The spectrum of mild cognitive impairment in dyslipidemic non-elderly type 1 diabetics
Bibliographic record
Abstract
Background: Diabetics often have reduced performance in numerous domains of cognitive function, a process termed as Diabetic encephalopathy. The exact pathophysiology of cognitive dysfunction in diabetes is not completely understood, but it is likely that hyperglycaemia, vascular disease, hypoglycemia, and insulin resistance play significant roles. Although cognitive dysfunction is quite common in elderly, however, its occurrence in non-elderly diabetics is not much investigated. Aim of the study was to identify the correlation among various components of lipid profile with mild cognitive impairment in non-elderly type 1 diabetics.Methods: 98 type 1 diabetics were enrolled justifying relevant inclusion &exclusion criteria. Anthropometric indices, biochemical and clinical parameters were measured. MoCA test was employed for the assessment of cognitive dysfunction. Receiver operating characteristic, partial correlation, and logistic regression analyzes were employed for evaluation.Results: 71.42% of enrolled diabetics had some degree of cognitive dysfunction. Duration of the disease had a significant impact on cognitive functioning (p=0.032).Gender, residential area as well as the age of onset of diabetes appeared to have an insignificant impact on cognitive functioning (p>0.05). Diabetics with poor glycemic control were more prone to develop MCI (p<0.001).On comparison of various component of MoCA test; it was seen that most significant parameter that was affected was attention (p<0.001), followed by delayed recall /memory, naming and abstraction (p<0.05).Conclusions: The results of our study suggest that dyslipidemia chiefly raised total cholesterol, triglycerides and LDL is quite common in non-elderly type 1 diabetics and are associated with poorer cognitive function. Cognitive dysfunction should be listed as one of the many complications of diabetes, along with retinopathy, neuropathy, nephropathy, and cardiovascular disease in the future.
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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 | 0.004 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".