Autistic traits are associated with faster pace of aging: Evidence from the Dunedin study at age 45
Bibliographic record
Abstract
Growing evidence indicates that the defining characteristics of autism spectrum disorder (ASD) are distributed throughout the general population; hence, understanding the correlates of aging in people with high autistic traits could shed light on ASD and aging. 915 members of the Dunedin longitudinal birth cohort completed a measure of autistic traits at age 45. A composite measure of the "pace of aging" was derived by tracking the decline in 19 biomarkers across ages 26, 32, 38, and 45 years. Facial age was also assessed. Reports of perceived health were collected from participants themselves, informants, and interviewers. Higher self-reported autistic traits significantly correlated with a faster pace of aging, older facial age, and poorer self-, informant-, and interviewer-rated health. After control for sex, SES and IQ, autistic traits were significantly associated with each variable: pace of aging (β = 0.09), facial age (β = 0.08), self- (β = -0.15), informant (β = -0.12), and interviewer-rated (β = -0.17) health. Autistic traits measured at age 45 are associated with faster aging. Participants with high autistic traits appear to be more vulnerable to poor health outcomes, as previously reported for those clinically diagnosed with ASD. Therefore, autistic traits may have important health implications. Replicating these findings in samples of autistic people is needed to identify the mechanism of their effect on aging and physical health to improve outcomes for those with ASD diagnoses or high autistic traits. LAY SUMMARY: The role that autistic traits have in relation to health outcomes has not been investigated. We looked at how physical health and aging (measured with self-reported questions and decline in multiple biological measures) were related to autistic traits (measured with a questionnaire, at age 45). We found that higher autistic traits were associated with poorer reports of physical health, and a faster pace of aging. This suggests that both those with autism and those with higher autistic traits may be more likely to experience poorer health outcomes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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 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".