56. Influence of Hypertension on β-amyloid Deposition and Alzheimer’s Disease Relationship in Older Adult Patients: A Systematic Review
Bibliographic record
Abstract
Background: Hypertension has been associated with Alzheimer’s disease (AD) as well as vascular dementia. However, underlying neuropathological mechanisms of hypertension to AD remain poorly understood. Commonly used biomarkers for AD early detection are positron emission tomography (PET) which allows amyloid-beta (Aβ) plaques imaging. Therefore, we estimated the impact of hypertension on the presence and severity of Aβ deposition. Objective: In this study, we assessed the relationships of hypertension with Aβ plaques deposition as AD pathology. Method: Studies were extracted from ScienceDirect, PMC, and Pubmed using MesH terminology with “Amyloid-PET” and “Hypertension” keywords on 6 January 2024. The derived data were evaluated based on the inclusion and exclusion criteria. Included publications were either randomized controlled trials, clinical trials, cohorts, or observational studies from the past 5 years. Systematic reviews, meta-analyses, and animal studies were excluded from the study. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the studies. Result: After screening forty four studies, five cohorts and one observational study were included in this study with a total of 1423 participants. All studies showed good quality based on NOS. Studies suggest that higher amyloid-PET SUVr values was related to hypertension in older adult patients. Hypertension can lead to blood-brain barrier dysfunction and cerebral blood flow reduction followed by increased Aβ deposition. Elevated Aβ may increase risk for AD. Conclusion: Hypertension is related to elevated Aβ deposition which increases risk for AD. Further research is needed to investigate possible confounders that may play a role in the association.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".