Statistical methods are usually applied in examining diet-disease associations, whereas factor analysis is commonly used for dietary pattern recognition. Recently, machine learning (ML) has been also proposed as an alternative technique in health classification. In this work, the predictive accuracy of statistical v. ML methodologies as regards the association of dietary patterns on CVD risk was tested. During 2001-2002, 3042 men and women (45 (sd 14) years) were enrolled in the ATTICA study. In 2011-2012, the 10-year CVD follow-up was performed among 2020 participants. Item Response Theory was applied to create a metric of combined 10-year cardiometabolic risk, the 'Cardiometabolic Health Score', that incorporated incidence of CVD, diabetes, hypertension and hypercholesterolaemia. Factor analysis was performed to extract dietary patterns, on the basis of either foods or nutrients consumed; linear regression analysis was used to assess their association with the cardiometabolic score. Two ML techniques (k-nearest-neighbor's algorithm and random-forests decision tree) were applied to evaluate participants' health based on dietary information. Factor analysis revealed five and three factors from foods and nutrients, respectively, explaining 54 and 65 % of the total variation in intake. Nutrient and food pattern regression models showed similar accuracy in correctly classifying an individual according to the cardiometabolic risk (R 2=9·6 % and R 2=8·3 %, respectively). ML techniques were superior compared with linear regression in correct classification of the individuals according to the Health Score (accuracy approximately 38 v. 6 %, respectively), whereas the two ML methods showed equal classification ability. Conclusively, ML methods could be a valuable tool in the field of nutritional epidemiology, leading to more accurate disease-risk evaluation.

In the last few years, the need for processing large amount of data in nutrition science was dramatically arose. This created the need to apply, primarily, advanced analytical research methods that could enable researchers to handle the large amount of information. Dietary pattern analysis is a commonly used approach to enable and incorporate this phenomenon in nutrition research. This article reviews the most common dietary pattern's assessment statistical methods, evaluating at the same time the up-to-day knowledge regarding the reliability and validity of the retrieved patterns. The review is based on both a-priori (diet scores) and a-posteriori (multivariate statistical analysis) methods. The reports from the existing few studies suggest that the use of both a-priori and a-posteriori pattern analyses in nutrition surveys should be made with consciousness. The suggestion of new statistical techniques for the control of repeatability of dietary patterns is considered essential.

Objective:The objective of this study was to examine the validity of EuroSCORE II in the Greek population.Methods:A prospective single-center study was performed during November 1, 2013 and November 5, 2016; 621 patients undergoing cardiac surgery were enrolled. The EuroSCORE II values and the actual mortality of the patients were recorded in a special database. Calibration of the model was evaluated with the Hosmer-Lemeshow goodness-of-fit test, and discrimination with the areas under the receiver operating characteristic (ROC) curve.Results:The observed in-hospital mortality rate was 3% (i.e. 18/621 patients). The median EuroSCORE II value was 1.3% (1st quartile: 0.86%, 3rd quartile: 2.46%), which indicates a low in-hospital mortality. Area under the ROC curve for EuroSCORE II was 0.85 (95% CI: 0.75-0.94), suggesting very good correct classification of the patients.Conclusion:The findings of the present work suggest that EuroSCORE II is a very good predictor of in-hospital mortality after cardiac surgery, in our population and, therefore can safely be used for quality assurance and risk assessment.

BackgroundUniform international measurement tools for assessing healthy ageing are currently lacking.ObjectivesThe study assessed the novel comprehensive global Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) Healthy Ageing Scale, using an Item Response Theory approach, for evaluating healthy ageing across populations.DesignPooled analysis of 16 international longitudinal studies.Setting38 countries in five continents.SubjectsInternational cohort (n=355 314), including 44.4% (n=153 597) males, aged (mean±SD) 61.7±11.5 years old.MethodsThe ATHLOS Healthy Ageing Scale (including 41 items related to intrinsic capacity and functional ability) was evaluated in a pooled international cohort (n=355 314 from 16 studies) according to gender, country of residence and age group. It was also assessed in a subset of eight cohorts with ≥3 waves of follow-up assessment. The independent samples t-test and Mann–Whitney test were applied for comparing normally and skewed continuous variables between groups, respectively.ResultsThe ATHLOS Scale (range: 12.49–68.84) had a mean (±SD) value of 50.2±10.0, with males and individuals >65 years old exhibiting higher and lower mean scores, respectively. Highest mean scores were detected in Switzerland, Japan and Denmark, while lowest in Ghana, India and Russia. When the ATHLOS Scale was evaluated in a subset of cohorts with ≥3 study waves, mean scores were significantly higher than those of the baseline cohort (mean scores in ≥3 study waves vs baseline: 51.6±9.4 vs 50.2±10.0; p<0.01).ConclusionsThe ATHLOS Healthy Ageing Scale may be adequately applied for assessing healthy ageing across populations.

We investigated the relation between alcohol drinking and healthy ageing by means of a validated health status metric, using individual data from the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project. For the purposes of this study, the ATHLOS harmonised dataset, which includes information from individuals aged 65+ in 38 countries, was analysed (n = 135,440). Alcohol drinking was reflected by means of three harmonised variables: alcohol drinking frequency, current and past alcohol drinker. A set of 41 self-reported health items and measured tests were used to generate a specific health metric. In the harmonised dataset, the prevalence of current drinking was 47.5% while of past drinking was 26.5%. In the pooled sample, current alcohol drinking was positively associated with better health status among older adults ((b-coef (95% CI): 1.32(0.45 to 2.19)) and past alcohol drinking was inversely related (b-coef (95% CI): −0.83 (−1.51 to −0.16)) with health status. Often alcohol consumption appeared to be beneficial only for females in all super-regions except Africa, both age group categories (65–80 years old and 80+), both age group categories, as well as among all the financial status categories (all p < 0.05). Regional analysis pictured diverse patterns in the association for current and past alcohol drinkers. Our results report the need for specific alcohol intake recommendations among older adults that will help them maintain a better health status throughout the ageing process.