High-fat meal increases peripheral blood mononuclear cell pro-inflammatory cytokine expression in African-American women
Bibliographic record
Abstract
African-American (AA) women have elevated predominance of inflammatory diseases concurrent with local inflammation resulting in compromised metabolic function. The purpose of the study was 2-fold: 1) to examine the gene and protein expression of pro- and anti-inflammatory cytokine secretion by peripheral blood mononuclear cells (PBMC) obtained from AA and Caucasian-American (CA) women in response to an acute high-fat meal; and 2) to explore the influence of race (AA vs. CA) on PBMC reactivity. Ten AA and 11 CA women consumed a high-fat meal with baseline and 4 h postprandial venous blood draws. PBMCs were incubated for 3 h then messenger RNA expression and supernatant protein concentration was used to examine inflammatory profiles. All women had a postprandial increase in interleukin (IL)-8 gene expression, IL-8 protein concentration, and tumor necrosis factor alpha (TNF-α) protein concentration (P < 0.05). AA women had a postprandial increase in IL-6, IL-8, and TNF-α protein concentration (P < 0.05). AA women had higher postprandial IL-1β protein concentration and IL-8 gene expression compared with CA women (P < 0.05). Our data uncovers the specific impact of race and time on pro-inflammatory PBMC (IL-1β, IL-6, IL-8, and TNF-α) expression profiles in response to an acute high-fat meal challenge. Novelty: African Americans have higher predominance of inflammatory disease. We explored the potential race impact on peripheral blood mononuclear cell reactivity in response to a meal. A pro-inflammatory response to an acute high-fat meal with race impact was observed possibly contributing to health disparities impacting African-American women.
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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) | 0.002 | 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".