Serum leptin concentrations and markers of immune function in overweight or obese postmenopausal women
Bibliographic record
Abstract
Experimental studies and case reports suggest a multifunctional role of leptin in immune function. However, clinical studies of leptin in healthy individuals with a comprehensive assessment of immunity are lacking. This study investigated associations between serum leptin concentrations and multiple biomarkers of cellular immunity and inflammation among 114 healthy postmenopausal, overweight, or obese women. Leptin was measured by RIA. C-reactive protein (CRP) and serum amyloid A (SAA) were measured by nephelometry. Flow cytometry was used to measure natural killer (NK) cell cytotoxicity and to enumerate and phenotype lymphocyte subsets. T-lymphocyte proliferation was assessed in response to phytohemagluttinin, as well as to anti-CD3 antibodies by the flow cytometric cell division tracking method. Multiple linear regression analysis with adjustment for confounding factors and log transformation, where appropriate, was used. Serum leptin concentrations were positively associated with serum CRP, SAA, and interleukin 6 (IL6) (P<0.0001, P=0.01, and P=0.04 respectively), more strongly among women with a body mass index (BMI) <30 kg/m(2). The associations were attenuated after adjustment for measured body composition, yet remained significant for CRP and SAA. No statistically significant associations were observed between leptin and NK cytotoxicity, lymphocyte subpopulations, or T-lymphocyte proliferation. This study fills an important gap in knowledge about the relationship between leptin concentrations and immune function in healthy individuals. Findings support an association between serum leptin and the inflammatory proteins CRP and SAA, which appears to be mediated only partly by adipose tissue. Our study does not support a link between leptin and other immune parameters among overweight or obese, but otherwise healthy postmenopausal women, perhaps because such effects are only present at low or deficient leptin concentrations.
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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) | 0.001 | 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".