Relationship between Alzheimer dementia and <scp>QT</scp> interval: A meta‐analysis
Bibliographic record
Abstract
Abstract While the link between aging and mortality from dementia is widely appreciated, the mechanism is not clear. The objective of this study was to determine whether there is a direct relationship between Alzheimer dementia (AD) and the QT interval, because the latter has been related to cardiac mortality. A systematic review and meta‐analysis were conducted after a Medline and EMBASE search using terms “Alzheimer disease or Dementia AND QT interval, QT dispersion or cardiac repolarization.” Four studies with control groups were identified. There were significant differences in QT interval between individuals with AD vs individuals without dementia (controls) (odds ratio (OR)1.665 [random effects model] and 1.879 [fixed effect model]) (p < 0.001). There were significant differences in QT interval between individuals with AD vs individuals with mild cognitive impairment (MCI) (OR 1.760 [random effects] and 1.810 [fixed effect]) (p < 0.001). A significant (p <0.001) correlation exists between the QTc and the Mini‐Mental State Exam (MMSE), a test of cognitive function. Two studies examined QT variability (the difference between the longest and shortest QT interval on a 12 lead ECG); the OR for QT variability AD vs MCI was 3.858 [random effects model] and 3.712 [fixed effects model] (p < 0.001). When compared to the control group, the OR for QT dispersion in AD was 6.358 [random effects model] or 5.143 ( P< 0.001) [fixed effects model]. A qualitative analysis of the data raised questions about paucity of data defining the nature of the control groups, the pathophysiologic mechanism, and the uniform use of a poor QT heart rate correction factor. The longer QT in AD, greater QT variability in AD, and the direct relationship between QT interval and AD severity supports a brain–heart connection in AD that might be fundamental to aging‐induced AD and mortality. Issues with defining the control group, limited number of studies, conflicting data in population studies, and the lack of a strong electrophysiological basis underscore the need for additional research in this field.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.034 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".