Abstract ObjectivesBlack older adults have a higher vascular burden compared to non‐Hispanic White (NHW) older adults, which may put them at risk for a form of depression known as vascular depression (VaDep). The literature examining VaDep in Black older adults is sparse. The current study addressed this important gap by examining whether vascular burden was associated with depressive symptoms in Black older adults. MethodsParticipants included 113 Black older adults from the Healthy Brain Project, a substudy of the Health, Aging, and Body Composition Study. In multiple regression analyses, clinical vascular burden (sum of vascular conditions) and white matter hyperintensity (WMH) volume predicted depressive symptoms as measured by the Center for Epidemiologic Studies Depression Scale, controlling for demographic variables. Follow‐up analyses compared the associations in the Black subsample and in 179 NHW older adults. ResultsHigher total WMH volume, but not clinically‐defined vascular burden, predicted higher concurrent depressive symptoms and higher average depressive symptoms over 4 years. Similar associations were found between uncinate fasciculus (UF) WMHs and concurrent depressive symptoms and between superior longitudinal fasciculus WMHs and average depressive symptoms. The association between depressive symptoms and UF WMH was stronger in Black compared to NHW individuals. ConclusionThis research is consistent with the VaDep hypothesis and extends it to Black older adults, a group that has historically been underrepresented in the literature. Results highlight WMH in the UF as particularly relevant to depressive symptoms in Black older adults and suggest this group may be particularly vulnerable to the negative effects of WMH. 
                        more » 
                        « less   
                    
                            
                            Common antiretroviral combinations are associated with somatic depressive symptoms in women with HIV
                        
                    
    
            Objective:While modern antiretroviral therapy (ART) is highly effective and safe, depressive symptoms have been associated with certain ART drugs. We examined the association between common ART regimens and depressive symptoms in women with HIV (WWH) with a focus on somatic vs. nonsomatic symptoms. Design:Analysis of longitudinal data from the Women's Interagency HIV Study. Methods:Participants were classified into three groups based on the frequency of positive depression screening (CES-D ≥16): chronic depression (≥50% of visits since study enrollment), infrequent depression (<50% of visits), and never depressed (no visits). Novel Bayesian machine learning methods building upon a subset-tree kernel approach were developed to estimate the combined effects of ART regimens on depressive symptoms in each group after covariate adjustment. Results:The analysis included 1538 WWH who participated in 12 924 (mean = 8.4) visits. The mean age was 49.9 years, 72% were Black, and 14% Hispanic. In the chronic depression group, combinations including tenofovir alafenamide and cobicistat-boosted elvitegravir and/or darunavir were associated with greater somatic symptoms of depression, whereas those combinations containing tenofovir disoproxil fumarate and efavirenz or rilpivirine were associated with less somatic depressive symptoms. ART was not associated with somatic symptoms in the infrequent depression or never depressed groups. ART regimens were not associated with nonsomatic symptoms in any group. Conclusions:Specific ART combinations are associated with somatic depressive symptoms in WWH with chronic depression. Future studies should consider specific depressive symptoms domains as well as complete drug combinations when assessing the relationship between ART and depression. 
        more » 
        « less   
        
    
                            - Award ID(s):
- 1918854
- PAR ID:
- 10549210
- Publisher / Repository:
- Lippincott Williams & Wilkins
- Date Published:
- Journal Name:
- AIDS
- Volume:
- 38
- Issue:
- 2
- ISSN:
- 0269-9370
- Page Range / eLocation ID:
- 167 to 176
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
- 
            
- 
            BackgroundThe treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo. MethodsThe study samples included training datasets (N = 271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N = 255) or placebo (N = 265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment. ResultsVariation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self‐esteem, and depressed feelings) assessed with the Children’s Depression Rating Scale‐Revised at 4–6 weeks predicted treatment outcomes with fluoxetine at 10–12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10–12 week outcomes at 4–6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo‐treated patients with accuracies of 67%. In placebo‐treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants. ConclusionsPGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression.more » « less
- 
            ImportanceMarked elevation in levels of depressive symptoms compared with historical norms have been described during the COVID-19 pandemic, and understanding the extent to which these are associated with diminished in-person social interaction could inform public health planning for future pandemics or other disasters. ObjectiveTo describe the association between living in a US county with diminished mobility during the COVID-19 pandemic and self-reported depressive symptoms, while accounting for potential local and state-level confounding factors. Design, Setting, and ParticipantsThis survey study used 18 waves of a nonprobability internet survey conducted in the United States between May 2020 and April 2022. Participants included respondents who were 18 years and older and lived in 1 of the 50 US states or Washington DC. Main Outcome and MeasureDepressive symptoms measured by the Patient Health Questionnaire-9 (PHQ-9); county-level community mobility estimates from mobile apps; COVID-19 policies at the US state level from the Oxford stringency index. ResultsThe 192 271 survey respondents had a mean (SD) of age 43.1 (16.5) years, and 768 (0.4%) were American Indian or Alaska Native individuals, 11 448 (6.0%) were Asian individuals, 20 277 (10.5%) were Black individuals, 15 036 (7.8%) were Hispanic individuals, 1975 (1.0%) were Pacific Islander individuals, 138 702 (72.1%) were White individuals, and 4065 (2.1%) were individuals of another race. Additionally, 126 381 respondents (65.7%) identified as female and 65 890 (34.3%) as male. Mean (SD) depression severity by PHQ-9 was 7.2 (6.8). In a mixed-effects linear regression model, the mean county-level proportion of individuals not leaving home was associated with a greater level of depression symptoms (β, 2.58; 95% CI, 1.57-3.58) after adjustment for individual sociodemographic features. Results were similar after the inclusion in regression models of local COVID-19 activity, weather, and county-level economic features, and persisted after widespread availability of COVID-19 vaccination. They were attenuated by the inclusion of state-level pandemic restrictions. Two restrictions, mandatory mask-wearing in public (β, 0.23; 95% CI, 0.15-0.30) and policies cancelling public events (β, 0.37; 95% CI, 0.22-0.51), demonstrated modest independent associations with depressive symptom severity. Conclusions and RelevanceIn this study, depressive symptoms were greater in locales and times with diminished community mobility. Strategies to understand the potential public health consequences of pandemic responses are needed.more » « less
- 
            PurposeThis study examined relations between four late-life depression subgroups (recent, >2 years ago, chronic, no depression) and regional brain volumes using structural MRI data from the National Alzheimer’s Coordinating Center (n=1,551). Data AnalysisMultiple linear regressions evaluated the effects of depression on 30 MRI biomarkers, while moderation analyses assessed how APOE ε4 and depression shape the connections between cognitive status and brain structure volumes. ResultsAfter adjusting for covariates and applying Hochberg’s method, recent depression (< 2 years) was associated with reduced total cerebrum cranial volume and left frontal lobe cortical gray matter volume. Chronic depression correlated with larger right lateral ventricle volume. ConclusionThese findings suggest that recent depression is linked to brain atrophy across specific regions and ventricular enlargement. Future research should investigate age-related impacts on these associations and whether restoration of brain volume occurs after depressive symptoms subside.more » « less
- 
            Summary Combination antiretroviral therapy (ART) with at least three different drugs has become the standard of care for people with HIV (PWH) due to its exceptional effectiveness in viral suppression. However, many ART drugs have been reported to associate with neuropsychiatric adverse effects including depression, especially when certain genetic polymorphisms exist. Pharmacogenetics is an important consideration for administering combination ART as it may influence drug efficacy and increase risk for neuropsychiatric conditions. Large-scale longitudinal HIV databases provide researchers opportunities to investigate the pharmacogenetics of combination ART in a data-driven manner. However, with more than 30 FDA-approved ART drugs, the interplay between the large number of possible ART drug combinations and genetic polymorphisms imposes statistical modeling challenges. We develop a Bayesian approach to examine the longitudinal effects of combination ART and their interactions with genetic polymorphisms on depressive symptoms in PWH. The proposed method utilizes a Gaussian process with a composite kernel function to capture the longitudinal combination ART effects by directly incorporating individuals’ treatment histories, and a Bayesian classification and regression tree to account for individual heterogeneity. Through both simulation studies and an application to a dataset from the Women’s Interagency HIV Study, we demonstrate the clinical utility of the proposed approach in investigating the pharmacogenetics of combination ART and assisting physicians to make effective individualized treatment decisions that can improve health outcomes for PWH.more » « less
 An official website of the United States government
An official website of the United States government 
				
			 
					 
					
 
                                    