Genetic Predictors of Response to Acupuncture or Cognitive Behavioral Therapy for Insomnia in Cancer Survivors: An Exploratory Analysis

Clinical-functional brain connectivity signature predicts longitudinal symptom improvement after acupuncture treatment in patients with functional dyspepsia

Whilst acupuncture has been shown to be an effective treatment for functional dyspepsia (FD), its efficacy varies significantly among patients. Knowing beforehand how each patient responds to acupuncture treatment will facilitate the ability to produce personalized prescriptions, therefore, improving acupuncture efficacy. The objective of this study was to construct the prediction model, based on the clinical-neuroimaging signature, to forecast the individual symptom improvement of FD patients following a 4-week acupuncture treatment and to identify the critical predictive features that could potentially serve as biomarkers for predicting the efficacy of acupuncture for FD. Clinical-functional brain connectivity signatures were extracted from samples in the training-test set (100 FD patients) and independent validation set (60 FD patients). Based on these signatures and support vector machine algorithms, prediction models were developed in the training test set, followed by model performance evaluation and predictive features extraction. Subsequently, the external robustness of the extracted predictive features in predicting acupuncture efficacy was evaluated by the independent validation set. The developed prediction models possessed an accuracy of 88% in predicting acupuncture responders, as well as an R2 of 0.453 in forecasting symptom relief. Factors that contributed significantly to stronger responsiveness of patients to acupuncture therapy included higher resting-state functional connectivity associated with the orbitofrontal gyrus, caudate, hippocampus, and anterior insula, as well as higher baseline scores of the Symptom Index of Dyspepsia and shorter durations of the condition. Furthermore, the robustness of these features in predicting the efficacy of acupuncture for FD was verified through various machine learning algorithms and independent samples and remained stable in univariate and multivariate analyses. These findings suggest that it is both feasible and reliable to predict the efficacy of acupuncture for FD based on the pre-treatment clinical-neuroimaging signature. The established prediction framework will promote the identification of suitable candidates for acupuncture treatment, thereby improving the efficacy and reducing the cost of acupuncture for FD.

COMT Val158Met Affects the Analgesic Response to Acupuncture Among Cancer Survivors With Chronic Pain

Catechol-O-methyltransferase (COMT) is the major enzyme involved in the catabolism of dopamine, a neurotransmitter in the brain's reward system. The common COMT polymorphism Val158Met (rs4680 G>A) modulates pain response to opioids through a reward-motivated mechanism; however, its role in nonpharmacological pain medicine has not been clinically characterized. We genotyped 325 participants from a randomized controlled trial of cancer survivors with chronic musculoskeletal pain. We found that carrying methionine at position 158 (158Met) of COMT, encoded by the A allele, significantly increased the analgesic response to electroacupuncture (74% vs 50%; odds ratio [OR]: 2.79; 95% confidence interval [CI]: 1.31, 6.05; P < .01), but not to auricular acupuncture (68% vs 60%; OR: 1.43; 95% CI: .65, 3.12; P = .37) or usual care (24% vs 18%; OR: 1.46; 95% CI: .38, 7.24; P = .61) compared to Val/Val. These findings raise the possibility that COMT Val158Met might be an important predictor of analgesic response to electroacupuncture, providing novel insights into precision nonpharmacologic pain management tailored to individual genetic backgrounds. PERSPECTIVE: This work suggests the modulating effects of the polymorphism in COMT Val158Met on the response to acupuncture. Further research needs to validate these findings, increase the mechanistic understanding of acupuncture, and guide further development of acupuncture as a precision pain management strategy.