Nutrients (Apr 2020)

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

  • Colleen X. Muñoz,
  • Evan C. Johnson,
  • Laura J. Kunces,
  • Amy L. McKenzie,
  • Michael Wininger,
  • Cory L. Butts,
  • Aaron Caldwell,
  • Adam Seal,
  • Brendon P. McDermott,
  • Jakob Vingren,
  • Abigail T. Colburn,
  • Skylar S. Wright,
  • Virgilio Lopez III,
  • Lawrence E. Armstrong,
  • Elaine C. Lee

DOI
https://doi.org/10.3390/nu12051276
Journal volume & issue
Vol. 12, no. 5
p. 1276

Abstract

Read online

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.

Keywords