Early life environments shape adult cardiometabolic health during rapid lifestyle change
Bibliographic record
Abstract
Early life environments can have long-lasting impacts on health and fitness, but the evolutionary significance of these effects remains debated. Two major classes of explanations have been proposed: developmental constraint (DC) explanations posit that early life adversity limits optimal development, leading to long-term costs, while predictive adaptive response (PAR) explanations posit that organisms use early life cues to predict adult conditions, resulting in detriments when adult environments do not match expectations. We tested these hypotheses using anthropological and biomedical data for the Orang Asli-the Indigenous peoples of Peninsular Malaysia-who are undergoing a rapid but heterogeneous transition from non-industrial, subsistence-based livelihoods to more industrialized, market-integrated conditions. Using questionnaire data, we show that this shift creates natural variation in the degree of similarity between early life and adult environments. Using anthropometric and health data, we find that more rural, subsistence-based early life environments are associated with shorter stature but better adult cardiometabolic health. Applying a quadratic regression framework, we find support for DC but not PAR in explaining adult cardiometabolic health, echoing findings and conclusions from other long-lived species. Overall, our results suggest that non-industrialized early life conditions can provide additive protection against common health issues associated with urban, industrialized lifestyle exposure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".