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Record W1893762565 · doi:10.1186/s13722-015-0040-7

Contribution of BDNF and DRD2 genetic polymorphisms to continued opioid use in patients receiving methadone treatment for opioid use disorder: an observational study

2015· article· en· W1893762565 on OpenAlexafffundabout
Monica Bawor, Brittany B. Dennis, Charlie Tan, Guillaume Paré, Michael Varenbut, Jeff Daiter, Carolyn Plater, Andrew Worster, David C. Marsh, Meir Steiner, Rebecca Anglin, Dipika Desai, Lehana Thabane, Zainab Samaan

Bibliographic record

VenueAddiction Science & Clinical Practice · 2015
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsNOSM UniversityMcMaster University Medical CentreSt. Joseph’s Healthcare HamiltonCanadian Centre on Substance Use and AddictionMcMaster UniversityPopulation Health Research Institute
FundersCanadian Institutes of Health Research
Keywordsrs6265Opioid use disorderMethadoneOpioidMediciners4680PsychiatryOdds ratioMethadone maintenanceInternal medicinePsychologyBrain-derived neurotrophic factorAlleleCatechol-O-methyl transferaseNeurotrophic factorsGeneticsBiology

Abstract

fetched live from OpenAlex

BACKGROUND: The heritability of opioid use disorder has been widely investigated; however, the influence of specific genes on methadone treatment outcomes is not well understood. The association between response to methadone treatment and genes that are involved in substance use behaviors and reward mechanisms is poorly understood, despite evidence suggesting their contribution to opioid use disorder. The aim of this study was to investigate the effect of brain-derived neurotrophic factor (BDNF) and dopamine receptor D2 (DRD2) polymorphisms on continued opioid use among patients on methadone treatment for opioid use disorder. METHODS: BDNF 196G>A (rs6265) and DRD2-241A>G (rs1799978) genetic variants were examined in patients with opioid use disorder who were recruited from methadone treatment clinics across Southern Ontario, Canada. We collected demographic information, substance use history, blood for genetic analysis, and urine to measure opioid use. We used regression analysis to examine the association between continued opioid use and genetic variants, adjusting for age, sex, ethnicity, methadone dose, duration in treatment, and number of urine screens. RESULTS: Among 240 patients treated with methadone for opioid use disorder, 36.3 percent (n = 87) and 11.3 percent (n = 27) had at least one risk allele for rs6265 and rs1799978, respectively. These genetic variants were not significantly associated with continued opioid use while on methadone maintenance treatment [rs6265: odds ratio (OR) = 1.37, 95 % confidence interval (CI) = 0.792, 2.371, p = 0.264; rs1799978: OR 1.27, 95 % CI 0.511, 3.182, p = 0.603]. CONCLUSIONS: Despite an association of BDNF rs6265 and DRD2 rs1799978 with addictive behaviors, these variants were not associated with continued illicit opioid use in patients treated with methadone. Problematic use of opioids throughout treatment with methadone may be attributed to nongenetic factors or a polygenic effect requiring further exploration. Additional research should focus on investigating these findings in larger samples and different populations.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.199
GPT teacher head0.448
Teacher spread0.250 · 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".

Quick stats

Citations27
Published2015
Admission routes3
Has abstractyes

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