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Record W2095762847 · doi:10.1139/jpn.0814

Serotonin transporter genotype interacts with paroxetine plasma levels to influence depression treatment response in geriatric patients

2008· article· en· W2095762847 on OpenAlexaffvenue
Francis E. Lotrich, Bruce G. Pollock, Margaret A. Kirshner, Robert F. Ferrell, Charles F. Reynolds

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2008
Typearticle
Languageen
FieldNeuroscience
TopicNeurotransmitter Receptor Influence on Behavior
Canadian institutionsBaycrest Hospital
FundersNational Center for Advancing Translational SciencesNational Institute of Mental Health
KeywordsParoxetineHamdSerotonin transporterInternal medicineAntidepressantGenotypeEndocrinologyPsychologyHamilton Rating Scale for DepressionDepression (economics)SerotoninAllelePlasma concentrationMedicineMajor depressive disorderSignificant differenceBiologyGeneticsReceptorGene

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate whether variable antidepressant response may be influenced by an interaction between the serotonin transporter promoter polymorphism (5-HTTLPR) and antidepressant concentration. METHODS: Elderly subjects with depression treated with paroxetine (n = 110) were genotyped and assessed with the Hamilton Rating Scale for Depression (HAMD). A mixed-effect analysis of repeated measures was used. RESULTS: There was an interaction between early paroxetine concentration and 5-HTTLPR genotype on symptomatic improvement over 12 weeks (F(18,59.5) = 1.8, p < 0.05), as well as main effects of both paroxetine concentration (F(68,55.3) = 2.4, p < 0.005) and genotype (F(2,74.2) = 5.7, p < 0.005). Paroxetine concentrations were correlated with change in HAMD scores after 2 weeks of treatment in subjects with the short (s) allele (r = 0.31, p < 0.05) but not in subjects homozygous for the long (l) allele. CONCLUSION: The results demonstrate a concentration-response relation for paroxetine in late-life depression and support the hypothesis for both a direct main effect and a moderating influence of 5-HTTLPR alleles on this concentration-response relation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.282
Teacher spread0.253 · 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.

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".

Quick stats

Citations42
Published2008
Admission routes2
Has abstractyes

Explore more

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