Snacking characteristics and patterns and their associations with diet quality and BMI in the Childhood Obesity Prevention and Treatment Research Consortium

Objective: To describe snacking characteristics and patterns in children and examine associations with diet quality and BMI. Design: Children's weight and height were measured. Participants/adult proxies completed multiple 24 h dietary recalls. Snack occasions were self-identified. Snack patterns were derived for each sample using exploratory factor analysis. Associations of snacking characteristics and patterns with Healthy Eating Index-2010 (HEI-2010) score and BMI were examined using multivariable linear regression models. Setting: Childhood Obesity Prevention and Treatment Research (COPTR) Consortium, USA: NET-Works, GROW, GOALS and IMPACT studies. Participants: Predominantly low-income, racial/ethnic minorities: NET-Works (n 534, 2-4-year-olds); GROW (n 610, 3-5-year-olds); GOALS (n 241, 7-11-year-olds); IMPACT (n 360, 10-13-year-olds).Results: Two snack patterns were derived for three studies: a meal-like pattern and a beverage pattern. The IMPACT study had a similar meal-like pattern and a dairy/grains pattern. A positive association was observed between meal-like pattern adherence and HEI-2010 score (P for trend < 0-01) and snack occasion frequency and HEI-2010 score (β coefficient (95 % CI): NET-Works, 0-14 (0-04, 0-23); GROW, 0-12 (0-02, 0-21)) among younger children. A preference for snacking while using a screen was inversely associated with HEI-2010 score in all studies except IMPACT (β coefficient (95 % CI): NET-Works, -3-15 (-5-37, -0-92); GROW, -2-44 (-4-27, -0-61); GOALS, -5-80 (-8-74, -2-86)). Associations with BMI were almost all null. Conclusions: Meal-like and beverage patterns described most children's snack intake, although patterns for non-Hispanic Blacks or adolescents may differ. Diets of 2-5-year-olds may benefit from frequent meal-like pattern snack consumption and diets of all children may benefit from decreasing screen use during eating occasions

Perspective: Dietary Biomarkers of Intake and Exposure - Exploration with Omics Approaches

While conventional nutrition research has yielded biomarkers such as doubly labeled water for energy metabolism and 24-h urinary nitrogen for protein intake, a critical need exists for additional, equally robust biomarkers that allow for objective assessment of specific food intake and dietary exposure. Recent advances in high-throughput MS combined with improved metabolomics techniques and bioinformatic tools provide new opportunities for dietary biomarker development. In September 2018, the NIH organized a 2-d workshop to engage nutrition and omics researchers and explore the potential of multiomics approaches in nutritional biomarker research. The current Perspective summarizes key gaps and challenges identified, as well as the recommendations from the workshop that could serve as a guide for scientists interested in dietary biomarkers research. Topics addressed included study designs for biomarker development, analytical and bioinformatic considerations, and integration of dietary biomarkers with other omics techniques. Several clear needs were identified, including larger controlled feeding studies, testing a variety of foods and dietary patterns across diverse populations, improved reporting standards to support study replication, more chemical standards covering a broader range of food constituents and human metabolites, standardized approaches for biomarker validation, comprehensive and accessible food composition databases, a common ontology for dietary biomarker literature, and methodologic work on statistical procedures for intake biomarker discovery. Multidisciplinary research teams with appropriate expertise are critical to moving forward the field of dietary biomarkers and producing robust, reproducible biomarkers that can be used in public health and clinical research

Author Correction: Genome-wide association analyses identify new Brugada syndrome risk loci and highlight a new mechanism of sodium channel regulation in disease susceptibility.

In the version of this article initially published, Federico Manevy’s name appeared with a middle initial in error. The name has been corrected in the HTML and PDF versions of the article