Secular trend for increasing birthweight in offspring of pregnant women with type 1 diabetes: is improved placentation the reason?
Bibliographic record
Abstract
Abstract Despite enormous progress in managing blood glucose levels, pregnancy in women with type 1 diabetes still carries risks for the growing fetus. While, previously, fetal undergrowth was not uncommon in these women, with improved maternal glycaemic control we now see an increased prevalence of fetal overgrowth. Besides short-term implications, offspring of women with type 1 diabetes are more likely to become obese and to develop diabetes and features of the metabolic syndrome. Here, we argue that the increase in birthweight is paradoxically related to improved glycaemic control in the pre- and periconceptional periods. Good glycaemic control reduces the prevalence of microangiopathy and improves placentation in early pregnancy, which may lead to unimpeded fetal nutrition. Even mild maternal hyperglycaemia may then later result in fetal overnutrition. This notion is supported by circumstantial evidence that lower HbA1c levels as well as increases in markers of placental size and function in early pregnancy are associated with large-for-gestational age neonates. We also emphasise that neonates with normal birthweight can have excessive fat deposition. This may occur when poor placentation leads to initial fetal undergrowth, followed by fetal overnutrition due to maternal hyperglycaemia. Thus, the complex interaction of glucose levels during different periods of pregnancy ultimately determines the risk of adiposity, which can occur in fetuses with both normal and elevated birthweight. Prevention of fetal adiposity calls for revised goal setting to enable pregnant women to maintain blood glucose levels that are closer to normal. This could be supported by continuous glucose monitoring throughout pregnancy and appropriate maternal gestational weight gain. Future research should consider the measurement of adiposity in neonates. Graphical abstract
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".