Blood pressure lowering to prevent dementia, the role or frailty, when not to treat?
Bibliographic record
Abstract
Abstract Background High blood pressure(HBP) is a risk factor for dementia. Clinical trials show antihypertensive treatment in those ∼60‐80 years lowers risk of incident dementia. 1 However, observational data 2 show differing patterns of benefit/risk for HBP and dementia in older age which may relate to the confounding influence of frailty. Frailty is a syndromal diagnosis that reflects a loss of resilience, and exacerbates expression of dementia pathology. 3,4 Since varying levels of frailty are present in clinical trial participants, we sought to examine its role as a potential moderator of the impact of antihypertensive treatment on incident dementia. Method Single‐stage individual‐participant‐data meta‐analysis. Data were merged from four landmark double‐blind placebo‐controlled trials of antihypertensive drugs with blinded adjudicated dementia endpoints. Frailty was assessed using a robust tool, the frailty index(FI) where scores range from 0.01 to 1.00. Frailty was modelled as a continuous and binary variable FI≤0.21 for none/mild frailty and FI>0.21 moderate‐severe frailty 5 . A multilevel multinomial regression model was used to determine the impact of baseline frailty on the impact of antihypertensive treatment on incident dementia taking account of the competing risk of death, unadjusted and adjusted for age, sex, education. Result Data on dementia and frailty were available for 24122 participants (mean age 68.5(SD9.31)yr, female 44%) with a median follow up of 4.5 years. Baseline FI median score was FI 0.16 (interquartile interval 0.11‐0.23). Adjusted analyses: There was no interaction between frailty (continuous) and antihypertensive treatment and incident dementia ( p = 0.47). For those an F ≤.21 the odds ratio for the impact of antihypertensive treatment was of 0.88(0.72,1.07) compared to 0.85(0.65,1.10) for an FI>0.21. Unadjusted results were similar. Conclusion Antihypertensive treatment is likely to reduce incident dementia in those aged∼60‐80 across varying levels of frailty. However, since clinical trial populations are biassed towards less frail participants, there are limitations to the generalizability of these data to more severe frailty in the general population. Further analyses will allow the investigation of the frailty/antihypertensive/dementia relationship in more detail. These data have implications for treatment guidelines for cardiovascular prevention alongside approaches to the management of people at high‐risk of dementia. 1. Eur Heart J 2022;43(48):4980–90 2. JAMA Intern Med;2022;182(2):142‐52 3. Lancet Neurol;2018;18(2):17784 4. Nat Aging;2021;1:651–65 5. Lancet Health Longev;2023;2(2)e96‐e104
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 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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".