Adherence to the mediterranean diet is inversely associated with the prevalence of metabolic syndrome in older people from the north of Spain

Background: The aim of this study was to relate adherence to the Mediterranean diet (MedDiet) to the prevalence of metabolic syndrome (MetS) in an elderly population from the north of Spain. Methods: We carried out an observational, descriptive, cross-sectional, and correlational study involving 556 non-institutionalised individuals aged 65 to 79 years. The MEDAS-14 questionnaire score was used to define the degree of adherence to the Mediterranean diet. The diagnosis of MetS was conducted using the International Diabetes Federation (IDF) criteria. Results: In 264 subjects with an average age of 71.9 (SD: ±4.2), 39% of whom were men, 36.4% had good adherence (score ? 9 in MEDAS-14), with no differences by gender or age. The prevalence of MetS was 40.2%, with 47.6% in men and 35.4% in women (p < 0.05). The prevalence of MetS was 2.4 times more frequent among individuals who consumed less than two servings (200 g) of vegetables daily compared with those who consumed two or more servings of vegetables daily (OR: 2.368, 95%CI: 1.141?4.916, p = 0.021). Low adherence to the MedDiet (MEDAS-14 score ? 8) was associated with an 82% higher prevalence of MetS (OR: 1.817, 95%CI: 1.072?3.081, p = 0.027). Conclusion: An inverse relationship was established between adherence to the MedDiet and the prevalence of MetS.Funding: This research received no external funding. Acknowledgments: We wish to acknowledge the European Atlantic University for providing trainee dietitians/nutritionists to carry out the fieldwork, the dietitians.nutritionists Carmen de la Vega-Hazas and Irene Doporto for their involvement in the fieldwork, and Beatríz Macías for their contribution to the English translation of the article

Changes in the lifestyle of the Spanish university population during confinement for COVID-19

The aim of this study was to evaluate the influence of the lockdown due to the COVID-19 pandemic, on eating and physical activity behavior, in a university population. A healthy diet such as the Mediterranean Diet (MD) pattern, rich in fruit and vegetables, can prevent degenerative diseases such as obesity, diabetes, cardiovascular diseases, etc. We conducted a cross-sectional study and data were collected by an anonymous online questionnaire. Participants completed a survey consisting of 3 sections: sociodemographic data; dietary behavior and physical activity; the Mediterranean Diet questionnaire (MEDAS-14) and the Emotional Eater Questionnaire (EEQ). A total of 168 participants completed the questionnaire: 66.7% were women, 79.2% were from Spain, 76.8% were students, 76.2% lived in their family home and 66.1% were of normal weight. During lockdown our population shopped for groceries 1 time or less per week (76.8%); maintained the same consumption of fruits (45.2%), vegetables (50.6%), dairy products (61.9%), pulses (64.9%), fish/seafood (57.7%), white meat (77.4%), red and processed meat (71.4%), pastries and snacks (48.2%), rice/pasta/potatoes (70.2%) and nuts (62.5%), spirits (98.8%) and sugary drinks (91.7%). Cooking time increased (73.2%) and the consumption decreased of low alcohol drinks (60.1%), spirits (75%) and sugary drinks (57.1%), and physical activity also diminished (49.4%). University Employees (UE) gained more weight (1.01 ± 0.02) than students (0.99 ± 0.03) (p < 0.05) during the confinement period. A total of 79.8% of the participants obtained a Medium/High Adherence to the MD during lockdown. Emotional and very emotional eaters were higher in the female group (p < 0.01). In the event of further confinement, strategies should be implemented to promote a balanced and healthy diet together with the practice of physical activity, taking special care of the female and UE groups

Adherence to the Mediterranean Diet Is Inversely Associated with the Prevalence of Metabolic Syndrome in Older People from the North of Spain

Background: The aim of this study was to relate adherence to the Mediterranean diet (MedDiet) to the prevalence of metabolic syndrome (MetS) in an elderly population from the north of Spain. Methods: We carried out an observational, descriptive, cross-sectional, and correlational study involving 556 non-institutionalised individuals aged 65 to 79 years. The MEDAS-14 questionnaire score was used to define the degree of adherence to the Mediterranean diet. The diagnosis of MetS was conducted using the International Diabetes Federation (IDF) criteria. Results: In 264 subjects with an average age of 71.9 (SD: &plusmn;4.2), 39% of whom were men, 36.4% had good adherence (score &ge; 9 in MEDAS-14), with no differences by gender or age. The prevalence of MetS was 40.2%, with 47.6% in men and 35.4% in women (p &lt; 0.05). The prevalence of MetS was 2.4 times more frequent among individuals who consumed less than two servings (200 g) of vegetables daily compared with those who consumed two or more servings of vegetables daily (OR: 2.368, 95%CI: 1.141&ndash;4.916, p = 0.021). Low adherence to the MedDiet (MEDAS-14 score &le; 8) was associated with an 82% higher prevalence of MetS (OR: 1.817, 95%CI: 1.072&ndash;3.081, p = 0.027). Conclusion: An inverse relationship was established between adherence to the MedDiet and the prevalence of MetS

Voxel Extraction and Multiclass Classification of Identified Brain Regions across Various Stages of Alzheimer’s Disease Using Machine Learning Approaches

This study sought to investigate how different brain regions are affected by Alzheimer’s disease (AD) at various phases of the disease, using independent component analysis (ICA). The study examines six regions in the mild cognitive impairment (MCI) stage, four in the early stage of Alzheimer’s disease (AD), six in the moderate stage, and six in the severe stage. The precuneus, cuneus, middle frontal gyri, calcarine cortex, superior medial frontal gyri, and superior frontal gyri were the areas impacted at all phases. A general linear model (GLM) is used to extract the voxels of the previously mentioned regions. The resting fMRI data for 18 AD patients who had advanced from MCI to stage 3 of the disease were obtained from the ADNI public source database. The subjects include eight women and ten men. The voxel dataset is used to train and test ten machine learning algorithms to categorize the MCI, mild, moderate, and severe stages of Alzheimer’s disease. The accuracy, recall, precision, and F1 score were used as conventional scoring measures to evaluate the classification outcomes. AdaBoost fared better than the other algorithms and obtained a phenomenal accuracy of 98.61%, precision of 99.00%, and recall and F1 scores of 98.00% each