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Record W4210437862 · doi:10.1002/alz.057465

Investigating the risk of cardiovascular risk factor subgroups in cognitively normal elderly on progression to AD: A latent class approach

2021· article· en· W4210437862 on OpenAlexaff
Myuri Ruthirakuhan, Hugo Cogo‐Moreira, Walter Swardfager, Nathan Herrmann, Krista L. Lanctôt, Sandra E. Black

Bibliographic record

VenueAlzheimer s & Dementia · 2021
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineInternal medicineNeuropathologyDiabetes mellitusBody mass indexDiseaseStroke (engine)Metabolic syndromeProportional hazards modelLatent class modelFamily historyCardiologyObesityEndocrinology

Abstract

fetched live from OpenAlex

Background Individual cardiovascular risk factors (CVRFs) have been associated with neurodegenerative processes. However, CVRFs often co-occur with one another and little is known regarding the extent of their clustering, and effect on progression to Alzheimer’s disease (AD). We identify classes of CVRFs in cognitively normal (CN) individuals, and investigate their risk on progression to AD. Method CN individuals were recruited from the National Alzheimer’s Coordinator Center dataset, with follow-up. To identify CVRF classes at baseline, a latent class analysis (LCA) was conducted with five vascular (hypertension, hypercholesterolemia, heart condition, stroke, and smoking history), and two metabolic (diabetes, and high body mass index (BMI)) indicators. Separate cox regressions were conducted to investigate the risk of CVRF class on progression to clinically-diagnosed and neuropathology-confirmed AD (clinically-diagnosed AD with intermediate/high AD Neuropathologic Change). Post-hoc analyses investigated differences in non-AD related neuropathologies between CVRF classes. Result This study included 12,412 CN individuals (age:70.9±10.5, male:35% (N=4312), MMSE:28.9±1.4, 6% (N=788) of whom progressed to AD). The LCA identified three phenotypes of baseline CVRF classes (Figure 1). One group had low probabilities of CVRFs (N=5398 (43%)) (reference group). The second group had higher probabilities of hypertension and hypercholesterolemia (vascular-dominant class) (N=5721 (46%)). The third group had higher probabilities of hypertension, hypercholesterolemia, diabetes, and high BMI (vascular/metabolic class) (N=1293 (10%)). Compared to the reference group, the vascular-dominant class (HR:1.14, 95%CI:1.02-1.27, p=.02), and vascular/metabolic class (HR:1.33, 95%CI:1.11-1.59, p=.002) were associated with an increased risk of progression to clinically-diagnosed AD. Compared to the reference group, only the vascular dominant class was associated with an increased risk of progression to neuropathology-confirmed AD (HR:1.40, 95%CI:1.00-1.96, p=.05). Post-hoc analyses determined that compared to other classes, the vascular/metabolic class had the greatest proportion of individuals with underlying cerebrovascular disease (CVD) (46% vs. ≤28%)(X2(2)=5.76, p=.05). Conclusion Presence of vascular-dominant, or a combination of vascular/metabolic CVRFs were associated with an increased risk of progression to clinically-diagnosed AD. The fact that vascular/metabolic class was not associated with an increased risk of progression to neuropathology-confirmed AD, suggests that the development of cognitive impairment in this group is due to underlying neurobiological processes such as CVD that are not related to AD.

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)0.006
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 score0.013
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.298
Teacher spread0.262 · 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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