Neurophysiological Study of Alzheimer’s Disease and Diabetes Mellitus Type 2 Patients. Is there a Common Link?
Bibliographic record
Abstract
Introduction: Recent studies have shown that insulin resistance and deficiency, a marker of Diabetes mellitus type 2 (DM), interact with beta amyloid and tau protein phosphorylation, which are the basic neuropathological hallmarks of Alzheimer’s Disease (AD). Based on these results, it was recently proposed that AD might be considered as ‘Diabetes type 3’. Aim of the current study is to assess the cognitive function of DM patients and Mild cognitive impairment (MCI) patients with neurophysiological and neuropsychological measures and seek possible correlations. Methods: The study participants were divided into two groups: group 1 and group 2. Group 1 consisted of 24 DM patients (7 men, 17 women; age 70.6±6.5 (mean ± SD) years; age range 55-86 years. Group 2 consisted of 16 MCI patients age 72.61±7.42 (mean ± SD)years; age range 58-89 years) age-matched (t-value=1.06, p=0.30) and gender matched (χ2=0.084, p=0.772) with group 1 patients. All patients were assessed with auditory event-related potentials (AERPs) and neuropsychological tests, which include MMSE, MOCA, IADL, and HAMILTON depression scale. Latencies and amplitudes of the major AERP waves (N200, P300 and Slow Wave) were determined, Results: No statistically significant difference was observed in the AERP characteristics and the performance of the patients in the neuropsychological tests between the two groups (p>0.05). Conclusions: From the results of the current study, it appears that the higher cognitive functions of DM patients as assessed with ERPs and neuropsychological tests are affected in a similar way with that of MCI patients; a finding which supports the existence of common pathophysiological mechanisms between the two diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".