Texture-based differences in eating rate influence energy intake for minimally processed and ultra-processed meals

Pey Sze Teo, Amanda JiaYing Lim, Ai Ting Goh, Janani R, Jie Ying Michelle Choy, Keri McCrickerd, Ciarán G Forde, Pey Sze Teo, Amanda JiaYing Lim, Ai Ting Goh, Janani R, Jie Ying Michelle Choy, Keri McCrickerd, Ciarán G Forde

Abstract

Background: Consumption of ultra-processed foods has been linked with higher energy intakes. Food texture is known to influence eating rate (ER) and energy intake to satiation, yet it remains unclear whether food texture influences energy intakes from minimally processed and ultra-processed meals.

Objectives: We examined the independent and combined effects of food texture and degree of processing on ad libitum food intake. We also investigated whether differences in energy intake during lunch influenced postmeal feelings of satiety and later food intake.

Methods: In this crossover study, 50 healthy-weight participants [n = 50 (24 men); mean ± SD age: 24.4 ± 3.1 y; BMI: 21.3 ± 1.9 kg/m2] consumed 4 ad libitum lunch meals consisting of "soft minimally processed," "hard minimally processed," "soft ultra-processed," and "hard ultra-processed" components. Meals were matched for total energy served, with some variation in meal energy density (±0.20 kcal/g). Ad libitum food intake (kcal and g) was measured and ER derived using behavioral coding of videos. Subsequent food intake was self-reported by food diary.

Results: There was a main effect of food texture on intake, whereby "hard minimally processed" and "hard ultra-processed" meals were consumed slower overall, produced a 21% and 26% reduction in food weight (g) and energy (kcal) consumed, respectively. Intakes were higher for "soft ultra-processed" and "soft minimally processed" meals (P < 0.001), after correcting for meal pleasantness. The effect of texture on food weight consumed was not influenced by processing levels (weight of food: texture*processing-effect, P = 0.376), but the effect of food texture on energy intake was (energy consumed: texture*processing-effect, P = 0.015). The least energy was consumed from the "hard minimally processed" meal (482.9 kcal; 95% CI: 431.9, 531.0 kcal) and the most from the "soft ultra-processed" meal (789.4 kcal; 95% CI: 725.9, 852.8 kcal; Δ=↓∼300 kcal). Energy intake was lowest when harder texture was combined with the "minimally processed" meals. Total energy intake across the day varied directly with energy intakes of the test meals (Δ15%, P < 0.001).

Conclusions: Findings suggest that food texture-based differences in ER and meal energy density contribute to observed differences in energy intake between minimally processed and ultra-processed meals.This trial was registered at clinicaltrials.gov as NCT04589221.

Keywords: NOVA food processing; ad libitum energy intake; eating rate; energy density; texture.

© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society for Nutrition.

Figures

FIGURE 1
FIGURE 1
ER (A) and EIR (B) of the 4 test meals. Values are mean (95% CI), n = 50 (24 men), repeated-measures linear mixed models. Within each graph, bars without a common letter differ (Bonferroni post hoc test, P < 0.001; ER: texture*processing-effect, P = 0.059; EIR: texture*processing-effect, P = 0.954). EIR, energy intake rate; ER, eating rate.
FIGURE 2
FIGURE 2
Intake of meals as weight (A) and energy (B) consumed across the 4 test meals, controlling for rated meal pleasantness. Values are mean (95% CI), n = 50 (24 men), repeated-measures linear mixed models. Within each graph, bars without a common letter differ (P < 0.05), with Bonferroni adjustment (weight of foods consumed: texture*processing-effect, P = 0.376; energy consumed: texture*processing-effect, P = 0.015).
FIGURE 3
FIGURE 3
Intake of meal components as weight (A) and energy (B) consumed across the 24 meal components in the 4 test meals, controlling for their respective rated meal component pleasantness. Values are mean (95% CI), n = 50 (24 men), repeated-measures linear mixed models (carbohydrate: weight of foods consumed: texture*processing-effect, P = 0.031; energy consumed: texture*processing-effect, P = 0.985; vegetable: weight of foods consumed: texture*processing-effect, P < 0.001; energy consumed: texture*processing-effect, P = 0.289; protein: weight of foods consumed: texture*processing-effect, P = 0.350; energy consumed: texture*processing-effect, P = 0.994; fruit: weight of foods consumed: texture*processing-effect, P = 0.414; energy consumed: texture*processing-effect, P < 0.001; dairy: weight of foods consumed: texture*processing-effect, P = 0.508; energy consumed: texture*processing-effect, P = 0.053; sauce: weight of foods consumed: texture*processing-effect, P = 0.053; energy consumed: texture*processing-effect, P = 0.228). Within each component of each graph, bars without a common letter differ (P < 0.05), with Bonferroni adjustment.
FIGURE 4
FIGURE 4
Total energy intake across each test day, controlling for rated meal pleasantness. Values are mean (95% CI), n = 50 (24 men), repeated-measures linear mixed models. Bars without a common letter differ (P < 0.05), with Bonferroni adjustment (texture*processing-effect, P = 0.625).
FIGURE 5
FIGURE 5
Changes in rated hunger (A), fullness (B), desire to eat (C), and prospective intake (D) from before lunch to after lunch and during subsequent 15-min intervals for 90 min postlunch, controlling for the baseline ratings and participants’ sex. The 4 test meals varied in texture and processing levels. Values are mean (95% CI), n = 50 (24 men), repeated-measures linear mixed models (hunger: texture*processing*time-effect, P = 0.65; fullness: texture*processing*time-effect, P = 0.17; desire to eat: texture*processing*time-effect, P = 0.025; prospective intake: texture*processing*time-effect, P = 0.58). *Significant difference between test meals at these time points, P < 0.05.

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Source: PubMed

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