Diabetes control, dyslipidemia, hsCRP and mild cognitive impairment in non-elderly people with type 2 diabetes mellitus
Bibliographic record
Abstract
Background: Mild Cognitive Impairment (MCI) a transitional stage between normal aging and dementia has been observed more in people with diabetes when compared with general population. The risk factors for MCI in type 2 diabetes mellitus (T2DM) have been defined in elderly patients and aging may itself contribute to declining in cognitive functions. As the large number people with T2DM are under 60years, the prevalence of MCI and factors contributing to it are not much studied. So, this study aimed to find out the factors contributing to MCI in non-elderly T2DM patients.Methods: In this cross-sectional study, 257 patients with T2DM underwent cognitive assessment by Montreal cognitive assessment test and the cognitive levels were correlated with their glycosylated hemoglobin, lipid profile, and highly sensitive C-reactive protein (hsCRP).Results: The prevalence of mild cognitive impairment (MCI) was 64.2%. MCI significantly correlated with duration of diabetes, socioeconomic status, HbA1c, serum triglycerides, low-density lipoprotein, very low-density lipoprotein and hsCRP levels. The factors that were statistically insignificant were body mass index and high-density lipoprotein levels.Conclusions: Cognitive impairment is seen even in non-elderly T2DM patients. It should be considered along with the other complications of diabetes and individuals with T2DM should be screened for cognitive impairment to prevent progression to dementia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".