In Utero Origins of Hypertension: Mechanisms and Targets for Therapy
Bibliographic record
Abstract
The developmental origins of health and disease theory is based on evidence that a suboptimal environment during fetal and neonatal development can significantly impact the evolution of adult-onset disease. Abundant evidence exists that a compromised prenatal (and early postnatal) environment leads to an increased risk of hypertension later in life. Hypertension is a silent, chronic, and progressive disease defined by elevated blood pressure (>140/90 mmHg) and is strongly correlated with cardiovascular morbidity/mortality. The pathophysiological mechanisms, however, are complex and poorly understood, and hypertension continues to be one of the most resilient health problems in modern society. Research into the programming of hypertension has proposed pharmacological treatment strategies to reverse and/or prevent disease. In addition, modifications to the lifestyle of pregnant women might impart far-reaching benefits to the health of their children. As more information is discovered, more successful management of hypertension can be expected to follow; however, while pregnancy complications such as fetal growth restriction, preeclampsia, preterm birth, etc., continue to occur, their offspring will be at increased risk for hypertension. This article reviews the current knowledge surrounding the developmental origins of hypertension, with a focus on mechanistic pathways and targets for therapeutic and pharmacologic interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".