As rates of ASD and ADHD rise, genetic contributions fall: Evidence for widening diagnostic criteria
Bibliographic record
Abstract
Importance: The incidence of ADHD and autism spectrum disorder (ASD) has increased markedly over recent decades, raising concerns about the emergence of new risk factors. Current literature typically attributes increased rates to changes in diagnostic practice, stigmatization, and awareness, but critically few studies have explored changes in underlying risk factors. Objective: To assess changes in the genetic risk profile of individuals diagnosed with ASD or ADHD according to year of incident diagnosis. Design: We used the iPSYCH2015 study, a population-based case-cohort with complete ascertainment of incident diagnoses for ASD and ADHD made from 1994 to 2016. Setting: Denmark. Participants: ASD (N=17,071) and ADHD (N=20,111). Exposure: Year of incident diagnosis. Regression models tested changes in the mean genetic risk profile of individuals diagnosed in each consecutive year (1994-2016), adjusting for age, sex, and ancestry. Main Outcomes: We used polygenic scores for psychiatric (ADHD, ASD, depression, bipolar, schizophrenia) and cognitive-behavioral (addiction, educational attainment, IQ, neuroticism, risk-taking) outcomes to capture the genetic risk profiles of diagnosed individuals. Results: A more recent ADHD diagnosis was associated (p<0.001) with less genetic risk for ADHD (β=-0.06 SD per 10 years) and other disorders (ASD, bipolar, schizophrenia). Similarly, a more recent ASD diagnosis was associated with less genetic risk for ASD (β=-0.07) and other disorders/traits (bipolar, schizophrenia, educational attainment). Conclusions and Relevance: Our novel approach suggests that over recent decades diagnostic practice around ADHD and ASD has evolved to capture a different profile of genetic risk. These findings support broadening diagnostic criteria as the explanation for the rise in incidence, with implications for understanding prevalence trends in relation to changes in risk factors and clinical practice. KEY POINTS: Our results suggest that recent increase in ASD and ADHD diagnoses coincide with a broadening of diagnostic criteria.
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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 | 0.032 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".