Longitudinal changes in circulating concentrations of inflammatory markers throughout pregnancy: are there associations with diet and weight status?
Bibliographic record
Abstract
The natural inflammation occurring during pregnancy can, under certain conditions, be associated with adverse pregnancy outcomes. This study aimed to (1) quantify changes in circulating concentrations of leptin, adiponectin, interleukin-6 (IL-6) and C-reactive protein (CRP) across trimesters of pregnancy, according to pre-pregnancy body mass index (ppBMI); and (2) examine the trimester-specific associations between the inflammatory markers’ concentrations, a Mediterranean diet score (MDS) and the dietary inflammatory index (DII). We measured leptin, adiponectin and IL-6 by ELISA and CRP by high-sensitivity immunonephelometry, in blood samples from 79 pregnant women (age: 32.1 ± 3.7 years; ppBMI: 25.7 ± 5.8 kg/m2). Three Web-based 24-h recalls were completed at each trimester and used to compute the MDS and the DII. CRP concentrations remained stable across trimesters, whereas concentrations of leptin and IL-6 increased, and adiponectin concentrations decreased (p < 0.001). Changes in leptin and adiponectin concentrations also differed according to ppBMI categories (p < 0.05). As for the dietary scores, the only significant association was observed in the second trimester between leptin concentrations and the MDS (r = −0.26, p < 0.05). In conclusion, ppBMI and the progression of pregnancy itself probably supplant the potential associations between diet and the inflammation occurring during that period. Novelty: Circulating leptin and IL-6 concentrations increased across trimesters whereas CRP was stable, and adiponectin decreased. Variations in circulating leptin and adiponectin concentrations differed by ppBMI categories. Very few associations were observed between dietary scores and inflammatory markers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".