Diet quality indices and dietary patterns are associated with plasma metabolites in colorectal cancer patients

Anne J M R Geijsen, Dieuwertje E Kok, Moniek van Zutphen, Pekka Keski-Rahkonen, David Achaintre, Audrey Gicquiau, Andrea Gsur, Flip M Kruyt, Cornelia M Ulrich, Matty P Weijenberg, Johannes H W de Wilt, Evertine Wesselink, Augustin Scalbert, Ellen Kampman, Fränzel J B van Duijnhoven, Anne J M R Geijsen, Dieuwertje E Kok, Moniek van Zutphen, Pekka Keski-Rahkonen, David Achaintre, Audrey Gicquiau, Andrea Gsur, Flip M Kruyt, Cornelia M Ulrich, Matty P Weijenberg, Johannes H W de Wilt, Evertine Wesselink, Augustin Scalbert, Ellen Kampman, Fränzel J B van Duijnhoven

Abstract

Purpose: Emerging evidence suggests that diet is linked to survival in colorectal cancer patients, although underlying mechanisms are not fully understood. The aim of this study was to evaluate whether dietary exposures are associated with metabolite concentrations in colorectal cancer patients.

Methods: Concentrations of 134 metabolites of the Biocrates AbsoluteIDQ p180 kit were quantified in plasma samples collected at diagnosis from 195 stage I-IV colorectal cancer patients. Food frequency questionnaires were used to calculate adherence to the World Cancer Research Fund (WCRF) dietary recommendations and the Dutch Healthy Diet (DHD15) index as well as to construct dietary patterns using Principal Component Analysis. Multivariable linear regression models were used to determine associations between dietary exposures and metabolite concentrations. All models were adjusted for age, sex, body mass index, smoking status, analytical batch, cancer stage, and multiple testing using false discovery rate.

Results: Participants had a mean (SD) age of 66 (9) years, were mostly men (60%), and mostly diagnosed with stage II and III cancer. For the dietary pattern analyses, Western, Carnivore, and Prudent patterns were identified. Better adherence to the WCRF dietary recommendations was associated with lower concentrations of ten phosphatidylcholines. Higher intake of the Carnivore pattern was associated with higher concentrations of two phosphatidylcholines. The DHD15-index, Western pattern, or Prudent pattern were not associated with metabolite concentrations.

Conclusion: In the current study, the WCRF dietary score and the Carnivore pattern are associated with phosphatidylcholines. Future research should elucidate the potential relevance of phosphatidylcholine metabolism in the colorectal cancer continuum.

Clinical trial registry: ClinicalTrials.gov Identifier: NCT03191110.

Keywords: Colorectal cancer patients; Diet quality indices; Dietary patterns; Metabolites; Metabolomics.

Conflict of interest statement

Cornelia M. Ulrich (Director of the Comprehensive Cancer Center at Huntsman Cancer Institute, Salt Lake City, U.S.) oversees research funded by several pharmaceutical companies but has not received funding directly herself that could constitute a conflict to the current work. Where authors are identified as personnel of the International Agency for Research on Cancer / World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer / World Health Organization.

© 2021. The Author(s).

Figures

Fig. 1
Fig. 1
Overview of food group loadings of the Western, Carnivore, and Prudent dietary patterns. Green and red bars represent positive and negative loading strengths, respectively. A more positive loading illustrates higher consumption of a specific food group, while a more negative loading characterizes lower consumption of the food group. Only food group loadings >|0.2| were considered to contribute to the dietary pattern and visualized to improve readability
Fig. 2
Fig. 2
Heatmap illustrating the observed top-15 metabolites (based on the smallest p-value for trend over tertiles) associated with the diet quality indices, i.e. the WCRF dietary score and DHD15-index, and the dietary patterns, i.e. the Western, Carnivore, and Prudent pattern. The color is correlated to the observed β values; a darker blue color corresponds with a more positive association, while a darker red color represents a more inverse association between the dietary exposure and the plasma metabolite. Statistically significant associations are presented by a black box around the cell

References

    1. van Zutphen M, Kampman E, Giovannucci EL, van Duijnhoven FJB. Lifestyle after colorectal cancer diagnosis in relation to survival and recurrence: a review of the literature. Curr Colorect Cancer Rep. 2017;13(5):370–401. doi: 10.1007/s11888-017-0386-1.
    1. Menni C, Zhai G, MacGregor A, PrehnRmisch-Margl CW, Suhre K, Adamski J, Cassidy A, Illig T, Spector TD, Valdes AM. Targeted metabolomics profiles are strongly correlated with nutritional patterns in women. Metabolomics. 2013;9(2):506–514. doi: 10.1007/s11306-012-0469-6.
    1. O'Sullivan A, Gibney MJ, Brennan L. Dietary intake patterns are reflected in metabolomic profiles: potential role in dietary assessment studies. Am J Clin Nutr. 2010;93(2):314–321. doi: 10.3945/ajcn.110.000950.
    1. Brennan L. Moving toward objective biomarkers of dietary intake. The Journal of Nutrition. 2018;148(6):821–822. doi: 10.1093/jn/nxy067.
    1. Brennan L. The nutritional metabolomics crossroads: how to ensure success for dietary biomarkers. Am J Clin Nutr. 2017;105(2):293–294. doi: 10.3945/ajcn.116.150847.
    1. 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(6):1286–1308. doi: 10.3945/ajcn.113.076133.
    1. Ulaszewska MM, Weinert CH, Trimigno A, Portmann R, Andres Lacueva C, Badertscher R, Brennan L, Brunius C, Bub A, Capozzi F. Nutrimetabolomics: an integrative action for metabolomic analyses in human nutritional studies. Mol Nutr Food Res. 2019;63(1):1800384. doi: 10.1002/mnfr.201800384.
    1. 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(2):637–649. doi: 10.3945/ajcn.116.150912.
    1. AlEssa HB, Malik VS, Yuan C, Willett WC, Huang T, Hu FB, Tobias DK. Dietary patterns and cardiometabolic and endocrine plasma biomarkers in US women, 2. Am J Clin Nutr. 2016;105(2):432–441. doi: 10.3945/ajcn.116.143016.
    1. Ahmad S, Moorthy M, Demler OV, et al. Assessment of risk factors and biomarkers associated with risk of cardiovascular disease among women consuming a Mediterranean diet. JAMA Network Open. 2018;1(8):e185708. doi: 10.1001/jamanetworkopen.2018.5708.
    1. Playdon MC, Moore SC, Derkach A, Reedy J, Subar AF, Sampson JN, Albanes D, Gu F, Kontto J, Lassale C. Identifying biomarkers of dietary patterns by using metabolomics. Am J Clin Nutr. 2017;105(2):450–465. doi: 10.3945/ajcn.116.144501.
    1. Schmidt JA, Rinaldi S, Ferrari P, Carayol M, Achaintre D, Scalbert A. Metabolic profiles of male meat eaters, fish eaters, vegetarians, and vegans from the EPIC-Oxford cohort. Am J Clin Nutr. 2015 doi: 10.3945/ajcn.115.111989.
    1. Wu GD, Compher C, Chen EZ, Smith SA, Shah RD, Bittinger K, Chehoud C, Albenberg LG, Nessel L, Gilroy E. Comparative metabolomics in vegans and omnivores reveal constraints on diet-dependent gut microbiota metabolite production. Gut. 2016;65(1):63–72. doi: 10.1136/gutjnl-2014-308209.
    1. Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol. 2002;13(1):3–9. doi: 10.1097/00041433-200202000-00002.
    1. Romaguera D, Ward H, Wark PA, Vergnaud A-C, Peeters PH, van Gils CH, Ferrari P, Fedirko V, Jenab M, Boutron-Ruault M-C. Pre-diagnostic concordance with the WCRF/AICR guidelines and survival in European colorectal cancer patients: a cohort study. BMC Med. 2015;13(1):107. doi: 10.1186/s12916-015-0332-5.
    1. Inoue-Choi M, Robien K, Lazovich D. Adherence to the WCRF/AICR guidelines for cancer prevention is associated with lower mortality among older female cancer survivors. Cancer Epidemiol Prevent Biomark. 2013;22(5):792–802. doi: 10.1158/1055-9965.EPI-13-0054.
    1. Vergnaud A-C, Romaguera D, Peeters PH, Van Gils CH, Chan DS, Romieu I, Freisling H, Ferrari P, Clavel-Chapelon F, Fagherazzi G. Adherence to the World Cancer Research Fund/American Institute for Cancer Research guidelines and risk of death in Europe: results from the European Prospective Investigation into Nutrition and Cancer cohort study. Am Clin Nutr. 2013;97(5):1107–1120. doi: 10.3945/ajcn.112.049569.
    1. Shams-White MM, Brockton NT, Mitrou P, Romaguera D, Brown S, Bender A, Kahle LL, Reedy J. Operationalizing the 2018 world cancer research fund/American institute for cancer research (WCRF/AICR) cancer prevention recommendations: a standardized scoring system. Nutrients. 2019;11(7):1572. doi: 10.3390/nu11071572.
    1. Looman M, Feskens EJ, de Rijk M, Meijboom S, Biesbroek S, Temme EH, de Vries J, Geelen A. Development and evaluation of the Dutch healthy diet index 2015. Public Health Nutr. 2017;20(13):2289–2299. doi: 10.1017/S136898001700091X.
    1. Van Lee L, Geelen A, Kiefte-de Jong J, Witteman J, Hofman A, Vonk N, Jankovic N, Van Huysduynen EH, De Vries J, Van't Veer P. Adherence to the Dutch dietary guidelines is inversely associated with 20-year mortality in a large prospective cohort study. Eur J Clin Nutr. 2016;70(2):262. doi: 10.1038/ejcn.2015.163.
    1. Fransen HP, Beulens JW, May AM, Struijk EA, Boer JM, de Wit GA, Onland-Moret NC, van der Schouw YT, Bueno-de-Mesquita HB, Hoekstra J. Dietary patterns in relation to quality-adjusted life years in the EPIC-NL cohort. Prev Med. 2015;77:119–124. doi: 10.1016/j.ypmed.2015.05.014.
    1. Meyerhardt JA, Niedzwiecki D, Hollis D, Saltz LB, Hu FB, Mayer RJ, Nelson H, Whittom R, Hantel A, Thomas J. Association of dietary patterns with cancer recurrence and survival in patients with stage III colon cancer. JAMA. 2007;298(7):754–764. doi: 10.1001/jama.298.7.754.
    1. van Ommen B, Keijer J, Heil SG, Kaput J. Challenging homeostasis to define biomarkers for nutrition related health. Mol Nutr Food Res. 2009;53(7):795–804. doi: 10.1002/mnfr.200800390.
    1. Gibney MJ, Walsh M, Brennan L, Roche HM, German B, van Ommen B. Metabolomics in human nutrition: opportunities and challenges. Am J Clin Nutr. 2005;82(3):497–503. doi: 10.1093/ajcn/82.3.497.
    1. Guasch-Ferré M, Bhupathiraju SN, Hu FB. Use of metabolomics in improving assessment of dietary intake. Clin Chem. 2018;64(1):82–98. doi: 10.1373/clinchem.2017.272344.
    1. Winkels RM, Heine-Bröring RC, Van Zutphen M, van Harten-Gerritsen S, Kok DE, Van Duijnhoven FJ, Kampman E. The COLON study: colorectal cancer: longitudinal, observational study on nutritional and lifestyle factors that may influence colorectal tumour recurrence, survival and quality of life. BMC Cancer. 2014;14(1):1. doi: 10.1186/1471-2407-14-374.
    1. Siebelink E, Geelen A, de Vries JH. Self-reported energy intake by FFQ compared with actual energy intake to maintain body weight in 516 adults. Br J Nutr. 2011;106(2):274–281. doi: 10.1017/S0007114511000067.
    1. Streppel MT, de Vries JH, Meijboom S, Beekman M, de Craen AJ, Slagboom PE, Feskens EJ. Relative validity of the food frequency questionnaire used to assess dietary intake in the Leiden longevity study. Nutr J. 2013;12(1):75. doi: 10.1186/1475-2891-12-75.
    1. Van Leersum N, Snijders H, Henneman D, Kolfschoten N, Gooiker G, Ten Berge M, Eddes E, Wouters M, Tollenaar R, Bemelman W. The Dutch surgical colorectal audit. Eur J Surg Oncol (EJSO) 2013;39(10):1063–1070. doi: 10.1016/j.ejso.2013.05.008.
    1. Kromhout D, Spaaij C, de Goede J, Weggemans R. The 2015 Dutch food-based dietary guidelines. Eur J Clin Nutr. 2016;70(8):869. doi: 10.1038/ejcn.2016.52.
    1. van Zutphen M, Boshuizen HC, Kok DE, van Baar H, Geijsen AJ, Wesselink E, Winkels RM, van Halteren HK, de Wilt JH, Kampman E. Colorectal cancer survivors only marginally change their overall lifestyle in the first 2 years following diagnosis. J Cancer Surviv. 2019;13:956–967. doi: 10.1007/s11764-019-00812-7.
    1. Netherlands Nutrition Center: NEVO Nederlandse Voedingsmiddelen Tabel 2011 (In English: Dutch Food Composition Table). . Accessed June 2018
    1. Borges CA, Rinaldi AE, Conde WL, Mainardi GM, Behar D, Slater B. Dietary patterns: a literature review of the methodological characteristics of the main step of the multivariate analyzes. Rev Br Epidemiol. 2015;18(4):837–857. doi: 10.1590/1980-5497201500040013.
    1. Reimann C, Filzmoser P, Garrett RG, Dutter R (2008) Statistical data analysis explained: applied environmental statistics with R. Principal component analysis (PCA) and factor analysis (FA).
    1. Fransen HP, May AM, Stricker MD, Boer JM, Hennig C, Rosseel Y, Ocke MC, Peeters PH, Beulens JW. A posteriori dietary patterns: how many patterns to retain? J Nutr. 2014;144(8):1274–1282. doi: 10.3945/jn.113.188680.
    1. McCann SE, Marshall JR, Brasure JR, Graham S, Freudenheim JL. Analysis of patterns of food intake in nutritional epidemiology: food classification in principal components analysis and the subsequent impact on estimates for endometrial cancer. Public Health Nutrition. 2001;4(5):989–997. doi: 10.1079/PHN2001168.
    1. Siskos AP, Jain P, Römisch-Margl W, Bennett M, Achaintre D, Asad Y, Marney L, Richardson L, Koulman A, Griffin JL. Interlaboratory reproducibility of a targeted metabolomics platform for analysis of human serum and plasma. Anal Chem. 2016;89(1):656–665. doi: 10.1021/acs.analchem.6b02930.
    1. Carayol M, Licaj I, Achaintre D, Sacerdote C, Vineis P, Key TJ, Moret NCO, Scalbert A, Rinaldi S, Ferrari P. Reliability of serum metabolites over a two-year period: a targeted metabolomic approach in fasting and non-fasting samples from EPIC. PLoS ONE. 2015;10(8):e0135437. doi: 10.1371/journal.pone.0135437.
    1. van Roekel EH, Trijsburg L, Assi N, Carayol M, Achaintre D, Murphy N, Rinaldi S, Schmidt JA, Stepien M, Kaaks R. Circulating metabolites associated with alcohol intake in the European prospective investigation into cancer and nutrition cohort. Nutrients. 2018;10(5):654. doi: 10.3390/nu10050654.
    1. Geijsen AJ, van Roekel EH, van Duijnhoven FJ, Achaintre D, Bachleitner-Hofmann T, Baierl A, Bergmann MM, Boehm J, Bours MJ, Brenner H. Plasma metabolites associated with colorectal cancer stage: findings from an international consortium. Int J Cancer. 2019;146(12):3256–3266. doi: 10.1002/ijc.32666.
    1. Cambiaghi A, Ferrario M, Masseroli M. Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration. Brief Bioinform. 2016 doi: 10.1093/bib/bbw031.
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc Ser B (Methodol) 1995;57(1):289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x.
    1. Fung T, Hu FB, Fuchs C, Giovannucci E, Hunter DJ, Stampfer MJ, Colditz GA, Willett WC. Major dietary patterns and the risk of colorectal cancer in women. Arch Intern Med. 2003;163(3):309–314. doi: 10.1001/archinte.163.3.309.
    1. Hu FB, Rimm E, Smith-Warner SA, Feskanich D, Stampfer MJ, Ascherio A, Sampson L, Willett WC. Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. Am J Clin Nutr. 1999;69(2):243–249. doi: 10.1093/ajcn/69.2.243.
    1. Kwan ML, Weltzien E, Kushi LH, Castillo A, Slattery ML, Caan BJ. Dietary patterns and breast cancer recurrence and survival among women with early-stage breast cancer. J Clin Oncol. 2008;27(6):919–926. doi: 10.1200/JCO.2008.19.4035.
    1. Varraso R, Garcia-Aymerich J, Monier F, Le Moual N, De Batlle J, Miranda G, Pison C, Romieu I, Kauffmann F, Maccario J. Assessment of dietary patterns in nutritional epidemiology: principal component analysis compared with confirmatory factor analysis. Am J Clin Nutr. 2012;96(5):1079–1092. doi: 10.3945/ajcn.112.038109.
    1. Bondia-Pons I, Martinez JA, de la Iglesia R, Lopez-Legarrea P, Poutanen K, Hanhineva K, MdlÁ Z. Effects of short-and long-term Mediterranean-based dietary treatment on plasma LC-QTOF/MS metabolic profiling of subjects with metabolic syndrome features: the metabolic syndrome reduction in Navarra (RESMENA) randomized controlled trial. Mol Nutr Food Res. 2015;59(4):711–728. doi: 10.1002/mnfr.201400309.
    1. Fung TT, Kashambwa R, Sato K, Chiuve SE, Fuchs CS, Wu K, Giovannucci E, Ogino S, Hu FB, Meyerhardt JA. Post diagnosis diet quality and colorectal cancer survival in women. PLoS ONE. 2014;9(12):e115377. doi: 10.1371/journal.pone.0115377.
    1. Ratjen I, Schafmayer C, di Giuseppe R, Waniek S, Plachta-Danielzik S, Koch M, Nothlings U, Hampe J, Schlesinger S, Lieb W. Postdiagnostic Mediterranean and healthy Nordic dietary patterns are inversely associated with all-cause mortality in long-term colorectal cancer survivors. J Nutrion. 2017;147(4):636–644. doi: 10.3945/jn.116.244129.
    1. Koundouros N, Poulogiannis G. Reprogramming of fatty acid metabolism in cancer. Br J Cancer. 2020;122(1):4–22. doi: 10.1038/s41416-019-0650-z.
    1. Ridgway ND. The role of phosphatidylcholine and choline metabolites to cell proliferation and survival. Crit Rev Biochem Mol Biol. 2013;48(1):20–38. doi: 10.3109/10409238.2012.735643.
    1. German JB, Gillies LA, Smilowitz JT, Zivkovic AM, Watkins SM. Lipidomics and lipid profiling in metabolomics. Curr Opin Lipidol. 2007;18(1):66–71.
    1. McGee EE, Kiblawi R, Playdon MC, Eliassen AH. Nutritional metabolomics in cancer epidemiology: current trends, challenges, and future directions. Curr Nut Rep. 2019;8:187–201. doi: 10.1007/s13668-019-00279-z.

Source: PubMed

赞助者和合作者

医疗条件

药物干预

3
订阅