Racial/ethnic differences in the association of lifestyle factors with biological aging in NHANES, 1999-2018
Bibliographic record
Abstract
Racial and ethnic disparities in healthy aging represent an emerging public health crisis that will only grow worse as our population grows older. Healthy lifestyle behaviors are proposed as a key strategy to promote healthy aging. However, the potential of lifestyle interventions to address aging health disparities is uncertain. We analyzed data from 42 625 adult participants (aged 20-85 years) participating in National Health and Nutrition Examination Survey (NHANES), 1999-2018 to evaluate relationships among healthy lifestyle behaviors and biological aging across White, Black, and Hispanic-identifying groups. We measured healthy lifestyle as adherence to a Mediterranean diet and level of leisure-time physical activity using established methods. We measured healthy aging using the PhenoAge biological age algorithm applied to blood chemistry data. We tested associations within each race/ethnic identity group and compared associations across groups using regression models with interaction terms. We found that within each race/ethnic identity group, greater adherence to a Mediterranean diet and higher levels of leisure-time physical activity were associated with younger biological age, independent of demographic and socioeconomic confounders, obesity, and smoking. However, these associations were stronger among White- as compared to non-Hispanic Black- and Hispanic-identifying adults. Results suggest that healthy lifestyle factors are likely to promote healthy aging across the population. However, lifestyle factors alone may not be sufficient to completely address race/ethnic disparities in healthy aging. Future studies will need to investigate additional ways to reduce racial and ethnic disparities in healthy 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".