Dietary Inflammatory and Insulinemic Potentials, Plasma Metabolome and Risk of Colorectal Cancer

Background: The inflammatory and insulinemic potentials of diets have been associated with colorectal cancer risk. However, it is unknown whether the plasma metabolite profiles related to inflammatory diets, or to insulinemic diets, underlie this association. The aim of this study was to evaluate the association between metabolomic profile scores related to the food-based empirical dietary inflammatory patterns (EDIP), the empirical dietary index for hyperinsulinemia (EDIH), and plasma inflammation (CRP, IL-6, TNFα-R2, adiponectin) and insulin (C-peptide) biomarkers, and colorectal cancer risk. Methods: Elastic net regression was used to derive three metabolomic profile scores for each dietary pattern among 6840 participants from the Nurses’ Health Study and Health Professionals Follow-up Study, and associations with CRC risk were examined using multivariableadjusted logistic regression, in a case-control study of 524 matched pairs nested in both cohorts. Results: Among 186 known metabolites, 27 were significantly associated with both the EDIP and inflammatory biomarkers, and 21 were significantly associated with both the EDIH and C-peptide. In men, odds ratios (ORs) of colorectal cancer, per 1 standard deviation (SD) increment in metabolomic score, were 1.91 (1.31–2.78) for the common EDIP and inflammatory-biomarker metabolome, 1.12 (0.78–1.60) for EDIP-only metabolome, and 1.65 (1.16–2.36) for the inflammatory-biomarkers-only metabolome. However, no association was found for EDIH-only, C-peptide-only, and the common metabolomic signatures in men. Moreover, the metabolomic signatures were not associated with colorectal cancer risk among women. Conclusion: Metabolomic profiles reflecting pro-inflammatory Metabolites 2023, 13, 744. https://doi.org/10.3390/metabo13060744 https://www.mdpi.com/journal/metabolites Metabolites 2023, 13, 744 2 of 14 diets and inflammation biomarkers were associated with colorectal cancer risk in men, while no association was found in women. Larger studies are needed to confirm our findings.

[1]  E. Rimm,et al.  Optimal dietary patterns for prevention of chronic disease , 2023, Nature Medicine.

[2]  E. Giovannucci,et al.  Are exposure-disease relationships assessed in cohorts of health professionals generalizable?: a comparative analysis based on WCRF/AICR systematic literature reviews , 2022, Cancer Causes & Control.

[3]  L. Liang,et al.  Plasma metabolite profiles related to plant-based diets and the risk of type 2 diabetes , 2022, Diabetologia.

[4]  L. Liang,et al.  Plasma Metabolite Profiles of Red Meat, Poultry, and Fish Consumption, and Their Associations with Colorectal Cancer Risk , 2022, Nutrients.

[5]  T. Frayling,et al.  Associations Between Glycemic Traits and Colorectal Cancer: A Mendelian Randomization Analysis , 2022, Journal of the National Cancer Institute.

[6]  M. Schulze,et al.  Prospective analysis of circulating metabolites and endometrial cancer risk , 2021, Gynecologic oncology.

[7]  W. Willett,et al.  Prospective Evaluation of Dietary and Lifestyle Pattern Indices with Risk of Colorectal Cancer in a Cohort of Younger Women. , 2021, Annals of oncology : official journal of the European Society for Medical Oncology.

[8]  J. Manson,et al.  Reproducibility and Validity of a Semi-quantitative Food Frequency Questionnaire in Men Assessed by Multiple Methods. , 2020, American journal of epidemiology.

[9]  J. Meyerhardt,et al.  Risk Factors and Incidence of Colorectal Cancer According to Major Molecular Subtypes , 2020, JNCI cancer spectrum.

[10]  Jingqin Luo,et al.  Sex differences in cancer mechanisms , 2020, Biology of Sex Differences.

[11]  Daniel S. Hitchcock,et al.  A prospective analysis of circulating plasma metabolites associated with ovarian cancer risk. , 2020, Cancer research.

[12]  E. Giovannucci,et al.  Global burden of colorectal cancer: emerging trends, risk factors and prevention strategies , 2019, Nature Reviews Gastroenterology & Hepatology.

[13]  James Y. Dai,et al.  Mendelian randomization analysis of C-reactive protein on colorectal cancer risk. , 2018, International journal of epidemiology.

[14]  Yan Shi,et al.  Diets That Promote Colon Inflammation Associate With Risk of Colorectal Carcinomas That Contain Fusobacterium nucleatum , 2018, Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association.

[15]  F. Hu,et al.  Association of dietary insulinemic potential and colorectal cancer risk in men and women. , 2018, The American journal of clinical nutrition.

[16]  E. Rimm,et al.  Relative Validity of Nutrient Intakes Assessed by Questionnaire, 24‐Hour Recalls, and Diet Records as Compared With Urinary Recovery and Plasma Concentration Biomarkers: Findings for Women , 2018, American journal of epidemiology.

[17]  L. Liang,et al.  An Empirical Dietary Inflammatory Pattern Score Is Associated with Circulating Inflammatory Biomarkers in a Multi-Ethnic Population of Postmenopausal Women in the United States. , 2018, The Journal of nutrition.

[18]  F. Hu,et al.  Association of Dietary Inflammatory Potential With Colorectal Cancer Risk in Men and Women , 2018, JAMA oncology.

[19]  J. Manson,et al.  Metabolic Predictors of Incident Coronary Heart Disease in Women , 2018, Circulation.

[20]  David A. Drew,et al.  Association Between Inflammatory Diet Pattern and Risk of Colorectal Carcinoma Subtypes Classified by Immune Responses to Tumor. , 2017, Gastroenterology.

[21]  R. Vasan,et al.  Dimethylguanidino valeric acid is a marker of liver fat and predicts diabetes , 2017, The Journal of clinical investigation.

[22]  Fred K Tabung,et al.  Dietary Patterns and Colorectal Cancer Risk: a Review of 17 Years of Evidence (2000–2016) , 2017, Current Colorectal Cancer Reports.

[23]  W. Willett,et al.  An Empirical Dietary Inflammatory Pattern Score Enhances Prediction of Circulating Inflammatory Biomarkers in Adults. , 2017, The Journal of nutrition.

[24]  E. Rimm,et al.  Validity of a Dietary Questionnaire Assessed by Comparison With Multiple Weighed Dietary Records or 24-Hour Recalls , 2017, American journal of epidemiology.

[25]  W. Willett,et al.  Development and validation of empirical indices to assess the insulinaemic potential of diet and lifestyle , 2016, British Journal of Nutrition.

[26]  W. Willett,et al.  Development and Validation of an Empirical Dietary Inflammatory Index. , 2016, The Journal of nutrition.

[27]  Caroline H. Johnson,et al.  Metabolomics: beyond biomarkers and towards mechanisms , 2016, Nature Reviews Molecular Cell Biology.

[28]  N. Rifai,et al.  Interleukin-6 and risk of colorectal cancer: results from the CLUE II cohort and a meta-analysis of prospective studies , 2015, Cancer Causes & Control.

[29]  Xuri Li,et al.  Circulating interleukin-6 and cancer: A meta-analysis using Mendelian randomization , 2015, Scientific Reports.

[30]  C. Clish,et al.  Metabolic control of type 1 regulatory T cell differentiation by AHR and HIF1-α , 2015, Nature Medicine.

[31]  J. Murabito,et al.  Distinct Metabolomic Signatures Are Associated with Longevity in Humans , 2015, Nature Communications.

[32]  Carol J Boushey,et al.  The Dietary Patterns Methods Project: synthesis of findings across cohorts and relevance to dietary guidance. , 2015, The Journal of nutrition.

[33]  Yurii B. Shvetsov,et al.  Associations of key diet-quality indexes with mortality in the Multiethnic Cohort: the Dietary Patterns Methods Project. , 2015, The American journal of clinical nutrition.

[34]  Tao Xi,et al.  C-reactive protein, interleukin-6 and the risk of colorectal cancer: a meta-analysis , 2014, Cancer Causes & Control.

[35]  P. Li,et al.  Circulating C-peptide level is a predictive factor for colorectal neoplasia: evidence from the meta-analysis of prospective studies , 2013, Cancer Causes & Control.

[36]  T. Fung,et al.  Dietary Patterns and the Risk of Colorectal Cancer , 2013, Current Nutrition Reports.

[37]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[38]  T. Erlinger,et al.  C‐reactive protein and colorectal cancer risk: A systematic review of prospective studies , 2008, International journal of cancer.

[39]  O. Herbarth,et al.  Modified nucleosides: an accurate tumour marker for clinical diagnosis of cancer, early detection and therapy control , 2006, British Journal of Cancer.

[40]  M. Zheng,et al.  Normal and modified urinary nucleosides represent novel biomarkers for colorectal cancer diagnosis and surgery monitoring , 2005, Journal of gastroenterology and hepatology.

[41]  Jing Ma,et al.  Inflammatory markers and the risk of coronary heart disease in men and women. , 2004, The New England journal of medicine.

[42]  D Spiegelman,et al.  Dietary fat and coronary heart disease: a comparison of approaches for adjusting for total energy intake and modeling repeated dietary measurements. , 1999, American journal of epidemiology.

[43]  D Feskanich,et al.  Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. , 1999, The American journal of clinical nutrition.

[44]  J. Manson,et al.  The Nurses' Health Study: 20-year contribution to the understanding of health among women. , 1997, Journal of women's health.

[45]  G A Colditz,et al.  Alcohol, height, and adiposity in relation to estrogen and prolactin levels in postmenopausal women. , 1995, Journal of the National Cancer Institute.

[46]  D Feskanich,et al.  Reproducibility and validity of food intake measurements from a semiquantitative food frequency questionnaire. , 1993, Journal of the American Dietetic Association.

[47]  G A Colditz,et al.  Reproducibility and validity of an expanded self-administered semiquantitative food frequency questionnaire among male health professionals. , 1992, American journal of epidemiology.

[48]  E. Rimm,et al.  Prospective study of alcohol consumption and risk of coronary disease in men , 1991, The Lancet.

[49]  C. Chute,et al.  Effect of transport conditions on the stability of biochemical markers in blood. , 1989, Clinical chemistry.

[50]  W. Willett,et al.  Reproducibility and validity of a semiquantitative food frequency questionnaire. , 1985, American journal of epidemiology.

[51]  A. Jemal,et al.  Cancer statistics, 2019 , 2019, CA: a cancer journal for clinicians.

[52]  F. Hu,et al.  Metabolomics-Based Dietary Biomarkers in Nutritional Epidemiology-Current Status and Future Opportunities. , 2018, Molecular nutrition & food research.

[53]  F. Hu,et al.  Use of Metabolomics in Improving Assessment of Dietary Intake. , 2018, Clinical chemistry.

[54]  Hanseul Kim,et al.  Sex differences in the association of obesity and colorectal cancer risk , 2016, Cancer Causes & Control.

[55]  N. Dubrawsky Cancer statistics , 1989, CA: a cancer journal for clinicians.