Association between plasma adipsin level and mild cognitive impairment in Chinese patients with type 2 diabetes: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The adipokine adipsin contributes to insulin resistance (IR), inflammation, and obesity, which are all regarded as high-risk factors for mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus. This research aimed to uncover the role of adipsin in Chinese type 2 diabetes mellitus (T2DM) population with early cognitive dysfunction and determine whether adipsin contributes to diabetic MCI caused by IR. METHODS: In our study, 126 patients with T2DM were enrolled. The Montreal Cognitive Assessment (MoCA) was used to assess cognitive impairment. Demographic data and neuropsychological test results were evaluated. Plasma adipsin level was measured by enzyme-linked immunosorbent assay. RESULTS: The MCI group (n = 57) presented higher plasma adipsin levels compared with the healthy controls (p = 0.018). After adjustment for educational attainment, and age, begative correlations were found between plasma adipsin levels and MoCA, Mini Mental State Exam, and Verbal Fluency Test scores(r = - 0.640, p < 0.001; r = - 0.612, p < 0.001; r = - 0.288, p = 0.035; respectively). Correlation analysis demonstrated that adipsin levels were significantly positively correlated with fasting C-peptide; homeostasis model of assessment for insulin resistance (HOMA-IR) (r = 0.368, p < 0.001; r = 0.494, p < 0.001; respectively). Multivariable regression analysis further indicated that high plasma adipsin level was a significant independent determinant of MCI in the Chinese population withT2DM (p = 0.017). CONCLUSIONS: Elevated plasma adipsin level was associated with MCI in Chinese T2DM patients. Further large-scale studies should be designed to determine whether adipsin is linked to IR-associated susceptibility to early cognitive decline in T2DM patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".