Does reducing blood pressure and cholesterol provide any HOPE for preventing cognitive decline and dementia?
Bibliographic record
Abstract
Dementia is a rapidly increasing global clinical and public health issue in aging populations, and insights from observation and clinical assessment aided by pathology consistently suggest that modifying cardiovascular risk factors may prevent cognitive decline.1 Such an approach was tested in the third Health Outcomes Prevention Evaluation (HOPE-3) trial,2 as reported in this issue of Neurology ®, in which an assessment of cognitive outcomes was included in an older subgroup (age ≥70 years) of 12,705 participants who had intermediate cardiovascular risk but had no prior cardiovascular events who were randomized to blood pressure (BP) lowering with a combination of candesartan and hydrochlorothiazide and cholesterol lowering with rosuvastatin. Of the initial 3,086 (24%) participants eligible for the cognitive substudy by age, 2,361 (77%) agreed to participate, and 1,626 (69%) were able to complete a series of tests emphasizing various aspects of cognitive function at baseline and at study end: the Digit Symbol Substitution Test (DSST), the modified Montreal Cognitive Assessment (mMoCA), and the Trail Making Test Part B (TMT-B). Despite reasonable reductions in systolic BP (by 6.0 mm Hg) and low-density lipoprotein cholesterol (LDL-C; 24.8 mg/dL) over a median of 5.7 years, there were no differences in any of the cognitive outcomes compared to placebo. However, in a post hoc analysis of the small group of participants (n = 181) at highest cardiovascular risk, defined by the highest tertiles of baseline systolic BP (>145 mm Hg) and LDL-C (>140 mg/dL), who were treated the most intensively with a combination of BP and cholesterol lowering, there was a reduction in cognitive decline on the DSST. What does this mean, and where do we go next?
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".