Untargeted Metabolomics Identifies Novel Potential Biomarkers of Habitual Food Intake in a Cross-Sectional Study of Postmenopausal Women

Untargeted Metabolomics Identifies Novel Potential Biomarkers of Habitual Food Intake in a... Abstract Background Recent studies suggest that untargeted metabolomics is a promising tool to identify novel biomarkers of individual foods. However, few large cross-sectional studies with comprehensive data on habitual diet and circulating metabolites have been conducted. Objective We aimed to identify potential food biomarkers and evaluate their predictive accuracy. Methods We conducted a cross-sectional analysis of consumption of 91 food groups or items, assessed by a 152-item food-frequency questionnaire, in relation to 1186 serum metabolites measured by mass spectrometry-based platforms from 1369 nonsmoking postmenopausal women (mean age = 68.3 y). Diet-metabolite associations were selected by Pearson's partial correlation analysis (P < 4.63 × 10−7, |r| > 0.2). The predictive accuracy of the selected food metabolites was evaluated from the area under the curve (AUC) calculated from receiver operating characteristic analysis conducted among women in the top and bottom quintiles of dietary intake. Results We identified 379 diet-metabolite associations. Forty-two food groups or items were correlated with 199 serum metabolites. We replicated 63 metabolites as biomarkers of habitual food intake reported in previous cross-sectional studies. Among those not previously shown to be associated with habitual diet, several are biologically plausible and were reported in acute feeding studies including: banana and dopamine 3-O-sulfate (r = 0.34, AUC = 76%) and dopamine 4-O-sulfate (r = 0.33, AUC = 74%), garlic and alliin (r = 0.24, AUC = 69%), N-acetylalliin (r = 0.27, AUC = 70%), and S-allylcysteine (r = 0.23, AUC = 69). Two unannotated metabolites were the strongest predictors for dark fish (X-02269, r = 0.51, AUC = 94%) and coffee intake (X-21442, r = 0.62, AUC = 98%). Conclusion In this comprehensive, cross-sectional analysis of habitual food intake and serum metabolites among postmenopausal women, we identified several potentially novel food biomarkers and replicated others. Our findings contribute to the limited literature on food-based biomarkers and highlight the significant and promising role that large cohort studies with archived blood samples could play in this field. This study was registered at clinicaltrials.gov as NCT03282812. diet, food biomarker, food frequency questionnaire, serum, untargeted metabolomics Introduction Nutritional epidemiologic studies primarily rely on self-reported dietary data which are subject to measurement error that may contribute to the inconsistent diet-disease associations (1). Dietary biomarkers are objective measures of diet and thus are not subject to self-reported measurement error. However, few reliable dietary biomarkers exist and they are primarily nutrient-based, such as 24-h urine nitrogen, potassium, and sodium as recovery biomarkers for protein, potassium, and sodium intakes, respectively (2–4). Additional objective dietary biomarkers, especially those correlated with food intake, are needed to advance understanding of the role of diet in disease risk. The rapid development of metabolomics technology in the past decade has enabled scientists to measure thousands of small molecules in human biofluids, cells, and tissues (5). The food metabolome (i.e., metabolites derived from foods and food constituents) is a promising resource to discover novel food biomarkers (6). Acute feeding studies and short- to medium-term intervention studies have identified several putative food biomarkers (6, 7). However, these studies only focus on one or a few specific type(s) of foods and reveal no information on other dietary origins of the identified biomarkers (6). In contrast, cross-sectional studies provide the advantage of examining multiple foods and other dietary sources simultaneously. Further, dietary data collected in large-scale observational studies reflect the wider distribution of food intakes in the population, whereas intervention studies usually only compare a limited number of exposure levels. Most large prospective cohort studies, including the Cancer Prevention Study-II (CPS-II) Nutrition Cohort, collected only blood samples from participants (8). Although fewer biomarkers are found in blood than in urine (9), several cross-sectional studies have identified >100 putative food biomarkers in blood samples, suggesting that blood samples are promising resources for food biomarker discovery (9–14). However, most previous studies were limited by the small number of food groups and metabolites assessed. In a cross-sectional analysis of 1369 nonsmoking postmenopausal women from the CPS-II Nutrition Cohort, we aimed to conduct a comprehensive analysis of individual foods to replicate previously identified food biomarkers and identify novel ones. We then evaluated the predictive accuracy of the identified biomarkers through the use of receiver operating characteristic (ROC) analysis. Methods Study population Women in the present study were drawn from a nested case-control study of breast cancer in the CPS-II Nutrition Cohort. The CPS-II Nutrition Cohort is a prospective cohort study of cancer incidence and mortality among 184,185 men and women, established by the American Cancer Society in 1992 (8). Participants completed a self-administered baseline questionnaire in 1992/1993 including demographic, medical, and lifestyle information. From June 1998 through May 2001, nonfasting blood specimens were collected from 21,963 women as described in detail elsewhere (8). All aspects of the CPS-II Nutrition Cohort were approved by the Emory University (Atlanta, GA) Institutional Review Board. Of the 21,963 women who provided a blood sample, we identified 1547 postmenopausal women who were cancer free (except nonmelanoma skin cancer) at blood draw and included in a breast cancer nested case-control study. For this food metabolomics analysis, we excluded women who were current smokers at blood draw (n = 119), and those with missing or unreliable dietary information from the 1999 FFQ (missing >70 line items or with energy intake >3500 or <600 kcal/d, n = 59), leaving 1369 postmenopausal women in the analysis (Supplemental Figure 1). Diet assessment In 1999–2000, dietary intake was self-reported on a semiquantita-tive modified Willett FFQ including 152 items, available online (https://www.cancer.org/content/dam/cancer-org/research/epidemiology/cps-II-nutrition-1999-long-survey-women.pdf). To provide a comprehensive evaluation of all food exposures assessed on the FFQ, we classified these food items into 91 food groups (Supplemental Table 1) belonging to 9 food classes (fruits, vegetables, grains, proteins, dairy, fats and oils, miscellaneous, alcohol, and beverages). Each food item on the FFQ was examined individually (e.g., oranges) and in combination with similar foods as a food group (e.g., total citrus fruits and juices). Although the FFQ was not collected at the time of blood draw, the interval was within 2 y. The median time between return of the 1999 FFQ and blood draw date was 7.7 mo. Three-quarters of women returned the FFQ ≤ 1.7 y before the date of blood draw, and 25% returned the FFQ ≤ 1.7 y after the date of blood draw. Metabolomics analysis Metabolomic profiling was conducted by Metabolon, Inc. (Durham, NC) with the use of ultrahigh-performance LC-tandem MS (UPLC-MS/MS) as described elsewhere (15). Briefly, serum samples were treated with methanol to precipitate proteins. Four sample fractions were dried and reconstituted in different solvents for measurement under 4 different platforms. Two fractions were for analysis by 2 separate reverse-phase UPLC-MS/MS methods with positive-ion-mode electrospray ionization (ESI), 1 fraction for analysis by reverse-phase UPLC-MS/MS method with negative-ion-mode ESI, and 1 for analysis by hydrophilic interaction chromatography UPLC-MS/MS with negative-ion-mode ESI. Individual metabolites were identified by comparison with a chemical library consisting of >3300 commercially available purified standard compounds. A total of 1385 metabolites were detected. Triplicates of 46 samples were used as quality controls to assess the reproducibility of the platform. The median intraclass correlation coefficient, calculated based on the quality-control samples, was 0.90 with an IQR of 0.74–0.96, suggesting a very high reproducibility. To reduce noise and increase statistical power, we excluded metabolites that were below the detection limit in ≥90% of the samples (n = 110) and metabolites with intraclass correlation coefficient <0.5 (n = 89). For the remaining metabolites, missing values were assigned the minimum detection value. To correct day-to-day variation from the platform, each metabolite was divided by its daily median. Statistical analysis Metabolite and food variables were generalized log transformed (16) and autoscaled before all analyses. Pearson's partial correlation was conducted with the use of the R package ppcor (17) to assess linear relations between each dietary variable and metabolite, controlling for age at blood draw (continuous), race (non-Hispanic white or other), education (no college, some college, or college graduate), smoking status (never or former), use of hormone replacement therapy (current or not current), physical activity (metabolic equivalent hours per week: <8.75, 8.75 to <17.5 or ≥17.5), BMI (kg/m2: <18.5, 18.5 to <25, 25 to <30, or ≥30), time since last meal (continuous in hours), ethanol intake (<14 g/d or ≥14 g/d, except for ethanol-containing items), and caloric intake (continuous). Associations were considered statistically significant if P values were less than the Bonferroni-corrected threshold 4.63 × 10−7 [0.05/(91 × 1186)]. To select more meaningful associations for evaluation, we further required that the absolute value of the correlation coefficient (|r|) be >0.2. Putative dietary biomarkers were further evaluated for predictive accuracy of discriminating high consumers (top quintile) from low consumers (bottom quintile), assessed from the AUC calculated from the ROC curve with the use of the R package pROC (18). We considered AUC <0.7 to be low, 0.7–0.8 to be moderate, and ≥0.8 to be high. For univariate ROC analysis of individual metabolites, the AUC and 95% CIs were estimated from 2000 times stratified bootstrap samples. We further conducted a multivariate ROC analysis by building a linear Support Vector Machine multivariate classification model (19) with all putative dietary biomarkers identified in the correlation analysis. The multivariate ROC analysis was conducted with the use of the Biomarker Analysis module of MetaboAnalyst 3.0 (20). To understand correlations among metabolites, we conducted pairwise Pearson's partial correlation analyses among all 1186 metabolites. The top 5 metabolites with |r| > 0.5 for each metabolite are provided in Supplemental Table 2. Results Participant characteristics Overall, 98% of women were white, with a mean age 68.3 ± 5.7 y (Supplemental Table 3); 40% of women had college or higher education, 61% were never smokers, 57% were currently using hormone replacement therapy, 59% engaged in recommended or greater levels of physical activity (≥8.75 metabolic equivalent hours per week), 50% had normal body weight, 32% were overweight, 16% were obese, and 84% had <14 g ethanol/d. Mean time since last meal was 2 h. Mean time between blood draw and breast cancer diagnosis was 5.2 y. Serum metabolites correlated with habitual dietary intake Usual servings per week of the 91 predefined food groups or items are shown in Supplemental Table 4. In total, we identified 1069 statistically significant diet-metabolite associations with P values less than the Bonferroni-corrected threshold of 4.63 × 10−7. Further requiring a |r| > 0.2 resulted in 379 potentially meaningful associations (Supplemental Table 5). Among the 379 associations, 42 food groups or items were correlated with ≥1 metabolite; 199 metabolites (111 known, 88 unknown identities) were associated with ≥1 food group. The majority of 199 metabolites belonged to unknown (43.3%) and xenobiotic (30.6%) superpathways, the rest belonged to lipids (15.8%), amino acids (6.3%), cofactors and vitamins (1.8%), peptides (0.8%), energy metabolism (0.8%), nucleotides (0.3%), and carbohydrates (0.3%) (Supplemental Figure 2). The 42 food groups or items belonged to 9 food classes: fruits, vegetables, proteins, alcohols, beverages, and others (grains, dairy, fats and oils, miscellaneous). As shown in Supplemental Table 5, 52 metabolites were correlated with 4 fruit groups or items (18 for total citrus fruits and juices, 17 for orange juice, 6 for banana, 11 for prune). The top 10 most predictive metabolites (all shown if <10), per AUC, are shown in Table 1. Stachydrine (also known as proline betaine) was the most predictive metabolite for total citrus fruits and juices (r = 0.53, AUC = 89%), and orange juice intake (r = 0.54, AUC = 87%), as has been consistently reported in similar studies (Table 2). Notably, 2 sulfonated dopamines were correlated with banana intake: dopamine 3-O-sulfate (r = 0.34, AUC = 76%) and dopamine 4-O-sulfate (r = 0.33, AUC = 74%). Twenty-two associations were found for 6 vegetable groups or items (1 for cruciferous vegetables, 1 for mushrooms, 7 for allium vegetables, 1 for onion, 9 for garlic, and 3 for tofu or soybeans), with the strongest association being between ergothioneine and mushrooms (r = 0.28, AUC = 75%). Other notable correlations were S-methylcysteine sulfoxide and cruciferous vegetables (r = 0.23, AUC = 69%), garlic and alliin (r = 0.24, AUC = 69%), N-acetylalliin (r = 0.27, AUC = 70%), and S-allylcysteine (r = 0.23, AUC = 69%). Next, 41 diet-metabolite associations were identified for 10 protein foods (1 for egg, 3 for red meat, 1 for processed meat, 2 for poultry, 7 for total fish, 10 for dark fish, 2 for shellfish, 7 for nuts, 7 for peanuts, and 1 for other nuts). An unknown metabolite X-02269 was the most predictive metabolite for dark fish intake (r = 0.51, AUC = 94%). Alcoholic beverages were the second largest category to show associations with serum metabolites. Seventy-three associations were identified for 6 types of alcohol (39 for total alcohol, 1 for beer, 16 for total wine, 3 for red wine, 2 for white wine, and 12 for liquor). Ethyl glucuronide was the most predictive metabolite for all types of alcohol except for beer (for total alcohol, r = 0.60, AUC = 92%). The highest number of associations identified in this study were for beverages, including 157 metabolite–beverage associations (74 for total coffee, 53 for caffeinated coffee, 24 for decaffeinated coffee, 2 for total tea, 2 for nonherbal tea, 1 for herbal tea, and 1 for diet soft drinks). An unknown X-21442 was the most predictive metabolite for total coffee consumption (r = 0.62, AUC = 98%), and decaffeinated coffee consumption (r = 0.31, AUC = 83%). The most predictive metabolite for caffeinated coffee was 1-methylxanthine, which is a caffeine metabolite. For all types of tea consumption, theanine was the most predictive biomarker, slightly stronger for nonherbal tea than herbal or decaffeinated tea, and strongest for total tea (r = 0.50, AUC = 84%). For other foods, notable correlations were found for milk (galactonate, r = 0.33, AUC = 76%; 2,8-quinolinediol sulfate, r = 0.27, AUC = 75%), butter [caprylate (8:0), r = 0.21, AUC = 67%; caprate (10:0), r = 0.26, AUC = 70%; and 10-undecenoate (11:1n-1), r = 0.24, AUC = 69%] and soy milk (4-ethylphenylsulfate, r = 0.20, AUC = 67%). TABLE 1 Top 10 predictive serum metabolites of 42 food groups/items among women in the CPS-II Nutrition Cohort (n = 1369)1 Food groups/items Metabolites Super-pathway r P AUC2 Q1 mean ± SD3 Q5 mean ± SD Fruits  Total citrus fruits and juices stachydrine XEN 0.53 3.6 × 10−99 0.89 0.5 ± 0.7 2.1 ± 1.2 X-247384 UKN 0.49 5.2 × 10−82 0.89 0.5 ± 1.2 2.7 ± 2.2 N-methylproline AA 0.50 8.6 × 10−86 0.87 0.6 ± 1.0 2.8 ± 1.9 chiro-inositol LIP 0.43 1.1 × 10−62 0.86 0.3 ± 0.6 1.5 ± 1.2 X-22836 UKN 0.42 1.3 × 10−58 0.83 0.4 ± 0.7 1.5 ± 1.2 X-23314 UKN 0.41 1.4 × 10−56 0.82 0.9 ± 1.2 3.3 ± 3.3 X-17350 UKN 0.37 4.2 × 10−44 0.80 1.0 ± 1.1 2.7 ± 2.3 methyl glucopyranoside (α + β) XEN 0.36 4.1 × 10−43 0.80 0.8 ± 0.9 2.0 ± 1.9 X-16947 UKN 0.36 2.5 × 10−42 0.80 1.7 ± 5.1 6.1 ± 8.6 β-cryptoxanthin XEN 0.35 1.0 × 10−39 0.80 0.8 ± 0.5 1.6 ± 0.9  Orange juice stachydrine XEN 0.54 4.5 × 10−104 0.87 0.6 ± 0.8 2.2 ± 1.2 X-24738 UKN 0.51 2.2 × 10−92 0.86 0.6 ± 1.2 2.8 ± 2.3 N-methylproline AA 0.52 6.8 × 10−93 0.86 0.7 ± 1.0 2.8 ± 1.9 X-23314 UKN 0.48 3.4 × 10−78 0.83 0.9 ± 1.4 3.5 ± 3.7 chiro-inositol LIP 0.46 2.6 × 10−72 0.83 0.4 ± 0.9 1.5 ± 1.3 X-17350 UKN 0.43 3.1 × 10−62 0.82 1.0 ± 1.2 2.8 ± 2.4 X-22836 UKN 0.44 4.5 × 10−64 0.82 0.4 ± 0.7 1.5 ± 1.2 X-16947 UKN 0.42 8.2 × 10−58 0.81 1.5 ± 4.5 6.9 ± 9.2 X-22515 UKN 0.40 3.0 × 10−54 0.81 0.6 ± 1.7 3.0 ± 4.2 X-19183 UKN 0.41 1.9 × 10−57 0.81 0.4 ± 0.9 1.2 ± 1.0  Banana dopamine 3-O-sulfate AA 0.34 1.0 × 10−37 0.76 1.3 ± 1.5 5.7 ± 7.0 dopamine 4-sulfate AA 0.33 2.5 × 10−36 0.74 0.9 ± 1.6 5.3 ± 7.5 S-methylmethionine AA 0.23 3.9 × 10−18 0.72 1.0 ± 2.2 2.3 ± 2.7 3-methoxytyramine sulfate AA 0.22 9.2 × 10−17 0.70 1.0 ± 0.5 1.5 ± 0.9 X-12729 UKN 0.21 1.8 × 10−15 0.68 1.3 ± 3.7 2.9 ± 5.0 5-hydroxyindoleacetate AA 0.21 1.1 × 10−14 0.68 0.8 ± 0.9 1.7 ± 1.9  Prunes X-11315 UKN 0.21 1.5 × 10−14 0.67 1.0 ± 0.3 1.2 ± 0.5 X-12818 UKN 0.20 5.3 × 10−14 0.62 1.0 ± 1.4 1.5 ± 1.8 hippurate XEN 0.22 7.1 × 10−16 0.61 1.2 ± 1.1 1.8 ± 1.7 benzoylcarnitine5 XEN 0.25 3.0 × 10−21 0.61 0.9 ± 1.1 1.4 ± 1.8 X-24757 UKN 0.25 6.1 × 10−21 0.60 1.0 ± 1.2 1.9 ± 3.0 5-hydroxymethyl-2-furoic acid AA 0.26 3.1 × 10−22 0.58 1.0 ± 3.7 2.4 ± 8.1 X-17367 UKN 0.23 3.4 × 10−17 0.58 1.1 ± 1.6 2.1 ± 4.0 X-17325 UKN 0.21 1.3 × 10−15 0.58 1.4 ± 1.8 2.3 ± 3.8 X-22475 UKN 0.23 5.9 × 10−18 0.57 0.6 ± 1.4 1.6 ± 4.0 catechol sulfate XEN 0.20 4.2 × 10−14 0.57 1.1 ± 0.9 1.5 ± 1.1 Vegetables  Cruciferous vegetables S-methylcysteine sulfoxide AA 0.24 1.7 × 10−18 0.69 1.0 ± 0.7 1.6 ± 1.2  Mushrooms ergothioneine XEN 0.28 3.0 × 10−25 0.75 1.0 ± 0.5 1.6 ± 0.8  Allium vegetables N-methyltaurine AA 0.28 1.4 × 10−26 0.73 0.5 ± 1.3 1.5 ± 1.9 N-acetylalliin XEN 0.22 4.4 × 10−16 0.67 0.8 ± 1.7 2.9 ± 11.1 piperine XEN 0.23 1.0 × 10−17 0.67 1.1 ± 1.2 2.0 ± 2.2 ergothioneine XEN 0.22 1.0 × 10−16 0.67 1.1 ± 0.6 1.4 ± 0.8 γ-CEHC CV −0.20 5.7 × 10−14 0.67 1.3 ± 0.7 0.9 ± 0.6 γ-CEHC glucuronide5 CV −0.20 2.8 × 10−14 0.67 1.3 ± 0.9 0.8 ± 0.8 X-12231 UKN 0.20 9.6 × 10−14 0.65 1.0 ± 1.2 1.5 ± 1.6  Onion N-methyltaurine AA 0.24 9.7 × 10−20 0.69 0.6 ± 1.6 1.4 ± 2.1  Garlic γ-CEHC glucuronide5 CV −0.22 7.7 × 10−17 0.72 1.3 ± 1.0 0.7 ± 0.6 X-18249 UKN −0.22 4.8 × 10−16 0.71 1.2 ± 0.5 0.9 ± 0.4 γ-CEHC CV −0.22 2.0 × 10−16 0.71 1.3 ± 0.7 0.9 ± 0.5 N-acetylalliin XEN 0.27 3.1 × 10−24 0.70 0.8 ± 3.3 2.9 ± 8.9 S-allylcysteine XEN 0.23 2.8 × 10−17 0.69 1.2 ± 3.5 3.2 ± 5.0 ergothioneine XEN 0.26 3.6 × 10−22 0.69 1.0 ± 0.5 1.5 ± 0.9 X-02269 UKN 0.21 6.6 × 10−15 0.69 1.2 ± 1.3 2.2 ± 2.4 alliin XEN 0.24 5.7 × 10−19 0.69 1.0 ± 5.6 3.8 ± 9.0 N-methyltaurine AA 0.24 1.5 × 10−18 0.68 0.6 ± 1.2 1.5 ± 2.1  Tofu or soybeans X-11847 UKN 0.22 5.7 × 10−16 0.75 1.7 ± 3.2 7.3 ± 11.8 X-11858 UKN 0.22 1.1 × 10−15 0.72 1.0 ± 3.4 8.7 ± 24.2 X-16649 UKN 0.21 1.1 × 10−14 0.62 0.8 ± 3.0 4.9 ± 12.7 Grains  Whole grains X-21752 UKN 0.20 5.8 × 10−14 0.65 0.5 ± 1.2 1.1 ± 1.7 Proteins  Eggs 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)5 LIP 0.22 8.4 × 10−17 0.71 1.0 ± 0.3 1.4 ± 0.5  Red meat 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)5 LIP 0.28 4.2 × 10−25 0.67 0.9 ± 0.3 1.2 ± 0.4 X-11381 UKN 0.21 3.3 × 10−15 0.66 1.0 ± 0.5 1.2 ± 0.6 1-(1-enyl-stearoyl)-2-oleoyl-GPE (P-18:0/18:1) LIP 0.23 6.9 × 10−18 0.65 1.0 ± 0.3 1.2 ± 0.4  Processed meat X-18922 UKN 0.20 3.9 × 10−14 0.70 0.8 ± 0.5 1.2 ± 0.6  Poultry X-13835 UKN 0.21 1.1 × 10−14 0.65 1.2 ± 1.8 1.7 ± 1.7 3-methylhistidine AA 0.21 3.6 × 10−15 0.64 1.3 ± 1.6 2.1 ± 2.2  Total fish X-02269 UKN 0.41 1.9 × 10−55 0.82 0.9 ± 1.1 2.7 ± 2.5 CMPF LIP 0.37 1.7 × 10−45 0.80 1.0 ± 2.0 2.7 ± 2.3 DHA LIP 0.33 1.1 × 10−36 0.77 0.9 ± 0.4 1.4 ± 0.7 docosahexaenoylcholine LIP 0.27 3.1 × 10−24 0.75 0.8 ± 0.4 1.4 ± 0.7 1-docosahexaenoylglycerol (22:6) LIP 0.28 1.2 × 10−26 0.74 0.8 ± 0.8 1.4 ± 0.8 EPA LIP 0.27 7.1 × 10−25 0.72 0.9 ± 0.6 1.7 ± 1.2 eicosapentaenoylcholine LIP 0.23 4.0 × 10−17 0.72 0.8 ± 0.7 1.5 ± 1.3  Dark fish X-02269 UKN 0.51 1.8 × 10−92 0.94 0.7 ± 0.9 3.6 ± 2.4 CMPF LIP 0.47 6.9 × 10−74 0.93 0.8 ± 1.4 3.6 ± 2.6 DHA LIP 0.37 5.6 × 10−44 0.86 0.9 ± 0.4 1.7 ± 0.8 EPA LIP 0.36 1.7 × 10−42 0.84 0.9 ± 0.6 2.0 ± 1.2 docosahexaenoylcholine LIP 0.27 2.1 × 10−24 0.80 0.9 ± 0.5 1.6 ± 0.7 sphingomyelin (d18:2/18:1)5 LIP −0.25 4.7 × 10−20 0.80 1.2 ± 0.4 0.8 ± 0.3 eicosapentaenoylcholine LIP 0.29 8.7 × 10−28 0.79 0.8 ± 0.7 1.9 ± 1.5 1-docosahexaenoylglycerol (22:6) LIP 0.28 1.1 × 10−26 0.79 0.8 ± 0.6 1.7 ± 0.9 docosapentaenoate (n-6 DPA; 22:5n-6) LIP −0.26 2.4 × 10−22 0.71 1.2 ± 0.5 0.8 ± 0.5 X-13866 UKN 0.21 1.8 × 10−14 0.69 1.2 ± 2.0 1.9 ± 1.9  Shellfish CMPF LIP 0.26 4.3 × 10−23 0.83 1.1 ± 1.8 2.9 ± 2.7 X-02269 UKN 0.25 4.2 × 10−20 0.81 1.0 ± 1.4 2.4 ± 1.7  Total nuts tryptophan betaine AA 0.41 2.2 × 10−55 0.80 0.8 ± 0.9 2.0 ± 1.6 X-23644 UKN 0.27 6.5 × 10−24 0.72 1.9 ± 3.4 4.5 ± 5.7 4-vinylphenol sulfate XEN 0.27 1.9 × 10−24 0.69 1.3 ± 1.7 2.6 ± 2.9 lignoceroylcarnitine (C24)5 LIP 0.25 5.9 × 10−21 0.69 0.9 ± 0.4 1.2 ± 0.5 γ-glutamylvaline PEP −0.25 1.7 × 10−21 0.68 1.2 ± 0.4 0.9 ± 0.4 behenoylcarnitine (C22)5 LIP 0.23 2.4 × 10−17 0.66 0.7 ± 0.5 1.1 ± 0.8 sphingomyelin (d18:2/23:1)5 LIP −0.22 4.2 × 10−16 0.66 1.1 ± 0.3 0.9 ± 0.3  Peanuts tryptophan betaine AA 0.45 2.3 × 10−68 0.83 0.8 ± 0.9 2.1 ± 1.7 X-23644 UKN 0.27 6.3 × 10−24 0.71 2.0 ± 3.6 4.6 ± 5.9 4-vinylphenol sulfate XEN 0.31 7.0 × 10−32 0.71 1.2 ± 1.6 2.7 ± 2.9 γ-glutamylvaline PEP −0.30 3.3 × 10−30 0.71 1.2 ± 0.4 0.9 ± 0.4 lignoceroylcarnitine (C24)5 LIP 0.25 2.5 × 10−20 0.66 1.0 ± 0.4 1.2 ± 0.5 behenoylcarnitine (C22)5 LIP 0.23 5.6 × 10−18 0.66 0.8 ± 0.6 1.2 ± 0.8 sphingomyelin (d18:2/231)5 LIP −0.22 1.7 × 10−16 0.65 1.1 ± 0.3 0.9 ± 0.2  Other nuts X-11315 UKN 0.22 1.2 × 10−15 0.66 1.0 ± 0.4 1.2 ± 0.4 Dairy  Milk galactonate CHO 0.33 1.5 × 10−35 0.76 0.8 ± 0.7 2.0 ± 1.8 2,8-quinolinediol sulfate XEN 0.27 2.6 × 10−24 0.75 0.4 ± 0.7 1.3 ± 1.5 phenylacetylglycine PEP 0.27 1.8 × 10−23 0.71 0.7 ± 0.8 1.5 ± 1.3 X-11381 UKN 0.23 3.3 × 10−18 0.71 0.9 ± 0.4 1.3 ± 0.5 X-12798 UKN 0.24 1.4 × 10−18 0.70 0.9 ± 0.4 1.2 ± 0.5  Soy milk X-16649 UKN 0.20 4.1 × 10−14 0.69 1.1 ± 4.9 5.7 ± 12.1 4-ethylphenylsulfate XEN 0.20 5.9 × 10−14 0.67 2.0 ± 4.6 8.4 ± 16.2  Yogurt X-21736 UKN −0.20 6.3 × 10−14 0.70 1.5 ± 1.2 0.9 ± 0.7 Fats and oils  Butter X-11438 UKN 0.24 6.2 × 10−20 0.71 1.0 ± 0.8 1.6 ± 1.0 caprate (10:0) LIP 0.26 1.2 × 10−21 0.70 1.1 ± 0.6 1.6 ± 1.0 10-undecenoate (11:1n-1) LIP 0.24 9.3 × 10−19 0.69 1.0 ± 0.5 1.4 ± 0.7 sphingomyelin (d18:1/25:0, d19:0/24:1, d20:1/23:0, d19:1/24:0)5 LIP 0.24 6.7 × 10−20 0.68 0.9 ± 0.4 1.2 ± 0.5 X-17337 UKN 0.21 6.7 × 10−15 0.67 1.0 ± 0.5 1.3 ± 0.6 caprylate (8:0) LIP 0.21 5.0 × 10−15 0.67 1.0 ± 0.4 1.3 ± 0.6 sphingomyelin (d17:1/16:0, d18:1/15:0, d16:1/17:0)5 LIP 0.23 1.5 × 10−17 0.65 1.0 ± 0.2 1.1 ± 0.3 Miscellaneous  French fries X-18899 UKN 0.26 8.0 × 10−22 0.84 1.0 ± 0.9 1.6 ± 0.7 X-11880 UKN 0.26 3.6 × 10−23 0.83 0.9 ± 0.5 1.6 ± 0.8 X-21339 UKN 0.29 7.6 × 10−27 0.81 0.9 ± 1.0 1.9 ± 1.1 X-11308 UKN 0.27 6.4 × 10−25 0.81 0.9 ± 0.5 1.5 ± 0.7 X-11549 UKN 0.27 1.1 × 10−24 0.81 0.9 ± 0.5 1.6 ± 0.9 X-11372 UKN 0.24 5.2 × 10−19 0.79 0.9 ± 0.4 1.4 ± 0.6 X-11378 UKN 0.23 1.0 × 10−17 0.76 0.9 ± 0.7 1.5 ± 0.7 X-16935 UKN 0.23 8.3 × 10−18 0.75 0.9 ± 1.1 1.9 ± 1.6 eicosanodioate LIP 0.21 8.8 × 10−15 0.73 1.0 ± 0.5 1.5 ± 0.8  Total candies X-13728 UKN 0.20 7.0 × 10−14 0.65 1.1 ± 1.2 2.0 ± 2.2  Chocolate candies X-13728 UKN 0.28 3.6 × 10−25 0.69 1.2 ± 1.4 2.3 ± 2.4 3-methylxanthine XEN 0.26 4.4 × 10−22 0.68 1.1 ± 1.1 1.9 ± 1.8 7-methylurate XEN 0.24 4.0 × 10−19 0.68 1.1 ± 1.2 1.9 ± 1.9 3,7-dimethylurate XEN 0.24 1.9 × 10−19 0.66 0.9 ± 1.0 1.5 ± 1.6 theobromine XEN 0.24 3.8 × 10−19 0.66 1.1 ± 1.1 1.9 ± 1.7 7-methylxanthine XEN 0.21 1.8 × 10−14 0.64 0.6 ± 0.8 1.1 ± 1.2  Desserts ergothioneine XEN −0.25 1.8 × 10−20 0.69 1.5 ± 0.8 1.0 ± 0.6 sphingomyelin (d18:2/18:1)5 LIP 0.21 7.8 × 10−15 0.65 0.9 ± 0.4 1.1 ± 0.4 Alcohol  Total alcohol ethyl glucuronide XEN 0.60 1.79 × 10−133 0.92 0.4 ± 0.5 9.2 ± 28.4 X-24293 UKN 0.54 1.63 × 10−102 0.87 0.8 ± 1.3 4.4 ± 6.6 X-21737 UKN 0.21 3.7 × 10−15 0.76 1.8 ± 11.6 3.0 ± 6.0 CMPF LIP 0.23 2.9 × 10−17 0.74 1.1 ± 1.7 2.2 ± 1.8 X-23655 UKN 0.22 5.3 × 10−16 0.73 0.5 ± 0.7 1.3 ± 1.3 X-24811 UKN 0.23 2.8 × 10−17 0.73 0.6 ± 0.8 1.3 ± 1.1 caffeine XEN 0.25 3.3 × 10−21 0.72 0.9 ± 1.3 2.1 ± 1.9 X-14473 UKN 0.26 5.0 × 10−22 0.72 0.8 ± 0.7 1.4 ± 0.9 sphingomyelin (d18:2/18:1)5 LIP −0.27 3.0 × 10−24 0.72 1.1 ± 0.4 0.8 ± 0.3 X-12230 UKN 0.20 9.7 × 10−14 0.72 1.0 ± 1.2 2.0 ± 1.9  Beer X-24293 UKN 0.27 1.5 × 10−24 0.72 1.2 ± 2.0 3.3 ± 6.8  Total wine ethyl glucuronide XEN 0.45 1.9 × 10−68 0.85 0.4 ± 0.5 5.5 ± 17.0 X-24293 UKN 0.37 4.8 × 10−46 0.79 0.8 ± 1.3 3.1 ± 4.0 2,3-dihydroxyisovalerate XEN 0.36 5.6 × 10−43 0.75 1.2 ± 1.3 3.0 ± 4.8 CMPF LIP 0.20 5.4 × 10−14 0.73 1.2 ± 1.6 2.2 ± 2.0 sphingomyelin (d18:2/18:1)5 LIP −0.23 7.5 × 10−18 0.71 1.1 ± 0.4 0.9 ± 0.3 X-18249 UKN −0.20 8.6 × 10−14 0.70 1.2 ± 0.5 0.8 ± 0.3 X-24473 UKN 0.25 1.6 × 10−20 0.70 1.2 ± 1.5 2.0 ± 2.9 oleoyl-linoleoyl-glycerol (18:1/18:2) (2)6 LIP −0.20 3.5 × 10−14 0.68 1.2 ± 0.6 0.9 ± 0.5 X-11795 UKN 0.22 2.1 × 10−16 0.65 1.1 ± 0.8 1.5 ± 1.6 androstenediol (3β,17β) monosulfate (2) LIP 0.21 8.6 × 10−15 0.65 1.1 ± 0.9 1.9 ± 2.0  Red wine ethyl glucuronide XEN 0.30 3.3 × 10−30 0.75 1.1 ± 6.3 4.5 ± 16.8 X-24293 UKN 0.27 1.4 × 10−24 0.72 1.0 ± 1.7 2.9 ± 4.8 2,3-dihydroxyisovalerate XEN 0.26 1.1 × 10−22 0.66 1.3 ± 1.4 2.5 ± 4.1  White wine ethyl glucuronide XEN 0.22 8.6 × 10−16 0.83 0.6 ± 1.6 6.7 ± 20.6 2,3-dihydroxyisovalerate XEN 0.23 1.9 × 10−17 0.74 1.3 ± 1.6 3.1 ± 5.1  Liquor ethyl glucuronide XEN 0.51 8.4 × 10−90 0.80 0.9 ± 5.5 8.4 ± 27.4 X-24293 UKN 0.44 2.4 × 10−65 0.79 1.0 ± 1.5 4.1 ± 6.7 X-01911 UKN 0.24 9.4 × 10−20 0.68 1.1 ± 1.2 1.8 ± 1.6 androstenediol (3β,17β) disulfate (1) LIP 0.28 1.7 × 10−26 0.67 1.1 ± 1.0 2.4 ± 3.4 androstenediol (3β,17β) monosulfate (2) LIP 0.28 2.3 × 10−26 0.67 1.1 ± 0.9 2.2 ± 3.1 X-21474 UKN 0.23 2.3 × 10−17 0.67 1.0 ± 1.2 1.7 ± 1.5 5α-androstan-3β,17β-diol disulfate LIP 0.29 8.7 × 10−27 0.66 1.2 ± 1.2 2.7 ± 5.0 X-21659 UKN 0.22 4.4 × 10−16 0.66 1.0 ± 1.2 1.7 ± 1.5 X-17335 UKN 0.20 5.1 × 10−14 0.62 1.0 ± 0.6 1.2 ± 0.7 5α-androstan-3α,17β-diol disulfate LIP 0.24 4.3 × 10−19 0.60 0.4 ± 0.8 1.0 ± 1.9 Beverages  Total coffee X-21442 UKN 0.62 6.3 × 10−146 0.98 0.1 ± 1.0 3.5 ± 4.5 trigonelline (N ′-methylnicotinate) CV 0.66 1.3 × 10−172 0.96 0.3 ± 0.4 2.0 ± 1.1 X-24811 UKN 0.62 7.5 × 10−145 0.95 0.2 ± 0.3 1.6 ± 1.1 X-23655 UKN 0.58 2.6 × 10−121 0.95 0.1 ± 0.4 1.5 ± 1.3 quinate XEN 0.66 1.7 × 10−170 0.95 0.3 ± 0.5 2.0 ± 1.3 3-hydroxypyridine sulfate XEN 0.61 1.5 × 10−138 0.95 0.2 ± 0.5 2.3 ± 1.6 X-12230 UKN 0.57 1.1 × 10−119 0.94 0.3 ± 0.5 2.3 ± 1.7 3-methyl catechol sulfate (1) XEN 0.59 1.9 × 10−127 0.93 0.4 ± 0.7 2.1 ± 1.4 X-17185 UKN 0.52 4.4 × 10−96 0.93 0.3 ± 0.5 3.1 ± 10.8 citraconate/glutaconate ENG 0.56 4.4 × 10−111 0.93 0.6 ± 0.4 2.0 ± 1.2  Caffeinated coffee 1-methylxanthine XEN 0.64 1.4 × 10−155 0.96 0.5 ± 0.7 3.2 ± 1.7 paraxanthine XEN 0.60 1.5 × 10−135 0.95 0.3 ± 0.5 2.3 ± 1.4 1-methylurate XEN 0.60 6.9 × 10−134 0.94 0.6 ± 0.9 3.2 ± 1.8 5-acetylamino-6-amino-3-methyluracil XEN 0.61 7.9 × 10−138 0.94 0.6 ± 0.9 3.1 ± 1.7 1,3-dimethylurate XEN 0.56 2.8 × 10−112 0.94 0.7 ± 4.3 2.5 ± 1.4 1,7-dimethylurate XEN 0.61 2.0 × 10−136 0.92 0.6 ± 0.7 2.3 ± 1.1 theophylline XEN 0.55 5.2 × 10−108 0.92 0.7 ± 3.5 2.5 ± 1.5 caffeine XEN 0.61 3.8 × 10−140 0.91 0.6 ± 1.0 2.7 ± 1.7 1,3,7-trimethylurate XEN 0.62 2.7 × 10−144 0.91 0.5 ± 0.8 2.2 ± 1.3 X-21442 UKN 0.41 4.1 × 10−57 0.88 0.7 ± 1.6 3.6 ± 4.2  Decaffeinated coffee X-21442 UKN 0.31 7.6 × 10−31 0.83 0.9 ± 1.9 3.4 ± 4.6 3-hydroxypyridine sulfate XEN 0.29 1.6 × 10−27 0.80 0.9 ± 1.3 2.5 ± 1.7 trigonelline (N ′-methylnicotinate) CV 0.26 6.5 × 10−23 0.79 0.9 ± 0.9 1.9 ± 1.2 quinate XEN 0.30 6.4 × 10−29 0.78 0.9 ± 1.1 2.1 ± 1.4 X-24811 UKN 0.26 3.4 × 10−22 0.78 0.7 ± 0.9 1.5 ± 1.2 X-23655 UKN 0.26 2.3 × 10−22 0.78 0.6 ± 1.0 1.6 ± 1.4 X-23649 UKN 0.24 2.6 × 10−19 0.78 0.6 ± 1.2 1.8 ± 1.8 X-12816 UKN 0.25 4.1 × 10−21 0.77 0.5 ± 1.0 1.5 ± 1.4 X-12230 UKN 0.23 2.3 × 10−18 0.77 1.1 ± 1.6 2.3 ± 1.7 2,3-dihydroxypyridine XEN 0.24 1.8 × 10−18 0.77 0.6 ± 1.0 1.5 ± 1.2  Total tea theanine XEN 0.50 2.6 × 10−87 0.84 1.0 ± 6.3 28.2 ± 53.0 X-21795 UKN 0.41 1.4 × 10−55 0.72 0.1 ± 0.3 1.1 ± 2.0  Nonherbal tea theanine XEN 0.47 3.7 × 10−74 0.84 5.3 ± 25.3 33.7 ± 58.7 X-21795 UKN 0.43 9.3 × 10−62 0.72 0.2 ± 0.8 1.3 ± 2.1  Herbal tea or decaffeinated tea theanine XEN 0.23 2.7 × 10−17 0.70 8.7 ± 33.9 17.9 ± 41.8  Diet soft drinks saccharin XEN 0.25 5.5 × 10−20 0.69 4.5 ± 23.5 17.9 ± 46.4 Food groups/items Metabolites Super-pathway r P AUC2 Q1 mean ± SD3 Q5 mean ± SD Fruits  Total citrus fruits and juices stachydrine XEN 0.53 3.6 × 10−99 0.89 0.5 ± 0.7 2.1 ± 1.2 X-247384 UKN 0.49 5.2 × 10−82 0.89 0.5 ± 1.2 2.7 ± 2.2 N-methylproline AA 0.50 8.6 × 10−86 0.87 0.6 ± 1.0 2.8 ± 1.9 chiro-inositol LIP 0.43 1.1 × 10−62 0.86 0.3 ± 0.6 1.5 ± 1.2 X-22836 UKN 0.42 1.3 × 10−58 0.83 0.4 ± 0.7 1.5 ± 1.2 X-23314 UKN 0.41 1.4 × 10−56 0.82 0.9 ± 1.2 3.3 ± 3.3 X-17350 UKN 0.37 4.2 × 10−44 0.80 1.0 ± 1.1 2.7 ± 2.3 methyl glucopyranoside (α + β) XEN 0.36 4.1 × 10−43 0.80 0.8 ± 0.9 2.0 ± 1.9 X-16947 UKN 0.36 2.5 × 10−42 0.80 1.7 ± 5.1 6.1 ± 8.6 β-cryptoxanthin XEN 0.35 1.0 × 10−39 0.80 0.8 ± 0.5 1.6 ± 0.9  Orange juice stachydrine XEN 0.54 4.5 × 10−104 0.87 0.6 ± 0.8 2.2 ± 1.2 X-24738 UKN 0.51 2.2 × 10−92 0.86 0.6 ± 1.2 2.8 ± 2.3 N-methylproline AA 0.52 6.8 × 10−93 0.86 0.7 ± 1.0 2.8 ± 1.9 X-23314 UKN 0.48 3.4 × 10−78 0.83 0.9 ± 1.4 3.5 ± 3.7 chiro-inositol LIP 0.46 2.6 × 10−72 0.83 0.4 ± 0.9 1.5 ± 1.3 X-17350 UKN 0.43 3.1 × 10−62 0.82 1.0 ± 1.2 2.8 ± 2.4 X-22836 UKN 0.44 4.5 × 10−64 0.82 0.4 ± 0.7 1.5 ± 1.2 X-16947 UKN 0.42 8.2 × 10−58 0.81 1.5 ± 4.5 6.9 ± 9.2 X-22515 UKN 0.40 3.0 × 10−54 0.81 0.6 ± 1.7 3.0 ± 4.2 X-19183 UKN 0.41 1.9 × 10−57 0.81 0.4 ± 0.9 1.2 ± 1.0  Banana dopamine 3-O-sulfate AA 0.34 1.0 × 10−37 0.76 1.3 ± 1.5 5.7 ± 7.0 dopamine 4-sulfate AA 0.33 2.5 × 10−36 0.74 0.9 ± 1.6 5.3 ± 7.5 S-methylmethionine AA 0.23 3.9 × 10−18 0.72 1.0 ± 2.2 2.3 ± 2.7 3-methoxytyramine sulfate AA 0.22 9.2 × 10−17 0.70 1.0 ± 0.5 1.5 ± 0.9 X-12729 UKN 0.21 1.8 × 10−15 0.68 1.3 ± 3.7 2.9 ± 5.0 5-hydroxyindoleacetate AA 0.21 1.1 × 10−14 0.68 0.8 ± 0.9 1.7 ± 1.9  Prunes X-11315 UKN 0.21 1.5 × 10−14 0.67 1.0 ± 0.3 1.2 ± 0.5 X-12818 UKN 0.20 5.3 × 10−14 0.62 1.0 ± 1.4 1.5 ± 1.8 hippurate XEN 0.22 7.1 × 10−16 0.61 1.2 ± 1.1 1.8 ± 1.7 benzoylcarnitine5 XEN 0.25 3.0 × 10−21 0.61 0.9 ± 1.1 1.4 ± 1.8 X-24757 UKN 0.25 6.1 × 10−21 0.60 1.0 ± 1.2 1.9 ± 3.0 5-hydroxymethyl-2-furoic acid AA 0.26 3.1 × 10−22 0.58 1.0 ± 3.7 2.4 ± 8.1 X-17367 UKN 0.23 3.4 × 10−17 0.58 1.1 ± 1.6 2.1 ± 4.0 X-17325 UKN 0.21 1.3 × 10−15 0.58 1.4 ± 1.8 2.3 ± 3.8 X-22475 UKN 0.23 5.9 × 10−18 0.57 0.6 ± 1.4 1.6 ± 4.0 catechol sulfate XEN 0.20 4.2 × 10−14 0.57 1.1 ± 0.9 1.5 ± 1.1 Vegetables  Cruciferous vegetables S-methylcysteine sulfoxide AA 0.24 1.7 × 10−18 0.69 1.0 ± 0.7 1.6 ± 1.2  Mushrooms ergothioneine XEN 0.28 3.0 × 10−25 0.75 1.0 ± 0.5 1.6 ± 0.8  Allium vegetables N-methyltaurine AA 0.28 1.4 × 10−26 0.73 0.5 ± 1.3 1.5 ± 1.9 N-acetylalliin XEN 0.22 4.4 × 10−16 0.67 0.8 ± 1.7 2.9 ± 11.1 piperine XEN 0.23 1.0 × 10−17 0.67 1.1 ± 1.2 2.0 ± 2.2 ergothioneine XEN 0.22 1.0 × 10−16 0.67 1.1 ± 0.6 1.4 ± 0.8 γ-CEHC CV −0.20 5.7 × 10−14 0.67 1.3 ± 0.7 0.9 ± 0.6 γ-CEHC glucuronide5 CV −0.20 2.8 × 10−14 0.67 1.3 ± 0.9 0.8 ± 0.8 X-12231 UKN 0.20 9.6 × 10−14 0.65 1.0 ± 1.2 1.5 ± 1.6  Onion N-methyltaurine AA 0.24 9.7 × 10−20 0.69 0.6 ± 1.6 1.4 ± 2.1  Garlic γ-CEHC glucuronide5 CV −0.22 7.7 × 10−17 0.72 1.3 ± 1.0 0.7 ± 0.6 X-18249 UKN −0.22 4.8 × 10−16 0.71 1.2 ± 0.5 0.9 ± 0.4 γ-CEHC CV −0.22 2.0 × 10−16 0.71 1.3 ± 0.7 0.9 ± 0.5 N-acetylalliin XEN 0.27 3.1 × 10−24 0.70 0.8 ± 3.3 2.9 ± 8.9 S-allylcysteine XEN 0.23 2.8 × 10−17 0.69 1.2 ± 3.5 3.2 ± 5.0 ergothioneine XEN 0.26 3.6 × 10−22 0.69 1.0 ± 0.5 1.5 ± 0.9 X-02269 UKN 0.21 6.6 × 10−15 0.69 1.2 ± 1.3 2.2 ± 2.4 alliin XEN 0.24 5.7 × 10−19 0.69 1.0 ± 5.6 3.8 ± 9.0 N-methyltaurine AA 0.24 1.5 × 10−18 0.68 0.6 ± 1.2 1.5 ± 2.1  Tofu or soybeans X-11847 UKN 0.22 5.7 × 10−16 0.75 1.7 ± 3.2 7.3 ± 11.8 X-11858 UKN 0.22 1.1 × 10−15 0.72 1.0 ± 3.4 8.7 ± 24.2 X-16649 UKN 0.21 1.1 × 10−14 0.62 0.8 ± 3.0 4.9 ± 12.7 Grains  Whole grains X-21752 UKN 0.20 5.8 × 10−14 0.65 0.5 ± 1.2 1.1 ± 1.7 Proteins  Eggs 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)5 LIP 0.22 8.4 × 10−17 0.71 1.0 ± 0.3 1.4 ± 0.5  Red meat 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)5 LIP 0.28 4.2 × 10−25 0.67 0.9 ± 0.3 1.2 ± 0.4 X-11381 UKN 0.21 3.3 × 10−15 0.66 1.0 ± 0.5 1.2 ± 0.6 1-(1-enyl-stearoyl)-2-oleoyl-GPE (P-18:0/18:1) LIP 0.23 6.9 × 10−18 0.65 1.0 ± 0.3 1.2 ± 0.4  Processed meat X-18922 UKN 0.20 3.9 × 10−14 0.70 0.8 ± 0.5 1.2 ± 0.6  Poultry X-13835 UKN 0.21 1.1 × 10−14 0.65 1.2 ± 1.8 1.7 ± 1.7 3-methylhistidine AA 0.21 3.6 × 10−15 0.64 1.3 ± 1.6 2.1 ± 2.2  Total fish X-02269 UKN 0.41 1.9 × 10−55 0.82 0.9 ± 1.1 2.7 ± 2.5 CMPF LIP 0.37 1.7 × 10−45 0.80 1.0 ± 2.0 2.7 ± 2.3 DHA LIP 0.33 1.1 × 10−36 0.77 0.9 ± 0.4 1.4 ± 0.7 docosahexaenoylcholine LIP 0.27 3.1 × 10−24 0.75 0.8 ± 0.4 1.4 ± 0.7 1-docosahexaenoylglycerol (22:6) LIP 0.28 1.2 × 10−26 0.74 0.8 ± 0.8 1.4 ± 0.8 EPA LIP 0.27 7.1 × 10−25 0.72 0.9 ± 0.6 1.7 ± 1.2 eicosapentaenoylcholine LIP 0.23 4.0 × 10−17 0.72 0.8 ± 0.7 1.5 ± 1.3  Dark fish X-02269 UKN 0.51 1.8 × 10−92 0.94 0.7 ± 0.9 3.6 ± 2.4 CMPF LIP 0.47 6.9 × 10−74 0.93 0.8 ± 1.4 3.6 ± 2.6 DHA LIP 0.37 5.6 × 10−44 0.86 0.9 ± 0.4 1.7 ± 0.8 EPA LIP 0.36 1.7 × 10−42 0.84 0.9 ± 0.6 2.0 ± 1.2 docosahexaenoylcholine LIP 0.27 2.1 × 10−24 0.80 0.9 ± 0.5 1.6 ± 0.7 sphingomyelin (d18:2/18:1)5 LIP −0.25 4.7 × 10−20 0.80 1.2 ± 0.4 0.8 ± 0.3 eicosapentaenoylcholine LIP 0.29 8.7 × 10−28 0.79 0.8 ± 0.7 1.9 ± 1.5 1-docosahexaenoylglycerol (22:6) LIP 0.28 1.1 × 10−26 0.79 0.8 ± 0.6 1.7 ± 0.9 docosapentaenoate (n-6 DPA; 22:5n-6) LIP −0.26 2.4 × 10−22 0.71 1.2 ± 0.5 0.8 ± 0.5 X-13866 UKN 0.21 1.8 × 10−14 0.69 1.2 ± 2.0 1.9 ± 1.9  Shellfish CMPF LIP 0.26 4.3 × 10−23 0.83 1.1 ± 1.8 2.9 ± 2.7 X-02269 UKN 0.25 4.2 × 10−20 0.81 1.0 ± 1.4 2.4 ± 1.7  Total nuts tryptophan betaine AA 0.41 2.2 × 10−55 0.80 0.8 ± 0.9 2.0 ± 1.6 X-23644 UKN 0.27 6.5 × 10−24 0.72 1.9 ± 3.4 4.5 ± 5.7 4-vinylphenol sulfate XEN 0.27 1.9 × 10−24 0.69 1.3 ± 1.7 2.6 ± 2.9 lignoceroylcarnitine (C24)5 LIP 0.25 5.9 × 10−21 0.69 0.9 ± 0.4 1.2 ± 0.5 γ-glutamylvaline PEP −0.25 1.7 × 10−21 0.68 1.2 ± 0.4 0.9 ± 0.4 behenoylcarnitine (C22)5 LIP 0.23 2.4 × 10−17 0.66 0.7 ± 0.5 1.1 ± 0.8 sphingomyelin (d18:2/23:1)5 LIP −0.22 4.2 × 10−16 0.66 1.1 ± 0.3 0.9 ± 0.3  Peanuts tryptophan betaine AA 0.45 2.3 × 10−68 0.83 0.8 ± 0.9 2.1 ± 1.7 X-23644 UKN 0.27 6.3 × 10−24 0.71 2.0 ± 3.6 4.6 ± 5.9 4-vinylphenol sulfate XEN 0.31 7.0 × 10−32 0.71 1.2 ± 1.6 2.7 ± 2.9 γ-glutamylvaline PEP −0.30 3.3 × 10−30 0.71 1.2 ± 0.4 0.9 ± 0.4 lignoceroylcarnitine (C24)5 LIP 0.25 2.5 × 10−20 0.66 1.0 ± 0.4 1.2 ± 0.5 behenoylcarnitine (C22)5 LIP 0.23 5.6 × 10−18 0.66 0.8 ± 0.6 1.2 ± 0.8 sphingomyelin (d18:2/231)5 LIP −0.22 1.7 × 10−16 0.65 1.1 ± 0.3 0.9 ± 0.2  Other nuts X-11315 UKN 0.22 1.2 × 10−15 0.66 1.0 ± 0.4 1.2 ± 0.4 Dairy  Milk galactonate CHO 0.33 1.5 × 10−35 0.76 0.8 ± 0.7 2.0 ± 1.8 2,8-quinolinediol sulfate XEN 0.27 2.6 × 10−24 0.75 0.4 ± 0.7 1.3 ± 1.5 phenylacetylglycine PEP 0.27 1.8 × 10−23 0.71 0.7 ± 0.8 1.5 ± 1.3 X-11381 UKN 0.23 3.3 × 10−18 0.71 0.9 ± 0.4 1.3 ± 0.5 X-12798 UKN 0.24 1.4 × 10−18 0.70 0.9 ± 0.4 1.2 ± 0.5  Soy milk X-16649 UKN 0.20 4.1 × 10−14 0.69 1.1 ± 4.9 5.7 ± 12.1 4-ethylphenylsulfate XEN 0.20 5.9 × 10−14 0.67 2.0 ± 4.6 8.4 ± 16.2  Yogurt X-21736 UKN −0.20 6.3 × 10−14 0.70 1.5 ± 1.2 0.9 ± 0.7 Fats and oils  Butter X-11438 UKN 0.24 6.2 × 10−20 0.71 1.0 ± 0.8 1.6 ± 1.0 caprate (10:0) LIP 0.26 1.2 × 10−21 0.70 1.1 ± 0.6 1.6 ± 1.0 10-undecenoate (11:1n-1) LIP 0.24 9.3 × 10−19 0.69 1.0 ± 0.5 1.4 ± 0.7 sphingomyelin (d18:1/25:0, d19:0/24:1, d20:1/23:0, d19:1/24:0)5 LIP 0.24 6.7 × 10−20 0.68 0.9 ± 0.4 1.2 ± 0.5 X-17337 UKN 0.21 6.7 × 10−15 0.67 1.0 ± 0.5 1.3 ± 0.6 caprylate (8:0) LIP 0.21 5.0 × 10−15 0.67 1.0 ± 0.4 1.3 ± 0.6 sphingomyelin (d17:1/16:0, d18:1/15:0, d16:1/17:0)5 LIP 0.23 1.5 × 10−17 0.65 1.0 ± 0.2 1.1 ± 0.3 Miscellaneous  French fries X-18899 UKN 0.26 8.0 × 10−22 0.84 1.0 ± 0.9 1.6 ± 0.7 X-11880 UKN 0.26 3.6 × 10−23 0.83 0.9 ± 0.5 1.6 ± 0.8 X-21339 UKN 0.29 7.6 × 10−27 0.81 0.9 ± 1.0 1.9 ± 1.1 X-11308 UKN 0.27 6.4 × 10−25 0.81 0.9 ± 0.5 1.5 ± 0.7 X-11549 UKN 0.27 1.1 × 10−24 0.81 0.9 ± 0.5 1.6 ± 0.9 X-11372 UKN 0.24 5.2 × 10−19 0.79 0.9 ± 0.4 1.4 ± 0.6 X-11378 UKN 0.23 1.0 × 10−17 0.76 0.9 ± 0.7 1.5 ± 0.7 X-16935 UKN 0.23 8.3 × 10−18 0.75 0.9 ± 1.1 1.9 ± 1.6 eicosanodioate LIP 0.21 8.8 × 10−15 0.73 1.0 ± 0.5 1.5 ± 0.8  Total candies X-13728 UKN 0.20 7.0 × 10−14 0.65 1.1 ± 1.2 2.0 ± 2.2  Chocolate candies X-13728 UKN 0.28 3.6 × 10−25 0.69 1.2 ± 1.4 2.3 ± 2.4 3-methylxanthine XEN 0.26 4.4 × 10−22 0.68 1.1 ± 1.1 1.9 ± 1.8 7-methylurate XEN 0.24 4.0 × 10−19 0.68 1.1 ± 1.2 1.9 ± 1.9 3,7-dimethylurate XEN 0.24 1.9 × 10−19 0.66 0.9 ± 1.0 1.5 ± 1.6 theobromine XEN 0.24 3.8 × 10−19 0.66 1.1 ± 1.1 1.9 ± 1.7 7-methylxanthine XEN 0.21 1.8 × 10−14 0.64 0.6 ± 0.8 1.1 ± 1.2  Desserts ergothioneine XEN −0.25 1.8 × 10−20 0.69 1.5 ± 0.8 1.0 ± 0.6 sphingomyelin (d18:2/18:1)5 LIP 0.21 7.8 × 10−15 0.65 0.9 ± 0.4 1.1 ± 0.4 Alcohol  Total alcohol ethyl glucuronide XEN 0.60 1.79 × 10−133 0.92 0.4 ± 0.5 9.2 ± 28.4 X-24293 UKN 0.54 1.63 × 10−102 0.87 0.8 ± 1.3 4.4 ± 6.6 X-21737 UKN 0.21 3.7 × 10−15 0.76 1.8 ± 11.6 3.0 ± 6.0 CMPF LIP 0.23 2.9 × 10−17 0.74 1.1 ± 1.7 2.2 ± 1.8 X-23655 UKN 0.22 5.3 × 10−16 0.73 0.5 ± 0.7 1.3 ± 1.3 X-24811 UKN 0.23 2.8 × 10−17 0.73 0.6 ± 0.8 1.3 ± 1.1 caffeine XEN 0.25 3.3 × 10−21 0.72 0.9 ± 1.3 2.1 ± 1.9 X-14473 UKN 0.26 5.0 × 10−22 0.72 0.8 ± 0.7 1.4 ± 0.9 sphingomyelin (d18:2/18:1)5 LIP −0.27 3.0 × 10−24 0.72 1.1 ± 0.4 0.8 ± 0.3 X-12230 UKN 0.20 9.7 × 10−14 0.72 1.0 ± 1.2 2.0 ± 1.9  Beer X-24293 UKN 0.27 1.5 × 10−24 0.72 1.2 ± 2.0 3.3 ± 6.8  Total wine ethyl glucuronide XEN 0.45 1.9 × 10−68 0.85 0.4 ± 0.5 5.5 ± 17.0 X-24293 UKN 0.37 4.8 × 10−46 0.79 0.8 ± 1.3 3.1 ± 4.0 2,3-dihydroxyisovalerate XEN 0.36 5.6 × 10−43 0.75 1.2 ± 1.3 3.0 ± 4.8 CMPF LIP 0.20 5.4 × 10−14 0.73 1.2 ± 1.6 2.2 ± 2.0 sphingomyelin (d18:2/18:1)5 LIP −0.23 7.5 × 10−18 0.71 1.1 ± 0.4 0.9 ± 0.3 X-18249 UKN −0.20 8.6 × 10−14 0.70 1.2 ± 0.5 0.8 ± 0.3 X-24473 UKN 0.25 1.6 × 10−20 0.70 1.2 ± 1.5 2.0 ± 2.9 oleoyl-linoleoyl-glycerol (18:1/18:2) (2)6 LIP −0.20 3.5 × 10−14 0.68 1.2 ± 0.6 0.9 ± 0.5 X-11795 UKN 0.22 2.1 × 10−16 0.65 1.1 ± 0.8 1.5 ± 1.6 androstenediol (3β,17β) monosulfate (2) LIP 0.21 8.6 × 10−15 0.65 1.1 ± 0.9 1.9 ± 2.0  Red wine ethyl glucuronide XEN 0.30 3.3 × 10−30 0.75 1.1 ± 6.3 4.5 ± 16.8 X-24293 UKN 0.27 1.4 × 10−24 0.72 1.0 ± 1.7 2.9 ± 4.8 2,3-dihydroxyisovalerate XEN 0.26 1.1 × 10−22 0.66 1.3 ± 1.4 2.5 ± 4.1  White wine ethyl glucuronide XEN 0.22 8.6 × 10−16 0.83 0.6 ± 1.6 6.7 ± 20.6 2,3-dihydroxyisovalerate XEN 0.23 1.9 × 10−17 0.74 1.3 ± 1.6 3.1 ± 5.1  Liquor ethyl glucuronide XEN 0.51 8.4 × 10−90 0.80 0.9 ± 5.5 8.4 ± 27.4 X-24293 UKN 0.44 2.4 × 10−65 0.79 1.0 ± 1.5 4.1 ± 6.7 X-01911 UKN 0.24 9.4 × 10−20 0.68 1.1 ± 1.2 1.8 ± 1.6 androstenediol (3β,17β) disulfate (1) LIP 0.28 1.7 × 10−26 0.67 1.1 ± 1.0 2.4 ± 3.4 androstenediol (3β,17β) monosulfate (2) LIP 0.28 2.3 × 10−26 0.67 1.1 ± 0.9 2.2 ± 3.1 X-21474 UKN 0.23 2.3 × 10−17 0.67 1.0 ± 1.2 1.7 ± 1.5 5α-androstan-3β,17β-diol disulfate LIP 0.29 8.7 × 10−27 0.66 1.2 ± 1.2 2.7 ± 5.0 X-21659 UKN 0.22 4.4 × 10−16 0.66 1.0 ± 1.2 1.7 ± 1.5 X-17335 UKN 0.20 5.1 × 10−14 0.62 1.0 ± 0.6 1.2 ± 0.7 5α-androstan-3α,17β-diol disulfate LIP 0.24 4.3 × 10−19 0.60 0.4 ± 0.8 1.0 ± 1.9 Beverages  Total coffee X-21442 UKN 0.62 6.3 × 10−146 0.98 0.1 ± 1.0 3.5 ± 4.5 trigonelline (N ′-methylnicotinate) CV 0.66 1.3 × 10−172 0.96 0.3 ± 0.4 2.0 ± 1.1 X-24811 UKN 0.62 7.5 × 10−145 0.95 0.2 ± 0.3 1.6 ± 1.1 X-23655 UKN 0.58 2.6 × 10−121 0.95 0.1 ± 0.4 1.5 ± 1.3 quinate XEN 0.66 1.7 × 10−170 0.95 0.3 ± 0.5 2.0 ± 1.3 3-hydroxypyridine sulfate XEN 0.61 1.5 × 10−138 0.95 0.2 ± 0.5 2.3 ± 1.6 X-12230 UKN 0.57 1.1 × 10−119 0.94 0.3 ± 0.5 2.3 ± 1.7 3-methyl catechol sulfate (1) XEN 0.59 1.9 × 10−127 0.93 0.4 ± 0.7 2.1 ± 1.4 X-17185 UKN 0.52 4.4 × 10−96 0.93 0.3 ± 0.5 3.1 ± 10.8 citraconate/glutaconate ENG 0.56 4.4 × 10−111 0.93 0.6 ± 0.4 2.0 ± 1.2  Caffeinated coffee 1-methylxanthine XEN 0.64 1.4 × 10−155 0.96 0.5 ± 0.7 3.2 ± 1.7 paraxanthine XEN 0.60 1.5 × 10−135 0.95 0.3 ± 0.5 2.3 ± 1.4 1-methylurate XEN 0.60 6.9 × 10−134 0.94 0.6 ± 0.9 3.2 ± 1.8 5-acetylamino-6-amino-3-methyluracil XEN 0.61 7.9 × 10−138 0.94 0.6 ± 0.9 3.1 ± 1.7 1,3-dimethylurate XEN 0.56 2.8 × 10−112 0.94 0.7 ± 4.3 2.5 ± 1.4 1,7-dimethylurate XEN 0.61 2.0 × 10−136 0.92 0.6 ± 0.7 2.3 ± 1.1 theophylline XEN 0.55 5.2 × 10−108 0.92 0.7 ± 3.5 2.5 ± 1.5 caffeine XEN 0.61 3.8 × 10−140 0.91 0.6 ± 1.0 2.7 ± 1.7 1,3,7-trimethylurate XEN 0.62 2.7 × 10−144 0.91 0.5 ± 0.8 2.2 ± 1.3 X-21442 UKN 0.41 4.1 × 10−57 0.88 0.7 ± 1.6 3.6 ± 4.2  Decaffeinated coffee X-21442 UKN 0.31 7.6 × 10−31 0.83 0.9 ± 1.9 3.4 ± 4.6 3-hydroxypyridine sulfate XEN 0.29 1.6 × 10−27 0.80 0.9 ± 1.3 2.5 ± 1.7 trigonelline (N ′-methylnicotinate) CV 0.26 6.5 × 10−23 0.79 0.9 ± 0.9 1.9 ± 1.2 quinate XEN 0.30 6.4 × 10−29 0.78 0.9 ± 1.1 2.1 ± 1.4 X-24811 UKN 0.26 3.4 × 10−22 0.78 0.7 ± 0.9 1.5 ± 1.2 X-23655 UKN 0.26 2.3 × 10−22 0.78 0.6 ± 1.0 1.6 ± 1.4 X-23649 UKN 0.24 2.6 × 10−19 0.78 0.6 ± 1.2 1.8 ± 1.8 X-12816 UKN 0.25 4.1 × 10−21 0.77 0.5 ± 1.0 1.5 ± 1.4 X-12230 UKN 0.23 2.3 × 10−18 0.77 1.1 ± 1.6 2.3 ± 1.7 2,3-dihydroxypyridine XEN 0.24 1.8 × 10−18 0.77 0.6 ± 1.0 1.5 ± 1.2  Total tea theanine XEN 0.50 2.6 × 10−87 0.84 1.0 ± 6.3 28.2 ± 53.0 X-21795 UKN 0.41 1.4 × 10−55 0.72 0.1 ± 0.3 1.1 ± 2.0  Nonherbal tea theanine XEN 0.47 3.7 × 10−74 0.84 5.3 ± 25.3 33.7 ± 58.7 X-21795 UKN 0.43 9.3 × 10−62 0.72 0.2 ± 0.8 1.3 ± 2.1  Herbal tea or decaffeinated tea theanine XEN 0.23 2.7 × 10−17 0.70 8.7 ± 33.9 17.9 ± 41.8  Diet soft drinks saccharin XEN 0.25 5.5 × 10−20 0.69 4.5 ± 23.5 17.9 ± 46.4 1Diet-metabolite correlations selected for presentation here had P < 4.63 × 10−7 and |r| > 0.2 from Pearson's partial correlation analysis. Adjusted for age at blood draw, race, education, smoking status, hormone replacement therapy, physical activity, BMI, ethanol consumption (except for alcohol-containing items), time since last meal, and caloric intake. AA, amino acid; CHO, carbohydrate; CMPF, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate; CPS-II, Cancer Prevention Study-II; CV, cofactor/vitamin; ENG, energy; LIP, lipid; PEP, peptide; Q, quintile; UKN, unknown; XEN, xenobiotic; γ-CEHC, γ-carboxyethyl hydrochroman. 2Estimated from receiver operating characteristic analysis using 2000-times stratified bootstrap samples. 3Raw data divided by the median of each metabolite, before data transformation by generalized log and autoscaling. 4Metabolites starting with X are unnamed; the superpathway of these is unknown. 5Putative identity that has not been officially confirmed based on a standard. 6(1) and (2) indicate that the metabolite differs from another with the same mass in the position of the R group. View Large TABLE 1 Top 10 predictive serum metabolites of 42 food groups/items among women in the CPS-II Nutrition Cohort (n = 1369)1 Food groups/items Metabolites Super-pathway r P AUC2 Q1 mean ± SD3 Q5 mean ± SD Fruits  Total citrus fruits and juices stachydrine XEN 0.53 3.6 × 10−99 0.89 0.5 ± 0.7 2.1 ± 1.2 X-247384 UKN 0.49 5.2 × 10−82 0.89 0.5 ± 1.2 2.7 ± 2.2 N-methylproline AA 0.50 8.6 × 10−86 0.87 0.6 ± 1.0 2.8 ± 1.9 chiro-inositol LIP 0.43 1.1 × 10−62 0.86 0.3 ± 0.6 1.5 ± 1.2 X-22836 UKN 0.42 1.3 × 10−58 0.83 0.4 ± 0.7 1.5 ± 1.2 X-23314 UKN 0.41 1.4 × 10−56 0.82 0.9 ± 1.2 3.3 ± 3.3 X-17350 UKN 0.37 4.2 × 10−44 0.80 1.0 ± 1.1 2.7 ± 2.3 methyl glucopyranoside (α + β) XEN 0.36 4.1 × 10−43 0.80 0.8 ± 0.9 2.0 ± 1.9 X-16947 UKN 0.36 2.5 × 10−42 0.80 1.7 ± 5.1 6.1 ± 8.6 β-cryptoxanthin XEN 0.35 1.0 × 10−39 0.80 0.8 ± 0.5 1.6 ± 0.9  Orange juice stachydrine XEN 0.54 4.5 × 10−104 0.87 0.6 ± 0.8 2.2 ± 1.2 X-24738 UKN 0.51 2.2 × 10−92 0.86 0.6 ± 1.2 2.8 ± 2.3 N-methylproline AA 0.52 6.8 × 10−93 0.86 0.7 ± 1.0 2.8 ± 1.9 X-23314 UKN 0.48 3.4 × 10−78 0.83 0.9 ± 1.4 3.5 ± 3.7 chiro-inositol LIP 0.46 2.6 × 10−72 0.83 0.4 ± 0.9 1.5 ± 1.3 X-17350 UKN 0.43 3.1 × 10−62 0.82 1.0 ± 1.2 2.8 ± 2.4 X-22836 UKN 0.44 4.5 × 10−64 0.82 0.4 ± 0.7 1.5 ± 1.2 X-16947 UKN 0.42 8.2 × 10−58 0.81 1.5 ± 4.5 6.9 ± 9.2 X-22515 UKN 0.40 3.0 × 10−54 0.81 0.6 ± 1.7 3.0 ± 4.2 X-19183 UKN 0.41 1.9 × 10−57 0.81 0.4 ± 0.9 1.2 ± 1.0  Banana dopamine 3-O-sulfate AA 0.34 1.0 × 10−37 0.76 1.3 ± 1.5 5.7 ± 7.0 dopamine 4-sulfate AA 0.33 2.5 × 10−36 0.74 0.9 ± 1.6 5.3 ± 7.5 S-methylmethionine AA 0.23 3.9 × 10−18 0.72 1.0 ± 2.2 2.3 ± 2.7 3-methoxytyramine sulfate AA 0.22 9.2 × 10−17 0.70 1.0 ± 0.5 1.5 ± 0.9 X-12729 UKN 0.21 1.8 × 10−15 0.68 1.3 ± 3.7 2.9 ± 5.0 5-hydroxyindoleacetate AA 0.21 1.1 × 10−14 0.68 0.8 ± 0.9 1.7 ± 1.9  Prunes X-11315 UKN 0.21 1.5 × 10−14 0.67 1.0 ± 0.3 1.2 ± 0.5 X-12818 UKN 0.20 5.3 × 10−14 0.62 1.0 ± 1.4 1.5 ± 1.8 hippurate XEN 0.22 7.1 × 10−16 0.61 1.2 ± 1.1 1.8 ± 1.7 benzoylcarnitine5 XEN 0.25 3.0 × 10−21 0.61 0.9 ± 1.1 1.4 ± 1.8 X-24757 UKN 0.25 6.1 × 10−21 0.60 1.0 ± 1.2 1.9 ± 3.0 5-hydroxymethyl-2-furoic acid AA 0.26 3.1 × 10−22 0.58 1.0 ± 3.7 2.4 ± 8.1 X-17367 UKN 0.23 3.4 × 10−17 0.58 1.1 ± 1.6 2.1 ± 4.0 X-17325 UKN 0.21 1.3 × 10−15 0.58 1.4 ± 1.8 2.3 ± 3.8 X-22475 UKN 0.23 5.9 × 10−18 0.57 0.6 ± 1.4 1.6 ± 4.0 catechol sulfate XEN 0.20 4.2 × 10−14 0.57 1.1 ± 0.9 1.5 ± 1.1 Vegetables  Cruciferous vegetables S-methylcysteine sulfoxide AA 0.24 1.7 × 10−18 0.69 1.0 ± 0.7 1.6 ± 1.2  Mushrooms ergothioneine XEN 0.28 3.0 × 10−25 0.75 1.0 ± 0.5 1.6 ± 0.8  Allium vegetables N-methyltaurine AA 0.28 1.4 × 10−26 0.73 0.5 ± 1.3 1.5 ± 1.9 N-acetylalliin XEN 0.22 4.4 × 10−16 0.67 0.8 ± 1.7 2.9 ± 11.1 piperine XEN 0.23 1.0 × 10−17 0.67 1.1 ± 1.2 2.0 ± 2.2 ergothioneine XEN 0.22 1.0 × 10−16 0.67 1.1 ± 0.6 1.4 ± 0.8 γ-CEHC CV −0.20 5.7 × 10−14 0.67 1.3 ± 0.7 0.9 ± 0.6 γ-CEHC glucuronide5 CV −0.20 2.8 × 10−14 0.67 1.3 ± 0.9 0.8 ± 0.8 X-12231 UKN 0.20 9.6 × 10−14 0.65 1.0 ± 1.2 1.5 ± 1.6  Onion N-methyltaurine AA 0.24 9.7 × 10−20 0.69 0.6 ± 1.6 1.4 ± 2.1  Garlic γ-CEHC glucuronide5 CV −0.22 7.7 × 10−17 0.72 1.3 ± 1.0 0.7 ± 0.6 X-18249 UKN −0.22 4.8 × 10−16 0.71 1.2 ± 0.5 0.9 ± 0.4 γ-CEHC CV −0.22 2.0 × 10−16 0.71 1.3 ± 0.7 0.9 ± 0.5 N-acetylalliin XEN 0.27 3.1 × 10−24 0.70 0.8 ± 3.3 2.9 ± 8.9 S-allylcysteine XEN 0.23 2.8 × 10−17 0.69 1.2 ± 3.5 3.2 ± 5.0 ergothioneine XEN 0.26 3.6 × 10−22 0.69 1.0 ± 0.5 1.5 ± 0.9 X-02269 UKN 0.21 6.6 × 10−15 0.69 1.2 ± 1.3 2.2 ± 2.4 alliin XEN 0.24 5.7 × 10−19 0.69 1.0 ± 5.6 3.8 ± 9.0 N-methyltaurine AA 0.24 1.5 × 10−18 0.68 0.6 ± 1.2 1.5 ± 2.1  Tofu or soybeans X-11847 UKN 0.22 5.7 × 10−16 0.75 1.7 ± 3.2 7.3 ± 11.8 X-11858 UKN 0.22 1.1 × 10−15 0.72 1.0 ± 3.4 8.7 ± 24.2 X-16649 UKN 0.21 1.1 × 10−14 0.62 0.8 ± 3.0 4.9 ± 12.7 Grains  Whole grains X-21752 UKN 0.20 5.8 × 10−14 0.65 0.5 ± 1.2 1.1 ± 1.7 Proteins  Eggs 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)5 LIP 0.22 8.4 × 10−17 0.71 1.0 ± 0.3 1.4 ± 0.5  Red meat 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)5 LIP 0.28 4.2 × 10−25 0.67 0.9 ± 0.3 1.2 ± 0.4 X-11381 UKN 0.21 3.3 × 10−15 0.66 1.0 ± 0.5 1.2 ± 0.6 1-(1-enyl-stearoyl)-2-oleoyl-GPE (P-18:0/18:1) LIP 0.23 6.9 × 10−18 0.65 1.0 ± 0.3 1.2 ± 0.4  Processed meat X-18922 UKN 0.20 3.9 × 10−14 0.70 0.8 ± 0.5 1.2 ± 0.6  Poultry X-13835 UKN 0.21 1.1 × 10−14 0.65 1.2 ± 1.8 1.7 ± 1.7 3-methylhistidine AA 0.21 3.6 × 10−15 0.64 1.3 ± 1.6 2.1 ± 2.2  Total fish X-02269 UKN 0.41 1.9 × 10−55 0.82 0.9 ± 1.1 2.7 ± 2.5 CMPF LIP 0.37 1.7 × 10−45 0.80 1.0 ± 2.0 2.7 ± 2.3 DHA LIP 0.33 1.1 × 10−36 0.77 0.9 ± 0.4 1.4 ± 0.7 docosahexaenoylcholine LIP 0.27 3.1 × 10−24 0.75 0.8 ± 0.4 1.4 ± 0.7 1-docosahexaenoylglycerol (22:6) LIP 0.28 1.2 × 10−26 0.74 0.8 ± 0.8 1.4 ± 0.8 EPA LIP 0.27 7.1 × 10−25 0.72 0.9 ± 0.6 1.7 ± 1.2 eicosapentaenoylcholine LIP 0.23 4.0 × 10−17 0.72 0.8 ± 0.7 1.5 ± 1.3  Dark fish X-02269 UKN 0.51 1.8 × 10−92 0.94 0.7 ± 0.9 3.6 ± 2.4 CMPF LIP 0.47 6.9 × 10−74 0.93 0.8 ± 1.4 3.6 ± 2.6 DHA LIP 0.37 5.6 × 10−44 0.86 0.9 ± 0.4 1.7 ± 0.8 EPA LIP 0.36 1.7 × 10−42 0.84 0.9 ± 0.6 2.0 ± 1.2 docosahexaenoylcholine LIP 0.27 2.1 × 10−24 0.80 0.9 ± 0.5 1.6 ± 0.7 sphingomyelin (d18:2/18:1)5 LIP −0.25 4.7 × 10−20 0.80 1.2 ± 0.4 0.8 ± 0.3 eicosapentaenoylcholine LIP 0.29 8.7 × 10−28 0.79 0.8 ± 0.7 1.9 ± 1.5 1-docosahexaenoylglycerol (22:6) LIP 0.28 1.1 × 10−26 0.79 0.8 ± 0.6 1.7 ± 0.9 docosapentaenoate (n-6 DPA; 22:5n-6) LIP −0.26 2.4 × 10−22 0.71 1.2 ± 0.5 0.8 ± 0.5 X-13866 UKN 0.21 1.8 × 10−14 0.69 1.2 ± 2.0 1.9 ± 1.9  Shellfish CMPF LIP 0.26 4.3 × 10−23 0.83 1.1 ± 1.8 2.9 ± 2.7 X-02269 UKN 0.25 4.2 × 10−20 0.81 1.0 ± 1.4 2.4 ± 1.7  Total nuts tryptophan betaine AA 0.41 2.2 × 10−55 0.80 0.8 ± 0.9 2.0 ± 1.6 X-23644 UKN 0.27 6.5 × 10−24 0.72 1.9 ± 3.4 4.5 ± 5.7 4-vinylphenol sulfate XEN 0.27 1.9 × 10−24 0.69 1.3 ± 1.7 2.6 ± 2.9 lignoceroylcarnitine (C24)5 LIP 0.25 5.9 × 10−21 0.69 0.9 ± 0.4 1.2 ± 0.5 γ-glutamylvaline PEP −0.25 1.7 × 10−21 0.68 1.2 ± 0.4 0.9 ± 0.4 behenoylcarnitine (C22)5 LIP 0.23 2.4 × 10−17 0.66 0.7 ± 0.5 1.1 ± 0.8 sphingomyelin (d18:2/23:1)5 LIP −0.22 4.2 × 10−16 0.66 1.1 ± 0.3 0.9 ± 0.3  Peanuts tryptophan betaine AA 0.45 2.3 × 10−68 0.83 0.8 ± 0.9 2.1 ± 1.7 X-23644 UKN 0.27 6.3 × 10−24 0.71 2.0 ± 3.6 4.6 ± 5.9 4-vinylphenol sulfate XEN 0.31 7.0 × 10−32 0.71 1.2 ± 1.6 2.7 ± 2.9 γ-glutamylvaline PEP −0.30 3.3 × 10−30 0.71 1.2 ± 0.4 0.9 ± 0.4 lignoceroylcarnitine (C24)5 LIP 0.25 2.5 × 10−20 0.66 1.0 ± 0.4 1.2 ± 0.5 behenoylcarnitine (C22)5 LIP 0.23 5.6 × 10−18 0.66 0.8 ± 0.6 1.2 ± 0.8 sphingomyelin (d18:2/231)5 LIP −0.22 1.7 × 10−16 0.65 1.1 ± 0.3 0.9 ± 0.2  Other nuts X-11315 UKN 0.22 1.2 × 10−15 0.66 1.0 ± 0.4 1.2 ± 0.4 Dairy  Milk galactonate CHO 0.33 1.5 × 10−35 0.76 0.8 ± 0.7 2.0 ± 1.8 2,8-quinolinediol sulfate XEN 0.27 2.6 × 10−24 0.75 0.4 ± 0.7 1.3 ± 1.5 phenylacetylglycine PEP 0.27 1.8 × 10−23 0.71 0.7 ± 0.8 1.5 ± 1.3 X-11381 UKN 0.23 3.3 × 10−18 0.71 0.9 ± 0.4 1.3 ± 0.5 X-12798 UKN 0.24 1.4 × 10−18 0.70 0.9 ± 0.4 1.2 ± 0.5  Soy milk X-16649 UKN 0.20 4.1 × 10−14 0.69 1.1 ± 4.9 5.7 ± 12.1 4-ethylphenylsulfate XEN 0.20 5.9 × 10−14 0.67 2.0 ± 4.6 8.4 ± 16.2  Yogurt X-21736 UKN −0.20 6.3 × 10−14 0.70 1.5 ± 1.2 0.9 ± 0.7 Fats and oils  Butter X-11438 UKN 0.24 6.2 × 10−20 0.71 1.0 ± 0.8 1.6 ± 1.0 caprate (10:0) LIP 0.26 1.2 × 10−21 0.70 1.1 ± 0.6 1.6 ± 1.0 10-undecenoate (11:1n-1) LIP 0.24 9.3 × 10−19 0.69 1.0 ± 0.5 1.4 ± 0.7 sphingomyelin (d18:1/25:0, d19:0/24:1, d20:1/23:0, d19:1/24:0)5 LIP 0.24 6.7 × 10−20 0.68 0.9 ± 0.4 1.2 ± 0.5 X-17337 UKN 0.21 6.7 × 10−15 0.67 1.0 ± 0.5 1.3 ± 0.6 caprylate (8:0) LIP 0.21 5.0 × 10−15 0.67 1.0 ± 0.4 1.3 ± 0.6 sphingomyelin (d17:1/16:0, d18:1/15:0, d16:1/17:0)5 LIP 0.23 1.5 × 10−17 0.65 1.0 ± 0.2 1.1 ± 0.3 Miscellaneous  French fries X-18899 UKN 0.26 8.0 × 10−22 0.84 1.0 ± 0.9 1.6 ± 0.7 X-11880 UKN 0.26 3.6 × 10−23 0.83 0.9 ± 0.5 1.6 ± 0.8 X-21339 UKN 0.29 7.6 × 10−27 0.81 0.9 ± 1.0 1.9 ± 1.1 X-11308 UKN 0.27 6.4 × 10−25 0.81 0.9 ± 0.5 1.5 ± 0.7 X-11549 UKN 0.27 1.1 × 10−24 0.81 0.9 ± 0.5 1.6 ± 0.9 X-11372 UKN 0.24 5.2 × 10−19 0.79 0.9 ± 0.4 1.4 ± 0.6 X-11378 UKN 0.23 1.0 × 10−17 0.76 0.9 ± 0.7 1.5 ± 0.7 X-16935 UKN 0.23 8.3 × 10−18 0.75 0.9 ± 1.1 1.9 ± 1.6 eicosanodioate LIP 0.21 8.8 × 10−15 0.73 1.0 ± 0.5 1.5 ± 0.8  Total candies X-13728 UKN 0.20 7.0 × 10−14 0.65 1.1 ± 1.2 2.0 ± 2.2  Chocolate candies X-13728 UKN 0.28 3.6 × 10−25 0.69 1.2 ± 1.4 2.3 ± 2.4 3-methylxanthine XEN 0.26 4.4 × 10−22 0.68 1.1 ± 1.1 1.9 ± 1.8 7-methylurate XEN 0.24 4.0 × 10−19 0.68 1.1 ± 1.2 1.9 ± 1.9 3,7-dimethylurate XEN 0.24 1.9 × 10−19 0.66 0.9 ± 1.0 1.5 ± 1.6 theobromine XEN 0.24 3.8 × 10−19 0.66 1.1 ± 1.1 1.9 ± 1.7 7-methylxanthine XEN 0.21 1.8 × 10−14 0.64 0.6 ± 0.8 1.1 ± 1.2  Desserts ergothioneine XEN −0.25 1.8 × 10−20 0.69 1.5 ± 0.8 1.0 ± 0.6 sphingomyelin (d18:2/18:1)5 LIP 0.21 7.8 × 10−15 0.65 0.9 ± 0.4 1.1 ± 0.4 Alcohol  Total alcohol ethyl glucuronide XEN 0.60 1.79 × 10−133 0.92 0.4 ± 0.5 9.2 ± 28.4 X-24293 UKN 0.54 1.63 × 10−102 0.87 0.8 ± 1.3 4.4 ± 6.6 X-21737 UKN 0.21 3.7 × 10−15 0.76 1.8 ± 11.6 3.0 ± 6.0 CMPF LIP 0.23 2.9 × 10−17 0.74 1.1 ± 1.7 2.2 ± 1.8 X-23655 UKN 0.22 5.3 × 10−16 0.73 0.5 ± 0.7 1.3 ± 1.3 X-24811 UKN 0.23 2.8 × 10−17 0.73 0.6 ± 0.8 1.3 ± 1.1 caffeine XEN 0.25 3.3 × 10−21 0.72 0.9 ± 1.3 2.1 ± 1.9 X-14473 UKN 0.26 5.0 × 10−22 0.72 0.8 ± 0.7 1.4 ± 0.9 sphingomyelin (d18:2/18:1)5 LIP −0.27 3.0 × 10−24 0.72 1.1 ± 0.4 0.8 ± 0.3 X-12230 UKN 0.20 9.7 × 10−14 0.72 1.0 ± 1.2 2.0 ± 1.9  Beer X-24293 UKN 0.27 1.5 × 10−24 0.72 1.2 ± 2.0 3.3 ± 6.8  Total wine ethyl glucuronide XEN 0.45 1.9 × 10−68 0.85 0.4 ± 0.5 5.5 ± 17.0 X-24293 UKN 0.37 4.8 × 10−46 0.79 0.8 ± 1.3 3.1 ± 4.0 2,3-dihydroxyisovalerate XEN 0.36 5.6 × 10−43 0.75 1.2 ± 1.3 3.0 ± 4.8 CMPF LIP 0.20 5.4 × 10−14 0.73 1.2 ± 1.6 2.2 ± 2.0 sphingomyelin (d18:2/18:1)5 LIP −0.23 7.5 × 10−18 0.71 1.1 ± 0.4 0.9 ± 0.3 X-18249 UKN −0.20 8.6 × 10−14 0.70 1.2 ± 0.5 0.8 ± 0.3 X-24473 UKN 0.25 1.6 × 10−20 0.70 1.2 ± 1.5 2.0 ± 2.9 oleoyl-linoleoyl-glycerol (18:1/18:2) (2)6 LIP −0.20 3.5 × 10−14 0.68 1.2 ± 0.6 0.9 ± 0.5 X-11795 UKN 0.22 2.1 × 10−16 0.65 1.1 ± 0.8 1.5 ± 1.6 androstenediol (3β,17β) monosulfate (2) LIP 0.21 8.6 × 10−15 0.65 1.1 ± 0.9 1.9 ± 2.0  Red wine ethyl glucuronide XEN 0.30 3.3 × 10−30 0.75 1.1 ± 6.3 4.5 ± 16.8 X-24293 UKN 0.27 1.4 × 10−24 0.72 1.0 ± 1.7 2.9 ± 4.8 2,3-dihydroxyisovalerate XEN 0.26 1.1 × 10−22 0.66 1.3 ± 1.4 2.5 ± 4.1  White wine ethyl glucuronide XEN 0.22 8.6 × 10−16 0.83 0.6 ± 1.6 6.7 ± 20.6 2,3-dihydroxyisovalerate XEN 0.23 1.9 × 10−17 0.74 1.3 ± 1.6 3.1 ± 5.1  Liquor ethyl glucuronide XEN 0.51 8.4 × 10−90 0.80 0.9 ± 5.5 8.4 ± 27.4 X-24293 UKN 0.44 2.4 × 10−65 0.79 1.0 ± 1.5 4.1 ± 6.7 X-01911 UKN 0.24 9.4 × 10−20 0.68 1.1 ± 1.2 1.8 ± 1.6 androstenediol (3β,17β) disulfate (1) LIP 0.28 1.7 × 10−26 0.67 1.1 ± 1.0 2.4 ± 3.4 androstenediol (3β,17β) monosulfate (2) LIP 0.28 2.3 × 10−26 0.67 1.1 ± 0.9 2.2 ± 3.1 X-21474 UKN 0.23 2.3 × 10−17 0.67 1.0 ± 1.2 1.7 ± 1.5 5α-androstan-3β,17β-diol disulfate LIP 0.29 8.7 × 10−27 0.66 1.2 ± 1.2 2.7 ± 5.0 X-21659 UKN 0.22 4.4 × 10−16 0.66 1.0 ± 1.2 1.7 ± 1.5 X-17335 UKN 0.20 5.1 × 10−14 0.62 1.0 ± 0.6 1.2 ± 0.7 5α-androstan-3α,17β-diol disulfate LIP 0.24 4.3 × 10−19 0.60 0.4 ± 0.8 1.0 ± 1.9 Beverages  Total coffee X-21442 UKN 0.62 6.3 × 10−146 0.98 0.1 ± 1.0 3.5 ± 4.5 trigonelline (N ′-methylnicotinate) CV 0.66 1.3 × 10−172 0.96 0.3 ± 0.4 2.0 ± 1.1 X-24811 UKN 0.62 7.5 × 10−145 0.95 0.2 ± 0.3 1.6 ± 1.1 X-23655 UKN 0.58 2.6 × 10−121 0.95 0.1 ± 0.4 1.5 ± 1.3 quinate XEN 0.66 1.7 × 10−170 0.95 0.3 ± 0.5 2.0 ± 1.3 3-hydroxypyridine sulfate XEN 0.61 1.5 × 10−138 0.95 0.2 ± 0.5 2.3 ± 1.6 X-12230 UKN 0.57 1.1 × 10−119 0.94 0.3 ± 0.5 2.3 ± 1.7 3-methyl catechol sulfate (1) XEN 0.59 1.9 × 10−127 0.93 0.4 ± 0.7 2.1 ± 1.4 X-17185 UKN 0.52 4.4 × 10−96 0.93 0.3 ± 0.5 3.1 ± 10.8 citraconate/glutaconate ENG 0.56 4.4 × 10−111 0.93 0.6 ± 0.4 2.0 ± 1.2  Caffeinated coffee 1-methylxanthine XEN 0.64 1.4 × 10−155 0.96 0.5 ± 0.7 3.2 ± 1.7 paraxanthine XEN 0.60 1.5 × 10−135 0.95 0.3 ± 0.5 2.3 ± 1.4 1-methylurate XEN 0.60 6.9 × 10−134 0.94 0.6 ± 0.9 3.2 ± 1.8 5-acetylamino-6-amino-3-methyluracil XEN 0.61 7.9 × 10−138 0.94 0.6 ± 0.9 3.1 ± 1.7 1,3-dimethylurate XEN 0.56 2.8 × 10−112 0.94 0.7 ± 4.3 2.5 ± 1.4 1,7-dimethylurate XEN 0.61 2.0 × 10−136 0.92 0.6 ± 0.7 2.3 ± 1.1 theophylline XEN 0.55 5.2 × 10−108 0.92 0.7 ± 3.5 2.5 ± 1.5 caffeine XEN 0.61 3.8 × 10−140 0.91 0.6 ± 1.0 2.7 ± 1.7 1,3,7-trimethylurate XEN 0.62 2.7 × 10−144 0.91 0.5 ± 0.8 2.2 ± 1.3 X-21442 UKN 0.41 4.1 × 10−57 0.88 0.7 ± 1.6 3.6 ± 4.2  Decaffeinated coffee X-21442 UKN 0.31 7.6 × 10−31 0.83 0.9 ± 1.9 3.4 ± 4.6 3-hydroxypyridine sulfate XEN 0.29 1.6 × 10−27 0.80 0.9 ± 1.3 2.5 ± 1.7 trigonelline (N ′-methylnicotinate) CV 0.26 6.5 × 10−23 0.79 0.9 ± 0.9 1.9 ± 1.2 quinate XEN 0.30 6.4 × 10−29 0.78 0.9 ± 1.1 2.1 ± 1.4 X-24811 UKN 0.26 3.4 × 10−22 0.78 0.7 ± 0.9 1.5 ± 1.2 X-23655 UKN 0.26 2.3 × 10−22 0.78 0.6 ± 1.0 1.6 ± 1.4 X-23649 UKN 0.24 2.6 × 10−19 0.78 0.6 ± 1.2 1.8 ± 1.8 X-12816 UKN 0.25 4.1 × 10−21 0.77 0.5 ± 1.0 1.5 ± 1.4 X-12230 UKN 0.23 2.3 × 10−18 0.77 1.1 ± 1.6 2.3 ± 1.7 2,3-dihydroxypyridine XEN 0.24 1.8 × 10−18 0.77 0.6 ± 1.0 1.5 ± 1.2  Total tea theanine XEN 0.50 2.6 × 10−87 0.84 1.0 ± 6.3 28.2 ± 53.0 X-21795 UKN 0.41 1.4 × 10−55 0.72 0.1 ± 0.3 1.1 ± 2.0  Nonherbal tea theanine XEN 0.47 3.7 × 10−74 0.84 5.3 ± 25.3 33.7 ± 58.7 X-21795 UKN 0.43 9.3 × 10−62 0.72 0.2 ± 0.8 1.3 ± 2.1  Herbal tea or decaffeinated tea theanine XEN 0.23 2.7 × 10−17 0.70 8.7 ± 33.9 17.9 ± 41.8  Diet soft drinks saccharin XEN 0.25 5.5 × 10−20 0.69 4.5 ± 23.5 17.9 ± 46.4 Food groups/items Metabolites Super-pathway r P AUC2 Q1 mean ± SD3 Q5 mean ± SD Fruits  Total citrus fruits and juices stachydrine XEN 0.53 3.6 × 10−99 0.89 0.5 ± 0.7 2.1 ± 1.2 X-247384 UKN 0.49 5.2 × 10−82 0.89 0.5 ± 1.2 2.7 ± 2.2 N-methylproline AA 0.50 8.6 × 10−86 0.87 0.6 ± 1.0 2.8 ± 1.9 chiro-inositol LIP 0.43 1.1 × 10−62 0.86 0.3 ± 0.6 1.5 ± 1.2 X-22836 UKN 0.42 1.3 × 10−58 0.83 0.4 ± 0.7 1.5 ± 1.2 X-23314 UKN 0.41 1.4 × 10−56 0.82 0.9 ± 1.2 3.3 ± 3.3 X-17350 UKN 0.37 4.2 × 10−44 0.80 1.0 ± 1.1 2.7 ± 2.3 methyl glucopyranoside (α + β) XEN 0.36 4.1 × 10−43 0.80 0.8 ± 0.9 2.0 ± 1.9 X-16947 UKN 0.36 2.5 × 10−42 0.80 1.7 ± 5.1 6.1 ± 8.6 β-cryptoxanthin XEN 0.35 1.0 × 10−39 0.80 0.8 ± 0.5 1.6 ± 0.9  Orange juice stachydrine XEN 0.54 4.5 × 10−104 0.87 0.6 ± 0.8 2.2 ± 1.2 X-24738 UKN 0.51 2.2 × 10−92 0.86 0.6 ± 1.2 2.8 ± 2.3 N-methylproline AA 0.52 6.8 × 10−93 0.86 0.7 ± 1.0 2.8 ± 1.9 X-23314 UKN 0.48 3.4 × 10−78 0.83 0.9 ± 1.4 3.5 ± 3.7 chiro-inositol LIP 0.46 2.6 × 10−72 0.83 0.4 ± 0.9 1.5 ± 1.3 X-17350 UKN 0.43 3.1 × 10−62 0.82 1.0 ± 1.2 2.8 ± 2.4 X-22836 UKN 0.44 4.5 × 10−64 0.82 0.4 ± 0.7 1.5 ± 1.2 X-16947 UKN 0.42 8.2 × 10−58 0.81 1.5 ± 4.5 6.9 ± 9.2 X-22515 UKN 0.40 3.0 × 10−54 0.81 0.6 ± 1.7 3.0 ± 4.2 X-19183 UKN 0.41 1.9 × 10−57 0.81 0.4 ± 0.9 1.2 ± 1.0  Banana dopamine 3-O-sulfate AA 0.34 1.0 × 10−37 0.76 1.3 ± 1.5 5.7 ± 7.0 dopamine 4-sulfate AA 0.33 2.5 × 10−36 0.74 0.9 ± 1.6 5.3 ± 7.5 S-methylmethionine AA 0.23 3.9 × 10−18 0.72 1.0 ± 2.2 2.3 ± 2.7 3-methoxytyramine sulfate AA 0.22 9.2 × 10−17 0.70 1.0 ± 0.5 1.5 ± 0.9 X-12729 UKN 0.21 1.8 × 10−15 0.68 1.3 ± 3.7 2.9 ± 5.0 5-hydroxyindoleacetate AA 0.21 1.1 × 10−14 0.68 0.8 ± 0.9 1.7 ± 1.9  Prunes X-11315 UKN 0.21 1.5 × 10−14 0.67 1.0 ± 0.3 1.2 ± 0.5 X-12818 UKN 0.20 5.3 × 10−14 0.62 1.0 ± 1.4 1.5 ± 1.8 hippurate XEN 0.22 7.1 × 10−16 0.61 1.2 ± 1.1 1.8 ± 1.7 benzoylcarnitine5 XEN 0.25 3.0 × 10−21 0.61 0.9 ± 1.1 1.4 ± 1.8 X-24757 UKN 0.25 6.1 × 10−21 0.60 1.0 ± 1.2 1.9 ± 3.0 5-hydroxymethyl-2-furoic acid AA 0.26 3.1 × 10−22 0.58 1.0 ± 3.7 2.4 ± 8.1 X-17367 UKN 0.23 3.4 × 10−17 0.58 1.1 ± 1.6 2.1 ± 4.0 X-17325 UKN 0.21 1.3 × 10−15 0.58 1.4 ± 1.8 2.3 ± 3.8 X-22475 UKN 0.23 5.9 × 10−18 0.57 0.6 ± 1.4 1.6 ± 4.0 catechol sulfate XEN 0.20 4.2 × 10−14 0.57 1.1 ± 0.9 1.5 ± 1.1 Vegetables  Cruciferous vegetables S-methylcysteine sulfoxide AA 0.24 1.7 × 10−18 0.69 1.0 ± 0.7 1.6 ± 1.2  Mushrooms ergothioneine XEN 0.28 3.0 × 10−25 0.75 1.0 ± 0.5 1.6 ± 0.8  Allium vegetables N-methyltaurine AA 0.28 1.4 × 10−26 0.73 0.5 ± 1.3 1.5 ± 1.9 N-acetylalliin XEN 0.22 4.4 × 10−16 0.67 0.8 ± 1.7 2.9 ± 11.1 piperine XEN 0.23 1.0 × 10−17 0.67 1.1 ± 1.2 2.0 ± 2.2 ergothioneine XEN 0.22 1.0 × 10−16 0.67 1.1 ± 0.6 1.4 ± 0.8 γ-CEHC CV −0.20 5.7 × 10−14 0.67 1.3 ± 0.7 0.9 ± 0.6 γ-CEHC glucuronide5 CV −0.20 2.8 × 10−14 0.67 1.3 ± 0.9 0.8 ± 0.8 X-12231 UKN 0.20 9.6 × 10−14 0.65 1.0 ± 1.2 1.5 ± 1.6  Onion N-methyltaurine AA 0.24 9.7 × 10−20 0.69 0.6 ± 1.6 1.4 ± 2.1  Garlic γ-CEHC glucuronide5 CV −0.22 7.7 × 10−17 0.72 1.3 ± 1.0 0.7 ± 0.6 X-18249 UKN −0.22 4.8 × 10−16 0.71 1.2 ± 0.5 0.9 ± 0.4 γ-CEHC CV −0.22 2.0 × 10−16 0.71 1.3 ± 0.7 0.9 ± 0.5 N-acetylalliin XEN 0.27 3.1 × 10−24 0.70 0.8 ± 3.3 2.9 ± 8.9 S-allylcysteine XEN 0.23 2.8 × 10−17 0.69 1.2 ± 3.5 3.2 ± 5.0 ergothioneine XEN 0.26 3.6 × 10−22 0.69 1.0 ± 0.5 1.5 ± 0.9 X-02269 UKN 0.21 6.6 × 10−15 0.69 1.2 ± 1.3 2.2 ± 2.4 alliin XEN 0.24 5.7 × 10−19 0.69 1.0 ± 5.6 3.8 ± 9.0 N-methyltaurine AA 0.24 1.5 × 10−18 0.68 0.6 ± 1.2 1.5 ± 2.1  Tofu or soybeans X-11847 UKN 0.22 5.7 × 10−16 0.75 1.7 ± 3.2 7.3 ± 11.8 X-11858 UKN 0.22 1.1 × 10−15 0.72 1.0 ± 3.4 8.7 ± 24.2 X-16649 UKN 0.21 1.1 × 10−14 0.62 0.8 ± 3.0 4.9 ± 12.7 Grains  Whole grains X-21752 UKN 0.20 5.8 × 10−14 0.65 0.5 ± 1.2 1.1 ± 1.7 Proteins  Eggs 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)5 LIP 0.22 8.4 × 10−17 0.71 1.0 ± 0.3 1.4 ± 0.5  Red meat 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)5 LIP 0.28 4.2 × 10−25 0.67 0.9 ± 0.3 1.2 ± 0.4 X-11381 UKN 0.21 3.3 × 10−15 0.66 1.0 ± 0.5 1.2 ± 0.6 1-(1-enyl-stearoyl)-2-oleoyl-GPE (P-18:0/18:1) LIP 0.23 6.9 × 10−18 0.65 1.0 ± 0.3 1.2 ± 0.4  Processed meat X-18922 UKN 0.20 3.9 × 10−14 0.70 0.8 ± 0.5 1.2 ± 0.6  Poultry X-13835 UKN 0.21 1.1 × 10−14 0.65 1.2 ± 1.8 1.7 ± 1.7 3-methylhistidine AA 0.21 3.6 × 10−15 0.64 1.3 ± 1.6 2.1 ± 2.2  Total fish X-02269 UKN 0.41 1.9 × 10−55 0.82 0.9 ± 1.1 2.7 ± 2.5 CMPF LIP 0.37 1.7 × 10−45 0.80 1.0 ± 2.0 2.7 ± 2.3 DHA LIP 0.33 1.1 × 10−36 0.77 0.9 ± 0.4 1.4 ± 0.7 docosahexaenoylcholine LIP 0.27 3.1 × 10−24 0.75 0.8 ± 0.4 1.4 ± 0.7 1-docosahexaenoylglycerol (22:6) LIP 0.28 1.2 × 10−26 0.74 0.8 ± 0.8 1.4 ± 0.8 EPA LIP 0.27 7.1 × 10−25 0.72 0.9 ± 0.6 1.7 ± 1.2 eicosapentaenoylcholine LIP 0.23 4.0 × 10−17 0.72 0.8 ± 0.7 1.5 ± 1.3  Dark fish X-02269 UKN 0.51 1.8 × 10−92 0.94 0.7 ± 0.9 3.6 ± 2.4 CMPF LIP 0.47 6.9 × 10−74 0.93 0.8 ± 1.4 3.6 ± 2.6 DHA LIP 0.37 5.6 × 10−44 0.86 0.9 ± 0.4 1.7 ± 0.8 EPA LIP 0.36 1.7 × 10−42 0.84 0.9 ± 0.6 2.0 ± 1.2 docosahexaenoylcholine LIP 0.27 2.1 × 10−24 0.80 0.9 ± 0.5 1.6 ± 0.7 sphingomyelin (d18:2/18:1)5 LIP −0.25 4.7 × 10−20 0.80 1.2 ± 0.4 0.8 ± 0.3 eicosapentaenoylcholine LIP 0.29 8.7 × 10−28 0.79 0.8 ± 0.7 1.9 ± 1.5 1-docosahexaenoylglycerol (22:6) LIP 0.28 1.1 × 10−26 0.79 0.8 ± 0.6 1.7 ± 0.9 docosapentaenoate (n-6 DPA; 22:5n-6) LIP −0.26 2.4 × 10−22 0.71 1.2 ± 0.5 0.8 ± 0.5 X-13866 UKN 0.21 1.8 × 10−14 0.69 1.2 ± 2.0 1.9 ± 1.9  Shellfish CMPF LIP 0.26 4.3 × 10−23 0.83 1.1 ± 1.8 2.9 ± 2.7 X-02269 UKN 0.25 4.2 × 10−20 0.81 1.0 ± 1.4 2.4 ± 1.7  Total nuts tryptophan betaine AA 0.41 2.2 × 10−55 0.80 0.8 ± 0.9 2.0 ± 1.6 X-23644 UKN 0.27 6.5 × 10−24 0.72 1.9 ± 3.4 4.5 ± 5.7 4-vinylphenol sulfate XEN 0.27 1.9 × 10−24 0.69 1.3 ± 1.7 2.6 ± 2.9 lignoceroylcarnitine (C24)5 LIP 0.25 5.9 × 10−21 0.69 0.9 ± 0.4 1.2 ± 0.5 γ-glutamylvaline PEP −0.25 1.7 × 10−21 0.68 1.2 ± 0.4 0.9 ± 0.4 behenoylcarnitine (C22)5 LIP 0.23 2.4 × 10−17 0.66 0.7 ± 0.5 1.1 ± 0.8 sphingomyelin (d18:2/23:1)5 LIP −0.22 4.2 × 10−16 0.66 1.1 ± 0.3 0.9 ± 0.3  Peanuts tryptophan betaine AA 0.45 2.3 × 10−68 0.83 0.8 ± 0.9 2.1 ± 1.7 X-23644 UKN 0.27 6.3 × 10−24 0.71 2.0 ± 3.6 4.6 ± 5.9 4-vinylphenol sulfate XEN 0.31 7.0 × 10−32 0.71 1.2 ± 1.6 2.7 ± 2.9 γ-glutamylvaline PEP −0.30 3.3 × 10−30 0.71 1.2 ± 0.4 0.9 ± 0.4 lignoceroylcarnitine (C24)5 LIP 0.25 2.5 × 10−20 0.66 1.0 ± 0.4 1.2 ± 0.5 behenoylcarnitine (C22)5 LIP 0.23 5.6 × 10−18 0.66 0.8 ± 0.6 1.2 ± 0.8 sphingomyelin (d18:2/231)5 LIP −0.22 1.7 × 10−16 0.65 1.1 ± 0.3 0.9 ± 0.2  Other nuts X-11315 UKN 0.22 1.2 × 10−15 0.66 1.0 ± 0.4 1.2 ± 0.4 Dairy  Milk galactonate CHO 0.33 1.5 × 10−35 0.76 0.8 ± 0.7 2.0 ± 1.8 2,8-quinolinediol sulfate XEN 0.27 2.6 × 10−24 0.75 0.4 ± 0.7 1.3 ± 1.5 phenylacetylglycine PEP 0.27 1.8 × 10−23 0.71 0.7 ± 0.8 1.5 ± 1.3 X-11381 UKN 0.23 3.3 × 10−18 0.71 0.9 ± 0.4 1.3 ± 0.5 X-12798 UKN 0.24 1.4 × 10−18 0.70 0.9 ± 0.4 1.2 ± 0.5  Soy milk X-16649 UKN 0.20 4.1 × 10−14 0.69 1.1 ± 4.9 5.7 ± 12.1 4-ethylphenylsulfate XEN 0.20 5.9 × 10−14 0.67 2.0 ± 4.6 8.4 ± 16.2  Yogurt X-21736 UKN −0.20 6.3 × 10−14 0.70 1.5 ± 1.2 0.9 ± 0.7 Fats and oils  Butter X-11438 UKN 0.24 6.2 × 10−20 0.71 1.0 ± 0.8 1.6 ± 1.0 caprate (10:0) LIP 0.26 1.2 × 10−21 0.70 1.1 ± 0.6 1.6 ± 1.0 10-undecenoate (11:1n-1) LIP 0.24 9.3 × 10−19 0.69 1.0 ± 0.5 1.4 ± 0.7 sphingomyelin (d18:1/25:0, d19:0/24:1, d20:1/23:0, d19:1/24:0)5 LIP 0.24 6.7 × 10−20 0.68 0.9 ± 0.4 1.2 ± 0.5 X-17337 UKN 0.21 6.7 × 10−15 0.67 1.0 ± 0.5 1.3 ± 0.6 caprylate (8:0) LIP 0.21 5.0 × 10−15 0.67 1.0 ± 0.4 1.3 ± 0.6 sphingomyelin (d17:1/16:0, d18:1/15:0, d16:1/17:0)5 LIP 0.23 1.5 × 10−17 0.65 1.0 ± 0.2 1.1 ± 0.3 Miscellaneous  French fries X-18899 UKN 0.26 8.0 × 10−22 0.84 1.0 ± 0.9 1.6 ± 0.7 X-11880 UKN 0.26 3.6 × 10−23 0.83 0.9 ± 0.5 1.6 ± 0.8 X-21339 UKN 0.29 7.6 × 10−27 0.81 0.9 ± 1.0 1.9 ± 1.1 X-11308 UKN 0.27 6.4 × 10−25 0.81 0.9 ± 0.5 1.5 ± 0.7 X-11549 UKN 0.27 1.1 × 10−24 0.81 0.9 ± 0.5 1.6 ± 0.9 X-11372 UKN 0.24 5.2 × 10−19 0.79 0.9 ± 0.4 1.4 ± 0.6 X-11378 UKN 0.23 1.0 × 10−17 0.76 0.9 ± 0.7 1.5 ± 0.7 X-16935 UKN 0.23 8.3 × 10−18 0.75 0.9 ± 1.1 1.9 ± 1.6 eicosanodioate LIP 0.21 8.8 × 10−15 0.73 1.0 ± 0.5 1.5 ± 0.8  Total candies X-13728 UKN 0.20 7.0 × 10−14 0.65 1.1 ± 1.2 2.0 ± 2.2  Chocolate candies X-13728 UKN 0.28 3.6 × 10−25 0.69 1.2 ± 1.4 2.3 ± 2.4 3-methylxanthine XEN 0.26 4.4 × 10−22 0.68 1.1 ± 1.1 1.9 ± 1.8 7-methylurate XEN 0.24 4.0 × 10−19 0.68 1.1 ± 1.2 1.9 ± 1.9 3,7-dimethylurate XEN 0.24 1.9 × 10−19 0.66 0.9 ± 1.0 1.5 ± 1.6 theobromine XEN 0.24 3.8 × 10−19 0.66 1.1 ± 1.1 1.9 ± 1.7 7-methylxanthine XEN 0.21 1.8 × 10−14 0.64 0.6 ± 0.8 1.1 ± 1.2  Desserts ergothioneine XEN −0.25 1.8 × 10−20 0.69 1.5 ± 0.8 1.0 ± 0.6 sphingomyelin (d18:2/18:1)5 LIP 0.21 7.8 × 10−15 0.65 0.9 ± 0.4 1.1 ± 0.4 Alcohol  Total alcohol ethyl glucuronide XEN 0.60 1.79 × 10−133 0.92 0.4 ± 0.5 9.2 ± 28.4 X-24293 UKN 0.54 1.63 × 10−102 0.87 0.8 ± 1.3 4.4 ± 6.6 X-21737 UKN 0.21 3.7 × 10−15 0.76 1.8 ± 11.6 3.0 ± 6.0 CMPF LIP 0.23 2.9 × 10−17 0.74 1.1 ± 1.7 2.2 ± 1.8 X-23655 UKN 0.22 5.3 × 10−16 0.73 0.5 ± 0.7 1.3 ± 1.3 X-24811 UKN 0.23 2.8 × 10−17 0.73 0.6 ± 0.8 1.3 ± 1.1 caffeine XEN 0.25 3.3 × 10−21 0.72 0.9 ± 1.3 2.1 ± 1.9 X-14473 UKN 0.26 5.0 × 10−22 0.72 0.8 ± 0.7 1.4 ± 0.9 sphingomyelin (d18:2/18:1)5 LIP −0.27 3.0 × 10−24 0.72 1.1 ± 0.4 0.8 ± 0.3 X-12230 UKN 0.20 9.7 × 10−14 0.72 1.0 ± 1.2 2.0 ± 1.9  Beer X-24293 UKN 0.27 1.5 × 10−24 0.72 1.2 ± 2.0 3.3 ± 6.8  Total wine ethyl glucuronide XEN 0.45 1.9 × 10−68 0.85 0.4 ± 0.5 5.5 ± 17.0 X-24293 UKN 0.37 4.8 × 10−46 0.79 0.8 ± 1.3 3.1 ± 4.0 2,3-dihydroxyisovalerate XEN 0.36 5.6 × 10−43 0.75 1.2 ± 1.3 3.0 ± 4.8 CMPF LIP 0.20 5.4 × 10−14 0.73 1.2 ± 1.6 2.2 ± 2.0 sphingomyelin (d18:2/18:1)5 LIP −0.23 7.5 × 10−18 0.71 1.1 ± 0.4 0.9 ± 0.3 X-18249 UKN −0.20 8.6 × 10−14 0.70 1.2 ± 0.5 0.8 ± 0.3 X-24473 UKN 0.25 1.6 × 10−20 0.70 1.2 ± 1.5 2.0 ± 2.9 oleoyl-linoleoyl-glycerol (18:1/18:2) (2)6 LIP −0.20 3.5 × 10−14 0.68 1.2 ± 0.6 0.9 ± 0.5 X-11795 UKN 0.22 2.1 × 10−16 0.65 1.1 ± 0.8 1.5 ± 1.6 androstenediol (3β,17β) monosulfate (2) LIP 0.21 8.6 × 10−15 0.65 1.1 ± 0.9 1.9 ± 2.0  Red wine ethyl glucuronide XEN 0.30 3.3 × 10−30 0.75 1.1 ± 6.3 4.5 ± 16.8 X-24293 UKN 0.27 1.4 × 10−24 0.72 1.0 ± 1.7 2.9 ± 4.8 2,3-dihydroxyisovalerate XEN 0.26 1.1 × 10−22 0.66 1.3 ± 1.4 2.5 ± 4.1  White wine ethyl glucuronide XEN 0.22 8.6 × 10−16 0.83 0.6 ± 1.6 6.7 ± 20.6 2,3-dihydroxyisovalerate XEN 0.23 1.9 × 10−17 0.74 1.3 ± 1.6 3.1 ± 5.1  Liquor ethyl glucuronide XEN 0.51 8.4 × 10−90 0.80 0.9 ± 5.5 8.4 ± 27.4 X-24293 UKN 0.44 2.4 × 10−65 0.79 1.0 ± 1.5 4.1 ± 6.7 X-01911 UKN 0.24 9.4 × 10−20 0.68 1.1 ± 1.2 1.8 ± 1.6 androstenediol (3β,17β) disulfate (1) LIP 0.28 1.7 × 10−26 0.67 1.1 ± 1.0 2.4 ± 3.4 androstenediol (3β,17β) monosulfate (2) LIP 0.28 2.3 × 10−26 0.67 1.1 ± 0.9 2.2 ± 3.1 X-21474 UKN 0.23 2.3 × 10−17 0.67 1.0 ± 1.2 1.7 ± 1.5 5α-androstan-3β,17β-diol disulfate LIP 0.29 8.7 × 10−27 0.66 1.2 ± 1.2 2.7 ± 5.0 X-21659 UKN 0.22 4.4 × 10−16 0.66 1.0 ± 1.2 1.7 ± 1.5 X-17335 UKN 0.20 5.1 × 10−14 0.62 1.0 ± 0.6 1.2 ± 0.7 5α-androstan-3α,17β-diol disulfate LIP 0.24 4.3 × 10−19 0.60 0.4 ± 0.8 1.0 ± 1.9 Beverages  Total coffee X-21442 UKN 0.62 6.3 × 10−146 0.98 0.1 ± 1.0 3.5 ± 4.5 trigonelline (N ′-methylnicotinate) CV 0.66 1.3 × 10−172 0.96 0.3 ± 0.4 2.0 ± 1.1 X-24811 UKN 0.62 7.5 × 10−145 0.95 0.2 ± 0.3 1.6 ± 1.1 X-23655 UKN 0.58 2.6 × 10−121 0.95 0.1 ± 0.4 1.5 ± 1.3 quinate XEN 0.66 1.7 × 10−170 0.95 0.3 ± 0.5 2.0 ± 1.3 3-hydroxypyridine sulfate XEN 0.61 1.5 × 10−138 0.95 0.2 ± 0.5 2.3 ± 1.6 X-12230 UKN 0.57 1.1 × 10−119 0.94 0.3 ± 0.5 2.3 ± 1.7 3-methyl catechol sulfate (1) XEN 0.59 1.9 × 10−127 0.93 0.4 ± 0.7 2.1 ± 1.4 X-17185 UKN 0.52 4.4 × 10−96 0.93 0.3 ± 0.5 3.1 ± 10.8 citraconate/glutaconate ENG 0.56 4.4 × 10−111 0.93 0.6 ± 0.4 2.0 ± 1.2  Caffeinated coffee 1-methylxanthine XEN 0.64 1.4 × 10−155 0.96 0.5 ± 0.7 3.2 ± 1.7 paraxanthine XEN 0.60 1.5 × 10−135 0.95 0.3 ± 0.5 2.3 ± 1.4 1-methylurate XEN 0.60 6.9 × 10−134 0.94 0.6 ± 0.9 3.2 ± 1.8 5-acetylamino-6-amino-3-methyluracil XEN 0.61 7.9 × 10−138 0.94 0.6 ± 0.9 3.1 ± 1.7 1,3-dimethylurate XEN 0.56 2.8 × 10−112 0.94 0.7 ± 4.3 2.5 ± 1.4 1,7-dimethylurate XEN 0.61 2.0 × 10−136 0.92 0.6 ± 0.7 2.3 ± 1.1 theophylline XEN 0.55 5.2 × 10−108 0.92 0.7 ± 3.5 2.5 ± 1.5 caffeine XEN 0.61 3.8 × 10−140 0.91 0.6 ± 1.0 2.7 ± 1.7 1,3,7-trimethylurate XEN 0.62 2.7 × 10−144 0.91 0.5 ± 0.8 2.2 ± 1.3 X-21442 UKN 0.41 4.1 × 10−57 0.88 0.7 ± 1.6 3.6 ± 4.2  Decaffeinated coffee X-21442 UKN 0.31 7.6 × 10−31 0.83 0.9 ± 1.9 3.4 ± 4.6 3-hydroxypyridine sulfate XEN 0.29 1.6 × 10−27 0.80 0.9 ± 1.3 2.5 ± 1.7 trigonelline (N ′-methylnicotinate) CV 0.26 6.5 × 10−23 0.79 0.9 ± 0.9 1.9 ± 1.2 quinate XEN 0.30 6.4 × 10−29 0.78 0.9 ± 1.1 2.1 ± 1.4 X-24811 UKN 0.26 3.4 × 10−22 0.78 0.7 ± 0.9 1.5 ± 1.2 X-23655 UKN 0.26 2.3 × 10−22 0.78 0.6 ± 1.0 1.6 ± 1.4 X-23649 UKN 0.24 2.6 × 10−19 0.78 0.6 ± 1.2 1.8 ± 1.8 X-12816 UKN 0.25 4.1 × 10−21 0.77 0.5 ± 1.0 1.5 ± 1.4 X-12230 UKN 0.23 2.3 × 10−18 0.77 1.1 ± 1.6 2.3 ± 1.7 2,3-dihydroxypyridine XEN 0.24 1.8 × 10−18 0.77 0.6 ± 1.0 1.5 ± 1.2  Total tea theanine XEN 0.50 2.6 × 10−87 0.84 1.0 ± 6.3 28.2 ± 53.0 X-21795 UKN 0.41 1.4 × 10−55 0.72 0.1 ± 0.3 1.1 ± 2.0  Nonherbal tea theanine XEN 0.47 3.7 × 10−74 0.84 5.3 ± 25.3 33.7 ± 58.7 X-21795 UKN 0.43 9.3 × 10−62 0.72 0.2 ± 0.8 1.3 ± 2.1  Herbal tea or decaffeinated tea theanine XEN 0.23 2.7 × 10−17 0.70 8.7 ± 33.9 17.9 ± 41.8  Diet soft drinks saccharin XEN 0.25 5.5 × 10−20 0.69 4.5 ± 23.5 17.9 ± 46.4 1Diet-metabolite correlations selected for presentation here had P < 4.63 × 10−7 and |r| > 0.2 from Pearson's partial correlation analysis. Adjusted for age at blood draw, race, education, smoking status, hormone replacement therapy, physical activity, BMI, ethanol consumption (except for alcohol-containing items), time since last meal, and caloric intake. AA, amino acid; CHO, carbohydrate; CMPF, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate; CPS-II, Cancer Prevention Study-II; CV, cofactor/vitamin; ENG, energy; LIP, lipid; PEP, peptide; Q, quintile; UKN, unknown; XEN, xenobiotic; γ-CEHC, γ-carboxyethyl hydrochroman. 2Estimated from receiver operating characteristic analysis using 2000-times stratified bootstrap samples. 3Raw data divided by the median of each metabolite, before data transformation by generalized log and autoscaling. 4Metabolites starting with X are unnamed; the superpathway of these is unknown. 5Putative identity that has not been officially confirmed based on a standard. 6(1) and (2) indicate that the metabolite differs from another with the same mass in the position of the R group. View Large TABLE 2 Replication of food metabolites found in previous cross-sectional studies for habitual intake among women in the CPS-II Nutrition Cohort (n = 1369)1 Food groups Metabolites Biospecimen Cross-sectional studies Citrus fruits and juices, orange juice β-cryptoxanthin serum (21) chiro-inositol serum, urine (9, 14) methyl glucopyranoside (α + β) serum (22) naringenin 7-glucuronide urine (23, 24) N-methylproline serum, urine (9, 10) stachydrine or proline betaine serum, urine (9, 10, 13, 14, 23, 25–27) X-17145 serum (9) X-17350 urine (9) Cruciferous vegetables S-methylcysteine sulfoxide serum (22) Mushrooms ergothioneine serum and plasma (13) Fish, shellfish CMPF serum, urine (9, 10, 13, 14) DHA serum (9, 10, 13, 14) EPA serum (9, 10, 13, 14) X-02269 serum (9, 13) Nuts, peanuts tryptophan betaine serum, urine (9, 10, 14) 4-vinylphenol sulfate serum, urine (9, 10, 14) X-11315 serum and plasma (13) Milk galactonate serum (22, 28) X-12798 serum and plasma (13) Butter caprate (10:0) serum (22) 10-undecenoate (11:1n-1) serum (13, 14) Chocolate 3,7-dimethylurate urine (29) 3-methylxanthine urine (29) 7-methylurate serum, urine (13, 25) 7-methylxanthine urine (29) theobromine serum (13, 14, 25) Alcohol ethyl glucuronide serum, urine (9, 14) Wine 2,3-dihydroxyisovalerate urine (9) X-01911 serum and plasma (13) X-11795 serum and plasma (13) Coffee 1,3,7-trimethylurate serum, urine (9–11, 30) 1,3-dimethylurate serum, urine (9, 30) 1,7-dimethylurate serum, urine (9–11, 30) 1-methylurate serum, urine (9–11, 30) 1-methylxanthine serum, urine (9–11, 13, 30) 3-(3-hydroxyphenyl)propionate serum (11) 3-hydroxyhippurate serum, urine (9, 11, 24, 30) 3-hydroxypyridine sulfate serum and plasma (13) 4-vinylguaiacol sulfate serum (11) 5-acetylamino-6-amino-3-methyluracil serum (9, 10) 5-acetylamino-6-formylamino-3-methyluracil urine (9) 3-methyl catechol sulfate (1) serum and plasma (13) caffeine serum, urine (9–11, 30) caffeic acid sulfate urine (24) catechol sulfate serum, urine (9, 11, 13) cinnamoylglycine serum (11) dihydroferulic acid serum (22) ferulic acid 4-sulfate urine (24) hippurate urine (9, 30) N-(2-furoyl)glycine serum, urine (9, 11) O-methylcatechol sulfate serum and plasma (13) paraxanthine serum, urine (9–11, 30) quinate serum, urine (9–11, 13) theophylline serum, urine (9, 11) trigonelline (N ′-methylnicotinate) serum (9, 11, 30) X-12230 serum and plasma (11, 13) X-12329 serum, urine (9, 11) X-12738 urine (9) X-12816 serum and plasma (11, 13) X-13844 urine (9) X-14473 serum (11, 13) X-17185 urine (9, 11) Tea theanine serum (22) Food groups Metabolites Biospecimen Cross-sectional studies Citrus fruits and juices, orange juice β-cryptoxanthin serum (21) chiro-inositol serum, urine (9, 14) methyl glucopyranoside (α + β) serum (22) naringenin 7-glucuronide urine (23, 24) N-methylproline serum, urine (9, 10) stachydrine or proline betaine serum, urine (9, 10, 13, 14, 23, 25–27) X-17145 serum (9) X-17350 urine (9) Cruciferous vegetables S-methylcysteine sulfoxide serum (22) Mushrooms ergothioneine serum and plasma (13) Fish, shellfish CMPF serum, urine (9, 10, 13, 14) DHA serum (9, 10, 13, 14) EPA serum (9, 10, 13, 14) X-02269 serum (9, 13) Nuts, peanuts tryptophan betaine serum, urine (9, 10, 14) 4-vinylphenol sulfate serum, urine (9, 10, 14) X-11315 serum and plasma (13) Milk galactonate serum (22, 28) X-12798 serum and plasma (13) Butter caprate (10:0) serum (22) 10-undecenoate (11:1n-1) serum (13, 14) Chocolate 3,7-dimethylurate urine (29) 3-methylxanthine urine (29) 7-methylurate serum, urine (13, 25) 7-methylxanthine urine (29) theobromine serum (13, 14, 25) Alcohol ethyl glucuronide serum, urine (9, 14) Wine 2,3-dihydroxyisovalerate urine (9) X-01911 serum and plasma (13) X-11795 serum and plasma (13) Coffee 1,3,7-trimethylurate serum, urine (9–11, 30) 1,3-dimethylurate serum, urine (9, 30) 1,7-dimethylurate serum, urine (9–11, 30) 1-methylurate serum, urine (9–11, 30) 1-methylxanthine serum, urine (9–11, 13, 30) 3-(3-hydroxyphenyl)propionate serum (11) 3-hydroxyhippurate serum, urine (9, 11, 24, 30) 3-hydroxypyridine sulfate serum and plasma (13) 4-vinylguaiacol sulfate serum (11) 5-acetylamino-6-amino-3-methyluracil serum (9, 10) 5-acetylamino-6-formylamino-3-methyluracil urine (9) 3-methyl catechol sulfate (1) serum and plasma (13) caffeine serum, urine (9–11, 30) caffeic acid sulfate urine (24) catechol sulfate serum, urine (9, 11, 13) cinnamoylglycine serum (11) dihydroferulic acid serum (22) ferulic acid 4-sulfate urine (24) hippurate urine (9, 30) N-(2-furoyl)glycine serum, urine (9, 11) O-methylcatechol sulfate serum and plasma (13) paraxanthine serum, urine (9–11, 30) quinate serum, urine (9–11, 13) theophylline serum, urine (9, 11) trigonelline (N ′-methylnicotinate) serum (9, 11, 30) X-12230 serum and plasma (11, 13) X-12329 serum, urine (9, 11) X-12738 urine (9) X-12816 serum and plasma (11, 13) X-13844 urine (9) X-14473 serum (11, 13) X-17185 urine (9, 11) Tea theanine serum (22) 1Unknown metabolites were only compared to studies using Metabolon platforms. CMPF, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate; CPS-II, Cancer Prevention Study-II. View Large TABLE 2 Replication of food metabolites found in previous cross-sectional studies for habitual intake among women in the CPS-II Nutrition Cohort (n = 1369)1 Food groups Metabolites Biospecimen Cross-sectional studies Citrus fruits and juices, orange juice β-cryptoxanthin serum (21) chiro-inositol serum, urine (9, 14) methyl glucopyranoside (α + β) serum (22) naringenin 7-glucuronide urine (23, 24) N-methylproline serum, urine (9, 10) stachydrine or proline betaine serum, urine (9, 10, 13, 14, 23, 25–27) X-17145 serum (9) X-17350 urine (9) Cruciferous vegetables S-methylcysteine sulfoxide serum (22) Mushrooms ergothioneine serum and plasma (13) Fish, shellfish CMPF serum, urine (9, 10, 13, 14) DHA serum (9, 10, 13, 14) EPA serum (9, 10, 13, 14) X-02269 serum (9, 13) Nuts, peanuts tryptophan betaine serum, urine (9, 10, 14) 4-vinylphenol sulfate serum, urine (9, 10, 14) X-11315 serum and plasma (13) Milk galactonate serum (22, 28) X-12798 serum and plasma (13) Butter caprate (10:0) serum (22) 10-undecenoate (11:1n-1) serum (13, 14) Chocolate 3,7-dimethylurate urine (29) 3-methylxanthine urine (29) 7-methylurate serum, urine (13, 25) 7-methylxanthine urine (29) theobromine serum (13, 14, 25) Alcohol ethyl glucuronide serum, urine (9, 14) Wine 2,3-dihydroxyisovalerate urine (9) X-01911 serum and plasma (13) X-11795 serum and plasma (13) Coffee 1,3,7-trimethylurate serum, urine (9–11, 30) 1,3-dimethylurate serum, urine (9, 30) 1,7-dimethylurate serum, urine (9–11, 30) 1-methylurate serum, urine (9–11, 30) 1-methylxanthine serum, urine (9–11, 13, 30) 3-(3-hydroxyphenyl)propionate serum (11) 3-hydroxyhippurate serum, urine (9, 11, 24, 30) 3-hydroxypyridine sulfate serum and plasma (13) 4-vinylguaiacol sulfate serum (11) 5-acetylamino-6-amino-3-methyluracil serum (9, 10) 5-acetylamino-6-formylamino-3-methyluracil urine (9) 3-methyl catechol sulfate (1) serum and plasma (13) caffeine serum, urine (9–11, 30) caffeic acid sulfate urine (24) catechol sulfate serum, urine (9, 11, 13) cinnamoylglycine serum (11) dihydroferulic acid serum (22) ferulic acid 4-sulfate urine (24) hippurate urine (9, 30) N-(2-furoyl)glycine serum, urine (9, 11) O-methylcatechol sulfate serum and plasma (13) paraxanthine serum, urine (9–11, 30) quinate serum, urine (9–11, 13) theophylline serum, urine (9, 11) trigonelline (N ′-methylnicotinate) serum (9, 11, 30) X-12230 serum and plasma (11, 13) X-12329 serum, urine (9, 11) X-12738 urine (9) X-12816 serum and plasma (11, 13) X-13844 urine (9) X-14473 serum (11, 13) X-17185 urine (9, 11) Tea theanine serum (22) Food groups Metabolites Biospecimen Cross-sectional studies Citrus fruits and juices, orange juice β-cryptoxanthin serum (21) chiro-inositol serum, urine (9, 14) methyl glucopyranoside (α + β) serum (22) naringenin 7-glucuronide urine (23, 24) N-methylproline serum, urine (9, 10) stachydrine or proline betaine serum, urine (9, 10, 13, 14, 23, 25–27) X-17145 serum (9) X-17350 urine (9) Cruciferous vegetables S-methylcysteine sulfoxide serum (22) Mushrooms ergothioneine serum and plasma (13) Fish, shellfish CMPF serum, urine (9, 10, 13, 14) DHA serum (9, 10, 13, 14) EPA serum (9, 10, 13, 14) X-02269 serum (9, 13) Nuts, peanuts tryptophan betaine serum, urine (9, 10, 14) 4-vinylphenol sulfate serum, urine (9, 10, 14) X-11315 serum and plasma (13) Milk galactonate serum (22, 28) X-12798 serum and plasma (13) Butter caprate (10:0) serum (22) 10-undecenoate (11:1n-1) serum (13, 14) Chocolate 3,7-dimethylurate urine (29) 3-methylxanthine urine (29) 7-methylurate serum, urine (13, 25) 7-methylxanthine urine (29) theobromine serum (13, 14, 25) Alcohol ethyl glucuronide serum, urine (9, 14) Wine 2,3-dihydroxyisovalerate urine (9) X-01911 serum and plasma (13) X-11795 serum and plasma (13) Coffee 1,3,7-trimethylurate serum, urine (9–11, 30) 1,3-dimethylurate serum, urine (9, 30) 1,7-dimethylurate serum, urine (9–11, 30) 1-methylurate serum, urine (9–11, 30) 1-methylxanthine serum, urine (9–11, 13, 30) 3-(3-hydroxyphenyl)propionate serum (11) 3-hydroxyhippurate serum, urine (9, 11, 24, 30) 3-hydroxypyridine sulfate serum and plasma (13) 4-vinylguaiacol sulfate serum (11) 5-acetylamino-6-amino-3-methyluracil serum (9, 10) 5-acetylamino-6-formylamino-3-methyluracil urine (9) 3-methyl catechol sulfate (1) serum and plasma (13) caffeine serum, urine (9–11, 30) caffeic acid sulfate urine (24) catechol sulfate serum, urine (9, 11, 13) cinnamoylglycine serum (11) dihydroferulic acid serum (22) ferulic acid 4-sulfate urine (24) hippurate urine (9, 30) N-(2-furoyl)glycine serum, urine (9, 11) O-methylcatechol sulfate serum and plasma (13) paraxanthine serum, urine (9–11, 30) quinate serum, urine (9–11, 13) theophylline serum, urine (9, 11) trigonelline (N ′-methylnicotinate) serum (9, 11, 30) X-12230 serum and plasma (11, 13) X-12329 serum, urine (9, 11) X-12738 urine (9) X-12816 serum and plasma (11, 13) X-13844 urine (9) X-14473 serum (11, 13) X-17185 urine (9, 11) Tea theanine serum (22) 1Unknown metabolites were only compared to studies using Metabolon platforms. CMPF, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate; CPS-II, Cancer Prevention Study-II. View Large The ROC curve for the most predictive metabolite for each of the 42 food groups or items is shown in Figure 1 by food class. Fold-change analysis after adjusting multiple covariates showed that the levels of the most predictive metabolites from quintile 1 to quintile 5 of intake had a 0.49- to 9-fold increase (Supplemental Figure 3). FIGURE 1 View largeDownload slide Receiver operating characteristic curves of the most predictive metabolite predicting quintile 1 and quintile 5 intake of each of the 42 food groups among women in the Cancer Prevention Study-II Nutrition Cohort (n = 1369). A, fruits; B, vegetables; C, proteins; D, alcohol; E, beverages; F, others. CMPF, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate; Decaf, decaffeinated; PE(P-18:0/20:4), 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4); γ-CEHC, γ-carboxyethyl hydrochroman. FIGURE 1 View largeDownload slide Receiver operating characteristic curves of the most predictive metabolite predicting quintile 1 and quintile 5 intake of each of the 42 food groups among women in the Cancer Prevention Study-II Nutrition Cohort (n = 1369). A, fruits; B, vegetables; C, proteins; D, alcohol; E, beverages; F, others. CMPF, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate; Decaf, decaffeinated; PE(P-18:0/20:4), 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4); γ-CEHC, γ-carboxyethyl hydrochroman. Multiple metabolites did not improve predictive accuracy compared to the most predictive metabolite More than one metabolite was correlated with 30 of the 42 food groups examined. A linear Support Vector Machine multivariate classification model was built with the use of all identified metabolites for each food shown in Supplemental Table 5, and a multivariate ROC analysis was conducted to calculate the AUC. Comparing the AUC calculated from the multivariate classification model with the AUC calculated from the univariate classification model revealed that there was no obvious improvement in predictive accuracy (AUC change <10%) by adding other metabolites into the classification model (Supplemental Table 6). Exceptions were garlic and decaffeinated coffee, of which the multivariate AUC increased by 14%, and 11%, respectively, compared to the AUC of the most predictive metabolite γ-carboxyethyl hydrochroman (γ-CEHC) glucuronide and X-21442. Sensitivity analyses Because the FFQ assessed average intake over the past 12 mo, in theory, blood collected after the FFQ may not correlate with the intake as well as blood collected before the FFQ was completed. Therefore, we conducted a sensitivity analysis by restricting the analyses to 1031 women who had their blood drawn after completion of the FFQ. A total of 363 diet-metabolite associations were identified, including 45 food groups and 189 different metabolites (data not shown). Stratification by case-control status revealed no noticeable differences between cases and controls (data not shown). Discussion Of the 199 diet-related metabolites, we replicated 63, including 49 known and 14 unknown metabolites that were reported as potential biomarkers of habitual food intake in previous cross-sectional studies (Table 2). The remaining metabolites were potentially novel or have only been identified as putative biomarkers in controlled intervention studies. For some biomarkers, such as those of citrus fruits and juices, fish, alcohol, and coffee, the sensitivity and specificity are both high; however, many other biomarkers have moderate to low sensitivity and specificity. We selectively discuss the plausible biomarkers by food class in the following text. Fruits We replicated 7 metabolites that have been correlated with total citrus fruits and juices or orange juice in previous cross-sectional studies (9, 10, 13, 14, 21, 23–27). The most significant biomarker—stachydrine—was first identified in an acute feeding study (31) and then validated as a biomarker of habitual citrus fruit intake in several cross-sectional datasets (9, 10, 13, 14, 23, 25–27). Together with our results, there is strong evidence that stachydrine is a reliable biomarker with high sensitivity and specificity for citrus, especially orange juice, intake. Other putative biomarkers of total citrus or orange juice intake in our study were the previously identified β-cryptoxanthin (21), chiro-inositol (9, 14), naringenin 7-glucuronide (23, 24), N-methylproline (9, 10), methyl glucopyranoside (α + β) (22), and 2 unknown metabolites referred to as X-17145 and X-17350. We validated 2 sulfonated dopamines as biomarkers for banana intake. Bananas are known to have the highest level of dopamine among many plants (32). Sulfonated dopamine, predominantly dopamine 3-O-sulfate, accounts for 90% of dopamine in blood (33). In a small feeding study (n = 6) of bananas, participants had elevated blood concentrations of conjugated dopamine that persisted for 8 h (34). Future studies are needed to confirm our findings. Vegetables Mushrooms are the primary dietary source of ergothioneine (35). Ergothioneine as a putative biomarker of mushroom intake was reported in a cross-sectional study of the TwinsUK cohort (13) and in a small feeding study (n = 10) of mushroom powder (36). Although ergothioneine was also correlated with intakes of allium vegetables, garlic, and desserts (inverse association) in our study, it is likely that these foods are frequently consumed with or without mushrooms. We also replicated S-methylcysteine sulfoxide as a biomarker for habitual cruciferous vegetable intake which was found in the Prostate, Lung, Colorectal and Ovarian (PLCO) cohort (22). Since the sensitivity and specificity were relatively low, future studies need to confirm our finding. For garlic intake, we identified 3 novel and biologically plausible biomarkers: alliin, N-acetylalliin, and S-allylcysteine. Alliin and S-allylcysteine are 2 compounds derived from a major component of garlic, namely γ-glutamylcysteines (37). In 2 feeding studies, urinary N-acetyl-S-allylcysteine increased after garlic consumption (38, 39). Although N-acetyl-I-allylcysteine was not detected or annotated in our study, we found that S-allylcysteine was significantly correlated with garlic intake. The moderate-to-low AUCs observed for these biomarkers were likely due to the day-to-day variation in consumption and measurement error of the self-administered FFQ. Protein foods We replicated 4 metabolites that have been associated with habitual consumption of fish and shellfish in previous cross-sectional studies: these are 3-carboxy-4-methyl-5-propyl-2-furanpropanoate (CMPF) (9, 10, 13, 14), DHA (9, 10, 13, 14), EPA (9, 10, 13, 14), and X-02269 (9, 13). The most predictive metabolite X-02269 for dark fish intake was also found in the TwinsUK cohort (13) and a US cohort (9). Tryptophan betaine and 4-vinylphenol sulfate were correlated with nut intake in our study, and have also been reported in 3 similar cross-sectional studies (9, 10, 14). These results are consistent with a feeding study of lactating women which showed that tryptophan betaine (or hypaphorine) was detected in human milk after peanut consumption (40), and with another study which showed that 4-vinylphenol (a polyphenol) was detected in roasted peanuts (41). Milk and butter For milk intake, we replicated an unknown metabolite X-12798 (13) and 2 biologically plausible biomarkers, galactonate and 2,8-quinolinediol sulfate. Galactonate is a metabolite of galactose through an anaerobic metabolic pathway (42) and has been associated with habitual milk intake (28). The other biomarker, 2,8-quinolinediol, recently detected in cow milk, might be hydrolyzed from quinoline, an alkaloid from various plant species, by cow gut microbiota (43). We found that higher butter intake was associated with 5 lipids in serum, including 3 medium-chain fatty acids, caprylate (8:0), caprate (10:0), and 10-undecenoate (11:1n-1); the latter 2 were associated with butter intake in other cohorts (13, 14, 22). We observed a low but novel correlation between soy milk intake and serum 4-ethylphenylsulfate. Similarly, Guertin et al. (14) observed a moderate association between tofu intake and 4-ethylphenylsulfate among 502 participants in the PLCO cohort. Animal studies suggest that 4-ethylphenylsulfate is derived from soy protein by gut microbiota (44). The low association observed in our study was likely due to the low intake level in this population. Future studies need to confirm this association in a population with higher intakes of soy products. Alcohol Ethyl glucuronide is metabolized directly from ethanol in the liver by UDP-glucuronosyltransferases (45). It has been suggested to be an optimal biomarker of alcohol consumption because of its high sensitivity and specificity, and it can be detected ≤80 h after the complete elimination of ethanol from the human body (46). For wine consumption, in addition to ethyl glucuronide, a potential biomarker was 2,3-dihydroxyisovalerate. Although the AUC we observed is low, it is a likely intermediate produced by yeast during wine fermentation (47), and was previously reported in a US cohort (9). Beverages Among the 74 metabolites associated with all categories of coffee intake reported in this study, 10 are involved in caffeine metabolism (caffeine, 1,3,7-trimethylurate, 1,3-dimethylurate, 1,7-dimethylurate, 1-methylurate, 1-methylxanthine, 5-acetyl-amino-6-amino-3-methyluracil, 5-acetylamino-6-formylamino-3-methyluracil, paraxanthine, and theophylline), and all have been reported in previous cross-sectional studies (9–11, 13, 30). Eight are involved in the metabolism of chlorogenic acid, one of the abundant polyphenols in coffee beans [quinate, hippurate, caffeic acid sulfate, ferulic acid 4-sulfate, dihydroferulic acid, feruloylglycine (novel), 3-(3-hydroxyphenyl)propionate, and 3-hydroxyhippurate]; formation of some of these metabolites also largely depend on gut microbiota metabolism (48, 49). Other notable metabolites correlated with coffee intake were other coffee constituents or their metabolites, such as trigonelline (N ′-methylnicotinate) found in green coffee beans (50), metabolites of catechol—a polyphenol (catechol sulfate, 3-methyl catechol sulfate, O-methylcatechol sulfate), and 2 metabolites of furan—formed via Maillard reaction during roasting [N-(2-furoyl)glycine, 2-furoic acid (novel)] (51). Together with 7 unknown metabolites, in total we replicated 32 metabolites found in previous studies for coffee intake. For all types of tea consumption, theanine was the most predictive biomarker in the present study. Theanine is a unique nonprotein amino acid in tea leaves and one of the major bioactive compounds in tea that has been suggested to exert neuroprotective effects and improve attention (52). It has also been associated with habitual intake in a large cohort study (22). Strengths and limitations The present study has several strengths, including its large sample size, comprehensive measurement of habitual diet, high reliability of the metabolomic platforms, and ability to adjust for time since last meal. Our findings support the great value of archived blood samples maintained in large cohort studies for dietary biomarker discovery. One limitation of our study is that the reference used to identify biomarkers was self-reported dietary data, which involve measurement error and can partially explain the low predictive accuracy for some potential metabolites. Nonetheless, measurement of habitual diet allows for the identification of biomarkers for a long-term diet that are likely to have longer half-lives or are relevant to foods that are frequently consumed. Another limitation is the cross-sectional study design. Future intervention studies are needed to confirm these biomarkers and test the dose-response relation with food intake. Furthermore, the generalizability of our findings might be limited since our study population was primarily older white women. Finally, as a general limitation in this field, we were unable to distinguish metabolites that are food intake biomarkers and metabolites that reflect diet-induced changes in metabolism. In conclusion, in this large and comprehensive analysis of habitual diet and serum metabolic profiles in a free-living population of postmenopausal women, we replicated 63 metabolites as food-based biomarkers that were previously reported in similar studies for citrus fruits and juices, cruciferous vegetables, mushrooms, fish and shellfish, nuts, milk, butter, chocolate, alcohol, wine, coffee, and tea intakes. We also identified hundreds of potentially novel associations (notably for garlic and coffee) and validated several putative biomarkers that have been only reported in small feeding studies (e.g., bananas). Given the relatively early stage of research on dietary—particularly food—biomarker discovery, our results demonstrate the important contribution large cohort studies with archived blood samples could make in this field. Future cross-sectional studies are needed to confirm our findings in a diverse population and intervention studies are needed to test the dose-response relation with food intake, and the kinetics of the potential biomarkers. Furthermore, studies with repeated measurements are needed to test the reproducibility of the potential biomarkers, which determines the reliability when these are used to study disease risk. Acknowledgments We thank Steven C Moore (National Cancer Institute) for assistance in obtaining partial financial support for this project. The authors’ responsibilities were as follows—YW and MLM: designed the research; YW and BDC: performed the statistical analysis; YW: wrote the paper; SMG, TJH, VLS, MMG, and MLM: provided critical review; YW: had primary responsibility for the final content; and all authors: read and approved the final manuscript. Notes The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study-II cohort. This work was supported, in part, through the Intramural Research Program of the National Cancer Institute, National Institutes of Health, Department of Health and Human Services. Supplemental Tables 1–6 and Supplemental Figures 1–3 are available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/jn/. Author disclosures: YW, SMG, BDC, TJH, VLS, MMG and MLM, no conflicts of interest. The views expressed here are those of the authors and do not necessarily represent the American Cancer Society or the American Cancer Society—Cancer Action Network. Abbreviations used: CMPF, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate; CPS-II, Cancer Prevention Study-II; ESI, electrospray ionization; ROC, receiver operating characteristic; UPLC-MS/MS, ultrahigh-performance LC-tandem MS; γ-CEHC, γ-carboxyethyl hydrochroman. References 1. Kipnis V , Freedman LS . Impact of exposure measurement error in nutritional epidemiology . J Natl Cancer Inst 2008 ; 100 : 1658 – 9 . Google Scholar CrossRef Search ADS PubMed 2. Bingham SA . Urine nitrogen as a biomarker for the validation of dietary protein intake . J Nutr 2003 ; 133 : S921–4 . Google Scholar CrossRef Search ADS 3. Tasevska N , Runswick SA , Bingham SA . Urinary potassium is as reliable as urinary nitrogen for use as a recovery biomarker in dietary studies of free living individuals . J Nutr 2006 ; 136 : 1334 – 40 . Google Scholar CrossRef Search ADS PubMed 4. Clark AJ , Mossholder S . Sodium and potassium intake measurements: dietary methodology problems . Am J Clin Nutr 1986 ; 43 : 470 – 6 . Google Scholar CrossRef Search ADS PubMed 5. Brennan L . Metabolomics in nutrition research: current status and perspectives . Biochem Soc Trans 2013 ; 41 : 670 – 3 . Google Scholar CrossRef Search ADS PubMed 6. Scalbert A , Brennan L , Manach C , Andres-Lacueva C , Dragsted LO , Draper J , Rappaport SM , van der Hooft JJ , Wishart DS . The food metabolome: a window over dietary exposure . Am J Clin Nutr 2014 ; 99 : 1286 – 308 . Google Scholar CrossRef Search ADS PubMed 7. Gibbons H , Brennan L . Metabolomics as a tool in the identification of dietary biomarkers . Proc Nutr Soc 2017 ; 76 : 42 – 53 . Google Scholar CrossRef Search ADS PubMed 8. Calle EE , Rodriguez C , Jacobs EJ , Almon ML , Chao A , McCullough ML , Feigelson HS , Thun MJ . The American Cancer Society Cancer Prevention Study II Nutrition Cohort: rationale, study design, and baseline characteristics . Cancer 2002 ; 94 : 2490 – 501 . Google Scholar CrossRef Search ADS PubMed 9. Playdon MC , Sampson JN , Cross AJ , Sinha R , Guertin KA , Moy KA , Rothman N , Irwin ML , Mayne ST , Stolzenberg-Solomon R , et al. Comparing metabolite profiles of habitual diet in serum and urine . Am J Clin Nutr 2016 ; 104 : 776 – 89 . Google Scholar CrossRef Search ADS PubMed 10. Zheng Y , Yu B , Alexander D , Steffen LM , Boerwinkle E . Human metabolome associates with dietary intake habits among African Americans in the atherosclerosis risk in communities study . Am J Epidemiol 2014 ; 179 : 1424 – 33 . Google Scholar CrossRef Search ADS PubMed 11. Guertin KA , Loftfield E , Boca SM , Sampson JN , Moore SC , Xiao Q , Huang WY , Xiong X , Freedman ND , Cross AJ , et al. Serum biomarkers of habitual coffee consumption may provide insight into the mechanism underlying the association between coffee consumption and colorectal cancer . Am J Clin Nutr 2015 ; 101 : 1000 – 11 . Google Scholar CrossRef Search ADS PubMed 12. Schmidt JA , Rinaldi S , Ferrari P , Carayol M , Achaintre D , Scalbert A , Cross AJ , Gunter MJ , Fensom GK , Appleby PN , et al. Metabolic profiles of male meat eaters, fish eaters, vegetarians, and vegans from the EPIC-Oxford cohort . Am J Clin Nutr 2015 ; 102 : 1518 – 26 . Google Scholar CrossRef Search ADS PubMed 13. Pallister T , Jennings A , Mohney RP , Yarand D , Mangino M , Cassidy A , MacGregor A , Spector TD , Menni C . Characterizing blood metabolomics profiles associated with self-reported food intakes in female twins . PLoS One 2016 ; 11 : e0158568 . Google Scholar CrossRef Search ADS PubMed 14. Guertin KA , Moore SC , Sampson JN , Huang WY , Xiao Q , Stolzenberg-Solomon RZ , Sinha R , Cross AJ . Metabolomics in nutritional epidemiology: identifying metabolites associated with diet and quantifying their potential to uncover diet-disease relations in populations . Am J Clin Nutr 2014 ; 100 : 208 – 17 . Google Scholar CrossRef Search ADS PubMed 15. Evans AM , DeHaven CD , Barrett T , Mitchell M , Milgram E . Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems . Anal Chem 2009 ; 81 : 6656 – 67 . Google Scholar CrossRef Search ADS PubMed 16. Huber W , Von Heydebreck A , Sültmann H , Poustka A , Vingron M . Variance stabilization applied to microarray data calibration and to the quantification of differential expression . Bioinformatics 2002 ; 18 : S96 – S104 . Google Scholar CrossRef Search ADS PubMed 17. Kim S . Ppcor: An R package for a fast calculation to semi-partial correlation coefficients . Commun Stat Appl Methods 2015 ; 22 : 665 . Google Scholar PubMed 18. Robin X , Turck N , Hainard A , Tiberti N , Lisacek F , Sanchez J-C , Müller M . pROC: an open-source package for R and S+ to analyze and compare ROC curves . BMC Bioinformatics 2011 ; 12 : 77 . Google Scholar CrossRef Search ADS PubMed 19. Joachims T . A support vector method for multivariate performance measures . Proceedings of the 22nd International Conference on Machine Learning, 2005 : ACM ; 2005 . p. 377 – 84 . 20. Xia J , Sinelnikov IV , Han B , Wishart DS . MetaboAnalyst 3.0—making metabolomics more meaningful . Nucleic Acids Res 2015 ; 43 : W251 – 7 . Google Scholar CrossRef Search ADS PubMed 21. Sugiura M , Kato M , Matsumoto H , Nagao A , Yano M . Serum concentration of β-cryptoxanthin in Japan reflects the frequency of Satsuma mandarin (Citrus unshiu Marc.) consumption . J Health Sci 2002 ; 48 : 350 – 3 . Google Scholar CrossRef Search ADS 22. Playdon MC , Ziegler RG , Sampson JN , Stolzenberg-Solomon R , Thompson HJ , Irwin ML , Mayne ST , Hoover RN , Moore SC . Nutritional metabolomics and breast cancer risk in a prospective study . Am J Clin Nutr 2017 ; 106 : 637 – 49 . Google Scholar CrossRef Search ADS PubMed 23. Pujos-Guillot E , Hubert J , Martin JF , Lyan B , Quintana M , Claude S , Chabanas B , Rothwell JA , Bennetau-Pelissero C , Scalbert A , et al. Mass spectrometry-based metabolomics for the discovery of biomarkers of fruit and vegetable intake: citrus fruit as a case study . J Proteome Res 2013 ; 12 : 1645 – 59 . Google Scholar CrossRef Search ADS PubMed 24. Edmands WM , Ferrari P , Rothwell JA , Rinaldi S , Slimani N , Barupal DK , Biessy C , Jenab M , Clavel-Chapelon F , Fagherazzi G , et al. Polyphenol metabolome in human urine and its association with intake of polyphenol-rich foods across European countries . Am J Clin Nutr 2015 ; 102 : 905 – 13 . Google Scholar CrossRef Search ADS PubMed 25. Andersen MB , Kristensen M , Manach C , Pujos-Guillot E , Poulsen SK , Larsen TM , Astrup A , Dragsted L . Discovery and validation of urinary exposure markers for different plant foods by untargeted metabolomics . Anal Bioanal Chem 2014 ; 406 : 1829 – 44 . Google Scholar CrossRef Search ADS PubMed 26. Lloyd AJ , Beckmann M , Fave G , Mathers JC , Draper J . Proline betaine and its biotransformation products in fasting urine samples are potential biomarkers of habitual citrus fruit consumption . Br J Nutr 2011 ; 106 : 812 – 24 . Google Scholar CrossRef Search ADS PubMed 27. Heinzmann SS , Brown IJ , Chan Q , Bictash M , Dumas ME , Kochhar S , Stamler J , Holmes E , Elliott P , Nicholson JK . Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption . Am J Clin Nutr 2010 ; 92 : 436 – 43 . Google Scholar CrossRef Search ADS PubMed 28. Playdon MC , Moore SC , Derkach A , Reedy J , Subar AF , Sampson JN , Albanes D , Gu F , Kontto J , Lassale C , et al. Identifying biomarkers of dietary patterns by using metabolomics . Am J Clin Nutr 2017 ; 105 : 450 – 65 . Google Scholar CrossRef Search ADS PubMed 29. Garcia-Aloy M , Llorach R , Urpi-Sarda M , Jáuregui O , Corella D , Ruiz-Canela M , Salas-Salvadó J , Fitó M , Ros E , Estruch R . A metabolomics-driven approach to predict cocoa product consumption by designing a multimetabolite biomarker model in free-living subjects from the PREDIMED study . Mol Nutr Food Res 2015 ; 59 : 212 – 20 . Google Scholar CrossRef Search ADS PubMed 30. Rothwell JA , Fillâtre Y , Martin J-F , Lyan B , Pujos-Guillot E , Fezeu L , Hercberg S , Comte B , Galan P , Touvier M . New biomarkers of coffee consumption identified by the non-targeted metabolomic profiling of cohort study subjects . PLoS One 2014 ; 9 : e93474 . Google Scholar CrossRef Search ADS PubMed 31. Atkinson W , Downer P , Lever M , Chambers ST , George PM . Effects of orange juice and proline betaine on glycine betaine and homocysteine in healthy male subjects . Eur J Nutr 2007 ; 46 : 446 – 52 . Google Scholar CrossRef Search ADS PubMed 32. Kulma A , Szopa J . Catecholamines are active compounds in plants . Plant Sci 2007 ; 172 : 433 – 40 . Google Scholar CrossRef Search ADS 33. Johnson GA , Baker CA , Smith RT . Radioenzymatic assay of sulfate conjugates of catecholamines and DOPA in plasma . Life Sci 1980 ; 26 : 1591 – 8 . Google Scholar CrossRef Search ADS PubMed 34. Davidson L , Vandongen R , Beilin LJ . Effects of eating bananas on plasma free and sulfate-conjugated catecholamines . Life Sci 1981 ; 29 : 1773 – 8 . Google Scholar CrossRef Search ADS PubMed 35. Kalaras MD , Richie JP , Calcagnotto A , Beelman RB . Mushrooms: A rich source of the antioxidants ergothioneine and glutathione . Food Chem 2017 ; 233 : 429 – 33 . Google Scholar CrossRef Search ADS PubMed 36. Weigand-Heller AJ , Kris-Etherton PM , Beelman RB . The bioavailability of ergothioneine from mushrooms (Agaricus bisporus) and the acute effects on antioxidant capacity and biomarkers of inflammation . Prev Med 2012 ; 54 Suppl : S75 – 8 . Google Scholar CrossRef Search ADS PubMed 37. Amagase H , Petesch BL , Matsuura H , Kasuga S , Itakura Y . Intake of garlic and its bioactive components . J Nutr 2001 ; 131 : S955 – 62 . Google Scholar CrossRef Search ADS 38. Verhagen H , Hageman GJ , Rauma AL , Versluis-de Haan G , van Herwijnen MH , de Groot J , Torronen R , Mykkanen H . Biomonitoring the intake of garlic via urinary excretion of allyl mercapturic acid . Br J Nutr 2001 ; 86 Suppl 1 : S111 – 4 . Google Scholar CrossRef Search ADS PubMed 39. de Rooij BM , Boogaard PJ , Rijksen DA , Commandeur JN , Vermeulen NP . Urinary excretion of N-acetyl-S-allyl-L-cysteine upon garlic consumption by human volunteers . Arch Toxicol 1996 ; 70 : 635 – 9 . Google Scholar CrossRef Search ADS PubMed 40. Keller BO , Wu BT , Li SS , Monga V , Innis SM . Hypaphorine is present in human milk in association with consumption of legumes . J Agric Food Chem 2013 ; 61 : 7654 – 60 . Google Scholar CrossRef Search ADS PubMed 41. Walradt JP , Pittet AO , Kinlin TE , Muralidhara R , Sanderson A . Volatile components of roasted peanuts . J Agric Food Chem 1971 ; 19 : 972 – 9 . Google Scholar CrossRef Search ADS 42. Leslie ND . Insights into the pathogenesis of galactosemia . Annu Rev Nutr 2003 ; 23 : 59 – 80 . Google Scholar CrossRef Search ADS PubMed 43. Rouge P , Cornu A , Biesse-Martin A-S , Lyan B , Rochut N , Graulet B . Identification of quinoline, carboline and glycinamide compounds in cow milk using HRMS and NMR . Food Chem 2013 ; 141 : 1888 – 94 . Google Scholar CrossRef Search ADS PubMed 44. Velenosi TJ , Hennop A , Feere DA , Tieu A , Kucey AS , Kyriacou P , McCuaig LE , Nevison SE , Kerr MA , Urquhart BL . Untargeted plasma and tissue metabolomics in rats with chronic kidney disease given AST-120 . Sci Rep 2016 ; 6 : 22526 . Google Scholar CrossRef Search ADS PubMed 45. Foti RS , Fisher MB . Assessment of UDP-glucuronosyltransferase catalyzed formation of ethyl glucuronide in human liver microsomes and recombinant UGTs . Forensic Sci Int 2005 ; 153 : 109 – 16 . Google Scholar CrossRef Search ADS PubMed 46. Wurst FM , Skipper GE , Weinmann W . Ethyl glucuronide—the direct ethanol metabolite on the threshold from science to routine use . Addiction 2003 ; 98 : 51 – 61 . Google Scholar CrossRef Search ADS PubMed 47. Generoso WC , Brinek M , Dietz H , Oreb M , Boles E . Secretion of 2,3-dihydroxyisovalerate as a limiting factor for isobutanol production in Saccharomyces cerevisiae . FEMS Yeast Res 2017 ; 17 ( 3 ): fox029 – fox029 . Google Scholar CrossRef Search ADS 48. Gonthier MP , Verny MA , Besson C , Remesy C , Scalbert A . Chlorogenic acid bioavailability largely depends on its metabolism by the gut microflora in rats . J Nutr 2003 ; 133 : 1853 – 9 . Google Scholar CrossRef Search ADS PubMed 49. Stalmach A , Mullen W , Barron D , Uchida K , Yokota T , Cavin C , Steiling H , Williamson G , Crozier A . Metabolite profiling of hydroxycinnamate derivatives in plasma and urine after the ingestion of coffee by humans: identification of biomarkers of coffee consumption . Drug Metab Dispos 2009 ; 37 : 1749 – 58 . Google Scholar CrossRef Search ADS PubMed 50. Allred KF , Yackley KM , Vanamala J , Allred CD . Trigonelline is a novel phytoestrogen in coffee beans . J Nutr 2009 ; 139 : 1833 – 8 . Google Scholar CrossRef Search ADS PubMed 51. Heinzmann SS , Holmes E , Kochhar S , Nicholson JK , Schmitt-Kopplin P . 2-Furoylglycine as a candidate biomarker of coffee consumption . J Agric Food Chem 2015 ; 63 : 8615 – 21 . Google Scholar CrossRef Search ADS PubMed 52. Nobre AC , Rao A , Owen GN . L-theanine, a natural constituent in tea, and its effect on mental state . Asia Pac J Clin Nutr 2008 ; 17 Suppl 1 : 167 – 8 . Google Scholar PubMed © 2018 American Society for Nutrition. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Nutrition Oxford University Press

Untargeted Metabolomics Identifies Novel Potential Biomarkers of Habitual Food Intake in a Cross-Sectional Study of Postmenopausal Women

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Oxford University Press
Copyright
© 2018 American Society for Nutrition.
ISSN
0022-3166
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1541-6100
D.O.I.
10.1093/jn/nxy027
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Abstract

Abstract Background Recent studies suggest that untargeted metabolomics is a promising tool to identify novel biomarkers of individual foods. However, few large cross-sectional studies with comprehensive data on habitual diet and circulating metabolites have been conducted. Objective We aimed to identify potential food biomarkers and evaluate their predictive accuracy. Methods We conducted a cross-sectional analysis of consumption of 91 food groups or items, assessed by a 152-item food-frequency questionnaire, in relation to 1186 serum metabolites measured by mass spectrometry-based platforms from 1369 nonsmoking postmenopausal women (mean age = 68.3 y). Diet-metabolite associations were selected by Pearson's partial correlation analysis (P < 4.63 × 10−7, |r| > 0.2). The predictive accuracy of the selected food metabolites was evaluated from the area under the curve (AUC) calculated from receiver operating characteristic analysis conducted among women in the top and bottom quintiles of dietary intake. Results We identified 379 diet-metabolite associations. Forty-two food groups or items were correlated with 199 serum metabolites. We replicated 63 metabolites as biomarkers of habitual food intake reported in previous cross-sectional studies. Among those not previously shown to be associated with habitual diet, several are biologically plausible and were reported in acute feeding studies including: banana and dopamine 3-O-sulfate (r = 0.34, AUC = 76%) and dopamine 4-O-sulfate (r = 0.33, AUC = 74%), garlic and alliin (r = 0.24, AUC = 69%), N-acetylalliin (r = 0.27, AUC = 70%), and S-allylcysteine (r = 0.23, AUC = 69). Two unannotated metabolites were the strongest predictors for dark fish (X-02269, r = 0.51, AUC = 94%) and coffee intake (X-21442, r = 0.62, AUC = 98%). Conclusion In this comprehensive, cross-sectional analysis of habitual food intake and serum metabolites among postmenopausal women, we identified several potentially novel food biomarkers and replicated others. Our findings contribute to the limited literature on food-based biomarkers and highlight the significant and promising role that large cohort studies with archived blood samples could play in this field. This study was registered at clinicaltrials.gov as NCT03282812. diet, food biomarker, food frequency questionnaire, serum, untargeted metabolomics Introduction Nutritional epidemiologic studies primarily rely on self-reported dietary data which are subject to measurement error that may contribute to the inconsistent diet-disease associations (1). Dietary biomarkers are objective measures of diet and thus are not subject to self-reported measurement error. However, few reliable dietary biomarkers exist and they are primarily nutrient-based, such as 24-h urine nitrogen, potassium, and sodium as recovery biomarkers for protein, potassium, and sodium intakes, respectively (2–4). Additional objective dietary biomarkers, especially those correlated with food intake, are needed to advance understanding of the role of diet in disease risk. The rapid development of metabolomics technology in the past decade has enabled scientists to measure thousands of small molecules in human biofluids, cells, and tissues (5). The food metabolome (i.e., metabolites derived from foods and food constituents) is a promising resource to discover novel food biomarkers (6). Acute feeding studies and short- to medium-term intervention studies have identified several putative food biomarkers (6, 7). However, these studies only focus on one or a few specific type(s) of foods and reveal no information on other dietary origins of the identified biomarkers (6). In contrast, cross-sectional studies provide the advantage of examining multiple foods and other dietary sources simultaneously. Further, dietary data collected in large-scale observational studies reflect the wider distribution of food intakes in the population, whereas intervention studies usually only compare a limited number of exposure levels. Most large prospective cohort studies, including the Cancer Prevention Study-II (CPS-II) Nutrition Cohort, collected only blood samples from participants (8). Although fewer biomarkers are found in blood than in urine (9), several cross-sectional studies have identified >100 putative food biomarkers in blood samples, suggesting that blood samples are promising resources for food biomarker discovery (9–14). However, most previous studies were limited by the small number of food groups and metabolites assessed. In a cross-sectional analysis of 1369 nonsmoking postmenopausal women from the CPS-II Nutrition Cohort, we aimed to conduct a comprehensive analysis of individual foods to replicate previously identified food biomarkers and identify novel ones. We then evaluated the predictive accuracy of the identified biomarkers through the use of receiver operating characteristic (ROC) analysis. Methods Study population Women in the present study were drawn from a nested case-control study of breast cancer in the CPS-II Nutrition Cohort. The CPS-II Nutrition Cohort is a prospective cohort study of cancer incidence and mortality among 184,185 men and women, established by the American Cancer Society in 1992 (8). Participants completed a self-administered baseline questionnaire in 1992/1993 including demographic, medical, and lifestyle information. From June 1998 through May 2001, nonfasting blood specimens were collected from 21,963 women as described in detail elsewhere (8). All aspects of the CPS-II Nutrition Cohort were approved by the Emory University (Atlanta, GA) Institutional Review Board. Of the 21,963 women who provided a blood sample, we identified 1547 postmenopausal women who were cancer free (except nonmelanoma skin cancer) at blood draw and included in a breast cancer nested case-control study. For this food metabolomics analysis, we excluded women who were current smokers at blood draw (n = 119), and those with missing or unreliable dietary information from the 1999 FFQ (missing >70 line items or with energy intake >3500 or <600 kcal/d, n = 59), leaving 1369 postmenopausal women in the analysis (Supplemental Figure 1). Diet assessment In 1999–2000, dietary intake was self-reported on a semiquantita-tive modified Willett FFQ including 152 items, available online (https://www.cancer.org/content/dam/cancer-org/research/epidemiology/cps-II-nutrition-1999-long-survey-women.pdf). To provide a comprehensive evaluation of all food exposures assessed on the FFQ, we classified these food items into 91 food groups (Supplemental Table 1) belonging to 9 food classes (fruits, vegetables, grains, proteins, dairy, fats and oils, miscellaneous, alcohol, and beverages). Each food item on the FFQ was examined individually (e.g., oranges) and in combination with similar foods as a food group (e.g., total citrus fruits and juices). Although the FFQ was not collected at the time of blood draw, the interval was within 2 y. The median time between return of the 1999 FFQ and blood draw date was 7.7 mo. Three-quarters of women returned the FFQ ≤ 1.7 y before the date of blood draw, and 25% returned the FFQ ≤ 1.7 y after the date of blood draw. Metabolomics analysis Metabolomic profiling was conducted by Metabolon, Inc. (Durham, NC) with the use of ultrahigh-performance LC-tandem MS (UPLC-MS/MS) as described elsewhere (15). Briefly, serum samples were treated with methanol to precipitate proteins. Four sample fractions were dried and reconstituted in different solvents for measurement under 4 different platforms. Two fractions were for analysis by 2 separate reverse-phase UPLC-MS/MS methods with positive-ion-mode electrospray ionization (ESI), 1 fraction for analysis by reverse-phase UPLC-MS/MS method with negative-ion-mode ESI, and 1 for analysis by hydrophilic interaction chromatography UPLC-MS/MS with negative-ion-mode ESI. Individual metabolites were identified by comparison with a chemical library consisting of >3300 commercially available purified standard compounds. A total of 1385 metabolites were detected. Triplicates of 46 samples were used as quality controls to assess the reproducibility of the platform. The median intraclass correlation coefficient, calculated based on the quality-control samples, was 0.90 with an IQR of 0.74–0.96, suggesting a very high reproducibility. To reduce noise and increase statistical power, we excluded metabolites that were below the detection limit in ≥90% of the samples (n = 110) and metabolites with intraclass correlation coefficient <0.5 (n = 89). For the remaining metabolites, missing values were assigned the minimum detection value. To correct day-to-day variation from the platform, each metabolite was divided by its daily median. Statistical analysis Metabolite and food variables were generalized log transformed (16) and autoscaled before all analyses. Pearson's partial correlation was conducted with the use of the R package ppcor (17) to assess linear relations between each dietary variable and metabolite, controlling for age at blood draw (continuous), race (non-Hispanic white or other), education (no college, some college, or college graduate), smoking status (never or former), use of hormone replacement therapy (current or not current), physical activity (metabolic equivalent hours per week: <8.75, 8.75 to <17.5 or ≥17.5), BMI (kg/m2: <18.5, 18.5 to <25, 25 to <30, or ≥30), time since last meal (continuous in hours), ethanol intake (<14 g/d or ≥14 g/d, except for ethanol-containing items), and caloric intake (continuous). Associations were considered statistically significant if P values were less than the Bonferroni-corrected threshold 4.63 × 10−7 [0.05/(91 × 1186)]. To select more meaningful associations for evaluation, we further required that the absolute value of the correlation coefficient (|r|) be >0.2. Putative dietary biomarkers were further evaluated for predictive accuracy of discriminating high consumers (top quintile) from low consumers (bottom quintile), assessed from the AUC calculated from the ROC curve with the use of the R package pROC (18). We considered AUC <0.7 to be low, 0.7–0.8 to be moderate, and ≥0.8 to be high. For univariate ROC analysis of individual metabolites, the AUC and 95% CIs were estimated from 2000 times stratified bootstrap samples. We further conducted a multivariate ROC analysis by building a linear Support Vector Machine multivariate classification model (19) with all putative dietary biomarkers identified in the correlation analysis. The multivariate ROC analysis was conducted with the use of the Biomarker Analysis module of MetaboAnalyst 3.0 (20). To understand correlations among metabolites, we conducted pairwise Pearson's partial correlation analyses among all 1186 metabolites. The top 5 metabolites with |r| > 0.5 for each metabolite are provided in Supplemental Table 2. Results Participant characteristics Overall, 98% of women were white, with a mean age 68.3 ± 5.7 y (Supplemental Table 3); 40% of women had college or higher education, 61% were never smokers, 57% were currently using hormone replacement therapy, 59% engaged in recommended or greater levels of physical activity (≥8.75 metabolic equivalent hours per week), 50% had normal body weight, 32% were overweight, 16% were obese, and 84% had <14 g ethanol/d. Mean time since last meal was 2 h. Mean time between blood draw and breast cancer diagnosis was 5.2 y. Serum metabolites correlated with habitual dietary intake Usual servings per week of the 91 predefined food groups or items are shown in Supplemental Table 4. In total, we identified 1069 statistically significant diet-metabolite associations with P values less than the Bonferroni-corrected threshold of 4.63 × 10−7. Further requiring a |r| > 0.2 resulted in 379 potentially meaningful associations (Supplemental Table 5). Among the 379 associations, 42 food groups or items were correlated with ≥1 metabolite; 199 metabolites (111 known, 88 unknown identities) were associated with ≥1 food group. The majority of 199 metabolites belonged to unknown (43.3%) and xenobiotic (30.6%) superpathways, the rest belonged to lipids (15.8%), amino acids (6.3%), cofactors and vitamins (1.8%), peptides (0.8%), energy metabolism (0.8%), nucleotides (0.3%), and carbohydrates (0.3%) (Supplemental Figure 2). The 42 food groups or items belonged to 9 food classes: fruits, vegetables, proteins, alcohols, beverages, and others (grains, dairy, fats and oils, miscellaneous). As shown in Supplemental Table 5, 52 metabolites were correlated with 4 fruit groups or items (18 for total citrus fruits and juices, 17 for orange juice, 6 for banana, 11 for prune). The top 10 most predictive metabolites (all shown if <10), per AUC, are shown in Table 1. Stachydrine (also known as proline betaine) was the most predictive metabolite for total citrus fruits and juices (r = 0.53, AUC = 89%), and orange juice intake (r = 0.54, AUC = 87%), as has been consistently reported in similar studies (Table 2). Notably, 2 sulfonated dopamines were correlated with banana intake: dopamine 3-O-sulfate (r = 0.34, AUC = 76%) and dopamine 4-O-sulfate (r = 0.33, AUC = 74%). Twenty-two associations were found for 6 vegetable groups or items (1 for cruciferous vegetables, 1 for mushrooms, 7 for allium vegetables, 1 for onion, 9 for garlic, and 3 for tofu or soybeans), with the strongest association being between ergothioneine and mushrooms (r = 0.28, AUC = 75%). Other notable correlations were S-methylcysteine sulfoxide and cruciferous vegetables (r = 0.23, AUC = 69%), garlic and alliin (r = 0.24, AUC = 69%), N-acetylalliin (r = 0.27, AUC = 70%), and S-allylcysteine (r = 0.23, AUC = 69%). Next, 41 diet-metabolite associations were identified for 10 protein foods (1 for egg, 3 for red meat, 1 for processed meat, 2 for poultry, 7 for total fish, 10 for dark fish, 2 for shellfish, 7 for nuts, 7 for peanuts, and 1 for other nuts). An unknown metabolite X-02269 was the most predictive metabolite for dark fish intake (r = 0.51, AUC = 94%). Alcoholic beverages were the second largest category to show associations with serum metabolites. Seventy-three associations were identified for 6 types of alcohol (39 for total alcohol, 1 for beer, 16 for total wine, 3 for red wine, 2 for white wine, and 12 for liquor). Ethyl glucuronide was the most predictive metabolite for all types of alcohol except for beer (for total alcohol, r = 0.60, AUC = 92%). The highest number of associations identified in this study were for beverages, including 157 metabolite–beverage associations (74 for total coffee, 53 for caffeinated coffee, 24 for decaffeinated coffee, 2 for total tea, 2 for nonherbal tea, 1 for herbal tea, and 1 for diet soft drinks). An unknown X-21442 was the most predictive metabolite for total coffee consumption (r = 0.62, AUC = 98%), and decaffeinated coffee consumption (r = 0.31, AUC = 83%). The most predictive metabolite for caffeinated coffee was 1-methylxanthine, which is a caffeine metabolite. For all types of tea consumption, theanine was the most predictive biomarker, slightly stronger for nonherbal tea than herbal or decaffeinated tea, and strongest for total tea (r = 0.50, AUC = 84%). For other foods, notable correlations were found for milk (galactonate, r = 0.33, AUC = 76%; 2,8-quinolinediol sulfate, r = 0.27, AUC = 75%), butter [caprylate (8:0), r = 0.21, AUC = 67%; caprate (10:0), r = 0.26, AUC = 70%; and 10-undecenoate (11:1n-1), r = 0.24, AUC = 69%] and soy milk (4-ethylphenylsulfate, r = 0.20, AUC = 67%). TABLE 1 Top 10 predictive serum metabolites of 42 food groups/items among women in the CPS-II Nutrition Cohort (n = 1369)1 Food groups/items Metabolites Super-pathway r P AUC2 Q1 mean ± SD3 Q5 mean ± SD Fruits  Total citrus fruits and juices stachydrine XEN 0.53 3.6 × 10−99 0.89 0.5 ± 0.7 2.1 ± 1.2 X-247384 UKN 0.49 5.2 × 10−82 0.89 0.5 ± 1.2 2.7 ± 2.2 N-methylproline AA 0.50 8.6 × 10−86 0.87 0.6 ± 1.0 2.8 ± 1.9 chiro-inositol LIP 0.43 1.1 × 10−62 0.86 0.3 ± 0.6 1.5 ± 1.2 X-22836 UKN 0.42 1.3 × 10−58 0.83 0.4 ± 0.7 1.5 ± 1.2 X-23314 UKN 0.41 1.4 × 10−56 0.82 0.9 ± 1.2 3.3 ± 3.3 X-17350 UKN 0.37 4.2 × 10−44 0.80 1.0 ± 1.1 2.7 ± 2.3 methyl glucopyranoside (α + β) XEN 0.36 4.1 × 10−43 0.80 0.8 ± 0.9 2.0 ± 1.9 X-16947 UKN 0.36 2.5 × 10−42 0.80 1.7 ± 5.1 6.1 ± 8.6 β-cryptoxanthin XEN 0.35 1.0 × 10−39 0.80 0.8 ± 0.5 1.6 ± 0.9  Orange juice stachydrine XEN 0.54 4.5 × 10−104 0.87 0.6 ± 0.8 2.2 ± 1.2 X-24738 UKN 0.51 2.2 × 10−92 0.86 0.6 ± 1.2 2.8 ± 2.3 N-methylproline AA 0.52 6.8 × 10−93 0.86 0.7 ± 1.0 2.8 ± 1.9 X-23314 UKN 0.48 3.4 × 10−78 0.83 0.9 ± 1.4 3.5 ± 3.7 chiro-inositol LIP 0.46 2.6 × 10−72 0.83 0.4 ± 0.9 1.5 ± 1.3 X-17350 UKN 0.43 3.1 × 10−62 0.82 1.0 ± 1.2 2.8 ± 2.4 X-22836 UKN 0.44 4.5 × 10−64 0.82 0.4 ± 0.7 1.5 ± 1.2 X-16947 UKN 0.42 8.2 × 10−58 0.81 1.5 ± 4.5 6.9 ± 9.2 X-22515 UKN 0.40 3.0 × 10−54 0.81 0.6 ± 1.7 3.0 ± 4.2 X-19183 UKN 0.41 1.9 × 10−57 0.81 0.4 ± 0.9 1.2 ± 1.0  Banana dopamine 3-O-sulfate AA 0.34 1.0 × 10−37 0.76 1.3 ± 1.5 5.7 ± 7.0 dopamine 4-sulfate AA 0.33 2.5 × 10−36 0.74 0.9 ± 1.6 5.3 ± 7.5 S-methylmethionine AA 0.23 3.9 × 10−18 0.72 1.0 ± 2.2 2.3 ± 2.7 3-methoxytyramine sulfate AA 0.22 9.2 × 10−17 0.70 1.0 ± 0.5 1.5 ± 0.9 X-12729 UKN 0.21 1.8 × 10−15 0.68 1.3 ± 3.7 2.9 ± 5.0 5-hydroxyindoleacetate AA 0.21 1.1 × 10−14 0.68 0.8 ± 0.9 1.7 ± 1.9  Prunes X-11315 UKN 0.21 1.5 × 10−14 0.67 1.0 ± 0.3 1.2 ± 0.5 X-12818 UKN 0.20 5.3 × 10−14 0.62 1.0 ± 1.4 1.5 ± 1.8 hippurate XEN 0.22 7.1 × 10−16 0.61 1.2 ± 1.1 1.8 ± 1.7 benzoylcarnitine5 XEN 0.25 3.0 × 10−21 0.61 0.9 ± 1.1 1.4 ± 1.8 X-24757 UKN 0.25 6.1 × 10−21 0.60 1.0 ± 1.2 1.9 ± 3.0 5-hydroxymethyl-2-furoic acid AA 0.26 3.1 × 10−22 0.58 1.0 ± 3.7 2.4 ± 8.1 X-17367 UKN 0.23 3.4 × 10−17 0.58 1.1 ± 1.6 2.1 ± 4.0 X-17325 UKN 0.21 1.3 × 10−15 0.58 1.4 ± 1.8 2.3 ± 3.8 X-22475 UKN 0.23 5.9 × 10−18 0.57 0.6 ± 1.4 1.6 ± 4.0 catechol sulfate XEN 0.20 4.2 × 10−14 0.57 1.1 ± 0.9 1.5 ± 1.1 Vegetables  Cruciferous vegetables S-methylcysteine sulfoxide AA 0.24 1.7 × 10−18 0.69 1.0 ± 0.7 1.6 ± 1.2  Mushrooms ergothioneine XEN 0.28 3.0 × 10−25 0.75 1.0 ± 0.5 1.6 ± 0.8  Allium vegetables N-methyltaurine AA 0.28 1.4 × 10−26 0.73 0.5 ± 1.3 1.5 ± 1.9 N-acetylalliin XEN 0.22 4.4 × 10−16 0.67 0.8 ± 1.7 2.9 ± 11.1 piperine XEN 0.23 1.0 × 10−17 0.67 1.1 ± 1.2 2.0 ± 2.2 ergothioneine XEN 0.22 1.0 × 10−16 0.67 1.1 ± 0.6 1.4 ± 0.8 γ-CEHC CV −0.20 5.7 × 10−14 0.67 1.3 ± 0.7 0.9 ± 0.6 γ-CEHC glucuronide5 CV −0.20 2.8 × 10−14 0.67 1.3 ± 0.9 0.8 ± 0.8 X-12231 UKN 0.20 9.6 × 10−14 0.65 1.0 ± 1.2 1.5 ± 1.6  Onion N-methyltaurine AA 0.24 9.7 × 10−20 0.69 0.6 ± 1.6 1.4 ± 2.1  Garlic γ-CEHC glucuronide5 CV −0.22 7.7 × 10−17 0.72 1.3 ± 1.0 0.7 ± 0.6 X-18249 UKN −0.22 4.8 × 10−16 0.71 1.2 ± 0.5 0.9 ± 0.4 γ-CEHC CV −0.22 2.0 × 10−16 0.71 1.3 ± 0.7 0.9 ± 0.5 N-acetylalliin XEN 0.27 3.1 × 10−24 0.70 0.8 ± 3.3 2.9 ± 8.9 S-allylcysteine XEN 0.23 2.8 × 10−17 0.69 1.2 ± 3.5 3.2 ± 5.0 ergothioneine XEN 0.26 3.6 × 10−22 0.69 1.0 ± 0.5 1.5 ± 0.9 X-02269 UKN 0.21 6.6 × 10−15 0.69 1.2 ± 1.3 2.2 ± 2.4 alliin XEN 0.24 5.7 × 10−19 0.69 1.0 ± 5.6 3.8 ± 9.0 N-methyltaurine AA 0.24 1.5 × 10−18 0.68 0.6 ± 1.2 1.5 ± 2.1  Tofu or soybeans X-11847 UKN 0.22 5.7 × 10−16 0.75 1.7 ± 3.2 7.3 ± 11.8 X-11858 UKN 0.22 1.1 × 10−15 0.72 1.0 ± 3.4 8.7 ± 24.2 X-16649 UKN 0.21 1.1 × 10−14 0.62 0.8 ± 3.0 4.9 ± 12.7 Grains  Whole grains X-21752 UKN 0.20 5.8 × 10−14 0.65 0.5 ± 1.2 1.1 ± 1.7 Proteins  Eggs 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)5 LIP 0.22 8.4 × 10−17 0.71 1.0 ± 0.3 1.4 ± 0.5  Red meat 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)5 LIP 0.28 4.2 × 10−25 0.67 0.9 ± 0.3 1.2 ± 0.4 X-11381 UKN 0.21 3.3 × 10−15 0.66 1.0 ± 0.5 1.2 ± 0.6 1-(1-enyl-stearoyl)-2-oleoyl-GPE (P-18:0/18:1) LIP 0.23 6.9 × 10−18 0.65 1.0 ± 0.3 1.2 ± 0.4  Processed meat X-18922 UKN 0.20 3.9 × 10−14 0.70 0.8 ± 0.5 1.2 ± 0.6  Poultry X-13835 UKN 0.21 1.1 × 10−14 0.65 1.2 ± 1.8 1.7 ± 1.7 3-methylhistidine AA 0.21 3.6 × 10−15 0.64 1.3 ± 1.6 2.1 ± 2.2  Total fish X-02269 UKN 0.41 1.9 × 10−55 0.82 0.9 ± 1.1 2.7 ± 2.5 CMPF LIP 0.37 1.7 × 10−45 0.80 1.0 ± 2.0 2.7 ± 2.3 DHA LIP 0.33 1.1 × 10−36 0.77 0.9 ± 0.4 1.4 ± 0.7 docosahexaenoylcholine LIP 0.27 3.1 × 10−24 0.75 0.8 ± 0.4 1.4 ± 0.7 1-docosahexaenoylglycerol (22:6) LIP 0.28 1.2 × 10−26 0.74 0.8 ± 0.8 1.4 ± 0.8 EPA LIP 0.27 7.1 × 10−25 0.72 0.9 ± 0.6 1.7 ± 1.2 eicosapentaenoylcholine LIP 0.23 4.0 × 10−17 0.72 0.8 ± 0.7 1.5 ± 1.3  Dark fish X-02269 UKN 0.51 1.8 × 10−92 0.94 0.7 ± 0.9 3.6 ± 2.4 CMPF LIP 0.47 6.9 × 10−74 0.93 0.8 ± 1.4 3.6 ± 2.6 DHA LIP 0.37 5.6 × 10−44 0.86 0.9 ± 0.4 1.7 ± 0.8 EPA LIP 0.36 1.7 × 10−42 0.84 0.9 ± 0.6 2.0 ± 1.2 docosahexaenoylcholine LIP 0.27 2.1 × 10−24 0.80 0.9 ± 0.5 1.6 ± 0.7 sphingomyelin (d18:2/18:1)5 LIP −0.25 4.7 × 10−20 0.80 1.2 ± 0.4 0.8 ± 0.3 eicosapentaenoylcholine LIP 0.29 8.7 × 10−28 0.79 0.8 ± 0.7 1.9 ± 1.5 1-docosahexaenoylglycerol (22:6) LIP 0.28 1.1 × 10−26 0.79 0.8 ± 0.6 1.7 ± 0.9 docosapentaenoate (n-6 DPA; 22:5n-6) LIP −0.26 2.4 × 10−22 0.71 1.2 ± 0.5 0.8 ± 0.5 X-13866 UKN 0.21 1.8 × 10−14 0.69 1.2 ± 2.0 1.9 ± 1.9  Shellfish CMPF LIP 0.26 4.3 × 10−23 0.83 1.1 ± 1.8 2.9 ± 2.7 X-02269 UKN 0.25 4.2 × 10−20 0.81 1.0 ± 1.4 2.4 ± 1.7  Total nuts tryptophan betaine AA 0.41 2.2 × 10−55 0.80 0.8 ± 0.9 2.0 ± 1.6 X-23644 UKN 0.27 6.5 × 10−24 0.72 1.9 ± 3.4 4.5 ± 5.7 4-vinylphenol sulfate XEN 0.27 1.9 × 10−24 0.69 1.3 ± 1.7 2.6 ± 2.9 lignoceroylcarnitine (C24)5 LIP 0.25 5.9 × 10−21 0.69 0.9 ± 0.4 1.2 ± 0.5 γ-glutamylvaline PEP −0.25 1.7 × 10−21 0.68 1.2 ± 0.4 0.9 ± 0.4 behenoylcarnitine (C22)5 LIP 0.23 2.4 × 10−17 0.66 0.7 ± 0.5 1.1 ± 0.8 sphingomyelin (d18:2/23:1)5 LIP −0.22 4.2 × 10−16 0.66 1.1 ± 0.3 0.9 ± 0.3  Peanuts tryptophan betaine AA 0.45 2.3 × 10−68 0.83 0.8 ± 0.9 2.1 ± 1.7 X-23644 UKN 0.27 6.3 × 10−24 0.71 2.0 ± 3.6 4.6 ± 5.9 4-vinylphenol sulfate XEN 0.31 7.0 × 10−32 0.71 1.2 ± 1.6 2.7 ± 2.9 γ-glutamylvaline PEP −0.30 3.3 × 10−30 0.71 1.2 ± 0.4 0.9 ± 0.4 lignoceroylcarnitine (C24)5 LIP 0.25 2.5 × 10−20 0.66 1.0 ± 0.4 1.2 ± 0.5 behenoylcarnitine (C22)5 LIP 0.23 5.6 × 10−18 0.66 0.8 ± 0.6 1.2 ± 0.8 sphingomyelin (d18:2/231)5 LIP −0.22 1.7 × 10−16 0.65 1.1 ± 0.3 0.9 ± 0.2  Other nuts X-11315 UKN 0.22 1.2 × 10−15 0.66 1.0 ± 0.4 1.2 ± 0.4 Dairy  Milk galactonate CHO 0.33 1.5 × 10−35 0.76 0.8 ± 0.7 2.0 ± 1.8 2,8-quinolinediol sulfate XEN 0.27 2.6 × 10−24 0.75 0.4 ± 0.7 1.3 ± 1.5 phenylacetylglycine PEP 0.27 1.8 × 10−23 0.71 0.7 ± 0.8 1.5 ± 1.3 X-11381 UKN 0.23 3.3 × 10−18 0.71 0.9 ± 0.4 1.3 ± 0.5 X-12798 UKN 0.24 1.4 × 10−18 0.70 0.9 ± 0.4 1.2 ± 0.5  Soy milk X-16649 UKN 0.20 4.1 × 10−14 0.69 1.1 ± 4.9 5.7 ± 12.1 4-ethylphenylsulfate XEN 0.20 5.9 × 10−14 0.67 2.0 ± 4.6 8.4 ± 16.2  Yogurt X-21736 UKN −0.20 6.3 × 10−14 0.70 1.5 ± 1.2 0.9 ± 0.7 Fats and oils  Butter X-11438 UKN 0.24 6.2 × 10−20 0.71 1.0 ± 0.8 1.6 ± 1.0 caprate (10:0) LIP 0.26 1.2 × 10−21 0.70 1.1 ± 0.6 1.6 ± 1.0 10-undecenoate (11:1n-1) LIP 0.24 9.3 × 10−19 0.69 1.0 ± 0.5 1.4 ± 0.7 sphingomyelin (d18:1/25:0, d19:0/24:1, d20:1/23:0, d19:1/24:0)5 LIP 0.24 6.7 × 10−20 0.68 0.9 ± 0.4 1.2 ± 0.5 X-17337 UKN 0.21 6.7 × 10−15 0.67 1.0 ± 0.5 1.3 ± 0.6 caprylate (8:0) LIP 0.21 5.0 × 10−15 0.67 1.0 ± 0.4 1.3 ± 0.6 sphingomyelin (d17:1/16:0, d18:1/15:0, d16:1/17:0)5 LIP 0.23 1.5 × 10−17 0.65 1.0 ± 0.2 1.1 ± 0.3 Miscellaneous  French fries X-18899 UKN 0.26 8.0 × 10−22 0.84 1.0 ± 0.9 1.6 ± 0.7 X-11880 UKN 0.26 3.6 × 10−23 0.83 0.9 ± 0.5 1.6 ± 0.8 X-21339 UKN 0.29 7.6 × 10−27 0.81 0.9 ± 1.0 1.9 ± 1.1 X-11308 UKN 0.27 6.4 × 10−25 0.81 0.9 ± 0.5 1.5 ± 0.7 X-11549 UKN 0.27 1.1 × 10−24 0.81 0.9 ± 0.5 1.6 ± 0.9 X-11372 UKN 0.24 5.2 × 10−19 0.79 0.9 ± 0.4 1.4 ± 0.6 X-11378 UKN 0.23 1.0 × 10−17 0.76 0.9 ± 0.7 1.5 ± 0.7 X-16935 UKN 0.23 8.3 × 10−18 0.75 0.9 ± 1.1 1.9 ± 1.6 eicosanodioate LIP 0.21 8.8 × 10−15 0.73 1.0 ± 0.5 1.5 ± 0.8  Total candies X-13728 UKN 0.20 7.0 × 10−14 0.65 1.1 ± 1.2 2.0 ± 2.2  Chocolate candies X-13728 UKN 0.28 3.6 × 10−25 0.69 1.2 ± 1.4 2.3 ± 2.4 3-methylxanthine XEN 0.26 4.4 × 10−22 0.68 1.1 ± 1.1 1.9 ± 1.8 7-methylurate XEN 0.24 4.0 × 10−19 0.68 1.1 ± 1.2 1.9 ± 1.9 3,7-dimethylurate XEN 0.24 1.9 × 10−19 0.66 0.9 ± 1.0 1.5 ± 1.6 theobromine XEN 0.24 3.8 × 10−19 0.66 1.1 ± 1.1 1.9 ± 1.7 7-methylxanthine XEN 0.21 1.8 × 10−14 0.64 0.6 ± 0.8 1.1 ± 1.2  Desserts ergothioneine XEN −0.25 1.8 × 10−20 0.69 1.5 ± 0.8 1.0 ± 0.6 sphingomyelin (d18:2/18:1)5 LIP 0.21 7.8 × 10−15 0.65 0.9 ± 0.4 1.1 ± 0.4 Alcohol  Total alcohol ethyl glucuronide XEN 0.60 1.79 × 10−133 0.92 0.4 ± 0.5 9.2 ± 28.4 X-24293 UKN 0.54 1.63 × 10−102 0.87 0.8 ± 1.3 4.4 ± 6.6 X-21737 UKN 0.21 3.7 × 10−15 0.76 1.8 ± 11.6 3.0 ± 6.0 CMPF LIP 0.23 2.9 × 10−17 0.74 1.1 ± 1.7 2.2 ± 1.8 X-23655 UKN 0.22 5.3 × 10−16 0.73 0.5 ± 0.7 1.3 ± 1.3 X-24811 UKN 0.23 2.8 × 10−17 0.73 0.6 ± 0.8 1.3 ± 1.1 caffeine XEN 0.25 3.3 × 10−21 0.72 0.9 ± 1.3 2.1 ± 1.9 X-14473 UKN 0.26 5.0 × 10−22 0.72 0.8 ± 0.7 1.4 ± 0.9 sphingomyelin (d18:2/18:1)5 LIP −0.27 3.0 × 10−24 0.72 1.1 ± 0.4 0.8 ± 0.3 X-12230 UKN 0.20 9.7 × 10−14 0.72 1.0 ± 1.2 2.0 ± 1.9  Beer X-24293 UKN 0.27 1.5 × 10−24 0.72 1.2 ± 2.0 3.3 ± 6.8  Total wine ethyl glucuronide XEN 0.45 1.9 × 10−68 0.85 0.4 ± 0.5 5.5 ± 17.0 X-24293 UKN 0.37 4.8 × 10−46 0.79 0.8 ± 1.3 3.1 ± 4.0 2,3-dihydroxyisovalerate XEN 0.36 5.6 × 10−43 0.75 1.2 ± 1.3 3.0 ± 4.8 CMPF LIP 0.20 5.4 × 10−14 0.73 1.2 ± 1.6 2.2 ± 2.0 sphingomyelin (d18:2/18:1)5 LIP −0.23 7.5 × 10−18 0.71 1.1 ± 0.4 0.9 ± 0.3 X-18249 UKN −0.20 8.6 × 10−14 0.70 1.2 ± 0.5 0.8 ± 0.3 X-24473 UKN 0.25 1.6 × 10−20 0.70 1.2 ± 1.5 2.0 ± 2.9 oleoyl-linoleoyl-glycerol (18:1/18:2) (2)6 LIP −0.20 3.5 × 10−14 0.68 1.2 ± 0.6 0.9 ± 0.5 X-11795 UKN 0.22 2.1 × 10−16 0.65 1.1 ± 0.8 1.5 ± 1.6 androstenediol (3β,17β) monosulfate (2) LIP 0.21 8.6 × 10−15 0.65 1.1 ± 0.9 1.9 ± 2.0  Red wine ethyl glucuronide XEN 0.30 3.3 × 10−30 0.75 1.1 ± 6.3 4.5 ± 16.8 X-24293 UKN 0.27 1.4 × 10−24 0.72 1.0 ± 1.7 2.9 ± 4.8 2,3-dihydroxyisovalerate XEN 0.26 1.1 × 10−22 0.66 1.3 ± 1.4 2.5 ± 4.1  White wine ethyl glucuronide XEN 0.22 8.6 × 10−16 0.83 0.6 ± 1.6 6.7 ± 20.6 2,3-dihydroxyisovalerate XEN 0.23 1.9 × 10−17 0.74 1.3 ± 1.6 3.1 ± 5.1  Liquor ethyl glucuronide XEN 0.51 8.4 × 10−90 0.80 0.9 ± 5.5 8.4 ± 27.4 X-24293 UKN 0.44 2.4 × 10−65 0.79 1.0 ± 1.5 4.1 ± 6.7 X-01911 UKN 0.24 9.4 × 10−20 0.68 1.1 ± 1.2 1.8 ± 1.6 androstenediol (3β,17β) disulfate (1) LIP 0.28 1.7 × 10−26 0.67 1.1 ± 1.0 2.4 ± 3.4 androstenediol (3β,17β) monosulfate (2) LIP 0.28 2.3 × 10−26 0.67 1.1 ± 0.9 2.2 ± 3.1 X-21474 UKN 0.23 2.3 × 10−17 0.67 1.0 ± 1.2 1.7 ± 1.5 5α-androstan-3β,17β-diol disulfate LIP 0.29 8.7 × 10−27 0.66 1.2 ± 1.2 2.7 ± 5.0 X-21659 UKN 0.22 4.4 × 10−16 0.66 1.0 ± 1.2 1.7 ± 1.5 X-17335 UKN 0.20 5.1 × 10−14 0.62 1.0 ± 0.6 1.2 ± 0.7 5α-androstan-3α,17β-diol disulfate LIP 0.24 4.3 × 10−19 0.60 0.4 ± 0.8 1.0 ± 1.9 Beverages  Total coffee X-21442 UKN 0.62 6.3 × 10−146 0.98 0.1 ± 1.0 3.5 ± 4.5 trigonelline (N ′-methylnicotinate) CV 0.66 1.3 × 10−172 0.96 0.3 ± 0.4 2.0 ± 1.1 X-24811 UKN 0.62 7.5 × 10−145 0.95 0.2 ± 0.3 1.6 ± 1.1 X-23655 UKN 0.58 2.6 × 10−121 0.95 0.1 ± 0.4 1.5 ± 1.3 quinate XEN 0.66 1.7 × 10−170 0.95 0.3 ± 0.5 2.0 ± 1.3 3-hydroxypyridine sulfate XEN 0.61 1.5 × 10−138 0.95 0.2 ± 0.5 2.3 ± 1.6 X-12230 UKN 0.57 1.1 × 10−119 0.94 0.3 ± 0.5 2.3 ± 1.7 3-methyl catechol sulfate (1) XEN 0.59 1.9 × 10−127 0.93 0.4 ± 0.7 2.1 ± 1.4 X-17185 UKN 0.52 4.4 × 10−96 0.93 0.3 ± 0.5 3.1 ± 10.8 citraconate/glutaconate ENG 0.56 4.4 × 10−111 0.93 0.6 ± 0.4 2.0 ± 1.2  Caffeinated coffee 1-methylxanthine XEN 0.64 1.4 × 10−155 0.96 0.5 ± 0.7 3.2 ± 1.7 paraxanthine XEN 0.60 1.5 × 10−135 0.95 0.3 ± 0.5 2.3 ± 1.4 1-methylurate XEN 0.60 6.9 × 10−134 0.94 0.6 ± 0.9 3.2 ± 1.8 5-acetylamino-6-amino-3-methyluracil XEN 0.61 7.9 × 10−138 0.94 0.6 ± 0.9 3.1 ± 1.7 1,3-dimethylurate XEN 0.56 2.8 × 10−112 0.94 0.7 ± 4.3 2.5 ± 1.4 1,7-dimethylurate XEN 0.61 2.0 × 10−136 0.92 0.6 ± 0.7 2.3 ± 1.1 theophylline XEN 0.55 5.2 × 10−108 0.92 0.7 ± 3.5 2.5 ± 1.5 caffeine XEN 0.61 3.8 × 10−140 0.91 0.6 ± 1.0 2.7 ± 1.7 1,3,7-trimethylurate XEN 0.62 2.7 × 10−144 0.91 0.5 ± 0.8 2.2 ± 1.3 X-21442 UKN 0.41 4.1 × 10−57 0.88 0.7 ± 1.6 3.6 ± 4.2  Decaffeinated coffee X-21442 UKN 0.31 7.6 × 10−31 0.83 0.9 ± 1.9 3.4 ± 4.6 3-hydroxypyridine sulfate XEN 0.29 1.6 × 10−27 0.80 0.9 ± 1.3 2.5 ± 1.7 trigonelline (N ′-methylnicotinate) CV 0.26 6.5 × 10−23 0.79 0.9 ± 0.9 1.9 ± 1.2 quinate XEN 0.30 6.4 × 10−29 0.78 0.9 ± 1.1 2.1 ± 1.4 X-24811 UKN 0.26 3.4 × 10−22 0.78 0.7 ± 0.9 1.5 ± 1.2 X-23655 UKN 0.26 2.3 × 10−22 0.78 0.6 ± 1.0 1.6 ± 1.4 X-23649 UKN 0.24 2.6 × 10−19 0.78 0.6 ± 1.2 1.8 ± 1.8 X-12816 UKN 0.25 4.1 × 10−21 0.77 0.5 ± 1.0 1.5 ± 1.4 X-12230 UKN 0.23 2.3 × 10−18 0.77 1.1 ± 1.6 2.3 ± 1.7 2,3-dihydroxypyridine XEN 0.24 1.8 × 10−18 0.77 0.6 ± 1.0 1.5 ± 1.2  Total tea theanine XEN 0.50 2.6 × 10−87 0.84 1.0 ± 6.3 28.2 ± 53.0 X-21795 UKN 0.41 1.4 × 10−55 0.72 0.1 ± 0.3 1.1 ± 2.0  Nonherbal tea theanine XEN 0.47 3.7 × 10−74 0.84 5.3 ± 25.3 33.7 ± 58.7 X-21795 UKN 0.43 9.3 × 10−62 0.72 0.2 ± 0.8 1.3 ± 2.1  Herbal tea or decaffeinated tea theanine XEN 0.23 2.7 × 10−17 0.70 8.7 ± 33.9 17.9 ± 41.8  Diet soft drinks saccharin XEN 0.25 5.5 × 10−20 0.69 4.5 ± 23.5 17.9 ± 46.4 Food groups/items Metabolites Super-pathway r P AUC2 Q1 mean ± SD3 Q5 mean ± SD Fruits  Total citrus fruits and juices stachydrine XEN 0.53 3.6 × 10−99 0.89 0.5 ± 0.7 2.1 ± 1.2 X-247384 UKN 0.49 5.2 × 10−82 0.89 0.5 ± 1.2 2.7 ± 2.2 N-methylproline AA 0.50 8.6 × 10−86 0.87 0.6 ± 1.0 2.8 ± 1.9 chiro-inositol LIP 0.43 1.1 × 10−62 0.86 0.3 ± 0.6 1.5 ± 1.2 X-22836 UKN 0.42 1.3 × 10−58 0.83 0.4 ± 0.7 1.5 ± 1.2 X-23314 UKN 0.41 1.4 × 10−56 0.82 0.9 ± 1.2 3.3 ± 3.3 X-17350 UKN 0.37 4.2 × 10−44 0.80 1.0 ± 1.1 2.7 ± 2.3 methyl glucopyranoside (α + β) XEN 0.36 4.1 × 10−43 0.80 0.8 ± 0.9 2.0 ± 1.9 X-16947 UKN 0.36 2.5 × 10−42 0.80 1.7 ± 5.1 6.1 ± 8.6 β-cryptoxanthin XEN 0.35 1.0 × 10−39 0.80 0.8 ± 0.5 1.6 ± 0.9  Orange juice stachydrine XEN 0.54 4.5 × 10−104 0.87 0.6 ± 0.8 2.2 ± 1.2 X-24738 UKN 0.51 2.2 × 10−92 0.86 0.6 ± 1.2 2.8 ± 2.3 N-methylproline AA 0.52 6.8 × 10−93 0.86 0.7 ± 1.0 2.8 ± 1.9 X-23314 UKN 0.48 3.4 × 10−78 0.83 0.9 ± 1.4 3.5 ± 3.7 chiro-inositol LIP 0.46 2.6 × 10−72 0.83 0.4 ± 0.9 1.5 ± 1.3 X-17350 UKN 0.43 3.1 × 10−62 0.82 1.0 ± 1.2 2.8 ± 2.4 X-22836 UKN 0.44 4.5 × 10−64 0.82 0.4 ± 0.7 1.5 ± 1.2 X-16947 UKN 0.42 8.2 × 10−58 0.81 1.5 ± 4.5 6.9 ± 9.2 X-22515 UKN 0.40 3.0 × 10−54 0.81 0.6 ± 1.7 3.0 ± 4.2 X-19183 UKN 0.41 1.9 × 10−57 0.81 0.4 ± 0.9 1.2 ± 1.0  Banana dopamine 3-O-sulfate AA 0.34 1.0 × 10−37 0.76 1.3 ± 1.5 5.7 ± 7.0 dopamine 4-sulfate AA 0.33 2.5 × 10−36 0.74 0.9 ± 1.6 5.3 ± 7.5 S-methylmethionine AA 0.23 3.9 × 10−18 0.72 1.0 ± 2.2 2.3 ± 2.7 3-methoxytyramine sulfate AA 0.22 9.2 × 10−17 0.70 1.0 ± 0.5 1.5 ± 0.9 X-12729 UKN 0.21 1.8 × 10−15 0.68 1.3 ± 3.7 2.9 ± 5.0 5-hydroxyindoleacetate AA 0.21 1.1 × 10−14 0.68 0.8 ± 0.9 1.7 ± 1.9  Prunes X-11315 UKN 0.21 1.5 × 10−14 0.67 1.0 ± 0.3 1.2 ± 0.5 X-12818 UKN 0.20 5.3 × 10−14 0.62 1.0 ± 1.4 1.5 ± 1.8 hippurate XEN 0.22 7.1 × 10−16 0.61 1.2 ± 1.1 1.8 ± 1.7 benzoylcarnitine5 XEN 0.25 3.0 × 10−21 0.61 0.9 ± 1.1 1.4 ± 1.8 X-24757 UKN 0.25 6.1 × 10−21 0.60 1.0 ± 1.2 1.9 ± 3.0 5-hydroxymethyl-2-furoic acid AA 0.26 3.1 × 10−22 0.58 1.0 ± 3.7 2.4 ± 8.1 X-17367 UKN 0.23 3.4 × 10−17 0.58 1.1 ± 1.6 2.1 ± 4.0 X-17325 UKN 0.21 1.3 × 10−15 0.58 1.4 ± 1.8 2.3 ± 3.8 X-22475 UKN 0.23 5.9 × 10−18 0.57 0.6 ± 1.4 1.6 ± 4.0 catechol sulfate XEN 0.20 4.2 × 10−14 0.57 1.1 ± 0.9 1.5 ± 1.1 Vegetables  Cruciferous vegetables S-methylcysteine sulfoxide AA 0.24 1.7 × 10−18 0.69 1.0 ± 0.7 1.6 ± 1.2  Mushrooms ergothioneine XEN 0.28 3.0 × 10−25 0.75 1.0 ± 0.5 1.6 ± 0.8  Allium vegetables N-methyltaurine AA 0.28 1.4 × 10−26 0.73 0.5 ± 1.3 1.5 ± 1.9 N-acetylalliin XEN 0.22 4.4 × 10−16 0.67 0.8 ± 1.7 2.9 ± 11.1 piperine XEN 0.23 1.0 × 10−17 0.67 1.1 ± 1.2 2.0 ± 2.2 ergothioneine XEN 0.22 1.0 × 10−16 0.67 1.1 ± 0.6 1.4 ± 0.8 γ-CEHC CV −0.20 5.7 × 10−14 0.67 1.3 ± 0.7 0.9 ± 0.6 γ-CEHC glucuronide5 CV −0.20 2.8 × 10−14 0.67 1.3 ± 0.9 0.8 ± 0.8 X-12231 UKN 0.20 9.6 × 10−14 0.65 1.0 ± 1.2 1.5 ± 1.6  Onion N-methyltaurine AA 0.24 9.7 × 10−20 0.69 0.6 ± 1.6 1.4 ± 2.1  Garlic γ-CEHC glucuronide5 CV −0.22 7.7 × 10−17 0.72 1.3 ± 1.0 0.7 ± 0.6 X-18249 UKN −0.22 4.8 × 10−16 0.71 1.2 ± 0.5 0.9 ± 0.4 γ-CEHC CV −0.22 2.0 × 10−16 0.71 1.3 ± 0.7 0.9 ± 0.5 N-acetylalliin XEN 0.27 3.1 × 10−24 0.70 0.8 ± 3.3 2.9 ± 8.9 S-allylcysteine XEN 0.23 2.8 × 10−17 0.69 1.2 ± 3.5 3.2 ± 5.0 ergothioneine XEN 0.26 3.6 × 10−22 0.69 1.0 ± 0.5 1.5 ± 0.9 X-02269 UKN 0.21 6.6 × 10−15 0.69 1.2 ± 1.3 2.2 ± 2.4 alliin XEN 0.24 5.7 × 10−19 0.69 1.0 ± 5.6 3.8 ± 9.0 N-methyltaurine AA 0.24 1.5 × 10−18 0.68 0.6 ± 1.2 1.5 ± 2.1  Tofu or soybeans X-11847 UKN 0.22 5.7 × 10−16 0.75 1.7 ± 3.2 7.3 ± 11.8 X-11858 UKN 0.22 1.1 × 10−15 0.72 1.0 ± 3.4 8.7 ± 24.2 X-16649 UKN 0.21 1.1 × 10−14 0.62 0.8 ± 3.0 4.9 ± 12.7 Grains  Whole grains X-21752 UKN 0.20 5.8 × 10−14 0.65 0.5 ± 1.2 1.1 ± 1.7 Proteins  Eggs 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)5 LIP 0.22 8.4 × 10−17 0.71 1.0 ± 0.3 1.4 ± 0.5  Red meat 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)5 LIP 0.28 4.2 × 10−25 0.67 0.9 ± 0.3 1.2 ± 0.4 X-11381 UKN 0.21 3.3 × 10−15 0.66 1.0 ± 0.5 1.2 ± 0.6 1-(1-enyl-stearoyl)-2-oleoyl-GPE (P-18:0/18:1) LIP 0.23 6.9 × 10−18 0.65 1.0 ± 0.3 1.2 ± 0.4  Processed meat X-18922 UKN 0.20 3.9 × 10−14 0.70 0.8 ± 0.5 1.2 ± 0.6  Poultry X-13835 UKN 0.21 1.1 × 10−14 0.65 1.2 ± 1.8 1.7 ± 1.7 3-methylhistidine AA 0.21 3.6 × 10−15 0.64 1.3 ± 1.6 2.1 ± 2.2  Total fish X-02269 UKN 0.41 1.9 × 10−55 0.82 0.9 ± 1.1 2.7 ± 2.5 CMPF LIP 0.37 1.7 × 10−45 0.80 1.0 ± 2.0 2.7 ± 2.3 DHA LIP 0.33 1.1 × 10−36 0.77 0.9 ± 0.4 1.4 ± 0.7 docosahexaenoylcholine LIP 0.27 3.1 × 10−24 0.75 0.8 ± 0.4 1.4 ± 0.7 1-docosahexaenoylglycerol (22:6) LIP 0.28 1.2 × 10−26 0.74 0.8 ± 0.8 1.4 ± 0.8 EPA LIP 0.27 7.1 × 10−25 0.72 0.9 ± 0.6 1.7 ± 1.2 eicosapentaenoylcholine LIP 0.23 4.0 × 10−17 0.72 0.8 ± 0.7 1.5 ± 1.3  Dark fish X-02269 UKN 0.51 1.8 × 10−92 0.94 0.7 ± 0.9 3.6 ± 2.4 CMPF LIP 0.47 6.9 × 10−74 0.93 0.8 ± 1.4 3.6 ± 2.6 DHA LIP 0.37 5.6 × 10−44 0.86 0.9 ± 0.4 1.7 ± 0.8 EPA LIP 0.36 1.7 × 10−42 0.84 0.9 ± 0.6 2.0 ± 1.2 docosahexaenoylcholine LIP 0.27 2.1 × 10−24 0.80 0.9 ± 0.5 1.6 ± 0.7 sphingomyelin (d18:2/18:1)5 LIP −0.25 4.7 × 10−20 0.80 1.2 ± 0.4 0.8 ± 0.3 eicosapentaenoylcholine LIP 0.29 8.7 × 10−28 0.79 0.8 ± 0.7 1.9 ± 1.5 1-docosahexaenoylglycerol (22:6) LIP 0.28 1.1 × 10−26 0.79 0.8 ± 0.6 1.7 ± 0.9 docosapentaenoate (n-6 DPA; 22:5n-6) LIP −0.26 2.4 × 10−22 0.71 1.2 ± 0.5 0.8 ± 0.5 X-13866 UKN 0.21 1.8 × 10−14 0.69 1.2 ± 2.0 1.9 ± 1.9  Shellfish CMPF LIP 0.26 4.3 × 10−23 0.83 1.1 ± 1.8 2.9 ± 2.7 X-02269 UKN 0.25 4.2 × 10−20 0.81 1.0 ± 1.4 2.4 ± 1.7  Total nuts tryptophan betaine AA 0.41 2.2 × 10−55 0.80 0.8 ± 0.9 2.0 ± 1.6 X-23644 UKN 0.27 6.5 × 10−24 0.72 1.9 ± 3.4 4.5 ± 5.7 4-vinylphenol sulfate XEN 0.27 1.9 × 10−24 0.69 1.3 ± 1.7 2.6 ± 2.9 lignoceroylcarnitine (C24)5 LIP 0.25 5.9 × 10−21 0.69 0.9 ± 0.4 1.2 ± 0.5 γ-glutamylvaline PEP −0.25 1.7 × 10−21 0.68 1.2 ± 0.4 0.9 ± 0.4 behenoylcarnitine (C22)5 LIP 0.23 2.4 × 10−17 0.66 0.7 ± 0.5 1.1 ± 0.8 sphingomyelin (d18:2/23:1)5 LIP −0.22 4.2 × 10−16 0.66 1.1 ± 0.3 0.9 ± 0.3  Peanuts tryptophan betaine AA 0.45 2.3 × 10−68 0.83 0.8 ± 0.9 2.1 ± 1.7 X-23644 UKN 0.27 6.3 × 10−24 0.71 2.0 ± 3.6 4.6 ± 5.9 4-vinylphenol sulfate XEN 0.31 7.0 × 10−32 0.71 1.2 ± 1.6 2.7 ± 2.9 γ-glutamylvaline PEP −0.30 3.3 × 10−30 0.71 1.2 ± 0.4 0.9 ± 0.4 lignoceroylcarnitine (C24)5 LIP 0.25 2.5 × 10−20 0.66 1.0 ± 0.4 1.2 ± 0.5 behenoylcarnitine (C22)5 LIP 0.23 5.6 × 10−18 0.66 0.8 ± 0.6 1.2 ± 0.8 sphingomyelin (d18:2/231)5 LIP −0.22 1.7 × 10−16 0.65 1.1 ± 0.3 0.9 ± 0.2  Other nuts X-11315 UKN 0.22 1.2 × 10−15 0.66 1.0 ± 0.4 1.2 ± 0.4 Dairy  Milk galactonate CHO 0.33 1.5 × 10−35 0.76 0.8 ± 0.7 2.0 ± 1.8 2,8-quinolinediol sulfate XEN 0.27 2.6 × 10−24 0.75 0.4 ± 0.7 1.3 ± 1.5 phenylacetylglycine PEP 0.27 1.8 × 10−23 0.71 0.7 ± 0.8 1.5 ± 1.3 X-11381 UKN 0.23 3.3 × 10−18 0.71 0.9 ± 0.4 1.3 ± 0.5 X-12798 UKN 0.24 1.4 × 10−18 0.70 0.9 ± 0.4 1.2 ± 0.5  Soy milk X-16649 UKN 0.20 4.1 × 10−14 0.69 1.1 ± 4.9 5.7 ± 12.1 4-ethylphenylsulfate XEN 0.20 5.9 × 10−14 0.67 2.0 ± 4.6 8.4 ± 16.2  Yogurt X-21736 UKN −0.20 6.3 × 10−14 0.70 1.5 ± 1.2 0.9 ± 0.7 Fats and oils  Butter X-11438 UKN 0.24 6.2 × 10−20 0.71 1.0 ± 0.8 1.6 ± 1.0 caprate (10:0) LIP 0.26 1.2 × 10−21 0.70 1.1 ± 0.6 1.6 ± 1.0 10-undecenoate (11:1n-1) LIP 0.24 9.3 × 10−19 0.69 1.0 ± 0.5 1.4 ± 0.7 sphingomyelin (d18:1/25:0, d19:0/24:1, d20:1/23:0, d19:1/24:0)5 LIP 0.24 6.7 × 10−20 0.68 0.9 ± 0.4 1.2 ± 0.5 X-17337 UKN 0.21 6.7 × 10−15 0.67 1.0 ± 0.5 1.3 ± 0.6 caprylate (8:0) LIP 0.21 5.0 × 10−15 0.67 1.0 ± 0.4 1.3 ± 0.6 sphingomyelin (d17:1/16:0, d18:1/15:0, d16:1/17:0)5 LIP 0.23 1.5 × 10−17 0.65 1.0 ± 0.2 1.1 ± 0.3 Miscellaneous  French fries X-18899 UKN 0.26 8.0 × 10−22 0.84 1.0 ± 0.9 1.6 ± 0.7 X-11880 UKN 0.26 3.6 × 10−23 0.83 0.9 ± 0.5 1.6 ± 0.8 X-21339 UKN 0.29 7.6 × 10−27 0.81 0.9 ± 1.0 1.9 ± 1.1 X-11308 UKN 0.27 6.4 × 10−25 0.81 0.9 ± 0.5 1.5 ± 0.7 X-11549 UKN 0.27 1.1 × 10−24 0.81 0.9 ± 0.5 1.6 ± 0.9 X-11372 UKN 0.24 5.2 × 10−19 0.79 0.9 ± 0.4 1.4 ± 0.6 X-11378 UKN 0.23 1.0 × 10−17 0.76 0.9 ± 0.7 1.5 ± 0.7 X-16935 UKN 0.23 8.3 × 10−18 0.75 0.9 ± 1.1 1.9 ± 1.6 eicosanodioate LIP 0.21 8.8 × 10−15 0.73 1.0 ± 0.5 1.5 ± 0.8  Total candies X-13728 UKN 0.20 7.0 × 10−14 0.65 1.1 ± 1.2 2.0 ± 2.2  Chocolate candies X-13728 UKN 0.28 3.6 × 10−25 0.69 1.2 ± 1.4 2.3 ± 2.4 3-methylxanthine XEN 0.26 4.4 × 10−22 0.68 1.1 ± 1.1 1.9 ± 1.8 7-methylurate XEN 0.24 4.0 × 10−19 0.68 1.1 ± 1.2 1.9 ± 1.9 3,7-dimethylurate XEN 0.24 1.9 × 10−19 0.66 0.9 ± 1.0 1.5 ± 1.6 theobromine XEN 0.24 3.8 × 10−19 0.66 1.1 ± 1.1 1.9 ± 1.7 7-methylxanthine XEN 0.21 1.8 × 10−14 0.64 0.6 ± 0.8 1.1 ± 1.2  Desserts ergothioneine XEN −0.25 1.8 × 10−20 0.69 1.5 ± 0.8 1.0 ± 0.6 sphingomyelin (d18:2/18:1)5 LIP 0.21 7.8 × 10−15 0.65 0.9 ± 0.4 1.1 ± 0.4 Alcohol  Total alcohol ethyl glucuronide XEN 0.60 1.79 × 10−133 0.92 0.4 ± 0.5 9.2 ± 28.4 X-24293 UKN 0.54 1.63 × 10−102 0.87 0.8 ± 1.3 4.4 ± 6.6 X-21737 UKN 0.21 3.7 × 10−15 0.76 1.8 ± 11.6 3.0 ± 6.0 CMPF LIP 0.23 2.9 × 10−17 0.74 1.1 ± 1.7 2.2 ± 1.8 X-23655 UKN 0.22 5.3 × 10−16 0.73 0.5 ± 0.7 1.3 ± 1.3 X-24811 UKN 0.23 2.8 × 10−17 0.73 0.6 ± 0.8 1.3 ± 1.1 caffeine XEN 0.25 3.3 × 10−21 0.72 0.9 ± 1.3 2.1 ± 1.9 X-14473 UKN 0.26 5.0 × 10−22 0.72 0.8 ± 0.7 1.4 ± 0.9 sphingomyelin (d18:2/18:1)5 LIP −0.27 3.0 × 10−24 0.72 1.1 ± 0.4 0.8 ± 0.3 X-12230 UKN 0.20 9.7 × 10−14 0.72 1.0 ± 1.2 2.0 ± 1.9  Beer X-24293 UKN 0.27 1.5 × 10−24 0.72 1.2 ± 2.0 3.3 ± 6.8  Total wine ethyl glucuronide XEN 0.45 1.9 × 10−68 0.85 0.4 ± 0.5 5.5 ± 17.0 X-24293 UKN 0.37 4.8 × 10−46 0.79 0.8 ± 1.3 3.1 ± 4.0 2,3-dihydroxyisovalerate XEN 0.36 5.6 × 10−43 0.75 1.2 ± 1.3 3.0 ± 4.8 CMPF LIP 0.20 5.4 × 10−14 0.73 1.2 ± 1.6 2.2 ± 2.0 sphingomyelin (d18:2/18:1)5 LIP −0.23 7.5 × 10−18 0.71 1.1 ± 0.4 0.9 ± 0.3 X-18249 UKN −0.20 8.6 × 10−14 0.70 1.2 ± 0.5 0.8 ± 0.3 X-24473 UKN 0.25 1.6 × 10−20 0.70 1.2 ± 1.5 2.0 ± 2.9 oleoyl-linoleoyl-glycerol (18:1/18:2) (2)6 LIP −0.20 3.5 × 10−14 0.68 1.2 ± 0.6 0.9 ± 0.5 X-11795 UKN 0.22 2.1 × 10−16 0.65 1.1 ± 0.8 1.5 ± 1.6 androstenediol (3β,17β) monosulfate (2) LIP 0.21 8.6 × 10−15 0.65 1.1 ± 0.9 1.9 ± 2.0  Red wine ethyl glucuronide XEN 0.30 3.3 × 10−30 0.75 1.1 ± 6.3 4.5 ± 16.8 X-24293 UKN 0.27 1.4 × 10−24 0.72 1.0 ± 1.7 2.9 ± 4.8 2,3-dihydroxyisovalerate XEN 0.26 1.1 × 10−22 0.66 1.3 ± 1.4 2.5 ± 4.1  White wine ethyl glucuronide XEN 0.22 8.6 × 10−16 0.83 0.6 ± 1.6 6.7 ± 20.6 2,3-dihydroxyisovalerate XEN 0.23 1.9 × 10−17 0.74 1.3 ± 1.6 3.1 ± 5.1  Liquor ethyl glucuronide XEN 0.51 8.4 × 10−90 0.80 0.9 ± 5.5 8.4 ± 27.4 X-24293 UKN 0.44 2.4 × 10−65 0.79 1.0 ± 1.5 4.1 ± 6.7 X-01911 UKN 0.24 9.4 × 10−20 0.68 1.1 ± 1.2 1.8 ± 1.6 androstenediol (3β,17β) disulfate (1) LIP 0.28 1.7 × 10−26 0.67 1.1 ± 1.0 2.4 ± 3.4 androstenediol (3β,17β) monosulfate (2) LIP 0.28 2.3 × 10−26 0.67 1.1 ± 0.9 2.2 ± 3.1 X-21474 UKN 0.23 2.3 × 10−17 0.67 1.0 ± 1.2 1.7 ± 1.5 5α-androstan-3β,17β-diol disulfate LIP 0.29 8.7 × 10−27 0.66 1.2 ± 1.2 2.7 ± 5.0 X-21659 UKN 0.22 4.4 × 10−16 0.66 1.0 ± 1.2 1.7 ± 1.5 X-17335 UKN 0.20 5.1 × 10−14 0.62 1.0 ± 0.6 1.2 ± 0.7 5α-androstan-3α,17β-diol disulfate LIP 0.24 4.3 × 10−19 0.60 0.4 ± 0.8 1.0 ± 1.9 Beverages  Total coffee X-21442 UKN 0.62 6.3 × 10−146 0.98 0.1 ± 1.0 3.5 ± 4.5 trigonelline (N ′-methylnicotinate) CV 0.66 1.3 × 10−172 0.96 0.3 ± 0.4 2.0 ± 1.1 X-24811 UKN 0.62 7.5 × 10−145 0.95 0.2 ± 0.3 1.6 ± 1.1 X-23655 UKN 0.58 2.6 × 10−121 0.95 0.1 ± 0.4 1.5 ± 1.3 quinate XEN 0.66 1.7 × 10−170 0.95 0.3 ± 0.5 2.0 ± 1.3 3-hydroxypyridine sulfate XEN 0.61 1.5 × 10−138 0.95 0.2 ± 0.5 2.3 ± 1.6 X-12230 UKN 0.57 1.1 × 10−119 0.94 0.3 ± 0.5 2.3 ± 1.7 3-methyl catechol sulfate (1) XEN 0.59 1.9 × 10−127 0.93 0.4 ± 0.7 2.1 ± 1.4 X-17185 UKN 0.52 4.4 × 10−96 0.93 0.3 ± 0.5 3.1 ± 10.8 citraconate/glutaconate ENG 0.56 4.4 × 10−111 0.93 0.6 ± 0.4 2.0 ± 1.2  Caffeinated coffee 1-methylxanthine XEN 0.64 1.4 × 10−155 0.96 0.5 ± 0.7 3.2 ± 1.7 paraxanthine XEN 0.60 1.5 × 10−135 0.95 0.3 ± 0.5 2.3 ± 1.4 1-methylurate XEN 0.60 6.9 × 10−134 0.94 0.6 ± 0.9 3.2 ± 1.8 5-acetylamino-6-amino-3-methyluracil XEN 0.61 7.9 × 10−138 0.94 0.6 ± 0.9 3.1 ± 1.7 1,3-dimethylurate XEN 0.56 2.8 × 10−112 0.94 0.7 ± 4.3 2.5 ± 1.4 1,7-dimethylurate XEN 0.61 2.0 × 10−136 0.92 0.6 ± 0.7 2.3 ± 1.1 theophylline XEN 0.55 5.2 × 10−108 0.92 0.7 ± 3.5 2.5 ± 1.5 caffeine XEN 0.61 3.8 × 10−140 0.91 0.6 ± 1.0 2.7 ± 1.7 1,3,7-trimethylurate XEN 0.62 2.7 × 10−144 0.91 0.5 ± 0.8 2.2 ± 1.3 X-21442 UKN 0.41 4.1 × 10−57 0.88 0.7 ± 1.6 3.6 ± 4.2  Decaffeinated coffee X-21442 UKN 0.31 7.6 × 10−31 0.83 0.9 ± 1.9 3.4 ± 4.6 3-hydroxypyridine sulfate XEN 0.29 1.6 × 10−27 0.80 0.9 ± 1.3 2.5 ± 1.7 trigonelline (N ′-methylnicotinate) CV 0.26 6.5 × 10−23 0.79 0.9 ± 0.9 1.9 ± 1.2 quinate XEN 0.30 6.4 × 10−29 0.78 0.9 ± 1.1 2.1 ± 1.4 X-24811 UKN 0.26 3.4 × 10−22 0.78 0.7 ± 0.9 1.5 ± 1.2 X-23655 UKN 0.26 2.3 × 10−22 0.78 0.6 ± 1.0 1.6 ± 1.4 X-23649 UKN 0.24 2.6 × 10−19 0.78 0.6 ± 1.2 1.8 ± 1.8 X-12816 UKN 0.25 4.1 × 10−21 0.77 0.5 ± 1.0 1.5 ± 1.4 X-12230 UKN 0.23 2.3 × 10−18 0.77 1.1 ± 1.6 2.3 ± 1.7 2,3-dihydroxypyridine XEN 0.24 1.8 × 10−18 0.77 0.6 ± 1.0 1.5 ± 1.2  Total tea theanine XEN 0.50 2.6 × 10−87 0.84 1.0 ± 6.3 28.2 ± 53.0 X-21795 UKN 0.41 1.4 × 10−55 0.72 0.1 ± 0.3 1.1 ± 2.0  Nonherbal tea theanine XEN 0.47 3.7 × 10−74 0.84 5.3 ± 25.3 33.7 ± 58.7 X-21795 UKN 0.43 9.3 × 10−62 0.72 0.2 ± 0.8 1.3 ± 2.1  Herbal tea or decaffeinated tea theanine XEN 0.23 2.7 × 10−17 0.70 8.7 ± 33.9 17.9 ± 41.8  Diet soft drinks saccharin XEN 0.25 5.5 × 10−20 0.69 4.5 ± 23.5 17.9 ± 46.4 1Diet-metabolite correlations selected for presentation here had P < 4.63 × 10−7 and |r| > 0.2 from Pearson's partial correlation analysis. Adjusted for age at blood draw, race, education, smoking status, hormone replacement therapy, physical activity, BMI, ethanol consumption (except for alcohol-containing items), time since last meal, and caloric intake. AA, amino acid; CHO, carbohydrate; CMPF, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate; CPS-II, Cancer Prevention Study-II; CV, cofactor/vitamin; ENG, energy; LIP, lipid; PEP, peptide; Q, quintile; UKN, unknown; XEN, xenobiotic; γ-CEHC, γ-carboxyethyl hydrochroman. 2Estimated from receiver operating characteristic analysis using 2000-times stratified bootstrap samples. 3Raw data divided by the median of each metabolite, before data transformation by generalized log and autoscaling. 4Metabolites starting with X are unnamed; the superpathway of these is unknown. 5Putative identity that has not been officially confirmed based on a standard. 6(1) and (2) indicate that the metabolite differs from another with the same mass in the position of the R group. View Large TABLE 1 Top 10 predictive serum metabolites of 42 food groups/items among women in the CPS-II Nutrition Cohort (n = 1369)1 Food groups/items Metabolites Super-pathway r P AUC2 Q1 mean ± SD3 Q5 mean ± SD Fruits  Total citrus fruits and juices stachydrine XEN 0.53 3.6 × 10−99 0.89 0.5 ± 0.7 2.1 ± 1.2 X-247384 UKN 0.49 5.2 × 10−82 0.89 0.5 ± 1.2 2.7 ± 2.2 N-methylproline AA 0.50 8.6 × 10−86 0.87 0.6 ± 1.0 2.8 ± 1.9 chiro-inositol LIP 0.43 1.1 × 10−62 0.86 0.3 ± 0.6 1.5 ± 1.2 X-22836 UKN 0.42 1.3 × 10−58 0.83 0.4 ± 0.7 1.5 ± 1.2 X-23314 UKN 0.41 1.4 × 10−56 0.82 0.9 ± 1.2 3.3 ± 3.3 X-17350 UKN 0.37 4.2 × 10−44 0.80 1.0 ± 1.1 2.7 ± 2.3 methyl glucopyranoside (α + β) XEN 0.36 4.1 × 10−43 0.80 0.8 ± 0.9 2.0 ± 1.9 X-16947 UKN 0.36 2.5 × 10−42 0.80 1.7 ± 5.1 6.1 ± 8.6 β-cryptoxanthin XEN 0.35 1.0 × 10−39 0.80 0.8 ± 0.5 1.6 ± 0.9  Orange juice stachydrine XEN 0.54 4.5 × 10−104 0.87 0.6 ± 0.8 2.2 ± 1.2 X-24738 UKN 0.51 2.2 × 10−92 0.86 0.6 ± 1.2 2.8 ± 2.3 N-methylproline AA 0.52 6.8 × 10−93 0.86 0.7 ± 1.0 2.8 ± 1.9 X-23314 UKN 0.48 3.4 × 10−78 0.83 0.9 ± 1.4 3.5 ± 3.7 chiro-inositol LIP 0.46 2.6 × 10−72 0.83 0.4 ± 0.9 1.5 ± 1.3 X-17350 UKN 0.43 3.1 × 10−62 0.82 1.0 ± 1.2 2.8 ± 2.4 X-22836 UKN 0.44 4.5 × 10−64 0.82 0.4 ± 0.7 1.5 ± 1.2 X-16947 UKN 0.42 8.2 × 10−58 0.81 1.5 ± 4.5 6.9 ± 9.2 X-22515 UKN 0.40 3.0 × 10−54 0.81 0.6 ± 1.7 3.0 ± 4.2 X-19183 UKN 0.41 1.9 × 10−57 0.81 0.4 ± 0.9 1.2 ± 1.0  Banana dopamine 3-O-sulfate AA 0.34 1.0 × 10−37 0.76 1.3 ± 1.5 5.7 ± 7.0 dopamine 4-sulfate AA 0.33 2.5 × 10−36 0.74 0.9 ± 1.6 5.3 ± 7.5 S-methylmethionine AA 0.23 3.9 × 10−18 0.72 1.0 ± 2.2 2.3 ± 2.7 3-methoxytyramine sulfate AA 0.22 9.2 × 10−17 0.70 1.0 ± 0.5 1.5 ± 0.9 X-12729 UKN 0.21 1.8 × 10−15 0.68 1.3 ± 3.7 2.9 ± 5.0 5-hydroxyindoleacetate AA 0.21 1.1 × 10−14 0.68 0.8 ± 0.9 1.7 ± 1.9  Prunes X-11315 UKN 0.21 1.5 × 10−14 0.67 1.0 ± 0.3 1.2 ± 0.5 X-12818 UKN 0.20 5.3 × 10−14 0.62 1.0 ± 1.4 1.5 ± 1.8 hippurate XEN 0.22 7.1 × 10−16 0.61 1.2 ± 1.1 1.8 ± 1.7 benzoylcarnitine5 XEN 0.25 3.0 × 10−21 0.61 0.9 ± 1.1 1.4 ± 1.8 X-24757 UKN 0.25 6.1 × 10−21 0.60 1.0 ± 1.2 1.9 ± 3.0 5-hydroxymethyl-2-furoic acid AA 0.26 3.1 × 10−22 0.58 1.0 ± 3.7 2.4 ± 8.1 X-17367 UKN 0.23 3.4 × 10−17 0.58 1.1 ± 1.6 2.1 ± 4.0 X-17325 UKN 0.21 1.3 × 10−15 0.58 1.4 ± 1.8 2.3 ± 3.8 X-22475 UKN 0.23 5.9 × 10−18 0.57 0.6 ± 1.4 1.6 ± 4.0 catechol sulfate XEN 0.20 4.2 × 10−14 0.57 1.1 ± 0.9 1.5 ± 1.1 Vegetables  Cruciferous vegetables S-methylcysteine sulfoxide AA 0.24 1.7 × 10−18 0.69 1.0 ± 0.7 1.6 ± 1.2  Mushrooms ergothioneine XEN 0.28 3.0 × 10−25 0.75 1.0 ± 0.5 1.6 ± 0.8  Allium vegetables N-methyltaurine AA 0.28 1.4 × 10−26 0.73 0.5 ± 1.3 1.5 ± 1.9 N-acetylalliin XEN 0.22 4.4 × 10−16 0.67 0.8 ± 1.7 2.9 ± 11.1 piperine XEN 0.23 1.0 × 10−17 0.67 1.1 ± 1.2 2.0 ± 2.2 ergothioneine XEN 0.22 1.0 × 10−16 0.67 1.1 ± 0.6 1.4 ± 0.8 γ-CEHC CV −0.20 5.7 × 10−14 0.67 1.3 ± 0.7 0.9 ± 0.6 γ-CEHC glucuronide5 CV −0.20 2.8 × 10−14 0.67 1.3 ± 0.9 0.8 ± 0.8 X-12231 UKN 0.20 9.6 × 10−14 0.65 1.0 ± 1.2 1.5 ± 1.6  Onion N-methyltaurine AA 0.24 9.7 × 10−20 0.69 0.6 ± 1.6 1.4 ± 2.1  Garlic γ-CEHC glucuronide5 CV −0.22 7.7 × 10−17 0.72 1.3 ± 1.0 0.7 ± 0.6 X-18249 UKN −0.22 4.8 × 10−16 0.71 1.2 ± 0.5 0.9 ± 0.4 γ-CEHC CV −0.22 2.0 × 10−16 0.71 1.3 ± 0.7 0.9 ± 0.5 N-acetylalliin XEN 0.27 3.1 × 10−24 0.70 0.8 ± 3.3 2.9 ± 8.9 S-allylcysteine XEN 0.23 2.8 × 10−17 0.69 1.2 ± 3.5 3.2 ± 5.0 ergothioneine XEN 0.26 3.6 × 10−22 0.69 1.0 ± 0.5 1.5 ± 0.9 X-02269 UKN 0.21 6.6 × 10−15 0.69 1.2 ± 1.3 2.2 ± 2.4 alliin XEN 0.24 5.7 × 10−19 0.69 1.0 ± 5.6 3.8 ± 9.0 N-methyltaurine AA 0.24 1.5 × 10−18 0.68 0.6 ± 1.2 1.5 ± 2.1  Tofu or soybeans X-11847 UKN 0.22 5.7 × 10−16 0.75 1.7 ± 3.2 7.3 ± 11.8 X-11858 UKN 0.22 1.1 × 10−15 0.72 1.0 ± 3.4 8.7 ± 24.2 X-16649 UKN 0.21 1.1 × 10−14 0.62 0.8 ± 3.0 4.9 ± 12.7 Grains  Whole grains X-21752 UKN 0.20 5.8 × 10−14 0.65 0.5 ± 1.2 1.1 ± 1.7 Proteins  Eggs 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)5 LIP 0.22 8.4 × 10−17 0.71 1.0 ± 0.3 1.4 ± 0.5  Red meat 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)5 LIP 0.28 4.2 × 10−25 0.67 0.9 ± 0.3 1.2 ± 0.4 X-11381 UKN 0.21 3.3 × 10−15 0.66 1.0 ± 0.5 1.2 ± 0.6 1-(1-enyl-stearoyl)-2-oleoyl-GPE (P-18:0/18:1) LIP 0.23 6.9 × 10−18 0.65 1.0 ± 0.3 1.2 ± 0.4  Processed meat X-18922 UKN 0.20 3.9 × 10−14 0.70 0.8 ± 0.5 1.2 ± 0.6  Poultry X-13835 UKN 0.21 1.1 × 10−14 0.65 1.2 ± 1.8 1.7 ± 1.7 3-methylhistidine AA 0.21 3.6 × 10−15 0.64 1.3 ± 1.6 2.1 ± 2.2  Total fish X-02269 UKN 0.41 1.9 × 10−55 0.82 0.9 ± 1.1 2.7 ± 2.5 CMPF LIP 0.37 1.7 × 10−45 0.80 1.0 ± 2.0 2.7 ± 2.3 DHA LIP 0.33 1.1 × 10−36 0.77 0.9 ± 0.4 1.4 ± 0.7 docosahexaenoylcholine LIP 0.27 3.1 × 10−24 0.75 0.8 ± 0.4 1.4 ± 0.7 1-docosahexaenoylglycerol (22:6) LIP 0.28 1.2 × 10−26 0.74 0.8 ± 0.8 1.4 ± 0.8 EPA LIP 0.27 7.1 × 10−25 0.72 0.9 ± 0.6 1.7 ± 1.2 eicosapentaenoylcholine LIP 0.23 4.0 × 10−17 0.72 0.8 ± 0.7 1.5 ± 1.3  Dark fish X-02269 UKN 0.51 1.8 × 10−92 0.94 0.7 ± 0.9 3.6 ± 2.4 CMPF LIP 0.47 6.9 × 10−74 0.93 0.8 ± 1.4 3.6 ± 2.6 DHA LIP 0.37 5.6 × 10−44 0.86 0.9 ± 0.4 1.7 ± 0.8 EPA LIP 0.36 1.7 × 10−42 0.84 0.9 ± 0.6 2.0 ± 1.2 docosahexaenoylcholine LIP 0.27 2.1 × 10−24 0.80 0.9 ± 0.5 1.6 ± 0.7 sphingomyelin (d18:2/18:1)5 LIP −0.25 4.7 × 10−20 0.80 1.2 ± 0.4 0.8 ± 0.3 eicosapentaenoylcholine LIP 0.29 8.7 × 10−28 0.79 0.8 ± 0.7 1.9 ± 1.5 1-docosahexaenoylglycerol (22:6) LIP 0.28 1.1 × 10−26 0.79 0.8 ± 0.6 1.7 ± 0.9 docosapentaenoate (n-6 DPA; 22:5n-6) LIP −0.26 2.4 × 10−22 0.71 1.2 ± 0.5 0.8 ± 0.5 X-13866 UKN 0.21 1.8 × 10−14 0.69 1.2 ± 2.0 1.9 ± 1.9  Shellfish CMPF LIP 0.26 4.3 × 10−23 0.83 1.1 ± 1.8 2.9 ± 2.7 X-02269 UKN 0.25 4.2 × 10−20 0.81 1.0 ± 1.4 2.4 ± 1.7  Total nuts tryptophan betaine AA 0.41 2.2 × 10−55 0.80 0.8 ± 0.9 2.0 ± 1.6 X-23644 UKN 0.27 6.5 × 10−24 0.72 1.9 ± 3.4 4.5 ± 5.7 4-vinylphenol sulfate XEN 0.27 1.9 × 10−24 0.69 1.3 ± 1.7 2.6 ± 2.9 lignoceroylcarnitine (C24)5 LIP 0.25 5.9 × 10−21 0.69 0.9 ± 0.4 1.2 ± 0.5 γ-glutamylvaline PEP −0.25 1.7 × 10−21 0.68 1.2 ± 0.4 0.9 ± 0.4 behenoylcarnitine (C22)5 LIP 0.23 2.4 × 10−17 0.66 0.7 ± 0.5 1.1 ± 0.8 sphingomyelin (d18:2/23:1)5 LIP −0.22 4.2 × 10−16 0.66 1.1 ± 0.3 0.9 ± 0.3  Peanuts tryptophan betaine AA 0.45 2.3 × 10−68 0.83 0.8 ± 0.9 2.1 ± 1.7 X-23644 UKN 0.27 6.3 × 10−24 0.71 2.0 ± 3.6 4.6 ± 5.9 4-vinylphenol sulfate XEN 0.31 7.0 × 10−32 0.71 1.2 ± 1.6 2.7 ± 2.9 γ-glutamylvaline PEP −0.30 3.3 × 10−30 0.71 1.2 ± 0.4 0.9 ± 0.4 lignoceroylcarnitine (C24)5 LIP 0.25 2.5 × 10−20 0.66 1.0 ± 0.4 1.2 ± 0.5 behenoylcarnitine (C22)5 LIP 0.23 5.6 × 10−18 0.66 0.8 ± 0.6 1.2 ± 0.8 sphingomyelin (d18:2/231)5 LIP −0.22 1.7 × 10−16 0.65 1.1 ± 0.3 0.9 ± 0.2  Other nuts X-11315 UKN 0.22 1.2 × 10−15 0.66 1.0 ± 0.4 1.2 ± 0.4 Dairy  Milk galactonate CHO 0.33 1.5 × 10−35 0.76 0.8 ± 0.7 2.0 ± 1.8 2,8-quinolinediol sulfate XEN 0.27 2.6 × 10−24 0.75 0.4 ± 0.7 1.3 ± 1.5 phenylacetylglycine PEP 0.27 1.8 × 10−23 0.71 0.7 ± 0.8 1.5 ± 1.3 X-11381 UKN 0.23 3.3 × 10−18 0.71 0.9 ± 0.4 1.3 ± 0.5 X-12798 UKN 0.24 1.4 × 10−18 0.70 0.9 ± 0.4 1.2 ± 0.5  Soy milk X-16649 UKN 0.20 4.1 × 10−14 0.69 1.1 ± 4.9 5.7 ± 12.1 4-ethylphenylsulfate XEN 0.20 5.9 × 10−14 0.67 2.0 ± 4.6 8.4 ± 16.2  Yogurt X-21736 UKN −0.20 6.3 × 10−14 0.70 1.5 ± 1.2 0.9 ± 0.7 Fats and oils  Butter X-11438 UKN 0.24 6.2 × 10−20 0.71 1.0 ± 0.8 1.6 ± 1.0 caprate (10:0) LIP 0.26 1.2 × 10−21 0.70 1.1 ± 0.6 1.6 ± 1.0 10-undecenoate (11:1n-1) LIP 0.24 9.3 × 10−19 0.69 1.0 ± 0.5 1.4 ± 0.7 sphingomyelin (d18:1/25:0, d19:0/24:1, d20:1/23:0, d19:1/24:0)5 LIP 0.24 6.7 × 10−20 0.68 0.9 ± 0.4 1.2 ± 0.5 X-17337 UKN 0.21 6.7 × 10−15 0.67 1.0 ± 0.5 1.3 ± 0.6 caprylate (8:0) LIP 0.21 5.0 × 10−15 0.67 1.0 ± 0.4 1.3 ± 0.6 sphingomyelin (d17:1/16:0, d18:1/15:0, d16:1/17:0)5 LIP 0.23 1.5 × 10−17 0.65 1.0 ± 0.2 1.1 ± 0.3 Miscellaneous  French fries X-18899 UKN 0.26 8.0 × 10−22 0.84 1.0 ± 0.9 1.6 ± 0.7 X-11880 UKN 0.26 3.6 × 10−23 0.83 0.9 ± 0.5 1.6 ± 0.8 X-21339 UKN 0.29 7.6 × 10−27 0.81 0.9 ± 1.0 1.9 ± 1.1 X-11308 UKN 0.27 6.4 × 10−25 0.81 0.9 ± 0.5 1.5 ± 0.7 X-11549 UKN 0.27 1.1 × 10−24 0.81 0.9 ± 0.5 1.6 ± 0.9 X-11372 UKN 0.24 5.2 × 10−19 0.79 0.9 ± 0.4 1.4 ± 0.6 X-11378 UKN 0.23 1.0 × 10−17 0.76 0.9 ± 0.7 1.5 ± 0.7 X-16935 UKN 0.23 8.3 × 10−18 0.75 0.9 ± 1.1 1.9 ± 1.6 eicosanodioate LIP 0.21 8.8 × 10−15 0.73 1.0 ± 0.5 1.5 ± 0.8  Total candies X-13728 UKN 0.20 7.0 × 10−14 0.65 1.1 ± 1.2 2.0 ± 2.2  Chocolate candies X-13728 UKN 0.28 3.6 × 10−25 0.69 1.2 ± 1.4 2.3 ± 2.4 3-methylxanthine XEN 0.26 4.4 × 10−22 0.68 1.1 ± 1.1 1.9 ± 1.8 7-methylurate XEN 0.24 4.0 × 10−19 0.68 1.1 ± 1.2 1.9 ± 1.9 3,7-dimethylurate XEN 0.24 1.9 × 10−19 0.66 0.9 ± 1.0 1.5 ± 1.6 theobromine XEN 0.24 3.8 × 10−19 0.66 1.1 ± 1.1 1.9 ± 1.7 7-methylxanthine XEN 0.21 1.8 × 10−14 0.64 0.6 ± 0.8 1.1 ± 1.2  Desserts ergothioneine XEN −0.25 1.8 × 10−20 0.69 1.5 ± 0.8 1.0 ± 0.6 sphingomyelin (d18:2/18:1)5 LIP 0.21 7.8 × 10−15 0.65 0.9 ± 0.4 1.1 ± 0.4 Alcohol  Total alcohol ethyl glucuronide XEN 0.60 1.79 × 10−133 0.92 0.4 ± 0.5 9.2 ± 28.4 X-24293 UKN 0.54 1.63 × 10−102 0.87 0.8 ± 1.3 4.4 ± 6.6 X-21737 UKN 0.21 3.7 × 10−15 0.76 1.8 ± 11.6 3.0 ± 6.0 CMPF LIP 0.23 2.9 × 10−17 0.74 1.1 ± 1.7 2.2 ± 1.8 X-23655 UKN 0.22 5.3 × 10−16 0.73 0.5 ± 0.7 1.3 ± 1.3 X-24811 UKN 0.23 2.8 × 10−17 0.73 0.6 ± 0.8 1.3 ± 1.1 caffeine XEN 0.25 3.3 × 10−21 0.72 0.9 ± 1.3 2.1 ± 1.9 X-14473 UKN 0.26 5.0 × 10−22 0.72 0.8 ± 0.7 1.4 ± 0.9 sphingomyelin (d18:2/18:1)5 LIP −0.27 3.0 × 10−24 0.72 1.1 ± 0.4 0.8 ± 0.3 X-12230 UKN 0.20 9.7 × 10−14 0.72 1.0 ± 1.2 2.0 ± 1.9  Beer X-24293 UKN 0.27 1.5 × 10−24 0.72 1.2 ± 2.0 3.3 ± 6.8  Total wine ethyl glucuronide XEN 0.45 1.9 × 10−68 0.85 0.4 ± 0.5 5.5 ± 17.0 X-24293 UKN 0.37 4.8 × 10−46 0.79 0.8 ± 1.3 3.1 ± 4.0 2,3-dihydroxyisovalerate XEN 0.36 5.6 × 10−43 0.75 1.2 ± 1.3 3.0 ± 4.8 CMPF LIP 0.20 5.4 × 10−14 0.73 1.2 ± 1.6 2.2 ± 2.0 sphingomyelin (d18:2/18:1)5 LIP −0.23 7.5 × 10−18 0.71 1.1 ± 0.4 0.9 ± 0.3 X-18249 UKN −0.20 8.6 × 10−14 0.70 1.2 ± 0.5 0.8 ± 0.3 X-24473 UKN 0.25 1.6 × 10−20 0.70 1.2 ± 1.5 2.0 ± 2.9 oleoyl-linoleoyl-glycerol (18:1/18:2) (2)6 LIP −0.20 3.5 × 10−14 0.68 1.2 ± 0.6 0.9 ± 0.5 X-11795 UKN 0.22 2.1 × 10−16 0.65 1.1 ± 0.8 1.5 ± 1.6 androstenediol (3β,17β) monosulfate (2) LIP 0.21 8.6 × 10−15 0.65 1.1 ± 0.9 1.9 ± 2.0  Red wine ethyl glucuronide XEN 0.30 3.3 × 10−30 0.75 1.1 ± 6.3 4.5 ± 16.8 X-24293 UKN 0.27 1.4 × 10−24 0.72 1.0 ± 1.7 2.9 ± 4.8 2,3-dihydroxyisovalerate XEN 0.26 1.1 × 10−22 0.66 1.3 ± 1.4 2.5 ± 4.1  White wine ethyl glucuronide XEN 0.22 8.6 × 10−16 0.83 0.6 ± 1.6 6.7 ± 20.6 2,3-dihydroxyisovalerate XEN 0.23 1.9 × 10−17 0.74 1.3 ± 1.6 3.1 ± 5.1  Liquor ethyl glucuronide XEN 0.51 8.4 × 10−90 0.80 0.9 ± 5.5 8.4 ± 27.4 X-24293 UKN 0.44 2.4 × 10−65 0.79 1.0 ± 1.5 4.1 ± 6.7 X-01911 UKN 0.24 9.4 × 10−20 0.68 1.1 ± 1.2 1.8 ± 1.6 androstenediol (3β,17β) disulfate (1) LIP 0.28 1.7 × 10−26 0.67 1.1 ± 1.0 2.4 ± 3.4 androstenediol (3β,17β) monosulfate (2) LIP 0.28 2.3 × 10−26 0.67 1.1 ± 0.9 2.2 ± 3.1 X-21474 UKN 0.23 2.3 × 10−17 0.67 1.0 ± 1.2 1.7 ± 1.5 5α-androstan-3β,17β-diol disulfate LIP 0.29 8.7 × 10−27 0.66 1.2 ± 1.2 2.7 ± 5.0 X-21659 UKN 0.22 4.4 × 10−16 0.66 1.0 ± 1.2 1.7 ± 1.5 X-17335 UKN 0.20 5.1 × 10−14 0.62 1.0 ± 0.6 1.2 ± 0.7 5α-androstan-3α,17β-diol disulfate LIP 0.24 4.3 × 10−19 0.60 0.4 ± 0.8 1.0 ± 1.9 Beverages  Total coffee X-21442 UKN 0.62 6.3 × 10−146 0.98 0.1 ± 1.0 3.5 ± 4.5 trigonelline (N ′-methylnicotinate) CV 0.66 1.3 × 10−172 0.96 0.3 ± 0.4 2.0 ± 1.1 X-24811 UKN 0.62 7.5 × 10−145 0.95 0.2 ± 0.3 1.6 ± 1.1 X-23655 UKN 0.58 2.6 × 10−121 0.95 0.1 ± 0.4 1.5 ± 1.3 quinate XEN 0.66 1.7 × 10−170 0.95 0.3 ± 0.5 2.0 ± 1.3 3-hydroxypyridine sulfate XEN 0.61 1.5 × 10−138 0.95 0.2 ± 0.5 2.3 ± 1.6 X-12230 UKN 0.57 1.1 × 10−119 0.94 0.3 ± 0.5 2.3 ± 1.7 3-methyl catechol sulfate (1) XEN 0.59 1.9 × 10−127 0.93 0.4 ± 0.7 2.1 ± 1.4 X-17185 UKN 0.52 4.4 × 10−96 0.93 0.3 ± 0.5 3.1 ± 10.8 citraconate/glutaconate ENG 0.56 4.4 × 10−111 0.93 0.6 ± 0.4 2.0 ± 1.2  Caffeinated coffee 1-methylxanthine XEN 0.64 1.4 × 10−155 0.96 0.5 ± 0.7 3.2 ± 1.7 paraxanthine XEN 0.60 1.5 × 10−135 0.95 0.3 ± 0.5 2.3 ± 1.4 1-methylurate XEN 0.60 6.9 × 10−134 0.94 0.6 ± 0.9 3.2 ± 1.8 5-acetylamino-6-amino-3-methyluracil XEN 0.61 7.9 × 10−138 0.94 0.6 ± 0.9 3.1 ± 1.7 1,3-dimethylurate XEN 0.56 2.8 × 10−112 0.94 0.7 ± 4.3 2.5 ± 1.4 1,7-dimethylurate XEN 0.61 2.0 × 10−136 0.92 0.6 ± 0.7 2.3 ± 1.1 theophylline XEN 0.55 5.2 × 10−108 0.92 0.7 ± 3.5 2.5 ± 1.5 caffeine XEN 0.61 3.8 × 10−140 0.91 0.6 ± 1.0 2.7 ± 1.7 1,3,7-trimethylurate XEN 0.62 2.7 × 10−144 0.91 0.5 ± 0.8 2.2 ± 1.3 X-21442 UKN 0.41 4.1 × 10−57 0.88 0.7 ± 1.6 3.6 ± 4.2  Decaffeinated coffee X-21442 UKN 0.31 7.6 × 10−31 0.83 0.9 ± 1.9 3.4 ± 4.6 3-hydroxypyridine sulfate XEN 0.29 1.6 × 10−27 0.80 0.9 ± 1.3 2.5 ± 1.7 trigonelline (N ′-methylnicotinate) CV 0.26 6.5 × 10−23 0.79 0.9 ± 0.9 1.9 ± 1.2 quinate XEN 0.30 6.4 × 10−29 0.78 0.9 ± 1.1 2.1 ± 1.4 X-24811 UKN 0.26 3.4 × 10−22 0.78 0.7 ± 0.9 1.5 ± 1.2 X-23655 UKN 0.26 2.3 × 10−22 0.78 0.6 ± 1.0 1.6 ± 1.4 X-23649 UKN 0.24 2.6 × 10−19 0.78 0.6 ± 1.2 1.8 ± 1.8 X-12816 UKN 0.25 4.1 × 10−21 0.77 0.5 ± 1.0 1.5 ± 1.4 X-12230 UKN 0.23 2.3 × 10−18 0.77 1.1 ± 1.6 2.3 ± 1.7 2,3-dihydroxypyridine XEN 0.24 1.8 × 10−18 0.77 0.6 ± 1.0 1.5 ± 1.2  Total tea theanine XEN 0.50 2.6 × 10−87 0.84 1.0 ± 6.3 28.2 ± 53.0 X-21795 UKN 0.41 1.4 × 10−55 0.72 0.1 ± 0.3 1.1 ± 2.0  Nonherbal tea theanine XEN 0.47 3.7 × 10−74 0.84 5.3 ± 25.3 33.7 ± 58.7 X-21795 UKN 0.43 9.3 × 10−62 0.72 0.2 ± 0.8 1.3 ± 2.1  Herbal tea or decaffeinated tea theanine XEN 0.23 2.7 × 10−17 0.70 8.7 ± 33.9 17.9 ± 41.8  Diet soft drinks saccharin XEN 0.25 5.5 × 10−20 0.69 4.5 ± 23.5 17.9 ± 46.4 Food groups/items Metabolites Super-pathway r P AUC2 Q1 mean ± SD3 Q5 mean ± SD Fruits  Total citrus fruits and juices stachydrine XEN 0.53 3.6 × 10−99 0.89 0.5 ± 0.7 2.1 ± 1.2 X-247384 UKN 0.49 5.2 × 10−82 0.89 0.5 ± 1.2 2.7 ± 2.2 N-methylproline AA 0.50 8.6 × 10−86 0.87 0.6 ± 1.0 2.8 ± 1.9 chiro-inositol LIP 0.43 1.1 × 10−62 0.86 0.3 ± 0.6 1.5 ± 1.2 X-22836 UKN 0.42 1.3 × 10−58 0.83 0.4 ± 0.7 1.5 ± 1.2 X-23314 UKN 0.41 1.4 × 10−56 0.82 0.9 ± 1.2 3.3 ± 3.3 X-17350 UKN 0.37 4.2 × 10−44 0.80 1.0 ± 1.1 2.7 ± 2.3 methyl glucopyranoside (α + β) XEN 0.36 4.1 × 10−43 0.80 0.8 ± 0.9 2.0 ± 1.9 X-16947 UKN 0.36 2.5 × 10−42 0.80 1.7 ± 5.1 6.1 ± 8.6 β-cryptoxanthin XEN 0.35 1.0 × 10−39 0.80 0.8 ± 0.5 1.6 ± 0.9  Orange juice stachydrine XEN 0.54 4.5 × 10−104 0.87 0.6 ± 0.8 2.2 ± 1.2 X-24738 UKN 0.51 2.2 × 10−92 0.86 0.6 ± 1.2 2.8 ± 2.3 N-methylproline AA 0.52 6.8 × 10−93 0.86 0.7 ± 1.0 2.8 ± 1.9 X-23314 UKN 0.48 3.4 × 10−78 0.83 0.9 ± 1.4 3.5 ± 3.7 chiro-inositol LIP 0.46 2.6 × 10−72 0.83 0.4 ± 0.9 1.5 ± 1.3 X-17350 UKN 0.43 3.1 × 10−62 0.82 1.0 ± 1.2 2.8 ± 2.4 X-22836 UKN 0.44 4.5 × 10−64 0.82 0.4 ± 0.7 1.5 ± 1.2 X-16947 UKN 0.42 8.2 × 10−58 0.81 1.5 ± 4.5 6.9 ± 9.2 X-22515 UKN 0.40 3.0 × 10−54 0.81 0.6 ± 1.7 3.0 ± 4.2 X-19183 UKN 0.41 1.9 × 10−57 0.81 0.4 ± 0.9 1.2 ± 1.0  Banana dopamine 3-O-sulfate AA 0.34 1.0 × 10−37 0.76 1.3 ± 1.5 5.7 ± 7.0 dopamine 4-sulfate AA 0.33 2.5 × 10−36 0.74 0.9 ± 1.6 5.3 ± 7.5 S-methylmethionine AA 0.23 3.9 × 10−18 0.72 1.0 ± 2.2 2.3 ± 2.7 3-methoxytyramine sulfate AA 0.22 9.2 × 10−17 0.70 1.0 ± 0.5 1.5 ± 0.9 X-12729 UKN 0.21 1.8 × 10−15 0.68 1.3 ± 3.7 2.9 ± 5.0 5-hydroxyindoleacetate AA 0.21 1.1 × 10−14 0.68 0.8 ± 0.9 1.7 ± 1.9  Prunes X-11315 UKN 0.21 1.5 × 10−14 0.67 1.0 ± 0.3 1.2 ± 0.5 X-12818 UKN 0.20 5.3 × 10−14 0.62 1.0 ± 1.4 1.5 ± 1.8 hippurate XEN 0.22 7.1 × 10−16 0.61 1.2 ± 1.1 1.8 ± 1.7 benzoylcarnitine5 XEN 0.25 3.0 × 10−21 0.61 0.9 ± 1.1 1.4 ± 1.8 X-24757 UKN 0.25 6.1 × 10−21 0.60 1.0 ± 1.2 1.9 ± 3.0 5-hydroxymethyl-2-furoic acid AA 0.26 3.1 × 10−22 0.58 1.0 ± 3.7 2.4 ± 8.1 X-17367 UKN 0.23 3.4 × 10−17 0.58 1.1 ± 1.6 2.1 ± 4.0 X-17325 UKN 0.21 1.3 × 10−15 0.58 1.4 ± 1.8 2.3 ± 3.8 X-22475 UKN 0.23 5.9 × 10−18 0.57 0.6 ± 1.4 1.6 ± 4.0 catechol sulfate XEN 0.20 4.2 × 10−14 0.57 1.1 ± 0.9 1.5 ± 1.1 Vegetables  Cruciferous vegetables S-methylcysteine sulfoxide AA 0.24 1.7 × 10−18 0.69 1.0 ± 0.7 1.6 ± 1.2  Mushrooms ergothioneine XEN 0.28 3.0 × 10−25 0.75 1.0 ± 0.5 1.6 ± 0.8  Allium vegetables N-methyltaurine AA 0.28 1.4 × 10−26 0.73 0.5 ± 1.3 1.5 ± 1.9 N-acetylalliin XEN 0.22 4.4 × 10−16 0.67 0.8 ± 1.7 2.9 ± 11.1 piperine XEN 0.23 1.0 × 10−17 0.67 1.1 ± 1.2 2.0 ± 2.2 ergothioneine XEN 0.22 1.0 × 10−16 0.67 1.1 ± 0.6 1.4 ± 0.8 γ-CEHC CV −0.20 5.7 × 10−14 0.67 1.3 ± 0.7 0.9 ± 0.6 γ-CEHC glucuronide5 CV −0.20 2.8 × 10−14 0.67 1.3 ± 0.9 0.8 ± 0.8 X-12231 UKN 0.20 9.6 × 10−14 0.65 1.0 ± 1.2 1.5 ± 1.6  Onion N-methyltaurine AA 0.24 9.7 × 10−20 0.69 0.6 ± 1.6 1.4 ± 2.1  Garlic γ-CEHC glucuronide5 CV −0.22 7.7 × 10−17 0.72 1.3 ± 1.0 0.7 ± 0.6 X-18249 UKN −0.22 4.8 × 10−16 0.71 1.2 ± 0.5 0.9 ± 0.4 γ-CEHC CV −0.22 2.0 × 10−16 0.71 1.3 ± 0.7 0.9 ± 0.5 N-acetylalliin XEN 0.27 3.1 × 10−24 0.70 0.8 ± 3.3 2.9 ± 8.9 S-allylcysteine XEN 0.23 2.8 × 10−17 0.69 1.2 ± 3.5 3.2 ± 5.0 ergothioneine XEN 0.26 3.6 × 10−22 0.69 1.0 ± 0.5 1.5 ± 0.9 X-02269 UKN 0.21 6.6 × 10−15 0.69 1.2 ± 1.3 2.2 ± 2.4 alliin XEN 0.24 5.7 × 10−19 0.69 1.0 ± 5.6 3.8 ± 9.0 N-methyltaurine AA 0.24 1.5 × 10−18 0.68 0.6 ± 1.2 1.5 ± 2.1  Tofu or soybeans X-11847 UKN 0.22 5.7 × 10−16 0.75 1.7 ± 3.2 7.3 ± 11.8 X-11858 UKN 0.22 1.1 × 10−15 0.72 1.0 ± 3.4 8.7 ± 24.2 X-16649 UKN 0.21 1.1 × 10−14 0.62 0.8 ± 3.0 4.9 ± 12.7 Grains  Whole grains X-21752 UKN 0.20 5.8 × 10−14 0.65 0.5 ± 1.2 1.1 ± 1.7 Proteins  Eggs 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)5 LIP 0.22 8.4 × 10−17 0.71 1.0 ± 0.3 1.4 ± 0.5  Red meat 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4)5 LIP 0.28 4.2 × 10−25 0.67 0.9 ± 0.3 1.2 ± 0.4 X-11381 UKN 0.21 3.3 × 10−15 0.66 1.0 ± 0.5 1.2 ± 0.6 1-(1-enyl-stearoyl)-2-oleoyl-GPE (P-18:0/18:1) LIP 0.23 6.9 × 10−18 0.65 1.0 ± 0.3 1.2 ± 0.4  Processed meat X-18922 UKN 0.20 3.9 × 10−14 0.70 0.8 ± 0.5 1.2 ± 0.6  Poultry X-13835 UKN 0.21 1.1 × 10−14 0.65 1.2 ± 1.8 1.7 ± 1.7 3-methylhistidine AA 0.21 3.6 × 10−15 0.64 1.3 ± 1.6 2.1 ± 2.2  Total fish X-02269 UKN 0.41 1.9 × 10−55 0.82 0.9 ± 1.1 2.7 ± 2.5 CMPF LIP 0.37 1.7 × 10−45 0.80 1.0 ± 2.0 2.7 ± 2.3 DHA LIP 0.33 1.1 × 10−36 0.77 0.9 ± 0.4 1.4 ± 0.7 docosahexaenoylcholine LIP 0.27 3.1 × 10−24 0.75 0.8 ± 0.4 1.4 ± 0.7 1-docosahexaenoylglycerol (22:6) LIP 0.28 1.2 × 10−26 0.74 0.8 ± 0.8 1.4 ± 0.8 EPA LIP 0.27 7.1 × 10−25 0.72 0.9 ± 0.6 1.7 ± 1.2 eicosapentaenoylcholine LIP 0.23 4.0 × 10−17 0.72 0.8 ± 0.7 1.5 ± 1.3  Dark fish X-02269 UKN 0.51 1.8 × 10−92 0.94 0.7 ± 0.9 3.6 ± 2.4 CMPF LIP 0.47 6.9 × 10−74 0.93 0.8 ± 1.4 3.6 ± 2.6 DHA LIP 0.37 5.6 × 10−44 0.86 0.9 ± 0.4 1.7 ± 0.8 EPA LIP 0.36 1.7 × 10−42 0.84 0.9 ± 0.6 2.0 ± 1.2 docosahexaenoylcholine LIP 0.27 2.1 × 10−24 0.80 0.9 ± 0.5 1.6 ± 0.7 sphingomyelin (d18:2/18:1)5 LIP −0.25 4.7 × 10−20 0.80 1.2 ± 0.4 0.8 ± 0.3 eicosapentaenoylcholine LIP 0.29 8.7 × 10−28 0.79 0.8 ± 0.7 1.9 ± 1.5 1-docosahexaenoylglycerol (22:6) LIP 0.28 1.1 × 10−26 0.79 0.8 ± 0.6 1.7 ± 0.9 docosapentaenoate (n-6 DPA; 22:5n-6) LIP −0.26 2.4 × 10−22 0.71 1.2 ± 0.5 0.8 ± 0.5 X-13866 UKN 0.21 1.8 × 10−14 0.69 1.2 ± 2.0 1.9 ± 1.9  Shellfish CMPF LIP 0.26 4.3 × 10−23 0.83 1.1 ± 1.8 2.9 ± 2.7 X-02269 UKN 0.25 4.2 × 10−20 0.81 1.0 ± 1.4 2.4 ± 1.7  Total nuts tryptophan betaine AA 0.41 2.2 × 10−55 0.80 0.8 ± 0.9 2.0 ± 1.6 X-23644 UKN 0.27 6.5 × 10−24 0.72 1.9 ± 3.4 4.5 ± 5.7 4-vinylphenol sulfate XEN 0.27 1.9 × 10−24 0.69 1.3 ± 1.7 2.6 ± 2.9 lignoceroylcarnitine (C24)5 LIP 0.25 5.9 × 10−21 0.69 0.9 ± 0.4 1.2 ± 0.5 γ-glutamylvaline PEP −0.25 1.7 × 10−21 0.68 1.2 ± 0.4 0.9 ± 0.4 behenoylcarnitine (C22)5 LIP 0.23 2.4 × 10−17 0.66 0.7 ± 0.5 1.1 ± 0.8 sphingomyelin (d18:2/23:1)5 LIP −0.22 4.2 × 10−16 0.66 1.1 ± 0.3 0.9 ± 0.3  Peanuts tryptophan betaine AA 0.45 2.3 × 10−68 0.83 0.8 ± 0.9 2.1 ± 1.7 X-23644 UKN 0.27 6.3 × 10−24 0.71 2.0 ± 3.6 4.6 ± 5.9 4-vinylphenol sulfate XEN 0.31 7.0 × 10−32 0.71 1.2 ± 1.6 2.7 ± 2.9 γ-glutamylvaline PEP −0.30 3.3 × 10−30 0.71 1.2 ± 0.4 0.9 ± 0.4 lignoceroylcarnitine (C24)5 LIP 0.25 2.5 × 10−20 0.66 1.0 ± 0.4 1.2 ± 0.5 behenoylcarnitine (C22)5 LIP 0.23 5.6 × 10−18 0.66 0.8 ± 0.6 1.2 ± 0.8 sphingomyelin (d18:2/231)5 LIP −0.22 1.7 × 10−16 0.65 1.1 ± 0.3 0.9 ± 0.2  Other nuts X-11315 UKN 0.22 1.2 × 10−15 0.66 1.0 ± 0.4 1.2 ± 0.4 Dairy  Milk galactonate CHO 0.33 1.5 × 10−35 0.76 0.8 ± 0.7 2.0 ± 1.8 2,8-quinolinediol sulfate XEN 0.27 2.6 × 10−24 0.75 0.4 ± 0.7 1.3 ± 1.5 phenylacetylglycine PEP 0.27 1.8 × 10−23 0.71 0.7 ± 0.8 1.5 ± 1.3 X-11381 UKN 0.23 3.3 × 10−18 0.71 0.9 ± 0.4 1.3 ± 0.5 X-12798 UKN 0.24 1.4 × 10−18 0.70 0.9 ± 0.4 1.2 ± 0.5  Soy milk X-16649 UKN 0.20 4.1 × 10−14 0.69 1.1 ± 4.9 5.7 ± 12.1 4-ethylphenylsulfate XEN 0.20 5.9 × 10−14 0.67 2.0 ± 4.6 8.4 ± 16.2  Yogurt X-21736 UKN −0.20 6.3 × 10−14 0.70 1.5 ± 1.2 0.9 ± 0.7 Fats and oils  Butter X-11438 UKN 0.24 6.2 × 10−20 0.71 1.0 ± 0.8 1.6 ± 1.0 caprate (10:0) LIP 0.26 1.2 × 10−21 0.70 1.1 ± 0.6 1.6 ± 1.0 10-undecenoate (11:1n-1) LIP 0.24 9.3 × 10−19 0.69 1.0 ± 0.5 1.4 ± 0.7 sphingomyelin (d18:1/25:0, d19:0/24:1, d20:1/23:0, d19:1/24:0)5 LIP 0.24 6.7 × 10−20 0.68 0.9 ± 0.4 1.2 ± 0.5 X-17337 UKN 0.21 6.7 × 10−15 0.67 1.0 ± 0.5 1.3 ± 0.6 caprylate (8:0) LIP 0.21 5.0 × 10−15 0.67 1.0 ± 0.4 1.3 ± 0.6 sphingomyelin (d17:1/16:0, d18:1/15:0, d16:1/17:0)5 LIP 0.23 1.5 × 10−17 0.65 1.0 ± 0.2 1.1 ± 0.3 Miscellaneous  French fries X-18899 UKN 0.26 8.0 × 10−22 0.84 1.0 ± 0.9 1.6 ± 0.7 X-11880 UKN 0.26 3.6 × 10−23 0.83 0.9 ± 0.5 1.6 ± 0.8 X-21339 UKN 0.29 7.6 × 10−27 0.81 0.9 ± 1.0 1.9 ± 1.1 X-11308 UKN 0.27 6.4 × 10−25 0.81 0.9 ± 0.5 1.5 ± 0.7 X-11549 UKN 0.27 1.1 × 10−24 0.81 0.9 ± 0.5 1.6 ± 0.9 X-11372 UKN 0.24 5.2 × 10−19 0.79 0.9 ± 0.4 1.4 ± 0.6 X-11378 UKN 0.23 1.0 × 10−17 0.76 0.9 ± 0.7 1.5 ± 0.7 X-16935 UKN 0.23 8.3 × 10−18 0.75 0.9 ± 1.1 1.9 ± 1.6 eicosanodioate LIP 0.21 8.8 × 10−15 0.73 1.0 ± 0.5 1.5 ± 0.8  Total candies X-13728 UKN 0.20 7.0 × 10−14 0.65 1.1 ± 1.2 2.0 ± 2.2  Chocolate candies X-13728 UKN 0.28 3.6 × 10−25 0.69 1.2 ± 1.4 2.3 ± 2.4 3-methylxanthine XEN 0.26 4.4 × 10−22 0.68 1.1 ± 1.1 1.9 ± 1.8 7-methylurate XEN 0.24 4.0 × 10−19 0.68 1.1 ± 1.2 1.9 ± 1.9 3,7-dimethylurate XEN 0.24 1.9 × 10−19 0.66 0.9 ± 1.0 1.5 ± 1.6 theobromine XEN 0.24 3.8 × 10−19 0.66 1.1 ± 1.1 1.9 ± 1.7 7-methylxanthine XEN 0.21 1.8 × 10−14 0.64 0.6 ± 0.8 1.1 ± 1.2  Desserts ergothioneine XEN −0.25 1.8 × 10−20 0.69 1.5 ± 0.8 1.0 ± 0.6 sphingomyelin (d18:2/18:1)5 LIP 0.21 7.8 × 10−15 0.65 0.9 ± 0.4 1.1 ± 0.4 Alcohol  Total alcohol ethyl glucuronide XEN 0.60 1.79 × 10−133 0.92 0.4 ± 0.5 9.2 ± 28.4 X-24293 UKN 0.54 1.63 × 10−102 0.87 0.8 ± 1.3 4.4 ± 6.6 X-21737 UKN 0.21 3.7 × 10−15 0.76 1.8 ± 11.6 3.0 ± 6.0 CMPF LIP 0.23 2.9 × 10−17 0.74 1.1 ± 1.7 2.2 ± 1.8 X-23655 UKN 0.22 5.3 × 10−16 0.73 0.5 ± 0.7 1.3 ± 1.3 X-24811 UKN 0.23 2.8 × 10−17 0.73 0.6 ± 0.8 1.3 ± 1.1 caffeine XEN 0.25 3.3 × 10−21 0.72 0.9 ± 1.3 2.1 ± 1.9 X-14473 UKN 0.26 5.0 × 10−22 0.72 0.8 ± 0.7 1.4 ± 0.9 sphingomyelin (d18:2/18:1)5 LIP −0.27 3.0 × 10−24 0.72 1.1 ± 0.4 0.8 ± 0.3 X-12230 UKN 0.20 9.7 × 10−14 0.72 1.0 ± 1.2 2.0 ± 1.9  Beer X-24293 UKN 0.27 1.5 × 10−24 0.72 1.2 ± 2.0 3.3 ± 6.8  Total wine ethyl glucuronide XEN 0.45 1.9 × 10−68 0.85 0.4 ± 0.5 5.5 ± 17.0 X-24293 UKN 0.37 4.8 × 10−46 0.79 0.8 ± 1.3 3.1 ± 4.0 2,3-dihydroxyisovalerate XEN 0.36 5.6 × 10−43 0.75 1.2 ± 1.3 3.0 ± 4.8 CMPF LIP 0.20 5.4 × 10−14 0.73 1.2 ± 1.6 2.2 ± 2.0 sphingomyelin (d18:2/18:1)5 LIP −0.23 7.5 × 10−18 0.71 1.1 ± 0.4 0.9 ± 0.3 X-18249 UKN −0.20 8.6 × 10−14 0.70 1.2 ± 0.5 0.8 ± 0.3 X-24473 UKN 0.25 1.6 × 10−20 0.70 1.2 ± 1.5 2.0 ± 2.9 oleoyl-linoleoyl-glycerol (18:1/18:2) (2)6 LIP −0.20 3.5 × 10−14 0.68 1.2 ± 0.6 0.9 ± 0.5 X-11795 UKN 0.22 2.1 × 10−16 0.65 1.1 ± 0.8 1.5 ± 1.6 androstenediol (3β,17β) monosulfate (2) LIP 0.21 8.6 × 10−15 0.65 1.1 ± 0.9 1.9 ± 2.0  Red wine ethyl glucuronide XEN 0.30 3.3 × 10−30 0.75 1.1 ± 6.3 4.5 ± 16.8 X-24293 UKN 0.27 1.4 × 10−24 0.72 1.0 ± 1.7 2.9 ± 4.8 2,3-dihydroxyisovalerate XEN 0.26 1.1 × 10−22 0.66 1.3 ± 1.4 2.5 ± 4.1  White wine ethyl glucuronide XEN 0.22 8.6 × 10−16 0.83 0.6 ± 1.6 6.7 ± 20.6 2,3-dihydroxyisovalerate XEN 0.23 1.9 × 10−17 0.74 1.3 ± 1.6 3.1 ± 5.1  Liquor ethyl glucuronide XEN 0.51 8.4 × 10−90 0.80 0.9 ± 5.5 8.4 ± 27.4 X-24293 UKN 0.44 2.4 × 10−65 0.79 1.0 ± 1.5 4.1 ± 6.7 X-01911 UKN 0.24 9.4 × 10−20 0.68 1.1 ± 1.2 1.8 ± 1.6 androstenediol (3β,17β) disulfate (1) LIP 0.28 1.7 × 10−26 0.67 1.1 ± 1.0 2.4 ± 3.4 androstenediol (3β,17β) monosulfate (2) LIP 0.28 2.3 × 10−26 0.67 1.1 ± 0.9 2.2 ± 3.1 X-21474 UKN 0.23 2.3 × 10−17 0.67 1.0 ± 1.2 1.7 ± 1.5 5α-androstan-3β,17β-diol disulfate LIP 0.29 8.7 × 10−27 0.66 1.2 ± 1.2 2.7 ± 5.0 X-21659 UKN 0.22 4.4 × 10−16 0.66 1.0 ± 1.2 1.7 ± 1.5 X-17335 UKN 0.20 5.1 × 10−14 0.62 1.0 ± 0.6 1.2 ± 0.7 5α-androstan-3α,17β-diol disulfate LIP 0.24 4.3 × 10−19 0.60 0.4 ± 0.8 1.0 ± 1.9 Beverages  Total coffee X-21442 UKN 0.62 6.3 × 10−146 0.98 0.1 ± 1.0 3.5 ± 4.5 trigonelline (N ′-methylnicotinate) CV 0.66 1.3 × 10−172 0.96 0.3 ± 0.4 2.0 ± 1.1 X-24811 UKN 0.62 7.5 × 10−145 0.95 0.2 ± 0.3 1.6 ± 1.1 X-23655 UKN 0.58 2.6 × 10−121 0.95 0.1 ± 0.4 1.5 ± 1.3 quinate XEN 0.66 1.7 × 10−170 0.95 0.3 ± 0.5 2.0 ± 1.3 3-hydroxypyridine sulfate XEN 0.61 1.5 × 10−138 0.95 0.2 ± 0.5 2.3 ± 1.6 X-12230 UKN 0.57 1.1 × 10−119 0.94 0.3 ± 0.5 2.3 ± 1.7 3-methyl catechol sulfate (1) XEN 0.59 1.9 × 10−127 0.93 0.4 ± 0.7 2.1 ± 1.4 X-17185 UKN 0.52 4.4 × 10−96 0.93 0.3 ± 0.5 3.1 ± 10.8 citraconate/glutaconate ENG 0.56 4.4 × 10−111 0.93 0.6 ± 0.4 2.0 ± 1.2  Caffeinated coffee 1-methylxanthine XEN 0.64 1.4 × 10−155 0.96 0.5 ± 0.7 3.2 ± 1.7 paraxanthine XEN 0.60 1.5 × 10−135 0.95 0.3 ± 0.5 2.3 ± 1.4 1-methylurate XEN 0.60 6.9 × 10−134 0.94 0.6 ± 0.9 3.2 ± 1.8 5-acetylamino-6-amino-3-methyluracil XEN 0.61 7.9 × 10−138 0.94 0.6 ± 0.9 3.1 ± 1.7 1,3-dimethylurate XEN 0.56 2.8 × 10−112 0.94 0.7 ± 4.3 2.5 ± 1.4 1,7-dimethylurate XEN 0.61 2.0 × 10−136 0.92 0.6 ± 0.7 2.3 ± 1.1 theophylline XEN 0.55 5.2 × 10−108 0.92 0.7 ± 3.5 2.5 ± 1.5 caffeine XEN 0.61 3.8 × 10−140 0.91 0.6 ± 1.0 2.7 ± 1.7 1,3,7-trimethylurate XEN 0.62 2.7 × 10−144 0.91 0.5 ± 0.8 2.2 ± 1.3 X-21442 UKN 0.41 4.1 × 10−57 0.88 0.7 ± 1.6 3.6 ± 4.2  Decaffeinated coffee X-21442 UKN 0.31 7.6 × 10−31 0.83 0.9 ± 1.9 3.4 ± 4.6 3-hydroxypyridine sulfate XEN 0.29 1.6 × 10−27 0.80 0.9 ± 1.3 2.5 ± 1.7 trigonelline (N ′-methylnicotinate) CV 0.26 6.5 × 10−23 0.79 0.9 ± 0.9 1.9 ± 1.2 quinate XEN 0.30 6.4 × 10−29 0.78 0.9 ± 1.1 2.1 ± 1.4 X-24811 UKN 0.26 3.4 × 10−22 0.78 0.7 ± 0.9 1.5 ± 1.2 X-23655 UKN 0.26 2.3 × 10−22 0.78 0.6 ± 1.0 1.6 ± 1.4 X-23649 UKN 0.24 2.6 × 10−19 0.78 0.6 ± 1.2 1.8 ± 1.8 X-12816 UKN 0.25 4.1 × 10−21 0.77 0.5 ± 1.0 1.5 ± 1.4 X-12230 UKN 0.23 2.3 × 10−18 0.77 1.1 ± 1.6 2.3 ± 1.7 2,3-dihydroxypyridine XEN 0.24 1.8 × 10−18 0.77 0.6 ± 1.0 1.5 ± 1.2  Total tea theanine XEN 0.50 2.6 × 10−87 0.84 1.0 ± 6.3 28.2 ± 53.0 X-21795 UKN 0.41 1.4 × 10−55 0.72 0.1 ± 0.3 1.1 ± 2.0  Nonherbal tea theanine XEN 0.47 3.7 × 10−74 0.84 5.3 ± 25.3 33.7 ± 58.7 X-21795 UKN 0.43 9.3 × 10−62 0.72 0.2 ± 0.8 1.3 ± 2.1  Herbal tea or decaffeinated tea theanine XEN 0.23 2.7 × 10−17 0.70 8.7 ± 33.9 17.9 ± 41.8  Diet soft drinks saccharin XEN 0.25 5.5 × 10−20 0.69 4.5 ± 23.5 17.9 ± 46.4 1Diet-metabolite correlations selected for presentation here had P < 4.63 × 10−7 and |r| > 0.2 from Pearson's partial correlation analysis. Adjusted for age at blood draw, race, education, smoking status, hormone replacement therapy, physical activity, BMI, ethanol consumption (except for alcohol-containing items), time since last meal, and caloric intake. AA, amino acid; CHO, carbohydrate; CMPF, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate; CPS-II, Cancer Prevention Study-II; CV, cofactor/vitamin; ENG, energy; LIP, lipid; PEP, peptide; Q, quintile; UKN, unknown; XEN, xenobiotic; γ-CEHC, γ-carboxyethyl hydrochroman. 2Estimated from receiver operating characteristic analysis using 2000-times stratified bootstrap samples. 3Raw data divided by the median of each metabolite, before data transformation by generalized log and autoscaling. 4Metabolites starting with X are unnamed; the superpathway of these is unknown. 5Putative identity that has not been officially confirmed based on a standard. 6(1) and (2) indicate that the metabolite differs from another with the same mass in the position of the R group. View Large TABLE 2 Replication of food metabolites found in previous cross-sectional studies for habitual intake among women in the CPS-II Nutrition Cohort (n = 1369)1 Food groups Metabolites Biospecimen Cross-sectional studies Citrus fruits and juices, orange juice β-cryptoxanthin serum (21) chiro-inositol serum, urine (9, 14) methyl glucopyranoside (α + β) serum (22) naringenin 7-glucuronide urine (23, 24) N-methylproline serum, urine (9, 10) stachydrine or proline betaine serum, urine (9, 10, 13, 14, 23, 25–27) X-17145 serum (9) X-17350 urine (9) Cruciferous vegetables S-methylcysteine sulfoxide serum (22) Mushrooms ergothioneine serum and plasma (13) Fish, shellfish CMPF serum, urine (9, 10, 13, 14) DHA serum (9, 10, 13, 14) EPA serum (9, 10, 13, 14) X-02269 serum (9, 13) Nuts, peanuts tryptophan betaine serum, urine (9, 10, 14) 4-vinylphenol sulfate serum, urine (9, 10, 14) X-11315 serum and plasma (13) Milk galactonate serum (22, 28) X-12798 serum and plasma (13) Butter caprate (10:0) serum (22) 10-undecenoate (11:1n-1) serum (13, 14) Chocolate 3,7-dimethylurate urine (29) 3-methylxanthine urine (29) 7-methylurate serum, urine (13, 25) 7-methylxanthine urine (29) theobromine serum (13, 14, 25) Alcohol ethyl glucuronide serum, urine (9, 14) Wine 2,3-dihydroxyisovalerate urine (9) X-01911 serum and plasma (13) X-11795 serum and plasma (13) Coffee 1,3,7-trimethylurate serum, urine (9–11, 30) 1,3-dimethylurate serum, urine (9, 30) 1,7-dimethylurate serum, urine (9–11, 30) 1-methylurate serum, urine (9–11, 30) 1-methylxanthine serum, urine (9–11, 13, 30) 3-(3-hydroxyphenyl)propionate serum (11) 3-hydroxyhippurate serum, urine (9, 11, 24, 30) 3-hydroxypyridine sulfate serum and plasma (13) 4-vinylguaiacol sulfate serum (11) 5-acetylamino-6-amino-3-methyluracil serum (9, 10) 5-acetylamino-6-formylamino-3-methyluracil urine (9) 3-methyl catechol sulfate (1) serum and plasma (13) caffeine serum, urine (9–11, 30) caffeic acid sulfate urine (24) catechol sulfate serum, urine (9, 11, 13) cinnamoylglycine serum (11) dihydroferulic acid serum (22) ferulic acid 4-sulfate urine (24) hippurate urine (9, 30) N-(2-furoyl)glycine serum, urine (9, 11) O-methylcatechol sulfate serum and plasma (13) paraxanthine serum, urine (9–11, 30) quinate serum, urine (9–11, 13) theophylline serum, urine (9, 11) trigonelline (N ′-methylnicotinate) serum (9, 11, 30) X-12230 serum and plasma (11, 13) X-12329 serum, urine (9, 11) X-12738 urine (9) X-12816 serum and plasma (11, 13) X-13844 urine (9) X-14473 serum (11, 13) X-17185 urine (9, 11) Tea theanine serum (22) Food groups Metabolites Biospecimen Cross-sectional studies Citrus fruits and juices, orange juice β-cryptoxanthin serum (21) chiro-inositol serum, urine (9, 14) methyl glucopyranoside (α + β) serum (22) naringenin 7-glucuronide urine (23, 24) N-methylproline serum, urine (9, 10) stachydrine or proline betaine serum, urine (9, 10, 13, 14, 23, 25–27) X-17145 serum (9) X-17350 urine (9) Cruciferous vegetables S-methylcysteine sulfoxide serum (22) Mushrooms ergothioneine serum and plasma (13) Fish, shellfish CMPF serum, urine (9, 10, 13, 14) DHA serum (9, 10, 13, 14) EPA serum (9, 10, 13, 14) X-02269 serum (9, 13) Nuts, peanuts tryptophan betaine serum, urine (9, 10, 14) 4-vinylphenol sulfate serum, urine (9, 10, 14) X-11315 serum and plasma (13) Milk galactonate serum (22, 28) X-12798 serum and plasma (13) Butter caprate (10:0) serum (22) 10-undecenoate (11:1n-1) serum (13, 14) Chocolate 3,7-dimethylurate urine (29) 3-methylxanthine urine (29) 7-methylurate serum, urine (13, 25) 7-methylxanthine urine (29) theobromine serum (13, 14, 25) Alcohol ethyl glucuronide serum, urine (9, 14) Wine 2,3-dihydroxyisovalerate urine (9) X-01911 serum and plasma (13) X-11795 serum and plasma (13) Coffee 1,3,7-trimethylurate serum, urine (9–11, 30) 1,3-dimethylurate serum, urine (9, 30) 1,7-dimethylurate serum, urine (9–11, 30) 1-methylurate serum, urine (9–11, 30) 1-methylxanthine serum, urine (9–11, 13, 30) 3-(3-hydroxyphenyl)propionate serum (11) 3-hydroxyhippurate serum, urine (9, 11, 24, 30) 3-hydroxypyridine sulfate serum and plasma (13) 4-vinylguaiacol sulfate serum (11) 5-acetylamino-6-amino-3-methyluracil serum (9, 10) 5-acetylamino-6-formylamino-3-methyluracil urine (9) 3-methyl catechol sulfate (1) serum and plasma (13) caffeine serum, urine (9–11, 30) caffeic acid sulfate urine (24) catechol sulfate serum, urine (9, 11, 13) cinnamoylglycine serum (11) dihydroferulic acid serum (22) ferulic acid 4-sulfate urine (24) hippurate urine (9, 30) N-(2-furoyl)glycine serum, urine (9, 11) O-methylcatechol sulfate serum and plasma (13) paraxanthine serum, urine (9–11, 30) quinate serum, urine (9–11, 13) theophylline serum, urine (9, 11) trigonelline (N ′-methylnicotinate) serum (9, 11, 30) X-12230 serum and plasma (11, 13) X-12329 serum, urine (9, 11) X-12738 urine (9) X-12816 serum and plasma (11, 13) X-13844 urine (9) X-14473 serum (11, 13) X-17185 urine (9, 11) Tea theanine serum (22) 1Unknown metabolites were only compared to studies using Metabolon platforms. CMPF, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate; CPS-II, Cancer Prevention Study-II. View Large TABLE 2 Replication of food metabolites found in previous cross-sectional studies for habitual intake among women in the CPS-II Nutrition Cohort (n = 1369)1 Food groups Metabolites Biospecimen Cross-sectional studies Citrus fruits and juices, orange juice β-cryptoxanthin serum (21) chiro-inositol serum, urine (9, 14) methyl glucopyranoside (α + β) serum (22) naringenin 7-glucuronide urine (23, 24) N-methylproline serum, urine (9, 10) stachydrine or proline betaine serum, urine (9, 10, 13, 14, 23, 25–27) X-17145 serum (9) X-17350 urine (9) Cruciferous vegetables S-methylcysteine sulfoxide serum (22) Mushrooms ergothioneine serum and plasma (13) Fish, shellfish CMPF serum, urine (9, 10, 13, 14) DHA serum (9, 10, 13, 14) EPA serum (9, 10, 13, 14) X-02269 serum (9, 13) Nuts, peanuts tryptophan betaine serum, urine (9, 10, 14) 4-vinylphenol sulfate serum, urine (9, 10, 14) X-11315 serum and plasma (13) Milk galactonate serum (22, 28) X-12798 serum and plasma (13) Butter caprate (10:0) serum (22) 10-undecenoate (11:1n-1) serum (13, 14) Chocolate 3,7-dimethylurate urine (29) 3-methylxanthine urine (29) 7-methylurate serum, urine (13, 25) 7-methylxanthine urine (29) theobromine serum (13, 14, 25) Alcohol ethyl glucuronide serum, urine (9, 14) Wine 2,3-dihydroxyisovalerate urine (9) X-01911 serum and plasma (13) X-11795 serum and plasma (13) Coffee 1,3,7-trimethylurate serum, urine (9–11, 30) 1,3-dimethylurate serum, urine (9, 30) 1,7-dimethylurate serum, urine (9–11, 30) 1-methylurate serum, urine (9–11, 30) 1-methylxanthine serum, urine (9–11, 13, 30) 3-(3-hydroxyphenyl)propionate serum (11) 3-hydroxyhippurate serum, urine (9, 11, 24, 30) 3-hydroxypyridine sulfate serum and plasma (13) 4-vinylguaiacol sulfate serum (11) 5-acetylamino-6-amino-3-methyluracil serum (9, 10) 5-acetylamino-6-formylamino-3-methyluracil urine (9) 3-methyl catechol sulfate (1) serum and plasma (13) caffeine serum, urine (9–11, 30) caffeic acid sulfate urine (24) catechol sulfate serum, urine (9, 11, 13) cinnamoylglycine serum (11) dihydroferulic acid serum (22) ferulic acid 4-sulfate urine (24) hippurate urine (9, 30) N-(2-furoyl)glycine serum, urine (9, 11) O-methylcatechol sulfate serum and plasma (13) paraxanthine serum, urine (9–11, 30) quinate serum, urine (9–11, 13) theophylline serum, urine (9, 11) trigonelline (N ′-methylnicotinate) serum (9, 11, 30) X-12230 serum and plasma (11, 13) X-12329 serum, urine (9, 11) X-12738 urine (9) X-12816 serum and plasma (11, 13) X-13844 urine (9) X-14473 serum (11, 13) X-17185 urine (9, 11) Tea theanine serum (22) Food groups Metabolites Biospecimen Cross-sectional studies Citrus fruits and juices, orange juice β-cryptoxanthin serum (21) chiro-inositol serum, urine (9, 14) methyl glucopyranoside (α + β) serum (22) naringenin 7-glucuronide urine (23, 24) N-methylproline serum, urine (9, 10) stachydrine or proline betaine serum, urine (9, 10, 13, 14, 23, 25–27) X-17145 serum (9) X-17350 urine (9) Cruciferous vegetables S-methylcysteine sulfoxide serum (22) Mushrooms ergothioneine serum and plasma (13) Fish, shellfish CMPF serum, urine (9, 10, 13, 14) DHA serum (9, 10, 13, 14) EPA serum (9, 10, 13, 14) X-02269 serum (9, 13) Nuts, peanuts tryptophan betaine serum, urine (9, 10, 14) 4-vinylphenol sulfate serum, urine (9, 10, 14) X-11315 serum and plasma (13) Milk galactonate serum (22, 28) X-12798 serum and plasma (13) Butter caprate (10:0) serum (22) 10-undecenoate (11:1n-1) serum (13, 14) Chocolate 3,7-dimethylurate urine (29) 3-methylxanthine urine (29) 7-methylurate serum, urine (13, 25) 7-methylxanthine urine (29) theobromine serum (13, 14, 25) Alcohol ethyl glucuronide serum, urine (9, 14) Wine 2,3-dihydroxyisovalerate urine (9) X-01911 serum and plasma (13) X-11795 serum and plasma (13) Coffee 1,3,7-trimethylurate serum, urine (9–11, 30) 1,3-dimethylurate serum, urine (9, 30) 1,7-dimethylurate serum, urine (9–11, 30) 1-methylurate serum, urine (9–11, 30) 1-methylxanthine serum, urine (9–11, 13, 30) 3-(3-hydroxyphenyl)propionate serum (11) 3-hydroxyhippurate serum, urine (9, 11, 24, 30) 3-hydroxypyridine sulfate serum and plasma (13) 4-vinylguaiacol sulfate serum (11) 5-acetylamino-6-amino-3-methyluracil serum (9, 10) 5-acetylamino-6-formylamino-3-methyluracil urine (9) 3-methyl catechol sulfate (1) serum and plasma (13) caffeine serum, urine (9–11, 30) caffeic acid sulfate urine (24) catechol sulfate serum, urine (9, 11, 13) cinnamoylglycine serum (11) dihydroferulic acid serum (22) ferulic acid 4-sulfate urine (24) hippurate urine (9, 30) N-(2-furoyl)glycine serum, urine (9, 11) O-methylcatechol sulfate serum and plasma (13) paraxanthine serum, urine (9–11, 30) quinate serum, urine (9–11, 13) theophylline serum, urine (9, 11) trigonelline (N ′-methylnicotinate) serum (9, 11, 30) X-12230 serum and plasma (11, 13) X-12329 serum, urine (9, 11) X-12738 urine (9) X-12816 serum and plasma (11, 13) X-13844 urine (9) X-14473 serum (11, 13) X-17185 urine (9, 11) Tea theanine serum (22) 1Unknown metabolites were only compared to studies using Metabolon platforms. CMPF, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate; CPS-II, Cancer Prevention Study-II. View Large The ROC curve for the most predictive metabolite for each of the 42 food groups or items is shown in Figure 1 by food class. Fold-change analysis after adjusting multiple covariates showed that the levels of the most predictive metabolites from quintile 1 to quintile 5 of intake had a 0.49- to 9-fold increase (Supplemental Figure 3). FIGURE 1 View largeDownload slide Receiver operating characteristic curves of the most predictive metabolite predicting quintile 1 and quintile 5 intake of each of the 42 food groups among women in the Cancer Prevention Study-II Nutrition Cohort (n = 1369). A, fruits; B, vegetables; C, proteins; D, alcohol; E, beverages; F, others. CMPF, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate; Decaf, decaffeinated; PE(P-18:0/20:4), 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4); γ-CEHC, γ-carboxyethyl hydrochroman. FIGURE 1 View largeDownload slide Receiver operating characteristic curves of the most predictive metabolite predicting quintile 1 and quintile 5 intake of each of the 42 food groups among women in the Cancer Prevention Study-II Nutrition Cohort (n = 1369). A, fruits; B, vegetables; C, proteins; D, alcohol; E, beverages; F, others. CMPF, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate; Decaf, decaffeinated; PE(P-18:0/20:4), 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (P-18:0/20:4); γ-CEHC, γ-carboxyethyl hydrochroman. Multiple metabolites did not improve predictive accuracy compared to the most predictive metabolite More than one metabolite was correlated with 30 of the 42 food groups examined. A linear Support Vector Machine multivariate classification model was built with the use of all identified metabolites for each food shown in Supplemental Table 5, and a multivariate ROC analysis was conducted to calculate the AUC. Comparing the AUC calculated from the multivariate classification model with the AUC calculated from the univariate classification model revealed that there was no obvious improvement in predictive accuracy (AUC change <10%) by adding other metabolites into the classification model (Supplemental Table 6). Exceptions were garlic and decaffeinated coffee, of which the multivariate AUC increased by 14%, and 11%, respectively, compared to the AUC of the most predictive metabolite γ-carboxyethyl hydrochroman (γ-CEHC) glucuronide and X-21442. Sensitivity analyses Because the FFQ assessed average intake over the past 12 mo, in theory, blood collected after the FFQ may not correlate with the intake as well as blood collected before the FFQ was completed. Therefore, we conducted a sensitivity analysis by restricting the analyses to 1031 women who had their blood drawn after completion of the FFQ. A total of 363 diet-metabolite associations were identified, including 45 food groups and 189 different metabolites (data not shown). Stratification by case-control status revealed no noticeable differences between cases and controls (data not shown). Discussion Of the 199 diet-related metabolites, we replicated 63, including 49 known and 14 unknown metabolites that were reported as potential biomarkers of habitual food intake in previous cross-sectional studies (Table 2). The remaining metabolites were potentially novel or have only been identified as putative biomarkers in controlled intervention studies. For some biomarkers, such as those of citrus fruits and juices, fish, alcohol, and coffee, the sensitivity and specificity are both high; however, many other biomarkers have moderate to low sensitivity and specificity. We selectively discuss the plausible biomarkers by food class in the following text. Fruits We replicated 7 metabolites that have been correlated with total citrus fruits and juices or orange juice in previous cross-sectional studies (9, 10, 13, 14, 21, 23–27). The most significant biomarker—stachydrine—was first identified in an acute feeding study (31) and then validated as a biomarker of habitual citrus fruit intake in several cross-sectional datasets (9, 10, 13, 14, 23, 25–27). Together with our results, there is strong evidence that stachydrine is a reliable biomarker with high sensitivity and specificity for citrus, especially orange juice, intake. Other putative biomarkers of total citrus or orange juice intake in our study were the previously identified β-cryptoxanthin (21), chiro-inositol (9, 14), naringenin 7-glucuronide (23, 24), N-methylproline (9, 10), methyl glucopyranoside (α + β) (22), and 2 unknown metabolites referred to as X-17145 and X-17350. We validated 2 sulfonated dopamines as biomarkers for banana intake. Bananas are known to have the highest level of dopamine among many plants (32). Sulfonated dopamine, predominantly dopamine 3-O-sulfate, accounts for 90% of dopamine in blood (33). In a small feeding study (n = 6) of bananas, participants had elevated blood concentrations of conjugated dopamine that persisted for 8 h (34). Future studies are needed to confirm our findings. Vegetables Mushrooms are the primary dietary source of ergothioneine (35). Ergothioneine as a putative biomarker of mushroom intake was reported in a cross-sectional study of the TwinsUK cohort (13) and in a small feeding study (n = 10) of mushroom powder (36). Although ergothioneine was also correlated with intakes of allium vegetables, garlic, and desserts (inverse association) in our study, it is likely that these foods are frequently consumed with or without mushrooms. We also replicated S-methylcysteine sulfoxide as a biomarker for habitual cruciferous vegetable intake which was found in the Prostate, Lung, Colorectal and Ovarian (PLCO) cohort (22). Since the sensitivity and specificity were relatively low, future studies need to confirm our finding. For garlic intake, we identified 3 novel and biologically plausible biomarkers: alliin, N-acetylalliin, and S-allylcysteine. Alliin and S-allylcysteine are 2 compounds derived from a major component of garlic, namely γ-glutamylcysteines (37). In 2 feeding studies, urinary N-acetyl-S-allylcysteine increased after garlic consumption (38, 39). Although N-acetyl-I-allylcysteine was not detected or annotated in our study, we found that S-allylcysteine was significantly correlated with garlic intake. The moderate-to-low AUCs observed for these biomarkers were likely due to the day-to-day variation in consumption and measurement error of the self-administered FFQ. Protein foods We replicated 4 metabolites that have been associated with habitual consumption of fish and shellfish in previous cross-sectional studies: these are 3-carboxy-4-methyl-5-propyl-2-furanpropanoate (CMPF) (9, 10, 13, 14), DHA (9, 10, 13, 14), EPA (9, 10, 13, 14), and X-02269 (9, 13). The most predictive metabolite X-02269 for dark fish intake was also found in the TwinsUK cohort (13) and a US cohort (9). Tryptophan betaine and 4-vinylphenol sulfate were correlated with nut intake in our study, and have also been reported in 3 similar cross-sectional studies (9, 10, 14). These results are consistent with a feeding study of lactating women which showed that tryptophan betaine (or hypaphorine) was detected in human milk after peanut consumption (40), and with another study which showed that 4-vinylphenol (a polyphenol) was detected in roasted peanuts (41). Milk and butter For milk intake, we replicated an unknown metabolite X-12798 (13) and 2 biologically plausible biomarkers, galactonate and 2,8-quinolinediol sulfate. Galactonate is a metabolite of galactose through an anaerobic metabolic pathway (42) and has been associated with habitual milk intake (28). The other biomarker, 2,8-quinolinediol, recently detected in cow milk, might be hydrolyzed from quinoline, an alkaloid from various plant species, by cow gut microbiota (43). We found that higher butter intake was associated with 5 lipids in serum, including 3 medium-chain fatty acids, caprylate (8:0), caprate (10:0), and 10-undecenoate (11:1n-1); the latter 2 were associated with butter intake in other cohorts (13, 14, 22). We observed a low but novel correlation between soy milk intake and serum 4-ethylphenylsulfate. Similarly, Guertin et al. (14) observed a moderate association between tofu intake and 4-ethylphenylsulfate among 502 participants in the PLCO cohort. Animal studies suggest that 4-ethylphenylsulfate is derived from soy protein by gut microbiota (44). The low association observed in our study was likely due to the low intake level in this population. Future studies need to confirm this association in a population with higher intakes of soy products. Alcohol Ethyl glucuronide is metabolized directly from ethanol in the liver by UDP-glucuronosyltransferases (45). It has been suggested to be an optimal biomarker of alcohol consumption because of its high sensitivity and specificity, and it can be detected ≤80 h after the complete elimination of ethanol from the human body (46). For wine consumption, in addition to ethyl glucuronide, a potential biomarker was 2,3-dihydroxyisovalerate. Although the AUC we observed is low, it is a likely intermediate produced by yeast during wine fermentation (47), and was previously reported in a US cohort (9). Beverages Among the 74 metabolites associated with all categories of coffee intake reported in this study, 10 are involved in caffeine metabolism (caffeine, 1,3,7-trimethylurate, 1,3-dimethylurate, 1,7-dimethylurate, 1-methylurate, 1-methylxanthine, 5-acetyl-amino-6-amino-3-methyluracil, 5-acetylamino-6-formylamino-3-methyluracil, paraxanthine, and theophylline), and all have been reported in previous cross-sectional studies (9–11, 13, 30). Eight are involved in the metabolism of chlorogenic acid, one of the abundant polyphenols in coffee beans [quinate, hippurate, caffeic acid sulfate, ferulic acid 4-sulfate, dihydroferulic acid, feruloylglycine (novel), 3-(3-hydroxyphenyl)propionate, and 3-hydroxyhippurate]; formation of some of these metabolites also largely depend on gut microbiota metabolism (48, 49). Other notable metabolites correlated with coffee intake were other coffee constituents or their metabolites, such as trigonelline (N ′-methylnicotinate) found in green coffee beans (50), metabolites of catechol—a polyphenol (catechol sulfate, 3-methyl catechol sulfate, O-methylcatechol sulfate), and 2 metabolites of furan—formed via Maillard reaction during roasting [N-(2-furoyl)glycine, 2-furoic acid (novel)] (51). Together with 7 unknown metabolites, in total we replicated 32 metabolites found in previous studies for coffee intake. For all types of tea consumption, theanine was the most predictive biomarker in the present study. Theanine is a unique nonprotein amino acid in tea leaves and one of the major bioactive compounds in tea that has been suggested to exert neuroprotective effects and improve attention (52). It has also been associated with habitual intake in a large cohort study (22). Strengths and limitations The present study has several strengths, including its large sample size, comprehensive measurement of habitual diet, high reliability of the metabolomic platforms, and ability to adjust for time since last meal. Our findings support the great value of archived blood samples maintained in large cohort studies for dietary biomarker discovery. One limitation of our study is that the reference used to identify biomarkers was self-reported dietary data, which involve measurement error and can partially explain the low predictive accuracy for some potential metabolites. Nonetheless, measurement of habitual diet allows for the identification of biomarkers for a long-term diet that are likely to have longer half-lives or are relevant to foods that are frequently consumed. Another limitation is the cross-sectional study design. Future intervention studies are needed to confirm these biomarkers and test the dose-response relation with food intake. Furthermore, the generalizability of our findings might be limited since our study population was primarily older white women. Finally, as a general limitation in this field, we were unable to distinguish metabolites that are food intake biomarkers and metabolites that reflect diet-induced changes in metabolism. In conclusion, in this large and comprehensive analysis of habitual diet and serum metabolic profiles in a free-living population of postmenopausal women, we replicated 63 metabolites as food-based biomarkers that were previously reported in similar studies for citrus fruits and juices, cruciferous vegetables, mushrooms, fish and shellfish, nuts, milk, butter, chocolate, alcohol, wine, coffee, and tea intakes. We also identified hundreds of potentially novel associations (notably for garlic and coffee) and validated several putative biomarkers that have been only reported in small feeding studies (e.g., bananas). Given the relatively early stage of research on dietary—particularly food—biomarker discovery, our results demonstrate the important contribution large cohort studies with archived blood samples could make in this field. Future cross-sectional studies are needed to confirm our findings in a diverse population and intervention studies are needed to test the dose-response relation with food intake, and the kinetics of the potential biomarkers. Furthermore, studies with repeated measurements are needed to test the reproducibility of the potential biomarkers, which determines the reliability when these are used to study disease risk. Acknowledgments We thank Steven C Moore (National Cancer Institute) for assistance in obtaining partial financial support for this project. The authors’ responsibilities were as follows—YW and MLM: designed the research; YW and BDC: performed the statistical analysis; YW: wrote the paper; SMG, TJH, VLS, MMG, and MLM: provided critical review; YW: had primary responsibility for the final content; and all authors: read and approved the final manuscript. Notes The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study-II cohort. This work was supported, in part, through the Intramural Research Program of the National Cancer Institute, National Institutes of Health, Department of Health and Human Services. Supplemental Tables 1–6 and Supplemental Figures 1–3 are available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/jn/. Author disclosures: YW, SMG, BDC, TJH, VLS, MMG and MLM, no conflicts of interest. The views expressed here are those of the authors and do not necessarily represent the American Cancer Society or the American Cancer Society—Cancer Action Network. Abbreviations used: CMPF, 3-carboxy-4-methyl-5-propyl-2-furanpropanoate; CPS-II, Cancer Prevention Study-II; ESI, electrospray ionization; ROC, receiver operating characteristic; UPLC-MS/MS, ultrahigh-performance LC-tandem MS; γ-CEHC, γ-carboxyethyl hydrochroman. References 1. Kipnis V , Freedman LS . Impact of exposure measurement error in nutritional epidemiology . J Natl Cancer Inst 2008 ; 100 : 1658 – 9 . Google Scholar CrossRef Search ADS PubMed 2. Bingham SA . Urine nitrogen as a biomarker for the validation of dietary protein intake . J Nutr 2003 ; 133 : S921–4 . Google Scholar CrossRef Search ADS 3. Tasevska N , Runswick SA , Bingham SA . Urinary potassium is as reliable as urinary nitrogen for use as a recovery biomarker in dietary studies of free living individuals . J Nutr 2006 ; 136 : 1334 – 40 . Google Scholar CrossRef Search ADS PubMed 4. Clark AJ , Mossholder S . Sodium and potassium intake measurements: dietary methodology problems . Am J Clin Nutr 1986 ; 43 : 470 – 6 . Google Scholar CrossRef Search ADS PubMed 5. Brennan L . Metabolomics in nutrition research: current status and perspectives . Biochem Soc Trans 2013 ; 41 : 670 – 3 . Google Scholar CrossRef Search ADS PubMed 6. Scalbert A , Brennan L , Manach C , Andres-Lacueva C , Dragsted LO , Draper J , Rappaport SM , van der Hooft JJ , Wishart DS . The food metabolome: a window over dietary exposure . Am J Clin Nutr 2014 ; 99 : 1286 – 308 . Google Scholar CrossRef Search ADS PubMed 7. Gibbons H , Brennan L . Metabolomics as a tool in the identification of dietary biomarkers . Proc Nutr Soc 2017 ; 76 : 42 – 53 . Google Scholar CrossRef Search ADS PubMed 8. Calle EE , Rodriguez C , Jacobs EJ , Almon ML , Chao A , McCullough ML , Feigelson HS , Thun MJ . The American Cancer Society Cancer Prevention Study II Nutrition Cohort: rationale, study design, and baseline characteristics . Cancer 2002 ; 94 : 2490 – 501 . Google Scholar CrossRef Search ADS PubMed 9. Playdon MC , Sampson JN , Cross AJ , Sinha R , Guertin KA , Moy KA , Rothman N , Irwin ML , Mayne ST , Stolzenberg-Solomon R , et al. Comparing metabolite profiles of habitual diet in serum and urine . Am J Clin Nutr 2016 ; 104 : 776 – 89 . Google Scholar CrossRef Search ADS PubMed 10. Zheng Y , Yu B , Alexander D , Steffen LM , Boerwinkle E . Human metabolome associates with dietary intake habits among African Americans in the atherosclerosis risk in communities study . Am J Epidemiol 2014 ; 179 : 1424 – 33 . Google Scholar CrossRef Search ADS PubMed 11. Guertin KA , Loftfield E , Boca SM , Sampson JN , Moore SC , Xiao Q , Huang WY , Xiong X , Freedman ND , Cross AJ , et al. Serum biomarkers of habitual coffee consumption may provide insight into the mechanism underlying the association between coffee consumption and colorectal cancer . Am J Clin Nutr 2015 ; 101 : 1000 – 11 . Google Scholar CrossRef Search ADS PubMed 12. Schmidt JA , Rinaldi S , Ferrari P , Carayol M , Achaintre D , Scalbert A , Cross AJ , Gunter MJ , Fensom GK , Appleby PN , et al. Metabolic profiles of male meat eaters, fish eaters, vegetarians, and vegans from the EPIC-Oxford cohort . Am J Clin Nutr 2015 ; 102 : 1518 – 26 . Google Scholar CrossRef Search ADS PubMed 13. Pallister T , Jennings A , Mohney RP , Yarand D , Mangino M , Cassidy A , MacGregor A , Spector TD , Menni C . Characterizing blood metabolomics profiles associated with self-reported food intakes in female twins . PLoS One 2016 ; 11 : e0158568 . Google Scholar CrossRef Search ADS PubMed 14. Guertin KA , Moore SC , Sampson JN , Huang WY , Xiao Q , Stolzenberg-Solomon RZ , Sinha R , Cross AJ . Metabolomics in nutritional epidemiology: identifying metabolites associated with diet and quantifying their potential to uncover diet-disease relations in populations . Am J Clin Nutr 2014 ; 100 : 208 – 17 . Google Scholar CrossRef Search ADS PubMed 15. Evans AM , DeHaven CD , Barrett T , Mitchell M , Milgram E . Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems . Anal Chem 2009 ; 81 : 6656 – 67 . Google Scholar CrossRef Search ADS PubMed 16. Huber W , Von Heydebreck A , Sültmann H , Poustka A , Vingron M . Variance stabilization applied to microarray data calibration and to the quantification of differential expression . Bioinformatics 2002 ; 18 : S96 – S104 . Google Scholar CrossRef Search ADS PubMed 17. Kim S . Ppcor: An R package for a fast calculation to semi-partial correlation coefficients . Commun Stat Appl Methods 2015 ; 22 : 665 . Google Scholar PubMed 18. Robin X , Turck N , Hainard A , Tiberti N , Lisacek F , Sanchez J-C , Müller M . pROC: an open-source package for R and S+ to analyze and compare ROC curves . BMC Bioinformatics 2011 ; 12 : 77 . Google Scholar CrossRef Search ADS PubMed 19. Joachims T . A support vector method for multivariate performance measures . Proceedings of the 22nd International Conference on Machine Learning, 2005 : ACM ; 2005 . p. 377 – 84 . 20. Xia J , Sinelnikov IV , Han B , Wishart DS . MetaboAnalyst 3.0—making metabolomics more meaningful . Nucleic Acids Res 2015 ; 43 : W251 – 7 . Google Scholar CrossRef Search ADS PubMed 21. Sugiura M , Kato M , Matsumoto H , Nagao A , Yano M . Serum concentration of β-cryptoxanthin in Japan reflects the frequency of Satsuma mandarin (Citrus unshiu Marc.) consumption . J Health Sci 2002 ; 48 : 350 – 3 . Google Scholar CrossRef Search ADS 22. Playdon MC , Ziegler RG , Sampson JN , Stolzenberg-Solomon R , Thompson HJ , Irwin ML , Mayne ST , Hoover RN , Moore SC . Nutritional metabolomics and breast cancer risk in a prospective study . Am J Clin Nutr 2017 ; 106 : 637 – 49 . Google Scholar CrossRef Search ADS PubMed 23. Pujos-Guillot E , Hubert J , Martin JF , Lyan B , Quintana M , Claude S , Chabanas B , Rothwell JA , Bennetau-Pelissero C , Scalbert A , et al. Mass spectrometry-based metabolomics for the discovery of biomarkers of fruit and vegetable intake: citrus fruit as a case study . J Proteome Res 2013 ; 12 : 1645 – 59 . Google Scholar CrossRef Search ADS PubMed 24. Edmands WM , Ferrari P , Rothwell JA , Rinaldi S , Slimani N , Barupal DK , Biessy C , Jenab M , Clavel-Chapelon F , Fagherazzi G , et al. Polyphenol metabolome in human urine and its association with intake of polyphenol-rich foods across European countries . Am J Clin Nutr 2015 ; 102 : 905 – 13 . Google Scholar CrossRef Search ADS PubMed 25. Andersen MB , Kristensen M , Manach C , Pujos-Guillot E , Poulsen SK , Larsen TM , Astrup A , Dragsted L . Discovery and validation of urinary exposure markers for different plant foods by untargeted metabolomics . Anal Bioanal Chem 2014 ; 406 : 1829 – 44 . Google Scholar CrossRef Search ADS PubMed 26. Lloyd AJ , Beckmann M , Fave G , Mathers JC , Draper J . Proline betaine and its biotransformation products in fasting urine samples are potential biomarkers of habitual citrus fruit consumption . Br J Nutr 2011 ; 106 : 812 – 24 . Google Scholar CrossRef Search ADS PubMed 27. Heinzmann SS , Brown IJ , Chan Q , Bictash M , Dumas ME , Kochhar S , Stamler J , Holmes E , Elliott P , Nicholson JK . Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption . Am J Clin Nutr 2010 ; 92 : 436 – 43 . Google Scholar CrossRef Search ADS PubMed 28. Playdon MC , Moore SC , Derkach A , Reedy J , Subar AF , Sampson JN , Albanes D , Gu F , Kontto J , Lassale C , et al. Identifying biomarkers of dietary patterns by using metabolomics . Am J Clin Nutr 2017 ; 105 : 450 – 65 . Google Scholar CrossRef Search ADS PubMed 29. Garcia-Aloy M , Llorach R , Urpi-Sarda M , Jáuregui O , Corella D , Ruiz-Canela M , Salas-Salvadó J , Fitó M , Ros E , Estruch R . A metabolomics-driven approach to predict cocoa product consumption by designing a multimetabolite biomarker model in free-living subjects from the PREDIMED study . Mol Nutr Food Res 2015 ; 59 : 212 – 20 . Google Scholar CrossRef Search ADS PubMed 30. Rothwell JA , Fillâtre Y , Martin J-F , Lyan B , Pujos-Guillot E , Fezeu L , Hercberg S , Comte B , Galan P , Touvier M . New biomarkers of coffee consumption identified by the non-targeted metabolomic profiling of cohort study subjects . PLoS One 2014 ; 9 : e93474 . Google Scholar CrossRef Search ADS PubMed 31. Atkinson W , Downer P , Lever M , Chambers ST , George PM . Effects of orange juice and proline betaine on glycine betaine and homocysteine in healthy male subjects . Eur J Nutr 2007 ; 46 : 446 – 52 . Google Scholar CrossRef Search ADS PubMed 32. Kulma A , Szopa J . Catecholamines are active compounds in plants . Plant Sci 2007 ; 172 : 433 – 40 . Google Scholar CrossRef Search ADS 33. Johnson GA , Baker CA , Smith RT . Radioenzymatic assay of sulfate conjugates of catecholamines and DOPA in plasma . Life Sci 1980 ; 26 : 1591 – 8 . Google Scholar CrossRef Search ADS PubMed 34. Davidson L , Vandongen R , Beilin LJ . Effects of eating bananas on plasma free and sulfate-conjugated catecholamines . Life Sci 1981 ; 29 : 1773 – 8 . Google Scholar CrossRef Search ADS PubMed 35. Kalaras MD , Richie JP , Calcagnotto A , Beelman RB . Mushrooms: A rich source of the antioxidants ergothioneine and glutathione . Food Chem 2017 ; 233 : 429 – 33 . Google Scholar CrossRef Search ADS PubMed 36. Weigand-Heller AJ , Kris-Etherton PM , Beelman RB . The bioavailability of ergothioneine from mushrooms (Agaricus bisporus) and the acute effects on antioxidant capacity and biomarkers of inflammation . Prev Med 2012 ; 54 Suppl : S75 – 8 . Google Scholar CrossRef Search ADS PubMed 37. Amagase H , Petesch BL , Matsuura H , Kasuga S , Itakura Y . Intake of garlic and its bioactive components . J Nutr 2001 ; 131 : S955 – 62 . Google Scholar CrossRef Search ADS 38. Verhagen H , Hageman GJ , Rauma AL , Versluis-de Haan G , van Herwijnen MH , de Groot J , Torronen R , Mykkanen H . Biomonitoring the intake of garlic via urinary excretion of allyl mercapturic acid . Br J Nutr 2001 ; 86 Suppl 1 : S111 – 4 . Google Scholar CrossRef Search ADS PubMed 39. de Rooij BM , Boogaard PJ , Rijksen DA , Commandeur JN , Vermeulen NP . Urinary excretion of N-acetyl-S-allyl-L-cysteine upon garlic consumption by human volunteers . Arch Toxicol 1996 ; 70 : 635 – 9 . Google Scholar CrossRef Search ADS PubMed 40. Keller BO , Wu BT , Li SS , Monga V , Innis SM . Hypaphorine is present in human milk in association with consumption of legumes . J Agric Food Chem 2013 ; 61 : 7654 – 60 . Google Scholar CrossRef Search ADS PubMed 41. Walradt JP , Pittet AO , Kinlin TE , Muralidhara R , Sanderson A . Volatile components of roasted peanuts . J Agric Food Chem 1971 ; 19 : 972 – 9 . Google Scholar CrossRef Search ADS 42. Leslie ND . Insights into the pathogenesis of galactosemia . Annu Rev Nutr 2003 ; 23 : 59 – 80 . Google Scholar CrossRef Search ADS PubMed 43. Rouge P , Cornu A , Biesse-Martin A-S , Lyan B , Rochut N , Graulet B . Identification of quinoline, carboline and glycinamide compounds in cow milk using HRMS and NMR . Food Chem 2013 ; 141 : 1888 – 94 . Google Scholar CrossRef Search ADS PubMed 44. Velenosi TJ , Hennop A , Feere DA , Tieu A , Kucey AS , Kyriacou P , McCuaig LE , Nevison SE , Kerr MA , Urquhart BL . Untargeted plasma and tissue metabolomics in rats with chronic kidney disease given AST-120 . Sci Rep 2016 ; 6 : 22526 . Google Scholar CrossRef Search ADS PubMed 45. Foti RS , Fisher MB . Assessment of UDP-glucuronosyltransferase catalyzed formation of ethyl glucuronide in human liver microsomes and recombinant UGTs . Forensic Sci Int 2005 ; 153 : 109 – 16 . Google Scholar CrossRef Search ADS PubMed 46. Wurst FM , Skipper GE , Weinmann W . Ethyl glucuronide—the direct ethanol metabolite on the threshold from science to routine use . Addiction 2003 ; 98 : 51 – 61 . Google Scholar CrossRef Search ADS PubMed 47. Generoso WC , Brinek M , Dietz H , Oreb M , Boles E . Secretion of 2,3-dihydroxyisovalerate as a limiting factor for isobutanol production in Saccharomyces cerevisiae . FEMS Yeast Res 2017 ; 17 ( 3 ): fox029 – fox029 . Google Scholar CrossRef Search ADS 48. Gonthier MP , Verny MA , Besson C , Remesy C , Scalbert A . Chlorogenic acid bioavailability largely depends on its metabolism by the gut microflora in rats . J Nutr 2003 ; 133 : 1853 – 9 . Google Scholar CrossRef Search ADS PubMed 49. Stalmach A , Mullen W , Barron D , Uchida K , Yokota T , Cavin C , Steiling H , Williamson G , Crozier A . Metabolite profiling of hydroxycinnamate derivatives in plasma and urine after the ingestion of coffee by humans: identification of biomarkers of coffee consumption . Drug Metab Dispos 2009 ; 37 : 1749 – 58 . Google Scholar CrossRef Search ADS PubMed 50. Allred KF , Yackley KM , Vanamala J , Allred CD . Trigonelline is a novel phytoestrogen in coffee beans . J Nutr 2009 ; 139 : 1833 – 8 . Google Scholar CrossRef Search ADS PubMed 51. Heinzmann SS , Holmes E , Kochhar S , Nicholson JK , Schmitt-Kopplin P . 2-Furoylglycine as a candidate biomarker of coffee consumption . J Agric Food Chem 2015 ; 63 : 8615 – 21 . Google Scholar CrossRef Search ADS PubMed 52. Nobre AC , Rao A , Owen GN . L-theanine, a natural constituent in tea, and its effect on mental state . Asia Pac J Clin Nutr 2008 ; 17 Suppl 1 : 167 – 8 . Google Scholar PubMed © 2018 American Society for Nutrition. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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Journal of NutritionOxford University Press

Published: May 15, 2018

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