The Association Between Plasma Fatty Acid and Cognitive Function Mediated by Inflammation in Patients With Type 2 Diabetes
Bibliographic record
Abstract
Abstract Background High fat diet may lead to systematic inflammation in patients with type 2 diabetes, which eventually result in neuronal damage and cognitive decline. However, there is a paucity of study examined whether inflammation mediated the association between diet fatty acids and cognitive function in type 2 patients with diabetes. Methods We evaluated the cognitive function of 372 Chinese patients with diabetes, using the mini-mental state examination (MMSE) and the montreal cognitive assessment (MoCA). Plasma fatty acids were measured by gas chromatography analysis. Inflammatory cytokines were determined by immune turbidimetric analysis and enzyme-linked immunosorbent assay (ELISA). Data was analyzed using multiple linear regression based on R program and mediation model was established by Mplus. Results In our study, we found the increasing of BMI may lead to cognitive impairment and induce inflammatory response. We also found higher SFAs levels in plasma were linked to cognitive decline, while higher MUFAs intake might be a protective factor for cognitive function. In addition, most PUFAs levels stood out as having increasing trends that were positively correlated to cognitive function scores, but n-6 PUFAs gave opposite results. In our study, we found higher SFAs led to higher proinflammatory factor levels. Apart from that, MUFAs, SCD-16 and SCD-18 were positively related to hs-CRP. Interestingly, we found PUFAs were negatively related to IL-10. Meanwhile, this result also indicated that C18:0 might reduce MoCA language skills scores by regulating plasma IL-10 levels. Conclusions Plasma fatty acids could improve or damage cognitive function by regulating IL-10, which suggested that plasma fatty acids can be evaluated as a potential indicator of cognitive function decline in 2 type diabetes.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".