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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).
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.
Only 6 to 8 % of the UK adults meet the daily recommendation for dietary fibre. Fava bean processing lead to vast amounts of high-fibre by-products such as hulls. Bean hull fortified bread was formulated to increase and diversify dietary fibre while reducing waste. This study assessed the bean hull: suitability as a source of dietary fibre; the systemic and microbial metabolism of its components and postprandial events following bean hull bread rolls. Nine healthy participants (53·9 ± 16·7 years) were recruited for a randomised controlled crossover study attending two 3 days intervention sessions, involving the consumption of two bread rolls per day (control or bean hull rolls). Blood and faecal samples were collected before and after each session and analysed for systemic and microbial metabolites of bread roll components using targeted LC-MS/MS and GC analysis. Satiety, gut hormones, glucose, insulin and gastric emptying biomarkers were also measured. Two bean hull rolls provided over 85 % of the daily recommendation for dietary fibre; but despite being a rich source of plant metabolites (P = 0·04 v. control bread), these had poor systemic bioavailability. Consumption of bean hull rolls for 3 days significantly increased plasma concentration of indole-3-propionic acid (P = 0·009) and decreased faecal concentration of putrescine (P = 0·035) and deoxycholic acid (P = 0·046). However, it had no effect on postprandial plasma gut hormones, bacterial composition and faecal short chain fatty acids amount. Therefore, bean hulls require further processing to improve their bioactives systemic availability and fibre fermentation.
Metabolites produced by microbial fermentation in the human intestine, especially short-chain fatty acids (SCFAs), are known to play important roles in colonic and systemic health. Our aim here was to advance our understanding of how and why their concentrations and proportions vary between individuals. We have analysed faecal concentrations of microbial fermentation acids from 10 human volunteer studies, involving 163 subjects, conducted at the Rowett Institute, Aberdeen, UK over a 7-year period. In baseline samples, the % butyrate was significantly higher, whilst % iso-butyrate and % iso-valerate were significantly lower, with increasing total SCFA concentration. The decreasing proportions of iso-butyrate and iso-valerate, derived from amino acid fermentation, suggest that fibre intake was mainly responsible for increased SCFA concentrations. We propose that the increase in % butyrate among faecal SCFA is largely driven by a decrease in colonic pH resulting from higher SCFA concentrations. Consistent with this, both total SCFA and % butyrate increased significantly with decreasing pH across five studies for which faecal pH measurements were available. Colonic pH influences butyrate production through altering the stoichiometry of butyrate formation by butyrate-producing species, resulting in increased acetate uptake and butyrate formation, and facilitating increased relative abundance of butyrate-producing species (notably Roseburia and Eubacterium rectale).
Errors inherent in self-reported measures of energy intake (EI) are substantial and well documented, but correlates of misreporting remain unclear. Therefore, potential predictors of misreporting were examined. In Study One, fifty-nine individuals (BMI = 26·1 (sd 3·8) kg/m2, age = 42·7 (sd 13·6) years, females = 29) completed a 14-d stay in a residential feeding behaviour suite where eating behaviour was continuously monitored. In Study Two, 182 individuals (BMI = 25·7 (sd 3·9) kg/m2, age = 42·4 (sd 12·2) years, females = 96) completed two consecutive days in a residential feeding suite and five consecutive days at home. Misreporting was directly quantified by comparing covertly measured laboratory weighed intakes (LWI) with self-reported EI (weighed dietary record (WDR), 24-h recall, 7-d diet history, FFQ). Personal (age, sex and %body fat) and psychological traits (personality, social desirability, body image, intelligence quotient and eating behaviour) were used as predictors of misreporting. In Study One, those with lower psychoticism (P = 0·009), openness to experience (P = 0·006) and higher agreeableness (P = 0·038) reduced EI on days participants knew EI was being measured to a greater extent than on covert days. Isolated associations existed between personality traits (psychoticism and openness to experience), eating behaviour (emotional eating) and differences between the LWI and self-reported EI, but these were inconsistent between dietary assessment techniques and typically became non-significant after accounting for multiplicity of comparisons. In Study Two, sex was associated with differences between LWI and the WDR (P = 0·009), 24-h recall (P = 0·002) and diet history (P = 0·050) in the laboratory, but not home environment. Personal and psychological correlates of misreporting identified displayed no clear pattern across studies or dietary assessment techniques and had little utility in predicting misreporting.
There is convincing evidence that sugar-sweetened beverages increase the risk of body weight (BW) gain and higher body mass index. In contrast, observational studies on regular consumption of 100% fruit juice provide conflicting results, with most indicating a neutral impact on BW and body mass index, while others suggest positive or even inverse associations. The lack of agreement may be due to confounding factors, or studies incorrectly grouping sugar-sweetened juice drinks with 100% fruit juice, which by law does not contain added sugars.
Intervention studies provide an opportunity to investigate the short-to-medium term impact of daily 100% orange juice (OJ) on BW. From 17 available studies (none of which individually reported statistically significant BW gain following OJ consumption), 6 randomised controlled trials in adults (n = 236) were selected for a meta-analysis. Inclusion criteria were an OJ intervention group and a non-OJ control group. Comparison with a control group was essential as some were weight loss studies with additional BW loss components, and for consistency this comparison between groups was used for all studies.
The meta-analysis focussed on the differences in BW change between OJ and control arms of each study. Where the standard deviation (SD) or standard error (SE) of the change was not reported and could not be obtained from the authors, it was imputed by using the mean SD from studies where it was available. The combined effect estimate was obtained from a random effects model using the DerSimonian-Laird estimator. Cochran's Q statistic was used to test for study heterogeneity.
The resulting forest plot revealed no statistically significant change in BW following OJ intakes of 250–500 ml per day for 4 to 12 weeks (which would theoretically provide 102–205 kcal on average). The Q statistic was 10.6 (I2 = 53%, p = 0.059) while the combined effect estimate was -0.34 kg (95% CI -1.11, 0.43). Interestingly, a similar finding was noted in a meta-analysis (Onakpoya et al. 2017) investigating BW change and daily grapefruit juice consumption from 3 intervention studies (n = 233); mean difference -0.45 kg [95% CI:-1.06 to 0.16; I2 = 53%].
In conclusion, daily consumption of OJ does not appear to have an adverse impact on BW. This may be due to dietary energy compensation (conscious or unconscious) or a satiety effect linked with the low glycemic index of OJ (GI = 50).
Onakpoya I et al. (2017) Crit Rev Food Sci Nutr 57: 602–612.
From 2008, the UK’s National Diet and Nutrition Survey (NDNS) changed the method of dietary data collection from a 7-d weighed diary to a 4-d unweighed diary, partly to reduce participant burden. This study aimed to test whether self-reported energy intake changed significantly over the 4-d recording period of the NDNS rolling programme. Analyses used data from the NDNS years 1 (2008/2009) to 8 (2015/2016) inclusive, from participants aged 13 years and older. Dietary records from participants who reported unusual amounts of food and drink consumed on one or more days were excluded, leaving 6932 participants. Mean daily energy intake was 7107 kJ (1698 kcal), and there was a significant decrease of 164 kJ (39 kcal) between days 1 and 4 (P < 0·001). There was no significant interaction of sex or low-energy reporter status (estimated from the ratio of reported energy intake:BMR) with the change in reported energy intake. The decrease in reported energy intake on day 4 compared with day 1 was greater (P < 0·019) for adults with higher BMI (>30 kg/m2) than it was for leaner adults. Reported energy intake decreased over the 4-d recording period of the NDNS rolling programme suggesting that participants change their diet more, or report less completely, with successive days of recording their diet. The size of the effect was relatively minor, however.
Placental weight is a valuable indicator of its function, predicting both pregnancy outcome and lifelong health. Population-based centile charts of weight-for-gestational-age and parity are useful for identifying extremes of placental weight but fail to consider maternal size. To address this deficit, a multiple regression model was fitted to derive coefficients for predicting normal placental weight using records from healthy pregnancies of nulliparous/multiparous women of differing height and weight (n = 107,170 deliveries, 37–43 weeks gestation). The difference between actual and predicted placental weight generated a z-score/individual centile for the entire cohort including women with pregnancy complications (n = 121,591). The association between maternal BMI and placental weight extremes defined by the new customised versus population-based standard was investigated by logistic regression, as was the association between low placental weight and pregnancy complications. Underweight women had a greater risk of low placental weight [<10thcentile, OR 1.84 (95% CI 1.66, 2.05)] and obese women had a greater risk of high placental weight [>90th centile, OR 1.98 (95% CI 1.88, 2.10)] using a population standard. After customisation, the risk of high placental weight in obese/morbidly obese women was attenuated [OR 1.17 (95% CI 1.09, 1.25)]/no longer significant, while their risk of low placental weight was 59%–129% higher (P < 0.001). The customised placental weight standard was more closely associated with stillbirth, hypertensive disease, placental abruption and neonatal death than the population standard. Our customised placental weight standard reveals higher risk of relative placental growth restriction leading to lower than expected birthweights in obese women, and a stronger association between low placental weight and pregnancy complications generally. Further, it provides an alternative tool for defining placental weight extremes with implications for the placental programming of chronic disease.
To examine associations between hours worked and diet quality, frequency of eating out and consuming takeaways.
Data were taken from the National Diet and Nutrition Survey (2008–2014). Associations between hours worked in paid employment and diet quality, assessed using the Diet Quality Index (DQI) and selected foods and nutrients, were tested using linear regression models. Associations between hours worked and frequency of eating out and consuming takeaways were tested using ordinal logistic regression models. All models were adjusted for sex, age, equivalised household income, household composition and household food role.
Adults (n 2154) aged 19–64 years in employment.
Mean (95 % CI) hours worked per week was 36·1 (35·6, 36·6) and mean DQI score was 41·9 (41·2, 42·5) %. Hours worked was not associated with DQI score, frequency of eating out or consuming takeaways. Hours worked was positively associated with consuming red meat, processed meat and alcohol intake. Adults working more hours had lower intake of fibre but higher total fat and saturated fat intakes if they lived in households with children.
Working hours may not be the main factor driving poor-quality diets among this sample of UK adults in employment. Focusing on consumption of foods prepared outside the household may not be the most efficient way to improve diet quality as effort is needed at all levels. Although it is unclear what is driving the differences in nutrient intakes according to household composition, they are important to consider when developing interventions to improve healthy eating.
To model dietary changes required to shift the UK population to diets that meet dietary recommendations for health, have lower greenhouse gas emissions (GHGE) and are affordable for different income groups.
Linear programming was used to create diets that meet dietary requirements for health and reduced GHGE (57 and 80 % targets) by income quintile, taking account of food budgets and foods currently purchased, thereby keeping dietary change to a minimum.
Nutrient composition, GHGE and price data were mapped to 101 food groups in household food purchase data (UK Living Cost and Food Survey (2013), 5144 households).
Current diets of all income quintiles had similar total GHGE, but the source of GHGE differed by types of meat and amount of fruit and vegetables. It was possible to create diets with a 57 % reduction in GHGE that met dietary and cost restraints in all income groups. In the optimised diets, the food sources of GHGE differed by income group due to the cost and keeping the level of deviation from current diets to a minimum. Broadly, the changes needed were similar across all groups; reducing animal-based products and increasing plant-based foods but varied by specific foods.
Healthy and lower-GHGE diets could be created in all income quintiles but tailoring changes to income groups to minimise deviation may make dietary changes more achievable. Specific attention must be given to make interventions and policies appropriate for all income groups.
Assessment of national dietary guidelines in a number of European countries reveals that some are based on cohort studies, focusing on total seafood consumption, while others are based on the content of EPA and DHA, distinguishing between oily and other fish. The mean actual intake of fish in most countries is around or below the recommended intake, with differences in intake of fish being present between sex and age groups. Many people do not reach the national recommendation for total fish intake. Dietary recommendations for fish and EPA/DHA are based mainly on data collected more than 10 years ago. However, methods of farmed fish production have changed considerably since then. The actual content of EPA and DHA in farmed salmon has nearly halved as the traditional finite marine ingredients fish meal and fish oil in salmon diets have been replaced with sustainable alternatives of terrestrial origin. As farmed salmon is an important source of EPA and DHA in many Western countries, our intake of these fatty acids is likely to have decreased. In addition, levels of vitamin D and Se are also found to have declined in farmed fish in the past decade. Significant changes in the EPA and DHA, vitamin D and Se content of farmed fish means that average intakes of these nutrients in Western populations are probably lower than before. This may have consequences for the health-giving properties of fish as well as future dietary recommendations for fish intake.
Consumers in the UK responded to the rapid increases in food prices between 2007 and 2009 partly by reducing the amount of food energy bought. Household food and drink waste has also decreased since 2007. The present study explored the combined effects of reductions in food purchases and waste on estimated food energy intakes and dietary energy density.
The amount of food energy purchased per adult equivalent was calculated from Kantar Worldpanel household food and drink purchase data for 2007 and 2012. Food energy intakes were estimated by adjusting purchase data for food and drink waste, using waste factors specific to the two years and scaled for household size.
Households in Scotland (n 2657 in 2007; n 2841 in 2012).
The amount of food energy purchased decreased between 2007 and 2012, from 8·6 to 8·2 MJ/adult equivalent per d (P<0·001). After accounting for the decrease in food waste, estimated food energy intake was not significantly different (7·3 and 7·2 MJ/adult equivalent per d for 2007 and 2012, respectively; P=0·186). Energy density of foods purchased increased slightly from 700 to 706 kJ/100 g (P=0·010).
While consumers in Scotland reduced the amount of food energy that they purchased between 2007 and 2012, this was balanced by reductions in household food and drink waste over the same time, resulting in no significant change in net estimated energy intake of foods brought into the home.
The effects of fish oil (FO) supplementation on glycaemic control are unclear, and positive effects may occur only when the phospholipid content of tissue membranes exceeds 14 % as n-3 PUFA. Subjects (n 36, thirty-three completed) were paired based on metabolic parameters and allocated into a parallel double-blind randomised trial with one of each pair offered daily either 6 g of FO (3·9 g n-3 PUFA) or 6 g of maize oil (MO) for 9 months. Hyperinsulinaemic–euglycaemic–euaminoacidaemic (HIEGEAA) clamps (with [6,6 2H2 glucose]) were performed at the start and end of the intervention. Endogenous glucose production (EGP) and whole-body protein turnover (WBPT) were each measured after an overnight fast. The primary outcome involved the effect of oil type on insulin sensitivity related to glycaemic control. The secondary outcome involved the effect of oil type on WBPT. Subjects on FO (n 16) had increased erythrocyte n-3 PUFA concentrations >14 %, whereas subjects on MO (n 17) had unaltered n-3 PUFA concentrations at 9 %. Type of oil had no effect on fasting EGP, insulin sensitivity or total glucose disposal during the HIEGEAA clamp. In contrast, under insulin-stimulated conditions, total protein disposal (P=0·007) and endogenous WBPT (P=0·001) were both increased with FO. In an associated pilot study (n 4, three completed), although n-3 PUFA in erythrocyte membranes increased to >14 % with the FO supplement, the enrichment in muscle membranes remained lower (8 %; P<0·001). In conclusion, long-term supplementation with FO, at amounts near the safety limits set by regulatory authorities in Europe and the USA, did not alter glycaemic control but did have an impact on WBPT.
High-protein diets are an effective means for weight loss (WL), but the mechanisms are unclear. One hypothesis relates to the release of gut hormones by either protein or amino acids (AA). The present study involved overweight and obese male volunteers (n 18, mean BMI 36·8 kg/m2) who consumed a maintenance diet for 7 d followed by fully randomised 10 d treatments with three iso-energetic WL diets, i.e. with either normal protein (NP, 15 % of energy) or high protein (HP, 30 %) or with a combination of protein and free AA, each 15 % of energy (NPAA). Psychometric ratings of appetite were recorded hourly. On day 10, plasma samples were taken at 30 min intervals over two consecutive 5 h periods (covering post-breakfast and post-lunch) and analysed for AA, glucose and hormones (insulin, total glucose-dependent insulinotropic peptide, active ghrelin and total peptide YY (PYY)) plus leucine kinetics (first 5 h only). Composite hunger was 16 % lower for the HP diet than for the NP diet (P< 0·01) in the 5 h period after both meals. Plasma essential AA concentrations were greatest within 60 min of each meal for the NPAA diet, but remained elevated for 3–5 h after the HP diet. The three WL diets showed no difference for either fasting concentrations or the postprandial net incremental AUC (net AUCi) for insulin, ghrelin or PYY. No strong correlations were observed between composite hunger scores and net AUCi for either AA or gut peptides. Regulation of hunger may involve subtle interactions, and a range of signals may need to be integrated to produce the overall response.
To date, no study has directly and simultaneously measured the discrepancy between what people actually eat and what they report eating under observation in the context of energy balance (EB). The present study aimed to objectively measure the ‘extent’ and ‘nature’ of misreporting of dietary intakes under conditions in which EB and feeding behaviour were continuously monitored. For this purpose, a total of fifty-nine adults were recruited for 12 d, involving two 3 d overt phases and two 3 d covert phases of food intake measurement in a randomised cross-over design. Subjects had ad libitum access to a variety of familiar foods. Food intake was covertly measured using a feeding behaviour suite to establish actual energy and nutrient intakes. During the overt phases, subjects were instructed to self-report food intake using widely accepted methods. Misreporting comprised two separate and synchronous phenomena. Subjects decreased energy intake (EI) when asked to record their food intake (observation effect). The effect was significant in women ( − 8 %, P< 0·001) but not in men ( − 3 %, P< 0·277). The reported EI was 5 to 21 % lower (reporting effect) than the actual intake, depending on the reporting method used. Semi-quantitative techniques gave larger discrepancies. These discrepancies were identical in men and women and non-macronutrient specific. The ‘observation’ and ‘reporting’ effects combined to constitute total misreporting, which ranged from 10 to 25 %, depending on the intake measurement assessed. When studied in a laboratory environment and EB was closely monitored, subjects under-reported their food intake and decreased the actual intake when they were aware that their intake was being monitored.
Previous work has shown that hunger and food intake are lower in individuals on high-protein (HP) diets when combined with low carbohydrate (LC) intakes rather than with moderate carbohydrate (MC) intakes and where a more ketogenic state occurs. The aim of the present study was to investigate whether the difference between HPLC and HPMC diets was associated with changes in glucose and ketone body metabolism, particularly within key areas of the brain involved in appetite control. A total of twelve men, mean BMI 34·9 kg/m2, took part in a randomised cross-over trial, with two 4-week periods when isoenergetic fixed-intake diets (8·3 MJ/d) were given, with 30 % of the energy being given as protein and either (1) a very LC (22 g/d; HPLC) or (2) a MC (182 g/d; HPMC) intake. An 18fluoro-deoxyglucose positron emission tomography scan of the brain was conducted at the end of each dietary intervention period, following an overnight fast (n 4) or 4 h after consumption of a test meal (n 8). On the next day, whole-body ketone and glucose metabolism was quantified using [1,2,3,4-13C]acetoacetate, [2,4-13C]3-hydroxybutyrate and [6,6-2H2]glucose. The composite hunger score was 14 % lower (P= 0·013) for the HPLC dietary intervention than for the HPMC diet. Whole-body ketone flux was approximately 4-fold greater for the HPLC dietary intervention than for the HPMC diet (P< 0·001). The 9-fold difference in carbohydrate intakes between the HPLC and HPMC dietary interventions led to a 5 % lower supply of glucose to the brain. Despite this, the uptake of glucose by the fifty-four regions of the brain analysed remained similar for the two dietary interventions. In conclusion, differences in the composite hunger score observed for the two dietary interventions are not associated with the use of alternative fuels by the brain.