Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adult depression (original) (raw)
Related papers
BackgroundMajor depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in MDD patients, and whether this process is associated with clinical characteristics in a large multi-center international dataset.MethodsWe performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 29 samples worldwide. Normative brain aging was estimated by predicting chronological age (10-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 1,147 male and 1,386 female controls from the ENIGMA MDD working group. The learned model parameters were applied to 1,089 male controls and 1,167 depressed males, and 1,326 female controls and 2,044 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted “brain age” and chronologi...
A Large-Scale ENIGMA Multisite Replication Study of Brain Age in Depression
ABSTRACTBackgroundSeveral studies have evaluated whether depressed persons have older appearing brains than their nondepressed peers. However, the estimated neuroimaging-derived “brain age gap” has varied from study to study, likely driven by differences in training and testing sample (size), age range, and used modality/features. To validate our previously developed ENIGMA brain age model and the identified brain age gap, we aim to replicate the presence and effect size estimate previously found in the largest study in depression to date (N=2,126 controls & N=2,675 cases; +1.08 years [SE 0.22], Cohen’s d=0.14, 95% CI: 0.08-0.20), in independent cohorts that were not part of the original study.MethodsA previously trained brain age model (www.photon-ai.com/enigma\_brainage) based on 77 FreeSurfer brain regions of interest was used to obtain unbiased brain age predictions in 751 controls and 766 persons with depression (18-75 years) from 13 new cohorts collected from 20 different scann...
NeuroImage, 2021
This paper describes the public repository that houses multimodal data collected as part of Aging Brain Cohort study being conducted at the University of South Carolina (ABC@UofSC). Ultimately, the ABC@UofSC Repository will contain diverse data from cross sectional (N ¼ 800, age ¼ 20-80) and longitudinal (N ¼ 200, age ¼ 60-80, interesting interval ¼ 4 years) samples of healthy South Carolinians which include socio demographic data, raw and preprocessed functional (resting-state and task based fMRI, ASL) and structural (T1, T2 FLAIR, DWI, SWI) MRI data, raw and preprocessed resting-state EEG, comprehensive blood work, measures of physical and sensory function, genetic data derived from whole blood and buccal swabs, and results from a unique constellation of social, emotional, cognitive and language measures. Currently, data has been collected from 65 participants (ages 60-80) in the longitudinal arm of the project. The ABC@UofSC Repository is unique in its broad range of measures, choice of state-of-the art MRI sequences, inclusion of complex language discourse measures, and demographic diversity. Data from the ABC@UofSC Repository are easily accessible upon request (abc.sc.edu), and our publicly available statistics and visualization tools provide collaborating researchers with the ability to identify associations between brain structure and function in relation to genetic variation and behavioral measures across the adult lifespan with unprecedented ease and rapidity.
Depression and cognition: how do they interrelate in old age?
The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry, 2013
To disentangle the reciprocal effects between depressive symptoms and cognitive functioning over time and to study the association between changes in their trajectories using 13 years of follow-up. Data were used from five waves of the population-based Longitudinal Aging Study Amsterdam. Subjects were included if data was present on depressive symptoms and cognitive performance on at least two occasions, which resulted in a study sample of N = 2,299. Depressive symptoms were assessed with the Center for Epidemiologic Studies Depression Scale. Cognitive functioning was assessed using the Mini-Mental State Examination (general cognitive functioning) and timed coding task (speed of information processing). Cross-domain latent change analyses showed that depression at baseline predicted both decline of general cognitive functioning and information processing speed, independent of relevant covariates. Conversely, information processing speed at baseline, but not general cognitive functio...
Frontiers in aging neuroscience, 2017
Objective: Late-life depression (LLD) is associated with development of different types of dementia. Identification of LLD patients, who will develop cognitive decline, i.e., the early stage of dementia would help to implement interventions earlier. The purpose of this study was to assess whether structural brain magnetic resonance imaging (MRI) in LLD patients can predict mild cognitive impairment (MCI) or dementia 1 year prior to the diagnosis. Methods: LLD patients underwent brain MRI at baseline and repeated clinical assessment after 1-year. Structural brain measurements were obtained using Freesurfer software (v. 5.1) from the T1W brain MRI images. MRI-based Random Forest classifier was used to discriminate between LLD who developed MCI or dementia after 1-year follow-up and cognitively stable LLD. Additionally, a previously established Random Forest model trained on 185 patients with Alzheimer's disease (AD) vs. 225 cognitively normal elderly from the Alzheimer's disea...
Aging Clinical and Experimental Research, 2004
Background and aims: Some prospective studies show that depression is a risk factor for cognitive decline. So far, the explanation for the background of this association has remained unclear. The present study investigated 1) whether depression is etiologically linked to cognitive decline; 2) whether depression and cognitive decline may be the consequence of the same underlying subcortical pathology, or 3) whether depression is a reaction to global cognitive deterioration. Methods: A cohort of 133 depressed and 144 non-depressed older persons was followed at eight successive observations over 3 years. All subjects were participants in the Longitudinal Aging Study Amsterdam (LASA). Depression symptoms were measured by means of the CES-D at eight successive waves. Cognitive function (memory function, information processing speed, global cognitive functioning) was assessed at baseline and at the last CES-D measurement. Results: The severity and duration of depressive symptoms were not associated with subsequent decline in memory functioning or global cognitive decline. There was an association between both chronic mild depression and chronic depression, and decline in speed of information processing. Conclusions: These results support the hypothesis that, in older persons, chronic depression as well as cognitive decline may be the consequence of the same underlying subcortical pathology.
Biological age and brain age in midlife: relationship to multimorbidity and mental health
Multimorbidity, co-occurrence of two or more chronic conditions, is one of the top priorities in global health research and has emerged as the gold standard approach to study disease accumulation. As aging underlies the development of many chronic conditions, surrogate aging biomarkers are not disease-specific and capture health at the whole person level, having the potential to improve our understanding of multimorbidity. Biological age has been examined in recent years as a surrogate biomarker to capture the process of aging. However, relatively few studies have investigated the relationship between biological age and multimorbidity. More research is needed to quantify biological age using a broad range of biological markers and multimorbidity based on a comprehensive set of chronic conditions. Brain age estimated by neuroimaging data and machine learning models is another surrogate aging biomarker predictive of a wide range of health outcomes. Little is known about the relationsh...
Depressive symptoms as a predictor of cognitive decline: MacArthur Studies of Successful Aging
The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry, 2007
The prevalence of dementia continues to rise, and yet, there are few known modifiable risk factors. Depression, as a treatable condition, may be important in the development of dementia. Our objective was to examine the association between depressive symptoms and longitudinal cognitive changes in older adults who were high-functioning at baseline. The authors analyzed data from a community-based cohort (aged 70-79 at baseline), who, at study entry, scored 7 or more (out of 9) on the Short Portable Mental Status Questionnaire (SPMSQ). Depressive symptoms were assessed at baseline using the depression subscale of the Hopkins Symptom Check List. Cognitive performance was measured at baseline and at seven-year follow up by the SPMSQ and by summary scores from standard tests of naming, construction, spatial recognition, abstraction, and delayed recall. After adjusting for potential confounders, including age, education, and chronic health conditions such as diabetes, heart attack, stroke...
Prediction of cognitive decline in healthy older adults using fMRI
Journal of Alzheimer's disease : JAD, 2010
Few studies have examined the extent to which structural and functional MRI, alone and in combination with genetic biomarkers, can predict future cognitive decline in asymptomatic elders. This prospective study evaluated individual and combined contributions of demographic information, genetic risk, hippocampal volume, and fMRI activation for predicting cognitive decline after an 18-month retest interval. Standardized neuropsychological testing, an fMRI semantic memory task (famous name discrimination), and structural MRI (sMRI) were performed on 78 healthy elders (73% female; mean age = 73 years, range = 65 to 88 years). Positive family history of dementia and presence of one or both apolipoprotein E (APOE) ε4 alleles occurred in 51.3% and 33.3% of the sample, respectively. Hippocampal volumes were traced from sMRI scans. At follow-up, all participants underwent a repeat neuropsychological examination. At 18 months, 27 participants (34.6%) declined by at least 1 SD on one of three ...
Biological Age, Not Chronological Age, Is Associated with Late-Life Depression
The Journals of Gerontology: Series A, 2017
Background: The pathophysiology of late-life depression (LLD) is complex and heterogeneous, with age-related processes implicated in its pathogenesis. This study examined the cross-sectional and longitudinal association between depressive symptoms and a baseline multibiomarker algorithm of biological age (BA) that aggregates indicators of inflammatory, metabolic, cardiovascular, lung, liver, and kidney functioning. Method: Data were analyzed from 2,776 men and women from the prospective observational Health Aging and Body Composition Study, who had both evaluable chronological age (CA) and BA. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression (CES-D) scale. Results: A covariate-adjusted regression model showed that BA (B = 0.03, p = .0471) but not CA (B = −0.01, p = .7185) is associated with baseline CES-D scores. The mean baseline BA for individuals with a CES-D ≥ 10 was 1.28 years greater than in those with a CES-D < 10. Comparatively, there is only a 0.05-year difference in mean CA between the two depression groups. A covariate-adjusted longitudinal model found that baseline BA predicts CES-D score at follow-up (B = 0.04, p = .0058), whereas CA does not (B = 0.03, p = .4125). Additionally, an older BA significantly predicted a CES-D ≥ 10 (B = 0.02, p = .032) over a 10-year period. Conclusions: A multibiomarker index of an older adult's BA outperformed their CA in predicting subsequent increased and clinically significant depressive symptoms. This result supports the evolving view of LLD as a brain disorder resulting from deleterious age-associated changes across numerous physiological systems.