedentary behavior (PS=64), alcohol (PS=52), tea (PS=36), dietary fiber (PS=31), milk/dairy (PS=29), micronutrients (PS _retinol =27 to PS _iron =38), vitamins (PS _B12 =22 to PS _vitD =91), soy (PS=24), isoflavones (PS=23), and sugar-sweetened beverages (PS=22). Conclusions and Impact: The prioritization framework can help identify impactful systematic reviews to complement TER conclusions and enhance our understanding of emerging research.

Restricted cubic spline analysis for waist-to-height ratio assessed close to diagnosis and all-cause, prostate cancer-specific and non-prostate cancer mortality.

Restricted cubic spline analysis for BMI assessed close to diagnosis and all-cause, prostate cancer–specific, and non–prostate cancer mortality. HRs from the Cox proportional hazards model with restricted cubic spline curves describing the association between BMI (kg/m2) collected before or after diagnosis combined and (A) all-cause mortality (deaths = 680), (B) prostate cancer–specific mortality (deaths = 331), and (C) non–prostate cancer mortality (deaths = 347); pre-diagnosis BMI and (D) all-cause mortality (deaths = 262), (E) prostate cancer–specific mortality (deaths = 124), and (F) non–prostate cancer mortality (deaths = 137); and post-diagnosis BMI and (G) all-cause mortality (deaths = 418; H), prostate cancer–specific mortality (deaths = 207), and (I) non–prostate cancer mortality (deaths = 210). HRs are based on the main model adjusted for age of diagnosis, year of diagnosis, smoking status, physical activity, sedentary activities, Townsend deprivation index, and alcohol intake frequency; knots at the 10th, 50th, and 90th percentiles of BMI. The median BMI of the individuals included in analyses was used as referent: 27.3 kg/m2 in the pre- or post-diagnosis analysis, 27.1 kg/m2 in the pre-diagnosis analysis, and 27.4 kg/m2 in the post-diagnosis analysis. The smooth density plot represents the density of the population across the spline variable.

Directed acyclic graph showing potential causal relations between the variables considered in the present study.

Cox proportional HRs and 95% CIs for the linear association between adiposity and prostate cancer–specific mortality (minimally versus main/fully adjusted model)

Restricted cubic spline analysis for waist circumference assessed close to diagnosis and all-cause, prostate cancer-specific and non-prostate cancer mortality.

Cox Proportional Hazard Ratios and 95%CIs for the linear association between adiposity close to diagnosis (pre- or post-diagnosis adiposity combined) and all-cause and prostate cancer-specific mortality in individuals with incident versus prevalent prostate cancer (fully adjusted/main model).

Relevant covariates available in UK Biobank database based on subject matter knowledge.