Urbanization exacerbates age-associated declines in cardiometabolic health in Turkana and Orang Asli
Bibliographic record
Abstract
Declines in cardiometabolic health among older individuals are so ubiquitous in Western, high-income countries that non-communicable diseases (NCDs) like type 2 diabetes, hypertension, and cardiovascular disease have been termed "diseases of aging". In contrast, research from non-industrial contexts has found low rates of cardiometabolic NCDs in old age, suggesting protective effects of lifestyle. To test if industrialization and urbanization generates or magnifies age-associated cardiometabolic health patterns, within-population analyses are needed. We worked with Turkana pastoralists of Kenya and Orang Asli mixed subsistence groups of Peninsular Malaysia-two groups that are transitioning from non-industrial to urban, market-integrated lifestyles. We find that rural, non-industrial environments produce minimal to modest age-dependent increases in body size, lipid, and blood pressure traits, and that urban environments significantly amplify age effects in repeatable ways across two distinct populations. However, we did not find that urban environments consistently accelerate biomarkers of more generalized functional capacity and biological aging, namely grip strength, walking speed, and epigenetic age. Together, these findings challenge the view that cardiometabolic "diseases of aging" are an intrinsic feature of aging, instead implicating urban lifestyle features as drivers of age-associated variation; however, these same lifestyle exposures may have heterogeneous effects on biological aging. These results underscore the urgency of understanding how rapid lifestyle changes shape aging trajectories, especially in populations undergoing industrial transitions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".