Compared Heritability of Chronotype Instruments in a Single Population Sample

It is well established that the oldest chronotype questionnaire, the morningness-eveningness questionnaire (MEQ), has significant heritability, and several associations have been reported between MEQ score and polymorphisms in candidate clock genes, a number of them reproducibly across populations. By contrast, there are no reports of heritability and genetic associations for the Munich chronotype questionnaire (MCTQ). Recent genome-wide association studies (GWAS) from large cohorts have reported multiple associations with chronotype as assessed by a single self-evaluation question. We have taken advantage of the availability of data from all these instruments from a single sample of 597 participants from the Brazilian Baependi Heart Study. The family-based design of the cohort allowed us to calculate the heritability (h2) for these measures. Heritability values for the best-fitted models were 0.37 for MEQ, 0.32 for MCTQ, and 0.28 for single-question chronotype (MEQ Question 19). We also calculated the heritability for the two major factors recently derived from MEQ, “Dissipation of sleep pressure” (0.32) and “Build-up of sleep pressure” (0.28). This first heritability comparison of the major chronotype instruments in current use provides the first quantification of the genetic component of MCTQ score, supporting its future use in genetic analysis. Our findings also suggest that the single chronotype question that has been used for large GWAS analyses captures a larger proportion of the dimensions of chronotype than previously thought

Pathophysiology of major depression by clinical stages

The comprehension of the pathophysiology of the major depressive disorder (MDD) is essential to the strengthening of precision psychiatry. In order to determine the relationship between the pathophysiology of the MDD and its clinical progression, analyzed by severity of the depressive symptoms and sleep quality, we conducted a study assessing different peripheral molecular biomarkers, including the levels of plasma C-reactive protein (CRP), serum mature brain-derived neurotrophic factor (mBDNF), serum cortisol (SC), and salivary cortisol awakening response (CAR), of patients with MDD (n = 58) and a control group of healthy volunteers (n = 62). Patients with the first episode of MDD (n = 30) had significantly higher levels of CAR and SC than controls (n = 32) and similar levels of mBDNF of controls. Patients with treatment-resistant depression (TRD, n = 28) presented significantly lower levels of SC and CAR, and higher levels of mBDNF and CRP than controls (n = 30). An increased severity of depressive symptoms and worse sleep quality were correlated with levels low of SC and CAR, and with high levels of mBDNF. These results point out a strong relationship between the stages clinical of MDD and changes in a range of relevant biological markers. This can assist in the development of precision psychiatry and future research on the biological tests for depression

Predicting depressive symptoms in middle-aged and elderly adults using sleep data and clinical health markers: a machine learning approach

Objectives: Comorbid depression is a highly prevalent and debilitating condition in middle-aged and elderly adults, particularly when associated with obesity, diabetes, and sleep disturbances. In this context, there is a growing need to develop efficient screening methods for cases based on clinical health markers for these comorbidities and sleep data. Thus, our objective was to detect depressive symptoms in these subjects, considering general biomarkers of obesity and diabetes and variables related to sleep and physical exercise through a machine learning approach. Methods: National Health and Nutrition Examination Survey (NHANES) 2015-2016 data were used and eighteen variables on self-reported physical activity, self-reported sleep habits, sleep disturbance indicative, anthropometric measurements, sociodemographic characteristics and plasma biomarkers of obesity and diabetes were selected as predictors. A total of 2,907 middle-aged and elderly subjects were eligible for the study. Supervised learning algorithms such as Lasso penalized Logistic Regression (LR), Random forest (RF) and Extreme Gradient Boosting (XGBoost) were implemented. Results: XGBoost provided greater accuracy and precision (87%), with a proportion of hits in cases with depressive symptoms above 80%. In addition, daytime sleepiness was the most significant predictor variable for predicting depressive symptoms. Conclusions: Sleep and physical activity variables, in addition to obesity and diabetes biomarkers, together assume significant importance to predict, with accuracy and precision of 87%, the occurrence of depressive symptoms in middle- aged and elderly individuals

Adherence to treatment in allergic rhinitis using mobile technology. The MASK Study

Background: Mobile technology may help to better understand the adherence to treatment. MASK-rhinitis (Mobile Airways Sentinel NetworK for allergic rhinitis) is a patient-centred ICT system. A mobile phone app (the Allergy Diary) central to MASK is available in 22 countries. Objectives: To assess the adherence to treatment in allergic rhinitis patients using the Allergy Diary App. Methods: An observational cross-sectional study was carried out on all users who filled in the Allergy Diary from 1 January 2016 to 1 August 2017. Secondary adherence was assessed by using the modified Medication Possession Ratio (MPR) and the Proportion of days covered (PDC) approach. Results: A total of 12 143 users were registered. A total of 6 949 users reported at least one VAS data recording. Among them, 1 887 users reported ≥7 VAS data. About 1 195 subjects were included in the analysis of adherence. One hundred and thirty-six (11.28%) users were adherent (MPR ≥70% and PDC ≤1.25), 51 (4.23%) were partly adherent (MPR ≥70% and PDC = 1.50) and 176 (14.60%) were switchers. On the other hand, 832 (69.05%) users were non-adherent to medications (MPR <70%). Of those, the largest group was non-adherent to medications and the time interval was increased in 442 (36.68%) users. Conclusion and clinical relevance: Adherence to treatment is low. The relative efficacy of continuous vs on-demand treatment for allergic rhinitis symptoms is still a matter of debate. This study shows an approach for measuring retrospective adherence based on a mobile app. This also represents a novel approach for analysing medication-taking behaviour in a real-world setting