Association of genetic and psychological factors with persistent pain after cosmetic thoracic surgery.

Abstract

The genetic control of pain has been repeatedly demonstrated in human association studies. In the present study, we assessed the relative contribution of 16 single nucleotide polymorphisms in pain-related genes, such as cathechol-O-methyl transferase gene (COMT), fatty acid amino hydrolase gene (FAAH), transient receptor potential cation channel, subfamily V, member 1 gene (TRPV1), and δ-opioid receptor gene (OPRD1), for postsurgical pain chronification. Ninety preoperatively pain-free male patients were assigned to good or poor outcome groups according to their intensity or disability score assessed at 1 week, 3 months, 6 months, and 1 year after funnel chest correction. The genetic effects were compared with those of two psychological predictors, the attentional bias toward positive words (dot-probe task) and the self-reported pain vigilance (Pain Vigilance and Awareness Questionnaire [PVAQ]), which were already shown to be the best predictors for pain intensity and disability at 6 months after surgery in the same sample, respectively. Cox regression analyses revealed no significant effects of any of the genetic predictors up to the end point of survival time at 1 year after surgery. Adding the genetics to the prediction by the attentional bias to positive words for pain intensity and the PVAQ for pain disability, again no significant additional explanation could be gained by the genetic predictors. In contrast, the preoperative PVAQ score was also, in the present enlarged sample, a meaningful predictor for lasting pain disability after surgery. Effect size measures suggested some genetic variables, for example, the polymorphism rs1800587G>A in the interleukin 1 alpha gene (IL1A) and the COMT haplotype rs4646312T>C/rs165722T>C/rs6269A>G/rs4633T>C/rs4818C>G/rs4680A>G, as possible relevant modulators of long-term postsurgical pain outcome. A comparison between pathophysiologically different predictor groups appears to be helpful in identifying clinically relevant predictors of chronic pain.