Ultra-endurance triathlon performance and markers of whole-body and gut-specific inflammation.

PURPOSE: To examine the influence of the Ultraman Florida triathlon (3 days of non-continuous racing; stage 1: 10 km swim and 144.8 km cycle; stage 2: 275.4 km cycle; stage 3: 84.4 km run) on circulating plasma concentrations of whole-body (C-reactive protein (CRP), interleukin (IL)-6 (IL-6), and IL-10 and surrogate gut-specific inflammatory markers (IL-17 and IL-23), and determine whether these variables are associated with performance. METHODS: Eighteen triathletes (N = 18; 15 men, 3 women; age: 37 ± 8 yrs) were evaluated at baseline and post-race for circulating concentrations of CRP, IL-6, IL-10, IL-17, and IL-23. Blood samples were drawn two days prior to stage 1 (1600 h) and one day after stage 3 (1200 h). RESULTS: Plasma CRP significantly increased from baseline (1985.8 ± 5962.3 ng/mL) to post-race (27,013.9 ± 12,888.8 ng/mL, p < 0.001, 13-fold increase). Both plasma IL-6 and IL-10 did not significantly change from baseline to post-race. Baseline and post-race concentrations of IL-17 and IL-23 were below detectable limits. Pearson's correlation between mean finish time and post-race IL-10 revealed a significant positive correlation (r = 0.54, p < 0.05). CONCLUSIONS: Our results suggest that cytokines such as IL-6 and IL-10 involved in the inflammatory response return to near-baseline concentrations rapidly even after ultra-endurance events of extreme duration. The absence of IL-17 and IL-23 may suggest positive gut adaptations from ultra-endurance training. A significant positive correlation between post-race IL-10 concentrations and mean finish time may indicate that a relationship between anti-inflammatory responses and performance exists.

Pre-sleep protein in casein supplement or whole-food form has no impact on resting energy expenditure or hunger in women.

The purpose of this study was to determine the effect of a whole-food protein (cottage cheese, CC) consumed before sleep on next-morning resting energy expenditure (REE), RER and appetite compared with an isoenergetic/isonitrogenous casein protein (CP) supplement and placebo (PL) in active women. In a beverage-blinded, randomised, cross-over design, ten active women (age, 23·1 (sd 1·9) years; body fat, 22·0 (sd 4·6) %) consumed pre-sleep CC (30 g of protein, 10 g of carbohydrate and 0 g of fat) or energy- and protein-matched liquid CP or PL (0 kJ). Participants arrived at 18.00 hours for an overnight stay in the laboratory. At 30-60 min before normal bed time (2 h post standard meal), participants consumed CC, CP or PL before measurement of REE. Upon waking (05.00-08.00 hours), REE was repeated and subjective appetite was recorded. Statistical analyses were conducted using repeated-measures ANOVA (SPSS). Significance was accepted at P≤0·05. There were no significant differences in acute REE (CC, 7217 (sd 1368); CP, 7188 (SD 895); PL, 7075 (sd 1108) kJ/d, P=0·95), acute RER (0·79 (sd 0·05), P=0·56), morning REE (CC, 5840 (sd 1225); CP, 5694 (sd 732); PL, 5991 (sd 903) kJ/d, P=0·79) or morning RER (0·77 (sd 0·03), P=0·52). Subjective measures of appetite were not different between groups. In active women, pre-sleep consumption of CC does not alter REE or RER more than a CP or PL beverage. These data suggest that the metabolic response from whole-food protein do not differ from the metabolic response of liquid protein.

Sleep Duration Correlates With Performance in Ultra-Endurance Triathlon.

The relationship between sleep duration, sleep quality, and race completion time during each stage of a 3-day ultra-endurance triathlon (stage 1: 10-km swim, 146-km cycle; stage 2: 276-km cycle; and stage 3: 84.4-km run) was investigated. Seventeen triathletes partook in sleep analysis throughout the ultra-endurance multiday triathlon using an actigraphy wristband. The participants wore the band to record objective sleep outcomes for approximately 4 days (1-2 d prerace, 3 race days, and 1 d postrace), except while racing. The total sleep time (TST; prerace: 414.1 [95.3] min, prestage 1: 392.2 [138.3] min, prestage 2: 355.6 [62.5] min, and prestage 3: 299.7 [107.0] min) significantly decreased over time (P < .05). Significant Pearson moment-product correlations were found between TST and subsequent race-day performance for race stage 1 (r = -.577; P = .019) and stage 3 (r = -.546; P = .035), with further analysis revealing that TST explained 33% and 30% of the variation in performance for stages 1 and 3, respectively. During a 3-day ultra-endurance triathlon, the TST was reduced and had a significant negative correlation to exercise performance, indicating that sleep loss was associated with slower performances. Sleep onset latency, wake episodes, and sleep efficiency did not significantly change over the course of this investigation, which may stem from the close proximity of exercise to sleep.

Rate normalization for sweat metabolomics biomarker discovery.

As the demand for real-time exercise performance feedback increases, excreted sweat has become a biosource of interest for continuous human performance assessment. For sweat to truly fulfill this requirement, analyte concentrations must be normalized to adequately assess day-to-day differences within and among individuals. In this manuscript, data are presented highlighting the use of accurate localized sweat rate as a means for ion and global metabolomic data normalization. The results illustrate large sweat rate variability among individuals over the course of two distinct exercises protocols. Furthermore, the data show sweat rate is not symmetrical at similar locations among right and left forearms of individuals (p = 0.0007). Sweat ion conductivity analysis suggest overall sweat rate normalization reduces variability collectively among ion values and participants with principal component analysis showing 77.8% of variation in the data set attributable to sweat rate normalization. Global metabolomic analysis of sweat illustrated overall rate normalization increases the variability among test subjects with 72.7% of the variation explained by sweat rate normalization. Finally, overall rate normalized metabolomic features of sweat significantly correlated (ρ ≥ 0.7, ρ ≤ -0.7) with measured performance metrics of the individual, establishing the potential for sweat to be used as a biosource for performance monitoring. Collectively, these data illustrate the importance of accurate localized sweat rate determination, for analyte data normalization, in support for the use of sweat in biomarker discovery efforts to predict human performance.