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Record W4390192603 · doi:10.1002/alz.079367

Trajectories in Depressive Symptoms and Midlife Brain Health

2023· article· en· W4390192603 on OpenAlexaboutno aff
Christina S. Dintica, Mohamad Habes, Guray Erus, Pamela J. Schreiner, Kristine Yaffe

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsDigit symbol substitution testStroop effectPsychologyMontreal Cognitive AssessmentHyperintensityEntorhinal cortexTrail Making TestWhite matterVerbal fluency testAudiologyCardiologyInternal medicineNeuropsychologyMedicinePsychiatryCognitionMagnetic resonance imagingHippocampusNeurosciencePathology

Abstract

fetched live from OpenAlex

Abstract Background Evidence in older adults suggests that depressive symptoms may be part of prodromal mood changes in dementia, rather than a risk factor. However, it is unclear whether variations in depressive symptoms in adulthood exhibit distinct characteristics in brain structure and cognitive function in midlife. Methods From the Coronary Artery Risk Development in Young Adults study, we identified 662 Black and White participants (age 23‐36 at baseline) who completed the Center for Epidemiological Studies Depression scale (CES‐D) at six time points over 20 years. 20 years after baseline, the participants underwent magnetic resonance imaging to characterize gray matter (GM) structures (total GM, temporal cortex, hippocampus, and entorhinal cortex) and white matter hyperintensities (WMHs). Participants also completed neuropsychological tests including the Digit Symbol Substitution Test (DSST), Rey‐Auditory Verbal Learning Test (RAVLT), Stroop Test, Montreal Cognitive Assessment (MoCA), and category and letter fluency tests, analyzed as z‐scores. Results Using growth mixture modeling, we identified four trajectories of depressive symptoms (Figure 1): consistently low scorers (“steady low”; n = 509, 76.9%), a class with an early peak and decline in symptoms (“declining”; n = 63, 9.5%), a class with late increases in symptoms (“increasing”; n = 49, 7.4%), and consistently high scorers (“steady high”; n = 41, 6.2%). Compared to the steady low class, the steady high class had lower entorhinal cortex volume (β: ‐180.80, 95% CI: ‐336.69 to ‐24.91). The increasing class, had more WMHs (β: 0.55, 95% CI: 0.22 to 0.89) and less total brain volume (β: ‐9269.25, 95% CI: ‐17104.67 to ‐1444.82). Both the steady high and the increasing classes had poorer performance on the Stroop task (β: ‐0.40, 95% CI: ‐0.70 to ‐0.10; β: ‐0.39, 95% CI: ‐0.64 to ‐0.14, respectively), however the steady high also had poorer DSST performance (β: ‐0.40, 95% CI: ‐0.67 to ‐0.13), while the increasing class had poorer performance on the MoCA (β: ‐1.20, 95% CI: ‐2.04 to ‐0.37). The declining group was not significantly different from the steady low group on any brain or cognitive measures. Conclusions Our findings suggest that trajectories in depressive symptoms in young to mid‐adulthood show different cognitive and brain phenotypes in midlife.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.340
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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