Acute Effects of Supervised Making Weight on Health Markers, Hormones and Body Composition in Muay Thai Fighters

Abstract

Making weight is a practice often used in combat sports. This consists of a rapid weight loss (RWL) and a subsequent rapid weight gain (RWG) in the days preceding competition. However, this practice is often carried out based on anecdotal information provided by ex-athletes or non-professionals, which has led to several adverse events. This study aimed to assess the acute effects of a supervised nutritional period of RWL/RWG on health markers, hormone concentrations, and body composition. We performed a single-arm repeated-measures (baseline, after RWL and after RWG) clinical trial with twenty-one (8F:16M) Italian Muay Thai fighters. Body mass was significantly lower after the RWL (-4.1%) while there was a significantly higher glucose availability after RWL and RWG. Blood urea nitrogen, lipid profile, and creatinine were within the normal range after RWL/RWG. Testosterone decrease significantly after RWL and RWG in the men group. Male fighters had a significant reduction in thyroid-stimulating hormone concentration after the RWL and RWG intervention, but no change was found in women at pre-competition. Bioelectrical parameters were almost fully restored after RWG. An evidence-based and individualized nutrition methodology reduces the adverse events after an RWL and RWG practice, although the impact on the hormonal profile is inevitable.

Keywords: blood chemical analysis; body composition; combat sports; dehydration; glycogen depletion; rapid weight loss; weight reduction diet.

Figure 1

Schematic representation of the study design.

Figure 2

CONSORT flow diagram.

Figure 3

Multi-paired Cumming estimation plots of analyzed variables. All raw data are plotted on the upper ax. This displays the means and their 95% CIs across each timepoint measure (baseline [t₀], after RWL [t₁] and after RWG [t₂]). The paired data are shown as lines (female = pink, male = cyan). In separate axes, beneath the raw data, the ES is shown with its 95% CI across three-time points for all participants (n = 21). Bootstrap resampling (BCa, 5000 bootstrap resamples) was performed to calculate the 95% CI of the mean difference. All confidence intervals were bias-corrected and accelerated with the DABEST ‘Data Analysis using Bootstrap-Coupled ESTimation’ software library within the R statistical computing environment. † Significance post-pre change in men (p < 0.05); ‡ Significance post-pre change in women (p < 0.05).

Figure 3

Multi-paired Cumming estimation plots of analyzed variables. All raw data are plotted on the upper ax. This displays the means and their 95% CIs across each timepoint measure (baseline [t₀], after RWL [t₁] and after RWG [t₂]). The paired data are shown as lines (female = pink, male = cyan). In separate axes, beneath the raw data, the ES is shown with its 95% CI across three-time points for all participants (n = 21). Bootstrap resampling (BCa, 5000 bootstrap resamples) was performed to calculate the 95% CI of the mean difference. All confidence intervals were bias-corrected and accelerated with the DABEST ‘Data Analysis using Bootstrap-Coupled ESTimation’ software library within the R statistical computing environment. † Significance post-pre change in men (p < 0.05); ‡ Significance post-pre change in women (p < 0.05).

Figure 3

Multi-paired Cumming estimation plots of analyzed variables. All raw data are plotted on the upper ax. This displays the means and their 95% CIs across each timepoint measure (baseline [t₀], after RWL [t₁] and after RWG [t₂]). The paired data are shown as lines (female = pink, male = cyan). In separate axes, beneath the raw data, the ES is shown with its 95% CI across three-time points for all participants (n = 21). Bootstrap resampling (BCa, 5000 bootstrap resamples) was performed to calculate the 95% CI of the mean difference. All confidence intervals were bias-corrected and accelerated with the DABEST ‘Data Analysis using Bootstrap-Coupled ESTimation’ software library within the R statistical computing environment. † Significance post-pre change in men (p < 0.05); ‡ Significance post-pre change in women (p < 0.05).

Figure 4

Difference-in-difference estimation plots for all variables. This graphic shows the difference of the differences, which is the calculation of the group means: (Male Δ—Female Δ). The effect chosen for examination is displayed as the triangle, with its 95% CI, against a floating different axis.

Figure 4

Difference-in-difference estimation plots for all variables. This graphic shows the difference of the differences, which is the calculation of the group means: (Male Δ—Female Δ). The effect chosen for examination is displayed as the triangle, with its 95% CI, against a floating different axis.

Figure 4

Difference-in-difference estimation plots for all variables. This graphic shows the difference of the differences, which is the calculation of the group means: (Male Δ—Female Δ). The effect chosen for examination is displayed as the triangle, with its 95% CI, against a floating different axis.