Sex differences in IL-3, IL-4, and IL-7 correlation with cholesterol and triglyceride levels in African Americans with high HbA1c
Bibliographic record
Abstract
Abstract Background Nearly 30 million Americans have diabetes; majority are diagnosed with type 2 diabetes (T2D). Low grade inflammation is associated with T2D which influences a network of serum cytokines. IL-3, IL-4, and IL-7 play coordinated pathogenic roles in many inflammatory diseases including T2D and non-fatty liver disease. It has been previously reported high circulating glucose influences serum cytokine levels and cholesterol metabolism. However, further studies are needed to delineate the relationship between cytokine serum levels, cholesterol, and triglycerides metabolism. Methods A total of 232 serum samples were collected from African Americans: 161 women and 71 men. This group consists of 95 normal HbA1c and 106 high HbA1c participants. Most participants had a BMI > 30 (n=171). Cytokines were measured using multiplexing assays from Luminex X-MAP® technology. Results In the presence of high HbA1c, there is a trend increase in serum levels of IL-4 in both men and women. In women participants, IL-3 (R=−0.41, P=0.02) is inversely correlated to BMI, but not in men. IL-3 is positively correlated to VLDL cholesterol in women with normal HbA1c. HDL is positively correlated with IL-3 (R=0.20, P=0.04). VLDL is positively correlated with IL-4 (R=0.79, P=0.007), in men with normal HbA1c. Triglycerides were positively correlated to IL-4 (R=0.80, P=0.006) and IL-3 (R=0.56, P=0.05) in men with normal HbA1c. Conclusion The involvement of IL-3, IL-4, IL-7 are linked to cholesterol metabolism but are possibly regulated differently between sexes. The evidence of these correlations provides a basis of understanding that could be utilized to provide intervention to reduce the complications of T2D. Supported by NIH NIMHD U54MD012392
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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) | 0.002 | 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".