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Background: The Environmental Determinants of Islet Autoimmunity (ENDIA) pregnancy-birth cohort investigates the developmental origins of type 1 diabetes (T1D), with recruitment between 2013 and 2019. ENDIA is the first study in the world with comprehensive data and biospecimen collection during pregnancy, at birth and through childhood from at-risk children who have a first-degree relative with T1D. Environmental exposures are thought to drive the progression to clinical T1D, with pancreatic islet autoimmunity (IA) developing in genetically susceptible individuals. The exposures and key molecular mechanisms driving this progression are unknown. Persistent IA is the primary outcome of ENDIA; defined as a positive antibody for at least one of IAA, GAD, ZnT8 or IA2 on two consecutive occasions and signifies high risk of clinical T1D. Method: A nested case-control (NCC) study design with 54 cases and 161 matched controls aims to investigate associations between persistent IA and longitudinal omics exposures in ENDIA. The NCC study will analyse samples obtained from ENDIA children who have either developed persistent IA or progressed to clinical T1D (cases) and matched control children at risk of developing persistent IA. Control children were matched on sex and age, with all four autoantibodies absent within a defined window of the case's onset date. Cases seroconverted at a median of 1.37 years (IQR 0.95, 2.56). Longitudinal omics data generated from approximately 16,000 samples of different biospecimen types, will enable evaluation of changes from pregnancy through childhood. Conclusions: This paper describes the ENDIA NCC study, omics platform design considerations and planned univariate and multivariate analyses for its longitudinal data. Methodologies for multivariate omics analysis with longitudinal data are discovery-focused and data driven. There is currently no single multivariate method tailored specifically for the longitudinal omics data that the ENDIA NCC study will generate and therefore omics analysis results will require either cross validation or independent validation. The ENDIA nested case-control study will utilize longitudinal omics data on approximately 16,000 samples from 190 unique children at risk of type 1 diabetes (T1D), including 54 who have developed islet autoimmunity (IA), followed during pregnancy, at birth and during early childhood, enabling the developmental origins of T1D to be explored. [ABSTRACT FROM AUTHOR]

Some athletes avoid dairy in the meal consumed before exercise due to fears about gastrointestinal discomfort. Regular exclusion of dairy foods may unnecessarily reduce intake of high quality proteins and calcium with possible implications for body composition and bone health. This study compared the effects of meals that included (Dairy) or excluded (Control) dairy foods on gastric comfort and subsequent cycling performance. Well-trained female cyclists (n = 32; mean ± SD; 24.3 ± 4.1 y; VO2peak 57.1 ± 4.9 ml/kg/min) completed two trials (randomized cross-over design) in which they consumed a meal (2 g/kg carbohydrate and 54 kJ/kg) 2 hr before a 90-min cycle session (80 min at 60% maximal aerobic power followed by a 10-min time trial; TT). The dairy meal contained 3 servings of dairy foods providing ~1350 mg calcium. Gut comfort and palatability were measured using questionnaires. Performance was measured as maximum mean power during the TT (MMP10min). There was no statistical or clinical evidence of an effect of meal type on MMP10min with a mean difference (Dairy – Control) of 4 W (95% CI [–2, 9]). There was no evidence of an association between pretrial gut comfort and meal type (p = .15) or between gut comfort delta scores and meal type postmeal (p = .31), preexercise (p = .17) or postexercise (p = .80). There was no statistical or clinical evidence of a difference in palatability between meal types. In summary, substantial amounts of dairy foods can be included in meals consumed before strenuous cycling without impairing either gut comfort or performance. [ABSTRACT FROM AUTHOR]

The Athlete Biological Passport (ABP) estimates individualized reference ranges for key blood markers, such as haemoglobin concentration ([Hb]), using predetermined population mean, between- and within-subject variances. Here, we aim to reassess previously published estimates for within-subject [Hb] variance and determine whether sex-, analyzer-, sport-, or season-specific values are required. Our reference population contains 7723 male (mean ± SD, 22.3 ± 4.6 years of age) and 6164 female (21.6 ± 4.3) athlete observations from 49 sports. [Hb] was calculated using one of three cytometers; Bayer-H3 (1997-1999, n = 4554), ADVIA-120 (1999-2010, n = 8636) and Sysmex XT-2000i (2010-2012, n = 697). The final model was a linear mixed model for [ Hb] with analyzer (H3, ADVIA, Sysmex), sex (male, female), sport (power-endurance, endurance, skill, team, disabled and non-athletes), season (summer, winter), and the interaction between sex and sport as fixed effects and athlete as a random effect. The model included an exponential correlation structure to allow for within-subject autocorrelation, and allowed different within-subject variances for each sport. Within-subject [Hb] variance (g2/L2) was significantly less for power endurance (35.09, 95% CI 33.50 to 36.76), disabled (25.82, 95% CI 21.71 to 35.28) and non-athletes (34.30, 95% CI 28.53 to 35.87) than for endurance (40.35, 95% CI 39.62 to 47.22) and team sports (38.70, 95% CI 37.68 to 39.76) athletes. No new evidence was found to justify adjusting the current within-subject [Hb] variance estimate. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]