Occurrence of mild cognitive impairment with hyperinsulinaemia in Africans with advanced type 2 diabetes mellitus
Bibliographic record
Abstract
There is paucity of information on the prevalence of mild cognitive impairment (MCI) among individuals with type 2 diabetes mellitus (T2DM) in sub-Saharan Africa, including Nigeria. In addition, the role of hyperinsulinaemia in the development of MCI needs further investigation. This study sought to assess cognition and hyperinsulinaemia, with the associated characteristics in patients with advanced T2DM. Cognition was assessed using Montreal cognitive assessment test (MoCA), while fasting plasma insulin was measured using an ELISA kit. Sixty one diabetic subjects and 32 non-diabetic controls, matched for age, gender and level of education were studied. The diabetics had MCI while the controls had normal cognitive function. About 88.5% of the diabetic subjects had MCI, in contrast with only 50% of the non-diabetic controls. The most significantly affected cognitive domains among the diabetics were executive function, naming, attention, abstraction and delayed recall. Among the diabetics, MCI correlated with age, weight and body mass index (BMI); and in addition, age and weight found to be significant predictors of MCI. Plasma insulin concentration among the diabetics (16.24 ± 13.5 µIU/ml) was more than twice that of the controls (7.59 ± 2.9 µIU/ml). Hyperinsulinaemia among the diabetics correlated with weight, BMI, blood pressure and fasting blood sugar (FBS). Glycated haemoglobin and FBS levels were higher among diabetics compared with the controls. In conclusion, Africans with advanced T2DM show multi-domain MCI with high prevalence, coexisting with hyperinsulinaemia. Majority of the patients have diabetic complications and poor glycaemic control. Hyperinsulinaemia may play a complementary role in the pathophysiology of MCI in T2DM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".