The relationship between the status of hypertension and lifestyle risk factors in middle-aged and elderly patients with mild cognitive impairment: a case-control study
Bibliographic record
Abstract
Abstract BACKGROUND In epidemiological studies, hypertension is related to the difference in the incidence of cognitive impairment, but the evidence is currently inconsistent regarding the association of disease duration, treatment, control, and life risk factors with mild cognitive impairment (MCI) in patients with hypertension. METHODS We selected 572 patients with hypertension treated in six medical centers in Nanjing from 2017 to 2020. The cognitive function of participants was assessed using MoCA. Potential risk factors were investigated by a structured questionnaire. Risk factors associated with the conversion of MCI occurring in hypertension were analyzed using multifactorial regression analysis. RESULTS MCI was observed in 256 of 572 individuals. MCI was more likely with increasing age (OR=1.15, 95%CI 1.10-1.20), and high education was better at preventing MCI (OR=0.47, 95%CI 0.32-0.71) among baseline clinical characteristics. Risk factors independently associated with MCI were diabetes (OR=2.40, 95%CI 1.53-3.76), Hyperlipidemia (OR=1.49, 95%=1.01-2.16), high salt diet (OR=2.27, 95%CI 1.34-3.84), and physical activity:༞2h/week (OR=0.65, 95%0.44-0.94). Controlling blood pressure to target values helped prevent MCI(OR=0.44, 95%CI 0.30-0.65), and these results were similar when stratified by age. CONCLUSION Our results suggest that it is necessary to popularize hypertension knowledge and a healthy lifestyle, and optimize treatment to achieve the goal of preventing cognitive decline in the middle-aged and elderly.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".