Background and aims Previous studies have shown beneficial associations between fruit and vegetable (FV) consumption and cardiometabolic risk factors. However, variety in FV, which may play an important role on cardiovascular health due to the different nutrient and phytochemical content among the different groups and subgroups of FV has been poorly investigated. We longitudinally investigated associations between 1-year changes in variety and quantity of FV and concurrent changes in cardiometabolic risk factors in elderly subjects with overweight/obesity and metabolic syndrome. Methods a one-year data longitudinal analysis of 6647 PREDIMED-plus study participants (48% women) was conducted. Data were collected at baseline, six months and 1-year of follow-up. Variety and quantity of FV were estimated using a food frequency questionnaire and continuous scores for variety were created based on items/month of FV. Linear mixed-models adjusted for potential confounders were performed to estimate associations (beta-coefficients and 95% confidence interval) between 1-year changes in FV variety and/or quantity and concurrent changes in cardiometabolic risk factors. Results Two points increment in the FV variety score over one year was associated with a concurrent decrease in glucose (-0.33 mg/dL (0.58, -0.07)), body weight (-0.07 kg (-0.13, -0.02)) and waist circumference (WC) (-0.08 cm (-0.16, -10.01)). An increment of 100 g/d of FV over one year was associated with a concurrent decrease in triglycerides (-0.50 mg/dL (-0.93, -0.08)), glucose (-0.21 mg/dL (-0.32, -0.11)), body weight (-0.11 kg (-0.15, -0.07)) and WC (-0.10 cm (-0.14, -0.06)) over 1-year. Changes in FV consumption which led to higher quantity and variety over one year were associated with downward changes in glucose (-1.26 mg/dL (-2.09, -0.43)), body weight (-0.40 kg (-0.58, -0.23)) and WC (-0.50 cm (-0.73, -0.28)). Conclusion Greater variety, in combination with higher quantity of FV was significantly associated with a decrease in several cardiometabolic risk factors among elderly subjects at high cardiovascular risk.

The early detection of symptoms and rapid testing are the basis of an efficient screening strategy to control COVID-19 transmission. The olfactory dysfunction is one of the most prevalent symptom and in many cases is the first symptom. This study aims to develop a machine learning COVID-19 predictive tool based on symptoms and a simple olfactory test, which consists of identifying the smell of an aromatized hydroalcoholic gel. A multi-centre population-based prospective study was carried out in the city of Reus (Catalonia, Spain). The study included consecutive patients undergoing a reverse transcriptase polymerase chain reaction test for presenting symptoms suggestive of COVID-19 or for being close contacts of a confirmed COVID-19 case. A total of 519 patients were included, 386 (74.4%) had at least one symptom and 133 (25.6%) were asymptomatic. A classification tree model including sex, age, relevant symptoms and the olfactory test results obtained a sensitivity of 0.97 (95% CI 0.91-0.99), a specificity of 0.39 (95% CI 0.34-0.44) and an AUC of 0.87 (95% CI 0.83-0.92). This shows that this machine learning predictive model is a promising mass screening for COVID-19.

Type-2 diabetes mellitus (T2DM) is a chronic metabolic disorder. The incidence and prevalence of patients with T2DM are increasing worldwide, even reaching epidemic values in most high- and middle-income countries. T2DM could be a risk factor of developing complications in other diseases. Indeed, some studies suggest a bidirectional interaction between T2DM and COVID-19. A growing body of evidence shows that COVID-19 prognosis in individuals with T2DM is worse compared with those without. Moreover, various studies have reported the emergence of newly diagnosed patients with T2DM after SARS-CoV-2 infection. The most common treatments for T2DM may influence SARS-CoV-2 and their implication in infection is briefly discussed in this review. A better understanding of the link between TD2M and COVID-19 could proactively identify risk factors and, as a result, develop strategies to improve the prognosis for these patients.