Adolescents and Young Adults with Germline CDH1 Variants and the Risk of Overtreatment.

Background: Adolescents and young adults (AYA) with germline CDH1 variants are at risk of overtreatment when precancer lesions are detected with endoscopic screening. We characterize diffuse-type gastric cancer prevalence and survival in AYA managed with prophylactic total gastrectomy (PTG) or endoscopic surveillance.

Methods: Prospective cohort study of 188 individuals aged 39 and younger enrolled from January 27, 2017, to May 1, 2023.

A Novel Method to Predict Carbohydrate and Energy Expenditure During Endurance Exercise Using Measures of Training Load.

Background: Sports nutrition guidelines recommend carbohydrate (CHO) intake be individualized to the athlete and modulated according to changes in training load. However, there are limited methods to assess CHO utilization during training sessions.

Objectives: We aimed to (1) quantify bivariate relationships between both CHO and overall energy expenditure (EE) during exercise and commonly used, non-invasive measures of training load across sessions of varying duration and intensity and (2) build and evaluate prediction models to estimate CHO utilization and EE with the same training load measures and easily quantified individual factors.

International society of sports nutrition position stand: ketogenic diets.

Position Statement: The International Society of Sports Nutrition (ISSN) provides an objective and critical review of the use of a ketogenic diet in healthy exercising adults, with a focus on exercise performance and body composition. However, this review does not address the use of exogenous ketone supplements. The following points summarize the position of the ISSN.

Predicting daily recovery during long-term endurance training using machine learning analysis.

Purpose: The aim of this study was to determine if machine learning models could predict the perceived morning recovery status (AM PRS) and daily change in heart rate variability (HRV change) of endurance athletes based on training, dietary intake, sleep, HRV, and subjective well-being measures.

Methods: Self-selected nutrition intake, exercise training, sleep habits, HRV, and subjective well-being of 43 endurance athletes ranging from professional to recreationally trained were monitored daily for 12 weeks (3572 days of tracking). Global and individualized models were constructed using machine learning techniques, with the single best algorithm chosen for each model.