Developmental programming of cardiovascular function: a translational perspective
Bibliographic record
Abstract
The developmental origins of health and disease (DOHaD) is a concept linking pre- and early postnatal exposures to environmental influences with long-term health outcomes and susceptibility to disease. It has provided a new perspective on the etiology and evolution of chronic disease risk, and as such is a classic example of a paradigm shift. What first emerged as the 'fetal origins of disease', the evolution of the DOHaD conceptual framework is a storied one in which preclinical studies played an important role. With its potential clinical applications of DOHaD, there is increasing desire to leverage this growing body of preclinical work to improve health outcomes in populations all over the world. In this review, we provide a perspective on the values and limitations of preclinical research, and the challenges that impede its translation. The review focuses largely on the developmental programming of cardiovascular function and begins with a brief discussion on the emergence of the 'Barker hypothesis', and its subsequent evolution into the more-encompassing DOHaD framework. We then discuss some fundamental pathophysiological processes by which developmental programming may occur, and attempt to define these as 'instigator' and 'effector' mechanisms, according to their role in early adversity. We conclude with a brief discussion of some notable challenges that hinder the translation of this preclinical work.
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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 | 0.002 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".