TNFα Dynamics During the Oral Glucose Tolerance Test Vary According to the Level of Insulin Resistance in Pregnant Women
Bibliographic record
Abstract
INTRODUCTION: TNFα is suspected to play a role in inflammation and insulin resistance leading to higher risk of metabolic impairment. Controversies exist concerning the role of TNFα in gestational insulin resistance. We investigated the interrelations between TNFα and insulin resistance in a large population-based cohort of pregnant women. METHODS: Women (n = 756) were followed prospectively at 5-16 weeks and 24-28 weeks of pregnancy. Anthropometric measures and blood samples were collected at both visits. A 75-g oral glucose tolerance test (OGTT) was conducted at the second trimester to assess insulin sensitivity status (homeostasis model of assessment of insulin resistance and Matsuda index). TNFα was measured at the first trimester (nonfasting) and at each time point of the OGTT. RESULTS: Participants were 28.4 ± 4.4 years old and had a mean body mass index of 25.5 ± 5.5 kg/m(2) at first trimester. Median TNFα levels were 1.56 (interquartile range, 1.18-2.06) pg/mL at first trimester and 1.61 (interquartile range, 1.12-2.13) pg/mL at second trimester (1 h after glucose load). At second trimester, higher TNFα levels were associated with higher insulin resistance index levels (r = 0.37 and -0.30 for homeostasis model of assessment of insulin resistance and Matsuda index, respectively; P < .0001), even after adjustment for age, body mass index, triglycerides, and adiponectin. Women with higher insulin resistance showed a continuing decrease in TNFα levels during the OGTT, whereas women who were more insulin sensitive showed an increase in TNFα at hour 1 and a decrease at hour 2 of the test. CONCLUSION: Higher insulin resistance is associated with higher levels of circulating TNFα at first and second trimesters of pregnancy. TNFα level dynamics during an OGTT at second trimester vary according to insulin-resistance state.
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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 | 0.003 |
| 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 | 0.001 | 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".