Adolescents perceive a low added sugar adequate fiber diet to be more satiating and equally palatable compared to a high added sugar low fiber diet in a randomized-crossover design controlled feeding pilot trial

Charting the Chronology of Research on Added Sugars: A Scoping Review and Evidence Map

The objective of this study was to conduct a scoping review and produce a publicly available database characterizing the design and reporting elements of the literature on dietary added sugars and select health outcomes. Relevant studies published from 1990 to 2021 were identified to create a database containing information on study and population characteristics, reported added sugars source and concentrations, dietary energy balance, total energy intake, and outcome measures related to body composition, obesity, cardiovascular disease, and diabetes mellitus. There were 245 publications identified, 22% of which describe interventions, and 78% describe observational studies. Publications pertaining to added sugars have risen dramatically since 2010, led by studies primarily assessing body composition (36%) or cardiovascular health (32%), including adults (65%), measuring liquid-only sources of added sugars (56%). Over 65% of studies reported total energy intake, 61% controlled for total energy intake in the design and analysis, and fewer than 5% of studies reported the energy balance of subjects. There has been a significant increase in research on added sugars since 2010, with substantial heterogeneity across all facets of methodology-study designs, exposures and outcomes of interest, terminology, and reporting of dietary intake data-thus limiting the ability to synthesize evidence in this scope of the literature. This evidence map highlights gaps and important areas for improvement to strengthen the state of research and better inform future policies and dietary recommendations on added sugars.

Associations Between Eating Speed, Diet Quality, Adiposity, and Cardiometabolic Risk Factors

OBJECTIVE

To assess the associations between eating speed, adiposity, cardiometabolic risk factors, and diet quality in a cohort of Spanish preschool-children.

STUDY DESIGN

A cross-sectional study in 1371 preschool age children (49% girls; mean age, 4.8 ± 1.0 years) from the Childhood Obesity Risk Assessment Longitudinal Study (CORALS) cohort was conducted. After exclusions, 956 participants were included in the analyses. The eating speed was estimated by summing the total minutes used in each of the 3 main meals and then categorized into slow, moderate, or fast. Multiple linear and logistic regression models were fitted to assess the β-coefficient, or OR and 95% CI, between eating speed and body mass index, waist circumference, fat mass index (FMI), blood pressure, fasting plasma glucose, and lipid profile.

RESULTS

Compared with participants in the slow-eating category, those in the fast-eating category had a higher prevalence risk of overweight/obesity (OR, 2.9; 95% CI, 1.8-4.4; P < .01); larger waist circumference (β, 2.6 cm; 95% CI, 1.5-3.8 cm); and greater FMI (β, 0.3 kg/m²; 95% CI, 0.1-0.5 kg/m²), systolic blood pressure (β, 2.8 mmHg; 95% CI, 0.6-4.9 mmHg), and fasting plasma glucose levels (β, 2.7 mg/dL, 95% CI, 1.2-4.2 mg/dL) but lower adherence to the Mediterranean diet (β, -0.5 points; 95% CI, -0.9 to -0.1 points).

CONCLUSIONS

Eating fast is associated with higher adiposity, certain cardiometabolic risk factors, and lower adherence to a Mediterranean diet. Further long-term and interventional studies are warranted to confirm these associations.

Estimating individualized treatment regimes from crossover designs

The field of precision medicine aims to tailor treatment based on patient-specific factors in a reproducible way. To this end, estimating an optimal individualized treatment regime (ITR) that recommends treatment decisions based on patient characteristics to maximize the mean of a prespecified outcome is of particular interest. Several methods have been proposed for estimating an optimal ITR from clinical trial data in the parallel group setting where each subject is randomized to a single intervention. However, little work has been done in the area of estimating the optimal ITR from crossover study designs. Such designs naturally lend themselves to precision medicine since they allow for observing the response to multiple treatments for each patient. In this paper, we introduce a method for estimating the optimal ITR using data from a 2 × 2 crossover study with or without carryover effects. The proposed method is similar to policy search methods such as outcome weighted learning; however, we take advantage of the crossover design by using the difference in responses under each treatment as the observed reward. We establish Fisher and global consistency, present numerical experiments, and analyze data from a feeding trial to demonstrate the improved performance of the proposed method compared to standard methods for a parallel study design.