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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
Oleh:
Nash, Sarah H.
;
Kristal, Alan R.
;
Hopkins, Scarlett E.
;
Boyer, Bert B.
;
O Brien, Diane M.
Jenis:
Article from Journal - ilmiah internasional
Dalam koleksi:
JN: The Journal of Nutrition vol. 144 no. 01 (Jan. 2014)
,
page 75-80.
Ketersediaan
Perpustakaan FK
Nomor Panggil:
J42.K.2014.01
Non-tandon:
1 (dapat dipinjam: 0)
Tandon:
tidak ada
Lihat Detail Induk
Isi artikel
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 (d13C) and nitrogen (d15N) 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 d13C and d15N in Yup’ik people. We also evaluated associations of sugar intake with fasting plasma glucose d13C. Finally, we evaluated relations between d13C and d15N 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 d13C was strongest for models based on RBCs and hair. There were no associations with fasting plasma glucose d13C (R2 = 0.03). The d13C and d15N 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.
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