Inflammation and Cognitive Function in Overweight and Obese Chinese Individuals
Bibliographic record
Abstract
BACKGROUND: The role(s) of inflammation in obesity-associated cognitive decline in overweight or obese populations is not completely understood. OBJECTIVE: To investigate the profile of plasma inflammatory cytokines in overweight and obese Chinese individuals and to assess the relationship between inflammation and cognitive function. METHODS: We evaluated the cognitive domains of 282 Chinese adults, aged 35 to 64 years, using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). The participants were classified into three groups according to their body mass index. Inflammatory cytokines were determined by immune turbidimetric analysis and enzyme-linked immunosorbent assay. Data were analyzed using covariance and partial correlation analyses after adjusting for gender, age, education level, hypertension, and hyperlipemia. RESULTS: The total MoCA scores of the overweight and obese groups were significantly lower than that of the control group. The obese group displayed a significantly higher level of tumor necrosis factor-α than the overweight and control groups and a significantly higher level of transforming growth factor-β than the control group. The overweight group displayed a significantly higher interleukin-4 level than the control and obese groups. After adjusting for confounding factors, however, we found no significant correlation between the level of plasma inflammatory cytokines and MMSE or MoCA total score. CONCLUSIONS: Compared to normal-weight Chinese participants, overweight and obese Chinese participants revealed significant differences in their inflammatory cytokines profile; however, the inflammatory cytokine levels did not correlate with the significantly lower cognitive scores observed in the overweight and obese groups.
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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 | 0.000 |
| 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".