Weight regain, body composition, and metabolic responses to weight loss in weight cycling athletes: A systematic review and meta-analyses.
Sarah BagotLéna PélissierBruno PereiraEmilie Chanséaume BussiereMartine DuclosAbdul G DullooJennifer Miles-ChanKeyne CharlotYves BoirieDavid ThivelLaurie IsaccoPublished in: Obesity reviews : an official journal of the International Association for the Study of Obesity (2023)
Depending on the nature of their sports, athletes may be engaged in successive weight loss (WL) and regain, conducing to "weight cycling." The aims of this paper were to systematically (and meta-analytically when possible) analyze the post-WL recovery of (i) body weight and (ii) fat mass; fat-free mass; and performance and metabolic responses in weight cycling athletes (18-55 years old, body mass index < 30 kg.m -2 ). MEDLINE, Embase, and SPORTDiscus databases were explored. The quality and risk of bias of the 74 included studies were assessed using the quality assessment tool for quantitative studies. Thirty-two studies were eligible for meta-analyses. Whatever the type of sports or methods used to lose weight, post-WL body weight does not seem affected compared with pre-WL. While similar results are observed for fat-free mass, strength sports athletes (also having longer WL and regain periods) do not seem to fully recover their initial fat mass (ES: -0.39, 95% CI: [-0.77; -0.00], p = 0.048, I 2 = 0.0%). Although the methods used by athletes to achieve WL might prevent them from a potential post-WL fat overshooting, further studies are needed to better understand WL episodes consequences on athletes' performance as well as short- and long-term physical, metabolic, and mental health.
Keyphrases
- body weight
- weight loss
- body mass index
- gastric bypass
- meta analyses
- high school
- body composition
- adipose tissue
- mental health
- physical activity
- bariatric surgery
- weight gain
- roux en y gastric bypass
- systematic review
- fatty acid
- case control
- high intensity
- randomized controlled trial
- glycemic control
- bone mineral density
- metabolic syndrome
- skeletal muscle
- high resolution
- big data
- obese patients
- machine learning
- human health
- risk assessment