Investigation of an Alternative Collection Method for Food Intake Biomarkers Discriminates a High Fat-High Protein Meat-Based Diet from a High Carbohydrate Vegan Diet. (29th May 2020)

• Record Type: Journal Article
• Authors: McNairn, Megan; Dillard, Kayla; Heath, Hannah; Pantaleon, Matthew; Fanter, Rob; Brito, Alex; Pilolla, Kari; Amin, Samir; Frano, Michael La
• Is Part Of: Current developments in nutrition. Volume 4(2020)Supplement 2
• Page Start: 1176
• Page End: 1176
• Publication Date: 2020-05-29
• Subjects: Nutrition -- Periodicals; Nutritional Physiological Phenomena; Nutrition; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 612.3
• DOI: 10.1093/cdn/nzaa056_023
• Languages: English
• ISSNs: 2475-2991

Abstract

Abstract: Objectives: Due to the challenges associated with accurate monitoring of dietary intake in humans, the use of food intake biomarker (FIB) analysis as a complement to dietary questionnaires has been explored. However, numerous limitations exist including the requirement of 24-hour urine collection, currently the best method of FIB assessment. As a potentially more convenient and accurate means of monitoring dietary intake, we explored the utility of postprandial dried blood spot (DBS) collection. The hypothesis of this study was that metabolic profiles in both DBS and urine under controlled feeding conditions would similarly reflect differences in diets of varying food type and macronutrient distribution. Methods: This was a randomized controlled trial with a crossover design including healthy volunteers ( n = 8) from the San Luis Obispo, CA. The standardized isocaloric diet interventions included a high fat, high protein meat diet (HFPM) versus a high carbohydrate vegan (HCV) diet. Participants attended two intervention days, separated by a two-week washout. During each intervention day, a finger-prick blood sample was collected one hour before each meal (at 8 am, 12 pm, and 4 pm). Participants also collected their urine for 24-hours. DBS and urine samples were analyzed by LC-MS in order to identify diet-discriminatory biomarkers. Results: Both the DBS and urine sample analysis identified biomarkers that differentiated the HFPM and HCV diets ( P < 0.05). Most notably, in both the DBS and urine, post-HFPM consumption had higher levels of amino acids and creatine while the HCV diet was associated with elevated sorbitol and hippuric acid ( P < 0.05). Unique to DBS, the HFPM diet had higher ketone bodies and triacylglycerols with <54 total carbons while post-HCV diets had higher levels of triacylglycerols with ≥54 total carbons ( P < 0.05). Exclusive to urine sample metabolite changes, post-HFPM had elevated cis-trans hydroxyproline and several nucleoside mono- and di-phosphates ( P < 0.05). Conclusions: DBS and urine collections were similarly capable of differentiating the diets consumed in this study. The potential use of DBS as a more efficient method of FIB identification deserves further investigation. Funding Sources: Cal Poly CAFES SURP and the Department of Food Science and Nutrition.