Dataset Information

Ultra-processed foods, dietary diversity and micronutrient intakes in the Australian population.

ABSTRACT:

Purpose

This cross-sectional study aimed to assess the association between ultra-processed foods consumption and dietary diversity and micronutrient intake in Australia.

Methods

As part of the Nutrition and Physical Activity Survey (2011-2012), 12,153 participants aged 2 years and above were recruited and interviewed. Dietary intake data were collected by two 24-h dietary recalls using the Automated Multiple-Pass Method. The NOVA classification system was used to group the food items based on the extent and purpose of industrial food processing. The mean micronutrient contents were calculated for the total diet, and for two diet fractions; one made up entirely of ultra-processed foods (NOVA group 4) and the other consisting of all non-ultra-processed foods (aggregation of NOVA food groups 1 to 3). The mean micronutrient content in the ultra-processed and non-ultra-processed food diet fractions were compared. Dietary diversity was measured using the ten Food Group Indicators (FGI) of the Food and Agriculture Organization and was defined as the sum number of FGIs per individual. Multiple linear regression models were used to assess the association between the quintiles of energy contribution of ultra-processed foods, dietary diversity, and micronutrient intake.

Results

A negative association was found between quintiles of energy contribution of ultra-processed foods and dietary diversity (β = - 0.43; p < 0.001). The overall micronutrient content was lower in the diet fraction dominated by ultra-processed foods compared to the non-ultra-processed food diet fraction in the study population. The dietary contents of vitamins A, E, C, B9, B12, zinc, calcium, iron, magnesium, potassium, and phosphorus were reduced significantly with increased consumption of ultra-processed foods, even after adjustment for sociodemographic factors and dietary diversity.

Conclusion

The quintiles of energy contribution of ultra-processed foods were negatively associated with dietary diversity and micronutrient intake in Australia.

SUBMITTER: Houshialsadat Z

PROVIDER: S-EPMC10798929 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Publications

Ultra-processed foods, dietary diversity and micronutrient intakes in the Australian population.

Houshialsadat Zeinab Z Cediel Gustavo G Sattamini Isabela I Scrinis Gyorgy G Machado Priscila P

European journal of nutrition 20231005 1

<h4>Purpose</h4>This cross-sectional study aimed to assess the association between ultra-processed foods consumption and dietary diversity and micronutrient intake in Australia.<h4>Methods</h4>As part of the Nutrition and Physical Activity Survey (2011-2012), 12,153 participants aged 2 years and above were recruited and interviewed. Dietary intake data were collected by two 24-h dietary recalls using the Automated Multiple-Pass Method. The NOVA classification system was used to group the food it ...[more]

PMID: 37798558

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Project description:BackgroundRapid simultaneous increases in ultra-processed food sales and obesity prevalence have been observed worldwide, including in Australia. Consumption of ultra-processed foods by the Australian population was previously shown to be systematically associated with increased risk of intakes of nutrients outside levels recommended for the prevention of obesity. This study aims to explore the association between ultra-processed food consumption and obesity among the Australian adult population and stratifying by age group, sex and physical activity level.MethodsA cross-sectional analysis of anthropometric and dietary data from 7411 Australians aged ≥20 years from the National Nutrition and Physical Activity Survey 2011-2012 was performed. Food consumption was evaluated through 24-h recall. The NOVA system was used to identify ultra-processed foods, i.e. industrial formulations manufactured from substances derived from foods and typically added of flavours, colours and other cosmetic additives, such as soft drinks, confectionery, sweet or savoury packaged snacks, microwaveable frozen meals and fast food dishes. Measured weight, height and waist circumference (WC) data were used to calculate the body mass index (BMI) and diagnosis of obesity and abdominal obesity. Regression models were used to evaluate the association of dietary share of ultra-processed foods (quintiles) and obesity indicators, adjusting for socio-demographic variables, physical activity and smoking.ResultsSignificant (P-trend ≤ 0.001) direct dose-response associations between the dietary share of ultra-processed foods and indicators of obesity were found after adjustment. In the multivariable regression analysis, those in the highest quintile of ultra-processed food consumption had significantly higher BMI (0.97 kg/m2; 95% CI 0.42, 1.51) and WC (1.92 cm; 95% CI 0.57, 3.27) and higher odds of having obesity (OR = 1.61; 95% CI 1.27, 2.04) and abdominal obesity (OR = 1.38; 95% CI 1.10, 1.72) compared with those in the lowest quintile of consumption. Subgroup analyses showed that the trend towards positive associations for all obesity indicators remained in all age groups, sex and physical activity level.ConclusionThe findings add to the growing evidence that ultra-processed food consumption is associated with obesity and support the potential role of ultra-processed foods in contributing to obesity in Australia.

Project description:Due to its rising prevalence, which parallels that of ultraprocessed food (UPF) consumption, inadequate micronutrient intake in childhood is a public health concern. This study aimed to evaluate the association between UPF consumption and inadequate intake of 20 micronutrients in a sample of children from the Mediterranean area. Cross-sectional information from participants in the "Seguimiento del Niño para un Desarrollo Óptimo" (SENDO) project 2015-2021 was used. Dietary information was gathered with a previously validated 147-item semi-quantitative food frequency questionnaire and the NOVA system was used to classify food items. Children were classified by tertiles of energy intake from UPF. Twenty micronutrients were evaluated, and inadequate intake was defined using the estimated average requirement as a cutoff. Crude and multivariable adjusted OR (95% CI) for the inadequacy of ≥ 3 micronutrients associated with UPF consumption were calculated fitting hierarchical models to take into account intra-cluster correlation between siblings. Analyses were adjusted for individual and family confounders. This study included 806 participants (51% boys) with a mean age of 5 years old (SD: 0.90) and an average energy intake from UPF of 37.64% (SD: 9.59). An inverse association between UPF consumption and the intake of 15 out of the 20 micronutrients evaluated was found (p < 0.01). After the adjustment for individual and family confounders, compared with children in the first tertile of UPF consumption, those in the third tertile showed higher odds of inadequate intake of ≥ 3 micronutrients (OR 2.57; 95%CI [1.51-4.40]). Conclusion: High UPF consumption is associated with increased odds of inadequate intake of micronutrients in childhood. What is Known: • Micronutrient deficiency is among the 20 most important risk factors for disease and affect around two billion people worldwide. • UPF are rich in total fat, carbohydrates and added sugar, but poor in vitamins and minerals. What is New: • Compared with children in the 1st tertile of UPF consumption, those in the 3rd tertile had 2.57 times higher odds (95% CI: 1.51-4.40) of inadequate intake of ≥ 3 micronutrients after adjusting for potential confounders. • The adjusted proportions of children with inadequate intake of ≥ 3 micronutrients were 23%, 27% and 35% in the 1st, 2nd, and 3rd tertiles of UPF consumption respectively.

Dataset Information

Ultra-processed foods, dietary diversity and micronutrient intakes in the Australian population.

Purpose

Methods

Results

Conclusion

Publications

Ultra-processed foods, dietary diversity and micronutrient intakes in the Australian population.

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