(1) Background: Artificial intelligence using machine learning techniques may help us to predict and prevent obesity. The aim was to design an interpretable prediction algorithm for overweight/obesity risk based on a combination of different machine learning techniques. (2) Methods: 38 variables related to sociodemographic, lifestyle, and health aspects from 1179 residents in Madrid were collected and used to train predictive models. Accuracy, precision, and recall metrics were tested and compared between nine classical machine learning techniques and the predictive model based on a combination of those classical machine learning techniques. Statistical validation was performed. The shapely additive explanation technique was used to identify the variables with the greatest impact on weight gain. (3) Results: Cascade classifier model combining gradient boosting, random forest, and logistic regression models showed the best predictive results for overweight/obesity compared to all machine learning techniques tested, reaching an accuracy of 79%, precision of 84%, and recall of 89% for predictions for weight gain. Age, sex, academic level, profession, smoking habits, wine consumption, and Mediterranean diet adherence had the highest impact on predicting obesity. (4) Conclusions: A combination of machine learning techniques showed a significant improvement in accuracy to predict risk of overweight/obesity than machine learning techniques separately.

(1) Background: In this work, we aim to describe influenza vaccine uptake among the diabetic population in Spain to assess the time trend from 2011 to 2020 and identify predictors of vaccine uptake among diabetes patients. (2) Methods: We conducted a descriptive cross-sectional study using the European Health Interview Survey for Spain (2014 and 2020) and the Spanish National Health Surveys (2011 and 2017). The independent variables analysed included socio-demographic characteristics, health-related variables and lifestyle variables. We matched each participant with diabetes with a non-diabetic participant based on age, sex, place of residence and year of survey. (3) Results: The overall coverage among diabetic adults was 52.1% compared to 40.6% for matched participants without diabetes (p < 0.01). The vaccine uptake among adults with diabetes was 52.6% in 2011, 54.38% in 2014 and 53.4% in 2017. The adjusted OR of having been vaccinated in 2020, with respect to 2011, was not significant at 0.87 (95% CI: 0.72–1.06). Factors such as being male, higher age, being affected by respiratory disease or cancer and being physically active were identified as positive predictors for influenza vaccination uptake, while smoking was a negative predictor. (4) Conclusions: The influenza vaccine uptake is below desirable levels among the adult diabetic population in Spain and has not improved from 2011 to 2020. More efforts should be made to increase influenza vaccine uptake in this high-risk group, especially for women, those aged 18–64 years, without other high-risk conditions and smokers.

(1) Background: This study aimed to analyze if the serum albumin levels of hospitalized SARS-CoV-2 (COVID-19) patients on admission could predict <30 days in-hospital all-cause mortality, and if glucose levels on admission affected this predictive ability. (2) Methods: A multicenter retrospective cohort of 1555 COVID-19-infected adult patients from public hospitals of the Madrid community were analyzed. (3) Results: Logistic regression analysis showed increased mortality for ages higher than 49 y. After adjusting for age, comorbidities and on-admission glucose levels, it was found that on-admission serum albumin ≥3.5 g/dL was significantly associated with reduced mortality (OR 0.48; 95%CI:0.36–0.62). There was an inverse concentration-dependent association between on-admission albumin levels and <30 days in-hospital all-cause mortality. However, when on-admission glucose levels were above 125 mg/dL, higher levels of serum albumin were needed to reach an association with survival. In vitro experiments showed that the spike protein S1 subunit of SARS-CoV-2 binds to native albumin. The binding ability of native albumin to the spike protein S1 subunit was decreased in the presence of an increasing concentration of glycated albumin. (4) Conclusions: On-admission serum albumin levels were inversely associated with <30 days in-hospital all-cause mortality. Native albumin binds the spike protein S1 subunit, suggesting that native albumin may act as a scavenger of the SARS-CoV-2 virus.

In this paper, we experimented with a set of machine-learning classifiers for predicting the risk of a person being overweight or obese, taking into account his/her dietary habits and socioeconomic information. We investigate with ten different machine-learning algorithms combined with four feature-selection strategies (two evolutionary feature-selection methods, one feature selection from the literature, and no feature selection). We tackle the problem under a binary classification approach with evolutionary feature selection. In particular, we use a genetic algorithm to select the set of variables (features) that optimize the accuracy of the classifiers. As an additional contribution, we designed a variant of the Stud GA, a particular structure of the selection operator of individuals where a reduced set of elitist solutions dominate the process. The genetic algorithm uses a direct binary encoding, allowing a more efficient evaluation of the individuals. We use a dataset with information from more than 1170 people in the Spanish Region of Madrid. Both evolutionary and classical feature-selection methods were successfully applied to Gradient Boosting and Decision Tree algorithms, reaching values up to 79% and increasing the average accuracy by two points, respectively.

Background and Objectives: Aspirin (ASA) is a commonly used antithrombotic drug that has been demonstrated to reduce venous thromboembolism. The aim was to analyze if geriatric COVID-19 patients undergoing a 100 mg/day Aspirin (ASA) treatment prior to hospitalization differ in hospital outcome compared to patients without previous ASA therapy. Materials and Methods: An observational retrospective study was carried out using an anonymized database including geriatric COVID-19 patients (March to April 2020) admitted to Madrid Hospitals Group. A group of COVID-19 patients were treated with low ASA (100 mg/day) prior to COVID-19 infection. Results: Geriatric ASA-treated patients were older (mean age over 70 years; n = 41), had higher frequency of hypertension and hyperlipidemia, and upon admission had higher D-dimer levels than non-ASA-treated patients (mean age over 73 years; n = 160). However, patients under ASA treatment did not show more frequent pulmonary thromboembolism (PE) than non-ASA-treated patients. ASA-treated geriatric COVID-19-infected patients in-hospital < 30 days all-cause mortality was more frequent than in non-ASA-treated COVID-19 patients. In ASA-treated COVID-19-infected geriatric patients, anticoagulant therapy with low molecular weight heparin (LMWH) significantly reduced need of ICU care, but tended to increase in-hospital < 30 days all-cause mortality. Conclusions: Prior treatment with a low dose of ASA in COVID-19-infected geriatric patients increased frequency of in-hospital < 30 days all-cause mortality, although it seemed to not increase PE frequency despite D-dimer levels upon admission being higher than in non-ASA users. In ASA-treated geriatric COVID-19-infected patients, addition of LMWH therapy reduced frequency of ICU care, but tended to increase in-hospital < 30 days all-cause mortality.

(1) Background: Radical prostatectomy has a high incidence of erectile dysfunction (ED). The aim was to determine if the expression of the nitric oxide synthase-3/soluble guanylate cyclase/phosphodiesterase 5 axis could be detected in buccal mucosa and if it could be differently expressed in patients with and without ED; (2) Methods: Erectile function from 38 subjects subjected to prostatectomy was evaluated using the International Index of Erectile Function-Erectile Function Domain before and one year after surgery. Nitric oxide synthase (NOS3), β1-subunit of soluble guanylate cyclase (sGC), phosphodiesterase-5 (PDE-5) expressions, and interleukin-6 and interleukin-10 content were measured in the buccal mucosa. PDE5A rs3806808 gene polymorphism was genotyped; (3) Results: One year after prostatectomy, 15 patients had recovered functional erection, and 23 showed ED. NOS3, β1-sGC, interleukin-6, and interleukin-10 expressions were not different between patients with and without ED after radical prostatectomy. Buccal mucosa levels of PDE-5 were higher in patients with ED compared to those who recovered erectile functionality. There were no differences found in the genotype of PDE5A polymorphism; (4) Conclusions: One year after prostatectomy, patients with ED had higher PDE5 levels in their buccal mucosa than patients who had recovered erectile function. Rs3806808 PDE5A gene polymorphism was not associated with increased PDE5 expression in buccal mucosa.