High Plasma Resistin Levels Portend the Insulin Resistance-Associated Susceptibility to Early Cognitive Decline in Patients with Type 2 Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: Metabolic disorders, including insulin resistance, obesity, and hyperlipidemia occur frequently prior to hyperglycemia in patients with type 2 diabetes mellitus (T2DM) and cause mild cognitive impairment (MCI). OBJECTIVE: We investigated the involvement of resistin in these metabolic abnormalities contributes to MCI in patients with T2DM. METHODS: A total of 138 hospitalized patients with T2DM were enrolled and categorized into MCI and non-MCI groups according to the Montreal Cognitive Assessment (MoCA) score. Metabolic indicators and cognitive state were assessed, and plasma resistin levels were determined by ELISA. RESULTS: The resistin levels and homeostasis model assessment of insulin resistance (HOMA-IR) scores of MCI and gender-stratified subgroups were significantly higher than those of controls without MCI (all p < 0.01). Correlation analysis showed that the resistin level was negatively associated with majority of cognitive domains, e.g., MoCA (r = -0.693, p < 0.001) and Mini-Mental State Examination (r = -0.571, p < 0.001), and was related to HOMA-IR (r = 0.667, p < 0.001) but not to obesity and lipid indices. Multivariable regression analysis indicated that resistin (β= -0.675, p < 0.001) and educational level (β= 0.177, p = 0.003) were independent risk factors of MoCA in patients with T2DM. CONCLUSIONS: High plasma resistin levels portend the insulin resistance-related susceptibility to early cognitive decline in Chinese patients with T2DM. The involvement of this adipokine in other metabolic disorders leading to diabetic MCI and its clinical value for early disease screening must be further studied.
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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 | 0.001 |
| 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".