National Diet And Nutrition Survey Research Articles

Association between Adiposity and Iron Status in Women of Reproductive Age: Data from the UK National Diet and Nutrition Survey (NDNS) 2008–2019

BackgroundOverweight/obesity and iron deficiency (ID) are highly prevalent in women of reproductive age (WRA), impacting on women’s health. Obesity is a risk factor for nutritional deficiencies but its association with ID is unclear. ObjectivesTo determine the association between adiposity and markers of iron status and ID prevalence in WRA. MethodsThis cross-sectional study analyzed the National Diet and Nutrition Survey (2008–2019) data, focusing on women aged 18–49 y with body mass index (BMI) ≥18.5 kg/m2. Prevalence of anemia, iron deficiency anemia (IDA), and ID were analyzed. Ferritin was adjusted for C-reactive protein. Iron status was assessed across high and low BMI, waist circumference (WC), waist-to-height ratio (WHtR), and waist-to-hip ratio (WHR). χ2, linear and logistic regressions were performed adjusting for covariates. ResultsAmong 1098 WRA, 496 normal weight and 602 overweight/obesity, prevalence rates were: anemia 9.2% and IDA 6.8%. Anemia was more prevalent in those with higher WHtR and WHR (11.9% compared with 5.9% and 16.7% compared with 6.5%, both P < 0.001). WRA with increased WC, WHtR, and WHR had higher IDA prevalence than those with lower adiposity (8.5% compared with 4.3%, P = 0.005; 9.4% compared with 3.3%, P < 0.001; 12.1% compared with 4.9%, P < 0.001). ID prevalence was 49.7% (ferritin cutoff 30 μg/L) and 19.6% (ferritin cutoff 15 μg/L), showing similar rates across adiposity groups. ID prevalence defined by soluble transferrin receptor (sTfR) was higher in women with increased WHR (P = 0.001). Higher WHR predicted ID categorized by sTfR (adjusted odds ratio [aOR]: 2.104, P = 0.004), and WHtR and WHR predicted anemia and IDA (anemia: WHtR aOR: 2.006, P = 0.036; WHR aOR: 4.489, P < 0.001 and IDA: WHtR aOR: 2.942, P = 0.012; WHR aOR: 4.142, P < 0.001). ConclusionsAt least 1 in 5 WRA in the UK are iron deficient, highlighting the need to revise current policies. Greater central adiposity was strongly associated with impaired iron status and the development of anemia, IDA, and ID.

Ultra-processed food consumption in UK adolescents: distribution, trends, and sociodemographic correlates using the National Diet and Nutrition Survey 2008/09 to 2018/19

PurposeWe quantified levels of ultra-processed food (UPF) consumption and investigated consumption patterns in a representative sample of UK adolescents.MethodsWe used data from 4-day food diaries from adolescents in the UK National Diet and Nutrition Survey (NDNS) (2008/09–2018/19). UPF were identified using the NOVA classification. We estimated the percentage of Total Energy Intake (%TEI) and the absolute weight (grams). Linear regression models quantified differences in UPF consumption across survey years and its association with participant’s individual characteristics. This was an analysis of the repeated cross-sectional data from the UK NDNS Rolling Programme waves 1–11 (2008/09–2018/19). A total of 2991 adolescents (11–18y) with complete information on dietary intake were included.ResultsMean UPF consumption was 861 (SD 442) g/d and this accounted for 65.9% (SD 13.4%) of TEI. Between 2008 and 2019, mean UPF consumption decreased from 996 to 776 g/d [ – 211 (95%CI – 302; – 120)] and from 67.7% to 62.8% of TEI [ – 4.8% (95%CI – 8.1; – 1.5)]. Higher %TEI was consumed by adolescents with lower socioeconomic status; white ethnicity and living in England North. A higher weight of UPF consumption (g/d) was associated with being male, white, age 18y, having parents with routine or manual occupation, living in England North, and living with obesity.ConclusionAverage energy intake from UPF has decreased over a decade in UK adolescents. We observed a social and regional patterning of UPF consumption, with higher consumption among adolescents from lower socioeconomic backgrounds, from a white ethnicity and living in England North. Our findings suggest inequalities associated with UPF intake and factors that might lie beyond individual choice.

Beans, peas and pulses for improved public and planetary health: Changing UK consumption patterns

Beans, peas and pulses offer significant nutritional, health and environmental benefits (1,2,3, 4) and the FAO states that including pulses in agrifood systems is key to achieving the Sustainable Development Goals(5). Recommended intakes vary across national food-based dietary guidelines(6), but higher intakes are associated with benefits including increased satiety, reduced blood pressure, lower risk of cardiovascular disease and improved gut microbiota composition and activity(7). Worldwide, the average consumption of pulses is 21g per person per day(8) but published analysis of UK intakes is scarce. The aim of this review was to analyse consumption trends using two UK government datasets.The Family Food module of the Living Costs and Food Survey details food and drink purchases from approximately 5000 households per year(9). The ‘UK – household purchases’ data include the average (mean) quantities purchased per person per week. Categories relating to beans, peas and pulses were identified, and data were presented as graphs of purchasing trends (1974–2021).The National Diet and Nutrition Survey (NDNS)(10) assesses the nutritional status of 1000 participants (1.5 years and over) annually. Personal-level dietary data (2008-2019) were evaluated for subgroups ‘baked beans’ and ‘beans and pulses including ready meal and homemade dishes’, including fresh, frozen and canned beans and pulses, and recipes containing them. Peas/ green beans were excluded because of the nature of the data aggregation. Food-level dietary data (2018-19) were assessed for the frequency of consumption of different types of peas, beans and pulses.Our analysis of the Family Food datasets shows that, at 28g per person per day, the current average (mean) consumption of beans, peas and pulses in the UK is suboptimal. Our parallel analysis of NDNS data showed that more than 40% of participants were not eating any beans and pulses (excluding peas).Dietary trends are shifting. The overall consumption of beans, peas and pulses has been falling steadily since the late 1980s. This is mostly due to the drop in consumption of peas and baked beans, though these are still the most frequently consumed legume categories in the UK diet. Canned pea purchases fell from 88g per person per week in 1974 to 14g per person per week in 2020-21. Baked bean purchases peaked at 133g per person per week in 1986, dropping to 78g in 2020-21.Purchases of other canned beans and pulses (excluding baked beans) have increased noticeably in the last decade, from 17g per person per week 2015-16 to 32g in 2020-21. Purchases of dried pulses have remained consistent, averaging 11g per person per week in 2020-21.This analysis indicates significant scope to deliver affordable, accessible health and environmental benefits through increased consumption of beans, peas and pulses in the UK.

2027 Protein consumption and determinants in community dwelling older adults: National Diet and Nutrition Survey (NDNS) years 9-11

Abstract Introduction Reference nutrient intake for protein amongst the general population is 0.75 grammes of protein per kilogram of body weight per day (g/kg BW/d). Expert groups recommend healthy adults over 65years have 1.0-1.2g/kg BW/d to support good health and maintain functionality (Deutz, Bauer and Barrazoni, Clinical Nutrition, 33(6):929-36). A recent paper suggested age specific recommendations of 1.2g/kg BW/d (Dorrington, Fallaize and Hobbs, Journal of Nutrition, 150(9):2245-2256). This study aimed to quantify percentage of community dwelling older adults meeting recommendations for protein intake and explore factors associated with low consumption. Methods The study population comprised &amp;gt;65s completing the NDNS survey years 9-11 (2016-2019). Dietary intake was recorded in food diaries. Protein consumption was calculated as grammes per kilogram adjusted body weight per day (g/kg aBW/d). Adjustment made for body mass index (BMI) below 22kg/m2 and above 27kg/m2. Percentage of participants meeting protein recommendations for 0.75, 1.0 and 1.2g/kg BW/d was calculated. Chi-squared test for independence was utilised to determine association between social, health and lifestyle factors and low protein intake. Results Data from 385 participants were included; 43% male, 98% white. Mean protein intake was 0.98g/kg aBW/d (SD ±0.25). Prevalence of protein intake below 0.75g/kg aBW/d was 16.4% (n=63), below 1.0g/kg aBW/d was 52.2% (n=201) and below 1.2g/kg aBW/d 82.1% (n=316). Current and ex-regular smoking was associated with protein intake &amp;lt;1g/kg aBW/d (p=0.01). No other analysis reached statistical significance although prevalence of low protein intake was higher in those without their own teeth (p=0.08), use of dentures (p=0.14) and BMI of 27-30kg/m2 (p=0.09). Conclusion A large percentage of older adults are below expert recommendations for protein intake. There is a need for clarity over recommendations so that a clear public message can be given to optimise health and function in ageing. Factors influencing poor protein intake require further examination.

Nutrients or processing? An analysis of food and drink items from the UK National Diet and Nutrition Survey based on nutrient content, the NOVA classification and front of package traffic light labelling.

UK front of package labelling (FOPL) informs consumers on the nutrient content of food. However, FOPL does not consider food processing, and with the UK government being urged to act on ultra-processed food (UPF), whether UPF should be added to FOPL is unclear. This study compared food and drink in the UK National Diet and Nutrition Survey (NDNS) Intake24 database based on FOPL, nutrient content and NOVA classification, to understand whether UPF are covered by dietary recommendations for foods high in fat, salt and sugar. NDNS items were coded into minimally processed food (MPF), processed culinary ingredients, processed food and UPF according to the NOVA classification and FOPL traffic lights. UPF contained greater energy, fat, saturated fat (SF), total sugar (TS) and salt than MPF. UPF had a greater odds of containing red FOPL and an unhealthier overall FOPL score (OR:4·59 (95 % CI: 3·79, 5·57); OR:7·0 (95 % CI: 6·1, 8·2), respectively) and lower odds of containing green FOPL (OR:0·05 (95 % CI: 0·03, 0·10)), compared with MPFs. For items with no red FOPL, UPF still contained greater energy, fat, SF, TS and salt than MPF. However, several UPF have healthier FOPL scores. UPF had an unhealthier nutritional profile and FOPL score than MPF. For items with no red FOPL, UPF still had an unhealthier profile than MPF, with a higher energy density. Importantly, not all UPF were unhealthy according to FOPL. These results indicate partial overlap between FOPL, nutrient content and NOVA classification of UK food and drink products, with implications for UK food and drink labelling.

Consumption of foods with the highest nutritional quality, and the lowest greenhouse gas emissions and price, differs between socio-economic groups in the UK population.

To establish a baseline understanding of whether consuming food with the highest nutritional quality, lowest greenhouse gas emissions (GHGE) and cost differs between different UK demographic and socio-economic population groups. Multiple linear regression models were fitted to evaluate the relationship between predictor socio-demographic variables in this study (i.e. sex, ethnic group, age, BMI and level of deprivation) and the response variables (i.e. consumption of items considered most nutritious, with a low GHGE and price, as a proportion of total items consumed). The UK. 1374 adult (18-65 years) participants from the National Diet and Nutrition Survey latest waves 9-11 (2016-2017 and 2018-2019). Based on the total energy consumption in a day, the average diet-based GHGE was significantly higher for participants with a higher BMI. Non-white and most deprived participants spent significantly (P < 0·001) less money per total energy consumption. Participants with a BMI between 18·6 and 39·9 kg/m2 and those living in the least deprived areas consumed a significantly (P < 0·001) higher amount of those items considered the most nutritious, with the lowest GHGE and cost per 100 kcal. Consumption of food with the highest nutritional quality, lowest GHGE and cost in the UK varies among those with different socio-demographic characteristics, especially the deprivation level of participants. Our analysis endorses the consideration of environmental sustainability and affordability, in addition to the consideration of nutritional quality from a health perspective, to make current dietary guidelines more encompassing and equitable.

Contribution of meat-free days, meat-free meals, and portion sizes to UK meat consumption declines

Abstract Background In the UK, reduced meat consumption is needed to protect planetary health and reduce noncommunicable disease. An analysis of the UK's National Diet and Nutrition Survey (NDNS) found from 2008-2019, daily meat consumption per capita decreased from 103.7g to 86.3g (∼1.7% per annum). This trend is not fast enough to meet reduction targets, such as the National Food Strategy's goal of a 30% reduction by 2030. Objectives This study aimed to examine behaviours driving the reduction in meat consumption. Specifically, we investigated two key strategies: reduced consumption frequency and smaller portion sizes. Design: Using NDNS rolling programme (2008/09-2018/19) data, we evaluated changes in: 1) the number of meat-eating days, 2) the number of meat-containing meal occasions, and 3) the portion size of meat per meat-containing meal occasion using Poisson and linear regression models. Meat consumption was based on disaggregated data from 4-day food diaries. Using decomposition analysis, we estimated the proportion of responsibility for each consumption behaviour relative to the overall decrease in consumption. Results The mean number of meat-eating days decreased from 3.3 to 3.0 days (P &amp;lt; 0.001), the mean number of daily meat-containing meal occasions decreased from 1.2 to 1.1 occasions (P = 0.01), and the mean portion size of meat decreased from 85.8g to 76.1g (P &amp;lt; 0.001). Portion size contributed most to the reductions in meat consumption (57%), followed by meat-eating days (37%), and meat-containing meal occasions (6%). Conclusions Our findings suggest the decrease in UK meat consumption can be largely attributed to smaller portion sizes of meat. Understanding the nuances of meat consumption behaviours (i.e., “meat-free days” vs. “meat-free meals” vs. “low-meat meals”) could help tailor behaviour change interventions by showing the need for a focus on promoting low-meat meals to accelerate reductions in meat consumption toward population and environmental health goals. Key messages • The decrease in UK meat consumption is largely being driven by reductions in portion size of meat at meal occasions. • Understanding nuances of meat consumption behaviours can help tailor behaviour change interventions by showing the need for a focus on promoting low-meat meals to accelerate reduced meat consumption.

Do markers of adiposity and glycaemia mediate the association between low carbohydrate diet and cardiovascular risk factors: findings from the UK National Diet and Nutrition Survey (NDNS) 2008–2016

ObjectivesTo examine the associations between low carbohydrate diet (LCD) and conventional cardiovascular risk factors and investigate whether these associations are mediated by body mass index (BMI), waist circumference (WC) and...

Machine learning models trained on synthetic datasets of multiple sample sizes for the use of predicting blood pressure from clinical data in a national dataset.

The potential for synthetic data to act as a replacement for real data in research has attracted attention in recent months due to the prospect of increasing access to data and overcoming data privacy concerns when sharing data. The field of generative artificial intelligence and synthetic data is still early in its development, with a research gap evidencing that synthetic data can adequately be used to train algorithms that can be used on real data. This study compares the performance of a series machine learning models trained on real data and synthetic data, based on the National Diet and Nutrition Survey (NDNS). Features identified to be potentially of relevance by directed acyclic graphs were isolated from the NDNS dataset and used to construct synthetic datasets and impute missing data. Recursive feature elimination identified only four variables needed to predict mean arterial blood pressure: age, sex, weight and height. Bayesian generalised linear regression, random forest and neural network models were constructed based on these four variables to predict blood pressure. Models were trained on the real data training set (n = 2408), a synthetic data training set (n = 2408) and larger synthetic data training set (n = 4816) and a combination of the real and synthetic data training set (n = 4816). The same test set (n = 424) was used for each model. Synthetic datasets demonstrated a high degree of fidelity with the real dataset. There was no significant difference between the performance of models trained on real, synthetic or combined datasets. Mean average error across all models and all training data ranged from 8.12 To 8.33. This indicates that synthetic data was capable of training equally accurate machine learning models as real data. Further research is needed on a variety of datasets to confirm the utility of synthetic data to replace the use of potentially identifiable patient data. There is also further urgent research needed into evidencing that synthetic data can truly protect patient privacy against adversarial attempts to re-identify real individuals from the synthetic dataset.

Ready meals, especially those that are animal-based and cooked in an oven, have lower nutritional quality and higher greenhouse gas emissions and are more expensive than equivalent home-cooked meals.

To examine whether ready meals and equivalent home-cooked meals differ in nutritional quality indicators, greenhouse gas emissions (GHGE) and cost. We performed a cross-sectional analysis of meal data from the National Diet and Nutrition Survey (NDNS) nutrient databank (2018/19). Additional data on nutrient composition, cost and cooking-related GHGE were calculated and compared between fifty-four ready meals and equivalent home-cooked meals. The UK. Not applicable. Ready meals, overall and those that were animal-based, had significantly higher levels of free sugar compared with equivalent home-cooked meals (P < 0·0001 and P < 0·0004, respectively). Animal-based ready meals had significantly higher levels of GHGE (P < 0·001), whereas the cost of ready meals, overall, was significantly higher (P < 0·001), compared with equivalent home-cooked meals. Animal-based meals, whether ready meals or equivalent homemade meals, had significantly higher levels of protein (P < 0·0001), contained significantly more kilocalories (P = 0·001), had significantly higher levels of GHGE (P < 0·0001) and were significantly more expensive (P < 0·0001), compared with plant-based meals. Overall, plant-based meals home-cooked on the gas or electric stove had the lowest GHGE and cost, whereas animal-based oven-cooked ready meals had the highest levels of GHGE and were most expensive. Ready meals have lower nutritional quality and higher GHGE and are more expensive than equivalent home-cooked meals, especially those meals that are animal-based and prepared in an oven.

Inequalities in energy drink consumption among UK adolescents: a mixed-methods study.

To examine energy drink consumption among adolescents in the United Kingdom (UK) and associations with deprivation and dietary inequalities. Quantitative dietary and demographic data from the National Diet and Nutrition Survey (NDNS) repeated cross-sectional survey were analysed using logistic regression models. Qualitative data from semi-structured interviews were analysed using inductive thematic analysis. UK. Quantitative data: nationally representative sample of 2587 adolescents aged 11-18 years. Qualitative data: 20 parents, 9 teachers, and 28 adolescents from Hampshire, UK. NDNS data showed adolescents' consumption of energy drinks was associated with poorer dietary quality (OR 0.46 per SD; 95% CI 0.37, 0.58; p<0.001). Adolescents from more deprived areas and lower income households were more likely to consume energy drinks than those in more affluent areas and households (OR 1.40; 95%CI 1.16, 1.69; p<0.001; OR 0.98 per £1000; 95%CI, 0.96, 0.99; p<0.001 respectively). Between 2008 and 2016, energy drink consumption among adolescents living in the most deprived areas increased, but decreased among those living in the most affluent neighbourhoods (p=0.04). Qualitative data identified three themes. First, many adolescents drink energy drinks because of their friends and because the unbranded drinks are cheap. Second, energy drink consumption clusters with other unhealthy eating behaviours and adolescents don't know why energy drinks are unhealthy. Third, adolescents believe voluntary bans in retail outlets and schools do not work. This study supports the introduction of age-dependent legal restrictions on the sale of energy drinks which may help curb existing socio-economic disparities in adolescents' energy drink intake.

Time to revisit the passive overconsumption hypothesis? Humans show sensitivity to calories in energy-rich meals

ABSTRACTBackgroundA possible driver of obesity is insensitivity (passive overconsumption) to food energy density (ED, kcal/g); however, it is unclear whether this insensitivity applies to all meals.ObjectivesWe assessed the influence of ED on energy intake (kcal) across a broad and continuous range of EDs comprised of noncovertly manipulated, real-world meals. We also allowed for the possibility that the association between energy intake and ED is nonlinear.MethodsWe completed a secondary analysis of 1519 meals which occurred in a controlled environment as part of a study conducted by Hall and colleagues to assess the effects of food ultra-processing on energy intake. To establish the generalizability of the findings, the analyses were repeated in 32,162 meals collected from free-living humans using data from the UK National Diet and Nutrition Survey (NDNS). Segmented regressions were performed to establish ED “breakpoints” at which the association between consumed meal ED and mean centered meal caloric intake (kcal) changed.ResultsSignificant breakpoints were found in both the Hall et al. data set (1.41 kcal/g) and the NDNS data set (1.75 and 2.94 kcal/g). Centered meal caloric intake did not increase linearly with consumed meal ED, and this pattern was captured by a 2-component (“volume” and “calorie content” [biologically derived from the sensing of fat, carbohydrate, and protein]) model of physical meal size (g), in which volume is the dominant signal with lower energy-dense foods and calorie content is the dominant signal with higher energy-dense foods.ConclusionsThese analyses reveal that, on some level, humans are sensitive to the energy content of meals and adjust meal size to minimize the acute aversive effects of overconsumption. Future research should consider the relative importance of volume and calorie-content signals, and how individual differences impact everyday dietary behavior and energy balance.

Characterizing Adolescents' Dietary Intake by Taste: Results From the UK National Diet and Nutrition Survey.

The taste of foods is a key factor for adolescents' food choices and intakes, yet, exploring taste characteristics of adolescents' diet is limited. Using food records for 284 adolescents (10–19 years old) from the National Diet and Nutrition Survey (NDNS), year 9 (2016–2017), we classified diets according to taste. Tastes for each food consumed were generated from a previous survey that asked participants to allocate one main taste to each food. Responses from that survey were processed and included in a Hierarchical Cluster Analysis (HCA) to identify taste clusters. The resulting tastes were then applied to the adolescents' food records in the NDNS. For each individual, the total weight of food per day for each taste was calculated. A linear regression model was used to explore dietary intakes from each taste. Findings reveal that adolescents' daily energy intake was highest (34%) from foods that taste sweet. Sweet foods were the main calorie contributors at breakfast and daytime snacking, while energy intake from neutral-tasting foods was higher at lunch and dinner. Sweet food intake was significantly positively associated with higher energy, sugar, and fat intakes. For each percentage increase in sweet foods, energy increased by 10 kcal/d (95% CI 6, 15; P < 0.01). Savory food intake was lower in carbohydrates and sugars; with neutral food consumption inversely associated with energy, carbohydrate, sugars, saturated and total fat. Higher salty food intake was linked to higher saturated fat as well as sodium consumption. Sweet and neutral foods dominate the UK adolescent diet, followed by savory tastes. Balancing the contributions of different tasting foods could assist in improving adolescent diet quality.

Assessment of energy and nutrient intakes among undergraduate students attending a University in the North of England.

Background: Unhealthy diets are typical of university students and are often thought to be unrepresentative of the general population. The main aim was to determine the energy and nutrient intakes of a large cohort of undergraduate university students; and to compare to gender-specific dietary reference values (DRVs) and nutrient data from the general population. Methodology: Data was collected from 639 university students aged 18-24 years who completed 4-day diet diaries. The energy and nutrient intake was determined and percentage energy values calculated and compared with dietary reference values (DRVs) and the National Diet and Nutrition Survey (NDNS) and Family Food Statistics. Logistic regression methods were used to identify micronutrients functioning as predictors of exceeding DRVs. Results: Energy intakes were lower than the DRV. The percentage total energy values for protein, fat, saturated fat and carbohydrate exceeded DRVs but the percentage energy from alcohol was below the maximum 5%. The DRVs were met for vitamin C, thiamin, and sodium/salt. Iron and calcium intakes were met in males but not in females. Intakes for fibre and vitamin A were below the DRV. Student data was comparable to the NDNS, with the exception of alcohol, fibre, vitamin A, calcium and sodium/salt, which were all lower than the NDNS. Conclusions: This study contradicts the stereotypical assumption that students are following a high energy, fat, saturated fat, total sugars, salt and alcohol diet compared with the general population.

Food-Level Analysis to Identify Dietary Choices With the Highest Nutritional Quality and Lowest Greenhouse Gas Emissions and Price.

IntroductionFood systems are challenged to provide healthy, sustainable and affordable foods. From a consumer perspective, identifying healthy, sustainable and affordable choices based on individual food products rather than diets could promote better shopping choices.ObjectiveTo identify foods and drinks with the highest nutritional quality and lowest greenhouse gas emissions (GHGE) and price. We also assessed how a combination of these indicators (e.g., nutritional quality, GHGE and price) for food categories aligned with current United Kingdom dietary recommendations.Materials and MethodsWe performed a secondary analysis of the National Diet and Nutrition Survey (NDNS) nutrient databank year 11 (2018/2019). Spearman correlation coefficients were used to assess the strength of relationships between nutritional quality, environmental impact and/or prices per 100 kcal. In addition, we developed an optimized nutritional quality, GHGE and price score for each food or drink item based on the overall medians for each of these indicators.ResultsMedian nutritional value was highest for fruit and vegetables, whilst median GHGE and price was lower for starchy carbohydrates, fats and items of which consumption should be limited. The relative proportions of foods considered the most nutritious and with a low GHGE and price in each of the food categories, on a per 100 kcal basis, were comparable to the proportions in the Eatwell Guide, except for the proportion of fruits and vegetables being smaller and the proportion of potatoes, bread, rice, pasta, and other starchy carbohydrates being larger in our analysis.ConclusionPublic health efforts should consider the impact of dietary choices not only in terms of nutritional quality but also in terms of environmental and economic impact. Our food-based analysis shows a large variation in nutritional quality, GHGE and price within and across food categories, which provides consumers with opportunities for “food swaps” that are more nutritious and have lower GHGE and price.

Nutritional Benefits from Fatty Acids in Organic and Grass-Fed Beef.

Livestock production is under increasing scrutiny as a component of the food supply chain with a large impact on greenhouse gas emissions. Amidst growing calls to reduce industrial ruminant production, there is room to consider differences in meat quality and nutritional benefits of organic and/or pasture-based management systems. Access to forage, whether fresh or conserved, is a key influencing factor for meat fatty acid profile, and there is increasing evidence that pasture access is particularly beneficial for meat’s nutritional quality. These composition differences ultimately impact nutrient supply to consumers of conventional, organic and grass-fed meat. For this review, predicted fatty acid supply from three consumption scenarios were modelled: i. average UK population National Diet and Nutrition Survey (NDNS) (<128 g/week) red meat consumption, ii. red meat consumption suggested by the UK National Health Service (NHS) (<490 g/week) and iii. red meat consumption suggested by the Eat Lancet Report (<98 g/week). The results indicate average consumers would receive more of the beneficial fatty acids for human health (especially the essential omega-3, alpha-linolenic acid) from pasture-fed beef, produced either organically or conventionally.

Diet Quality Index for older adults (DQI-65): development and use in predicting adherence to dietary recommendations and health markers in the UK National Diet and Nutrition Survey.

Diet quality indexes (DQI) are useful tools for assessing diet quality in relation to health and guiding delivery of personalised nutritional advice; however, existing DQI are limited in their applicability to older adults (aged ≥ 65 years). Therefore, this research aimed to develop a novel evidence-based DQI specific to older adults (DQI-65). Three DQI-65 variations were developed to assess the impacts of different component quantitation methods and inclusion of physical activity. These were Nutrient and Food-based DQI-65 (NFDQI-65), NFDQI-65 with Physical Activity (NFDQI-65+PA) and Food-based DQI-65 with Physical Activity (FDQI-65+PA). To assess their individual efficacy, the NFDQI-65, NFDQI-65+PA and FDQI-65+PA were explored alongside the validated Healthy Eating Index-2015 (HEI-2015) and Alternative Healthy Eating Index-2010 (AHEI-2010) using data from the cross-sectional UK National Diet and Nutrition Survey (NDNS) rolling programme. Scores for DQI-65 variations, the HEI-2015 and AHEI-2010 were calculated for adults ≥ 65 years from years 2-6 of the NDNS (n 871). Associations with nutrient intake, nutrient status and health markers were analysed using linear and logistic regression. Higher DQI-65 and HEI-2015 scores were associated with increased odds of meeting almost all our previously proposed age-specific nutritional recommendations, and with important health markers of importance for older adults, including lower BMI, lower medication use and lower C-reactive protein (P < 0·01). Few associations were observed for the AHEI-2010. This analysis suggests value of all three DQI-65 as measures of dietary quality in UK older adults. However, methodological limitations mean further investigations are required to assess validity and reliability of the DQI-65.

Examination of dietary intake of UK preschool children by varying carers: evidence from the 2008-2016 UK National Diet and Nutrition Survey.

Early years caregivers can play a key role in young children's eating and the prevention of childhood obesity. The UK National Diet and Nutrition Survey (NDNS) is a large representative survey collecting detailed food and nutrition consumption data. Using these data, the aim of this study was to investigate the relationship between dietary intake of preschool children in the UK aged 2 to 4 years and accompanying adult/s. Nutrition consumption data from 1218 preschool children from years 1 to 8 of the 2008-2016 NDNS were accessed. Dietary data were captured using 3 or 4 day estimated food diaries. Regression analyses revealed significant differences in consumption when children were not accompanied by their parents. Compared with when children were with parents, children consumed significantly more energy dense meals (0·32 kJ/g, 95% CI 0·1-0·6 kJ/g), energy (62 kJ/g, (95% CI 27-97 kJ)) Na (19 mg, (95 % CI 6, 32)), added sugars (0·6 g, (95 % CI 0·1, 1·1)), vegetables (3 g, (95 % CI 1, 4)), total grams (12 g, (95 % CI 3, 21)) and saturated fat (0·2 g, (95 % CI 0·1, 0·4)) per eating occasion when accompanied by wider family. When children were accompanied by a formal childcare provider, they consumed significantly lower energy dense meals (-0·9 kJ/g, (95% CI -1·4 - -0·3 kJ/g)), less added sugars (-1·6 g, (95 % CI -2·4, -0·8)) and more fruit (12 g, (95 % CI 3, 21)) per eating occasion than when they were with their parents. The results demonstrate that non-parental caregivers might be an important target to promote healthy eating in young children. Further research is needed to establish which caregivers would benefit most.

Consistency between the Eatwell Guide and nutrient profiling models in the UK: an observational study

BackgroundNutrient profile models (NPMs) are algorithms that classify or rank foods according to their nutritional composition. An NPM was developed in 2005 in the UK to regulate marketing of foods to children. The model uses a scoring system that balances points awarded for the protein, fibre, fruit, vegetable, and nut content of foods and drinks against points awarded for energy, saturated fats, sodium, and sugars. Foods that score above 4 and drinks that score above 1 face marketing restrictions. The NPM was revised in 2018, but this version has not been finalised. The Eatwell Guide, the UK's food-based dietary guidelines, provides healthy eating advice. The aim of this study was to evaluate the extent that the 2005 and the 2018 versions of the NPM are consistent with the Eatwell Guide. MethodsIn this observational study, we obtained individual-level data from years 9 to 11 (2016/17 to 2018/19) of the National Diet and Nutrition Survey (NDNS). The NDNS is an annual survey measuring food consumption in the UK from a representative sample of approximately 1000 people per year. We estimated the healthiness of individuals' diets according to an Eatwell Guide dietary index (ie, number of food-based and nutrient-based recommendations achieved) and an NPM dietary index (ie, a weighted average of the NPM score of all foods eaten). We compared the agreement between diet healthiness according to the Eatwell Guide and the NPM indices by calculating the Cohen's κ coefficient. No specific ethics approval or patient consent was required for this analysis of NDNS data. Findings3028 individual diets were assessed. Individuals with a higher (ie, healthier) Eatwell Guide index score consumed a diet with, on average, a lower (ie, healthier) NPM index score both for the 2005 and 2018 versions. Agreement between the Eatwell Guide and the NPM dietary index in classifying a diet as “healthy” was moderate for relatively healthier diets, irrespective of the NPM version (Cohen's κ 0·39 [p<0·0001] for the 2005 version and 0·46 [p<0·0001] for the 2018 version, using an NPM cutoff of 4). InterpretationThis study suggests that the Eatwell Guide and NPM dietary indices are broadly concordant when assessing the healthiness of UK diets. As NPMs and food-based dietary guidelines evolve it is crucial to improve alignment between dietary advice and NPM-based food policies to ensure consistent information is conveyed to the public. FundingNIHR Academic Clinical Fellowship, Nuffield Department of Population Health (University of Oxford), NIHR Oxford Biomedical Research Centre, and the British Heart Foundation.

Relationships Between Food Groups and Eating Time Slots According to Diabetes Status in Adults From the UK National Diet and Nutrition Survey (2008-2017).

Time of eating is associated with diabetes and obesity but little is known about less healthy foods and specific time of their intake over the 24 h of the day. In this study, we aimed to identify potential relationships between foods and their eating time and to see whether these associations may vary by diabetes status. The National Diet and Nutrition Survey (NDNS) including 6,802 adults (age ≥ 19 years old) collected 749,026 food recordings by a 4-day-diary. The contingency table cross-classifying 60 food groups with 7 pre-defined eating time slots (6–9 a.m., 9 a.m.–12 p.m., 12–2 p.m., 2–5 p.m., 8–10 p.m., 10 p.m.–6 a.m.) was analyzed by Correspondence Analysis (CA). CA biplots were generated for all adults and separately by diabetes status (self-reported, pre-diabetes, undiagnosed-diabetes, and non-diabetics) to visually explore the associations between food groups and time of eating across diabetes strata. For selected food groups, odds ratios (OR, 99% CI) were derived of consuming unhealthy foods at evening/night (8 p.m.–6 a.m.) vs. earlier time in the day, by logistic regression models with generalized estimating equations. The biplots suggested positive associations between evening/night and consumption of puddings, regular soft drinks, sugar confectioneries, chocolates, beers, ice cream, biscuits, and crisps for all adults in the UK. The OR (99% CIs) of consuming these foods at evening/night were, respectively, 1.43 (1.06, 1.94), 1.72 (1.44, 2.05), 1.84 (1.31, 2.59), 3.08 (2.62, 3.62), 7.26 (5.91, 8.92), 2.45 (1.84, 3.25), 1.90 (1.68, 2.16), and 1.49 (1.22, 1.82) vs. earlier time in the day adjusted for age, sex, body mass index (BMI), and social-economic levels. Stratified biplots found that sweetened beverages, sugar-confectioneries appeared more strongly associated with evening/night among undiagnosed diabetics. Foods consumed in the evening/night time tend to be highly processed, easily accessible, and rich in added sugar or saturated fat. Individuals with undiagnosed diabetes are more likely to consume unhealthy foods at night. Further longitudinal studies are required to ascertain the causal direction of the association between late-eating and diabetes status.