Study on the correlation between visfatin and mild cognitive impairment in elderly patients with diabetes
Bibliographic record
Abstract
Objective To explore the relationship between visfatin and mild cognitive impairment(MCI)in patients with type 2 diabetes mellitus(T2DM). Methods A perspective study involving 75 hospitalized T2DM patients were divided into groups with(MCI, n=35)and without(NMCI, n=40)mild cognitive impairment.Another 30 non-diabetic patients were chosen as normal control(NC). Fasting plasma levels of glucose(FPG), insulin(FINS), lipid, glycosylated hemoglobin(HbA1c), HOMA-IR and visfatin were measured and calculated. Results The serum visfatin level was higher in MCI(28.81±3.32)μg/L than in NMCI(20.69±3.40)μg/L and NC(19.06±2.35)μg/L(F=96.491, P<0.01). Visfatin was negatively correlated with Montreal Cognitive Assessment(MoCA)total score(MoCA-TS)(r=-0.646, P<0.01), but positively correlated with course of disease, waist hip ratio, FPG, HbA1c, FINS, HOMA-IR and triglyceride(r=0.282, 0.276, 0.318, 0.496, 0.339, 0.433, 0.309, P<0.05 or P<0.01). MoCA-TS was negatively correlated with course of disease, HbA1c, HOMA-IR, triglyceride, total cholesterol, low density lipoprotein cholesterol(r=-0.582, -0.365, -0.234, -0.330, -0.277, -0.238, P<0.05 or P<0.01), but positively correlated with high density lipoprotein cholesterol(r=0.290, P<0.05). Higher values of visfatin(OR=3.246, P<0.01), HbA1c(OR=2.308, P<0.01)and course of disease(OR=1.634, P<0.05)were the risk factors for MCI. Conclusions The elevated visfatin level might be a risk factor for MCI in T2DM patients. Key words: Diabetes, type 2; Cognition disorders; Adipokines
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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 | 0.000 | 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".