What Characterizes Depression in Old Age? Results from the Bruneck Study

Effectiveness of subjective support-focused cognitive behavioral therapy on depressive symptoms among (pre)frail community-dwelling older adults: A randomized controlled trial

BACKGROUND

Subjective support could ameliorate the adverse effect of (pre)frailty on depressive symptoms. However, there is scarce evidence regarding subjective support-focused intervention in preventing depression among (pre)frail community-dwelling older adults. This study aims to explore the effectiveness of subjective support-focused cognitive behavioral therapy (SS-CBT) in preventing depression among this group of population.

METHODS

A total of 100 community-dwelling (pre)frail older adults were recruited from six communities in a Chinese city and were randomized to an 8-week SS-CBT group or a wait-list control group. Depressive symptoms and subjective support were assessed at baseline (T0), and at 8 week (T1), 12 week (T2), 16 week (T3) after randomization. Generalized estimating equation was used to examine the effectiveness of SS-CBT on depressive symptoms and subjective support. Hierarchical linear regression models and Bootstrapping method were used to examine whether subjective support mediated the effectiveness of SS-CBT on depressive symptoms.

RESULTS

Participants in SS-CBT group reported significant reduction in depressive symptoms (Wald χ2 = 20.800, p < 0.001) and improvement in subjective support (Wald χ2 = 92.855, p < 0.001) compared to those in wait-list control group. Changes in subjective support mediated the effectiveness of SS-CBT on changes in depressive symptoms.

LIMITATIONS

Restricted regions to recruit participants, inclusion of the most motivated participants, lack of diagnosis of depression, potential experimenter bias and contamination, short follow-up period, and lack of an active control group.

CONCLUSIONS

The findings support the benefits of SS-CBT in preventing depression among (pre)frail community-dwelling older adults, and provide insight into possible mechanisms.

Symptoms of depression are related to sedentary behavior and sleep duration in elderly individuals: A cross-sectional study of 49,317 older Chinese adults

OBJECTIVE

Depression is a common mental disorder in older adults. This study aimed to determine the incidence of depression and the relationship between sedentary behavior and sleep duration in elderly individuals.

METHOD

A total of 49,317 elderly adults from eight health centers in six Chinese provinces completed an online self-reported health questionnaire. Logistic regression was used to explore the association between sedentary behavior, sleep duration, and depression symptoms.

RESULTS

Symptoms of depression were present in 20.1% of older adults. Sedentary behaviors for >6 h/day (OR = 3.16; 95% CI: 2.90-3.44; p < 0.001) were a greater risk factor for depressive symptoms in older adults than sedentary behaviors for less than 3 h/day. Elderly individuals with short sleep durations (<7 h/day) were more likely to experience depression (OR = 2.20; 95% CI: 2.07-2.34; p < 0.001).

CONCLUSION

This study suggested that sedentary behavior and short sleep duration in older Chinese adults are associated with depressive symptoms. In China, further improvement of the mental health and lifestyle of elderly individuals may be warranted.

Association between Ultraviolet B Exposure Levels and Depression in Taiwanese Adults: A Nested Case-Control Study

Depression is a common mental disorder that affects more than 264 million people worldwide. Anxiety, diabetes, Alzheimer’s disease, myocardial infarction, and cancer, among other disorders, are known to increase the risk of depression. Exposure to ultraviolet B (UVB) can cause human serotonin levels to increase. The vitamin D pathway is one mechanism through which ultraviolet light absorbed through the skin can affect mood; however, UVB exposure is known to increase the risk of cancer. In this study, we explored the effects of prolonged exposure to UVB on depression. Data were retrieved from the Taiwan National Health Insurance Research Database for 2008 to 2013. Each patient with depression was matched 1:4 with a comparison patient by sex and age (±5 years); thus, the study included 23,579 patients with depression and 94,316 healthy controls for comparison. The patients had been exposed to UVB for at least 1 year to observe the cumulative effect of UVB exposure. Based on the World Health Organization UV index, we divided the observation period data into five UV levels: low, moderate, high, very high, and extreme. A multivariate Poisson regression model was used to assess the risk of depression according to UVB exposure level, adjusting for sex, age, income, urbanization level, month, and comorbidities. The results revealed that the incidence rate ratio (IRR) for patients with depression was 0.889 for moderate levels (95% CI 0.835–0.947), 1.134 for high levels (95% CI: 1.022–1.260), 1.711 for very high levels (95% CI: 1.505–1.945), and 2.785 for extreme levels (95% CI: 2.439–3.180) when compared to low levels. Moderate levels of UVB lowered the risk of depression, while high levels of UVB gradually increased the risk. We propose that UVB at normal concentrations can effectively improve depression. However, exposure to high concentrations of UVB damage DNA results in physical diseases such as skin cancer, which increase the risk of depression.

Predicted Cognitive Conversion in Guiding Early Decision-Tailoring on Patients With Cognitive Impairment

Background

Cognitive decline is the most dominant and patient-oriented symptom during the development of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). This study was designed to test the feasibility of hybrid convolutional neural networks and long-short-term memory (CNN-LSTM) modeling driven early decision-tailoring with the predicted long-term cognitive conversion in AD and MCI.

Methods

Characteristics of patients with AD or MCI covering demographic features, clinical features, and time-dependent neuropsychological-related features were fused into the hybrid CNN-LSTM modeling to predict cognitive conversion based on a 4-point change in the AD assessment scale-cognition score. Treatment reassignment rates were estimated based on the actual and predicted cognitive conversion at 3 and 6 months according to the prespecified principle; that is if the ADAS-cog score of the patient declines less than 4 points or increases at either follow-up time point, the medical treatment recommended upon their diagnosis would be considered insufficient. Therefore, it is recommended to upgrade the medical treatment upon diagnosis. Actual and predicted treatment reassignment rates were compared in the general population and subpopulations categorized by age, gender, symptom severity, and the intervention subtypes.

Results

A total of 224 patients were included in the analysis. The hybrid CNN-LSTM model achieved the mean AUC of 0.735 (95% CI: 0.701–0.769) at 3 months and 0.853 (95% CI: 0.814–0.892) at 6 months in predicting cognitive conversion status. The AUC at 6 months was significantly impacted when data collected at 3 months were withdrawn. The predicted cognitive conversion suggested a revision of medical treatment in 46.43% (104/224) of patients at 3 months and 54.02% (121/224) at 6 months as compared with 62.05% (139/224) at 3 months (p = 0.001) and 62.50% (140/224) at 6 months (p = 0.069) according to their actual cognitive conversion. No significant differences were detected between treatment reassignment rates estimated based on actual and predicted cognitive conversion in all directions at 6 months.

Conclusion

Using the synergistic advances of deep learning modeling and featured longitudinal information, our hypothesis was preliminarily verified with the comparable predictive performance in cognitive conversion. Results provided the possibility of reassigned recommended treatment for those who may suffer from cognitive decline in the future. Considering the limited diversity of treatment strategies applied in this study, the real-world medical situation should be further simulated.

Ginkgo Biloba Extract Is Comparable With Donepezil in Improving Functional Recovery in Alzheimer's Disease: Results From a Multilevel Characterized Study Based on Clinical Features and Resting-State Functional Magnetic Resonance Imaging

Background: Ginkgo biloba extract (GBE) and donepezil have been reported to be effective in patients with Alzheimer’s disease (AD). Nonetheless, how these drugs impact spontaneous brain activities and how they consequently improve functional recovery are currently unclear.

Objectives: This study was to explore the efficacy of GBE vs. donepezil and their add-on efficacy on functional recovery and the adaption of spontaneous brain activities following pharmacologic treatment in patients with AD.

Methods: Patients with AD were enrolled and assigned to the GBE group (n = 50), the donepezil group (n = 50), or the combined group (n = 50). Neuropsychological assessments, including minimum mental state examination (MMSE), Alzheimer’s disease assessment scale-cognition (ADAS-Cog), instrumental activity of daily living (IADL), geriatric depression scale (GDS), neuropsychiatric inventory (NPI), and quality of life in Alzheimer’s disease (QOL-AD), were conducted at baseline, 1 month, 3 months, and 6 months. Resting-state functional magnetic resonance imaging (rs-fMRI) was collected to compare the amplitude of low-frequency fluctuation (ALFF), percent amplitude of fluctuation (PerAF), regional homogeneity (ReHo), and degree centrality (DC) at baseline and 6 months.

Results: No major significant differences were detected in all comparisons between groups across all follow-up time points. For intragroup comparison, MMSE and ADAS-Cog scores differed significantly across all follow-ups in three groups. The combined group showed significant improvement of GDS scores between baseline and 6 months (p = 0.007). The GBE group (p = 0.044) and donepezil group (p = 0.012) demonstrated significant improvement of NPI scores between baseline and 6 months. Significant correlations were observed between IADL and ALFF in the right gyrus rectus (p = 0.03) and in the left superior cerebellum gyrus (p = 0.01), between GDS and ALFF in the right middle temporal gyrus (p = 0.01), between NPI and PerAF in the left fusiform gyrus (p = 0.03), and between MMSE and ReHo in right superior frontal gyrus (p = 0.04).

Conclusion: GBE was comparable with donepezil in the improvement of functional recovery in patients with AD while the combined application of GBE and donepezil seems unnecessary. GBE-mediated improvement of functional recovery was characterized by decreased ALFF values in the right gyrus rectus and decreased PerAF values in the left fusiform gyrus. These featured variations of imaging metrics in specific brain regions may serve as biomarkers in the monitoring of the therapeutic efficacy of GBE.