Objective To compare the relative validity of food group intakes derived from a comprehensive self-administered diet history questionnaire (DHQ) and a brief-type DHQ (BDHQ) developed for the ...assessment of Japanese diets during the previous month using semi-weighed dietary records (DR) as a reference method. Design Between November 2002 and September 2003, a 4 d DR (covering four non-consecutive days), a DHQ (150-item semi-quantitative questionnaire) and a BDHQ (fifty-eight-item fixed-portion-type questionnaire) were completed four times (once per season) at 3-month intervals. Setting Three areas in Japan: Osaka, Nagano and Tottori. Subjects Ninety-two Japanese women aged 31-69 years and ninety-two Japanese men aged 32-76 years. Results Median food group intakes were estimated well for approximately half of the food groups. No statistically significant differences were noted between a 16 d DR and the first DHQ (DHQ1) or between the DR and the first BDHQ (BDHQ1) in fifteen (44 %) and fifteen (52 %) food items for women and in fourteen (41 %) and sixteen (55 %) food items for men, respectively, indicating that both questionnaires estimated median values reasonably well. Median Spearman's correlation coefficients with the DR were 0·43 (range: -0·09 to 0·77) for DHQ1 and 0·44 (range: 0·14 to 0·82) for BDHQ1 in women, with respective values of 0·44 (range: 0·08 to 0·87) and 0·48 (range: 0·22 to 0·83) in men, indicating reasonable ranking ability. Similar results were observed for mean values of the four DHQ and BDHQ. Conclusions In terms of food intake estimates, both the DHQ and the BDHQ showed reasonable validity.
•A systematized food waste accounting at macro scale level was developed for Europe.•The accounting follows Mass Flow Analysis concepts, ensuring closed mass balance.•Disaggregated values per food ...supply chain stages and per food groups are presented.•Hotspots and food groups with higher share of food waste were identified.•The stage contributing the most to food waste varies depending on the food group.
In 2015, the United Nations defined the Sustainable Development Goals (SDG), which include a target (12.3) on food waste. The target requires “by 2030, to halve per capita global food waste at the retail and consumer levels and to reduce food losses along production and supply chains, including post-harvest losses”. The target has increased awareness about the food waste problem and boosted research in food waste quantification. Nevertheless, there is a lack of studies that adopt a systematic approach to account for food waste providing disaggregated values per food supply chain stage and per food groups. Such an approach could support policy makers in prioritizing interventions for food waste reduction. To fill this gap, this paper presents a high-level top-down approach to food waste accounting in the European Union. The study aims to support the understanding of the mass flows associated with food production, consumption, and waste, addressing different food groups along the food supply chain. The method for accountin is the mass flow analysis. According to the results, cereals, fruit, and vegetables as the food groups are responsible for the highest amount of food waste, and the consumption stage to be responsible for the largest share of food waste for most food groups. This work highlights the need for further primary research on food waste generation in the EU. Ultimately, this would allow to robustly estimate the food waste generated at EU level, and establishing a more accurate baseline to track the progress towards SDG target 12.3.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Diets should be healthy for the benefits of both humans and the environment. The World Index for Sustainability and Health (WISH) was developed to assess both diets' healthiness and environmental ...sustainability, and the index was applied in this study. Food intake quantities for single foods were calculated based on the data collected from four 24-h recalls during two seasons in 2019/2020 with women of reproductive age in two rural areas each in Kenya, Tanzania and Uganda (
= 1152). Single foods were grouped into 13 food groups, and the amount of each food group consumed was converted to an overall WISH score and four sub-scores. The food groups with a low WISH score were fruits, vegetables, dairy foods, fish, unsaturated oils and nuts, meaning that their consumption was outside the recommended range for a healthy and sustainable diet. Contrariwise, the intake of red meat and poultry was partly above the recommended intake for those women who consumed them. The overall WISH score and sub-scores showed that the consumption of "protective" food groups needed to increase in the study population, while the consumption of "limiting" food groups was sufficient or should decrease. For future application, we recommend dividing food groups that are critical for nutrition, e.g., vegetables, into sub-groups to further understand their contribution to this index.
Evaluate the validity of the PortionSize application.
In this pilot study, 14 adults used PortionSize to record their free-living food intake over 3 consecutive days. Digital photography was the ...criterion measure, and the main outcomes were estimated intake of food (grams), energy (kilocalories), and food groups. Equivalence tests with ±25% equivalence bounds and Bland-Altman analysis were performed.
Estimated gram intake from PortionSize was equivalent (P < 0.001) to digital photography estimates. PortionSize and digital photography estimated energy intake, however, were not equivalent (P = 0.08), with larger estimates from PortionSize. In addition, PortionSize and digital photography were equivalent for vegetable intake (P = 0.01), but PortionSize had larger estimates of fruits, grains, dairy, and protein intake (P >0.07; error range 11% to 23%).
Compared with digital photography, PortionSize accurately estimated food intake and had reasonable error rates for other nutrients; however, it overestimated energy intake, indicating further application improvements are needed for free-living conditions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The Diet Quality Questionnaire (DQQ) is a rapid dietary assessment tool designed to enable feasible measuring and monitoring of diet quality at population level in the general public.
To evaluate ...validity of the DQQ for collecting population-level food group consumption data required for calculating diet quality indicators by comparing them with a multipass 24-h dietary recall (24hR) as the reference.
Cross-sectional data were collected among female participants aged 15–49 y in Ethiopia (n = 488), 18–49 y in Vietnam (n = 200), and 19–69 y in Solomon Islands (n = 65) to compare DQQ and 24hR data in proportional differences in food group consumption prevalence, percentage of participants achieving Minimum Dietary Diversity for Women (MDD-W), percent agreement, percentage misreporting food group consumption, and diet quality scores of Food Group Diversity Score (FGDS), noncommunicable disease (NCD)-Protect, NCD-Risk, and the Global Dietary Recommendation (GDR) score using a nonparametric analysis.
The mean (standard deviation) percentage point difference between DQQ and 24hR in population prevalence of food group consumption was 0.6 (0.7), 2.4 (2.0), and 2.5 (2.7) in Ethiopia, Vietnam, and Solomon Islands, respectively. Percent agreement of food group consumption data ranged from 88.6% (10.1) in Solomon Islands to 96.3% (4.9) in Ethiopia. There was no significant difference between DQQ and 24hR in population prevalence of achieving MDD-W except for Ethiopia (DQQ 6.1 percentage points higher, P < 0.01). Median (25th–75th percentiles) scores of FGDS, NCD-Protect, NCD-Risk, and GDR score were comparable between the tools.
The DQQ is a suitable tool for collecting population-level food group consumption data for estimating diet quality with food group-based indicators such as the MDD-W, FGDS, NCD-Protect, NCD-Risk, and GDR score.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The present commentary contains a clear and simple guide designed to identify ultra-processed foods. It responds to the growing interest in ultra-processed foods among policy makers, academic ...researchers, health professionals, journalists and consumers concerned to devise policies, investigate dietary patterns, advise people, prepare media coverage, and when buying food and checking labels in shops or at home. Ultra-processed foods are defined within the NOVA classification system, which groups foods according to the extent and purpose of industrial processing. Processes enabling the manufacture of ultra-processed foods include the fractioning of whole foods into substances, chemical modifications of these substances, assembly of unmodified and modified food substances, frequent use of cosmetic additives and sophisticated packaging. Processes and ingredients used to manufacture ultra-processed foods are designed to create highly profitable (low-cost ingredients, long shelf-life, emphatic branding), convenient (ready-to-consume), hyper-palatable products liable to displace all other NOVA food groups, notably unprocessed or minimally processed foods. A practical way to identify an ultra-processed product is to check to see if its list of ingredients contains at least one item characteristic of the NOVA ultra-processed food group, which is to say, either food substances never or rarely used in kitchens (such as high-fructose corn syrup, hydrogenated or interesterified oils, and hydrolysed proteins), or classes of additives designed to make the final product palatable or more appealing (such as flavours, flavour enhancers, colours, emulsifiers, emulsifying salts, sweeteners, thickeners, and anti-foaming, bulking, carbonating, foaming, gelling and glazing agents).
This meta-analysis summarizes the evidence of a prospective association between the intake of foods whole grains, refined grains, vegetables, fruit, nuts, legumes, eggs, dairy, fish, red meat, ...processed meat, and sugar-sweetened beverages (SSBs) and risk of general overweight/obesity, abdominal obesity, and weight gain. PubMed and Web of Science were searched for prospective observational studies until August 2018. Summary RRs and 95% CIs were estimated from 43 reports for the highest compared with the lowest intake categories, as well as for linear and nonlinear relations focusing on each outcome separately: overweight/obesity, abdominal obesity, and weight gain. The quality of evidence was evaluated with use of the NutriGrade tool. In the dose-response meta-analysis, inverse associations were found for whole-grain (RRoverweight/obesity: 0.93; 95% CI: 0.89, 0.96), fruit (RRoverweight/obesity: 0.93; 95% CI: 0.86, 1.00; RRweight gain: 0.91; 95% CI: 0.86, 0.97), nut (RRabdominal obesity: 0.42; 95% CI: 0.31, 0.57), legume (RRoverweight/obesity: 0.88; 95% CI: 0.84, 0.93), and fish (RRabdominal obesity: 0.83; 95% CI: 0.71, 0.97) consumption and positive associations were found for refined grains (RRoverweight/obesity: 1.05; 95% CI: 1.00, 1.10), red meat (RRabdominal obesity: 1.10; 95% CI: 1.04, 1.16; RRweight gain: 1.14; 95% CI: 1.03, 1.26), and SSBs (RRoverweight/obesity: 1.05; 95% CI: 1.00, 1.11; RRabdominal obesity: 1.12; 95% CI: 1.04, 1.20). The dose-response meta-analytical findings provided very low to low quality of evidence that certain food groups have an impact on different measurements of adiposity risk. To improve the quality of evidence, better-designed observational studies, inclusion of intervention trials, and use of novel statistical methods (e.g., substitution analyses or network meta-analyses) are needed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The gut microbiome is shaped by diet and influences host metabolism; however, these links are complex and can be unique to each individual. We performed deep metagenomic sequencing of 1,203 gut ...microbiomes from 1,098 individuals enrolled in the Personalised Responses to Dietary Composition Trial (PREDICT 1) study, whose detailed long-term diet information, as well as hundreds of fasting and same-meal postprandial cardiometabolic blood marker measurements were available. We found many significant associations between microbes and specific nutrients, foods, food groups and general dietary indices, which were driven especially by the presence and diversity of healthy and plant-based foods. Microbial biomarkers of obesity were reproducible across external publicly available cohorts and in agreement with circulating blood metabolites that are indicators of cardiovascular disease risk. While some microbes, such as Prevotella copri and Blastocystis spp., were indicators of favorable postprandial glucose metabolism, overall microbiome composition was predictive for a large panel of cardiometabolic blood markers including fasting and postprandial glycemic, lipemic and inflammatory indices. The panel of intestinal species associated with healthy dietary habits overlapped with those associated with favorable cardiometabolic and postprandial markers, indicating that our large-scale resource can potentially stratify the gut microbiome into generalizable health levels in individuals without clinically manifest disease.
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GEOZS, IJS, IMTLJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZAGLJ
From the 1960s to the early 21st-century adherence to the Mediterranean diet (MD) declined around the world. This was partly due to the westernization of eating habits. However, in the last decade a ...new variable came into play, the economic crisis, which may have affected dietary patterns.
We analyzed worldwide trends of adherence to the MD between the periods 1961-1965, 2000-2003 and 2004-2011.
Data was obtained from the Food and Agriculture Organization Food Balance Sheets in three study periods: 1961-1965, 2000-2003 and 2004-2011. The Mediterranean Adequacy Index (MAI) was calculated for 41 selected countries using the averages of available energy intake for different food groups. Changes in MAI indicated the trends in adherence in the different periods.
In many countries, MAI deteriorated from 1961 to 1965 and 2004 to 2011, yet an increase was observed in 16 countries. Between the last two observation periods, MAI values stabilized in 16 of the 41 selected countries. Regional rankings for the three study periods based on descending MAI scores were: Southern Mediterranean, Mediterranean Europe, Central Europe and Northern Europe.
Adherence to the MD significantly decreased between 1961-65 and 2000-03, whereas from 2004-2011 there was a stabilization of MAI values and even an increase among 16 countries. Efforts are needed to preserve the dietary traditions and lifestyle habits within the Mediterranean region in order to counteract increasing rates of chronic disease.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, VSZLJ, ZAGLJ
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