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Record W4206409982 · doi:10.1002/alz.055618

Self‐reported and informant‐reported depressive symptoms are independent and divergent predictors of new onset dementia in older adults

2021· article· en· W4206409982 on OpenAlexaff
Tanaeem Rehman, Alexander McGirr, Zahinoor Ismail

Bibliographic record

VenueAlzheimer s & Dementia · 2021
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsDementiaGeriatric Depression ScaleDepression (economics)Proportional hazards modelClinical Dementia RatingPsychologyPsychiatryDepressive symptomsCognitionClinical psychologyMedicineInternal medicineCognitive impairmentDisease

Abstract

fetched live from OpenAlex

Abstract Background Depressive symptoms in older adults are important predictors of incident dementia. Symptoms can be assessed in several ways, including self‐reported or informant‐reported measures. It is unclear whether one is superior in predicting dementia onset or how to interpret discordant reports. Here, in a large sample of non‐dementia older adults for whom depressive symptoms were assessed via both self‐report and informant‐report, we test independent and divergent prognostic utility for dementia. Method Data from 10,684 non‐dementia subjects from the National Alzheimer’s Coordinating Centre were analysed. This included individuals with normal cognition (NC; CDR=0) and Mild Cognitive Impairment (MCI; CDR=0.5). Geriatric Depression Scale‐15 (GDS) was used for self‐report and the Neuropsychiatric Inventory Questionnaire depression item (NPI‐Q‐D) was used for informant‐report. Cox regression and Kaplan Meier (KM) survival analyses were used to explore the measures as independent predictors of dementia, and the putative interaction between them. Outcome was defined as change to CDR≥1 at follow‐up. The cox model was adjusted for age, sex, and education. CDR category was included to determine applicability of findings across cognitive categories. GDS‐15 and NPI‐Q‐D scores were added as continuous predictors. For the KM analysis, depressive symptoms were dichotomized as GDS+/GDS‐ and NPI‐Q‐D+/NPI‐Q‐D‐ using cut‐off scores of ≥5 for GDS‐15 and ≥2 for NPI‐Q‐Depression. Result GDS (HR: 1.096; CI: 1.070–1.122) and NPI‐Q‐D (HR: 1.428; CI: 1.291–1.580) independently predicted dementia, with a significant interaction between them (p<0.000) even after controlling for CDR. KM analyses showed that in the self‐reported non‐depressed group (GDS‐), there was a significant difference in the hazard of incident dementia based on the informant report. The discordant group i.e., GDS‐/NPI‐Q‐D+ progressed to dementia significantly faster (9.6yrs; SD=0.292) compared to the GDS‐/NPIQ‐D‐ group (12.0yrs; SD=0.055) (p<0.000; Log‐rank test). No significant difference was found based on informant reports in the GDS+ group (p=0.114). Conclusion Self‐reported and informant‐reported depressive symptoms are not duplicative, and independently predict dementia risk across cognitive categories. Informant report is particularly important when self‐report is negative, to detect individuals with compromised insight at higher risk for progression. Discordance of self‐informant reports require greater clinical and research consideration in NC and MCI populations.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.016
GPT teacher head0.277
Teacher spread0.261 · 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
Published2021
Admission routes1
Has abstractyes

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