BACKGROUND: Healthy diet patterns are associated with lower risk of cancer and other chronic diseases. Metabolomics has the potential to expand dietary biomarker development to include dietary patterns, which may provide a complement or alternative to self-reported diet. OBJECTIVE: This study examined the correlation of serum untargeted metabolomic markers with 4 diet pattern scores-the alternate Mediterranean diet score (aMED), alternate Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension (DASH) diet, and the Healthy Eating Index (HEI)-2015-and used multivariate methods to identify discriminatory metabolites for each pattern. METHODS: Among 1367 US postmenopausal women with serum metabolomic data in the Cancer Prevention Study-II Nutrition Cohort, we conducted partial correlation analysis, adjusted for demographic and lifestyle variables, to examine cross-sectional correlations between serum metabolomic markers and healthy diet pattern scores. In a randomly selected "training" set (50%), we conducted orthogonal partial least-squares discriminant analysis to identify metabolites that discriminated the top from bottom diet score quintiles. Combinations of metabolites with a variable importance in projection (VIP) score ≥2.5 were tested for predictability in the "testing" set based on the use of receiver operating characteristic curves. RESULTS: Out of 1186 metabolites, 32 unique metabolites were considered discriminatory based on a VIP score ≥2.5 in the training dataset with some overlap across scores (aMED = 16; AHEI = 17; DASH = 13; HEI = 12). Spearman partial correlation analyses, applying a cut-point (|r| ≥ 0.15) and Bonferroni correction (P < 1.05 × 10-5), identified similar key metabolites. The top 5 metabolites for each pattern mostly distinguished high compared with low scores; 4 of the 5 (fish-derived) metabolites were the same for aMED and AHEI, 2 of which were identified for HEI; 4 DASH metabolites were unique. CONCLUSIONS: Metabolomic methods that used a split-sample approach identified potential biomarkers for 4 healthy diet patterns. Similar metabolites across scores reflect fish consumption in healthy dietary patterns. These findings should be replicated in independent populations.
BACKGROUND: Healthy diet patterns are associated with lower risk of cancer and other chronic diseases. Metabolomics has the potential to expand dietary biomarker development to include dietary patterns, which may provide a complement or alternative to self-reported diet. OBJECTIVE: This study examined the correlation of serum untargeted metabolomic markers with 4 diet pattern scores-the alternate Mediterranean diet score (aMED), alternate Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension (DASH) diet, and the Healthy Eating Index (HEI)-2015-and used multivariate methods to identify discriminatory metabolites for each pattern. METHODS: Among 1367 US postmenopausal women with serum metabolomic data in the Cancer Prevention Study-II Nutrition Cohort, we conducted partial correlation analysis, adjusted for demographic and lifestyle variables, to examine cross-sectional correlations between serum metabolomic markers and healthy diet pattern scores. In a randomly selected "training" set (50%), we conducted orthogonal partial least-squares discriminant analysis to identify metabolites that discriminated the top from bottom diet score quintiles. Combinations of metabolites with a variable importance in projection (VIP) score ≥2.5 were tested for predictability in the "testing" set based on the use of receiver operating characteristic curves. RESULTS: Out of 1186 metabolites, 32 unique metabolites were considered discriminatory based on a VIP score ≥2.5 in the training dataset with some overlap across scores (aMED = 16; AHEI = 17; DASH = 13; HEI = 12). Spearman partial correlation analyses, applying a cut-point (|r| ≥ 0.15) and Bonferroni correction (P < 1.05 × 10-5), identified similar key metabolites. The top 5 metabolites for each pattern mostly distinguished high compared with low scores; 4 of the 5 (fish-derived) metabolites were the same for aMED and AHEI, 2 of which were identified for HEI; 4 DASH metabolites were unique. CONCLUSIONS: Metabolomic methods that used a split-sample approach identified potential biomarkers for 4 healthy diet patterns. Similar metabolites across scores reflect fish consumption in healthy dietary patterns. These findings should be replicated in independent populations.
Authors: Maura E Walker; Rebecca J Song; Xiang Xu; Robert E Gerszten; Debby Ngo; Clary B Clish; Laura Corlin; Jiantao Ma; Vanessa Xanthakis; Paul F Jacques; Ramachandran S Vasan Journal: Nutrients Date: 2020-05-19 Impact factor: 5.717
Authors: Anne M Evans; Claire O'Donovan; Mary Playdon; Chris Beecher; Richard D Beger; John A Bowden; David Broadhurst; Clary B Clish; Surendra Dasari; Warwick B Dunn; Julian L Griffin; Thomas Hartung; Ping- Ching Hsu; Tao Huan; Judith Jans; Christina M Jones; Maureen Kachman; Andre Kleensang; Matthew R Lewis; María Eugenia Monge; Jonathan D Mosley; Eric Taylor; Fariba Tayyari; Georgios Theodoridis; Federico Torta; Baljit K Ubhi; Dajana Vuckovic Journal: Metabolomics Date: 2020-10-12 Impact factor: 4.290
Authors: Casey M Rebholz; Yan Gao; Sameera Talegawkar; Katherine L Tucker; Lisandro D Colantonio; Paul Muntner; Debby Ngo; Zsu Zsu Chen; Daniel Cruz; Daniel H Katz; Usman A Tahir; Clary Clish; Robert E Gerszten; James G Wilson Journal: Mol Nutr Food Res Date: 2021-03-11 Impact factor: 5.914
Authors: Talha Rafiq; Sandi M Azab; Koon K Teo; Lehana Thabane; Sonia S Anand; Katherine M Morrison; Russell J de Souza; Philip Britz-McKibbin Journal: Adv Nutr Date: 2021-12-01 Impact factor: 8.701
Authors: Marian L Neuhouser; Mary Pettinger; Johanna W Lampe; Lesley F Tinker; Stephanie M George; Jill Reedy; Xiaoling Song; Bharat Thyagarajan; Shirley A Beresford; Ross L Prentice Journal: Am J Epidemiol Date: 2021-11-02 Impact factor: 4.897
Authors: Hyunju Kim; Emily A Hu; Kari E Wong; Bing Yu; Lyn M Steffen; Sara B Seidelmann; Eric Boerwinkle; Josef Coresh; Casey M Rebholz Journal: J Nutr Date: 2021-01-04 Impact factor: 4.798