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Open AccessArticle

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

1
Department of Health Sciences, University of Hartford, West Hartford, CT 06117, USA
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Division of Kinesiology & Health, University of Wyoming, Laramie, WY 82071, USA
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Onegevity Health, New York, NY 10019, USA
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Virta Health, San Francisco, CA 94105, USA
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Yale School of Public Health, New Haven, CT 06511, USA
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Department of Veterans Affairs, West Haven, CT 06516, USA
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Department of Health Promotion & Human Performance Weber State University, University of Arkansas, Fayetteville, AR 72701, USA
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California Polytechnic State University, San Luis Obispo, CA 93407, USA
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Department of Biological Sciences, University of North Texas, Denton, TX 76203, USA
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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; https://doi.org/10.3390/nu12051276
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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