Negative fateful life events in midlife and advanced predicted brain aging

Hatton, Sean N.; Franz, Carol E.; Elman, Jeremy A.; Panizzon, Matthew S.; Hagler, Donald J.; Fennema-Notestine, Christine; Eyler, Lisa T.; McEvoy, Linda K.; Lyons, Michael J.; Dale, Anders M.; Kremen, William S.

doi:10.1016/j.neurobiolaging.2018.03.004

Cited by 38 publications

(35 citation statements)

References 51 publications

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“…Higher brain-PAD scores suggesting accelerated aging trajectories were related to greater cognitive impairment in depression (Hatton et al, 2018). One proposed explanation was the common biological mechanism underlying depression and brain aging (Franceschi et al, 2000;L.…”

Section: Discussionmentioning

confidence: 99%

The stage‐specifically accelerated brain aging in never‐treated first‐episode patients with depression

Han

Chen

Zheng

et al. 2021

Human Brain Mapping

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Depression associated with structural brain abnormalities is hypothesized to be related with accelerated brain aging. However, there is far from a unified conclusion because of clinical variations such as medication status, cumulative illness burden. To explore whether brain age is accelerated in never-treated first-episode patients with depression and its association with clinical characteristics, we constructed a prediction model where gray matter volumes measured by voxel-based morphometry derived from T1-weighted MRI scans were treated as features. The prediction model was first validated using healthy controls (HCs) in two Chinese Han datasets (Dataset 1, N = 130 for HCs and N = 195 for patients with depression; Dataset 2, N = 270 for HCs) separately or jointly, then the trained prediction model using HCs (N = 400) was applied to never-treated first-episode patients with depression (N = 195). The brain-predicted age difference (brain-PAD) scores defined as the difference between predicted brain age and chronological age, were calculated for all participants and compared between patients with age-, gender-, educational level-matched HCs in Dataset 1. Overall, patients presented higher brain-PAD scores suggesting patients Shaoqiang Han and Yuan Chen contributed equally to this study.

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Section: Discussionmentioning

confidence: 99%

The stage‐specifically accelerated brain aging in never‐treated first‐episode patients with depression

Han

Chen

Zheng

et al. 2021

Human Brain Mapping

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“…Higher brain-PAD scores have been associated with greater cognitive impairment, 13 increased morbidity, 10 and exposure to cumulative negative fateful life events (e.g., death of a close family member, financial hardship, or divorce). 14…”

Section: Introductionmentioning

confidence: 99%

Brain Aging in Major Depressive Disorder: Results from the ENIGMA Major Depressive Disorder working group

Han

Dinga

Hahn

et al. 2019

Preprint

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222Background: Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, 223 aging-related diseases, and mortality. We examined potential advanced brain aging in MDD patients, and 224 whether this process is associated with clinical characteristics in a large multi-center international dataset. 225Methods: We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI 226 scans from 29 samples worldwide. Normative brain aging was estimated by predicting chronological age 227 (10-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and 228 total intracranial volume measures separately in 1,147 male and 1,386 female controls from the ENIGMA 229 MDD working group. The learned model parameters were applied to 1,089 male controls and 1,167 230 depressed males, and 1,326 female controls and 2,044 depressed females to obtain independent 231 unbiased brain-based age predictions. The difference between predicted "brain age" and chronological 232 age was calculated to indicate brain predicted age difference (brain-PAD). 233Findings: On average, MDD patients showed a higher brain-PAD of +0.90 (SE 0.21) years (Cohen's 234 d=0.12, 95% CI 0.06-0.17) compared to controls. Relative to controls, first-episode and currently 235 depressed patients showed higher brain-PAD (+1.2 [0.3] years), and the largest effect was observed in 236 those with late-onset depression (+1.7 [0.7] years). In addition, higher brain-PAD was associated with 237 higher self-reported depressive symptomatology (b=0.05, p=0.004). 238Interpretation: This highly powered collaborative effort showed subtle patterns of abnormal structural 239 brain aging in MDD. Substantial within-group variance and overlap between groups were observed. 240Longitudinal studies of MDD and somatic health outcomes are needed to further assess the predictive 241 value of these brain-PAD estimates. 242 Funding: This work was supported, in part, by NIH grants U54 EB020403 and R01 MH116147.243 244 245 246 247248 . 29 Alternatively, brain age may be more sensitive to subjective (BDI) than to objectively 595 (HDRS-17) rated experiences, consistent with the finding of Kwak and colleagues (2018) that the 596 subjective experience of aging was closely related to predicted brain age. 30 However, it is important to 597 20 bear in mind the small effect size (b=0.05). Nonetheless, positive associations with current depressive 598 symptom severity have been previously reported with more advanced levels of biological aging, as 599 indicated by shorter telomere length 31 and increased epigenetic aging. 19 601This study showed relatively largest effect size of advanced brain aging in patients with a late-life onset of 602 depression (>55 years old) compared to controls. However, we did not find significant differences 603 between early vs. adult vs. late onset of depression groups. Additionally, no differences between remitted 604 (N=344) and acute patients (N=2,179) were found, leading to the speculatio...

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“…Interestingly, the effect of dietary patterns on BrA has been also investigated, but only in baboons: a premature brain aging (+2.7 years) was observed in young female adults who had experienced moderate fetal undernutrition, compared to healthy controls (43). More recently, Hatton and colleagues (44) reported an association between negative fateful life events and advanced brain aging (+2.3 years), after controlling for physical, psychological, and lifestyle factors.…”

Section: Brain-based Biological Agementioning

confidence: 99%

Machine Learning Approaches for the Estimation of Biological Aging: The Road Ahead for Population Studies

et al. 2019

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In recent years, different machine learning algorithms have been developed for the estimation of Biological Age (BA), defined as the hypothetical underlying age of an organism. BA can be computed based on different circulating and non-circulating biomarkers. In this perspective, identifying biomarkers with a prominent influence on BA and developing reliable models for its estimation is of fundamental importance for monitoring healthy aging, and could provide new tools to screen health status and the risk of clinical events in the general population. Here, we briefly review the different machine learning (ML) approaches used for BA estimation, focusing on those methods with potential application to the Moli-sani study, a prospective population-based cohort study of 24,325 subjects (35–99 years). In particular, we discuss the potential of BA estimation based on blood biomarkers, which likely represents the easiest and most immediate way to compute organismal BA. Similarly, we describe ML methods for the estimation of brain age based on structural neuroimaging features. For each method, we discuss the relation with epidemiological variables (e.g., mortality), genetic and environmental factors, and common age-related diseases (e.g., Alzheimer disease), to examine the potential as aging biomarker in the general population. Finally, we hypothesize new approaches for BA estimation, both at the single organ and at the whole organism level. Overall, here we trace the road ahead in the Big Data era for our and other prospective general population cohorts, presenting ways to exploit the notable amount of data available nowadays.

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Negative fateful life events in midlife and advanced predicted brain aging

Cited by 38 publications

References 51 publications

The stage‐specifically accelerated brain aging in never‐treated first‐episode patients with depression

The stage‐specifically accelerated brain aging in never‐treated first‐episode patients with depression

Brain Aging in Major Depressive Disorder: Results from the ENIGMA Major Depressive Disorder working group

Machine Learning Approaches for the Estimation of Biological Aging: The Road Ahead for Population Studies

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