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F38. PHENOTYPIC CLUSTERING OF BIPOLAR DISORDER SUPPORTS STRATIFICATION BY LITHIUM RESPONSIVENESS, NOT DIAGNOSTIC SUBTYPES

2023· article· en· W4387501611 on OpenAlexaff
Katie Scott, Martin Alda, Abraham Nunes

Bibliographic record

VenueEuropean Neuropsychopharmacology · 2023
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPopulation stratificationBipolar disorderPsychopathologyPhenotypeGenetic heterogeneityPopulationHomogeneousLithium (medication)Internal medicinePsychologyBiologyMedicineClinical psychologyGeneticsGenotypeSingle-nucleotide polymorphism

Abstract

fetched live from OpenAlex

Background Bipolar disorder (BD) affects 1% of the global population and functionally impairs 83% of the afflicted. Although BD is a highly heritable and biological disorder, phenotypic heterogeneity complicates elucidation of its etiology. While genetic studies have been fruitful, the effect sizes of genetic markers are generally too small to support clinical applications. Phenotypic stratification of BD may resolve this heterogeneity and provide more homogeneous groups for future studies. One common stratification approach involves categorizing BD into Types I and II, but it has been criticized for imposing categorical boundaries on a dimensional psychopathological condition, let alone the contested validity of BD-II as a diagnosis. A promising alternative subtype is responsiveness to prophylactic lithium. Clinical phenotypic profiles differentiate excellent from poor lithium responders, and lithium responsiveness is heritable, suggesting a genetic component. The present study adjudicates between stratification strategies based on (A) BD I/II subtype and (B) lithium responsiveness, by ascertaining which approach captures more phenotypic information. Specifically, we use detailed clinical data from lithium-treated patients with BD-I or II. Using their detailed clinical profiles, we identify data-driven phenotypic clusters, and measure the information these clusters carry about either BD subtype or lithium responsiveness. Our results may inform diagnostic nosology and stratification approaches for future genetic studies of BD. Methods We included adult patients with BD-I or II (N = 477 across four sites) who were treated with lithium as their principal mood stabilizer for at least one year. Treatment responsiveness was defined using the dichotomized Alda score. We performed hierarchical clustering on phenotypes defined by over 50 features, covering demographics, clinical course , family history, suicide behaviour , and comorbid conditions. We then measured the amount of information that inferred clusters carried about (A) BD subtype or (B) lithium responsiveness using adjusted mutual information (AMI) scores. Detailed phenotypic profiles across clusters were then evaluated with univariate comparisons. Results Two clusters were identified (n = 52 and n = 425), which captured significantly less information about BD subtype (AMI range 0.004 to 0.011 [SE range: 2e-4 to 4e-4]) than lithium responsiveness, (AMI 0.033 to 0.133 [1e-3 to 2e-3]). The smaller cluster had disproportionately more lithium responders (n = 42 [80.8%] vs.108 [25.4%]; p = 0.026), attention-deficit hyperactivity disorder (5 [9.6%] vs. 15 [3.5%]; p = 0.026), and learning disabilities (4 [7.7%] vs. 15 [3.5%]; p = 0.026). Discussion Detailed clinical phenotypes may offer more information about lithium responsiveness than diagnostic subtype, supporting lithium responsiveness rather than BD-I/II classification as a valid approach to stratification in clinical samples.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.017
GPT teacher head0.293
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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