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Growing evidence suggests that individuals with anxiety disorder have an elevated risk of cardiovascular disease (CVD) but few studies have assessed this association independently of or jointly with depression.
We conducted a prospective cohort study using UK Biobank. Diagnoses of anxiety disorder, depression, and CVDs were ascertained through linked hospital admission and mortality data. Individual and joint associations between anxiety disorder and depression and CVD overall, as well as each of myocardial infarction, stroke/transient ischemic attack, and heart failure, were analyzed using Cox proportional hazard models and interaction tests.
Among the 431,973 participants, the risk of CVD was higher among those who had been diagnosed with anxiety disorder only (hazard ratio [HR] 1.72; 95% confidence interval [CI] 1.32–2.24), depression only (HR 2.07; 95% CI 1.79–2.40), and both conditions (HR 2.89; 95% CI 2.03–4.11) compared to those without these conditions, respectively. There was very little evidence of multiplicative or additive interaction. Results were similar for myocardial infarction, stroke/transient ischemic attack, and heart failure.
Having anxiety is associated with the same magnitude of increased risk of CVD among people who do not have depression and those who do. Anxiety disorder should be considered for inclusion in CVD risk prediction and stratification, in addition to depression.
To investigate the relationship of a healthy eating score with depression in Chilean older adults.
Older adults from the Chilean National Health Survey 2016–2017. Associations were analysed using complex samples multivariable logistic regressions adjusted for age, sex, socio-demographic, lifestyles (physical activity, smoking, alcohol consumption and sleep duration), BMI and clinical conditions (hypertension, diabetes, hypercholesterolaemia and cardiovascular diseases).
The number of participants was 2031 (≥ 60 years). The Composite International Diagnostic Interview-Short Form was applied to establish the diagnosis of major depressive episode. Six healthy eating habits were considered to produce the healthy eating score (range: 0–12): consumption of seafood, whole grain, dairy, fruits, vegetables and legumes. Participants were categorised according to their final scores as healthy (≥ 9), average (5–8) and unhealthy (≤ 4).
Participants with a healthy score had a higher educational level, physical activity and regular sleep hours than participants with an average and unhealthiest healthy eating score. Participants classified in the healthiest healthy eating score had an inverse association with depression (OR: 0·28, (95 % CI 0·10, 0·74)). Food items that contributed the most to this association were legumes (15·2 %) and seafood (12·7 %).
Older adults classified in the healthiest healthy eating score, characterised by a high consumption of legumes and seafood, showed a lower risk for depression in a representative sample of Chilean population.
To identify sex-specific cut-off points for waist circumference (WC) in the definition of metabolic syndrome (MetS) for the Chilean adult population.
MetS was defined as the presence of at least two out of four of the following criteria: TAG ≥1·7 mmol/l; HDL-cholesterol: <1·3 mmol/l in women and <1·0 mmol/l in men; systolic blood pressure ≥130 mmHg and/or diastolic blood pressure ≥85 mmHg; and fasting glucose ≥ 5·6 mmol/l or current treatment for diabetes. The receiver operating characteristics curve and the AUC were computed to derive the specificity and sensitivity using bootstrapping (10 000 iterations restricted to have at least between 40 and 60 % of the original population). The optimal cut-off point for the Chilean population was computed by sex.
A representative sample of the Chilean population aged ≥15 years.
8182 participants (60 % women) from the three available Chilean National Health Surveys conducted in 2003, 2009–2010 and 2016–2017.
WC had a good predictive ability for MetS (AUC for men 0·74 (95 % CI 0·72, 0·76); AUC for women 0·71 (95 % CI 0·68, 0·73)). The optimal cut-off points for WC, in the definition of MetS, were 92·3 cm (95 % CI 90·5, 94·4) and 87·6 cm (95 % CI 85·8, 92·1) for men and women, respectively.
The mentioned cut-off points should be used for WC in the definition of MetS in Chile. As a result, the current recommendation (WHO/International Diabetes Federation) for WC, in the identification of MetS, is not supported by these findings in a representative sample of the Chilean adult population.
Newly available data from big scale studies conducted in the UK, such as the UK Biobank, offers the possibility to further explore the prospective association between a diet-quality score and health outcomes after accounting for the effect of important confounding factors. The aim of this work, therefore, was to investigate the association between a diet-quality score, with the incidence of cardiovascular diseases (CVDs), cancer and all-cause mortality.
Material and methods
This study includes 345,343 participants (age range: 39–73, 55.1% women) from the UK Biobank, a prospective population-based study. Using 21 standardised variables of diet (alcohol, bread, bread type, cereal, dried fruit, water, coffee, tea, cheese, oily fish, non-oily fish, salt added to food, spread type, fresh fruit, cooked vegetable, raw vegetables, milk type, poultry, beef, lamb, and pork) we created a diet-quality score (very healthy, healthy, unhealthy and very unhealthy) using principal-component factor analysis. Associations between the dietary-quality score (very unhealthy individuals were the reference group) and health outcomes (all-cause mortality, CVD and cancer incidence) were investigated using Cox-proportional hazard models. All analyses were performed using STATA 14 statistical software.
In comparison to individuals with a very unhealthy diet, those with a better diet-quality had a lower risk of all-cause mortality and cancer as well as incidence of CVD and cancer. For example, individuals classified in the very healthy group had a 12% lower risk of all-cause mortality (HR: 0.88 [95% CI: 0.82 to 0.95]), 12% lower risk of CVD incidence (HR: 0.88 [95% CI: 0.80 to 0.98]), 17% of all-cancer mortality (HR: 0.83 [95% CI: 0.75 to 0.93]), and 10% lower risk all-cancer incidence (HR: 0.90 [95% CI: 0.85 to 0.94]). Those in the healthy group had a 12% lower risk of all-cause (HR: 0.88 [95% CI: 0.83 to 0.93]) and 15% lower risk of all-cancer mortality (HR: 0.85 [95% CI: 0.78 to 0.93]). There was no significant association between CVD mortality and any diet-quality group. These findings were independent of major confounding factors including socio-demographic covariates, prevalent of diseases and lifestyle factors.
Our findings indicate that individuals with a healthy diet in the UK biobank cohort are associated with a lower risk of premature mortality, and incidence of CVDs and cancer independently of major confounding factors.
Obesity remains one of the biggest health challenges worldwide. Sarcopenia, a progressive loss of muscle strength, is associated with a higher risk of disability and lower quality of life. Both conditions can occur independently of each other; however, share a common inflammatory pathway, leading to serious health problems. Previous studies have shown a positive association between severe sarcopenia and respiratory disease incidence/mortality, however, it is unclear if this association is modified by obesity. The aim of this work, therefore, was to investigate the association of severe sarcopenia and severe sarcopenic-obesity with respiratory incidence and mortality in the UK Biobank cohort.
Material and methods
242,572 white participants from the UK biobank study were included. Severe sarcopenia was defined as the combination of low muscle mass, low grip strength and slow gait speed. Severe sarcopenic-obesity was defined, using 3 different criteria. The combination of severe sarcopenia plus at least one of the following criteria: BMI ≥ 30 kg/m2, waist circumference (WC) > 88 cm in women and > 102 cm in men, or the two highest quintiles of body fat (60%). Associations between severe sarcopenic and severe sarcopenic-obesity and respiratory incidence and mortality were investigated using Cox-proportional hazard models.
In people without sarcopenia, high BMI, WC and body fat were associated with a reduced risk of respiratory disease mortality (HR: 0.70 [0.52; 0.85], HR: 0.74 [95%CI: 062: 088] and HR: 0.74 [95%CI: 0.63; 0.88], respectively). In comparison to people without sarcopenia or obesity, those with severe sarcopenia had three times higher risk of respiratory disease incidence (HR: 3.13 [95%CI: 2.25; 4.35]) and five times higher risk of mortality (HR: 5.37 [95%CI: 2.96: 9.74]). However, sarcopenic-obesity, based on WC and body fat, was only associated with a moderately increased respiratory disease incidence (HR 1.60 [95%CI: 1.04; 2.46] and HR: 1.52 [1.04: 2.22], respectively). There were no associations between respiratory mortality and sarcopenic-obesity.
Higher levels of adiposity may be a protective factor against respiratory mortality and could reduce the effect of severe sarcopenia over this disease. However, the mechanism behind this association needs to elucidate.
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.
The aim of the study was to determine the main factors (sociodemographic, anthropometric, lifestyle and health status) associated with high Na excretion in a representative population of Chile.
Na excretion (g/d), a valid marker of Na intake, was determined by urine analysis and Tanaka’s formulas. Blood pressure was measured by trained staff and derived from the mean of three readings recorded after 15 min rest. The associations of Na excretion with blood pressure and the primary correlates of high Na excretion were determined using logistic regression.
Chileans aged ≥15 years.
Participants (n 2913) from the Chilean National Health Survey 2009–2010.
Individuals aged 25 years or over, those who were obese and those who had hypertension, diabetes or metabolic syndrome were more likely to have higher Na excretion. The odds for hypertension increased by 10·2 % per 0·4 g/d increment in Na excretion (OR=1·10; 95 % CI 1·06, 1·14; P < 0·0001). These findings were independent of major confounding factors.
Age, sex, adiposity, sitting behaviours and existing co-morbidities such as diabetes were associated with higher Na excretion levels in the Chilean population. These findings could help policy makers to implement public health strategies tailored towards individuals who are more likely to consume high levels of dietary salt.
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.
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.
Improving diet and other lifestyle behaviours has considerable potential for reducing the global burden of non-communicable diseases, promoting better health across the life-course and increasing wellbeing. However, realising this potential will require the development, testing and implementation of much more effective behaviour change interventions than are used conventionally. Evidence-based, personalised (or stratified) interventions which incorporate effective behaviour change techniques (BCT) and which are delivered digitally are likely to be an important route to scalable and sustainable interventions. Progress in developing such interventions will depend on the outcomes of research on: (i) the best bases for personalisation of dietary advice; (ii) identification of BCT which are proven to enhance intervention efficacy; (iii) suitable platforms (digital-based tools) for collection of relevant participant characteristics (e.g. socioeconomic information, current diet and lifestyle and dietary preferences) linked with intelligent systems which use those characteristics to offer tailored feedback and advice in a cost-effective and acceptable manner. Future research should focus on such interventions aiming to reduce health inequalities and to improve overall public health.
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