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Stable Isotope Models of Sugar Intake Using Hair, Red Blood Cells, and Plasma, but Not Fasting Plasma Glucose, Predict Sugar Intake in a Yup'ik Study Population.

TitleStable Isotope Models of Sugar Intake Using Hair, Red Blood Cells, and Plasma, but Not Fasting Plasma Glucose, Predict Sugar Intake in a Yup'ik Study Population.
Publication TypeJournal Article
Year of Publication2014
AuthorsNash, SH, Kristal, AR, Hopkins, SE, Boyer, BB, O'Brien, DM
JournalJ Nutr
Volume144
Issue1
Pagination75-80
Date Published2014 Jan
ISSN1541-6100
Abstract

Objectively measured biomarkers will help to resolve the controversial role of sugar intake in the etiology of obesity and related chronic diseases. We recently validated a dual-isotope model based on RBC carbon (δ(13)C) and nitrogen (δ(15)N) isotope ratios that explained a large percentage of the variation in self-reported sugar intake in a Yup'ik study population. Stable isotope ratios can easily be measured from many tissues, including RBCs, plasma, and hair; however, it is not known how isotopic models of sugar intake compare among these tissues. Here, we compared self-reported sugar intake with models based on RBCs, plasma, and hair δ(13)C and δ(15)N in Yup'ik people. We also evaluated associations of sugar intake with fasting plasma glucose δ(13)C. Finally, we evaluated relations between δ(13)C and δ(15)N values in hair, plasma, RBCs, and fasting plasma glucose to allow comparison of isotope ratios across tissue types. Models using RBCs, plasma, or hair isotope ratios explained similar amounts of variance in total sugar, added sugar, and sugar-sweetened beverage intake (∼53%, 48%, and 34%, respectively); however, the association with δ(13)C was strongest for models based on RBCs and hair. There were no associations with fasting plasma glucose δ(13)C (R(2) = 0.03). The δ(13)C and δ(15)N values of RBCs, plasma, and hair showed strong, positive correlations; the slopes of these relations did not differ from 1. This study demonstrates that RBC, plasma, and hair isotope ratios predict sugar intake and provides data that will allow comparison of studies using different sample types.

DOI10.3945/jn.113.182113
Alternate JournalJ. Nutr.
PubMed ID24198311
PubMed Central IDPMC3861795
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