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Occurrence of Psychosis and Bipolar Disorder in Individuals With Attention-Deficit/Hyperactivity Disorder Treated With Stimulants

2025· article· en· W4413946474 on OpenAlexaffabout
Gonzalo Salazar de Pablo, Clàudia Aymerich, Juan Pablo Chart‐Pascual, Marco Solmi, Javier Torres-Cortes, Nessma Abdelhafez, Ana Catalán, Olivier Corbeil, Nicoletta Adamo, Philip Shaw, Paolo Fusar‐Poli, Samuele Cortese

Bibliographic record

VenueJAMA Psychiatry · 2025
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsInstitut Universitaire en Santé Mentale de QuébecOttawa HospitalUniversité LavalUniversity of Ottawa
Fundersnot available
KeywordsBipolar disorderAttention deficit hyperactivity disorderMeta-analysisPsychiatryMethylphenidatePsycINFOPsychosisMedicineObservational studyOdds ratioSchizophrenia (object-oriented programming)Systematic reviewPsychologyMEDLINEInternal medicineMood

Abstract

fetched live from OpenAlex

Importance: Individuals with attention-deficit/hyperactivity disorder (ADHD) may present with psychosis or bipolar disorder (BD) following treatment with stimulants. The extent to which this occurs is currently unclear. Objective: To meta-analytically quantify the occurrence of psychosis or BD after exposure to stimulants in individuals with ADHD and assess possible moderating factors. Data Sources: PubMed, Web of Science, Ovid/PsycINFO, and Cochrane Central Register of Reviews were searched from inception until October 1, 2024, without language restrictions. Study Selection: Studies of any design with DSM or International Classification of Diseases-defined ADHD populations exposed to stimulants, where psychosis or BD outcomes were evaluated. Data Extraction and Synthesis: PRISMA Preferred Reporting Items for Systematic Reviews and Meta-analyses and MOOSE Meta-analysis of Observational Studies in Epidemiology guidelines were followed, the protocol was registered, and the Newcastle-Ottawa scale and Cochrane risk of bias-2 tool were used for quality appraisal. Random-effects meta-analysis, subgroup analyses, and meta-regressions were conducted. Main Outcomes and Measures: For the proportion of individuals developing psychotic symptoms, psychotic disorders, and BD, effect sizes are reported as percentages with 95% CIs. For the comparison between amphetamines and methylphenidate, effect sizes are presented as odds ratios with 95% CIs. Results: Sixteen studies (N = 391 043; mean [range] age, 12.6 [8.5-31.1] years; 288 199 [73.7%] male) were eligible. Among individuals with ADHD prescribed stimulants, 2.76% (95% CI, 0.73-9.88; k = 10; n = 237 035), 2.29% (95% CI, 1.52-3.40; k = 4; n = 91 437), and 3.72% (95% CI, 0.77-16.05; k = 4; n = 92 945) developed psychotic symptoms, a psychotic disorder, and BD, respectively. Heterogeneity across the studies was significant (I2 > 95%). Psychosis occurrence risk was significantly higher in individuals exposed to amphetamines than to methylphenidate (odds ratio [OR], 1.57, 95% CI, 1.15-2.16; k = 3, n = 231 325). Subgroup analyses showed significantly higher prevalence of psychotic symptoms in studies from North America and in those with longer follow-up periods. Increased psychosis occurrence was associated with a higher proportion of female participants, smaller sample sizes, and higher dose of stimulants. Conclusions and Relevance: This systematic review and meta-analysis found a nonnegligible occurrence of psychotic symptoms, psychotic disorders, or BD in individuals with ADHD treated with stimulants. Amphetamines were associated with higher occurrence compared to methylphenidate. The included studies cannot establish causality, highlighting the need for further research, including randomized clinical trials and mirror-image studies comparing individuals exposed and not exposed to stimulants. Nonetheless, clinicians should inform patients about the increased occurrence of psychosis or BD when discussing stimulant pharmacotherapy and systematically monitor for these conditions throughout treatment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.296
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes2
Has abstractyes

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