Association Between the Frailty and Cognitive Impairment Among Patients With Hypertension—A Post Hoc Analysis of the SPRINT Trial
Bibliographic record
Abstract
Background Frailty and cognitive impairment are common in the elderly, with various shared risk factors like hypertension. Frailty is a marker for future cognitive function. Moreover, whether intensive blood pressure interacted with frailty and cognitive impairment is unknown. Methods and Results We performed a post hoc analysis of data from SPRINT (Systolic Blood Pressure Intervention Trial). The relationship between frailty and a composite of probable dementia (PD) and mild cognitive impairment (MCI) was analyzed. Also, we evaluated the interaction of intensive blood pressure lowering in the relationship between frailty and cognitive impairment. A total of 8537 patients were included in our study, and 35.1% were women. The mean age of these participants was 67.9±9.3 years. According to the baseline frailty index, 1670, 4637, and 2230 patients were in fit, less fit, and frail statuses, respectively. During a mean follow‐up of 4.61 years, 871 cases of PD or MCI occurred. Compared with those in fit status, those with less fit (hazard ratio [HR], 2.14 [95% CI, 1.65–2.77]) and frailty (HR, 4.28 [95% CI, 3.26–5.61]) status had a higher incidence of a composite of PD and MCI. Blood pressure control strategy interacted with the correlation between frailty and cognitive impairment. Intensive blood pressure control (HR, 2.4 [95% CI, 2.0–2.8]) accelerated the relationship between frailty and incidence of PD and MCI compared with the standard treatment group (HR, 1.8 [95% CI, 1.5–2.1]; P for interaction=0.009). Conclusions This study found that the baseline frailty status was a possible marker for the incidence of a composite of PD and MCI. Intensive blood pressure control may strengthen this correlation. Registration URL: https://clinicaltrials.gov/ ; Unique identifier: NCT01206062.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".