BIOMARKERS OF HEALTH AND AGING IN A COHORT OF VIETNAMESE ADULTS AGED 60 AND OLDER
Bibliographic record
Abstract
Abstract War and traumatic events have negative health impacts across the life course, and most wars are situated in lower- and middle-income countries, where many survivors are now or will soon be entering old age. To date, however, most aging research has focused on health effects in high-income countries, neglecting the burdens of war exposure for the broader global population. The Vietnam Health and Aging Study (VHAS), a longitudinal study of 2,447 individuals aged 60+, investigates how stress and trauma experienced during the American War impacts health and aging of Vietnamese survivors. Using biomarker and anthropometric data collected in 2021-2022, we examined associations of recruitment characteristics (age, sex, district, military participation) with select cardiometabolic and inflammatory biomarkers (e.g., C-reactive protein [CRP], Interleukin-6 [IL-6]). Preliminary analyses revealed variation in associations of key demographic and outcome measures. Hypertensive risks were significantly higher for males, and overweight/obesity (OW/OB) risk significantly higher for females, whereas age was positively associated with hypertensive risk and inversely associated with OW/OB risk. Hypertensive risk was lower for those with formal military experience, while proinflammatory markers (i.e., CRP, IL-6) increased with age but did not vary by sex or military participation. District residence, which reflects differences in bombing intensity during the war, showed varied associations with inflammatory and cardiometabolic biomarkers. These findings suggest inflammatory and cardiometabolic risks are not uniformly coupled across participants in this study, underscoring the complexities of how prior war trauma interacts with other factors to influence health and well-being during aging.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".