Reappearance of C-Peptide During the Third Trimester of Pregnancy in Type 1 Diabetes: Pancreatic Regeneration or Fetal Hyperinsulinism?
Bibliographic record
Abstract
OBJECTIVE We assessed longitudinal patterns of maternal C-peptide concentration to examine the hypothesis of β-cell regeneration in pregnancy with type 1 diabetes. RESEARCH DESIGN AND METHODS C-peptide was measured on maternal serum samples from 127 participants (12, 24, and 34 weeks) and cord blood during the Continuous Glucose Monitoring in Women With Type 1 Diabetes in Pregnancy Trial (CONCEPTT). C-peptide was measured using a highly sensitive direct and solid-phase competitive electrochemiluminescent immunoassay. RESULTS Three discrete patterns of maternal C-peptide trajectory were identified: pattern 1, undetectable throughout pregnancy, n = 74 (58%; maternal C-peptide <3 pmol/L); pattern 2, detectable at baseline, n = 22 (17%; maternal C-peptide 7–272 pmol/L at baseline); and pattern 3, undetectable maternal C-peptide at 12 and 24 weeks, which first became detectable at 34 weeks, n = 31 (24%; maternal C-peptide 4–26 pmol/L at 34 weeks). Baseline characteristics and third trimester glucose profiles of women with pattern 1 and pattern 3 C-peptide trajectories were similar, but women in pattern 3 had suboptimal glycemia (50% time above range) at 24 weeks’ gestation. Offspring of women with pattern 3 C-peptide trajectories had elevated cord blood C-peptide (geometric mean 1,319 vs. 718 pmol/L; P = 0.007), increased rates of large for gestational age (90% vs. 60%; P = 0.002), neonatal hypoglycemia (42% vs. 14%; P = 0.001), and neonatal intensive care admission (45% vs. 23%; P = 0.023) compared with pattern 1 offspring. CONCLUSIONS First maternal C-peptide appearance at 34 weeks was associated with midtrimester hyperglycemia, elevated cord blood C-peptide, and high rates of neonatal complications. This suggests transfer of C-peptide from fetal to maternal serum and is inconsistent with pregnancy-related β-cell regeneration.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| 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".