Stopping the Intergenerational Risk of Diabetes—From Mechanisms to Interventions: A Report on Research Supported by Pathway to Stop Diabetes
Bibliographic record
Abstract
Embedded in the developmental origins of health and disease (DOHaD) hypothesis, maternal hyperglycemia in utero, from preexisting diabetes or gestational diabetes mellitus, predisposes the offspring to excess adiposity and heightened risk of prediabetes and type 2 diabetes development. This transmission creates a vicious cycle increasing the presence of diabetes from one generation to another, leading to the question: How can we interrupt this vicious cycle? In this article, we present the current state of knowledge on the intergenerational transmission of diabetes from epidemiological life course studies. Then, we discuss the potential mechanisms implicated in the intergenerational transmission of diabetes with a focus on epigenetics. We present novel findings stemming from epigenome-wide association studies of offspring DNA methylation in blood and placental tissues, which shed light on potential molecular mechanisms implicated in the mother-offspring transmission of diabetes. Lastly, with a perspective on how to break the cycle, we consider interventions to prevent offspring obesity and diabetes development before puberty, as a critical period of the intergenerational cycle. This article is part of a series of perspectives that report on research funded by the American Diabetes Association Pathway to Stop Diabetes program.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".