No Consistent Evidence that Ageism Is Linked to Biological Aging Status in the US Health and Retirement Study
Bibliographic record
Abstract
INTRODUCTION: Ageism, defined as stereotype, prejudice, and discrimination against people based on their age, has been shown to have unfavorable impacts on health. While discrimination has often been shown to negatively impact health, whether ageism might accelerate biological aging itself is unclear. METHODS: We conducted secondary analyses of the Health and Retirement Study (HRS, 2008, 2012, and 2016 waves). Ageism was estimated using self-perception of aging (SPA) and perceived age discrimination (PAD). Other types of discrimination (e.g., racism, sexism) were also considered. The Everyday Discrimination Scale was used to assess PAD and other types of discrimination. Biological aging was measured through homeostatic dysregulation (HD, n = 3,443, 2016 wave, six measures), epigenetic age (n = 1,484, 2016 wave, five measures), and telomere length (n = 1,981, 2008 wave). Biological aging measures were modeled as a function of ageism within and across waves. RESULTS: Within waves, SPA score was associated with some elevated HD (e.g., β = 0.11, p < 0.001, quantified by 44 biomarkers) and epigenetic age indices (e.g., β = 0.61, p < 0.001, Hannum Epi Age). After controlling for comorbidities and social participation, these variables were no longer associated. Effects were similar but weaker in predicting 2016 biological aging from SPA in 2008 and 2012. PAD was not associated with biological aging measures, in contrast to other types of discrimination, which were. CONCLUSIONS: We found no consistent evidence linking ageism to biological aging status. Further research should investigate why; potentially, ageism has less time to become biologically embedded, compared to racism and sexism, which might be experienced throughout one's life, but measurement challenges could also be present.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".