Total and free sugar levels and main types of sugars used in 18,784 local and imported pre‐packaged foods and beverages sold in Hong Kong

There is limited information regarding the free sugar content of pre‐packaged foods in Hong Kong. This study aims to assess the free sugar content and identify the most frequently used free sugar ingredients (FSI) in pre‐packaged foods in Hong Kong. Data from 18,784 products from the 2019 FoodSwitch Hong Kong database were used in this analysis. Ingredient lists were screened to identify FSI. Total sugar content was derived from nutrition labels on packaging. Free sugar content was estimated based on adaptation of a previously established systematic methodology. Descriptive statistics of the total sugar and free sugar content, as well as the mean ±SD contribution of free sugar to total sugar of the audited products were calculated, stratified by food groups. Almost two‐thirds (64.5%) of the pre‐packaged foods contained at least one FSI. ‘Sugar (sucrose)’ was the most popular FSI that was found in more than half (54.7%) of the products. ‘Fruit and vegetable juices’ (median 10.0; IQR 8.3–11.5 g/100 mL) were found to have a higher median free sugar content than ‘Soft drinks’ (8.0; 6.0–10.6 g/100 mL). Mean ±SD contribution of free sugar to the total sugar content was 65.8 ± 43.4%, with 8 out of 14 food groups having > 70% total sugar as free sugar. To conclude, free sugar, especially sucrose, was extensively used in a wide variety of pre‐packaged products sold in Hong Kong. Further studies are needed to assess the population intake of free sugar in Hong Kong to inform public health policy on free sugar reduction

An innovative machine learning approach to predict the dietary fiber content of packaged foods

Underconsumption of dietary fiber is prevalent worldwide and is associated with multiple adverse health conditions. Despite the importance of fiber, the labeling of fiber content on packaged foods and beverages is voluntary in most countries, making it challenging for consumers and policy makers to monitor fiber consumption. Here, we developed a machine learning approach for automated and systematic prediction of fiber content using nutrient information commonly available on packaged products. An Australian packaged food dataset with known fiber content information was divided into training (n = 8986) and test datasets (n = 2455). Utilization of a k-nearest neighbors machine learning algorithm explained a greater proportion of variance in fiber content than an existing manual fiber prediction approach (R2 = 0.84 vs. R2 = 0.68). Our findings highlight the opportunity to use machine learning to efficiently predict the fiber content of packaged products on a large scale

Estimating the potential impact of the Australian government's reformulation targets on household sugar purchases

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

Socio-economic difference in purchases of ultra-processed foods in Australia: an analysis of a nationally representative household grocery purchasing panel

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

Socio-economic difference in purchases of ultra-processed foods in Australia: an analysis of a nationally representative household grocery purchasing panel

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

Polysaccharopeptide enhances the anticancer activity of doxorubicin and etoposide on human breast cancer cells ZR-75-30

In search of natural bioactive microbial compounds with adjuvant properties, we have previously showed that the polysaccharopeptide (PSP), isolated from Chinese medicinal mushroom Coriolus versicolor, was able to enhance the cytotoxicity of certain S-phase targeted-drugs on human leukemic HL-60 cells via some cell-cycle and apoptotic-dependent pathways. The present study aimed to investigate whether the synergism of mechanisms of PSP with certain chemotherapeutic drugs also applies to human breast cancer. PSP treatment enhanced the cytotoxicity of doxorubicin (Doxo), etoposide (VP-16) but not cytarabine (Ara-C). Bivariate bromodeoxyuridine (BrdUrd)/DNA flow cytometry analysis estimated a longer DNA synthesis time (Ts) for the PSP treated cancerous cells suggesting that PSP enhanced the apoptotic effect of Doxo and VP-16 via creating an S-phase trap in the human breast cancer cell line ZR-75-30. The participation of PSP in the apoptotic machinery of the chemotherapeutic agents was further supported by a reduced ratio of protein expression of Bcl-xL/Bax of the cancer cells. This study provides further insight into the synergistic mechanisms of PSP and supports the hypothesis that the anti-cancer potentials of PSP is not limited to leukemia but may also be used as an adjuvant therapy for breast cancers.link_to_OA_fulltex

Sodium concentration of pre-packaged foods sold in Hong Kong

© The Author(s), 2020. Published by Cambridge University Press on behalf of The Nutrition Society. Objective:To describe the Na concentration of pre-packaged foods available in Hong Kong.Design:The Na concentrations (mg/100 g or mg/100 ml or per serving) of all pre-packaged foods available for sale in major supermarket chains in Hong Kong were obtained from the 2017 Hong Kong FoodSwitch database. Median and interquartile range (IQR) of Na concentration for different food groups and the proportion of foods and beverages considered low and high Na (600 mg/100 g or mg/100 ml, respectively) were determined.Setting:Hong Kong.Participants:Not applicable.Results:We analysed 11 518 pre-packaged products. 'Fruit and vegetables (including table salt)' had the highest variability in Na concentration ranging from 0 to 39 000 mg/100 g, followed by 'sauces, dressings, spreads and dips' ranging from 0 to 34 130. The latter also had the highest median Na concentration (mg/100 g or mg/100 ml) at 1180 (IQR 446-3520), followed by meat and meat products (median 800, IQR 632-1068) and snack foods (median 650, IQR 453-926). Fish and fish products (median 531, 364-791) and meat and meat products (median 444, IQR 351-593) had the highest Na concentration per serving. Overall, 46·7 and 26·7 % of products were low and high in Na, respectively.Conclusions:Our results can serve as a baseline for food supply interventions in Hong Kong. We have identified several food groups as priority areas for reformulation, demonstrating the potential of such initiatives to improve the healthiness of the food supply in Hong Kong

A Machine Learning Approach to Predict the Added Sugar Content of Packaged Foods.

BACKGROUND: Dietary guidelines recommend limiting the intake of added sugars. However, despite the public health importance, most countries have not mandated the labeling of added sugar content on packaged foods and beverages, making it difficult for consumers to avoid products with added sugar, and limiting the ability of policymakers to identify priority products for intervention. OBJECTIVE: To develop a machine learning approach for the prediction of added sugar content in packaged products using available nutrient, ingredient, and food category information. DESIGN: The added sugar prediction algorithm was developed using k-Nearest Neighbors (KNN) and packaged food information from the US Label Insight dataset (n = 70,522). A synthetic dataset of Australian packaged products (n = 500) was used to assess validity and generalization. Performance metrics included the coefficient of determination (R2), mean absolute error (MAE), and Spearman rank correlation (ρ). To benchmark the KNN approach, the KNN approach was compared to an existing added sugar prediction approach that relies on a series of manual steps. RESULTS: Compared to the existing added sugar prediction approach, the KNN approach was similarly apt at explaining variation in added sugar content (R2 = 0.96 vs. 0.97 respectively) and ranking products from highest to lowest in added sugar content (ρ = 0.91 vs. 0.93 respectively), while less apt at minimizing absolute deviations between predicted and true values (MAE = 1.68 g vs. 1.26 g per 100 g or 100 mL respectively). CONCLUSIONS: KNN can be used to predict added sugar content in packaged products with a high degree of validity. Being automated, KNN can easily be applied to large datasets. Such predicted added sugar levels can be used to monitor the food supply and inform interventions aimed at reducing added sugar intake

An Innovative Machine Learning Approach to Predict the Dietary Fiber Content of Packaged Foods.

Underconsumption of dietary fiber is prevalent worldwide and is associated with multiple adverse health conditions. Despite the importance of fiber, the labeling of fiber content on packaged foods and beverages is voluntary in most countries, making it challenging for consumers and policy makers to monitor fiber consumption. Here, we developed a machine learning approach for automated and systematic prediction of fiber content using nutrient information commonly available on packaged products. An Australian packaged food dataset with known fiber content information was divided into training (n = 8986) and test datasets (n = 2455). Utilization of a k-nearest neighbors machine learning algorithm explained a greater proportion of variance in fiber content than an existing manual fiber prediction approach (R2 = 0.84 vs. R2 = 0.68). Our findings highlight the opportunity to use machine learning to efficiently predict the fiber content of packaged products on a large scale

Modelling of the impact of universal added sugar reduction through food reformulation

© 2017 The Author(s). Food reformulation has been suggested to be one of the strategies to reduce population added sugar (AS) intake. This study aims to investigate the untested assumption that a reduction in AS through reformulation will result in a reduction in population intakes of AS and energy. Plausible dietary data from 4,140 respondents of an Australian national nutrition survey were used. Dietary modelling was performed at AS reductions of 10%, 15%, and 25% using four strategies: simple removal of AS or replacement with non-nutritive sweeteners (NNS), and replacement of AS with NNS and either: polyols, 50% fibres or 50% maltodextrin. Paired t-tests were conducted to compare the intake of energy, fat, and AS pre- and post-reformulation. The chosen reformulation strategies resulted in a projected reduction in AS and energy, with the greatest reduction found in 25% reformulation which was the highest level modelled. The overall projected mean (SD) reduction in energy and AS after 25% reformulation was 114 (92) kJ/day and 11.73 (7.52) g/day, p < 0.001. To conclude, product reformulation may be a potentially useful strategy for reducing AS intake. Although the magnitude of projected reduction was small at the individual level, the impact may be meaningful at a population level