BACKGROUND AND AIMS: It is not clear whether alterations in the intestinal microbiota of children with celiac disease (CD) cause the disease or are a result of disease and/or its treatment with a gluten-free diet (GFD). METHODS: We obtained 167 fecal samples from 141 children (20 with newonset CD, 45 treated with a GFD, 57 healthy children, and 19 unaffected siblings of children with CD) in Glasgow, Scotland. Samples were analyzed by 16S ribosomal RNA sequencing, and diet-related metabolites were measured by gas chromatography. We obtained fecal samples from 13 children with new-onset CD after 6 and 12 months on a GFD. Relationships between microbiota with diet composition, gastrointestinal function, and biomarkers of GFD compliance were explored. RESULTS: Microbiota a diversity did not differ among groups. Microbial dysbiosis was not observed in children with new-onset CD. In contrast, 2.8% (Bray-Curtis

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Estimation of RMR using prediction equations is the basis for calculating energy requirements. In the present study, RMR was predicted by Harris–Benedict, Schofield, Henry, Mifflin–St Jeor and Owen equations and measured by indirect calorimetry in 125 healthy adult women of varying BMI (17–44 kg/m2). Agreement between methods was assessed by Bland–Altman analyses and each equation was assessed for accuracy by calculating the percentage of individuals predicted within ± 10 % of measured RMR. Slopes and intercepts of bias as a function of average RMR (mean of predicted and measured RMR) were calculated by regression analyses. Predictors of equation bias were investigated using univariate and multivariate linear regression. At group level, bias (the difference between predicted and measured RMR) was not different from zero only for Mifflin–St Jeor (0 (sd 153) kcal/d (0 (sd 640) kJ/d)) and Henry (8 (sd 163) kcal/d (33 (sd 682) kJ/d)) equations. Mifflin–St Jeor and Henry equations were most accurate at the individual level and predicted RMR within 10 % of measured RMR in 71 and 66 % of participants, respectively. For all equations, limits of agreement were wide, slopes of bias were negative, and intercepts of bias were positive and significantly (P < 0⋅05) different from zero. Increasing age, height and BMI were associated with underestimation of RMR, but collectively these variables explained only 15 % of the variance in estimation bias. Overall accuracy of equations for prediction of RMR is low at the individual level, particularly in women with low and high RMR. The Mifflin–St Jeor equation was the most accurate for this dataset, but prediction errors were still observed in about one-third of participants.