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Eur Neuropsychopharmacol. 2015 Apr;25(4):441-53. doi: 10.1016/j.euroneuro.2015.01.001. Epub 2015 Feb 2.

The combined effect of genetic polymorphisms and clinical parameters on treatment outcome in treatment-resistant depression.

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Department of Psychiatry and Psychotherapy, Medical University of Vienna, Währinger Gürtel 18-20A-1090 Vienna, Austria.
Université Libre de Bruxelles, Belgium; Psy Pluriel Centre Europèen de Psychologie Medicale, Belgium.
Imperial College, University of London, United Kingdom.
School of Medicine, Free University of Brussels, Belgium.
Psychiatric Division, Chaim Sheba Medical Center, Israel.
Department of Biomedical and NeuroMotor Sciences, University of Bologna, Italy.
Department of Psychiatry and Psychotherapy, Medical University of Vienna, Währinger Gürtel 18-20A-1090 Vienna, Austria. Electronic address:


For over a decade, the European Group for the Study of Resistant Depression (GSRD) has examined single nucleotide polymorphisms (SNP) and clinical parameters in regard to treatment outcome. However, an interaction based model combining these factors has not been established yet. Regarding the low effect of individual SNPs, a model investigating the interactive role of SNPs and clinical variables in treatment-resistant depression (TRD) seems auspicious. Thus 225 patients featured in previous work of the GSRD were enrolled in this investigation. According to data availability and previous positive results, 12 SNPs in HTR2A, COMT, ST8SIA2, PPP3CC and BDNF as well as 8 clinical variables featured in other GSRD studies were chosen for this investigation. Random forests algorithm were used for variable shrinkage and k-means clustering for surfacing variable characteristics determining treatment outcome. Using these machine learning and clustering algorithms, we detected a set of 3 SNPs and a clinical variable that was significantly associated with treatment response. About 62% of patients exhibiting the allelic combination of GG-GG-TT for rs6265, rs7430 and rs6313 of the BDNF, PPP3CC and HTR2A genes, respectively, and without melancholia showed a HAM-D decline under 17 compared to about 34% of the whole study sample. Our random forests prediction model for treatment outcome showed that combining clinical and genetic variables gradually increased the prediction performance recognizing correctly 25% of responders using all 4 factors. Thus, we could confirm our previous findings and furthermore show the strength of an interaction-based model combining statistical algorithms in identifying and operating treatment predictors.


Clinical factors in depression; Clustering; Genetics in depression; Random forests; Treatment outcome; Treatment-resistant-depression

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