Characterization of Physiological Biomarkers in Long-Term Kratom (Mitragyna speciosa Korth.) Users: A Preliminary Study

Abstract

A neurophysiological outcome associated with long-term kratom chewing in traditional use context is still unknown. Thus, the primary aim of this study was to investigate biomarkers of neurological response to the long-term kratom chewing. The fifty-two participants (controls; n=24 and long-term kratom users (LKU) who chewed kratom leaves; n = 28) were recruited with background-matched control group. Neurophysiological parameters with the proposed EEG (Theta/alpha ratio (TAR) and power function variance (PVFA), and all domains of ultra-short heart rate variability (HRV) heart rate variability were assessed during resting-state. Cognitive performance (Working memory) and kratom dependence score rating were also examined. All the proposed features were compared between the controls and long-term kratom chewers and determined in the relevant factors (age, duration, and daily quantity of kratom use). The statistically significant proposed features were proved by 1) path analysis for evaluating the causal relationship, and 2) the recognized machine-learning algorithms (Random Forest, Support vector machine, k Nearest neighbor, and Logistic regression) for binary classification. The results showed that only the proposed EEG feature (TAR) was significantly increased, compared to the control in the same age range of 50 years. The increased TAR and decreased PVF in the alpha band (PVFA) were direct effects of kratom leaves use and were significantly observed in LKU with a very high dose use. In addition, PVFA was a negative correlation with Kratom dependence. The results were also confirmed by the support vector machine achieved the highest performance to classify LKU with different doses of Kratom consuming by using the combination features TAR (both electrodes and average) and PVF in the alpha band. These preliminary results first highlighted the sensitive EEG biomarkers to characterize the LKU with a large effect size. These findings may lead to effective machine learning approaches based on EEG biomarkers for screening excessive Kratom users that might eventually develop Kratom dependence.