Background: Sugar-sweetened beverage (SSB) consumption may be associated with cancer mortality independent of, or indirectly through, established influences on increased body adiposity. Methods: We examined the associations of SSBs and artificially-sweetened beverages (ASB) with mortality from all-cancers combined, obesity-related cancers combined, and 20 cancer types, among men and women in the Cancer Prevention Study-II (CPS-II) prospective cohort. In 1982, 934,777 cancer-free participants provided information on usual SSB and ASB consumption. Deaths were identified through 2016. Multivariable Cox proportional hazards regression models examined associations of beverage types with cancer mortality, without and with BMI adjustment. Results: During follow-up, 135,093 CPS-II participants died from cancer. Consumption of ≥2 SSB drinks/day vs. never was not associated with all-cancer mortality, but was associated with increased risk of obesity-related cancers [HR, 1.05; 95% confidence intervals (CI), 1.01–1.08; Ptrend = 0.057], which became null after adjustment for BMI. SSBs were associated with increased mortality from colorectal (HR, 1.09; 95% CI, 1.02–1.17; Ptrend = 0.011), and kidney (HR, 1.17; 95% CI, 1.03–1.34; Ptrend = 0.056) cancers, which remained after BMI adjustment. A positive association of ASB consumption with obesity-related cancers (HR, 1.05; 95% CI, 1.01–1.08; Ptrend = 0.001) was null after controlling for BMI; however, an increased risk of pancreatic cancer was robust to BMI adjustment (HR, 1.11; 95% CI, 1.02–1.20; Ptrend < 0.008). Conclusions: SSB consumption was associated with higher mortality from certain cancers, partially mediated through obesity. Associations of ASB consumption and increased pancreatic cancer risk merit further study. Impact: Future research should consider the role of BMI in studies of sweetened beverages and cancer risk. These results should inform policy regarding sweetened beverage consumption.

Previous metabolomic studies have identified putative blood biomarkers of dietary intake. These biomarkers need to be replicated in other populations and tested for reproducibility over time for the potential use in future epidemiological studies. We conducted a metabolomics analysis among 671 racially/ethnically diverse men and women included in a diet validation study to examine the correlation between >100 food groups/items (101 by a food frequency questionnaire (FFQ), 105 by 24-h diet recalls (24HRs)) with 1141 metabolites measured in fasting plasma sample replicates, six months apart. Diet–metabolite associations were examined by Pearson’s partial correlation analysis. Biomarker reproducibility was assessed using intraclass correlation coefficients (ICCs). A total of 677 diet–metabolite associations were identified after Bonferroni adjustment for multiple comparisons and restricting absolute correlation coefficients to greater than 0.2 (601 associations using the FFQ and 395 using 24HRs). The median ICCs of the 238 putative biomarkers was 0.56 (interquartile range 0.46–0.68). In this study, with repeated FFQs, 24HRs and plasma metabolic profiles, we identified several potentially novel food biomarkers and replicated others found in our previous study. Our findings contribute to the growing literature on food-based biomarkers and provide important information on biomarker reproducibility which could facilitate their utilization in future nutritional epidemiological studies.

Untargeted metabolomic studies have identified potential biomarkers of colorectal cancer risk, but evidence is still limited and broadly inconsistent. Among 39,239 Cancer Prevention Study II Nutrition cohort participants who provided a blood sample between 1998–2001, 517 newly diagnosed colorectal cancers were identified through 30 June 2015. In this nested case–control study, controls were matched 1:1 to cases on age, sex, race and date of blood draw. Mass spectroscopy-based metabolomic analyses of pre-diagnostic plasma identified 886 named metabolites, after quality control exclusions. Conditional logistic regression models estimated multivariable-adjusted odds ratios (OR) and 95% confidence intervals (CI) for 1 standard deviation (SD) increase in each metabolite with risk of colorectal cancer. Six metabolites were associated with colorectal cancer risk at a false discovery rate < 0.20. These metabolites were of several classes, including cofactors and vitamins, nucleotides, xenobiotics, lipids and amino acids. Five metabolites (guanidinoacetate, 2’-O-methylcytidine, vanillylmandelate, bilirubin (E,E) and N-palmitoylglycine) were positively associated (OR per 1 SD = 1.29 to 1.32), and one (3-methylxanthine) was inversely associated with CRC risk (OR = 0.79, 95% CI, 0.69–0.89). We did not replicate findings from two earlier prospective studies of 250 cases each after adjusting for multiple comparisons. Large pooled prospective analyses are warranted to confirm or refute these findings and to discover and replicate metabolites associated with colorectal cancer risk.

Background: Few standardized and open-source tools exist for calculating dietary pattern indexes from dietary intake data in epidemiological and clinical studies. Miscalculations of dietary indexes, with suspected erroneous findings, are occasionally noted in the literature. Objective: The primary aim is to develop and validate dietaryindex, a user-friendly and versatile R package that standardizes the calculation of dietary indexes. Methods: Dietaryindex utilizes a two-step process: an initial calculation of serving size for each food and nutrient category, followed by the calculation of individual dietary indexes. It includes generic functions that accept any preprocessed serving sizes of food groups and nutrients, with the standard serving sizes defined according to the methodologies used in well-known prospective cohort studies. For ease of use, dietaryindex also offers one-step functions that directly reference common datasets and tools, including the National Health and Nutrition Examination Survey (NHANES) and Block Food Frequency Questionnaire, eliminating the need for data preprocessing. At least two independent researchers validated the serving size definitions and scoring algorithms of dietaryindex. Results: Dietaryindex can calculate multiple dietary indexes of high interest in research, including Healthy Eating Index (HEI) - 2020, Alternative Healthy Eating Index 2010, Dietary Approaches to Stop Hypertension Index, Alternate Mediterranean Diet Score, Dietary Inflammatory Index, American Cancer Society 2020 dietary index, and Planetary Health Diet Index from the EAT-Lancet Commission. In our validation process, dietaryindex demonstrated full accuracy (100%) in all generic functions with two-decimal rounding precision in comparison to hand-calculated results. Similarly, using NHANES 2017-2018 data and ASA24 and DHQ3 example data, the HEI2015 outputs from dietaryindex aligned (99.95% - 100%) with results using the SAS codes from the National Cancer Institute. Conclusions: Dietaryindex is a user-friendly, versatile, and validated informatics tool for standardized dietary index calculations. We have open-sourced all the validation files and codes with detailed tutorials on GitHub ( https://github.com/jamesjiadazhan/dietaryindex ).

Previous cross-sectional metabolomics studies have identified many potential dietary biomarkers, mostly in blood. Few studies examined urine samples although urine is preferred for dietary biomarker discovery. Furthermore, little is known regarding the reproducibility of urinary metabolomic biomarkers over time. We aimed to identify urinary metabolomic biomarkers of diet and assess their reproducibility over time. We conducted a metabolomics analysis among 648 racially/ethnically diverse men and women in the Diet Assessment Sub-study of the Cancer Prevention Study-3 cohort to examine the correlation between >100 food groups/items [101 by a food frequency questionnaire (FFQ), and 105 by repeated 24 h diet recalls (24HRs)] and 1391 metabolites measured in 24 h urine sample replicates, six months apart. Diet–metabolite associations were examined by Pearson’s partial correlation analysis. Biomarkers were evaluated for prediction accuracy assessed using area under the curve (AUC) calculated from the receiver operating characteristic curve and for reproducibility assessed using intraclass correlation coefficients (ICCs). A total of 1708 diet–metabolite associations were identified after Bonferroni correction for multiple comparisons and restricting correlation coefficients to >0.2 or <−0.2 (1570 associations using the FFQ and 933 using 24HRs), 513 unique metabolites correlated with 79 food groups/items. The median ICCs of the 513 putative biomarkers was 0.53 (interquartile range 0.42–0.62). In this study, with comprehensive dietary data and repeated 24 h urinary metabolic profiles, we identified a large number of diet–metabolite correlations and replicated many found in previous studies. Our findings revealed the promise of urine samples for dietary biomarker discovery in a large cohort study and provide important information on biomarker reproducibility, which could facilitate their utilization in future clinical and epidemiological studies.