Comorbidity Between Metabolic Syndrome and Depression Exacerbates Oxidative Stress and Inflammation in the Brain
Bibliographic record
Abstract
Major cardiovascular disease risk factors, such as metabolic syndrome (MetS) and depression, are associated with a chronic inflammatory state and increased oxidative stress. As in other peripheral microvessels, inflammation and oxidative stress impair the microvasculature of the brain, leading to increased risk of stroke. It is unclear how the comorbidity between MetS and depression affect the inflammatory and oxidative stress conditions in the brain. To determine if depression and MetS together further exacerbate cerebral inflammation and oxidative stress compared to either disease alone, markers of oxidative stress were assessed through dihydroethidium (DHE) and DAF‐FM assays along with PCR and ELISA to evaluate the inflammatory response in the brains of obese Zucker rats (OZRs), a model of MetS, and lean Zucker rats (LZRs), healthy controls. Reactivity of the middle cerebral artery (MCA) was used as a measure of vascular function. The MCA was isolated and then hung in a pressurized vessel myobath to be exposed to increasing doses of acetylcholine (Ach) to test endothelium‐dependent dilation. OZRs had increased superoxide production in the basilar arteriole (p<0.05), which was exacerbated with UCMS. Similarly, the OZRs and OZR‐UCMS had a significant decrease in nitric oxide production as compared to both LZR groups. Concentrations of anti‐inflammatory cytokines, IL‐4 and IL‐10, were reduced in OZRs and further reduced in OZR‐UCMS. Inflammatory marker (TNFα and CD68) mRNA abundance was elevated in OZRs and to a higher extent in OZR‐UCMS. This oxidative stress and inflammation contributed to decreased MCA reactivity in OZR controls from that of LZR controls in response to Ach (p<0.01). In both LZRs and OZRs, MCA dilation was further attenuated with UCMS as compared to their respective control values. These data show that the comorbidity of MetS and depression exacerbates the oxidative stress and inflammatory states associated with each disease separately. Our results suggest that the cerebral environment is drastically changed during this comorbidity and leads to vascular dysfunction, which can cause an increased risk of poor cardiovascular outcomes. Support or Funding Information National Institute of Health (5P20GM109098)
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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 | 0.000 | 0.000 |
| 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".