DOES AGEISM ACCELERATE BIOLOGICAL AGING
Bibliographic record
Abstract
Abstract Defined as, “stereotype, prejudice, and discrimination directly towards people because of their age”, ageism may contribute to adverse health outcomes, accelerate aging process, and increase the burden on health and social services. Little is known about the ageism impact on biological aging. Secondary analysis of the American Health and Retirement Study (2012 and 2016 waves) was carried out. Participants were asked: the self-perception of aging (SPA), the causes of receiving discrimination, including ageism as one of the causes, and the frequency of receiving such discrimination. The aging rate was measured using two distinct measurements: homeostatic dysregulation (using Mahalanobis distance on 44 biomarkers, n= 9934, 2016 wave) and epigenetic aging clocks (n=4018, 2016 wave). The influence of perceived ageism (current or previous waves) on the aging rate was modelled with linear models using biological aging (aka. homeostatic dysregulation and epigenetic age) as the dependent variable (outcome), ageism as the exposure, with considering confounders: sex, depressive symptom. The results show that more negative SPA, either from the previous (2012) or the same wave (2016), is associated with elevated homeostatic dysregulation (e.g. the slope increases from 1.20 to 1.34, p< 0.001, previous wave) and increasing epigenetic age (e.g. DNAm PhenoAge, the slope increases from 53.81 to 61.14, p< 0.001, current wave). The association between the ageism receiving frequency and biological aging is similar but less significant. The results demonstrate that ageism is associated with accelerated biological aging. More interventions are called to combat ageism and foster the health and wellbeing of the older adults.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".