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Little is known about who would benefit from Internet-based personalised nutrition (PN) interventions. This study aimed to evaluate the characteristics of participants who achieved greatest improvements (i.e. benefit) in diet, adiposity and biomarkers following an Internet-based PN intervention. Adults (n 1607) from seven European countries were recruited into a 6-month, randomised controlled trial (Food4Me) and randomised to receive conventional dietary advice (control) or PN advice. Information on dietary intake, adiposity, physical activity (PA), blood biomarkers and participant characteristics was collected at baseline and month 6. Benefit from the intervention was defined as ≥5 % change in the primary outcome (Healthy Eating Index) and secondary outcomes (waist circumference and BMI, PA, sedentary time and plasma concentrations of cholesterol, carotenoids and omega-3 index) at month 6. For our primary outcome, benefit from the intervention was greater in older participants, women and participants with lower HEI scores at baseline. Benefit was greater for individuals reporting greater self-efficacy for ‘sticking to healthful foods’ and who ‘felt weird if [they] didn’t eat healthily’. Participants benefited more if they reported wanting to improve their health and well-being. The characteristics of individuals benefiting did not differ by other demographic, health-related, anthropometric or genotypic characteristics. Findings were similar for secondary outcomes. These findings have implications for the design of more effective future PN intervention studies and for tailored nutritional advice in public health and clinical settings.
In the UK, 11.8% of expectant mothers undergo an elective caesarean section (ELCS) representing 92 000 births per annum. It is not known to what extent this procedure has an impact on mental well-being in the longer term.
To determine the prevalence and postpartum progression of anxiety and depression symptoms in women undergoing ELCS in Wales.
Prevalence of depression and anxiety were determined in women at University Hospital Wales (2015–16; n = 308) through completion of the Edinburgh Postnatal Depression Scale (EPDS; ≥13) and State-Trait Anxiety Inventory (STAI; ≥40) questionnaires 1 day prior to ELCS, and three postpartum time points for 1 year. Maternal characteristics were determined from questionnaires and, where possible, confirmed from National Health Service maternity records.
Using these criteria the prevalence of reported depression symptoms was 14.3% (95% CI 10.9–18.3) 1 day prior to ELCS, 8.0% (95% CI 4.2–12.5) within 1 week, 8.7% (95% CI 4.2–13.8) at 10 weeks and 12.4% (95% CI 6.4–18.4) 1 year postpartum. Prevalence of reported anxiety symptoms was 27.3% (95% CI 22.5–32.4), 21.7% (95% CI 15.8–28.0), 25.3% (95% CI 18.5–32.7) and 35.1% (95% CI 26.3–44.2) at these same stages. Prenatal anxiety was not resolved after ELCS more than 1 year after delivery.
Women undergoing ELCS experience prolonged anxiety postpartum that merits focused clinical attention.
Traditionally, personalised nutrition was delivered at an individual level. However, the concept of delivering tailored dietary advice at a group level through the identification of metabotypes or groups of metabolically similar individuals has emerged. Although this approach to personalised nutrition looks promising, further work is needed to examine this concept across a wider population group. Therefore, the objectives of this study are to: (1) identify metabotypes in a European population and (2) develop targeted dietary advice solutions for these metabotypes. Using data from the Food4Me study (n 1607), k-means cluster analysis revealed the presence of three metabolically distinct clusters based on twenty-seven metabolic markers including cholesterol, individual fatty acids and carotenoids. Cluster 2 was identified as a metabolically healthy metabotype as these individuals had the highest Omega-3 Index (6·56 (sd 1·29) %), carotenoids (2·15 (sd 0·71) µm) and lowest total saturated fat levels. On the basis of its fatty acid profile, cluster 1 was characterised as a metabolically unhealthy cluster. Targeted dietary advice solutions were developed per cluster using a decision tree approach. Testing of the approach was performed by comparison with the personalised dietary advice, delivered by nutritionists to Food4Me study participants (n 180). Excellent agreement was observed between the targeted and individualised approaches with an average match of 82 % at the level of delivery of the same dietary message. Future work should ascertain whether this proposed method could be utilised in a healthcare setting, for the rapid and efficient delivery of tailored dietary advice solutions.
Individual response to dietary interventions can be highly variable. The phenotypic characteristics of those who will respond positively to personalised dietary advice are largely unknown. The objective of this study was to compare the phenotypic profiles of differential responders to personalised dietary intervention, with a focus on total circulating cholesterol. Subjects from the Food4Me multi-centre study were classified as responders or non-responders to dietary advice on the basis of the change in cholesterol level from baseline to month 6, with lower and upper quartiles defined as responder and non-responder groups, respectively. There were no significant differences between demographic and anthropometric profiles of the groups. Furthermore, with the exception of alcohol, there was no significant difference in reported dietary intake, at baseline. However, there were marked differences in baseline fatty acid profiles. The responder group had significantly higher levels of stearic acid (18 : 0, P=0·034) and lower levels of palmitic acid (16 : 0, P=0·009). Total MUFA (P=0·016) and total PUFA (P=0·008) also differed between the groups. In a step-wise logistic regression model, age, baseline total cholesterol, glucose, five fatty acids and alcohol intakes were selected as factors that successfully discriminated responders from non-responders, with sensitivity of 82 % and specificity of 83 %. The successful delivery of personalised dietary advice may depend on our ability to identify phenotypes that are responsive. The results demonstrate the potential use of metabolic profiles in identifying response to an intervention and could play an important role in the development of precision nutrition.
According to many works on English phonology, word-final alveolar consonants – and only alveolar consonants – assimilate to following word-initial consonants, e.g. ran quickly → ra[ŋ] quickly. Some phonologists explain the readiness of alveolar consonants to assimilate (vs. the resistance of velar and labial articulations) by proposing that they have underspecified place of articulation (e.g. Avery & Rice 1989). Labial or dorsal nasals do not undergo assimilation because their place nodes are specified. There are reports that velar and labial consonants sometimes assimilate in English, but these are anecdotal observations, with no available audio and no statistics on their occurrence. We find evidence of assimilation of labial and velar nasals in the Audio British National Corpus, motivating a new, quantitative phonological framework: a statistical model of underspecification and variation which captures typical as well as less common but systematic patterns seen in non-coronal assimilation.
To characterise clusters of individuals based on adherence to dietary recommendations and to determine whether changes in Healthy Eating Index (HEI) scores in response to a personalised nutrition (PN) intervention varied between clusters.
Food4Me study participants were clustered according to whether their baseline dietary intakes met European dietary recommendations. Changes in HEI scores between baseline and month 6 were compared between clusters and stratified by whether individuals received generalised or PN advice.
Individuals in cluster 1 (C1) met all recommended intakes except for red meat, those in cluster 2 (C2) met two recommendations, and those in cluster 3 (C3) and cluster 4 (C4) met one recommendation each. C1 had higher intakes of white fish, beans and lentils and low-fat dairy products and lower percentage energy intake from SFA (P<0·05). C2 consumed less chips and pizza and fried foods than C3 and C4 (P<0·05). C1 were lighter, had lower BMI and waist circumference than C3 and were more physically active than C4 (P<0·05). More individuals in C4 were smokers and wanted to lose weight than in C1 (P<0·05). Individuals who received PN advice in C4 reported greater improvements in HEI compared with C3 and C1 (P<0·05).
The cluster where the fewest recommendations were met (C4) reported greater improvements in HEI following a 6-month trial of PN whereas there was no difference between clusters for those randomised to the Control, non-personalised dietary intervention.
To characterise participants who dropped out of the Food4Me Proof-of-Principle study.
The Food4Me study was an Internet-based, 6-month, four-arm, randomised controlled trial. The control group received generalised dietary and lifestyle recommendations, whereas participants randomised to three different levels of personalised nutrition (PN) received advice based on dietary, phenotypic and/or genotypic data, respectively (with either more or less frequent feedback).
Seven recruitment sites: UK, Ireland, The Netherlands, Germany, Spain, Poland and Greece.
Adults aged 18–79 years (n 1607).
A total of 337 (21 %) participants dropped out during the intervention. At baseline, dropouts had higher BMI (0·5 kg/m2; P<0·001). Attrition did not differ significantly between individuals receiving generalised dietary guidelines (Control) and those randomised to PN. Participants were more likely to drop out (OR; 95 % CI) if they received more frequent feedback (1·81; 1·36, 2·41; P<0·001), were female (1·38; 1·06, 1·78; P=0·015), less than 45 years old (2·57; 1·95, 3·39; P<0·001) and obese (2·25; 1·47, 3·43; P<0·001). Attrition was more likely in participants who reported an interest in losing weight (1·53; 1·19, 1·97; P<0·001) or skipping meals (1·75; 1·16, 2·65; P=0·008), and less likely if participants claimed to eat healthily frequently (0·62; 0·45, 0·86; P=0·003).
Attrition did not differ between participants receiving generalised or PN advice but more frequent feedback was related to attrition for those randomised to PN interventions. Better strategies are required to minimise dropouts among younger and obese individuals participating in PN interventions and more frequent feedback may be an unnecessary burden.
The interplay between the fat mass- and obesity-associated (FTO) gene variants and diet has been implicated in the development of obesity. The aim of the present analysis was to investigate associations between FTO genotype, dietary intakes and anthropometrics among European adults. Participants in the Food4Me randomised controlled trial were genotyped for FTO genotype (rs9939609) and their dietary intakes, and diet quality scores (Healthy Eating Index and PREDIMED-based Mediterranean diet score) were estimated from FFQ. Relationships between FTO genotype, diet and anthropometrics (weight, waist circumference (WC) and BMI) were evaluated at baseline. European adults with the FTO risk genotype had greater WC (AAv. TT: +1·4 cm; P=0·003) and BMI (+0·9 kg/m2; P=0·001) than individuals with no risk alleles. Subjects with the lowest fried food consumption and two copies of the FTO risk variant had on average 1·4 kg/m2 greater BMI (Ptrend=0·028) and 3·1 cm greater WC (Ptrend=0·045) compared with individuals with no copies of the risk allele and with the lowest fried food consumption. However, there was no evidence of interactions between FTO genotype and dietary intakes on BMI and WC, and thus further research is required to confirm or refute these findings.
An efficient and robust method to measure vitamin D (25-hydroxy vitamin D3 (25(OH)D3) and 25-hydroxy vitamin D2 in dried blood spots (DBS) has been developed and applied in the pan-European multi-centre, internet-based, personalised nutrition intervention study Food4Me. The method includes calibration with blood containing endogenous 25(OH)D3, spotted as DBS and corrected for haematocrit content. The methodology was validated following international standards. The performance characteristics did not reach those of the current gold standard liquid chromatography-MS/MS in plasma for all parameters, but were found to be very suitable for status-level determination under field conditions. DBS sample quality was very high, and 3778 measurements of 25(OH)D3 were obtained from 1465 participants. The study centre and the season within the study centre were very good predictors of 25(OH)D3 levels (P<0·001 for each case). Seasonal effects were modelled by fitting a sine function with a minimum 25(OH)D3 level on 20 January and a maximum on 21 July. The seasonal amplitude varied from centre to centre. The largest difference between winter and summer levels was found in Germany and the smallest in Poland. The model was cross-validated to determine the consistency of the predictions and the performance of the DBS method. The Pearson’s correlation between the measured values and the predicted values was r 0·65, and the sd of their differences was 21·2 nmol/l. This includes the analytical variation and the biological variation within subjects. Overall, DBS obtained by unsupervised sampling of the participants at home was a viable methodology for obtaining vitamin D status information in a large nutritional study.
Vanished from history is the story of the ‘Mariner's Calculator’, invented and patented at the Great Seal Patent Office, London, by Mrs Janet Taylor, in 1834. Dismissed by the Admiralty, it had no commercial future and only one instrument is known to remain in existence. The article traces the invention from its inception and provides relevant biographical details of its inventor. The authors then analyse the assessment by the Admiralty to determine if it was fair and outline the endeavour in 2004 to reassess the achievement by a reconstruction of the Mariner's Calculator from its original patent.
To use more effectively the limited resources available for conservation there is an urgent need to identify which conservation approaches are most likely to succeed. However, measuring conservation success is often difficult, as it is achieved outside the project time frame. Measures of implementation are often reported to donors to demonstrate achievement but it is unclear whether they really predict conservation success. We applied a conceptual framework and score-card developed by the Cambridge Conservation Forum (CCF) to a sample of 60 conservation activities to determine the predictive power of implementation measures versus measures of key outcomes (later steps in the models defined in the CCF tools). We show that assessing key outcomes is often more difficult than quantifying the degree of implementation of a project but that, while implementation is a poor predictor of success, key outcomes provide a feasible and much more reliable proxy for whether a project will deliver real conservation benefits. The CCF framework and evaluation tool provide a powerful basis for synthesizing past experience and, with wider application, will help to identify factors that affect the success of conservation activities.
There has been significant anxiety among prescribers regarding the potential for cardiac adverse effects associated with acetylcholinesterase (AChE) inhibitors in Alzheimer's disease. There is no consensus on how to manage this cardiovascular risk, and memory clinics vary widely in their practice. Review of published evidence reveals that the incidence of cardiovascular side-effects is low, and that serious adverse events are rare. Intensive cardiovascular screening such as pre-treatment electrocardiograms or 24 h cardiac monitoring is not justified. Furthermore, there are no high-risk groups to target. This article suggests pragmatic guidelines for managing cardiovascular risk in patients receiving AChE inhibitors. The guidelines are intended to be easy to incorporate into routine clinical practice in a memory clinic.