Kaitlyn M Mazzilli1, Kathleen M McClain1, Loren Lipworth2, Mary C Playdon3, Joshua N Sampson1, Clary B Clish4, Robert E Gerszten5, Neal D Freedman1, Steven C Moore1. 1. Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA. 2. Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. 3. Division of Cancer Population Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA. 4. Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA. 5. Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Abstract
BACKGROUND: Metabolomics has proven useful for detecting objective biomarkers of diet that may help to improve dietary measurement. Studies to date, however, have focused on a relatively narrow set of lipid classes. OBJECTIVE: The aim of this study was to uncover candidate dietary biomarkers by identifying serum metabolites correlated with self-reported diet, particularly metabolites in underinvestigated lipid classes, e.g. triglycerides and plasmalogens. METHODS: We assessed dietary questionnaire data and serum metabolite correlations from 491 male and female participants aged 55-75 y in an exploratory cross-sectional study within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO). Self-reported intake was categorized into 50 foods, food groups, beverages, and supplements. We examined 522 identified metabolites using 2 metabolomics platforms (Broad Institute and Massachusetts General Hospital). Correlations were identified using partial Pearson's correlations adjusted for age, sex, BMI, smoking status, study site, and total energy intake [Bonferroni-corrected level of 0.05/(50 × 522) = 1.9 × 10-6]. We assessed prediction of dietary intake by multiple-metabolite linear models with the use of 10-fold crossvalidation least absolute shrinkage and selection operator (LASSO) regression. RESULTS: Eighteen foods, beverages, and supplements were correlated with ≥1 serum metabolite at the Bonferroni-corrected significance threshold, for a total of 102 correlations. Of these, only 5 have been reported previously, to our knowledge. Our strongest correlations were between citrus and proline betaine (r = 0.55), supplements and pantothenic acid (r = 0.46), and fish and C40:9 phosphatidylcholine (PC) (r = 0.35). The multivariate analysis similarly found reasonably large correlations between metabolite profiles and citrus (r = 0.59), supplements (r = 0.57), and fish (r = 0.44). CONCLUSIONS: Our study of PLCO participants identified many novel food-metabolite associations and replicated 5 previous associations. These candidate biomarkers of diet may help to complement measures of self-reported diet in nutritional epidemiology studies, though further validation work is still needed. Published by Oxford University Press on behalf of the American Society for Nutrition 2019.
BACKGROUND: Metabolomics has proven useful for detecting objective biomarkers of diet that may help to improve dietary measurement. Studies to date, however, have focused on a relatively narrow set of lipid classes. OBJECTIVE: The aim of this study was to uncover candidate dietary biomarkers by identifying serum metabolites correlated with self-reported diet, particularly metabolites in underinvestigated lipid classes, e.g. triglycerides and plasmalogens. METHODS: We assessed dietary questionnaire data and serum metabolite correlations from 491 male and female participants aged 55-75 y in an exploratory cross-sectional study within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO). Self-reported intake was categorized into 50 foods, food groups, beverages, and supplements. We examined 522 identified metabolites using 2 metabolomics platforms (Broad Institute and Massachusetts General Hospital). Correlations were identified using partial Pearson's correlations adjusted for age, sex, BMI, smoking status, study site, and total energy intake [Bonferroni-corrected level of 0.05/(50 × 522) = 1.9 × 10-6]. We assessed prediction of dietary intake by multiple-metabolite linear models with the use of 10-fold crossvalidation least absolute shrinkage and selection operator (LASSO) regression. RESULTS: Eighteen foods, beverages, and supplements were correlated with ≥1 serum metabolite at the Bonferroni-corrected significance threshold, for a total of 102 correlations. Of these, only 5 have been reported previously, to our knowledge. Our strongest correlations were between citrus and proline betaine (r = 0.55), supplements and pantothenic acid (r = 0.46), and fish and C40:9 phosphatidylcholine (PC) (r = 0.35). The multivariate analysis similarly found reasonably large correlations between metabolite profiles and citrus (r = 0.59), supplements (r = 0.57), and fish (r = 0.44). CONCLUSIONS: Our study of PLCO participants identified many novel food-metabolite associations and replicated 5 previous associations. These candidate biomarkers of diet may help to complement measures of self-reported diet in nutritional epidemiology studies, though further validation work is still needed. Published by Oxford University Press on behalf of the American Society for Nutrition 2019.
Authors: Mary C Playdon; Regina G Ziegler; Joshua N Sampson; Rachael Stolzenberg-Solomon; Henry J Thompson; Melinda L Irwin; Susan T Mayne; Robert N Hoover; Steven C Moore Journal: Am J Clin Nutr Date: 2017-06-28 Impact factor: 7.045
Authors: Eugene P Rhee; Susan Cheng; Martin G Larson; Geoffrey A Walford; Gregory D Lewis; Elizabeth McCabe; Elaine Yang; Laurie Farrell; Caroline S Fox; Christopher J O'Donnell; Steven A Carr; Ramachandran S Vasan; Jose C Florez; Clary B Clish; Thomas J Wang; Robert E Gerszten Journal: J Clin Invest Date: 2011-03-14 Impact factor: 14.808
Authors: V Neveu; J Perez-Jiménez; F Vos; V Crespy; L du Chaffaut; L Mennen; C Knox; R Eisner; J Cruz; D Wishart; A Scalbert Journal: Database (Oxford) Date: 2010-01-08 Impact factor: 3.451
Authors: Sheila A Bingham; Robert Luben; Ailsa Welch; Nicholas Wareham; Kay-Tee Khaw; Nicholas Day Journal: Lancet Date: 2003-07-19 Impact factor: 79.321
Authors: Nina P Paynter; Raji Balasubramanian; Franco Giulianini; Dong D Wang; Lesley F Tinker; Shuba Gopal; Amy A Deik; Kevin Bullock; Kerry A Pierce; Justin Scott; Miguel A Martínez-González; Ramon Estruch; JoAnn E Manson; Nancy R Cook; Christine M Albert; Clary B Clish; Kathryn M Rexrode Journal: Circulation Date: 2018-02-20 Impact factor: 29.690
Authors: Bing Yu; Krista A Zanetti; Marinella Temprosa; Demetrius Albanes; Nathan Appel; Clara Barrios Barrera; Yoav Ben-Shlomo; Eric Boerwinkle; Juan P Casas; Clary Clish; Caroline Dale; Abbas Dehghan; Andriy Derkach; A Heather Eliassen; Paul Elliott; Eoin Fahy; Christian Gieger; Marc J Gunter; Sei Harada; Tamara Harris; Deron R Herr; David Herrington; Joel N Hirschhorn; Elise Hoover; Ann W Hsing; Mattias Johansson; Rachel S Kelly; Chin Meng Khoo; Mika Kivimäki; Bruce S Kristal; Claudia Langenberg; Jessica Lasky-Su; Deborah A Lawlor; Luca A Lotta; Massimo Mangino; Loïc Le Marchand; Ewy Mathé; Charles E Matthews; Cristina Menni; Lorelei A Mucci; Rachel Murphy; Matej Oresic; Eric Orwoll; Jennifer Ose; Alexandre C Pereira; Mary C Playdon; Lucilla Poston; Jackie Price; Qibin Qi; Kathryn Rexrode; Adam Risch; Joshua Sampson; Wei Jie Seow; Howard D Sesso; Svati H Shah; Xiao-Ou Shu; Gordon C S Smith; Ulla Sovio; Victoria L Stevens; Rachael Stolzenberg-Solomon; Toru Takebayashi; Therese Tillin; Ruth Travis; Ioanna Tzoulaki; Cornelia M Ulrich; Ramachandran S Vasan; Mukesh Verma; Ying Wang; Nick J Wareham; Andrew Wong; Naji Younes; Hua Zhao; Wei Zheng; Steven C Moore Journal: Am J Epidemiol Date: 2019-06-01 Impact factor: 4.897
Authors: L O Dragsted; Q Gao; A Scalbert; G Vergères; M Kolehmainen; C Manach; L Brennan; L A Afman; D S Wishart; C Andres Lacueva; M Garcia-Aloy; H Verhagen; E J M Feskens; G Praticò Journal: Genes Nutr Date: 2018-05-30 Impact factor: 5.523
Authors: Rachel S Kelly; Kevin M Mendez; Mengna Huang; Brian D Hobbs; Clary B Clish; Robert Gerszten; Michael H Cho; Craig E Wheelock; Michael J McGeachie; Su H Chu; Juan C Celedón; Scott T Weiss; Jessica Lasky-Su Journal: Am J Respir Crit Care Med Date: 2022-02-01 Impact factor: 21.405
Authors: Hyunju Kim; Alice H Lichtenstein; Kari E Wong; Lawrence J Appel; Josef Coresh; Casey M Rebholz Journal: Mol Nutr Food Res Date: 2020-12-28 Impact factor: 6.575
Authors: Kristen D Brantley; Oana A Zeleznik; Bernard Rosner; Rulla M Tamimi; Julian Avila-Pacheco; Clary B Clish; A Heather Eliassen Journal: Cancer Epidemiol Biomarkers Prev Date: 2022-04-01 Impact factor: 4.090
Authors: Fenglei Wang; Paulette D Chandler; Oana A Zeleznik; Kana Wu; You Wu; Kanhua Yin; Rui Song; Julian Avila-Pacheco; Clary B Clish; Jeffrey A Meyerhardt; Xuehong Zhang; Mingyang Song; Shuji Ogino; I-Min Lee; A Heather Eliassen; Liming Liang; Stephanie A Smith-Warner; Walter C Willett; Edward L Giovannucci Journal: Nutrients Date: 2022-02-25 Impact factor: 5.717
Authors: Sophie Hellstrand; Filip Ottosson; Einar Smith; Louise Brunkwall; Stina Ramne; Emily Sonestedt; Peter M Nilsson; Olle Melander; Marju Orho-Melander; Ulrika Ericson Journal: Nutrients Date: 2021-05-09 Impact factor: 5.717
Authors: Hyunju Kim; Cheryl Am Anderson; Emily A Hu; Zihe Zheng; Lawrence J Appel; Jiang He; Harold I Feldman; Amanda H Anderson; Ana C Ricardo; Zeenat Bhat; Tanika N Kelly; Jing Chen; Ramachandran S Vasan; Paul L Kimmel; Morgan E Grams; Josef Coresh; Clary B Clish; Eugene P Rhee; Casey M Rebholz Journal: J Nutr Date: 2021-10-01 Impact factor: 4.687