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Record W2735362140 · doi:10.3389/fphar.2017.00402

Association between CYP2D6 Genotypes and the Risk of Antidepressant Discontinuation, Dosage Modification and the Occurrence of Maternal Depression during Pregnancy

2017· article· en· W2735362140 on OpenAlexaff
Anick Bérard, Andrea Gaedigk, Odile Sheehy, Christina Chambers, Mark J. Roth, Pina Bozzo, Diana Johnson, Kelly Kao, Sharon Lavigne, Lori Wolfe, Dee Quinn, Kristen Dieter, Jin‐Ping Zhao

Bibliographic record

VenueFrontiers in Pharmacology · 2017
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsHospital for Sick ChildrenUniversité de MontréalSickKids FoundationCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsPregnancyMedicineAntidepressantDiscontinuationOdds ratioDepression (economics)AnxietyConfoundingBeck Depression InventoryObstetricsInternal medicinePsychiatryBiology

Abstract

fetched live from OpenAlex

IMPORTANCE Polymorphic expression of drug metabolizing enzymes affects the metabolism of antidepressants, and thus can contribute to drug response and/or adverse events. Pregnancy itself can affect CYP2D6 activity with profound variations determined by CYP2D6 genotype. OBJECTIVE To investigate the association between CYP2D6 genotype and the risk of antidepressant discontinuation, dosage modification, and the occurrence of maternal depression during pregnancy. SETTING Data from the Organization of Teratology Information Specialists (OTIS) Antidepressants in Pregnancy Cohort, 2006-2010. Women were eligible if they were within 14 completed weeks of pregnancy at recruitment and exposed to an antidepressant or having any exposures considered non-teratogenic. MAIN OUTCOMES AND MEASURES Gestational antidepressant usage was self-reported and defined as continuous/discontinued use, and non-use; dosage modification was further documented. Maternal depression and anxiety were measured every trimester using the telephone interviewer-administered Edinburgh Postnatal Depression Scale and the Beck Anxiety Inventory, respectively. Saliva samples were used for CYP2D6 genotype analyses. Logistic regression models were used to calculate crude and adjusted odds ratios (OR) with 95% confidence intervals. RESULTS A total of 246 pregnant women were included in the study. The majority were normal metabolizers (n=204, 83%); 3.3% (n=8) were ultrarapid metabolizers, 5.7 % (n=14) poor metabolizers, and 8.1% (n=20) intermediate metabolizers. Among study subjects, 139 women were treated with antidepressants at the beginning of pregnancy, and 21 antidepressant users (15%) discontinued therapy during pregnancy. Adjusting for depressive symptoms, and other potential confounders, the risk of discontinuing antidepressants during pregnancy was nearly four times higher in slow metabolizers (poor or intermediate metabolizers) compared to those with a faster metabolism rate (normal or ultrarapid metabolizers), aOR= 3.57 (95% CI: 1.15-11.11). Predicted CYP2D6 metabolizer status did not impact dosage modifications. Compared with slow metabolizers, significantly higher proportion of women in the fast metabolizer group had depressive symptom in the first trimester (19.81% vs. 5.88%, P=0.049). CONCLUSIONS AND RELEVANCE Prior knowledge of CYP2D6 genotype may help to identify pregnant women at greater risk of antidepressant discontinuation. Twenty percent of women exposed to antidepressants during pregnancy remained depressed, indicating an urgent need for personalized treatment of depression during pregnancy.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.194

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.310
Teacher spread0.296 · 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

Citations23
Published2017
Admission routes1
Has abstractyes

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