Metabolomic Analysis of Renal Cell Carcinoma in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial.

Background: In the US in 2021, 76,080 kidney cancers are expected and >80% are renal cell carcinomas (RCCs). Along with excess fat, metabolic dysfunction is implicated in RCC etiology. To identify RCC-associated metabolites, we conducted a 1:1 matched case−control study nested within the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Methods: We measured 522 serum metabolites in 267 cases/control pairs. Cases were followed for a median 7.1 years from blood draw to diagnosis. Using conditional logistic regression, we computed adjusted odds ratios (ORs) and 95% confidence intervals (CIs) comparing risk between 90th and 10th percentiles of log metabolite intensity, with the significance threshold at a false discovery rate <0.20. Results: Four metabolites were inversely associated with risk of RCC during follow-up­C38:4 PI, C34:0 PC, C14:0 SM, and C16:1 SM (ORs ranging from 0.33−0.44). Two were positively associated with RCC risk­C3-DC-CH3 carnitine and C5 carnitine (ORs = 2.84 and 2.83, respectively). These results were robust when further adjusted for metabolic risk factors (body mass index (BMI), physical activity, diabetes/hypertension history). Metabolites associated with RCC had weak correlations (|r| < 0.2) with risk factors of BMI, physical activity, smoking, alcohol, and diabetes/hypertension history. In mutually adjusted models, three metabolites (C38:4 PI, C14:0 SM, and C3-DC-CH3 carnitine) were independently associated with RCC risk. Conclusions: Serum concentrations of six metabolites were associated with RCC risk, and three of these had independent associations from the mutually adjusted model. These metabolites may point toward new biological pathways of relevance to this malignancy.

A Pilot Feasibility Study of an Online Youth Tobacco Survey Administration among High School Students.

The COVID-19 pandemic restrictions forced many schools to shift to remote or hybrid learning, disrupting surveillance systems such as the New Jersey Youth Tobacco Survey, traditionally administered in schools by paper and pencil. In spring 2021, we conducted a feasibility study among a convenience sample of six public high schools to assess the use of an online survey to allow for remote participation. In each school, 4 to 6 classes were selected randomly, and all students within a sampled class were selected to participate in the survey. A total of 702 students completed surveys. School contacts were asked to provide qualitative feedback about the survey administration. Feedback was generally positive, with a few suggestions for improvement. Approximately 19% of students reported the ever use of e-cigarettes. Among current e-cigarette users, there was a shift in popularity from prefilled or refillable pods or cartridges (23.3%) to disposable e-cigarettes (53.5%). Less than 10% of current e-cigarette users reported using tobacco-flavored e-cigarettes, despite a statewide flavor ban on all other flavors.

COMETS Analytics: An Online Tool for Analyzing and Meta-Analyzing Metabolomics Data in Large Research Consortia.

Consortium-based research is crucial for producing reliable, high-quality findings, but existing tools for consortium studies have important drawbacks with respect to data protection, ease of deployment, and analytical rigor. To address these concerns, we developed COnsortium of METabolomics Studies (COMETS) Analytics to support and streamline consortium-based analyses of metabolomics and other -omics data. The application requires no specialized expertise and can be run locally to guarantee data protection or through a Web-based server for convenience and speed. Unlike other Web-based tools, COMETS Analytics enables standardized analyses to be run across all cohorts, using an algorithmic, reproducible approach to diagnose, document, and fix model issues. This eliminates the time-consuming and potentially error-prone step of manually customizing models by cohort, helping to accelerate consortium-based projects and enhancing analytical reproducibility. We demonstrated that the application scales well by performing 2 data analyses in 45 cohort studies that together comprised measurements of 4,647 metabolites in up to 134,742 participants. COMETS Analytics performed well in this test, as judged by the minimal errors that analysts had in preparing data inputs and the successful execution of all models attempted. As metabolomics gathers momentum among biomedical and epidemiologic researchers, COMETS Analytics may be a useful tool for facilitating large-scale consortium-based research.

A Metabolomics Analysis of Postmenopausal Breast Cancer Risk in the Cancer Prevention Study II.

Breast cancer is the most common cancer in women, but its incidence can only be partially explained through established risk factors. Our aim was to use metabolomics to identify novel risk factors for breast cancer and to validate recently reported metabolite-breast cancer findings. We measured levels of 1275 metabolites in prediagnostic serum in a nested case-control study of 782 postmenopausal breast cancer cases and 782 matched controls. Metabolomics analysis was performed by Metabolon Inc using ultra-performance liquid chromatography and a Q-Exactive high resolution/accurate mass spectrometer. Controls were matched by birth date, date of blood draw, and race/ethnicity. Odds ratios (ORs) and 95% confidence intervals (CIs) of breast cancer at the 90th versus 10th percentile (modeled on a continuous basis) of metabolite levels were estimated using conditional logistic regression, with adjustment for age. Twenty-four metabolites were significantly associated with breast cancer risk at a false discovery rate <0.20. For the nine metabolites positively associated with risk, the ORs ranged from 1.75 (95% CI: 1.29-2.36) to 1.45 (95% CI: 1.13-1.85), and for the 15 metabolites inversely associated with risk, ORs ranged from 0.59 (95% CI: 0.43-0.79) to 0.69 (95% CI: 0.55-0.87). These metabolites largely comprised carnitines, glycerolipids, and sex steroid metabolites. Associations for three sex steroid metabolites validated findings from recent studies and the remainder were novel. These findings contribute to growing data on metabolite-breast cancer associations by confirming prior findings and identifying novel leads for future validation efforts.

Identification of 102 Correlations between Serum Metabolites and Habitual Diet in a Metabolomics Study of the Prostate, Lung, Colorectal, and Ovarian Cancer Trial.

BACKGROUND: Metabolomics has proven useful for detecting objective biomarkers of diet that may help to improve dietary measurement. Studies to date, however, have focused on a relatively narrow set of lipid classes. OBJECTIVE: The aim of this study was to uncover candidate dietary biomarkers by identifying serum metabolites correlated with self-reported diet, particularly metabolites in underinvestigated lipid classes, e.g. triglycerides and plasmalogens. METHODS: We assessed dietary questionnaire data and serum metabolite correlations from 491 male and female participants aged 55-75 y in an exploratory cross-sectional study within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO). Self-reported intake was categorized into 50 foods, food groups, beverages, and supplements. We examined 522 identified metabolites using 2 metabolomics platforms (Broad Institute and Massachusetts General Hospital). Correlations were identified using partial Pearson's correlations adjusted for age, sex, BMI, smoking status, study site, and total energy intake [Bonferroni-corrected level of 0.05/(50 × 522) = 1.9 × 10-6]. We assessed prediction of dietary intake by multiple-metabolite linear models with the use of 10-fold crossvalidation least absolute shrinkage and selection operator (LASSO) regression. RESULTS: Eighteen foods, beverages, and supplements were correlated with ≥1 serum metabolite at the Bonferroni-corrected significance threshold, for a total of 102 correlations. Of these, only 5 have been reported previously, to our knowledge. Our strongest correlations were between citrus and proline betaine (r = 0.55), supplements and pantothenic acid (r = 0.46), and fish and C40:9 phosphatidylcholine (PC) (r = 0.35). The multivariate analysis similarly found reasonably large correlations between metabolite profiles and citrus (r = 0.59), supplements (r = 0.57), and fish (r = 0.44). CONCLUSIONS: Our study of PLCO participants identified many novel food-metabolite associations and replicated 5 previous associations. These candidate biomarkers of diet may help to complement measures of self-reported diet in nutritional epidemiology studies, though further validation work is still needed.

Weight Training and Risk of 10 Common Types of Cancer.

INTRODUCTION: Ample data support that leisure time aerobic moderate to vigorous physical activity (MVPA) is associated with lower risk of at least seven types of cancer. However, the link between muscle-strengthening activities and cancer etiology is not well understood. Our objective was to determine the association of weight lifting with incidence of 10 common cancer types. METHODS: We used multivariable Cox regression to estimate hazard ratios (HR) and 95% confidence intervals (CI) for association of weight lifting with incidence of 10 cancer types in the National Institutes of Health-American Association of Retired Persons Diet and Health Study follow-up. Weight lifting was modeled continuously and categorically. Dose-response relationships were evaluated using cubic restricted spline models. We explored whether associations varied by subgroups defined by sex, age, and body mass index using the Wald test for homogeneity. We examined joint categories of MVPA and weight lifting in relation to cancer risk for significant associations. RESULTS: After adjusting for all covariates including MVPA, we observed a statistically significant lower risk of colon cancer (Ptrend = 0.003) in individuals who weight lifted; the HR and 95% CI associated with low and high weight lifting as compared with no weight lifting were 0.75 (95% CI, 0.66-0.87) and 0.78 (95% CI, 0.61-0.98), respectively. The weight lifting-colon cancer relationship differed between men and women (any weight lifting vs no weight lifting: HRmen = 0.91; 95% CI, 0.84-0.98; HRwomen = 1.00; 95% CI, 0.93-1.08; Pinteraction = 0.008). A lower risk of kidney cancer among weight lifters was observed but became nonsignificant after adjusting for MVPA (Ptrend = 0.06), resulting in an HR of 0.94 (95% CI, 0.78-1.12) for low weight lifting and 0.80 (95% CI, 0.59-1.11) for high weight lifting. CONCLUSIONS: Participants who engaged in weight lifting had a significantly lower risk of colon cancer and a trend toward a lower risk of kidney cancer than participants who did not weight lift.