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Record W4390199081 · doi:10.1002/alz.079952

Late‐life hypertension acts together with amyloid‐β pathology to promote early cognitive decline

2023· article· en· W4390199081 on OpenAlexaff
Lucas Uglione Da Ros, João Pedro Ferrari‐Souza, Marco Antônio De Bastiani, Lucas Augusto Hauschild, Firoza Z Lussier, Mira Chamoun, Gleb Bezgin, Andréa Lessa Benedet, Tharick A. Pascoal, Pedro Rosa‐Neto

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsCohortDementiaCognitive declineMedicineNeuropsychologyLongitudinal studyInternal medicineCohort studyDiseaseRisk factorCognitionBlood pressureNeuropsychological assessmentOncologyPathologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Midlife hypertension (HTN) is a known risk factor for Alzheimer’s Disease (AD) development. However, whether the same effect is observed in older individuals, at risk for AD, remains to be elucidated. Here, we aimed to assess whether late‐life HTN and the presence of amyloid‐β pathology (Aβ) interact to promote longitudinal cognitive decline in cognitively unimpaired (CU) individuals, and if systolic blood pressure (SBP) levels moderate this interaction. Interactive effect of disease pathophysiology is crucial for dementia prevention strategies. Method We used two independent cohorts. We evaluated 475 CU individuals over 65 years of age from the ADNI cohort, with available baseline medical data and CSF Elecsys biomarkers (Aβ1‐42 and p‐tau181), as well as longitudinal clinical assessments with neuropsychological testing (up to 6 years); the individuals were classified as (A)+ or (A)‐ based on a previously proposed cut‐off of CSF p‐tau181/Aβ1‐42 lower than 0.025. We also evaluated 162 CU individuals over 65 years of age from the TRIAD cohort with baseline clinical data and Aβ‐PET, as well as longitudinal clinical assessments with neuropsychological testing (up to 3 years). The individuals were classified as (A)+ or (A)‐ based on Aβ‐PET positivity. All individuals were classified as positive or negative for hypertension based on medical history. For the ADNI cohort, we could also evaluate the SBP levels as a continuous variable. Result Linear mixed‐effects (LME) models showed that HTN and Aβ acted together to promote longitudinal cognitive decline (ADNI: HTN X Aβ X Time, β = ‐0.44, p = 0.001, figure 1, TRIAD: HTN X Aβ X Time, β = ‐0.64, p < 0.001, figure 2). Also, we could see that higher SBP levels acted together with Aβ to promote further cognitive decline (SBP values X Aβ X Time, β = ‐0.011, p = 0.03, figure 3). Conclusion Our results support a framework in which late‐life HTN is a modifiable risk factor for cognitive decline in CU individuals at increased risk for AD. This supports further investigation in determining the best target of SBP levels for individuals at risk for AD, in the context of offering precision medicine for this population.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.312
Teacher spread0.274 · 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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