Impact of Fetal and Infant Exposure to the Chinese Great Famine on the Risk of Hypertension in Adulthood
Bibliographic record
Abstract
BACKGROUND: Famine provides quasi-experimental conditions for testing the hypothesis of "programming" health effects by poor nutrition in early life. It remains uncertain whether early life exposure to famine increases the risk of hypertension in adulthood. There is a lack of data on the relative impact of exposure to famine during fetal development versus infancy (<2 years postnatal). We sought to assess the impact of exposure to the 1959-1961 Chinese Great Famine (the largest in human history) during fetal development and infancy on the risks of hypertension, short stature and obesity in adulthood. METHODOLOGY/PRINCIPAL FINDINGS: We conducted a retrospective cohort study of 12,065 adults (46-53 years of age) born 1957-1964 in the Zhongshan and Nanhai municipalities of Guangdong province, China. Adjusting for socio-demographic and lifestyle characteristics, as compared to subjects who were unexposed to famine, the risk of hypertension was not significantly elevated in subjects exposed to famine during fetal development only overall, but was 1.36-fold higher in those exposed during the first trimester of pregnancy only [adjusted odds ratio (OR) 1.36 (95% confidence intervals 1.03-1.79)], 1.83-fold higher in those exposed during infancy only [adjusted OR 1.83 (1.61-2.08)], and 1.31-fold higher in those exposed during both fetal development and infancy [adjusted OR 1.31 (1.14-1.51)]. Exposure to famine during infancy increased the risk of short stature. Early life exposure to famine did not increase the risk of obesity. CONCLUSIONS/SIGNIFICANCE: Exposure to the Chinese Great Famine during the first trimester of pregnancy only, or during infancy only, or during both fetal development and infancy increased the risk of hypertension in adulthood, suggesting an important role of changes in exposure to famine during fetal development and from prenatal to early postnatal life in developmental "programming" cardiovascular disease risk.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".