Preliminary findings and feasibility of mid‐life cognitive and blood‐based biomarker assessments in adults with and without prospectively studied ADHD diagnosis in childhood
Bibliographic record
Abstract
BACKGROUND: Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder associated with long-term, impairing symptoms of inattention and/or impulsivity/hyperactivity-restlessness into adulthood and late-life. Emerging epidemiological evidence suggests that ADHD is associated with an increased risk for mild cognitive impairment (MCI) and Alzheimer's disease (AD). Furthermore, genetic predisposition to ADHD has been linked to cognitive decline and AD-related pathophysiology. It is unclear whether AD risk in ADHD is driven by reduced brain resilience to the effects of AD pathophysiology (e.g., amyloid pathology) or shared risk factors that directly affect AD pathology (e.g., cardiovascular health). Pathways to AD have been principally tested in samples free from neurodevelopmental differences, making it impossible to know the applicability of commonly studied AD pathways in vulnerable groups. Moreover, studies of ADHD in AD have been hampered by reliance on electronic health records, which can present biased estimates of ADHD prevalence due to unclear diagnostic accuracy. METHOD: =44.5, 28% Black or multiple race, 24% women. We anticipate 50 assessments by 06/25. This research is studying feasibility of procedures in the larger PALS sample and gathering preliminary data about group differences in cognitive functioning pertinent to ADHD and AD risk and blood-based biomarkers of AD, some of which are known to be altered in preclinical stages of the disease. Example cognitive tests are MoCA and Rey Auditory Verbal Learning Test-Immediate/Recall; example blood-based biomarkers are plasma Ab42/40, tau (p-tau181, 217), and inflammation-related markers. RESULT: We will report preliminary group comparisons, emphasizing effect sizes given interim study progress. CONCLUSION: Understanding the association between rigorous, prospectively diagnosed ADHD and prevalent age-related diseases, such as AD, is a pressing concern given the increased prevalence of ADHD in adulthood. Our research is beginning to address this need including demonstrating feasibility of measuring cognition and AD biomarkers in midlife adults with ADHD histories.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".