A few studies showed that both adherence to Mediterranean diet (MedDiet) and physical activity practice have a positive impact on pulmonary function in subjects with lung disease. These associations are not well studied in subjects free from lung disease. In a cross-sectional study conducted in 3020 middle-aged subjects free of lung disease, adherence to the MedDiet using the Mediterranean Diet Adherence Screener, and physical activity practice using the International Physical Activity Questionnaire short form were recorded. Respiratory function was assessed using forced spirometry and the results were evaluated according to the Global initiative for Chronic Obstructive Lung Disease. Logistic regression models were used to analyze the associations between adherence to the MedDiet and physical activity practice with the presence of ventilatory defects. Participants with a high adherence to MedDiet, in comparison to those with low adherence, had both higher forced vital capacity (FVC; 100 (87–109) vs. 94 (82–105) % of predicted, p = 0.003) and forced expired volume in the first second (FEV1; 100 (89–112) vs. 93 (80–107) % of predicted, p < 0.001). According to their degree of physical activity, those subjects with a high adherence also had both higher FVC (100 (88–107) vs. 94 (83–105) % of predicted, p = 0.027) and FEV1 (100 (89–110) vs. 95 (84–108) % of predicted, p = 0.047) in comparison with those with low adherence. The multivariable logistic regression models showed a significant and independent association between both low adherence to MedDiet and low physical activity practice, and the presence of altered pulmonary patterns, with differences between men and women. However, no joint effect between adherence to MedDiet and physical activity practice on respiratory function values was observed. Low adherence to MedDiet and low physical activity practice were independently associated with pulmonary impairment. Therefore, the lung mechanics seem to benefit from heart-healthy lifestyle behaviors.

We evaluated the influence of untreated obstructive sleep apnoea (OSA) on the magnitude of cognitive decline and on several cognitive subdomains in patients with mild-to-moderate Alzheimer's disease.In this single-centre study, 144 patients were recruited prospectively from a cognitive impairment unit and underwent overnight polysomnography.The mean±sdchange in the Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-cog) score at 12 months was 3.19±5.61 in the non-OSA group and 0.08±5.62 in the OSA group, with an intergroup difference of −3.36 (95% CI 0.19–0.16; p=0.002). We did not observe a significant difference in any cognitive subdomains at 12 months. Regarding Mini-Mental State Examination scores at 36 months, the mean change was 1.69 (95% CI −1.26–4.64; p=0.445). No significant differences were found among different OSA severity groups.We observed that ADAS-cog scores were better in the OSA group than in the non-OSA group by a statistically but not clinically significant margin. We did not find differences in the different cognitive subdomains after 1 year or in global cognition after 3 years of follow-up.

Alzheimer's disease (AD) and sleep disorders exhibit a close association, where disruptions in sleep patterns often precede the onset of Mild Cognitive Impairment (MCI) and early-stage AD. This study delves into the potential of utilizing sleep-related electroencephalography (EEG) signals acquired through polysomnography (PSG) for the early detection of AD. Our primary focus is on exploring semi-supervised Deep Learning techniques for the classification of EEG signals due to the clinical scenario characterized by the limited data availability. The methodology entails testing and comparing the performance of semi-supervised models, benchmarked against an unsupervised and a supervised model. The study highlights the significance of spatial and temporal analysis capabilities, conducting independent analyses of each sleep stage. Results demonstrate the effectiveness of one semi-supervised model in leveraging limited labeled data, achieving stable metrics across all sleep stages, and reaching 90% accuracy in its supervised form. Comparative analyses reveal this superior performance over the unsupervised model, while the supervised model ranges between $92-94\%$ . These findings underscore the potential of semi-supervised models in early AD detection, particularly in overcoming the challenges associated with the scarcity of labeled data. Ablation tests affirm the critical role of spatio-temporal feature extraction in semi-supervised predictive performance, and t-SNE visualizations validate the model's proficiency in distinguishing AD patterns. Overall, this research contributes to the advancement of AD detection through innovative Deep Learning approaches, highlighting the crucial role of semi-supervised learning in addressing data limitations.

Type 2 diabetes exerts a negative impact on sleep breathing. It is unknown whether a long-term improvement in glycemic control ameliorates this effect. We conducted an interventional study with 35 patients with type 2 diabetes and obstructive sleep apnea (OSA) to explore this. At home, sleep breathing parameters were assessed at baseline and after a 4-month period in which antidiabetic therapy was intensified. Patients who decreased their body mass index ≥2kg/m² were excluded. Those with an HbA1c reduction ≥0.5% were considered good responders (n = 24). After the follow-up, good responders exhibited an improvement in the apnea–hypopnea index (AHI: 26-1 (95% IC: 8.6–95.0) vs. 20.0 (4.0–62.4) events/hour, p = 0.002) and in time with oxygen saturation below 90% (CT90: 13.3 (0.4–69.0) vs. 8.1 (0.4–71.2) %, p = 0.002). No changes were observed in the group of non–responders (p = 0.722 and p = 0.138, respectively). The percentage of moderate and severe OSA decreased among good responders (p = 0.040). In the wider population, the change in HbA1c correlated positively to decreases in AHI (r = 0.358, p = 0.035) and negatively to increases in the minimum arterial oxygen saturation (r = −0.386, p = 0.039). Stepwise multivariate regression analysis showed that baseline AHI and the absolute change in HbA1c independently predicted decreased AHI (R² = 0.496). The improvement of glycemic control exerts beneficial effects on sleep breathing parameters in type 2 diabetes, which cannot be attributed merely to weight loss.

Respiratory failure is a recognized late complication of amyotrophic lateral sclerosis. It is related to the neurological progression of the diseases with the impairment of the respiratory musculature. Survival and quality of life of amyotrophic lateral sclerosis patients is improved by using noninvasive mechanical ventilation. The rate of long-term mechanical ventilation is different within and between countries. Cultural factors, socioeconomic conditions, and physician attitude often influence the decision to start noninvasive ventilation. Technical elements, like the choice of the correct interface, solid caregivers support, and the communication between the patient and the physician are essential for achieving therapeutic goals, especially in the case of continuous treatment.