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Background Consumption of ultra-processed foods is associated with increased risk of obesity and non-communicable diseases. Little is known about current patterns of ultra-processed foods intake in Australia. The aim of this study was to examine the amount and type of ultra-processed foods purchased by Australian households in 2019 and determine whether purchases differed by socio-economic status (SES). We also assessed whether purchases of ultra-processed foods changed between 2015 and 2019.  Methods We used grocery purchase data from a nationally representative consumer panel in Australia to assess packaged and unpackaged grocery purchases that were brought home between 2015 to 2019. Ultra-processed foods were identified according to the NOVA system, which classifies foods according to the nature, extent and purpose of industrial food processing. Purchases of ultra-processed foods were calculated per capita, using two outcomes: grams/day and percent of total energy. The top food categories contributing to purchases of ultra-processed foods in 2019 were identified, and differences in ultra-processed food purchases by SES (Index of Relative Social Advantage and Disadvantage) were assessed using survey-weighted linear regression. Changes in purchases of ultra-processed foods between 2015 to 2019 were examined overall and by SES using mixed linear models. Results In 2019, the mean ± SD total grocery purchases made by Australian households was 881.1 ± 511.9 g/d per capita. Of this, 424.2 ± 319.0 g/d per capita was attributable to purchases of ultra-processed foods, which represented 56.4% of total energy purchased. The largest food categories contributing to total energy purchased included mass-produced, packaged breads (8.2% of total energy purchased), chocolate and sweets (5.7%), biscuits and crackers (5.7%) and ice-cream and edible ices (4.3%). In 2019, purchases of ultra-processed foods were significantly higher for the lowest SES households compared to all other SES quintiles ( P  < 0.001). There were no major changes in purchases of ultra-processed foods overall or by SES over the five-year period. Conclusions Between 2015 and 2019, ultra-processed foods have consistently made up the majority of groceries purchased by Australians, particularly for the lowest SES households. Policies that reduce ultra-processed food consumption may reduce diet-related health inequalities.

To examine the labelling status of -fat of pre-packaged foods sold in Hong Kong. Data from 19 027 items in the 2019 FoodSwitch Hong Kong database were used. Ingredient lists were screened to identify specific (e.g. partially hydrogenated vegetable oil, PHVO) and non-specific -fat ingredient indicators (e.g. hydrogenated oil). -fat content was obtained from the on-pack nutrition labels, which was converted into proportion of total fat (% ). Descriptive statistics were calculated for -fat content and the number of specific, non-specific and total -fat ingredients indicators found on the ingredients lists. Comparisons were made between regions using one-way ANOVA and for continuous and categorical variables, respectively. Cross-sectional audit. Not applicable. A total of 729 items (3·8 % of all products) reported to contain industrially produced -fat, with a median of 0·4 g/100 g or 100 ml (interquartile range (IQR): 0·1-0·6) and 1·2 % (IQR: 0·6-2·9). 'Bread and bakery products' had the highest proportion of items with industrially produced -fat (18·9 %). 'Non-alcoholic beverages' had the highest proportion of products of 'false negatives' labelling (e.g. labelled as 0 -fat but contains PHVO; 59·3 %). The majority of products with -fat indicator originated from Asia (70 %). According to the labelling ∼4 % of pre-packaged food and beverages sold in Hong Kong in 2019 contained industrially produced -fat, and a third of these had -fat >2 % . The ambiguous -fat labelling in Hong Kong may not effectively assist consumers in identifying products free from industrially produced -fat.

Background This study investigates the associations between coffee consumption and metabolic syndrome and its components, as well as the effect of milk, sugar, and artificial sweeteners on these associations. Methods A cross‐sectional analysis was conducted with 351805 UK Biobank participants. Coffee consumption data were collected via food frequency questionnaires and 24‐h recall. Metabolic syndrome was identified through blood biochemistry and self‐reported medication use. Odds ratios were calculated using multivariable logistic regression, and results were verified with two‐sample Mendelian randomization. Results Consuming up to two cups of coffee per day was inversely associated with metabolic syndrome (1 cup/day: odds ratio [OR]: 0.88, 95% confidence interval [CI]: 0.85–0.92; 2 cups/day: OR: 0.90, 95% CI: 0.86–0.93). Higher intakes showed near‐null associations. Mendelian randomization did not support a causal link between coffee intake and metabolic syndrome. Both self‐reported and genetically predicted high coffee consumption (four cups per day or more) were associated with central obesity. The inverse association between coffee consumption and metabolic syndrome was more profound among drinkers of ground coffee than those of instant coffee. Results were similar when stratified by the use of milk and sugar, yet the use of artificial sweetener with coffee was positively associated with metabolic syndrome and all component conditions. Conclusions Coffee consumption may increase the risk of central obesity but is unlikely to impact the risk of metabolic syndrome. The potential health effects of artificial sweeteners in coffee need further investigation. Highlights Coffee consumption ≤2 cups/day inversely linked to metabolic syndrome (MetSyn). No causal relationship between coffee intake and MetSyn. High coffee consumption (4+ cups/day) associated with central obesity. Ground coffee showed stronger inverse association with MetSyn than instant coffee. Use of artificial sweetener with coffee linked to MetSyn and its components.

To describe a standardized method to assign glycemic index (GI) values to food items, obtained from 3 × 24-h recalls among Aboriginal and Torres Strait Islander and non-Indigenous Australian children, which can be adapted for use with simple food composition databases. Four published GI databases were used as the source of GI values. Changes were made to a previously published methodology for GI value assignment to accommodate the needs of the Many Rivers Diabetes Prevention Project. There were 1132 food items in the recall database. Two hundred nineteen (19.3%) food items were directly linked to the FoodWorks GI database and 545 (48.1%) items were assigned the GI value of a “closely related” food item in the four GI databases used. Among the top carbohydrate contributors, 113 (35.3%) items have a direct linkage with the FoodWorks GI database. The mean ± SEM dietary GI and glycemic load (GL) of the study population resulting from this methodology are 57.5 ± 0.3 and 143.4 ± 2.6, respectively. This simple method provides opportunities for countries without food composition database that are comprehensive for GI/GL but which contain accurate information on carbohydrates in foods to assign high-quality GI values to food items in epidemiological studies based on 24-h recalls.

Background Countries around the world are putting in place sugar reformulation targets for packaged foods to reduce excess sugar consumption. The Australian government released its voluntary sugar reformulation targets for nine food categories in 2020. We estimated the potential impact of these targets on household sugar purchases and examined differences by income. For comparison, we also modelled the potential impact of the UK sugar reduction targets on per capita sugar purchases as the UK has one of the most comprehensive sugar reduction strategies in the world. Methods Grocery purchase data from a nationally representative consumer panel ( n =7,188) in Australia was linked with a large database (FoodSwitch) with product-specific sugar content information for packaged foods ( n =25,261); both datasets were collected in 2018. Potential reductions in per capita sugar purchases were calculated overall and by food category. Differences in sugar reduction across income level were assessed by analysis of variance. Results In 2018, the total sugar acquired from packaged food and beverage purchases consumed at-home was 56.1 g/day per capita. Australia’s voluntary reformulation targets for sugar covered 2,471/25,261 (9.8%) unique products in the FoodSwitch dataset. Under the scenario that all food companies adhered to the voluntary targets, sugar purchases were estimated to be reduced by 0.9 g/day per capita, which represents a 1.5% reduction in sugar purchased from packaged foods. However, if Australia adopted the UK targets, over twice as many products would be covered ( n =4,667), and this would result in a more than four times greater reduction in sugar purchases (4.1 g/day per capita). It was also estimated that if all food companies complied with Australia’s voluntary sugar targets, reductions to sugar would be slightly greater in low-income households compared with high-income households by 0.3 g/day (95%CI 0.2 - 0.4 g/day, p <0.001). Conclusions Sugar-reduction policies have the potential to substantially reduce population sugar consumption and may help to reduce health inequalities related to excess sugar consumption. However, the current reformulation targets in Australia are estimated to achieve only a small reduction to sugar intakes, particularly in comparison to the UK’s sugar reduction program.

The placement of nutrition information on the front of food packages has been proposed as a method of providing simplified and visible nutrition information. This study aimed to determine the most acceptable and effective front-of-pack food labelling system for Australian consumers. Consumers' preferences and ability to compare the healthiness of mock food products were assessed for different front-of-pack labelling systems. Four systems were tested, including two variations of the Percentage Daily Intake system (Monochrome %DI and Colour-Coded %DI), which displays the proportion of daily nutrient contribution that a serve of food provides; and two variations of the Traffic Light (TL) system (Traffic Light and Traffic Light + Overall Rating), which uses colour-coding to indicate nutrient levels. Intercept surveys with 790 consumers were conducted, where each participant was exposed to a single labelling system for performance testing. Participants indicated strong support for the inclusion of nutrient information on total fat, saturated fat, sugar and sodium on the front of packages, and a consistent labelling format across all products. Using the TL system, participants were five times more likely to identify healthier foods compared with the Monochrome %DI system [odds ratio (OR) = 5.18; p < 0.001], and three times more likely compared with the Colour-Coded %DI system (OR = 3.01; p < 0.05). Consumers supported the introduction of consistent front-of-pack food labelling. The TL system was the most effective in assisting consumers to identify healthier foods. Mandatory TL labelling regulations are recommended to assist consumers in making healthy food choices.

There are no published data regarding the overall dietary glycaemic index (GI) and glycaemic load (GL) of Australian children and adolescents. We therefore aim to describe the dietary GI and GL of participants of the 2007 Australian National Children's Nutrition and Physical Activity Survey (2007ANCNPAS), and to identify the main foods contributing to their GL. Children, aged 2–16 years, who provided two 24 h recalls in the 2007ANCNPAS were included. A final dataset of 4184 participants was analysed. GI of each food item was assigned using a previously published method. GL was calculated, and food groups contributing to the GL were described by age group and sex. The weighted mean dietary GI and GL of the participants were 54 (sd 5) and 136 (sd 44), respectively. Among the nutrients examined, Ca had the highest inverse relationship with GI (P < 0·001), while percentage energy from starch was most positively associated with GI. The association between fibre density and GI was modest, and percentage energy from sugar had an inverse relationship with GI. Daily dietary GL contributed by energy-dense and/or nutrient-poor (EDNP) items in subjects aged 14–16 years was more than doubled that of subjects aged 2–3 years. To conclude, Australian children and adolescents were having a high-GI dietary pattern characterised by high-starchy food intake and low Ca intake. A significant proportion of their dietary GL was from EDNP foods. Efforts to reduce dietary GI and GL in children and adolescents should focus on energy-dense starchy foods.

The present study aimed to assess the longitudinal associations of coffee and tea consumption with metabolic syndrome and its component conditions in a group of Australian older adults who participated in the Blue Mountains Eye Study (n 2554, mean age: 64 years, 43 % female). Participants’ coffee and tea intake were measured using a validated food frequency questionnaire. Hazard ratios (HRs) over a 10-year period were estimated using Cox hazard regression models adjusting for lifestyle factors. Results showed that coffee consumption was not associated with the incidence of metabolic syndrome, high fasting glucose, high triglycerides, central obesity, high blood pressure and low HDL-cholesterol (HDL-C). Tea consumption was not associated with incidence of metabolic syndrome and the component conditions except for the risk of having low HDL-C, in which a nominally inverse association was observed (multivariate-adjusted HR at 2–3 cups/d: 0⋅48, 95 % CI 0⋅26, 0⋅87, P = 0⋅016; 4 cups/d or more: 0⋅50, 95 % CI 0⋅27, 0⋅93, P = 0⋅029). After stratifying for fruit consumption (Pinteraction between tea and fruit = 0⋅007), consuming four cups of tea per day was nominally associated with lower incidence of metabolic syndrome among those with high fruit consumption (multivariable-adjusted HR: 0⋅44, 95 % CI 0⋅20, 0⋅93, P = 0⋅033). Our results did not support a significant association between tea and coffee consumption and metabolic syndrome. Tea consumption may be associated with a lower risk of having low HDL-C, while high tea and fruit consumption together may be associated with a lower risk of developing metabolic syndrome.

Previous studies in Australian children/adolescents and adults examining added sugar (AS) intake were based on now out-of-date national surveys. We aimed to examine the AS and free sugar (FS) intakes and the main food sources of AS among Australians, using plausible dietary data collected by a multiple-pass, 24-h recall, from the 2011–12 Australian Health Survey respondents (n 8202). AS and FS intakes were estimated using a previously published method, and as defined by the WHO, respectively. Food groups contributing to the AS intake were described and compared by age group and sex by one-way ANOVA. Linear regression was used to test for trends across age groups. Usual intake of FS (as percentage energy (%EFS)) was computed using a published method and compared with the WHO cut-off of <10 %EFS. The mean AS intake of the participants was 60·3 (sd 52·6) g/d. Sugar-sweetened beverages accounted for the greatest proportion of the AS intake of the Australian population (21·4 (sd 30·1) %), followed by sugar and sweet spreads (16·3 (sd 24·5) %) and cakes, biscuits, pastries and batter-based products (15·7 (sd 24·4) %). More than half of the study population exceeded the WHO’s cut-off for FS, especially children and adolescents. Overall, 80–90 % of the daily AS intake came from high-sugar energy-dense and/or nutrient-poor foods. To conclude, the majority of Australian adults and children exceed the WHO recommendation for FS intake. Efforts to reduce AS intake should focus on energy-dense and/or nutrient-poor foods.

Despite tremendous growth in the consumption of gluten-free (GF) foods, there is a lack of evaluation of their nutritional profile and how they compare with non-GF foods. The present study evaluated the nutritional quality of GF and non-GF foods in core food groups, and a wide range of discretionary products in Australian supermarkets. Nutritional information on the Nutrition Information Panel was systematically obtained from all packaged foods at four large supermarkets in Sydney, Australia in 2013. Food products were classified as GF if a GF declaration appeared anywhere on the product packaging, or non-GF if they contained gluten, wheat, rye, triticale, barley, oats or spelt. The primary outcome was the ‘Health Star Rating’ (HSR: lowest score 0·5; optimal score 5), a nutrient profiling scheme endorsed by the Australian Government. Differences in the content of individual nutrients were explored in secondary analyses. A total of 3213 food products across ten food categories were included. On average, GF plain dry pasta scored nearly 0·5 stars less (P< 0·001) compared with non-GF products; however, there were no significant differences in the mean HSR for breads or ready-to-eat breakfast cereals (P≥ 0·42 for both). Relative to non-GF foods, GF products had consistently lower average protein content across all the three core food groups, in particular for pasta and breads (52 and 32 % less, P< 0·001 for both). A substantial proportion of foods in discretionary categories carried GF labels (e.g. 87 % of processed meats), and the average HSR of GF discretionary foods were not systematically superior to those of non-GF products. The consumption of GF products is unlikely to confer health benefits, unless there is clear evidence of gluten intolerance.