Hypertension and Cognitive Trajectories: A Prospective Cohort Study in the Elderly Indian Population
Bibliographic record
Abstract
Objectives: We studied the progression of mild cognitive impairment (MCI) to dementia. The relationship between dementia and several demographic parameters as well as cardiovascular risk factors (mainly hypertension) was also studied. We also ascertained the effect of antihypertensive medication compliance/adherence on the progression of dementia in adults with MCI during a 2-year period. Various biochemical parameters, which can be used as markers, were also examined. Materials and Methods: The investigation was carried out at multispecialty hospitals. The experimental protocol was approved by the ethical committee constituted as per the guidelines of Indian Council of Medical Research. Subjects enrolled were aged ≥50 years and with hypertension ≥5 years. These participants were followed up for 28.69 ± 11.85 months. Subjects were assessed by Hindi Mental State Examination (HMSE) it is a Hindi version of Mini Mental State Examination (MMSE), as well as Montreal Cognitive Assessment (MoCA) test was used for MCI, further assessed by various cognitive domain specific tests. Statistical Analysis: Analysis was done using SPSS (Statistical Package for the Social Sciences) software version 27.0. Results: The mean age of the study sample was 62.41 ± 8.09, with 45% of the population belonging to a rural background. Executive function was most affected. Age was negatively correlated with HMSE score ( P < 0.05), whereas education and medicine adherence were positively correlated with both HMSE and MoCA scores ( P < 0.001). Conclusion: Slowing down the rate of dementia progression is critical in developing economies to avoid the devastating economic and social burden that dementia causes. This can be achieved by managing dementia risk factors and implementing a comprehensive cognitive assessment in susceptible subjects (such as hypertension). Blood biomarkers can be beneficial in conjunction with early and easy diagnosis (MCI) and treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".