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.

Objective: To investigate associations of a oxidative balance score (OBS) with blood levels of total cholesterol, low-density lipoprotein-(LDL)-cholesterol, high-density lipoprotein-(HDL) cholesterol and triglycerides, and biomarkers of inflammation (serum C-reactive protein [CRP], albumin and venous total white blood cell [WBC] counts) among 19,825 participants in a nationwide study. Methods: Using cross-sectional data 14 dietary and lifestyle components were incorporated into the OBS and the resulting score (range 3–26) was then divided into five equal intervals. Multivariable-adjusted odds ratios (ORs) for abnormal biomarker levels and 95% confidence intervals (CIs) were calculated using logistic regression models. Results: The ORs (95% CIs) comparing those in the highest relative to those in the lowest OBS equal interval categories were 0.50 (0.38–0.66) for CRP, 0.50 (0.36–0.71) for the total WBC count, and 0.75 (0.58–0.98) for LDL-cholesterol; all three p-values for trend were <0.001. The OBS-HDL-cholesterol association was statistically significantly inverse among females, but not among males. The OBS was not associated with serum albumin or triglycerides. Conclusion: Our findings suggest that an OBS may be associated with some, but not all, circulating lipids/lipoproteins and biomarkers of inflammation.

Over-the-counter analgesic use is common and is typically assessed through self-report; therefore, it is subject to misclassification. Detection of drug metabolites in biofluids offers a viable tool for validating self-reported analgesic use. Thus, the aim of this study was to determine the utility of a metabolomics approach for the validation of acetaminophen and ibuprofen use in blood samples. Untargeted mass spectrometry-based metabolomics analysis was conducted in serum samples from 1547 women and plasma samples from 556 men. The presence of two metabolites each for acetaminophen and ibuprofen at levels at or above a defined cutoff value was used to determine concordance with self-reported use. For acetaminophen use based on the presence of both acetaminophen and acetamidophenylglucuronide, concordance was 98.5⁻100% among individuals reporting use today, and 79.8⁻91.4% for those reporting never or rare use. Ibuprofen use based on the presence of both carboxyibuprofen and hydroxyibuprofen resulted in concordance of 51.3⁻52.5% for individuals reporting use today and 99.4⁻100% for those reporting never or rare use. Our findings suggest that an untargeted metabolomics approach in blood samples may be useful for validating self-reported acetaminophen use. However, this approach appears unlikely to be suitable for validating ibuprofen use.

Background: To evaluate whether circulating proteins are associated with survival after lung cancer diagnosis, and whether they can improve prediction of prognosis. Methods: We measured up to 1159 proteins in blood samples from 708 participants in 6 cohorts. Samples were collected within 3 years prior to lung cancer diagnosis. We used Cox proportional hazards models to identify proteins associated with overall mortality after lung cancer diagnosis. To evaluate model performance, we used a round-robin approach in which models were fit in 5 cohorts and evaluated in the 6th cohort. Specifically, we fit a model including 5 proteins and clinical parameters and compared its performance with clinical parameters only. Findings: There were 86 proteins nominally associated with mortality (p < 0.05), but only CDCP1 remained statistically significant after accounting for multiple testing (hazard ratio per standard deviation: 1.19, 95% CI: 1.10–1.30, unadjusted p = 0.00004). The external C-index for the protein-based model was 0.63 (95% CI: 0.61–0.66), compared with 0.62 (95% CI: 0.59–0.64) for the model with clinical parameters only. Inclusion of proteins did not provide a statistically significant improvement in discrimination (C-index difference: 0.015, 95% CI: −0.003 to 0.035). Interpretation: Blood proteins measured within 3 years prior to lung cancer diagnosis were not strongly associated with lung cancer survival, nor did they importantly improve prediction of prognosis beyond clinical information. Funding: No explicit funding for this study. Authors and data collection supported by the US National Cancer Institute ( U19CA203654), INCA (France, 2019-1-TABAC-01), Cancer Research Foundation of Northern Sweden ( AMP19-962), and Swedish Department of Health Ministry.

Background: Cannabis use is increasing, including among smokers, an at-risk population for cancer. Research is equivocal on whether using cannabis inhibits quitting cigarettes. The current longitudinal study investigated associations between smoking cannabis and subsequently quitting cigarettes. Methods: Participants were 4,535 adult cigarette smokers from a cohort enrolled in the American Cancer Society’s Cancer Prevention Study-3 in 2009–2013. Cigarette quitting was assessed on a follow-up survey in 2015–2017, an average of 3.1 years later. Rates of quitting cigarettes at follow-up were examined by retrospectively assessed baseline cannabis smoking status (never, former, recent), and by frequency of cannabis smoking among recent cannabis smokers (low: ≤3 days/month; medium: 4–19 days/month; high: ≥20 days/month). Logistic regression models adjusted for sociodemographic factors, smoking- and health-related behaviors, and time between baseline and follow-up. Results: Adjusted cigarette quitting rates at follow-up did not differ significantly by baseline cannabis smoking status [never 36.2%, 95% confidence interval (CI), 34.5–37.8; former 34.1%, CI, 31.4–37.0; recent 33.6%, CI, 30.1–37.3], nor by frequency of cannabis smoking (low 31.4%, CI, 25.6–37.3; moderate 36.7%, CI, 30.7–42.3; high 34.4%, CI, 28.3–40.2) among recent baseline cannabis smokers. In cross-sectional analyses conducted at follow-up, the proportion of cigarette smokers intending to quit smoking cigarettes in the next 30 days did not differ by cannabis smoking status (P ¼ 0.83). Conclusions: Results do not support the hypothesis that cannabis smoking inhibits quitting cigarette smoking among adults. Impact: Future longitudinal research should include follow-ups of >1 year, and assess effects of intensity/frequency of cannabis use and motivation to quit on smoking cessation.

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 compar-isons 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.

Purpose: Oxidative stress is defined as an imbalance between pro-oxidants and antioxidants. Previous research found that a single comprehensive oxidative balance score (OBS) that includes individual pro- and anti-oxidant exposures may be associated with various conditions (including prostate cancer) in the absence of associations with the individual factors. We investigated an OBS-incident prostate cancer association among 43,325 men in the Cancer Prevention Study II Nutrition Cohort. Methods: From 1999-2007, 3386 incident cases were identified. Twenty different components, used in two ways (unweighted or weighted based on literature reviews), were incorporated into the OBS, and the resulting scores were then expressed as three types of variables (continuous, quartiles, or six equal intervals). Multivariable-adjusted rate ratios were calculated using Cox proportional hazards models. Results: We hypothesized that the OBS would be inversely associated with prostate cancer risk; however, the rate ratios (95% confidence intervals) comparing the highest with the lowest OBS categories ranged from 1.17 (1.04-1.32) to 1.39 (0.90-2.15) for all cases, 1.14 (0.87-1.50) to 1.59 (0.57-4.40) for aggressive disease (American Joint Committee on Cancer stage III/IV or Gleason score 8-10), and 0.91 (0.62-1.35) to 1.02 (1.02-1.04) for nonaggressive disease. Conclusions: Our findings are not consistent with the hypothesis that oxidative balance-related exposures collectively affect risk for prostate cancer.

Context: Oxidative balance score (OBS) is a composite measure of multiple pro- and antioxidant exposures. Objective: To investigate associations of OBS with F2-isoprostanes (FIP), mitochondrial DNA copy number (mtDNA), and fluorescent oxidative products (FOP), and assess inter-relationships among the biomarkers. Methods: In a cross-sectional study, associations of a thirteen-component OBS with biomarker levels were assessed using multivariable regression models. Results: Association of OBS with FIP, but not with FOP, was in the hypothesized direction. The results for mtDNA were unstable and analysis-dependent. The three biomarkers were not inter-correlated. Conclusions: Different biomarkers of oxidative stress may reflect different biological processes.

Persons of African ancestry (AA) have a twofold higher risk for multiple myeloma (MM) compared with persons of European ancestry (EA). Genome-wide association studies (GWASs) support a genetic contribution to MM etiology in individuals of EA. Little is known about genetic risk factors for MM in individuals of AA. We performed a meta-analysis of 2 GWASs ofMMin 1813 cases and 8871 controls and conducted an admixture mapping scan to identify risk alleles. We fine-mapped the 23 known susceptibility loci to find markers that could better capture MM risk in individuals of AA and constructed a polygenic risk score (PRS) to assess the aggregated effect of known MM risk alleles. In GWAS meta-analysis, we identified 2 suggestive novel loci located at 9p24.3 and 9p13.1 at P < 1 × 10-6; however, no genome-wide significant association was noted. In admixture mapping, we observed a genome-wide significant inverse association between local AA at 2p24.1-23.1 and MM risk in AA individuals. Of the 23 known EA risk variants, 20 showed directional consistency, and 9 replicated at P < .05 in AA individuals. In 8 regions, we identified markers that better captureMMrisk in persons with AA. AA individuals with a PRS in the top 10% had a 1.82-fold (95% confidence interval, 1.56-2.11) increased MM risk compared with those with average risk (25%-75%). The strongest functional association was between the risk allele for variant rs56219066 at 5q15 and lower ELL2 expression (P = 5.1 × 10-12). Our study shows that common genetic variation contributes to MM risk in individuals with AA.