Exploring the Relationship between Balance and Cognition in Middle-Aged Individuals with Diabetes and Hypertension: A Cross-sectional Study
Bibliographic record
Abstract
Background: Diabetes and hypertension are commonly occurring non-communicablediseases across the world. India is known as the diabetescapital of the world since more than 62 million individuals are presentlysuffering from diabetes. The risk of dementia increases in individualswith type 2 diabetes mellitus (DM). Similarly, in individuals withhypertension (HTN), there is an increased risk of balance impairmentdue to reduced sensory inputs from peripheral nerves to the CNS. Thisstudy has been taken up to evaluate the correlation between balanceand cognition in the adult Indian population suffering from diabetesand hypertension using the Berg Balance and Montreal CognitiveAssessment scale.Method: Two hundred and ninety-eight middle-aged individuals werescreened for hypertension and/ or diabetes mellitus for this crosssectionalstudy. Each subject was assessed for balance and cognitionusing the Berg Balance Scale and Montreal Cognitive Assessment scalerespectively. The data that were obtained were tabulated and analysed.Result: The Pearson correlation analysis suggested a negative correlationbetween diastolic blood pressure (DBP) and cognition (r = -0.267; p =0.020) indicating that higher DBP causes cognition to deteriorate inhypertensive patients. It was seen that an increase in systolic bloodpressure correlated with a decline in cognitive ability among diabeticpeople.Conclusion: Hypertension with increased diastolic pressure resultsin detrimental cognitive decline. No correlation was found betweensystolic blood pressure (SBP), DBP and balance. An increase in theglycaemic level affects cognitive ability and increases the risk of falls. How to cite this article:Kumar S, Chitra J, Fernandes J, Shetty A, Nale A,George CM, Yadav N. Exploring the Relationshipbetween Balance and Cognition in Middle-AgedIndividuals with Diabetes and Hypertension: ACross-sectional Study. Chettinad Health City MedJ. 2023;12(3):56-61. DOI: https://doi.org/10.24321/2278.2044.202352
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".