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Dieting is theorized as a risk factor for loss-of-control (LOC)-eating (i.e., feeling a sense of lack of control while eating). Support for this association has largely relied on retrospective self-report data, which does not always correlate with objectively assessed eating behavior in youth. We hypothesized that during a laboratory-based LOC-eating paradigm, children and adolescents who reported current (at the time of the visit) dieting would consume meals consistent with LOC-eating (greater caloric intake, and intake of carbohydrates and fats, but less intake of protein). Participants were presented with a buffet-style meal and instructed to "Let yourself go and eat as much as you want." Current dieting (i.e., any deliberate change to the amount or type of food eaten to influence shape or weight, regardless of how effective the changes are) was assessed via interview. General linear models were adjusted for fat mass (%), lean mass (kg), height, sex, protocol, race and ethnicity, pre-meal hunger and minutes since consumption of a breakfast shake. Of 337 participants (M age 12.8 ± 2.7y; 62.3 % female; 45.7 % non- Hispanic White and 26.1 % non-Hispanic Black; M BMIz 0.78 ± 1.11), only 33 (9.8 %) reported current dieting. Current dieting was not significantly associated with total energy intake (F = 1.63, p = .20, η p 2  = 0.005), or intake from carbohydrates (F = 2.45, p = .12, η p 2  = 0.007), fat (F = 2.65, p = .10, η p 2  = 0.008), or protein (F = 0.39, p = .53, η p 2  = 0.001). Contrary to theories that dieting promotes LOC-eating, current dieting was not associated with youth's eating behavior in a laboratory setting. Experimental approaches for investigating dieting are needed to test theories that implicate dieting in pediatric LOC-eating., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Published by Elsevier Ltd.)

Among youth, greater heart rate (HR) and lesser HR variability (HRV) are precursors to loss-of-control (LOC) eating episodes in the natural environment. However, there are limited data examining whether pre-meal HR and HRV are associated with greater LOC-eating in the laboratory setting. We therefore examined temporal relationships between pre-meal HR, frequency- and time-based metrics of pre-meal HRV, perceived LOC-eating, and energy intake during a meal designed to simulate a LOC-eating episode. Among 209 participants (54.5% female, 12.58 ± 2.72 years, 0.52 ± 1.02 BMIz), 19 reported LOC-eating in the prior month. Perceived LOC-eating during the laboratory meal was not significantly linked to pre-meal HR ( p = 0.37), but was positively related to pre-meal HRV (ps = 0.02-0.04). This finding was driven by youth with recent LOC-eating, as these associations were not significant when analyses were run only among participants without recent reported LOC-eating ( p = 0.15-0.99). Pre-meal HR and HRV were not significantly related to total energy intake (ps = 0.27-0.81). Additional research is required to determine whether early-stage pediatric LOC-eating is preceded by a healthy pre-meal stress response. Longitudinal studies could help clarify whether this pattern becomes less functional over time among youth who develop recurrent LOC-eating episodes.

We investigated whether ultra-processed foods affect energy intake in 20 weight-stable adults, aged (mean ± SE) 31.2 ± 1.6 years and BMI = 27 ± 1.5 kg/m 2 . Subjects were admitted to the NIH Clinical Center and randomized to receive either ultra-processed or unprocessed diets for 2 weeks immediately followed by the alternate diet for 2 weeks. Meals were designed to be matched for presented calories, energy density, macronutrients, sugar, sodium, and fiber. Subjects were instructed to consume as much or as little as desired. Energy intake was greater during the ultra-processed diet (508 ± 106 kcal/day; p = 0.0001), with increased consumption of carbohydrate (280 ± 54 kcal/day; p < 0.0001) and fat (230 ± 53 kcal/day; p = 0.0004), but not protein (-2 ± 12 kcal/day; p = 0.85). Weight changes were highly correlated with energy intake (r = 0.8, p < 0.0001), with participants gaining 0.9 ± 0.3 kg (p = 0.009) during the ultra-processed diet and losing 0.9 ± 0.3 kg (p = 0.007) during the unprocessed diet. Limiting consumption of ultra-processed foods may be an effective strategy for obesity prevention and treatment., (Published by Elsevier Inc.)

Despite literature supporting the importance of diet during rehabilitation, minimal research quantifies dietary intake during treatment for alcohol use disorder (AUD). The aim was to quantify dietary intake and energy balance of patients with AUD during inpatient treatment. This was a secondary analysis of data from a 4-week observational protocol. Participants self-selected food from a room service menu. Dietary intake was recorded by patients and reviewed by nutrition staff. To quantify nutrient and food group intake, data were coded into Nutrition Data Systems for Research software, versions 2016 and 2017. Daily average intake was calculated for all dietary variables. Participants (n = 22) were adults seeking treatment for AUD at the National Institutes of Health Clinical Center (Bethesda, MD) between September 2016 and September 2017 and who were enrolled in a study examining the microbiome during AUD rehabilitation. Four participants discontinued protocol participation before study week 4 and were not included in analyses examining change over time. Weight change, daily energy, and macronutrient and select micronutrient intakes were the main outcome measures included. Mean differences in intake and weight were assessed using nonparametric tests. Sixty-four percent of participants were male; mean ± SD age was 46.3 ± 13.0 years, mean ± SD body mass index (calculated as kg/m2) was 23.9 ± 2.5, and mean intake was 2,665 kcal/d (consisting of 45.9% carbohydrate, 34.9% fat, and 19.1% protein). Eighty percent or more of this sample met the Estimated Average Requirement for 10 of 16 micronutrients assessed. Male participants consumed more energy than estimated needs (P =.003) and gained a mean ± SD of 2.67 ± 1.84 kg (P =.006) when an outlier with weight loss and acute pancreatitis was removed from analysis. Female participants did not gain weight or consume more than estimated energy needs. Overall macronutrient intake was within recommended ranges, but intake of other dietary components and weight gain were variable, supporting the need for individualized nutrition care during AUD treatment. [ABSTRACT FROM AUTHOR]

Minimal recent research explores how alcohol use disorder (AUD) affects dietary intake during periods of alcohol consumption. Therefore, we aimed to assess the feasibility and utility of a computerized food frequency questionnaire, the Diet History Questionnaire II (DHQ II), to quantify dietary intake in adults with AUD. Participants were adult men and women seeking treatment for AUD at the National Institutes of Health Clinical Center. Participants (N = 21) were enrolled in an observational study examining the microbiome in AUD. The Web-based DHQ II, reflecting dietary intake over the past year, was administered to participants within 2 weeks of admission to an inpatient alcohol rehabilitation program. Descriptive data are reported as mean ± SEM. Analyses were run in the Statistical Package for the Social Sciences, and P <.05 was considered significant. Participants (67% male) were 46.3 ± 2.8 years old with a body mass index of 23.8 ± 0.6 kg/m2. Average energy intake was 27961.7 ± 3205.8 kj, alcohol intake was 4723.0 ± 75.8 g (45% kcal), carbohydrate intake was 514.3 ± 66.8 g (31% kcal), fat intake was 101.2 ± 13.6 g (16% kcal), and protein intake was 122.8 ± 17.0 g (8% kcal). In this population of adults with AUD, DHQ II results showed energy and micronutrient intakes that were higher than, but macronutrient distribution that was similar to, previous findings in similar populations. [ABSTRACT FROM AUTHOR]