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Open AccessArticle

Impact of Nutrient Intake on Hydration Biomarkers Following Exercise and Rehydration Using a Clustering-Based Approach

Department of Health Sciences, University of Hartford, West Hartford, CT 06117, USA
Division of Kinesiology & Health, University of Wyoming, Laramie, WY 82071, USA
Onegevity Health, New York, NY 10019, USA
Virta Health, San Francisco, CA 94105, USA
Yale School of Public Health, New Haven, CT 06511, USA
Department of Veterans Affairs, West Haven, CT 06516, USA
Department of Health Promotion & Human Performance Weber State University, University of Arkansas, Fayetteville, AR 72701, USA
California Polytechnic State University, San Luis Obispo, CA 93407, USA
Department of Biological Sciences, University of North Texas, Denton, TX 76203, USA
Department of Kinesiology, Human Performance Laboratory, University of Connecticut, Storrs, CT 06269, USA
Author to whom correspondence should be addressed.
Nutrients 2020, 12(5), 1276;
Received: 30 March 2020 / Revised: 21 April 2020 / Accepted: 27 April 2020 / Published: 30 April 2020
(This article belongs to the Special Issue Body Water Regulation and Nutrient Intake)
We investigated the impact of nutrient intake on hydration biomarkers in cyclists before and after a 161 km ride, including one hour after a 650 mL water bolus consumed post-ride. To control for multicollinearity, we chose a clustering-based, machine learning statistical approach. Five hydration biomarkers (urine color, urine specific gravity, plasma osmolality, plasma copeptin, and body mass change) were configured as raw- and percent change. Linear regressions were used to test for associations between hydration markers and eight predictor terms derived from 19 nutrients merged into a reduced-dimensionality dataset through serial k-means clustering. Most predictor groups showed significant association with at least one hydration biomarker: (1) Glycemic Load + Carbohydrates + Sodium, (2) Protein + Fat + Zinc, (3) Magnesium + Calcium, (4) Pinitol, (5) Caffeine, (6) Fiber + Betaine, and (7) Water; potassium + three polyols, and mannitol + sorbitol showed no significant associations with any hydration biomarker. All five hydration biomarkers were associated with at least one nutrient predictor in at least one configuration. We conclude that in a real-life scenario, some nutrients may serve as mediators of body water, and urine-specific hydration biomarkers may be more responsive to nutrient intake than measures derived from plasma or body mass. View Full-Text
Keywords: hydration; nutrition; sport nutrition; exercise; copeptin; collinearity; clustering hydration; nutrition; sport nutrition; exercise; copeptin; collinearity; clustering
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Muñoz, C.X.; Johnson, E.C.; Kunces, L.J.; McKenzie, A.L.; Wininger, M.; Butts, C.L.; Caldwell, A.; Seal, A.; McDermott, B.P.; Vingren, J.; Colburn, A.T.; Wright, S.S.; Lopez III, V.; Armstrong, L.E.; Lee, E.C. Impact of Nutrient Intake on Hydration Biomarkers Following Exercise and Rehydration Using a Clustering-Based Approach. Nutrients 2020, 12, 1276.

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