Australian National Nutrition Research Articles

Association between protein intake, diet quality, and obesity in Australian adults: A comparison of measurement units

The role of protein in decreasing the risk of cardiometabolic diseases has been proposed, yet the findings are inconsistent, possibly due to how protein intake was expressed(1). This study aimed to examine how different ways of expressing protein intake may influence its relationships with diet quality and obesity. This study used data from the Australian National Nutrition and Physical Activity Survey (NNPAS) 2011-12, focusing on adults aged ≥19 years, excluding those who were pregnant or lactating, and had both anthropometric and dietary data (n = 7637). Total protein intake was assessed by up to two 24-hour dietary recalls and reported in two measurement units, namely g/day and % of energy intake. Usual protein (g) and energy intakes (kJ) were modelled using the Multiple Source Method(2). Diet quality was assessed using the Dietary Guidelines Index (DGI)(3). Body mass index (BMI) and waist circumference were used as measures of obesity. Multiple linear regressions were performed stratified by sex, and adjusting for age, country of birth, Socio-economic Indexes for Areas, physical activity level, energy misreporting, usual energy intake (for diet quality), and non-protein energy intake (for obesity). Positive associations between protein intake and diet quality were confirmed across measurement units. Protein intake expressed in % of energy intake (β-coefficient: men = 0.97; women = 1.21, p<0.001) had a higher β-coefficient than those reported in g/day (β-coefficient: men = 0.19; women = 0.30, p<0.001). Linear regression models showed a positive association between protein intake and BMI for men and women, either expressed in g/day (β-coefficient: men = 0.02, p<0.001; women = 0.03, p = 0.001) or % of energy intake (β-coefficient: men = 0.14, p<0.001; women = 0.12, p = 0.002). The relationship between protein intake expressed in g/day and waist circumference was also statistically significant (β-coefficient: men = 0.04, p = 0.004; women = 0.05, p = 0.035). However, protein intake expressed as % of energy intake was correlated with waist circumference for men only (β-coefficient: men = 0.26, p = 0.001; women = 0.19, p = 0.075). The consistent findings across measurement units suggested that the unit of g/day and % of energy intake can be used for examining associations between protein intake and diet quality. However, associations between protein intake and measures of obesity varied between protein units and sex. These findings suggest that the selection of protein measurement units in relation to obesity outcomes might need to consider the characteristics of study population (e.g., sex, energy intake). Overall, the outcomes of this study suggest that how protein is expressed may impact the associations between protein intake, diet quality, and obesity, and therefore require further considerations when examining the role of protein in cardiometabolic health.

Happy Little Vegemites™! An analysis of the contribution of yeast extract spreads and tomato-based sauces to nutrient intake adequacy in Australia.

Yeast extract spreads and tomato-based sauces (i.e., ketchup) are consumed regularly by the Australian population. Therefore, there is a need to explore the contribution of these condiments to nutrient intakes among Australians. The present study comprises a secondary analysis of data from the 2011-2012 Australian National Nutrition and Physical Activity Survey. Dietary intake data were undertaken for 12,153 Australians aged ≥ 2 years, using 24-h recalls. Yeast extract spreads and tomato-based sauces were categorised based on how they were defined in the Australian Food and Nutrient (AUSNUT) 2011-2013 database. Kruskal-Wallis H tests and the post-hoc Dunn's test with Bonferroni correction were applied to test whether a significant difference existed in the percentage contribution of yeast extract spreads and tomato-based sauces to intakes of select nutrients. In total, 19.6% (n = 2384) of the population sample consumed yeast extract spreads and/or tomato-based sauces during the 24-h recall. The percentage contribution of yeast extract spreads to daily intakes of sodium, potassium, thiamine, riboflavin, niacin, folate, magnesium, iron, zinc and iodine were significantly higher in line with a greater quantity of yeast extract spread consumed (p < 0.05). The percentage contribution of tomato-based sauces to daily intakes of sodium, potassium, riboflavin, niacin, folate, beta-carotene, magnesium, iron, zinc and iodine was increased significantly with a greater quantity of tomato-based sauces consumed (p < 0.05). Consumption of yeast extracts and tomato-based sauces contribute to greater intake of key nutrients, such as B-vitamins and beta-carotene, and may assist in meeting key nutrient reference values. However, consumption of these sauces and condiments also resulted in greater intakes of sodium, contributing to population intakes exceeding recommendations. Reducing sodium content of frequently consumed condiments may potentially assist in lowering population intakes, at the same time as preserving intakes of other important nutrients.

Using Popular Foods Consumed to Inform Development of Digital Tools for Dietary Assessment and Monitoring.

Knowing the type and quality of the most popular foods consumed by a population can be useful in the design of technologies for monitoring food intake and interventions. The aim of this research was to determine the most frequently consumed foods and beverages among the Australian population and provide recommendations for progressing the design of dietary assessment technologies. Analysis of the first 24 h recall of the most recent Australian National Nutrition and Physical Activity Survey was conducted. The most popular foods and beverages consumed by energy (kJ) and by frequency were calculated. There were 4515 separate foods and beverages reported by 12,153 people. Overall, the top 10 foods that contributed most energy included full fat milk, beer, white rice, white bread, red wine, cola soft drinks, bananas, red apples, wholewheat breakfast cereal and white sugar. The five most frequently reported foods and beverages were tap water, black tea, full fat milk, instant coffee, and sugar. Understanding the most popular foods and beverages consumed can support innovations in the design of digital tools for dietary surveillance and to reduce under-reporting and food omissions. These findings could also guide the development of more tailored and relevant food databases that underpin these technologies.

Macronutrient (im)balance drives energy intake in an obesogenic food environment: An ecological analysis.

The protein leverage hypothesis (PLH) postulates that strong regulation of protein intake drives energy overconsumption and obesity when human diets are diluted by fat and carbohydrates. The two predictions of the PLH are that humans (i) regulate intake to maintain protein within a narrow range and that (ii) energy intake is an inverse function of percentage energy from protein because absolute protein intake is maintained within narrow limits. Multidimensional nutritional geometry was used to test the predictions of the PLH using dietary data from the Australian National Nutrition and Physical Activity Survey. Both predictions of the PLH were confirmed in a population setting: the mean protein intake was 18.4%, and energy intake decreased with increasing energy from protein (L=-0.18, p &lt; 0.0001). It was demonstrated that highly processed discretionary foods are a significant diluent of protein and associated with increased energy but not increased protein intake. These results support an integrated ecological and mechanistic explanation for obesity, in which low-protein highly processed foods lead to higher energy intake because of the biological response to macronutrient imbalance driven by a dominant appetite for protein. This study supports a central role for protein in the obesity epidemic, with significant implications for global health.

Do sauces, condiments and seasonings contribute important amounts of nutrients to Australian dietary intakes?

Dietary assessment commonly focuses on particular foods/food groups as indicators of overall dietary intake. Accompaniments such as sauces are not often a focus. The present study describes daily intakes of sauces, condiments and seasonings (SCS) using the most recent Australian National Nutrition and Physical Activity Survey (NNPAS), as well as the contribution to total energy and selected nutrient intakes. NNPAS dietary data were collected by one 24-h recall for 12,153 individuals aged ≥ 2 years (53% female, 29% aged 31-50 years). SCS (i.e., any food items not normally consumed as a food itself, consumed as an addition to a dish after cooking/preparation to enhance flavour) were identified/coded within the dietary data and reported in terms of how they were consumed, primary composition, and contribution to total daily energy and selected macro- and micronutrient intakes. Most participants (85.1%) reported consuming at least one SCS on the day of the recall (median [interquartile range], 2 [1-4]). SCS were predominantly consumed within main meals (breakfast, lunch, dinner) (73.9%), and were predominantly sugar/sugar products (e.g., white sugar) (35.0%), or fats and oils (e.g., butter) (25.9%). SCS contributed a median (interquartile range) of 3.8% (1.1-7.9) of total energy, 5.3% (0.0-15.5) of fat, 2.3% (0.1-6.6) of carbohydrate and 0.2% (0.01-1.2) of protein intake. SCS made the largest contribution towards vitamin E (females median 3.6%; males median 3.4%) and sodium intakes (females median 3.0%; males median 2.9%). Although SCS contribute a small proportion of total energy and nutrient intakes in the Australian population, the contribution is more substantial for some nutrients and population groups.

A Comparison of the Australian Dietary Guidelines to the NOVA Classification System in Classifying Foods to Predict Energy Intakes and Body Mass Index.

NOVA classification distinguishes foods by level of processing, with evidence suggesting that a high intake of ultra-processed foods (UPFs, NOVA category 4) leads to obesity. The Australian Dietary Guidelines, in contrast, discourage excess consumption of “discretionary foods” (DFs), defined according to their composition. Here, we (i) compare the classification of Australian foods under the two systems, (ii) evaluate their performance in predicting energy intakes and body mass index (BMI) in free-living Australians, and (iii) relate these outcomes to the protein leverage hypothesis of obesity. Secondary analysis of the Australian National Nutrition and Physical Activity Survey was conducted. Non-protein energy intake increased by 2.1 MJ (p < 0.001) between lowest and highest tertiles of DF intake, which was significantly higher than UPF (0.6 MJ, p < 0.001). This demonstrates that, for Australia, the DF classification better distinguishes foods associated with high energy intakes than does the NOVA system. BMI was positively associated with both DFs (−1. 0, p = 0.0001) and UPFs (−1.1, p = 0.0001) consumption, with no difference in strength of association. For both classifications, macronutrient and energy intakes conformed closely to the predictions of protein leverage. We account for the similarities and differences in performance of the two systems in an analysis of Australian foods.

The Profiling of Diet and Physical Activity in Reproductive Age Women and Their Association with Body Mass Index.

Pre-pregnancy, pregnancy and postpartum are critical life stages associated with higher weight gain and obesity risk. Among these women, the sociodemographic groups at highest risk for suboptimal lifestyle behaviours and core lifestyle components associated with excess adiposity are unclear. This study sought to identify subgroups of women meeting diet/physical activity (PA) recommendations in relation to sociodemographics and assess diet/PA components associated with body mass index (BMI) across these life stages. Cross-sectional data (Australian National Nutrition and Physical Activity Survey 2011–2012) were analysed for pre-pregnancy, pregnant and postpartum women. The majority (63–95%) of women did not meet dietary or PA recommendations at all life stages. Core and discretionary food intake differed by sociodemographic factors. In pre-pregnant women, BMI was inversely associated with higher whole grain intake (β = −1.58, 95% CI −2.96, −0.21; p = 0.025) and energy from alcohol (β = −0.08, −0.14, −0.005; p = 0.035). In postpartum women, BMI was inversely associated with increased fibre (β = −0.06, 95% CI −0.11, −0.004; p = 0.034) and PA (β = −0.002, 95% CI −0.004, −0.001; p = 0.013). This highlights the need for targeting whole grains, fibre and PA to prevent obesity across life stages, addressing those most socioeconomically disadvantaged.

Sensory swap: Modelling the impact of swapping discretionary choices for similar tasting core foods on the energy, nutrients and sensory properties of Australian diets

Food choice is strongly driven by the sensory characteristics of foods with sweet, salty and fatty mouthfeel considered highly palatable and rewarding. Attempts to improve diet quality have not addressed sensory characteristics of diets before. This report describes a data modelling exercise that could underpin a dietary strategy to help support consumption of higher quality diets without compromising sensory preferences. This study used the Australian National Nutrition and Physical Activity Survey data (in 9341 adults) and the CSIRO sensory-diet database. A method was developed to find core food swaps which had a similar sensory profile as discretionary foods. This study investigated the impact of such swaps on energy and nutrient intake and the impact of the swaps on servings of food groups. The modelling resulted in a similar sensory profile of core foods to that of discretionary foods with hardness, sweetness and fatty mouthfeel all within 1–3% but the saltiness approached a 4% change. There was a small (3.6%) increase in energy intake. This swap strategy decreased the intake of risk nutrients such as saturated fat and added sugars, but not sodium, while increasing the intake of beneficial nutrients like calcium, zinc and vitamin C. Results also show that there was an increase in the intake of servings of core food groups such as fruits, grains, and dairy products but little change in vegetables. In conclusion, similar sensory swaps are possible and could underpin a diet strategy, that could be further refined through food appropriateness, to improve quality.

Characterizing children’s eating patterns: does the choice of eating occasion definition matter?

BackgroundRecommendations to define eating occasions (EO) currently exist for research in adults, but not for children or adolescents. We examined how varying EO definitions affect the characterization of eating patterns in children and adolescents.MethodsCross-sectional dietary data collected using a 24-h recall data during the 2011–12 Australian National Nutrition and Physical Activity Survey (1364 boys and 1337 girls aged 2–18 years) were analyzed. Eight definitions were applied: participant-identified, time-of-day, and 6 neutral definitions (EO separated by 15- or 60-min and/or an additional energy criterion of 21 or 210 kJ). Frequency of and total energy intake from meals, snacks, and all EO were estimated. F tests stratified by gender and age-group, were used to assess differences between definitions. Agreement between definitions of meal and snack frequencies was assessed using intraclass correlation coefficients (ICC). Linear regression was used to estimate the proportion of variance in total energy intake (kJ) and BMI z-score predicted by each definition.ResultsMean frequencies of meals and snacks differed between the participant-identified and time-of-day definitions, in boys and girls and for all age groups (P < 0.01). Across the six neutral definitions, there were differences between mean frequencies of EO with the largest mean difference observed for children aged 2–3 y (boys: 2.3, girls: 2.5; P < 0.003). Between the participant-identified and time-of-day definitions, there was good agreement for frequencies of snacks (ICC for both genders: 0.93) but not meals (boys: 0.36; girls: 0.38). The 15-min time interval plus 210 kJ definition of an EO consistently predicted the most variance in total energy intake (R2 range = 8.1–34.8). Definitions that delineated meals and snacks better predicted variance in BMI z-score, when compared to the neutral definitions.ConclusionsHow eating patterns are characterized vary depending on the EO definitions employed, particularly in young children. Variance in total energy intake was best predicted by a variation of the neutral definition whereas definitions that delineated meals and snacks performed better in relation to predicting BMI variance. Further international research that compares EO definitions in children will help inform a standard approach.

Do Older Women of Reproductive Age Have Better Diet Quality than Younger Women of Reproductive Age?

There is increasing recognition of the importance of nutrition for reproductive health, but little is known regarding the diet quality of younger vs. older reproductive aged women, and how their intakes relate to dietary recommendations. The purpose of the study was to examine the diets of younger (19–35 years old) compared to older (35–50 years old) reproductive aged women, and how they align with dietary recommendations. Women aged 19–50 years from the 2011–13 Australian National Nutrition and Physical Activity Survey were included (n = 2323). Dietary intakes were assessed by a single 24-h dietary recall and were compared to (i) Australian Dietary Guidelines; (ii) Acceptable Macronutrient Distribution for protein, carbohydrates, and fat; and (iii) Dietary Guideline Index (DGI). Regression analyses comparing younger and older women against recommendations were undertaken, with confounders determined a priori. There was no difference between older and younger women in meeting food group recommendations, with 26% of all women meeting recommendations for fruit, and meat and alternatives, and <20% meeting recommendations for vegetables and alternatives, grains, and dairy. Although there was no difference between older and younger women in total DGI score (mean (SE) 75.6 (1.7) vs. 74.5 (2.5), p > 0.05), older women had higher component scores in limiting saturated fat, consuming low-fat milk, and limiting adding salt during cooking. Continued health promotion for women of reproductive age should be a key priority to improve their own health and that of future generations.

Balanced carbohydrate ratios are associated with improved diet quality in Australia: A nationally representative cross-sectional study.

BackgroundCarbohydrate quality influences major health outcomes; however, the best criteria to assess carbohydrate quality remain unknown.ObjectiveThe objectives were to: i) evaluate whether a diet that meets a carbohydrate ratio (simple, modified or dual ratio) is associated with higher nutrient intakes and diet quality, and ii) model the impact of substituting carbohydrate foods that meet the proposed ratios in place of foods that do not, on nutrient intakes.DesignA secondary analysis of cross-sectional data from the 2011–12 Australian National Nutrition and Physical Activity Survey.Participants/SettingNational data from participants aged 2 years and older (n = 12,153).Main outcome measuresRatios were defined as (i) simple ratio, 10:1 (10g carbohydrate:≥1g dietary fiber); (ii) modified ratio, 10:1:2 (10g carbohydrate:≥1g dietary fiber:≤2g free sugars); and (iii) dual ratio, 10:1 & 1:2 (10g carbohydrate:≥1g dietary fiber & ≤2g free sugars per 1g dietary fiber). Ratios were compared to nutrient intakes obtained via automated multiple-pass 24-hour dietary recall and diet quality calculated using the Australian Healthy Eating Index.Statistical analyses performedSubstitution dietary modelling was performed. Data were analyzed using paired and independent sample t-tests.ResultsRatio adherence was highest for simple (50.2% adults; 28.6% children), followed by dual (40.6% adults; 21.7% children), then modified (32.7% adults; 18.6% children) ratios. Participants who met any ratio reported higher nutrient intake and diet quality compared to those who failed to meet the respective ratio (P < .001 for all), with the greatest nutrient intakes found for those who met modified or dual ratios. Dietary modelling improved nutrient intakes for all ratios, with the greatest improvement found for the dual ratio.ConclusionsAll carbohydrate ratios were associated with higher diet quality, with a free sugars constraint in the dual ratio providing the greatest improvements.

Characterizing Children’s Eating Patterns: Does the Choice of Eating Occasion Definition Matter?

ObjectivesCurrently, there are recommendations to defining eating occasions (EO) for use in studies of adults, but none exist for children or adolescents. This study examined how varying definitions of EO influence the characterization of eating patterns in children and adolescents. MethodsCross-sectional 24-h dietary recall data collected during the 2011–12 Australian National Nutrition and Physical Activity Survey (n = 2701, age range: 2–18 years) were analyzed. Eight definitions were applied: participant-identified, time-of-day, and 6 neutral definitions (individual EO separated by 15- or 60-min and/or an additional energy criterion of 5 kcal or 50 kcal). Frequency of and total energy intake from meals, snacks, and all EO were estimated. Differences between definitions were tested using F tests, stratified by sex and age-group. Agreement between definitions of meal and snack frequencies was assessed using intraclass correlation coefficients (ICC). For each definition, linear regression was used to estimate the proportion of variance in total energy intake (kcal) and BMI z-score predicted by frequency of meals and snacks and all EO. ResultsIn boys and girls and across all age groups, mean frequencies of meals and snacks differed between the participant-identified and time-of-day definitions (P < 0.01). There were differences between mean frequencies of EO across the 6 neutral definitions with the largest mean difference observed for children aged 2–3 y (boys: 2.3, girls: 2.5; P < 0.003). There was good agreement (ICC) for snacks (boys: 0.93, girls: 0.93) but not meal frequencies (boys: 0.36; girls: 0.38) between the participant-identified and time-of-day definitions. The definition of 15-min time interval plus 210 kJ consistently predicted the most variance in total energy intake (R2 range = 8.1–34.8). Generally, definitions that delineated meals and snacks best predicted variance in BMI z-score. ConclusionsVariations in the definition of EO affect how eating patterns are characterized, particularly in young children. A variation of the neutral definition best predicted variance in total energy intake and findings for BMI were mixed. Further research that compares EO definitions in younger and older children is needed to inform a standard approach. Funding SourcesNational Heart Foundation of Australia; National Health and Medical Research Council.

Unhealthy Diet Pattern Mediates the Disproportionate Prevalence of Obesity among Adults with Socio-Economic Disadvantage: An Australian Representative Cross-Sectional Study.

The role of unhealthy dietary pattern in the association between socio-economic factors and obesity is unclear. The aim was to examine the association between socio-economic disadvantage and obesity and to assess mediation effect of unhealthy dietary pattern defined using the Mediterranean diet criteria. The data source was the Australian National Nutrition and Physical Activity Survey. The study sample included 7744 participants aged 18 years and over, 28% of whom had obesity. We used the Australian Socio-Economic Indexes for Areas (SEIFA) classification system for categorizing socio-economic disadvantage; calculated the Mediterranean Diet Score (MDS) using standard criteria; and used measured body mass index to define obesity. We conducted a mediation analysis using log–binomial models to generate the prevalence ratio for obesity and the proportion mediated by the MDS. The most disadvantaged group was associated with higher level of obesity after controlling for covariates (1.40, 95% CI 1.25, 1.56) compared to the least disadvantaged group, and in a dose–response way for each decreasing SEIFA quintile. The relationship between socio-economic disadvantage and obesity was mediated by the MDS (4.0%, 95% CI 1.9, 8.0). Public health interventions should promote healthy dietary patterns, such as the Mediterranean diet, to reduce obesity, especially in communities with high socio-economic disadvantage.

Retrospectively Estimating Energy Intake and Misreporting From a Qualitative Food Frequency Questionnaire: An Example Using Australian Cohort and National Survey Data.

Qualitative food frequency questionnaires (Q-FFQ) omit portion size information from dietary assessment. This restricts researchers to consumption frequency data, limiting investigations of dietary composition (i.e., energy-adjusted intakes) and misreporting. To support such researchers, we provide an instructive example of Q-FFQ energy intake estimation that derives typical portion size information from a reference survey population and evaluates misreporting. A sample of 1,919 Childhood Determinants of Adult Health Study (CDAH) participants aged 26–36 years completed a 127-item Q-FFQ. We assumed sex-specific portion sizes for Q-FFQ items using 24-h dietary recall data from the 2011–2012 Australian National Nutrition and Physical Activity Survey (NNPAS) and compiled energy density values primarily using the Australian Food Composition Database. Total energy intake estimation was daily equivalent frequency × portion size (g) × energy density (kJ/g) for each Q-FFQ item, summed. We benchmarked energy intake estimates against a weighted sample of age-matched NNPAS respondents (n = 1,383). Median (interquartile range) energy intake was 9,400 (7,580–11,969) kJ/day in CDAH and 9,055 (6,916–11,825) kJ/day in weighted NNPAS. Median energy intake to basal metabolic rate ratios were 1.43 (1.15–1.78) in CDAH and 1.35 (1.03–1.74) in weighted NNPAS, indicating notable underreporting in both samples, with increased levels of underreporting among the overweight and obese. Using the Goldberg and predicted total energy expenditure methods for classifying misreporting, 65 and 41% of CDAH participants had acceptable/plausible energy intake estimates, respectively. Excluding suspected CDAH misreporters improved the plausibility of energy intake estimates, concordant with expected body weight associations. This process can assist researchers wanting an estimate of energy intake from a Q-FFQ and to evaluate misreporting, broadening the scope of diet–disease investigations that depend on consumption frequency data.

What do Australian adults eat for breakfast? A latent variable mixture modelling approach for understanding combinations of foods at eating occasions

BackgroundThe patterning of food intake at eating occasions is a poorly understood, albeit important, step towards achieving a healthy dietary pattern. However, to capture the many permutations of food combinations at eating occasions, novel analytic approaches are required. We applied a latent variable mixture modelling (LVMM) approach to understand how foods are consumed in relation to each other at breakfast.MethodsDietary intake at breakfast (n = 8145 occasions) was assessed via 24-h recall during the 2011–12 Australian National Nutrition and Physical Activity Survey (n = 3545 men and n = 4127 women, ⩾19 y). LVMM was used to determine breakfast food profiles based on 35 food group variables, reflecting compliance with Australian Dietary Guidelines. F and adjusted-chi2 tests assessed differences in timing of consumption and participant characteristics between the breakfast profiles. Regression models, adjusted for covariates, were used to examine associations between breakfast food profiles and objective adiposity measures (BMI and waist circumference).ResultsFive distinct profiles were found. Three were similar for men and women. These were labelled: “Wholegrain cereals and milks” (men: 16%, women: 17%), “Protein-foods” (men and women: 11%) and “Mixed cereals and milks” (men: 33%, women: 37%). Two “Breads and spreads” profiles were also found that were differentiated by their accompanying beverages (men) or type of grain (women). Profiles were found to vary by timing of consumption, participant characteristics and adiposity indicators. For example, the “Protein-foods” profile occurred more frequently on weekends and after 9 am. Men with a “Bread and spreads (plus tea/coffee)” profile were older (P < 0.001) and had lower income and education levels (P < 0.05), when compared to the other profiles. Women with a “Protein-foods” profile were younger (P < 0.001) and less likely to be married (P < 0.01). Both men and women with a “Wholegrain cereals and milks” profile had the most favourable adiposity estimates (P < 0.05).ConclusionsWe identified five breakfast food profiles in adults that varied by timing of consumption, participant characteristics and adiposity indicators. LVMM was a useful approach for capturing the complexity of food combinations at breakfast. Future research could collect contextual information about eating occasions to understand the complex factors that influence food choices.

Defining whole-grain foods - does it change estimations of intakes and associations with CVD risk factors: an Australian and Swedish perspective.

Historically, there are inconsistencies in the calculation of whole-grain intake, particularly through use of highly variable whole-grain food definitions. The current study aimed to determine the impact of using a whole-grain food definition on whole-grain intake estimation in Australian and Swedish national cohorts and investigate impacts on apparent associations with CVD risk factors. This utilised the Australian National Nutrition and Physical Activity Survey 2011-2012, the Swedish Riksmaten adults 2010-2011 and relevant food composition databases. Whole-grain intakes and associations with CVD risk factors were determined based on consumption of foods complying with the Healthgrain definition (≥30 % whole grain (dry weight), more whole than refined grain and meeting accepted standards for 'healthy foods' based on local regulations) and compared with absolute whole-grain intake. Compliance of whole-grain containing foods with the Healthgrain definition was low in both Sweden (twenty-nine of 155 foods) and Australia (214 of 609 foods). Significant mean differences of up to 24·6 g/10 MJ per d of whole-grain intake were highlighted using Swedish data. Despite these large differences, application of a whole-grain food definition altered very few associations with CVD risk factors, specifically, changes with body weight and blood glucose associations in Australian adults where a whole-grain food definition was applied, and some anthropometric measures in Swedish data where a high percentage of whole-grain content was included. Use of whole-grain food definitions appears to have limited impact on measuring whole-grain health benefits but may have greater relevance in public health messaging.

Strategies to Reduce Consumption of Unhealthy Foods and Beverages: Scenario Modeling to Estimate the Impact on the Australian Population’s Energy and Nutrient Intakes

BackgroundOverconsumption of energy-dense, nutrient-poor “discretionary” foods and beverages can indicate poor diet quality, which is a risk factor for obesity and chronic disease. With 60% of Australians exceeding the recommended intake of discretionary foods, there is a need for interventions to reduce their consumption. ObjectiveThe objective was to model the impact of 26 specific scenarios to limit discretionary food intake on energy and nutrient intake. DesignThe scenario modeling analysis was based on reduction strategies targeting portion size, frequency, or variety of discretionary food and beverage items consumed. Participants/SettingData from 12,153 respondents aged 2 to 85 years from the 2011-12 Australian National Nutrition and Physical Activity Survey were used. Outcome measuresThe outcome measures were change in servings of discretionary foods and key nutrients (energy, total fat, saturated fat, sugar, alcohol, sodium, trans fat) for the population, and by sex, age group, weight status, and socioeconomic status. Statistical analyses performedDescriptive and inferential statistical analysis were conducted. ResultsScenarios reduced total energy intake by up to 26% across the population as a whole. The removal of discretionary foods (not beverages) resulted in the greatest reduction in servings and energy (74.8% and 25.6%, respectively), followed by the removal of discretionary items in portions greater than 143 kcal (59.1% and 24.3%) and not consuming discretionary items at main meals (51.2% and 17.8%). Targeting single categories of discretionary foods reduced energy intake by an average of 5.6% for the removal of cakes and biscuits, 4.4% for alcohol, and 3.9% for sugar-sweetened beverages. Strategies reduced total fat, saturated fat, and sugar by up to 35%, 38%, and 40% respectively. ConclusionsStrategies that are specific to discretionary food and beverage intake targeting reductions in portion size, frequency, or variety have the potential to reduce energy intake and improve diet quality. These findings have implications for designing interventions with potential to tailor messages to current dietary habits. Exploration of how these strategies could be effectively implemented will be the focus of further research.

Estimated health benefits, costs, and cost-effectiveness of eliminating industrial trans-fatty acids in Australia: A modelling study.

trans-fatty acids (TFAs) are a well-known risk factor of ischemic heart disease (IHD). In Australia, the highest TFA intake is concentrated to the most socioeconomically disadvantaged groups. Elimination of industrial TFA (iTFA) from the Australian food supply could result in reduced IHD mortality and morbidity while improving health equity. However, such legislation could lead to additional costs for both government and food industry. Thus, we assessed the potential cost-effectiveness, health gains, and effects on health equality of an iTFA ban from the Australian food supply. Markov cohort models were used to estimate the impact on IHD burden and health equity, as well as the cost-effectiveness of a national ban of iTFA in Australia. Intake of TFA was assessed using the 2011-2012 Australian National Nutrition and Physical Activity Survey. The IHD burden attributable to TFA was calculated by comparing the current level of TFA intake to a counterfactual setting where consumption was lowered to a theoretical minimum distribution with a mean of 0.5% energy per day (corresponding to TFA intake only from nonindustrial sources, e.g., dairy foods). Policy costs, avoided IHD events and deaths, health-adjusted life years (HALYs) gained, and changes in IHD-related healthcare costs saved were estimated over 10 years and lifetime of the adult Australian population. Cost-effectiveness was assessed by calculation of incremental cost-effectiveness ratios (ICERs) using net policy cost and HALYs gained. Health benefits and healthcare cost changes were also assessed in subgroups based on socioeconomic status, defined by Socio-Economic Indexes for Areas (SEIFA) quintile, and remoteness. Compared to a base case of no ban and current TFA intakes, elimination of iTFA was estimated to prevent 2,294 (95% uncertainty interval [UI]: 1,765; 2,851) IHD deaths and 9,931 (95% UI: 8,429; 11,532) IHD events over the first 10 years. The greatest health benefits were accrued to the most socioeconomically disadvantaged quintiles and among Australians living outside of major cities. The intervention was estimated to be cost saving (net cost <0 AUD) or cost-effective (i.e., ICER < AUD 169,361/HALY) regardless of the time horizon, with ICERs of 1,073 (95% UI: dominant; 3,503) and 1,956 (95% UI: 1,010; 2,750) AUD/HALY over 10 years and lifetime, respectively. Findings were robust across several sensitivity analyses. Key limitations of the study include the lack of recent data of TFA intake and the small sample sizes used to estimate intakes in subgroups. As with all simulation models, our study does not prove that a ban of iTFA will prevent IHD, rather, it provides the best quantitative estimates and corresponding uncertainty of a potential effect in the absence of stronger direct evidence. Our model estimates that a ban of iTFAs could avert substantial numbers of IHD events and deaths in Australia and would likely be a highly cost-effective strategy to reduce social-economic and urban-rural inequalities in health. These findings suggest that elimination of iTFA can cost-effectively improve health and health equality even in countries with low iTFA intake.

Have we compromised too much? A critical analysis of nutrition policy in Australia 2007-2018.

A comprehensive nutrition policy containing a broad package of cross-sector and synergistic policy actions is required to attenuate the systemic drivers of poor nutrition. The current study aims to critically analyse trends in the scope of federal nutrition policy actions in Australia between 2007 and 2018 by: (1) describing the changes in nutrition policy actions, benchmarked against an international best-practice policy framework and (2) investigating how and why the scope of these policy actions have changed over time by examining the decision-making processes that led to the establishment of Australia's Healthy Food Partnership (the Partnership). Qualitative case study involving documentary analysis and key-informant interviews. Australian federal government documents (n 10) were analysed against the NOURISHING framework. Key informants (n 6) were interviewed and asked about the Partnership's decision-making and establishment processes. Australia. Executive Committee (the Partnership's governing body) and working group members. From 2007 to 2018, the scope of Australian national nutrition policy has fluctuated from evidence-informed recommendations for a comprehensive policy to the mostly discrete policy actions of the Partnership. Themes of 'pragmatism and compromise', 'actor relationships and lobbying' and 'political context' were critical drivers for establishing the Partnership. The narrowing of Australian nutrition policy reflects a response to political expediency and compromise. This political dynamic highlights a dilemma facing nutrition policy advocates: should (and if so, how) a balance be sought between the aspirational but possibly unrealistic goals, and the limited but likely deliverable outcomes during policy-making processes? These findings have relevance for developing a future comprehensive national nutrition policy.

First Nations Peoples' Participation in the Development of Population-Wide Food and Nutrition Policy in Australia: A Political Economy and Cultural Safety Analysis.

Background: Healthy and sustainable food systems underpin the well-being of Indigenous peoples. Increasingly governments are taking action to improve diets via population-wide policies. The United Nations Declaration on the Rights of Indigenous People states that Indigenous peoples have the right to participate in all decisions that affect them. We analysed Australian national food and nutrition policy processes to determine: (i) the participation of Aboriginal organisations, (ii) the issues raised in Aboriginal organisations’ policy submissions, and (iii) the extent to which Aboriginal organisations’ recommendations were addressed in final policy documents. Methods: Political economy and cultural safety lenses informed the study design. We analysed publicly-available documents for Australian population-wide food and nutrition policy consultations occurring 2008-2018. Data sources were policy documents, committee reports, terms of reference and consultation submissions. The submissions made by Aboriginal organisations were thematically analysed and key policy recommendations extracted. We examined the extent to which key recommendations made by Aboriginal organisations were included in the subsequent policy documents. Results: Five food and nutrition policy processes received submissions from Aboriginal organisations. Key themes centred on self-determination, culturally-appropriate approaches to health, and the need to address food insecurity and social determinants of health. These messages were underrepresented in final policy documents, and Aboriginal people were not included in any committees overseeing policy development processes. Conclusion: This analysis suggests that very few Aboriginal organisations have participated in Australian population-wide food and nutrition policy processes and that these policy development processes are culturally unsafe. In order to operationalise First Nations peoples’ right to self-determination, alternative mechanisms are required to redress the power imbalances preventing the full participation of Aboriginal and Torres Strait Islander peoples in population-wide food and nutrition policy decisions. This means reflecting on deeply embedded institutional structures and the normative assumptions upon which they rest.