The impact of hypertension on longitudinal cognitive decline in cognitively unimpaired individuals
Bibliographic record
Abstract
Abstract Background Mid‐life hypertension (HTN) is considered a risk factor for the development of clinical Alzheimer´s Disease (AD). Yet, it is not known whether late‐life hypertension relates to AD´s pathophysiology, with current literature being contradictory. We recently demonstrated that a vascular risk factor composite, which includes HTN, interact with the AD pathophysiology to promote cognitive decline. Here, we aimed to assess whether late‐life HTN alone and AD biomarkers interact to promote longitudinal cognitive decline, and if controlling the blood pressure affects this relationship. Method We evaluated 503 cognitively unimpaired (CU) individuals from the ADNI cohort, with available baseline medical data and cerebrospinal fluid (CSF) Elecsys biomarkers (Aβ1‐42 and p‐tau181), as well as longitudinal clinical assessments with neuropsychological testing (up to 6 years). Individuals with both Aβ1‐42 and p‐tau181 positivity were defined as having preclinical AD ((AT)+), and individuals with previous diagnosis of HTN or those taking anti‐hypertensive medication were considered HTN+. These participants were further divided based on two different measures of systolic blood pressure, being classified as controlled (C) if at least one of them was inferior to 140 mmHg, or non‐controlled (NC) if both measures were over 140 mmHg. The modified version of Preclinical Alzheimer’s Cognitive Composite (mPACC) was used as the outcome to evaluate the cognitive trajectory. Result Linear mixed‐effects (LME) models showed that HTN interacted with the AD pathophysiology to promote longitudinal cognitive decline (HTN X AD pathophysiology X Time, β= ‐0.45, p= 0.014), and when the individuals with HTN were further stratified between C and NC, we could see that this effect was seen only in the NC group (HTN+C X AD pathophysiology X Time, β= ‐0.29, p=0.13; Group HTN+NC X AD pathophysiology X Time, β= ‐0.79, p=0.002). Conclusion We observed that in CU individuals, HTN acted as a risk factor for cognitive decline in (AT)+ patients. Additionally, HTN + NC had an increased risk compared with HTN + C individuals. Thus late‐life HTN is a risk factor for the emerging of cognitive symptoms among the elderly and controlling the blood pressure may reduce this risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".