The association between plasma fatty acid and cognitive function mediated by inflammation in patients with type 2 diabetes mellitus
Bibliographic record
Abstract
Abstract In this study, we evaluated the cognitive function of 372 Chinese patients (214 males and 158 females; the average age was 57.09 ± 9.00 years) with type 2 diabetes mellitus (T2DM) by using the mini-mental state examination (MMSE) and the Montreal cognitive assessment (MoCA), with Plasma fatty acids measured by gas chromatography analysis and inflammatory cytokines determined by immune turbidimetric analysis and enzyme-linked immunosorbent assay (ELISA) to investigate whether there was a correlation between the plasma fatty acids, plasma inflammatory cytokine levels and cognitive test scores in Chinese patients with T2DM. We found the increasing of body mass index (BMI) might lead to cognitive impairment and induce inflammatory response. Higher saturated fatty acids (SFAs) levels in plasma were linked to cognitive decline, while higher monounsaturated fatty acids (MUFAs) intake might be a protective factor for cognitive function. In addition, most polyunsaturated fatty acids (PUFAs) levels stood out as having increasing trends that were positively correlated to cognitive function scores. In our study, we found higher SFAs led to higher proinflammatory factor levels. Apart from that, MUFAs and stearoyl-CoA desaturase-18 (SCD-18) were positively related to hypersensitive C-reactive protein (hs-CRP) (P<0.05; P<0.05; P<0.05). Meanwhile, our result also indicated that the increasing of C18:0 might reduce MoCA language skill scores by regulating plasma IL-10 levels. Plasma fatty acids could improve or damage cognitive function by regulating IL-10, which suggested plasma fatty acids could be evaluated as a potential indicator of cognitive function decline 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 | 0.001 |
| 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".