Objectives: To investigate if depression risk modifies the association between frailty and mortality in older adults. Design: Ongoing cohort study. Setting: Albacete city, Spain Participants: Eight hundred subjects, 58.8% women, over 70 years of age from the Frailty and Dependence in Albacete (FRADEA) study. Measurements: Frailty phenotype, Geriatric Depression Scale (GDS), comorbidity, disability, and drug use were collected at baseline. Six groups were categorized: (G1: non-frail/no depression risk; G2: non-frail/depression risk; G3: prefrail/no depression risk; G4: prefrail/depression risk; G5: frail/no depression risk; and G6: frail/depression risk). Mean follow-up was 2542 days (SD 1006). GDS was also analyzed as a continuous variable. The association between frailty and depression risk with 10-year mortality was analyzed. Results: Mean age was 78.5 years. Non-frail was 24.5%, prefrail 56.3%, frail 19.3%, and 33.5% at depression risk. Mean GDS score was 3.7 (SD 3.2), increasing with the number of frailty criteria (p < 0.001). Ten-year mortality rate was 44.9%. Mortality was 21.4% for the non-frail, 45.6% for the prefrail, and 72.7% for the frail participants, 56% for those with depression risk, and 39.3% for those without depression risk. Mean survival times for groups G1 to G6 were, respectively, 3390, 3437, 2897, 2554, 1887, and 1931 days. Adjusted mortality risk was higher for groups G3 (HR 2.1; 95% confidence interval (CI) 1.4–3.1), G4 (HR 2.5; 95% CI 1.7–3.8), G5 (HR 3.8; 95% CI 2.4–6.1), and G6 (HR 4.0; 95% CI 2.6–6.2), compared with G1 (p < 0.001). Interaction was found between frailty and depression risk, although they were independently associated with mortality. Conclusions: Depression risk increases mortality risk in prefrail older adults but not in non-frail and frail ones. Depression should be monitored in these older adults to optimize health outcomes. Factors modulating the relationship between frailty and depression should be explored in future studies.

Arterial stiffness has been shown to be a subclinical marker associated with cardiovascular disease. Meanwhile, long-term exercise has been demonstrated to reduce arterial stiffness, providing a decrease in cardiovascular risk. However, the acute effect of exercise on arterial stiffness is unclear. This systematic review and meta-analysis aimed to assess the acute effect of exercise interventions on arterial stiffness in healthy adults. We searched the Cochrane Central Register of Controlled Trials, MEDLINE (via Pubmed), Scopus, and Web of Science databases, from their inception to 30 June 2020. A meta-analysis was performed to evaluate the acute effect of exercise on arterial stiffness using random-effects models to calculate pooled effect size estimates and their corresponding 95% CI. Pulse wave velocity was measured as an arterial stiffness index. The 30 studies included in the meta-analysis showed that pulse wave velocity was not modified immediately after exercise (0 min post) (ES: 0.02; 95% CI: −0.22, 0.26), but subsequently decreased 30 min after exercise (ES: −0.27; 95% CI: −0.43, −0.12). Thereafter, pulse wave velocity increased to its initial value 24 h after exercise (ES: −0.07; 95% CI: −0.21, 0.07). Our results show that, although there is a significant reduction in pulse wave velocity 30 min after exercise, the levels of arterial stiffness return to their basal levels after 24 h. These findings could imply that, in order to achieve improvements in pulse wave velocity, exercise should be performed on a daily basis.

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Parkinson’s disease is a chronic, progressive, and disabling neurodegenerative disease which evolves until the end of life and triggers different mood and organic alterations that influence health-related quality of life. The objective of our study was to identify the factors that negatively impact the quality of life of patients with Parkinson’s disease and construct a predictive model of health-related quality of life in these patients. Methods: An analytical, prospective observational study was carried out, including Parkinson’s patients at different stages in the Albacete Health Area. The sample consisted of 155 patients (T0) who were followed up at one (T1) and two years (T2). The instruments used were a purpose-designed data collection questionnaire and the “Parkinson’s Disease Questionnaire” (PDQ-39), with a global index where a higher score indicates a worse quality of life. A multivariate analysis was performed by multiple linear regression at T0. Next, the model’s predictive capacity was evaluated at T1 and T2 using the area under the ROC curve (AUROC). Results: Predictive factors were: sex, living in a residence, using a cane, using a wheelchair, having a Parkinson’s stage of HY > 2, having Alzheimer’s disease or a major neurocognitive disorder, having more than five non-motor symptoms, polypharmacy, and disability greater than 66%. This model showed good predictive capacity at one year and two years of follow-up, with an AUROC of 0.89 (95% CI: 0.83–0.94) and 0.83 (95% CI: 0.76–0.89), respectively. Conclusions: A predictive model constructed with nine variables showed a good discriminative capacity to predict the quality of life of patients with Parkinson’s disease at one and two years of follow-up.