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Otitis externa accounts for 1.1–1.3 per cent of patient presentations in primary care and 25 per cent of urgent referrals to ENT. This study aimed to explore otitis externa clinical decision-making at the primary-secondary care interface, otitis externa prevalence and recent trends in antimicrobial resistance in otitis externa related bacterial isolates and ototopical prescribing.
This is a mixed-methods study drawing on data from primary and secondary care and open National Health Service sources.
A total of 101 general practitioner survey respondents reported frequently prescribing oral antibiotics for otitis externa. General practitioner consultations for otitis externa increased 25 per cent over 15 years. General practitioner ototopical preparations cost the National Health Service £7 410 440 in 2006 and £11 325 241 in 2016. A total of 162 consecutive hospital otitis externa-related bacterial isolates yielded 128 pseudomonas species, with 18 that were resistant to gentamicin and 7 that were resistant to ciprofloxacin. Ten guidelines reviewed showed systematic inconsistencies.
General practitioners reported regularly prescribing oral antibiotics for otitis externa. Antimicrobial drug resistance is common in otitis externa. The available guidance is suboptimal.
Obtaining objective, dietary exposure information from individuals is challenging because of the complexity of food consumption patterns and the limitations of self-reporting tools (e.g., FFQ and diet diaries). This hinders research efforts to associate intakes of specific foods or eating patterns with population health outcomes.
Dietary exposure can be assessed by the measurement of food-derived chemicals in urine samples. We aimed to develop methodologies for urine collection that minimised impact on the day-to-day activities of participants but also yielded samples that were data-rich in terms of targeted biomarker measurements.
Urine collection methodologies were developed within home settings.
Different cohorts of free-living volunteers.
Home collection of urine samples using vacuum transfer technology was deemed highly acceptable by volunteers. Statistical analysis of both metabolome and selected dietary exposure biomarkers in spot urine collected and stored using this method showed that they were compositionally similar to urine collected using a standard method with immediate sample freezing. Even without chemical preservatives, samples can be stored under different temperature regimes without any significant impact on the overall urine composition or concentration of forty-six exemplar dietary exposure biomarkers. Importantly, the samples could be posted directly to analytical facilities, without the need for refrigerated transport and involvement of clinical professionals.
This urine sampling methodology appears to be suitable for routine use and may provide a scalable, cost-effective means to collect urine samples and to assess diet in epidemiological studies.
There is strong evidence that foods containing dietary fibre protect against colorectal cancer, resulting at least in part from its anti-proliferative properties. This study aimed to investigate the effects of supplementation with two non-digestible carbohydrates, resistant starch (RS) and polydextrose (PD), on crypt cell proliferative state (CCPS) in the macroscopically normal rectal mucosa of healthy individuals. We also investigated relationships between expression of regulators of apoptosis and of the cell cycle on markers of CCPS. Seventy-five healthy participants were supplemented with RS and/or PD or placebo for 50 d in a 2 × 2 factorial design in a randomised, double-blind, placebo-controlled trial (the Dietary Intervention, Stem cells and Colorectal Cancer (DISC) Study). CCPS was assessed, and the expression of regulators of the cell cycle and of apoptosis was measured by quantitative PCR in rectal mucosal biopsies. SCFA concentrations were quantified in faecal samples collected pre- and post-intervention. Supplementation with RS increased the total number of mitotic cells within the crypt by 60 % (P = 0·001) compared with placebo. This effect was limited to older participants (aged ≥50 years). No other differences were observed for the treatments with PD or RS as compared with their respective controls. PD did not influence any of the measured variables. RS, however, increased cell proliferation in the crypts of the macroscopically-normal rectum of older adults. Our findings suggest that the effects of RS on CCPS are not only dose, type of RS and health status-specific but are also influenced by age.
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.
Describe the epidemiological and molecular characteristics of an outbreak of Klebsiella pneumoniae carbapenemase (KPC)–producing organisms and the novel use of a cohorting unit for its control.
A 566-room academic teaching facility in Milwaukee, Wisconsin.
Solid-organ transplant recipients.
Infection control bundles were used throughout the time of observation. All KPC cases were intermittently housed in a cohorting unit with dedicated nurses and nursing aids. The rooms used in the cohorting unit had anterooms where clean supplies and linens were placed. Spread of KPC-producing organisms was determined using rectal surveillance cultures on admission and weekly thereafter among all consecutive patients admitted to the involved units. KPC-positive strains underwent pulsed-field gel electrophoresis and whole-genome sequencing.
A total of 8 KPC cases (5 identified by surveillance) were identified from April 2016 to April 2017. After the index patient, 3 patients acquired KPC-producing organisms despite implementation of an infection control bundle. This prompted the use of a cohorting unit, which immediately halted transmission, and the single remaining KPC case was transferred out of the cohorting unit. However, additional KPC cases were identified within 2 months. Once the cohorting unit was reopened, no additional KPC cases occurred. The KPC-positive species identified during this outbreak included Klebsiella pneumoniae, Enterobacter cloacae complex, and Escherichia coli. blaKPC was identified on at least 2 plasmid backbones.
A complex KPC outbreak involving both clonal and plasmid-mediated dissemination was controlled using weekly surveillances and a cohorting unit.
Colorectal cancer (CRC) is the third most common cancer globally. CRC risk is increased by obesity, and by its lifestyle determinants notably physical inactivity and poor nutrition. Obesity results in increased inflammation and oxidative stress which cause genomic damage and contribute to mitochondrial dysregulation and CRC risk. The mitochondrial dysfunction associated with obesity includes abnormal mitochondrial size, morphology and reduced autophagy, mitochondrial biogenesis and expression of key mitochondrial regulators. Although there is strong evidence that increased adiposity increases CRC risk, evidence for the effects of intentional weight loss on CRC risk is much more limited. In model systems, energy depletion leads to enhanced mitochondrial integrity, capacity, function and biogenesis but the effects of obesity and weight loss on mitochondria in the human colon are not known. We are using weight loss following bariatric surgery to investigate the effects of altered adiposity on mitochondrial structure and function in human colonocytes. In summary, there is strong and consistent evidence in model systems and more limited evidence in human subjects that over-feeding and/or obesity result in mitochondrial dysfunction and that weight loss might mitigate or reverse some of these effects.
Bowel cancer risk is strongly influenced by lifestyle factors including diet and physical activity. Several studies have investigated the effects of adherence to the World Cancer Research Fund (WCRF)/American Institute for Cancer Research (AICR) cancer prevention recommendations on outcomes such as all-cause and cancer-specific mortality, but the relationships with molecular mechanisms that underlie the effects on bowel cancer risk are unknown. This study aimed to investigate the relationships between adherence to the WCRF/AICR cancer prevention recommendations and wingless/integrated (WNT)-pathway-related markers of bowel cancer risk, including the expression of WNT pathway genes and regulatory microRNA (miRNA), secreted frizzled-related protein 1 (SFRP1) methylation and colonic crypt proliferative state in colorectal mucosal biopsies. Dietary and lifestyle data from seventy-five healthy participants recruited as part of the DISC Study were used. A scoring system was devised including seven of the cancer prevention recommendations and smoking status. The effects of total adherence score and scores for individual recommendations on the measured outcomes were assessed using Spearman’s rank correlation analysis and unpaired t tests, respectively. Total adherence score correlated negatively with expression of Myc proto-oncogene (c-MYC) (P=0·039) and WNT11 (P=0·025), and high adherers had significantly reduced expression of cyclin D1 (CCND1) (P=0·042), WNT11 (P=0·012) and c-MYC (P=0·048). Expression of axis inhibition protein 2 (AXIN2), glycogen synthase kinase (GSK3β), catenin β1 (CTNNB1) and WNT11 and of the oncogenic miRNA miR-17 and colonic crypt kinetics correlated significantly with scores for individual recommendations, including body fatness, red meat intake, plant food intake and smoking status. The findings from this study provide evidence for positive effects of adherence to the WCRF/AICR cancer prevention recommendations on WNT-pathway-related markers of bowel cancer risk.
n-3 Fatty acids are associated with better cardiovascular and cognitive health. However, the concentration of EPA, DPA and DHA in different plasma lipid pools differs and factors influencing this heterogeneity are poorly understood. Our aim was to evaluate the association of oily fish intake, sex, age, BMI and APOE genotype with concentrations of EPA, DPA and DHA in plasma phosphatidylcholine (PC), NEFA, cholesteryl esters (CE) and TAG. Healthy adults (148 male, 158 female, age 20–71 years) were recruited according to APOE genotype, sex and age. The fatty acid composition was determined by GC. Oily fish intake was positively associated with EPA in PC, CE and TAG, DPA in TAG, and DHA in all fractions (P≤0·008). There was a positive association between age and EPA in PC, CE and TAG, DPA in NEFA and CE, and DHA in PC and CE (P≤0·034). DPA was higher in TAG in males than females (P<0·001). There was a positive association between BMI and DPA and DHA in TAG (P<0·006 and 0·02, respectively). APOE genotype×sex interactions were observed: the APOE4 allele associated with higher EPA in males (P=0·002), and there was also evidence for higher DPA and DHA (P≤0·032). In conclusion, EPA, DPA and DHA in plasma lipids are associated with oily fish intake, sex, age, BMI and APOE genotype. Such insights may be used to better understand the link between plasma fatty acid profiles and dietary exposure and may influence intake recommendations across population subgroups.
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.
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.
A number of socio-economic, biological and lifestyle characteristics change with advancing age and place very old adults at increased risk of micronutrient deficiencies. The aim of this study was to assess vitamin and mineral intakes and respective food sources in 793 75-year-olds (302 men and 491 women) in the North-East of England, participating in the Newcastle 85+ Study. Micronutrient intakes were estimated using a multiple-pass recall tool (2×24 h recalls). Determinants of micronutrient intake were assessed with multinomial logistic regression. Median vitamin D, Ca and Mg intakes were 2·0 (interquartile range (IQR) 1·2–6·5) µg/d, 731 (IQR 554–916) mg/d and 215 (IQR 166–266) mg/d, respectively. Fe intake was 8·7 (IQR 6·7–11·6) mg/d, and Se intake was 39·0 (IQR 27·3–55·5) µg/d. Cereals and cereal products were the top contributors to intakes of folate (31·5 %), Fe (49·2 %) and Se (46·7 %) and the second highest contributors to intakes of vitamin D (23·8 %), Ca (27·5 %) and K (15·8 %). More than 95 % (n 756) of the participants had vitamin D intakes below the UK’s Reference Nutrient Intake (10 µg/d). In all, >20 % of the participants were below the Lower Reference Nutrient Intake for Mg (n 175), K (n 238) and Se (n 418) (comparisons with dietary reference values (DRV) do not include supplements). As most DRV are not age specific and have been extrapolated from younger populations, results should be interpreted with caution. Participants with higher education, from higher social class and who were more physically active had more nutrient-dense diets. More studies are needed to inform the development of age-specific DRV for micronutrients for the very old.
Very old people (referred to as those aged 85 years and over) are the fastest growing age segment of many Western societies owing to the steady rise of life expectancy and decrease in later life mortality. In the UK, there are now more than 1·5 million very old people (2·5 % of total population) and the number is projected to rise to 3·3 million or 5 % over the next 20 years. Reduced mobility and independence, financial constraints, higher rates of hospitalisation, chronic diseases and disabilities, changes in body composition, taste perception, digestion and absorption of food all potentially influence either nutrient intake or needs at this stage of life. The nutritional needs of the very old have been identified as a research priority by the British Nutrition Foundation's Task Force report, Healthy Ageing: The Role of Nutrition and Lifestyle. However, very little is known about the dietary habits and nutritional status of the very old. The Newcastle 85+ study, a cohort of more than 1000 85-year olds from the North East of England and the Life and Living in Advanced Age study (New Zealand), a bicultural cohort study of advanced ageing of more than 900 participants from the Bay of Plenty and Rotorua regions of New Zealand are two unique cohort studies of ageing, which aim to assess the spectrum of health in the very old as well as examine the associations of health trajectories and outcomes with biological, clinical and social factors as each cohort ages. The nutrition domain included in both studies will help to fill the evidence gap by identifying eating patterns, and measures of nutritional status associated with better, or worse, health and wellbeing. This review will explore some of this ongoing work.
Food and nutrient intake data are scarce in very old adults (85 years and older) – one of the fastest growing age segments of Western societies, including the UK. Our primary objective was to assess energy and macronutrient intakes and respective food sources in 793 85-year-olds (302 men and 491 women) living in North-East England and participating in the Newcastle 85+ cohort Study. Dietary information was collected using a repeated multiple-pass recall (2×24 h recalls). Energy, macronutrient and NSP intakes were estimated, and the contribution (%) of food groups to nutrient intake was calculated. The median energy intake was 6·65 (interquartile ranges (IQR) 5·49–8·16) MJ/d – 46·8 % was from carbohydrates, 36·8 % from fats and 15·7 % from proteins. NSP intake was 10·2 g/d (IQR 7·3–13·7). NSP intake was higher in non-institutionalised, more educated, from higher social class and more physically active 85-year-olds. Cereals and cereal products were the top contributors to intakes of energy and most macronutrients (carbohydrates, non-milk extrinsic sugars, NSP and fat), followed by meat and meat products. The median intakes of energy and NSP were much lower than the estimated average requirement for energy (9·6 MJ/d for men and 7·7 MJ/d for women) and the dietary reference value (DRV) for NSP (≥18 g/d). The median SFA intake was higher than the DRV (≤11 % of dietary energy). This study highlights the paucity of data on dietary intake and the uncertainties about DRV for this age group.
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.