Prediction of depressive symptoms at high age (80+) by psychological, biological and functional factors.

BACKGROUND: Late-life depression (LLD) is highly prevalent, especially in people aged 80 years and older. We aimed to investigate predictors and their influence on depressive symptoms in LLD. METHODS: We analysed data from the NRW80+ study, a population-based cross-sectional study of individuals aged 80 years and older. Data from n = 926 cognitively unimpaired participants were included. We reduced 95 variables to 21 predictors of depressive symptoms by using a two-step cluster analysis (TSCA), which were assigned to one of four factors (function, values and lifestyle, autonomy and contentment, biological-somatic) according to a principal component analysis. A second TSCA with complete data sets (n = 879) was used to define clusters of participants. Using weighted mean composite scores (CS) for each factor group, binary logistic regression analyses were performed to predict depressive symptoms for each cluster and the total population. RESULTS: The second TSCA yielded two clusters (cluster 1 (n = 688), cluster 2 (n = 191)). The proportion of participants with depressive symptoms was significantly higher in cluster 2 compared to cluster 1 (39 % vs. 15 %; OR = 3.6; 95 % CI 2.5-5.1; p < .001). Participants in cluster 2 were significantly older (mean age 88 vs. 85 years; p < .001), with a higher proportion of women (56 % vs. 46 %; OR = 1.5; 95 % CI 1.1-2.0; p = .016), had a higher BMI (p = .017), lower financial resources (OR = 2.3; 95 % CI 1.6-3.5; p < .001), lower educational level (OR = 1.8; 95 % CI 1.2-2.5; p = .002), higher proportion of single, separated or widowed participants (OR = 1.9; 95 % CI 1.3-2.6; p < .001) and a smaller mean social network (p = .044) compared to cluster 1. Binary logistic regression analyses showed that the weighted mean CS including the autonomy and contentment predictors explained the largest proportion of variance (22.8 %) for depressive symptoms in the total population (Nagelkerke's R2 = 0.228, p < .001) and in both clusters (cluster 1: Nagelkerke's R2 = 0.171, p < .001; cluster 2: Nagelkerke's R2 = 0.213, p < .001), respectively. LIMITATIONS: The main limitations are the restriction to cognitively unimpaired individuals and the use of a self-rated questionnaire to assess depressive symptoms. CONCLUSIONS: Psychological factors such as autonomy and contentment are critical for the occurrence of depressive symptoms at higher age, independent of the functional and somatic status and may serve as specific targets for psychotherapy.

Quality of health care for patients with coronary heart disease and comorbid mental disorders: a prospective cohort study.

BACKGROUND: Coronary heart disease (CHD) is often associated with mental disorders (MDs). Comorbid MDs reduce the quality of life and increase cardiac morbidity and mortality. Nevertheless, there is little and inconsistent research on the management of MDs in CHD patients. To bridge this gap, this study aims to gain insight into the long-term course of MD-related health care for patients with CHD, in order to identify opportunities for care improvement. METHODS: In this prospective cohort study, we investigated whether CHD patients with or without expert-rated MD at baseline (N = 364) received different MD-related health care from either their general practitioner (GP) or cardiologist at follow-up, M = 2.7 [2.0-4.0] years later. In the follow-up assessment, N = 131 CHD patients participated and received questionnaires capturing sociodemographic, mental health, and MD-related health care characteristics. Descriptive statistics, t-tests and chi-squared tests were used for analyses. RESULTS: We found significant differences in MD-related health care. CHD patients with MD were more likely to be examined psychologically/psychiatrically (MD 55.9%, non-MD 16.7%, p = < .001) and diagnosed with MD (MD 55.9%, non-MD 13.5%, p = .020) by their GP or cardiologist. Recommendations for and responses to requests for psychotherapy were more likely in MD patients compared to non-MD patients (MD 38.7%, non-MD 11.8%, p = .012 and MD 38.5%, non-MD 11.8%, p = .031, respectively). No significant differences were found concerning physicians' active demand for patients' mental health, referral to a specialist for additional diagnostics, provision of information about the diagnosed MD and further treatment options, response to the patients' request for psychopharmacotherapy, help received in finding psychotherapy or psychopharmacotherapy, and actual receipt of these treatments. CONCLUSIONS: The results indicate differences in MD-related health care of CHD patients with and without comorbid MD. However, they still highlight the need to further encourage primary care physicians treating CHD to adequately address MDs, provide further diagnostics, support, and information to affected patients. To address this, physicians may benefit from awareness training on the association between CHD and MDs and on appropriate communication with MD patients. TRIAL REGISTRATION: German clinical trials register (Deutsches Register Klinischer Studien, DRKS) Registration Number: ID DRKS00022154, date of registration: 02.11.2021.

Symptomatic Clusters Related to Amyloid Positivity in Cognitively Unimpaired Individuals.

Background: The NIA-AA Research Framework on Alzheimer's disease (AD) proposes a transitional stage (stage 2) characterized by subtle cognitive decline, subjective cognitive decline (SCD) and mild neurobehavioral symptoms (NPS). Objective: To identify participant clusters based on stage 2 features and assess their association with amyloid positivity in cognitively unimpaired individuals. Methods: We included baseline data of N = 338 cognitively unimpaired participants from the DELCODE cohort with data on cerebrospinal fluid biomarkers for AD. Classification into the AD continuum (i.e., amyloid positivity, A+) was based on Aß42/40 status. Neuropsychological test data were used to assess subtle objective cognitive dysfunction (OBJ), the subjective cognitive decline interview (SCD-I) was used to detect SCD, and the Neuropsychiatric Inventory Questionnaire (NPI-Q) was used to assess NPS. A two-step cluster analysis was carried out and differences in AD biomarkers between clusters were analyzed. Results: We identified three distinct participant clusters based on presented symptoms. The highest rate of A+ participants (47.6%) was found in a cluster characterized by both OBJ and SCD. A cluster of participants that presented with SCD and NPS (A+:26.6%) and a cluster of participants with overall few symptoms (A+:19.7%) showed amyloid positivity in a range that was not higher than the expected A+ rate for the age group. Across the full sample, participants with a combination of SCD and OBJ in the memory domain showed a lower Aß42/ptau181 ratio compared to those with neither SCD nor OBJ. Conclusions: The cluster characterized by participants with OBJ and concomitant SCD was enriched for amyloid pathology.