Association of cardiovascular and metabolic diseases with risk of dementia in the urban population of North India
Bibliographic record
Abstract
Background/Aim: Dementia has become a public health problem due to its association with biological risk factors; obesity, diabetes, hypertension and hypercholesterolaemia. Most of these risk factors, appear to be associated with dementia as well as with risk of coronary artery diseases (CADs) and stroke. This study aimed to find out the association of biological risk factors with cognitive impairment and dementia. Methods: Cross-sectional survey in a hospital was performed. After written informed consent and approval from hospital ethic committee, all subjects (n = 2002) above 25 years of age (1016 males and 986 females) were randomly selected and recruited from urban population of Moradabad, North India. Clinical data and risk factors were recorded with the help of case record form and validated questionnaires. Assessment of cognitive decline and dementia was made by Singh's memory function rating scale and biological risk factors by physical examination, sphygmomanometer and electrocardiography. The association of biological risk factors with dementia was calculated by multivariate logistic regression analysis after adjustment of age and sex. Results: Obesity, diabetes, hypertension, CAD and hypercholesterolemia were highly prevalent independent risk factors among patients with dementia. Multivariate logistic regression analysis showed that regardless of age and body mass index, diabetes mellitus and CAD were highly significant (p < 0.001) risk factors of dementia. Hypertension and family history of dementia were weakly but significantly(p < 0.05) associated with dementia. Conclusion: It is possible that increased frequency of obesity, diabetes, hypertension and CAD may increase the risk of dementia in an ageing population. Prevention and control of these biological risk factors may cause decline in the risk of dementia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".