Associations Between Gestational Weight Gain, Gestational Diabetes, and Childhood Obesity Incidence

INTRODUCTION

Excessive maternal gestational weight gain (GWG) is strongly correlated with childhood obesity, yet how excess maternal weight gain and gestational diabetes mellitus (GDM) interact to affect early childhood obesity is poorly understood. The purpose of this study was to investigate whether overall and trimester-specific maternal GWG and GDM were associated with obesity in offspring by age 6 years.

METHODS

A cohort of 10,335 maternal-child dyads was established from electronic health records. Maternal weights at conception and delivery were estimated from weight trajectory fits using functional principal components analysis. Kaplan-Meier curves and Cox regression, together with generalized raking, examined time-to-childhood-obesity.

RESULTS

Obesity diagnosed prior to age 6 years was estimated at 19.7% (95% CI: 18.3, 21.1). Maternal weight gain during pregnancy was a strong predictor of early childhood obesity (p < 0.0001). The occurrence of early childhood obesity was lower among mothers with GDM compared with those without diabetes (adjusted hazard ratio = 0.58, p = 0.014). There was no interaction between maternal weight gain and GDM (p = 0.55). Higher weight gain during the first trimester was associated with lower risk of early childhood obesity (p = 0.0002) whereas higher weight gain during the second and third trimesters was associated with higher risk (p < 0.0001).

DISCUSSION

Results indicated total and trimester-specific maternal weight gain was a strong predictor of early childhood obesity, though obesity risk by age 6 was lower for children of mothers with GDM. Additional research is needed to elucidate underlying mechanisms directly related to trimester-specific weight gain and GDM that impede or protect against obesity prevalence during early childhood.

Ultra-processed food consumption and BMI-Z among children at risk for obesity from low-income households

OBJECTIVE

To evaluate the association between baseline ultra-processed food consumption in early childhood and child BMI Z-score over 36 months.

METHODS

We conducted a prospective cohort analysis as a secondary data analysis of the Growing Right Onto Wellness randomised trial. Dietary intake was measured via 24-h diet recalls. The primary outcome was child BMI-Z, measured at baseline and at 3-, 9-, 12-, 24- and 36-month timepoints. Child BMI-Z was modelled using a longitudinal mixed-effects model, adjusting for covariates and stratifying by age.

RESULTS

Among 595 children, median (Q1-Q3) baseline age was 4.3 (3.6-5.0) years, 52.3% of the children were female, 65.4% had normal weight, 33.8% were overweight, 0.8% were obese and 91.3% of parents identified as Hispanic. Model-based estimates suggest that, compared with low ultra-processed consumption (300 kcals/day), high ultra-processed intake (1300 kcals/day) was associated with a 1.2 higher BMI-Z at 36 months for 3-year-olds (95% CI = 0.5, 1.9; p < 0.001) and a 0.6 higher BMI-Z for 4-year-olds (95% CI = 0.2, 1.0; p = 0.007). The difference was not statistically significant for 5-year-olds or overall.

CONCLUSIONS

In 3- and 4-year-old children, but not in 5-year-old children, high ultra-processed food intake at baseline was significantly associated with higher BMI-Z at 36-month follow-up, adjusting for total daily kcals. This suggests that it might not be only the total number of calories in a child's daily intake that influences child weight status, but also the number of calories from ultra-processed foods.

Reliability and validity of assigning ultraprocessed food categories to 24-h dietary recall data

BACKGROUND

The Nova classification system categorizes foods into 4 processing levels, including ultraprocessed foods (UPFs). Consumption of UPFs is extensive in the United States, and high UPF consumption is associated with chronic disease risk. A reliable and valid method to Nova-categorize foods would advance understanding of UPF consumption and its relationship to health outcomes.

OBJECTIVES

Test the reliability and validity of training coders and assigning Nova categories to individual foods collected via 24-h dietary recalls.

DESIGN

A secondary analysis of 24-h dietary recalls from 610 children who participated in a randomized controlled trial and were 3-5 y old at baseline was conducted. The Nutrition Data System for Research (NDSR) software was used to collect 2-3 dietary recalls at baseline and yearly for 3 y. Trained and certified coder pairs independently categorized foods into one of 4 Nova categories (minimally processed, processed culinary ingredients, processed, and ultraprocessed). Interrater reliability was assessed by percent concordance between coder pairs and by Cohen's κ coefficient. Construct validity was evaluated by comparing the average daily macronutrient content of foods between Nova categories.

RESULTS

In 5546 valid recall days, 3099 unique foods were categorized: minimally processed (18%), processed culinary ingredients (0.4%), processed (15%), and ultraprocessed (67%). Coder concordance = 88.3%, and κ coefficient = 0.75. Descriptive comparisons of macronutrient content across 66,531 diet recall food entries were consistent with expectations. On average, UPFs were 62% (SD 19) of daily calories, and a disproportionally high percentage of daily added sugar (94%; SD 16) and low percentage of daily protein (47%; SD 24). Minimally processed foods were 30% (SD 17) of daily calories, and a disproportionally low percentage of daily added sugar (1%; SD 8) and high percentage of daily protein (43%; SD 24).

CONCLUSIONS

This method of Nova classifying NDSR-based 24-h dietary recalls was reliable and valid for identifying individual intake of processed foods, including UPFs.