Background The optimal distribution between physical activity (PA) levels and sedentary behaviour (SB) for the greatest benefits for body composition among older adults with overweight/obesity and chronic health conditions remains unclear. We aimed to determine the prospective association between changes in PA and in SB with concurrent changes in body composition and to examine whether reallocating inactive time into different physical activity levels was associated with 12-month change to body composition in older adults. Methods Longitudinal assessment nested in the PREDIMED-Plus trial. A subsample (n = 1564) of men and women (age 55–75 years) with overweight/obesity and metabolic syndrome from both arms of the PREDIMED-Plus trial was included in the present analysis. Participants were followed up at 6 and 12 months. Physical activity and SB were assessed using validated questionnaires. Out of 1564 participants, 388 wore an accelerometer to objectively measure inactive time and PA over a 7-day period. At each time point, participants’ body composition was measured using dual-energy X-ray absorptiometry (DXA). Standard covariate-adjusted and isotemporal substitution modelling were applied to linear mixed-effects models. Results Increasing 30 min of total PA and moderate-to-vigorous physical activity (MVPA) was associated with significant reductions in body fat (β − 0.07% and − 0.08%) and visceral adipose tissue (VAT) (− 13.9 g, and − 15.6 g) at 12 months (all p values < 0.001). Reallocating 30 min of inactive time to MVPA was associated with reductions in body fat and VAT and with an increase in muscle mass and muscle-to-fat mass ratio (all p values < 0.001). Conclusions At 12 months, increasing total PA and MVPA and reducing total SB and TV-viewing SB were associated with improved body composition in participants with overweight or obesity, and metabolic syndrome. This was also observed when substituting 30 min of inactive time with total PA, LPA and MVPA, with the greatest benefits observed with MVPA. Trial registration International Standard Randomized Controlled Trial (ISRCTN), 89898870. Retrospectively registered on 24 July 2014

Objective: To examine the cross-sectional and longitudinal (2-year follow-up) associations between dietary diversity (DD) and depressive symptoms. Design: An energy-adjusted dietary diversity score (DDS) was assessed using a validated FFQ and was categorised into quartiles (Q). The variety in each food group was classified into four categories of diversity (C). Depressive symptoms were assessed with Beck Depression Inventory-II (Beck II) questionnaire and depression cases defined as physician-diagnosed or Beck II >= 18. Linear and logistic regression models were used. Setting: Spanish older adults with metabolic syndrome (MetS). Participants: A total of 6625 adults aged 55–75 years from the PREDIMED-Plus study with overweight or obesity and MetS. Results: Total DDS was inversely and statistically significantly associated with depression in the cross-sectional analysis conducted; OR Q4 v. Q1 = 0·76 (95 % CI (0·64, 0·90)). This was driven by high diversity compared to low diversity (C3 v. C1) of vegetables (OR = 0·75, 95 % CI (0·57, 0·93)), cereals (OR = 0·72 (95 % CI (0·56, 0·94)) and proteins (OR = 0·27, 95 % CI (0·11, 0·62)). In the longitudinal analysis, there was no significant association between the baseline DDS and changes in depressive symptoms after 2 years of follow-up, except for DD in vegetables C4 v. C1 = (β = 0·70, 95 % CI (0·05, 1·35)). Conclusions: According to our results, DD is inversely associated with depressive symptoms, but eating more diverse does not seem to reduce the risk of future depression. Additional longitudinal studies (with longer follow-up) are needed to confirm these findings.

SARS-CoV-2 infection has a high transmission level. At the present time there is not a specific treatment approved but it is known that, in vitro, chloroquine and hydroxychloroquine can inhibit the coronavirus. Objective: verifying if patients with autoimmune diseases that are on treatment with HCQ have less incidence and severity on COVID-19. Material and methods: this is a retrospective cohort study. The exposed cohort was formed by individuals with autoimmune diseases with HCQ treatment. The control cohort was randomly selected using the Health Card database. To deal with confounding variables and evaluate the effect of HCQ on the incidence and severity of SARS-CoV-2 infection, propensity score matching was used. Risk difference and paired percentage difference between exposed and non-exposed groups was estimated. Results: 919 individuals formed the exposed cohort and 1351 the control cohort. After matching, there were 690 patients on each group. During the time of the study, in the exposed group there were 42 (6.1%) individuals with suspected COVID-19, 12(1.7%) with confirmed COVID-19 and 3(0.4%) were hospitalized. In the control group there were 30(4.3%) individuals with suspected COVID-19, 13(1.9%) with confirmed COVID-19 and 2(0.3%) were hospitalized. The risk difference between each cohort was: 0.017(-0.05-0.04) for suspected COVID-19; -0.014(-0.015-0.012) for confirmed COVID-19 and 0.001(-0.007-0.007) for hospitalized patients. There were not significant differences. Conclusion: there is no difference neither on the incidence nor on the severity of COVID-19 between patients with autoimmune diseases with HCQ treatment and patients that do not take HCQ.

Background: Breast cancer is the most common type of malignancy and the leading cause of cancer-related death among women. Among its risk factors, excess body fat is one of the most remarkable. Body Mass Index (BMI) is the most frequently used indice to determine body fat percentage. However, other estimators also exist, such as Clinica Universidad de Navarra e Body Adiposity Estimator (CUN-BAE). Our aim is to compare the attributable fraction of body fat among postmenopausal women with breast cancer by comparing BMI versus CUN-BAE.Methods: We performed a case-control study by using the MCC-Spain database. It is a population multi-case control study that includes high incidence tumours in Spain. We calculated the BMI and CUN-BAE after dividing the total number of cases into four respective categories. Lastly, we compared the population attributable fraction of body fat with both indices.Results: We included a total of 2176 women, 1143 (52.52%) in the control group and 1033 (47,47%) cases of women with breast cancer. The body fat distribution data for the different BMI groups in cases and controls were the following: 36,5% vs 45,6%, 38,8% vs 34,6%, 18,5 vs 14,7%,6 % vs 5%, respectively. The data for CUN-BAE in cases and control were: 14% vs 20%, 31,7% vs 33%, 33,5% vs 29%, 2 % vs 17%, respectively. (Table ). As a result, the population attributable fraction was 28,6% by using the BMI and 46,2% in CUN-BAE. Conclusions:The increase in body fat determined by CUN-BAE, after adjusting it based on the menopausal status and hormonal factors, has shown to directly correlate with an increased risk of breast cancer. We conclude that CUN-BAE is a more precise measure than BMI. Table .