# Genetic Variations Associated with Long-Term Treatment Response in Bipolar Depression
## 4 Discussion
Due to the observational uncontrolled design of this study, other available clinicopathological characteristics of the patient sample were included in the statistical models to avoid the detection of false correlations with pharmacogenetic variables. Among non-pharmacogenetic variables associated with long-term patients' status, the amount of tobacco and the treatment with anxiolytes were significantly positively correlated with the CGI-BP-M scores. The polycyclic aromatic hydrocarbons in cigarette smoke have been shown to induce CYP1A2, and different studies have reported decreased plasmonic concentrations of drugs primarily metabolized by CYP1A2 in smokers, including the antipsychotics clozapine and olanzapine [54; 55], with the consequent poorer treatment response. We could not find a correlation between smokers with higher CGI-BP-M scores and treatment with clozapine or olanzapine, probably because of the small sample used in this study and the low number of patients treated with those drugs (\(n\leq 5\)). Additionally, other patients' characteristics, such as the severity of the disease itself may account for increased tobacco consumption. Likewise, treatment with anxiolytes, as a marker of anxiety in the study sample, was also associated with the long-term severity in our cohort. Finally, the number of adverse effects was found to be associated with the number of previous episodes and the presence of psychiatric comorbidities, which might be indirectly indicative of a higher number of medication trials or polymedication in these patients. The predictors described in the present study could be of value for targeting patients with BD that most benefit from a particular medication in the long term. However, this study has some limitations. First, the reported R\({}^{2}\) for the models obtained was between 0.14 and 0.33, meaning that 67% to 86% of the variation in the clinical outcomes assessed would not be explained by the models. Nevertheless, since the statistic is strongly influenced by variation in the independent variables, a small R\({}^{2}\) may not necessarily be indicative of a weak relationship [56]. Additionally, a low R\({}^{2}\) can be a good model in certain cases, such as in fields of study that have an inherently greater amount of variation, making it inevitable to obtain lower R\({}^{2}\) values. This is the scenario found in the genetic study of many phenotypes. While in monogenic disorders such as cystic fibrosis or Duchenne muscular dystrophy a single gene/mutation is the cause of the disorder, complex disorders such as diabetes, cancer susceptibility, or psychiatric disorders themselves are known to be polygenic, being influenced by several genes/variants, each of them having a small contribution, and often in conjunction with environmental factors. Drug response is indeed a complex trait, affected by several pharmacokinetic and pharmacodynamic genes, but also by the biology of the treated disease itself and a number of environmental factors. It is in this context that the predictive value of the significant variables in this study is valuable, adding to the body of evidence of the clinical validity of these gene-drug interactions in bipolar disorder. Second, the high number of medications per patient in the study population made it impossible to assign each patient to only one response-related category (i.e., efficacy, metabolism, side effects, or standard/wild-type genotype) according to the Neuropharmagen(r) report. Most patients were treated with different medications associated with different response categories and, in some cases, one medication was associated with more than one category. The frequency of the medications prescribed in the study population was markedly unequal: lithium, SSRIs, or quetiapine were used in many cases, while other psychotropic medications were much less used. In addition, the pharmacogenetic tool did not provide information for some of the medications used in some patients (black bars in Figure 3).