Data-Driven Dietary Patterns, Nutrient Intake and Body Weight Status in a Cross-Section of Singaporean Children Aged 6–12 Years

Pattern analysis of children’s diet may provide insights into chronic disease risk in adolescence and adulthood. This study aimed to assess dietary patterns of young Singaporean children using cluster analysis. An existing dataset included 15,820 items consumed by 561 participants (aged 6–12 years) over 2 days of dietary recall. Thirty-seven food groups were defined and expressed as a percentage contribution of total energy. Dietary patterns were identified using k-means cluster analysis. Three clusters were identified, “Western”, “Convenience” and “Local/hawker”, none of which were defined by more prudent dietary choices. The “Convenience” cluster group had the lowest total energy intake (mean 85.8 ± SD 25.3 of Average Requirement for Energy) compared to the other groups (95.4 ± 25.9 for “Western” and 93.4 ± 25.3 for “Local/hawker”, p < 0.001) but also had the lowest calcium intake (66.3 ± 34.7 of Recommended Dietary Allowance), similar to intake in the “Local/hawker” group (69.5 ± 38.9) but less than the “Western” group (82.8 ± 36.1, p < 0.001). These findings highlight the need for longitudinal analysis of dietary habit in younger Singaporeans in order to better define public health messaging targeted at reducing risk of major noncommunicable disease

Independent and combined impact of texture manipulation on oral processing behaviours among faster and slower eaters

Background: Food texture can moderate eating rate and ad libitum energy intake. Many foods are combined with condiments when consumed and the texture and eating properties differ considerably between condiments and carrier foods. Little is known about how combinations of textures impact oral processing or whether these differences are affected by individual eating-styles. Objective: We investigated the impact of texture parameters (unit size, thickness, hardness and lubrication) on oral processing behaviours for carrots and rice-crackers, and tested whether these behaviours differ between 'faster' and 'slower' eaters. Method: Seventy participants (34 males, 26.0 ± 5.4 years, BMI = 21.5 ± 1.7 kg m-2) consumed 24 weight-matched carrot samples varying in unit size (large/medium/small), thickness (thick/thin), hardness (hard/soft) and lubrication (with/without mayonnaise). In a second step, participants consumed 8 weight-matched cracker samples varying in unit size (large/small), hardness (hard/soft) and lubrication (with/without mayonnaise). Sample consumption was video-recorded for post hoc behavioural annotation to derive specific oral processing behaviours. Participants were divided into 'faster' or 'slower' eater groups using a post hoc median split based on eating rate of raw carrot. Results: Across texture parameters, hardness had the largest influence (p thickness > lubrication > unit size. For crackers, the rank order of eating rate was hardness > lubrication > unit size. Harder carrot samples with decreased unit size and reduced thickness combined had a larger synergistic effect in reducing eating rate (p < 0.001) than manipulation of any single texture parameter alone. Reducing the unit size of crackers while increasing hardness without lubrication combined (p = 0.015) to produce the largest reduction in eating rate. There were no significant differences between fast and slow eaters on their oral processing behaviours across texture manipulations. Conclusions: Combinations of texture manipulations have the largest impact in moderating oral processing behaviours, and this is consistent across 'faster' and 'slower' eaters. Changing food-texture presents an effective strategy to guide reformulation of product sensory properties to better regulate eating rate and energy intake, regardless of an individual's natural eating-style

Increased oral processing and a slower eating rate increase glycaemic, insulin and satiety responses to a mixed meal tolerance test

Purpose: Variations in specific oral processing behaviours may contribute to differences in glucose, insulin and satiety responses to a standardised test meal. This study tested how natural variations in oral processing between slower and faster eaters contribute to differences in post-prandial glucose (PP glucose), insulin response (PP insulin) and post-meal satiety for a standardised test meal. Methods: Thirty-three participants with higher risk for type 2 diabetes consumed a standardised test-meal while being video recorded to derive specific oral processing behaviours. Plasma glucose, insulin and satiety measures were collected at baseline, during and post meal. Participants were split into slower and faster eaters using median split based on their eating rates and individual bolus properties were analysed at the point of swallow. Results: There were large variations in eating rate (p 0.05), slower eaters showed significantly higher PP insulin between 45 and 60 min (p < 0.001). Slower eaters had longer oro-sensory exposure and increased bolus saliva uptake which was associated with higher PP glucose iAUC. Faster eating rate and larger bolus particle size at swallow correlated with lower PP glucose iAUC. A slower eating rate with greater chews per bite significantly increased insulin iAUC. Faster eaters also consistently rated their hunger and desire to eat higher than slower eaters (p < 0.05). Conclusions: Natural variations in eating rate and the associated oral processing contributed to differences in PP glucose, PP insulin and satiety responses. Encouraging increased chewing and longer oral-exposure time during consumption, may promote early glucose absorption and greater insulin and satiety responses, and help support euglycaemia. Trial Registration: ClinicalTrials.gov identifier: NCT04522063