Attentional impulsivity in methamphetamine use disorder: Assessing its mediating role between nicotine dependence and methamphetamine use disorder severity in the Chinese population

Attentional impulsivity in methamphetamine use disorder: Assessing its mediating role between nicotine dependence and methamphetamine use disorder severity in the Chinese population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Attentional impulsivity in methamphetamine use disorder: Assessing its mediating role between nicotine dependence and methamphetamine use disorder severity in the Chinese population Yiming Ji, Yilian Zhao, Hengyu Li, Chuansheng Wang, Xian Li, Desheng Zhai, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8615424/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Methamphetamine use disorder (MUD) lacks effective treatments. MUD, nicotine dependence, alcohol dependence, and trait impulsivity may be correlated, however, the precise magnitude of the correlation remains unclear between these four aspects. This study aimed to construct comprehensive models to analyze the mediating role of trait impulsivity on nicotine/alcohol dependence and MUD severity. Methods This study included 1230 participants from five drug rehabilitation centers in China. After semi-structured interviews and standardized tests, 163 participants were divided into three groups based on MUD severity. Trait impulsivity was evaluated using the Chinese version of Barratt Impulsiveness Scale, 11th version. Results The mediation analyses revealed that the positive direct effect of nicotine dependence on MUD severity was 0.319 (95% CI: 0.231, 0.413), and the positive indirect effect of nicotine dependence on MUD severity via trait impulsivity was 0.079 (95% CI: 0.040, 0.126), accounting for 19.85% of the total effect. However, the parallel multiple mediation analysis indicated that the indirect effects of nicotine dependence on MUD severity via motor impulsivity and non-planning impulsivity were invalid ( P > 0.05). The positive indirect effect of nicotine dependence on MUD severity via attentional impulsivity (AI) alone was 0.064 (95% CI: 0.027, 0.109), accounting for 16.08% of the total effect. Conclusions Trait impulsivity, especially AI, mediated the positive effects of nicotine dependence on MUD severity in a Chinese population. In the context of the causalities, high levels of nicotine dependence and trait impulsivity can accelerate the progression of severe MUD. Smoking cessation and impulsivity reduction may help prevent MUD. Health sciences/Diseases Health sciences/Medical research Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology methamphetamine use disorder nicotine dependence trait impulsivity attentional impulsivity mediation analysis Figures Figure 1 Figure 2 INTRODUCTION Methamphetamine (MA), an illicit psychostimulant, is tremendously abused globally ( 1 ). The United Nations Office on Drugs and Crime reported that the number of harmful MA users reached 14 million in Asia alone, even during the coronavirus disease 2019 pandemic ( 2 ). In China, MA is one of the most prevalent drugs, accounting for 50.8% of nearly 1 million users of substances at the end of 2023 ( 3 ). For Chinese administrations, MA use disorder (MUD) from MA abuse leads to various negative outcomes, such as severe medical, psychiatric, and social complications ( 4 ). Owing to current insufficient understanding, MUD lacks effective treatments, in contrast to methadone for opioid use disorder ( 5 ). Nicotine, a dominant psychoactive substance in cigarettes (including electronic cigarettes), is commonly a ‘gateway’ stimulant that facilitates substance use disorders (SUDs) ( 6 ). Based on the genetic and neurobiological mechanisms, nicotine dependence promotes subsequent MUD in humans and rodents ( 7 , 8 ). The proportion of nicotine dependence in the MUD population (≥ 74%) has increased in the United States and China during the past decade ( 9 , 10 ). Additionally, alcohol dependence seemed to be pertinent to MUD ( 11 ). However, the precise magnitude of the association of nicotine and/or alcohol dependence with MUD remains unclear. Further analysis is theoretically fundamental to elucidating the potential pathways of nicotine and/or alcohol dependence in MUD. Trait impulsivity is a tendency to act without competent forethought of the consequences of behavior ( 12 ). This personality trait may be a predictive marker for the onset and progression of SUDs ( 13 , 14 ). Studies have reported that impulsivity is concerned with the initiation and maintenance of MUD in the American population ( 15 , 16 ). Additionally, motor impulsivity (MI) and non-planning impulsivity (NPI) were relevant to individuals with nicotine dependence, while sensation seeking impulsivity was relevant to alcohol dependence ( 17 , 18 ). To ensure efficient interventions in clinical practice, the mutual effects between nicotine and/or alcohol dependence, trait impulsivity, and MUD should be clarified. Previous studies have mainly focused on the interrelationships between MUD and either nicotine/alcohol dependence or trait impulsivity; however, few studies have linked these four aspects simultaneously. Mediation analysis can statistically ascertain the causal nexus and magnitude of the associations between variables ( 19 ). This study aimed to construct comprehensive models to analyze the mediating role of trait impulsivity on nicotine/alcohol dependence and MUD severity in a Chinese population. We believe that our findings would expand the novel neuropsychiatric mechanisms and help prevent and treat MUD by elucidating its relationship with nicotine/alcohol dependence and trait impulsivity. METHODS Participants and study design This multi-center study was conducted between 2017 and 2025. The study participants were consecutively selected from five drug rehabilitation centers in China: the affiliated Kangning Hospital of Ningbo University (Zhejiang), Shanghai Mental Health Center (Shanghai), Xinkaipu Isolated Compulsory Drug Rehabilitation Center (Hunan), Wuhan Mental Health Center (Hubei), and Xinxiang Compulsory Rehabilitation Center (Henan). They were screened in the clinical data-sharing database ( http://www.medresman.org.cn ) and the biological sample bank ( http://xy2bims.fulcruminfo.cn ) for substance-related disorders and behavior addictions. The participants were recruited through face-to-face interviews and/or assessments by experienced psychiatrists who had received professional interview training (Fig. 1 ). The interviews were conducted during 7-day to 2-month from admission. Participants with MUD had no access to MA, tobacco, alcohol, or other drugs during the research. All participants signed informed consent forms and were educated on the purpose, methods, risks, and conflicts of interest of this study. We ensured the security and confidentiality of the participants’ personal information and test results. [Insert Fig. 1 about here] This study is a part of the National Key Research and Development Project of China (2017YFC1310400) and is registered with the Chinese Clinical Trial Registry (ChiCTR2000032198, Registration Date: 2020.4.22). This study was endorsed by the Ethics Committee of Henan Medical University (XYLL-2017016) and the Jawatankuasa Etika Penyelidikan Manusia Universiti Sains Malaysia (USM/JEPeM/PP/24100916), which adhered to the Declaration of Helsinki. Sample size The sample size was calculated with the formula suggested by Fritz and MacKinnon: n = L/f 2 + k + 1 ( 20 ). For this calculation, f = 0.26 for halfway between the values for small and medium effect sizes, L = 7.85 for a type one error α of 0.05 and a statistical power of 0.8, and k = 11 as variables to be entered in the model. Hence, 128 was the minimal estimated sample size for this study. Inclusion and exclusion criteria The inclusion criteria were as follows: ( 1 ) MUD confirmed by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) ( 21 ), ( 2 ) aged between 18 and 60 years, and ( 3 ) positive MA urine test (< 7 days after admission). The exclusion criteria were ( 1 ) withdrawal symptoms (i.e., dysphoric mood, insomnia or hypersomnia, and increased appetite, etc.), ( 2 ) comorbid psychiatric disorders (i.e., schizophrenia, bipolar disorder, and social phobia, etc.), or ( 3 ) other severe physical illnesses (i.e., epilepsy, stroke, and brain trauma, etc.). Individuals with polysubstance use were not excluded unless other SUDs were diagnosed. Measures Sociodemographic characteristics were collected using the Chinese version of the Semi-Structured Assessment of Drug and Alcohol Dependence (SSADAA) ( 22 ), which exhibited good reliability and validity. Characteristic variables included age, sex, education, marital status, income, and employment status. The MA use characteristics gathered by the Chinese SSADAA included the age of onset and years of MA use. MUD severity was assessed using the stimulant use disorder diagnostic criteria in DSM-5. Trait impulsivity was assessed using the Barratt Impulsiveness Scale, 11th version (BIS-11) ( 23 ). While exhibiting satisfactory psychometric properties, the Chinese version of the BIS-11 (30 items) comprises three subscales: attentional impulsivity (AI), MI, and NPI ( 24 ). The higher total score indicates higher self-reported impulsivity, and the Cronbach’s α value of the total scale was 0.886 in this study. Nicotine dependence was measured with the Fagerström Test for Nicotine Dependence (FTND) ( 25 ). This test has been extensively used to evaluate nicotine dependence. Based on total scores, nicotine dependence was divided into two groups: none or low nicotine dependence (0 − 3 points) and medium- to high-nicotine dependence (4 − 10 points) ( 26 ). Alcohol dependence was evaluated with the Chinese version of the Alcohol Use Disorders Identification Test (AUDIT) ( 27 ). The AUDIT scale is reliable and valid for measuring alcohol dependence, as well as hazardous and harmful alcohol consumption. According to the total scores, alcohol dependence was divided into two groups: none or low hazardous alcohol use (0 − 7 points) and high hazardous alcohol use, harmful alcohol use or alcohol use disorder (8 − 40 points) ( 28 ). Statistical analysis For continuous data, the skewness and kurtosis of the distribution were used to test the normality( 29 ). One-way analysis of variance (three groups) or Student’s t -test (two groups) was used to compare normally distributed data, and the Kruskal-Wallis H test was used for non-normally distributed multigroup variables. Categorical variables were reported as numbers (n) and percentages (%) and were compared with the χ 2 test. For the mediation analysis, we first assessed the correlations between nicotine dependence, alcohol dependence, trait impulsivity, and MUD severity. Then, we performed mediation analyses using the Hayes PROCESS macro (version 4.0) for SPSS to generate bootstrapped (n = 5000) bias-corrected regression estimates and confidence intervals (CIs). All analyses were undertaken in SPSS (version 26.0), and the threshold for statistically significance was set at P < 0.05. Mediation analyses adopted Model 4 of the PROCESS macro. Distinct paths were created in the mediation models: path a, depicting the effect of the predictor (nicotine dependence) on the mediators (total impulsivity in Fig. 2 A; AI, MI, and NPI in Fig. 2 B, 2 C); path b, representing the effect of the mediators (see above) on the outcome (MUD severity); path a × b (indirect effect), representing the mediating effect of the predictor on the outcome by the mediators; path c, depicting the total effect (indirect effect + direct effect) of the predictor on the outcome; and path c’ (direct effect), depicting the residual effect of the predictor on the outcome not mediated by the mediators in the models (Fig. 2 A − C). [Insert Fig. 2 about here] Eight confounding variables were investigated: age, sex, education, marital status, employment, income, age of onset, and years of MA use. When these confounding variables were introduced as covariates into the models, the coefficients were not all available, and the results did not change the significance of the indirect effects. Therefore, confounding variables were excluded from the final mediation models. To avoid the bias induced by the absence of nicotine dependence, we conducted sensitivity analyses by excluding participants who scored 0 on the FTND scale in the mediation models. The adjusted total impulsivity as the mediator was fitted in the PROCESS macro. RESULTS Differences in sociodemographic and substance use characteristics The chronology of the interviews and assessments in this study is summarized in Fig. 1 . Initially, 1230 participants were assessed for sociodemographic and substance use characteristics by the SSADAA. Of 1055 participants who were screened following the inclusion and exclusion criteria, 447 were enrolled and filled out the questionnaires (SSADDA, DSM-5, BIS-11, FTND, and AUDIT). Finally, 163 participants completed all interviews and questionnaires and were divided into three groups based on MUD severity: mild (n = 52), moderate (n = 50), and severe (n = 61). [Insert Table 1 about here] Table 1 Comparisons of sociodemographic and substance use characteristics among the MUD population Mild MUD (n = 52) Moderate MUD (n = 50) Severe MUD (n = 61) F/c 2 P value Age (years) 33.92 ± 7.54 30.62 ± 6.72 34.18 ± 8.34 3.556 0.031 Sex [n (%)] 8.313 0.016 Male 39(75.0%) 37(74.0%) 32(52.5%) Female 13(25.0%) 13(26.0%) 29(47.5%) Education (years) 9.58 ± 3.23 9.16 ± 3.05 9.11 ± 2.45 0.414 0.662 Marital status [n (%)] 2.598 0.273 Unmarried 33(63.5%) 30(60.0%) 45(73.8%) Married 19(36.5%) 20(40.0%) 16(26.2%) Employment [n (%)] 2.617 0.270 Employed 32(61.5%) 23(46.0%) 31(50.8%) Unemployed 20(38.5%) 27(54.0%) 30(49.2%) Income (¥ / month), median (Q1, Q3) 7250 (3250,14750) 8000 (4000,13500) 4000 (3000,8000) 10.286 0.006 Age of onset (years) 29.85 ± 7.79 25.84 ± 6.42 28.33 ± 7.91 3.757 0.025 Years of MA use (years) 4.08 ± 2.23 4.78 ± 2.89 5.85 ± 4.20 4.209 0.017 Nicotine dependence [n (%)] 26.736 < 0.001 None − low 38(73.1%) 30(60.0%) 16(26.2%) Medium − high 14(26.9%) 20(40.0%) 45(73.8%) Alcohol dependence [n (%)] 4.723 0.094 None − low hazardous 28(53.8%) 17(34.0%) 31(50.8%) High hazardous − dependence 24(46.2%) 33(66.0%) 30(49.2%) MA: methamphetamine; MUD: methamphetamine use disorder; (¥) is the currency abbreviation for the China yuan renminbi (CNY); P < 0.05 was considered significant. Table 1 provides comparisons of sociodemographic characteristics among the MUD population with different addiction severities. Age ( P = 0.031), sex ( P = 0.016), and monthly income ( P = 0.006) significantly differed between the MUD groups, but no significant differences were observed with other sociodemographic characteristics. Regarding MA use characteristics, the age of onset in the mild MUD group was higher than that in the moderate and severe MUD groups ( P = 0.025), and the severe MUD group exhibited longer years of MA use than the mild and moderate groups ( P = 0.017). Additionally, medium-high nicotine dependence was predominant in the severe MUD group, while none-low nicotine dependence was predisposed to mild MUD ( P 0.05). Differences in impulsivity among MUD populations According to the BIS-11, the severe MUD group (54.48 ± 17.08) had higher total scores than the mild and moderate MUD groups (35.45 ± 14.82, 43.35 ± 13.25; P < 0.001). Among the three MUD groups, the scores for AI ( P < 0.001), MI ( P < 0.001), and NPI ( P < 0.001) were significant different (Table 2 ). Table 2 Impulsivity differences among MUD populations BIS-11 Total (`x ± s) Attentional Motor Non-planning MUD severity (n) Mild (52) 35.45 ± 14.82 32.02 ± 16.94 37.02 ± 18.96 37.31 ± 18.84 Moderate (50) 43.35 ± 13.25 42.40 ± 17.61 43.00 ± 16.49 44.65 ± 17.61 Severe (61) 54.48 ± 17.08 51.84 ± 17.02 52.79 ± 21.34 58.81 ± 20.74 F 22.217 18.706 9.768 18.419 P value < 0.001 < 0.001 < 0.001 < 0.001 Nicotine dependence (n) None - low (84) 40.10 ± 15.65 38.81 ± 18.22 41.01 ± 19.35 40.48 ± 19.16 Medium - high (79) 50.20 ± 17.24 46.68 ± 19.00 48.73 ± 20.46 55.19 ± 20.71 t 3.920 2.699 2.476 4.712 P value < 0.001 0.008 0.014 < 0.001 Alcohol dependence (n) None - low hazardous (76) 43.44 ± 18.58 41.18 ± 20.48 43.13 ± 21.21 46.02 ± 22.56 High hazardous - dependence (87) 46.35 ± 15.79 43.88 ± 17.55 46.18 ± 19.31 48.99 ± 19.94 t 1.080 0.905 0.962 0.894 P value 0.282 0.367 0.338 0.373 MUD: methamphetamine use disorder; BIS-11: Barratt impulsiveness scale (11th version); P < 0.05 was considered significant. The BIS-11 total score in the medium-high nicotine dependence group (50.20 ± 17.24) was higher than that in the none-low group (40.10 ± 15.65, P < 0.001). Compared with the none-low nicotine dependence group, the medium-high group had remarkably greater scores on all three subscales (AI, MI, and NPI) ( P = 0.008, P = 0.014, and P 0.05) (Table 2 ). [Insert Table 2 about here] Correlations between MUD severity, impulsivity, nicotine dependence, and alcohol dependence According to Pearson’s product-moment correlations, MUD severity was positively correlated with nicotine dependence (r = 0.554, P < 0.001). MUD severity was positively associated with total impulsivity (total BIS-11 score) (r = 0.459, P < 0.001) as well as with its three subscales (AI, MI, and NPI) (r = 0.424, P < 0.001; r = 0.319, P < 0.001; and r = 0.431, P < 0.001, respectively). Furthermore, total impulsivity, AI, MI, and NPI were significantly associated with nicotine dependence (r = 0.376, P < 0.001; r = 0.322, P < 0.001; r = 0.230, P = 0.003; and r = 0.405, P 0.05), except for total impulsivity (r = 0.167, P = 0.033) and MI (r = 0.182, P = 0.020). These results revealed that MUD severity positively correlated with both nicotine dependence and trait impulsivity (total impulsivity, AI, MI, and NPI) (Table 3 ). Table 3 Correlations between MUD severity, nicotine dependence, alcohol dependence, and impulsivity in MUD populations (r value) 1 2 3 4 5 6 7 1. MUD severity 1 2. Nicotine dependence 0.554 *** 1 3. Alcohol dependence 0.124 0.123 1 4. BIS-11 Total 0.459 *** 0.376 *** 0.167 * 1 5. BIS-11 Attentional 0.424 *** 0.322 *** 0.136 0.862 *** 1 6. BIS-11 Motor 0.319 *** 0.230 ** 0.182 * 0.836 *** 0.592 *** 1 7. BIS-11 Non-planning 0.431 *** 0.405 *** 0.110 0.860 *** 0.634 *** 0.547 *** 1 MUD: methamphetamine use disorder; BIS-11: Barratt impulsiveness scale (11th version); * P < 0.05, ** P < 0.01, *** P < 0.001; P < 0.05 was considered significant. In the BIS-11, the total score was significantly related to all three subscales (AI, MI, and NPI) (r = 0.862, P < 0.001; r = 0.836, P < 0.001; and r = 0.860, P < 0.001, respectively), and each subscale score was correlated with one another from 0.547 to 0.634 (all P < 0.001) (Table 3 ). [Insert Table 3 about here] Mediation analyses According to the mediation analyses, path c (nicotine dependence positively towards MUD severity) was available (R 2 = 0.307, F = 71.175; c = 0.554, P < 0.001), path a (nicotine dependence positively towards total impulsivity) was meaningful (R 2 = 0.141, F = 26.430; a = 0.376, P < 0.001), and both path c’ (nicotine dependence directly and positively towards MUD severity) and path b (total impulsivity directly and positively towards MUD severity) were significant (R 2 = 0.380, F = 49.001; c’=0.444, P < 0.001 and b = 0.292, P < 0.001, respectively) (Fig. 2 A, Table S1 ). Additionally, the direct effect (path c’) of nicotine dependence on MUD severity was 0.319 (95% CI: 0.231, 0.413), and the indirect effect (path a × b) of nicotine dependence on MUD severity through total impulsivity was 0.079 (95% CI: 0.040, 0.126), accounting for 19.85% of the total effect (path c, Table S2). As zero was not included in the 95% CIs, this condition indicated that the indirect effect was remarkable. Thus, total impulsivity partially mediated the relationship between nicotine dependence and MUD severity (Fig. 2 A). Considering the multidimensionality of the mediator (total impulsivity), parallel multiple mediation analysis was further conducted to ascertain the mediating roles of its sub-traits (AI, MI, and NPI). However, the indirect effects (paths a × b) of nicotine dependence on MUD severity via MI and NPI were 0.008 (95% CI: -0.018, 0.036) and 0.032 (95% CI: -0.012, 0.084) (Tables S3 and S4). Since the 95% CIs straddled zero ( P > 0.05), the indirect effects of MI and NPI were invalid (Fig. 2 B). The mediating effect of AI was exclusively analyzed to avoid biased data and underpowered mediation effects. In Fig. 2 C, path a (nicotine dependence positively towards AI) was significant (R 2 = 0.104, F = 18.639; a = 0.322, P < 0.001), and both paths c’ (nicotine dependence directly and positively towards MUD severity) and b (AI directly and positively towards MUD severity) were significant (R 2 = 0.374, F = 47.815; c’=0.465, P < 0.001 and b = 0.275, P = 0.0001, respectively) (Table S5). Meanwhile, the direct effect (path c’) of nicotine dependence on MUD severity was 0.334 (95% CI: 0.245, 0.422), and the indirect effect (path a × b) of nicotine dependence on MUD severity via AI was 0.064 (95% CI: 0.027, 0.109), accounting for 16.08% of the total effect (path c, Table S6). As the 95% CIs did not include any zeroes, the results revealed that the indirect effect was significant; thus, AI served as an efficient mediator in this scenario. Relative to MI and NPI, AI played a substantial mediating role between nicotine dependence and MUD severity (Fig. 2 C). Additionally, the data of 152 participants were retested in the mediation models after excluding 11 participants who scored 0 on the FTND scale. The results of the sensitivity analyses showed that total impulsivity remained a mediator of nicotine dependence on MUD severity after adjusting for confounders. The indirect effect of the adjusted total impulsivity was 0.091 (95% CI: 0.046, 0.146), accounting for 22.86% of the total effect (Tables S7 and S8). DISCUSSION This study clarified the mediation of trait impulsivity from nicotine dependence to MUD severity in a Chinese population. First, varying MUD severity showed significant differences with nicotine dependence and trait impulsivity; however, no significant difference was observed with alcohol dependence. Furthermore, MUD severity, trait impulsivity (total impulsivity, AI, MI, and NPI), and nicotine dependence were positively correlated. However, alcohol dependence was not related to MUD severity or nicotine dependence. Mediation analyses demonstrated that trait impulsivity (19.85%) mediated the positive effects of nicotine dependence on MUD severity. Compared with MI and NPI, only AI (16.08%) exerted a partial mediating effect between nicotine dependence and MUD severity in models of trait impulsivity. Our data showed that more severe MUD was frequently associated with higher nicotine dependence, and the findings further elucidated the relationship between nicotine dependence and MUD. Based on behavioral economics and epidemiology, studies have shown that the aggravated MA demand is correlated with higher nicotine dependence in Americans ( 30 , 31 ), which is consistent with our results. However, Bujarski et al. reported no differences between MUD and nicotine dependence in 60 American non-treatment users ( 32 ). Their findings may be due to the relatively small sample size and non-standard assessment of nicotine dependence. According to neurobiological studies, nicotine exposure in adolescents increased the subsequent rates of MUD in adult rats ( 33 , 34 ). We believe that the association between MUD and nicotine dependence partially shares a common pathway in the dopamine reward system, especially in the nucleus accumbens ( 35 ). Regarding alcohol dependence and MUD, studies suggested an equivocal relationship between Australian and Japanese populations ( 11 , 36 ). However, our data did not reveal a correlation between MUD severity and alcohol dependence based on AUDIT scores in the Chinese population. Multiracial investigations may help to clarify this puzzle. We observed that MUD severity was positively correlated with trait impulsivity and its three sub-traits (AI, MI, and NPI) in a Chinese population. Moallem et al. ( 37 ) preliminarily observed that increased MUD severity corresponded to higher impulsivity in 177 Americans. Additionally, in a virtual digital psychotherapeutic study, AI was positively related to drug craving among 47 patients with MUD ( 38 ), whereas 157 American individuals with higher AI and MI commenced MA exposure at a younger age ( 15 ). These subtle differences between MUD and impulsivity may be due to diverse sociocultural backgrounds and administrative policies. Our data showed that total impulsivity and its sub-traits correlated with nicotine dependence in MUD. Studies reported that higher impulsivity was related to higher nicotine dependence among American and Iranian college students ( 39 , 40 ). Mittal et al. ( 41 ) reported that 137 young adults with high impulsivity smoked electronic cigarettes more frequently. Janine et al. ( 42 ) documented a correlation between NPI and nicotine dependence in 1284 American adults. The similarity between our findings and those of previous studies suggests that total impulsivity and its certain sub-traits are associated with MUD severity and nicotine dependence. Based on these associations, this study constructed mediation models and performed overall quantitative analyses of the relationship between trait impulsivity, nicotine dependence, and MUD severity. When trait impulsivity was controlled for, the strength of the relationship between nicotine dependence and MUD severity reduced, indicating that trait impulsivity partially (19.85%) mediated the effect. In other words, decreased impulsivity is a protective factor against this effect. Our findings imply that the increased risk of MUD severity reasonably originates from higher nicotine dependence via the mediating effect of total impulsivity, contributing a novel insight into addiction theory. Similarly, a study on a Taiwanese (China) population reported that impulsivity was a mediator between cigarette smoking initiation and MUD severity, explaining 19.41% of the total effect ( 43 ). Instead of their self-designed questionnaire, we used a validated scale (FTND) to objectively assess nicotine dependence. Although both studies supported the mediating role of trait impulsivity, it is more reliable to assess the level of nicotine dependence rather than the initiation of nicotine dependence. Nor does every exposure inevitably transition to SUDs since only approximately 10% of people become addicted ( 5 , 44 ). In addition, some research reported that trait impulsivity also exerted some effects in other SUDs (including cannabis and cocaine) ( 17 , 45 ). Hence, the findings indicate that trait impulsivity and nicotine dependence could be considered targets to predict and intervene against the risk of developing MUD in the future. In the context of impulsivity sub-traits, recent studies have shown that certain sub-traits play specific roles in SUDs ( 46 , 47 ). Since the paths of MI and NPI were unavailable in the following parallel multiple mediation tests, we observed a positive indirect effect mediated by AI alone (16.08%). Therefore, we believe that AI, rather than MI or NPI, plays a pivotal role in the mechanism underlying the effect of nicotine dependence. An elevated AI can aggravate the effect of nicotine dependence on MUD severity. Huang et al. ( 43 ) reported that AI (15.78%) and NPI (10.37%) had indirect effects on nicotine dependence and MUD severity, where total impulsivity and the three sub-traits were entered into the model simultaneously as mediators. However, this remains debatable because the total BIS-11 score was formulated from its three subscales by inter-algorithms. We suggest that total impulsivity and its sub-traits should be validated separately since none of the mediators can tangle with one another in a parallel model ( 48 ). Similarly, Su et al. ( 49 ) reported that AI was strongly related to MUD in a gene polymorphic study, providing molecular genetic evidence for AI as a crucial mediator. It is postulated that the mediation by AI may result from the fact that individuals with high AI cannot concentrate easily, have extraneous thoughts when thinking, and often encounter difficulty completing tasks ( 23 ). Strengths and limitations The tools involving scales and tests used in this study had better generality and standardization, and the mediation models and preliminary algorithms had relative advantages in machine learning, all of which may contribute to the potential applications of generative artificial intelligence in MUD prediction and intervention in the future. Nonetheless, this study had certain limitations. First, we used a cross-sectional multi-center study design. Thus, the causalities from these data should be further validated in future longitudinal studies. Second, this study utilized a static assessment of trait impulsivity; it would be better to assess impulsivity at multiplicities, such as impulsive choices and actions. Third, volatility in the data may be attributed to social desirability and recall biases. CONCLUSIONS This study confirmed the mediation of trait impulsivity, especially AI, from nicotine dependence to MUD severity in a Chinese population. In the context of the causalities, high levels of nicotine dependence and trait impulsivity can accelerate the progression of severe MUD. Smoking cessation and impulsivity reduction may help prevent MUD, supportively responding to the Framework Convention on Tobacco Control (WHO). Additionally, mediation paths and algorithms may provide a new neuropsychiatric strategy for the accurate prediction and holistic intervention for individuals with SUDs. Abbreviations MA methamphetamine MUD methamphetamine use disorder SUD substance use disorder DSM-5 Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition SSADAA Semi-Structured Assessment of Drug and Alcohol Dependence BIS-11 Barratt impulsiveness scale, 11th version AI attentional impulsivity MI motor impulsivity NPI non-planning impulsivity FTND Fagerström Test for Nicotine Dependence AUDIT Alcohol Use Disorders Identification Test. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Henan Medical University (XYLL-2017016) and the Jawatankuasa Etika Penyelidikan Manusia Universiti Sains Malaysia (USM/JEPeM/PP/24100916). All participants signed informed consent forms. All procedures followed were in accordance with the Helsinki Declaration. Consent for publication Not applicable. Data Availability The database can be visited with data user agreements. Any reasonable requests to access these datasets, please contact to BZ and RLZ. Competing interests The authors declare that they have no competing interests. Funding This research was supported by the Henan Collaborative Innovation Center (XTgh04), the Natural Science Foundation of Henan Province (242300421307), the Henan Province Science and Technology Research and Development Plan Joint Fund (235101610004), the Medical Science and Technology Research Project of Henan Province (222102310710), and the National Key Research and Development Project of China (2017YFC1310402). Authors’ Contributions Conceptualization: BZ, RLZ, MFILA, YMJ, WH; Methodology: BZ, RLZ, YMJ, YLZ, HYL, DSZ, LW, XJZ, MAM; Formal analysis: BZ, RLZ, YMJ, YLZ, HYL, CSW, DSZ, LW, XJZ, MAM; Investigation: BZ, RLZ, YMJ, YLZ, HYL, CSW, XL, LW, XJZ; Data curation: RLZ, YMJ, DSZ, XJZ; Writing-original draft: BZ, RLZ, YMJ, YLZ, HYL; Writing-review and editing: BZ, RLZ, MFILA, YMJ, CSW, XL, DSZ, LW, JQZ, XJZ, WH, MAM; Funding acquisition: BZ, RLZ, YMJ, CSW, JQZ, WH; Supervision: MFILA, WH; Project administration: BZ, RLZ, MFILA, CSW, XL; Validation: BZ, HYL, CSW, DSZ, LW, JQZ. All authors read and approved the final manuscript. Acknowledgements We appreciate all the researchers in this study. We are grateful to the peers engaged in data collection, specifically Kangfeng Zhu, Kang Liu, Yan Li, Wanwan Guo, Youhui Sun. References Farrell, M. et al. Responding to global stimulant use: challenges and opportunities. Lancet 394 (10209), 1652–1667 (2019). United Nations Office on Drugs and Crime. World Drug Report. United Nations publication, 2024. Accessed 2024 Jun 26. (2024). Available from: https://www.unodc.org/unodc/en/data-and-analysis/world-drug-report-2024.html China National Narcotics Control Committee. China Drug Situation Report. China Narcotics Control. Accessed 2024 Jun 19. (2023). Available from: http://www.nncc626.com/20240619/d1a1ffb1f3fb4c93bb05f6e73fb50414/c.html Paulus, M. P., Stewart, J. L. & Neurobiology Clinical Presentation, and Treatment of Methamphetamine Use Disorder: A Review. JAMA Psychiatry . 77 (9), 959–966 (2020). Volkow, N. D. & Blanco, C. Substance use disorders: a comprehensive update of classification, epidemiology, neurobiology, clinical aspects, treatment and prevention. World Psychiatry . 22 (2), 203–229 (2023). Kandel, D. & Kandel, E. The Gateway Hypothesis of substance abuse: developmental, biological and societal perspectives. Acta Paediatr. 104 (2), 130–137 (2015). Cross, S. J., Lotfipour, S. & Leslie, F. M. Mechanisms and genetic factors underlying co-use of nicotine and alcohol or other drugs of abuse. Am. J. Drug Alcohol Abuse . 43 (2), 171–185 (2017). Pipkin, J. A. et al. Nicotine exposure beginning in adolescence enhances the acquisition of methamphetamine self-administration, but not methamphetamine-primed reinstatement in male rats. Drug Alcohol Depend. 142 , 341–344 (2014). Shearer, R. D., Howell, B. A., Bart, G. & Winkelman, T. N. A. Substance use patterns and health profiles among US adults who use opioids, methamphetamine, or both, 2015–2018. Drug Alcohol Depend. 214 , 108162 (2020). Li, J. et al. Depression in Chinese men with methamphetamine dependence: Prevalence, correlates and relationship with alexithymia. J. Affect. Disord . 319 , 235–243 (2022). Leslie, E. M. et al. Simultaneous use of alcohol with methamphetamine but not ecstasy linked with aggression among young adult stimulant users. Addict. Behav. 70 , 27–34 (2017). Brooks, S. J. et al. The impact of cognitive training in substance use disorder: the effect of working memory training on impulse control in methamphetamine users. Psychopharmacol. (Berl) . 234 (12), 1911–1921 (2017). MacKillop, J., Weafer, J., Oshri, J. C. G., Palmer, A. & de Wit, A. The latent structure of impulsivity: impulsive choice, impulsive action, and impulsive personality traits. Psychopharmacol. (Berl) . 233 (18), 3361–3370 (2016). Jentsch, J. D. et al. Dissecting impulsivity and its relationships to drug addictions. Ann. N Y Acad. Sci. 1327 , 1–26 (2014). Cservenka, A. & Ray, L. A. Self-reported attentional and motor impulsivity are related to age at first methamphetamine use. Addict. Behav. 65 , 7–12 (2017). Kogachi, S., Chang, L., Alicata, D., Cunningham, E. & Ernst, T. Sex differences in impulsivity and brain morphometry in methamphetamine users. Brain Struct. Funct. 222 (1), 215–227 (2017). Round, J. T., Fozard, T. E., Harrison, A. A. & Kolokotroni, K. Z. Disentangling the effects of cannabis and cigarette smoking on impulsivity. J. Psychopharmacol. 34 (9), 955–968 (2020). Waddell, J. T., King, S. E., Okey, S. A., Marohnic, S. D. & Corbin, W. R. Prospective Effects of UPPS-P Impulsivity and Typical Drinking Context on Future Drinking Behavior. J. Stud. Alcohol Drugs . 83 (2), 212–222 (2022). Hayes, A. F. & Preacher, K. J. Statistical mediation analysis with a multicategorical independent variable. Br. J. Math. Stat. Psychol. 67 (3), 451–470 (2014). Fritz, M. S. & Mackinnon, D. P. Required sample size to detect the mediated effect. Psychol. Sci. 18 (3), 233–239 (2007). American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. Arlington, VA, American Psychiatric Association, (2013). Ma, Y. J. et al. Reliability and validity of DSM-IV and DSM-5 methamphetamine use disorder diagnoses using the Chinese Version of the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA). Drug Alcohol Depend. ; (2021). 229(Pt B):109047. Patton, J. H., Stanford, M. S. & Barratt, E. S. Factor structure of the Barratt impulsiveness scale. J. Clin. Psychol. 51 (6), 768–774 (1995). An, J., Phillips, M. R. & Conner, K. R. Validity of proxy-based reports of impulsivity and aggression in Chinese research on suicidal behavior. Crisis 31 (3), 137–142 (2010). Huang, C. L., Lin, H. H. & Wang, H. H. The psychometric properties of the Chinese version of the Fagerstrom Test for Nicotine Dependence. Addict. Behav. 31 (12), 2324–2327 (2006). Fagerstrom, K., Russ, C., Yu, C. R., Yunis, C. & Foulds, J. The Fagerstrom Test for Nicotine Dependence as a predictor of smoking abstinence: a pooled analysis of varenicline clinical trial data. Nicotine Tob. Res. 14 (12), 1467–1473 (2012). Li, Q., Babor, T. F., Hao, W. & Chen, X. The Chinese translations of Alcohol Use Disorders Identification Test (AUDIT) in China: a systematic review. Alcohol Alcohol . 46 (4), 416–423 (2011). Dybek, I. et al. The reliability and validity of the Alcohol Use Disorders Identification Test (AUDIT) in a German general practice population sample. J. Stud. Alcohol . 67 (3), 473–481 (2006). Kim, H. Y. Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restor. Dent. Endod . 38 (1), 52–54 (2013). Yoon, J. H. et al. Assessment of demand for methamphetamine and cigarettes among individuals with methamphetamine use disorder. Exp. Clin. Psychopharmacol. 29 (4), 334–344 (2021). Shoptaw, S. et al. Frequency of reported methamphetamine use linked to prevalence of clinical conditions, sexual risk behaviors, and social adversity in diverse men who have sex with men in Los Angeles. Drug Alcohol Depend. 232 , 109320 (2022). Bujarski, S. et al. The relationship between methamphetamine and alcohol use in a community sample of methamphetamine users. Drug Alcohol Depend. 142 , 127–132 (2014). Harmony, Z. R., Alderson, E. M., Garcia-Carachure, I., Bituin, L. D. & Crawford, C. A. Effects of nicotine exposure on oral methamphetamine self-administration, extinction, and drug-primed reinstatement in adolescent male and female rats. Drug Alcohol Depend. 209 , 107927 (2020). Cardenas, A. & Lotfipour, S. Age- and Sex-Dependent Nicotine Pretreatment Effects on the Enhancement of Methamphetamine Self-administration in Sprague-Dawley Rats. Nicotine Tob. Res. 24 (8), 1186–1192 (2022). Fan, J. et al. Cross-talks between gut microbiota and tobacco smoking: a two-sample Mendelian randomization study. BMC Med. 21 (1), 163 (2023). Yamamoto, T., Kimura, T., Tamakoshi, A. & Matsumoto, T. Variables associated with methamphetamine use within the past year and sex differences among patients with methamphetamine use disorder: A cross-sectional study in Japan. Am. J. Addict. 31 (2), 134–141 (2022). Moallem, N. R., Courtney, K. E. & Ray, L. A. The relationship between impulsivity and methamphetamine use severity in a community sample. Drug Alcohol Depend. 187 , 1–7 (2018). Chen, T. et al. Virtual Digital Psychotherapist App-Based Treatment in Patients With Methamphetamine Use Disorder (Echo-APP): Single-Arm Pilot Feasibility and Efficacy Study. JMIR Mhealth Uhealth . 11 , e40373 (2023). Balevich, E. C., Wein, N. D. & Flory, J. D. Cigarette smoking and measures of impulsivity in a college sample. Subst. Abus . 34 (3), 256–262 (2013). Rezvanfard, M., Ekhtiari, H., Mokri, A., Djavid, G. & Kaviani, H. Psychological and behavioral traits in smokers and their relationship with nicotine dependence level. Arch. Iran. Med. 13 (5), 395–405 (2010). Mittal, A. et al. Impulsivity-Related Personality Traits as Predictors of E-Cigarette Use among Young Adults over Time. Subst. Use Misuse . 57 (7), 1007–1013 (2022). Flory, J. D. & Manuck, S. B. Impulsiveness and cigarette smoking. Psychosom. Med. 71 (4), 431–437 (2009). Huang, C. Y., Hung, C. C., Ho, Y. J. & Fang, S. C. Trait Impulsivity as a Mediator Between Early Cigarette Smoking Initiation and Addiction Severity in Patients with Methamphetamine Use Disorder. Int. J. Ment Health Ad . 22 (1), 279–298 (2024). Bechara, A. Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. Nat. Neurosci. 8 (11), 1458–1463 (2005). Foltin, R. W., Luba, R., Chen, Y., Wang, Y. & Evans, S. M. Impulsivity in cocaine users compared to matched controls: Effects of sex and preferred route of cocaine use. Drug Alcohol Depend. 226 , 108840 (2021). Verges, A., Littlefield, A. K., Arriaza, T. & Alvarado, M. E. Impulsivity facets and substance use initiation: A comparison of two models of impulsivity. Addict. Behav. 88 , 61–66 (2019). Bertin, L., Benca-Bachman, C. E., Kogan, S. M. & Palmer, R. H. C. Examining the differential effects of latent impulsivity factors on substance use outcomes in African American men. Addict. Behav. 117 , 106847 (2021). Coutts, J. J. & Hayes, A. F. Questions of value, questions of magnitude: An exploration and application of methods for comparing indirect effects in multiple mediator models. Behav. Res. Methods . 55 (7), 3772–3785 (2023). Su, H. et al. An association between BDNF Val66Met polymorphism and impulsivity in methamphetamine abusers. Neurosci. Lett. 582 , 16–20 (2014). Additional Declarations No competing interests reported. Supplementary Files Additionaltables.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Apr, 2026 Reviews received at journal 01 Apr, 2026 Reviews received at journal 21 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers invited by journal 02 Feb, 2026 Editor invited by journal 21 Jan, 2026 Editor assigned by journal 17 Jan, 2026 Submission checks completed at journal 17 Jan, 2026 First submitted to journal 16 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8615424","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":584361643,"identity":"aa916cf0-4fc3-4a6c-a28b-f2a300a7a03f","order_by":0,"name":"Yiming Ji","email":"","orcid":"","institution":"the Second Affiliated Hospital of Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yiming","middleName":"","lastName":"Ji","suffix":""},{"id":584361644,"identity":"0937a4f8-5a0b-44ca-be2b-6388234e508e","order_by":1,"name":"Yilian Zhao","email":"","orcid":"","institution":"the Second Affiliated Hospital of Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yilian","middleName":"","lastName":"Zhao","suffix":""},{"id":584361645,"identity":"cd85d4b5-be87-47cd-be1e-11556a24e198","order_by":2,"name":"Hengyu Li","email":"","orcid":"","institution":"the Second Affiliated Hospital of Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hengyu","middleName":"","lastName":"Li","suffix":""},{"id":584361646,"identity":"9fcd0815-353d-40f9-a5d1-5a57b82afb8c","order_by":3,"name":"Chuansheng Wang","email":"","orcid":"","institution":"Henan Academy of Innovations in Medical Science","correspondingAuthor":false,"prefix":"","firstName":"Chuansheng","middleName":"","lastName":"Wang","suffix":""},{"id":584361647,"identity":"fe756392-cfae-4204-aa8f-2b42c621fa43","order_by":4,"name":"Xian Li","email":"","orcid":"","institution":"Xinxiang compulsory rehabilitation center","correspondingAuthor":false,"prefix":"","firstName":"Xian","middleName":"","lastName":"Li","suffix":""},{"id":584361648,"identity":"d2b216a9-99b8-44f5-9f0f-75a26ac5c0c3","order_by":5,"name":"Desheng Zhai","email":"","orcid":"","institution":"the Second Affiliated Hospital of Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Desheng","middleName":"","lastName":"Zhai","suffix":""},{"id":584361649,"identity":"6994aaa5-1ff0-4e59-8afb-499e869671d6","order_by":6,"name":"Lai Wei","email":"","orcid":"","institution":"the Second Affiliated Hospital of Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lai","middleName":"","lastName":"Wei","suffix":""},{"id":584361650,"identity":"893083ac-a16a-42c8-bbf5-f1712627fd44","order_by":7,"name":"Junqiang Zhao","email":"","orcid":"","institution":"the Second Affiliated Hospital of Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junqiang","middleName":"","lastName":"Zhao","suffix":""},{"id":584361651,"identity":"8442b48b-ed2e-4c56-b092-c74f25451cf6","order_by":8,"name":"Xiaojie Zhang","email":"","orcid":"","institution":"The Second Xiangya Hospital of Central South University, National Center for Mental Disorders","correspondingAuthor":false,"prefix":"","firstName":"Xiaojie","middleName":"","lastName":"Zhang","suffix":""},{"id":584361652,"identity":"8af45da4-a0c4-4faf-99e6-7a97ba057d4b","order_by":9,"name":"Wei Hao","email":"","orcid":"","institution":"The Second Xiangya Hospital of Central South University, National Center for Mental Disorders","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Hao","suffix":""},{"id":584361653,"identity":"693a6755-469e-48f5-8f58-4fe25139fc18","order_by":10,"name":"Mohd Afifuddin Bin Mohamad","email":"","orcid":"","institution":"Universiti Sains Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Mohd","middleName":"Afifuddin Bin","lastName":"Mohamad","suffix":""},{"id":584361654,"identity":"30310c98-8579-4d48-89bf-f907659cf630","order_by":11,"name":"Mohammad Farris Iman Leong Bin Abdullah","email":"","orcid":"","institution":"Universiti Sultan Zainal Abidin","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Farris Iman Leong Bin","lastName":"Abdullah","suffix":""},{"id":584361655,"identity":"751c5f6b-61bf-421d-bde7-af339f9000c7","order_by":12,"name":"Ruiling Zhang","email":"","orcid":"","institution":"the Second Affiliated Hospital of Henan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruiling","middleName":"","lastName":"Zhang","suffix":""},{"id":584361656,"identity":"267dbb14-23bc-4057-b7cb-a6d872fdd6ae","order_by":13,"name":"Bin Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYHACA2YGAwYGfiiPsYFoLZINpGkBkQeI1WLe3rztcUHBHTnjGzmmm3kYbGQ3HGB+9gCfFpkzx8qNZxg8MzY7cyztNg9DmvGGA2zmBvi0SEjkmEnzGBxO3Ha8+RhQy+HEDQd42CSI0VK/uZmxDajlP/FaEgzYwbYcIEILD9AvQC2GM4B+uTnHINl45mE2M/xa2IEhxvPnsDz/jByzG28q7GT7jjc/w6sFCNiQ2KCgYiagHk3LKBgFo2AUjAIsAAAw9EUS/nTIZwAAAABJRU5ErkJggg==","orcid":"","institution":"the Second Affiliated Hospital of Henan Medical University","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2026-01-16 05:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8615424/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8615424/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101848920,"identity":"bdb3b742-eedd-468d-8db8-7c53917b54f3","added_by":"auto","created_at":"2026-02-04 09:43:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1190380,"visible":true,"origin":"","legend":"\u003cp\u003eFlow-chart for the study\u003c/p\u003e\n\u003cp\u003eMUD: methamphetamine use disorder; SSADAA: Semi-Structured Assessment of Drug and Alcohol Dependence; DSM-5: Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; FTND: Fagerström Test for Nicotine Dependence; AUDIT: Alcohol Use Disorders Identification Test; BIS-11: Barratt Impulsiveness Scale (11th version)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8615424/v1/ea928fb0e90f35230af8a296.png"},{"id":101848911,"identity":"831ed7e3-8d65-4d86-a69b-d3df3337adc1","added_by":"auto","created_at":"2026-02-04 09:42:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2138690,"visible":true,"origin":"","legend":"\u003cp\u003eMediation analyses of impulsivity between nicotine dependence and MUD severity\u003c/p\u003e\n\u003cp\u003e(a) Nicotine dependence on MUD severity mediated by total impulsivity. Path c, nicotine dependence positively towards MUD severity (R\u003csup\u003e2\u003c/sup\u003e=0.307, F=71.175, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001); Path a, nicotine dependence positively towards total impulsivity (R\u003csup\u003e2\u003c/sup\u003e=0.141, F=26.430, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001); Path c’ (nicotine dependence directly and positively towards MUD severity) and path b (total impulsivity directly and positively towards MUD severity) (R\u003csup\u003e2\u003c/sup\u003e=0.380, F=49.001, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). (b) Nicotine dependence on MUD severity mediated by attentional impulsivity, motor impulsivity, and non-planning impulsivity (parallel multiple mediation). Given that zero were included in the 95% CIs, the indirect effects of motor impulsivity and non-planning impulsivity were not significant. (c) Nicotine dependence on MUD severity mediated by attentional impulsivity. Path c, nicotine dependence positively towards MUD severity (R\u003csup\u003e2\u003c/sup\u003e=0.307, F=71.176, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001); Path a, nicotine dependence positively towards attentional impulsivity (R\u003csup\u003e2\u003c/sup\u003e=0.104, F=18.639, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001); Path c’ (nicotine dependence directly and positively towards MUD severity) and path b (attentional impulsivity directly and positively towards MUD severity) (R\u003csup\u003e2\u003c/sup\u003e=0.374, F=47.815, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). MUD: methamphetamine use disorder. X: nicotine dependence, M (mediator): total impulsivity (Figure 2a); attentional impulsivity (Figure 2c), Y: MUD severity. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001. \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 was considered significant.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8615424/v1/e528965429984dc2dbc66159.png"},{"id":101942823,"identity":"c24c93c4-8fd6-47ae-9909-8900c3111bb0","added_by":"auto","created_at":"2026-02-05 09:38:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4352047,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8615424/v1/af253ba5-85b6-4c5d-ac18-ed4051672738.pdf"},{"id":101848923,"identity":"512822cf-0623-42d3-84a8-a9195cbab1b7","added_by":"auto","created_at":"2026-02-04 09:43:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":36602,"visible":true,"origin":"","legend":"","description":"","filename":"Additionaltables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8615424/v1/5e96ab25c4cb85be5c7343b9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Attentional impulsivity in methamphetamine use disorder: Assessing its mediating role between nicotine dependence and methamphetamine use disorder severity in the Chinese population","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMethamphetamine (MA), an illicit psychostimulant, is tremendously abused globally (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The United Nations Office on Drugs and Crime reported that the number of harmful MA users reached 14\u0026nbsp;million in Asia alone, even during the coronavirus disease 2019 pandemic (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In China, MA is one of the most prevalent drugs, accounting for 50.8% of nearly 1\u0026nbsp;million users of substances at the end of 2023 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). For Chinese administrations, MA use disorder (MUD) from MA abuse leads to various negative outcomes, such as severe medical, psychiatric, and social complications (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Owing to current insufficient understanding, MUD lacks effective treatments, in contrast to methadone for opioid use disorder (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNicotine, a dominant psychoactive substance in cigarettes (including electronic cigarettes), is commonly a \u0026lsquo;gateway\u0026rsquo; stimulant that facilitates substance use disorders (SUDs) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Based on the genetic and neurobiological mechanisms, nicotine dependence promotes subsequent MUD in humans and rodents (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The proportion of nicotine dependence in the MUD population (\u0026ge;\u0026thinsp;74%) has increased in the United States and China during the past decade (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Additionally, alcohol dependence seemed to be pertinent to MUD (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, the precise magnitude of the association of nicotine and/or alcohol dependence with MUD remains unclear. Further analysis is theoretically fundamental to elucidating the potential pathways of nicotine and/or alcohol dependence in MUD.\u003c/p\u003e \u003cp\u003eTrait impulsivity is a tendency to act without competent forethought of the consequences of behavior (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This personality trait may be a predictive marker for the onset and progression of SUDs (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Studies have reported that impulsivity is concerned with the initiation and maintenance of MUD in the American population (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Additionally, motor impulsivity (MI) and non-planning impulsivity (NPI) were relevant to individuals with nicotine dependence, while sensation seeking impulsivity was relevant to alcohol dependence (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). To ensure efficient interventions in clinical practice, the mutual effects between nicotine and/or alcohol dependence, trait impulsivity, and MUD should be clarified.\u003c/p\u003e \u003cp\u003ePrevious studies have mainly focused on the interrelationships between MUD and either nicotine/alcohol dependence or trait impulsivity; however, few studies have linked these four aspects simultaneously. Mediation analysis can statistically ascertain the causal nexus and magnitude of the associations between variables (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This study aimed to construct comprehensive models to analyze the mediating role of trait impulsivity on nicotine/alcohol dependence and MUD severity in a Chinese population. We believe that our findings would expand the novel neuropsychiatric mechanisms and help prevent and treat MUD by elucidating its relationship with nicotine/alcohol dependence and trait impulsivity.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and study design\u003c/h2\u003e \u003cp\u003eThis multi-center study was conducted between 2017 and 2025. The study participants were consecutively selected from five drug rehabilitation centers in China: the affiliated Kangning Hospital of Ningbo University (Zhejiang), Shanghai Mental Health Center (Shanghai), Xinkaipu Isolated Compulsory Drug Rehabilitation Center (Hunan), Wuhan Mental Health Center (Hubei), and Xinxiang Compulsory Rehabilitation Center (Henan). They were screened in the clinical data-sharing database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.medresman.org.cn\u003c/span\u003e\u003cspan address=\"http://www.medresman.org.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the biological sample bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://xy2bims.fulcruminfo.cn\u003c/span\u003e\u003cspan address=\"http://xy2bims.fulcruminfo.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for substance-related disorders and behavior addictions. The participants were recruited through face-to-face interviews and/or assessments by experienced psychiatrists who had received professional interview training (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The interviews were conducted during 7-day to 2-month from admission. Participants with MUD had no access to MA, tobacco, alcohol, or other drugs during the research. All participants signed informed consent forms and were educated on the purpose, methods, risks, and conflicts of interest of this study. We ensured the security and confidentiality of the participants\u0026rsquo; personal information and test results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here]\u003c/p\u003e \u003cp\u003eThis study is a part of the National Key Research and Development Project of China (2017YFC1310400) and is registered with the Chinese Clinical Trial Registry (ChiCTR2000032198, Registration Date: 2020.4.22). This study was endorsed by the Ethics Committee of Henan Medical University (XYLL-2017016) and the Jawatankuasa Etika Penyelidikan Manusia Universiti Sains Malaysia (USM/JEPeM/PP/24100916), which adhered to the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eThe sample size was calculated with the formula suggested by Fritz and MacKinnon: n\u0026thinsp;=\u0026thinsp;L/f\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;k\u0026thinsp;+\u0026thinsp;1 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). For this calculation, f\u0026thinsp;=\u0026thinsp;0.26 for halfway between the values for small and medium effect sizes, L\u0026thinsp;=\u0026thinsp;7.85 for a type one error α of 0.05 and a statistical power of 0.8, and k\u0026thinsp;=\u0026thinsp;11 as variables to be entered in the model. Hence, 128 was the minimal estimated sample size for this study.\u003c/p\u003e\n\u003ch3\u003eInclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003eThe inclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) MUD confirmed by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) aged between 18 and 60 years, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) positive MA urine test (\u0026lt;\u0026thinsp;7 days after admission). The exclusion criteria were (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) withdrawal symptoms (i.e., dysphoric mood, insomnia or hypersomnia, and increased appetite, etc.), (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) comorbid psychiatric disorders (i.e., schizophrenia, bipolar disorder, and social phobia, etc.), or (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) other severe physical illnesses (i.e., epilepsy, stroke, and brain trauma, etc.). Individuals with polysubstance use were not excluded unless other SUDs were diagnosed.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eSociodemographic characteristics were collected using the Chinese version of the Semi-Structured Assessment of Drug and Alcohol Dependence (SSADAA) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), which exhibited good reliability and validity. Characteristic variables included age, sex, education, marital status, income, and employment status.\u003c/p\u003e \u003cp\u003eThe MA use characteristics gathered by the Chinese SSADAA included the age of onset and years of MA use. MUD severity was assessed using the stimulant use disorder diagnostic criteria in DSM-5.\u003c/p\u003e \u003cp\u003eTrait impulsivity was assessed using the Barratt Impulsiveness Scale, 11th version (BIS-11) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). While exhibiting satisfactory psychometric properties, the Chinese version of the BIS-11 (30 items) comprises three subscales: attentional impulsivity (AI), MI, and NPI (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The higher total score indicates higher self-reported impulsivity, and the Cronbach\u0026rsquo;s α value of the total scale was 0.886 in this study.\u003c/p\u003e \u003cp\u003eNicotine dependence was measured with the Fagerstr\u0026ouml;m Test for Nicotine Dependence (FTND) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This test has been extensively used to evaluate nicotine dependence. Based on total scores, nicotine dependence was divided into two groups: none or low nicotine dependence (0\u0026thinsp;\u0026minus;\u0026thinsp;3 points) and medium- to high-nicotine dependence (4\u0026thinsp;\u0026minus;\u0026thinsp;10 points) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlcohol dependence was evaluated with the Chinese version of the Alcohol Use Disorders Identification Test (AUDIT) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The AUDIT scale is reliable and valid for measuring alcohol dependence, as well as hazardous and harmful alcohol consumption. According to the total scores, alcohol dependence was divided into two groups: none or low hazardous alcohol use (0\u0026thinsp;\u0026minus;\u0026thinsp;7 points) and high hazardous alcohol use, harmful alcohol use or alcohol use disorder (8\u0026thinsp;\u0026minus;\u0026thinsp;40 points) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFor continuous data, the skewness and kurtosis of the distribution were used to test the normality(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). One-way analysis of variance (three groups) or Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test (two groups) was used to compare normally distributed data, and the Kruskal-Wallis H test was used for non-normally distributed multigroup variables. Categorical variables were reported as numbers (n) and percentages (%) and were compared with the χ\u003csup\u003e2\u003c/sup\u003e test. For the mediation analysis, we first assessed the correlations between nicotine dependence, alcohol dependence, trait impulsivity, and MUD severity. Then, we performed mediation analyses using the Hayes PROCESS macro (version 4.0) for SPSS to generate bootstrapped (n\u0026thinsp;=\u0026thinsp;5000) bias-corrected regression estimates and confidence intervals (CIs). All analyses were undertaken in SPSS (version 26.0), and the threshold for statistically significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eMediation analyses adopted Model 4 of the PROCESS macro. Distinct paths were created in the mediation models: path a, depicting the effect of the predictor (nicotine dependence) on the mediators (total impulsivity in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; AI, MI, and NPI in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC); path b, representing the effect of the mediators (see above) on the outcome (MUD severity); path a \u0026times; b (indirect effect), representing the mediating effect of the predictor on the outcome by the mediators; path c, depicting the total effect (indirect effect\u0026thinsp;+\u0026thinsp;direct effect) of the predictor on the outcome; and path c\u0026rsquo; (direct effect), depicting the residual effect of the predictor on the outcome not mediated by the mediators in the models (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026thinsp;\u0026minus;\u0026thinsp;C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e about here]\u003c/p\u003e \u003cp\u003eEight confounding variables were investigated: age, sex, education, marital status, employment, income, age of onset, and years of MA use. When these confounding variables were introduced as covariates into the models, the coefficients were not all available, and the results did not change the significance of the indirect effects. Therefore, confounding variables were excluded from the final mediation models.\u003c/p\u003e \u003cp\u003eTo avoid the bias induced by the absence of nicotine dependence, we conducted sensitivity analyses by excluding participants who scored 0 on the FTND scale in the mediation models. The adjusted total impulsivity as the mediator was fitted in the PROCESS macro.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in sociodemographic and substance use characteristics\u003c/h2\u003e \u003cp\u003eThe chronology of the interviews and assessments in this study is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Initially, 1230 participants were assessed for sociodemographic and substance use characteristics by the SSADAA. Of 1055 participants who were screened following the inclusion and exclusion criteria, 447 were enrolled and filled out the questionnaires (SSADDA, DSM-5, BIS-11, FTND, and AUDIT). Finally, 163 participants completed all interviews and questionnaires and were divided into three groups based on MUD severity: mild (n\u0026thinsp;=\u0026thinsp;52), moderate (n\u0026thinsp;=\u0026thinsp;50), and severe (n\u0026thinsp;=\u0026thinsp;61).\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparisons of sociodemographic and substance use characteristics among the MUD population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild MUD (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate MUD (n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSevere MUD (n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eF/c\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.92\u0026thinsp;\u0026plusmn;\u0026thinsp;7.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.62\u0026thinsp;\u0026plusmn;\u0026thinsp;6.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.18\u0026thinsp;\u0026plusmn;\u0026thinsp;8.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39(75.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(74.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(52.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(26.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(47.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.16\u0026thinsp;\u0026plusmn;\u0026thinsp;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33(63.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45(73.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(36.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(61.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(46.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(50.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(54.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(49.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome (\u0026yen; / month), median (Q1, Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7250 (3250,14750)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8000 (4000,13500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4000 (3000,8000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of onset (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.85\u0026thinsp;\u0026plusmn;\u0026thinsp;7.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.84\u0026thinsp;\u0026plusmn;\u0026thinsp;6.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.33\u0026thinsp;\u0026plusmn;\u0026thinsp;7.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of MA use (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.08\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.78\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.85\u0026thinsp;\u0026plusmn;\u0026thinsp;4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNicotine dependence [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u0026thinsp;\u0026minus;\u0026thinsp;low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(73.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u0026thinsp;\u0026minus;\u0026thinsp;high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(26.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45(73.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol dependence [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u0026thinsp;\u0026minus;\u0026thinsp;low hazardous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(53.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(34.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(50.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh hazardous\u0026thinsp;\u0026minus;\u0026thinsp;dependence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(46.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(66.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(49.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eMA: methamphetamine; MUD: methamphetamine use disorder; (\u0026yen;) is the currency abbreviation for the China yuan renminbi (CNY); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides comparisons of sociodemographic characteristics among the MUD population with different addiction severities. Age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031), sex (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016), and monthly income (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) significantly differed between the MUD groups, but no significant differences were observed with other sociodemographic characteristics. Regarding MA use characteristics, the age of onset in the mild MUD group was higher than that in the moderate and severe MUD groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), and the severe MUD group exhibited longer years of MA use than the mild and moderate groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017). Additionally, medium-high nicotine dependence was predominant in the severe MUD group, while none-low nicotine dependence was predisposed to mild MUD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, alcohol dependence did not show a statistically significant difference across the MUD groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDifferences in impulsivity among MUD populations\u003c/h3\u003e\n\u003cp\u003eAccording to the BIS-11, the severe MUD group (54.48\u0026thinsp;\u0026plusmn;\u0026thinsp;17.08) had higher total scores than the mild and moderate MUD groups (35.45\u0026thinsp;\u0026plusmn;\u0026thinsp;14.82, 43.35\u0026thinsp;\u0026plusmn;\u0026thinsp;13.25; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among the three MUD groups, the scores for AI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), MI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and NPI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significant different (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImpulsivity differences among MUD populations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eBIS-11\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (`x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAttentional\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMotor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-planning\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMUD severity (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.45\u0026thinsp;\u0026plusmn;\u0026thinsp;14.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.02\u0026thinsp;\u0026plusmn;\u0026thinsp;16.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.02\u0026thinsp;\u0026plusmn;\u0026thinsp;18.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.31\u0026thinsp;\u0026plusmn;\u0026thinsp;18.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.35\u0026thinsp;\u0026plusmn;\u0026thinsp;13.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.40\u0026thinsp;\u0026plusmn;\u0026thinsp;17.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.00\u0026thinsp;\u0026plusmn;\u0026thinsp;16.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.65\u0026thinsp;\u0026plusmn;\u0026thinsp;17.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere (61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.48\u0026thinsp;\u0026plusmn;\u0026thinsp;17.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.84\u0026thinsp;\u0026plusmn;\u0026thinsp;17.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.79\u0026thinsp;\u0026plusmn;\u0026thinsp;21.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.81\u0026thinsp;\u0026plusmn;\u0026thinsp;20.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNicotine dependence (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone - low (84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.10\u0026thinsp;\u0026plusmn;\u0026thinsp;15.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.81\u0026thinsp;\u0026plusmn;\u0026thinsp;18.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.01\u0026thinsp;\u0026plusmn;\u0026thinsp;19.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.48\u0026thinsp;\u0026plusmn;\u0026thinsp;19.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium - high (79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.20\u0026thinsp;\u0026plusmn;\u0026thinsp;17.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.68\u0026thinsp;\u0026plusmn;\u0026thinsp;19.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.73\u0026thinsp;\u0026plusmn;\u0026thinsp;20.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.19\u0026thinsp;\u0026plusmn;\u0026thinsp;20.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol dependence (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone - low hazardous (76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.44\u0026thinsp;\u0026plusmn;\u0026thinsp;18.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.18\u0026thinsp;\u0026plusmn;\u0026thinsp;20.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.13\u0026thinsp;\u0026plusmn;\u0026thinsp;21.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.02\u0026thinsp;\u0026plusmn;\u0026thinsp;22.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh hazardous - dependence (87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.35\u0026thinsp;\u0026plusmn;\u0026thinsp;15.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.88\u0026thinsp;\u0026plusmn;\u0026thinsp;17.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.18\u0026thinsp;\u0026plusmn;\u0026thinsp;19.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.99\u0026thinsp;\u0026plusmn;\u0026thinsp;19.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eMUD: methamphetamine use disorder; BIS-11: Barratt impulsiveness scale (11th version); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe BIS-11 total score in the medium-high nicotine dependence group (50.20\u0026thinsp;\u0026plusmn;\u0026thinsp;17.24) was higher than that in the none-low group (40.10\u0026thinsp;\u0026plusmn;\u0026thinsp;15.65, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Compared with the none-low nicotine dependence group, the medium-high group had remarkably greater scores on all three subscales (AI, MI, and NPI) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). However, the BIS-11 total score and the three subscale scores did not differ significantly for alcohol dependence (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e about here]\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations between MUD severity, impulsivity, nicotine dependence, and alcohol dependence\u003c/h2\u003e \u003cp\u003eAccording to Pearson\u0026rsquo;s product-moment correlations, MUD severity was positively correlated with nicotine dependence (r\u0026thinsp;=\u0026thinsp;0.554, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). MUD severity was positively associated with total impulsivity (total BIS-11 score) (r\u0026thinsp;=\u0026thinsp;0.459, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as well as with its three subscales (AI, MI, and NPI) (r\u0026thinsp;=\u0026thinsp;0.424, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; r\u0026thinsp;=\u0026thinsp;0.319, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; and r\u0026thinsp;=\u0026thinsp;0.431, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). Furthermore, total impulsivity, AI, MI, and NPI were significantly associated with nicotine dependence (r\u0026thinsp;=\u0026thinsp;0.376, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; r\u0026thinsp;=\u0026thinsp;0.322, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; r\u0026thinsp;=\u0026thinsp;0.230, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; and r\u0026thinsp;=\u0026thinsp;0.405, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively). However, alcohol dependence was not associated with MUD severity, nicotine dependence, or trait impulsivity (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), except for total impulsivity (r\u0026thinsp;=\u0026thinsp;0.167, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033) and MI (r\u0026thinsp;=\u0026thinsp;0.182, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020). These results revealed that MUD severity positively correlated with both nicotine dependence and trait impulsivity (total impulsivity, AI, MI, and NPI) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between MUD severity, nicotine dependence, alcohol dependence, and impulsivity in MUD populations (r value)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. MUD severity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Nicotine dependence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.554\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Alcohol dependence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. BIS-11 Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.459\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.376\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.167\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. BIS-11 Attentional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.424\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.322\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.862\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. BIS-11 Motor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.319\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.230\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.182\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.836\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.592\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. BIS-11 Non-planning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.431\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.405\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.860\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.634\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.547\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eMUD: methamphetamine use disorder; BIS-11: Barratt impulsiveness scale (11th version); \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the BIS-11, the total score was significantly related to all three subscales (AI, MI, and NPI) (r\u0026thinsp;=\u0026thinsp;0.862, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; r\u0026thinsp;=\u0026thinsp;0.836, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; and r\u0026thinsp;=\u0026thinsp;0.860, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively), and each subscale score was correlated with one another from 0.547 to 0.634 (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e about here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMediation analyses\u003c/h2\u003e \u003cp\u003eAccording to the mediation analyses, path c (nicotine dependence positively towards MUD severity) was available (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.307, F\u0026thinsp;=\u0026thinsp;71.175; c\u0026thinsp;=\u0026thinsp;0.554, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), path a (nicotine dependence positively towards total impulsivity) was meaningful (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.141, F\u0026thinsp;=\u0026thinsp;26.430; a\u0026thinsp;=\u0026thinsp;0.376, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and both path c\u0026rsquo; (nicotine dependence directly and positively towards MUD severity) and path b (total impulsivity directly and positively towards MUD severity) were significant (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.380, F\u0026thinsp;=\u0026thinsp;49.001; c\u0026rsquo;=0.444, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and b\u0026thinsp;=\u0026thinsp;0.292, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Additionally, the direct effect (path c\u0026rsquo;) of nicotine dependence on MUD severity was 0.319 (95% CI: 0.231, 0.413), and the indirect effect (path a \u0026times; b) of nicotine dependence on MUD severity through total impulsivity was 0.079 (95% CI: 0.040, 0.126), accounting for 19.85% of the total effect (path c, Table S2). As zero was not included in the 95% CIs, this condition indicated that the indirect effect was remarkable. Thus, total impulsivity partially mediated the relationship between nicotine dependence and MUD severity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eConsidering the multidimensionality of the mediator (total impulsivity), parallel multiple mediation analysis was further conducted to ascertain the mediating roles of its sub-traits (AI, MI, and NPI). However, the indirect effects (paths a \u0026times; b) of nicotine dependence on MUD severity via MI and NPI were 0.008 (95% CI: -0.018, 0.036) and 0.032 (95% CI: -0.012, 0.084) (Tables S3 and S4). Since the 95% CIs straddled zero (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), the indirect effects of MI and NPI were invalid (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThe mediating effect of AI was exclusively analyzed to avoid biased data and underpowered mediation effects. In Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, path a (nicotine dependence positively towards AI) was significant (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.104, F\u0026thinsp;=\u0026thinsp;18.639; a\u0026thinsp;=\u0026thinsp;0.322, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and both paths c\u0026rsquo; (nicotine dependence directly and positively towards MUD severity) and b (AI directly and positively towards MUD severity) were significant (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.374, F\u0026thinsp;=\u0026thinsp;47.815; c\u0026rsquo;=0.465, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and b\u0026thinsp;=\u0026thinsp;0.275, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001, respectively) (Table S5). Meanwhile, the direct effect (path c\u0026rsquo;) of nicotine dependence on MUD severity was 0.334 (95% CI: 0.245, 0.422), and the indirect effect (path a \u0026times; b) of nicotine dependence on MUD severity via AI was 0.064 (95% CI: 0.027, 0.109), accounting for 16.08% of the total effect (path c, Table S6). As the 95% CIs did not include any zeroes, the results revealed that the indirect effect was significant; thus, AI served as an efficient mediator in this scenario. Relative to MI and NPI, AI played a substantial mediating role between nicotine dependence and MUD severity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eAdditionally, the data of 152 participants were retested in the mediation models after excluding 11 participants who scored 0 on the FTND scale. The results of the sensitivity analyses showed that total impulsivity remained a mediator of nicotine dependence on MUD severity after adjusting for confounders. The indirect effect of the adjusted total impulsivity was 0.091 (95% CI: 0.046, 0.146), accounting for 22.86% of the total effect (Tables S7 and S8).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study clarified the mediation of trait impulsivity from nicotine dependence to MUD severity in a Chinese population. First, varying MUD severity showed significant differences with nicotine dependence and trait impulsivity; however, no significant difference was observed with alcohol dependence. Furthermore, MUD severity, trait impulsivity (total impulsivity, AI, MI, and NPI), and nicotine dependence were positively correlated. However, alcohol dependence was not related to MUD severity or nicotine dependence. Mediation analyses demonstrated that trait impulsivity (19.85%) mediated the positive effects of nicotine dependence on MUD severity. Compared with MI and NPI, only AI (16.08%) exerted a partial mediating effect between nicotine dependence and MUD severity in models of trait impulsivity.\u003c/p\u003e \u003cp\u003eOur data showed that more severe MUD was frequently associated with higher nicotine dependence, and the findings further elucidated the relationship between nicotine dependence and MUD. Based on behavioral economics and epidemiology, studies have shown that the aggravated MA demand is correlated with higher nicotine dependence in Americans (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), which is consistent with our results. However, Bujarski \u003cem\u003eet al.\u003c/em\u003e reported no differences between MUD and nicotine dependence in 60 American non-treatment users (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Their findings may be due to the relatively small sample size and non-standard assessment of nicotine dependence. According to neurobiological studies, nicotine exposure in adolescents increased the subsequent rates of MUD in adult rats (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). We believe that the association between MUD and nicotine dependence partially shares a common pathway in the dopamine reward system, especially in the nucleus accumbens (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Regarding alcohol dependence and MUD, studies suggested an equivocal relationship between Australian and Japanese populations (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). However, our data did not reveal a correlation between MUD severity and alcohol dependence based on AUDIT scores in the Chinese population. Multiracial investigations may help to clarify this puzzle.\u003c/p\u003e \u003cp\u003eWe observed that MUD severity was positively correlated with trait impulsivity and its three sub-traits (AI, MI, and NPI) in a Chinese population. Moallem \u003cem\u003eet al.\u003c/em\u003e (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) preliminarily observed that increased MUD severity corresponded to higher impulsivity in 177 Americans. Additionally, in a virtual digital psychotherapeutic study, AI was positively related to drug craving among 47 patients with MUD (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), whereas 157 American individuals with higher AI and MI commenced MA exposure at a younger age (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). These subtle differences between MUD and impulsivity may be due to diverse sociocultural backgrounds and administrative policies.\u003c/p\u003e \u003cp\u003eOur data showed that total impulsivity and its sub-traits correlated with nicotine dependence in MUD. Studies reported that higher impulsivity was related to higher nicotine dependence among American and Iranian college students (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Mittal \u003cem\u003eet al.\u003c/em\u003e (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) reported that 137 young adults with high impulsivity smoked electronic cigarettes more frequently. Janine \u003cem\u003eet al.\u003c/em\u003e (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) documented a correlation between NPI and nicotine dependence in 1284 American adults. The similarity between our findings and those of previous studies suggests that total impulsivity and its certain sub-traits are associated with MUD severity and nicotine dependence.\u003c/p\u003e \u003cp\u003eBased on these associations, this study constructed mediation models and performed overall quantitative analyses of the relationship between trait impulsivity, nicotine dependence, and MUD severity. When trait impulsivity was controlled for, the strength of the relationship between nicotine dependence and MUD severity reduced, indicating that trait impulsivity partially (19.85%) mediated the effect. In other words, decreased impulsivity is a protective factor against this effect. Our findings imply that the increased risk of MUD severity reasonably originates from higher nicotine dependence via the mediating effect of total impulsivity, contributing a novel insight into addiction theory. Similarly, a study on a Taiwanese (China) population reported that impulsivity was a mediator between cigarette smoking initiation and MUD severity, explaining 19.41% of the total effect (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Instead of their self-designed questionnaire, we used a validated scale (FTND) to objectively assess nicotine dependence. Although both studies supported the mediating role of trait impulsivity, it is more reliable to assess the level of nicotine dependence rather than the initiation of nicotine dependence. Nor does every exposure inevitably transition to SUDs since only approximately 10% of people become addicted (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). In addition, some research reported that trait impulsivity also exerted some effects in other SUDs (including cannabis and cocaine) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Hence, the findings indicate that trait impulsivity and nicotine dependence could be considered targets to predict and intervene against the risk of developing MUD in the future.\u003c/p\u003e \u003cp\u003eIn the context of impulsivity sub-traits, recent studies have shown that certain sub-traits play specific roles in SUDs (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Since the paths of MI and NPI were unavailable in the following parallel multiple mediation tests, we observed a positive indirect effect mediated by AI alone (16.08%). Therefore, we believe that AI, rather than MI or NPI, plays a pivotal role in the mechanism underlying the effect of nicotine dependence. An elevated AI can aggravate the effect of nicotine dependence on MUD severity. Huang \u003cem\u003eet al.\u003c/em\u003e (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) reported that AI (15.78%) and NPI (10.37%) had indirect effects on nicotine dependence and MUD severity, where total impulsivity and the three sub-traits were entered into the model simultaneously as mediators. However, this remains debatable because the total BIS-11 score was formulated from its three subscales by inter-algorithms. We suggest that total impulsivity and its sub-traits should be validated separately since none of the mediators can tangle with one another in a parallel model (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Similarly, Su \u003cem\u003eet al.\u003c/em\u003e (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) reported that AI was strongly related to MUD in a gene polymorphic study, providing molecular genetic evidence for AI as a crucial mediator. It is postulated that the mediation by AI may result from the fact that individuals with high AI cannot concentrate easily, have extraneous thoughts when thinking, and often encounter difficulty completing tasks (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThe tools involving scales and tests used in this study had better generality and standardization, and the mediation models and preliminary algorithms had relative advantages in machine learning, all of which may contribute to the potential applications of generative artificial intelligence in MUD prediction and intervention in the future. Nonetheless, this study had certain limitations. First, we used a cross-sectional multi-center study design. Thus, the causalities from these data should be further validated in future longitudinal studies. Second, this study utilized a static assessment of trait impulsivity; it would be better to assess impulsivity at multiplicities, such as impulsive choices and actions. Third, volatility in the data may be attributed to social desirability and recall biases.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study confirmed the mediation of trait impulsivity, especially AI, from nicotine dependence to MUD severity in a Chinese population. In the context of the causalities, high levels of nicotine dependence and trait impulsivity can accelerate the progression of severe MUD. Smoking cessation and impulsivity reduction may help prevent MUD, supportively responding to the Framework Convention on Tobacco Control (WHO). Additionally, mediation paths and algorithms may provide a new neuropsychiatric strategy for the accurate prediction and holistic intervention for individuals with SUDs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emethamphetamine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMUD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emethamphetamine use disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSUD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esubstance use disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDSM-5\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiagnostic and Statistical Manual of Mental Disorders, Fifth Edition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSSADAA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSemi-Structured Assessment of Drug and Alcohol Dependence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBIS-11\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBarratt impulsiveness scale, 11th version\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eattentional impulsivity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emotor impulsivity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-planning impulsivity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFTND\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFagerstr\u0026ouml;m Test for Nicotine Dependence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUDIT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlcohol Use Disorders Identification Test.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Henan Medical University (XYLL-2017016) and the Jawatankuasa Etika Penyelidikan Manusia Universiti Sains Malaysia (USM/JEPeM/PP/24100916). All participants signed informed consent forms. All procedures followed were in accordance with the Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe database can be visited with data user agreements. Any reasonable requests to access these datasets, please contact to BZ and RLZ.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Henan Collaborative Innovation Center (XTgh04), the Natural Science Foundation of Henan Province (242300421307), the Henan Province Science and Technology Research and Development Plan Joint Fund (235101610004), the Medical Science and Technology Research Project of Henan Province (222102310710), and the National Key Research and Development Project of China (2017YFC1310402).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: BZ, RLZ, MFILA, YMJ, WH; Methodology: BZ, RLZ, YMJ, YLZ, HYL, DSZ, LW, XJZ, MAM; Formal analysis: BZ, RLZ, YMJ, YLZ, HYL, CSW, DSZ, LW, XJZ, MAM; Investigation: BZ, RLZ, YMJ, YLZ, HYL, CSW, XL, LW, XJZ; Data curation: RLZ, YMJ, DSZ, XJZ; Writing-original draft: BZ, RLZ, YMJ, YLZ, HYL; Writing-review and editing: BZ, RLZ, MFILA, YMJ, CSW, XL, DSZ, LW, JQZ, XJZ, WH, MAM; Funding acquisition: BZ, RLZ, YMJ, CSW, JQZ, WH; Supervision: MFILA, WH; Project administration: BZ, RLZ, MFILA, CSW, XL; Validation: BZ, HYL, CSW, DSZ, LW, JQZ. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate all the researchers in this study. We are grateful to the peers engaged in data collection, specifically Kangfeng Zhu, Kang Liu, Yan Li, Wanwan Guo, Youhui Sun.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFarrell, M. et al. Responding to global stimulant use: challenges and opportunities. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e394\u003c/b\u003e (10209), 1652\u0026ndash;1667 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations Office on Drugs and Crime. World Drug Report. United Nations publication, 2024. Accessed 2024 Jun 26. (2024). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.unodc.org/unodc/en/data-and-analysis/world-drug-report-2024.html\u003c/span\u003e\u003cspan address=\"https://www.unodc.org/unodc/en/data-and-analysis/world-drug-report-2024.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChina National Narcotics Control Committee. China Drug Situation Report. China Narcotics Control. Accessed 2024 Jun 19. (2023). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.nncc626.com/20240619/d1a1ffb1f3fb4c93bb05f6e73fb50414/c.html\u003c/span\u003e\u003cspan address=\"http://www.nncc626.com/20240619/d1a1ffb1f3fb4c93bb05f6e73fb50414/c.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaulus, M. P., Stewart, J. L. \u0026amp; Neurobiology Clinical Presentation, and Treatment of Methamphetamine Use Disorder: A Review. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e. \u003cb\u003e77\u003c/b\u003e (9), 959\u0026ndash;966 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolkow, N. D. \u0026amp; Blanco, C. Substance use disorders: a comprehensive update of classification, epidemiology, neurobiology, clinical aspects, treatment and prevention. \u003cem\u003eWorld Psychiatry\u003c/em\u003e. \u003cb\u003e22\u003c/b\u003e (2), 203\u0026ndash;229 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKandel, D. \u0026amp; Kandel, E. The Gateway Hypothesis of substance abuse: developmental, biological and societal perspectives. \u003cem\u003eActa Paediatr.\u003c/em\u003e \u003cb\u003e104\u003c/b\u003e (2), 130\u0026ndash;137 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCross, S. J., Lotfipour, S. \u0026amp; Leslie, F. M. Mechanisms and genetic factors underlying co-use of nicotine and alcohol or other drugs of abuse. \u003cem\u003eAm. J. Drug Alcohol Abuse\u003c/em\u003e. \u003cb\u003e43\u003c/b\u003e (2), 171\u0026ndash;185 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePipkin, J. A. et al. Nicotine exposure beginning in adolescence enhances the acquisition of methamphetamine self-administration, but not methamphetamine-primed reinstatement in male rats. \u003cem\u003eDrug Alcohol Depend.\u003c/em\u003e \u003cb\u003e142\u003c/b\u003e, 341\u0026ndash;344 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShearer, R. D., Howell, B. A., Bart, G. \u0026amp; Winkelman, T. N. A. Substance use patterns and health profiles among US adults who use opioids, methamphetamine, or both, 2015\u0026ndash;2018. \u003cem\u003eDrug Alcohol Depend.\u003c/em\u003e \u003cb\u003e214\u003c/b\u003e, 108162 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, J. et al. Depression in Chinese men with methamphetamine dependence: Prevalence, correlates and relationship with alexithymia. \u003cem\u003eJ. Affect. Disord\u003c/em\u003e. \u003cb\u003e319\u003c/b\u003e, 235\u0026ndash;243 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeslie, E. M. et al. Simultaneous use of alcohol with methamphetamine but not ecstasy linked with aggression among young adult stimulant users. \u003cem\u003eAddict. Behav.\u003c/em\u003e \u003cb\u003e70\u003c/b\u003e, 27\u0026ndash;34 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrooks, S. J. et al. The impact of cognitive training in substance use disorder: the effect of working memory training on impulse control in methamphetamine users. \u003cem\u003ePsychopharmacol. (Berl)\u003c/em\u003e. \u003cb\u003e234\u003c/b\u003e (12), 1911\u0026ndash;1921 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacKillop, J., Weafer, J., Oshri, J. C. G., Palmer, A. \u0026amp; de Wit, A. The latent structure of impulsivity: impulsive choice, impulsive action, and impulsive personality traits. \u003cem\u003ePsychopharmacol. (Berl)\u003c/em\u003e. \u003cb\u003e233\u003c/b\u003e (18), 3361\u0026ndash;3370 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJentsch, J. D. et al. Dissecting impulsivity and its relationships to drug addictions. \u003cem\u003eAnn. N Y Acad. Sci.\u003c/em\u003e \u003cb\u003e1327\u003c/b\u003e, 1\u0026ndash;26 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCservenka, A. \u0026amp; Ray, L. A. Self-reported attentional and motor impulsivity are related to age at first methamphetamine use. \u003cem\u003eAddict. Behav.\u003c/em\u003e \u003cb\u003e65\u003c/b\u003e, 7\u0026ndash;12 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKogachi, S., Chang, L., Alicata, D., Cunningham, E. \u0026amp; Ernst, T. Sex differences in impulsivity and brain morphometry in methamphetamine users. \u003cem\u003eBrain Struct. Funct.\u003c/em\u003e \u003cb\u003e222\u003c/b\u003e (1), 215\u0026ndash;227 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRound, J. T., Fozard, T. E., Harrison, A. A. \u0026amp; Kolokotroni, K. Z. Disentangling the effects of cannabis and cigarette smoking on impulsivity. \u003cem\u003eJ. Psychopharmacol.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e (9), 955\u0026ndash;968 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaddell, J. T., King, S. E., Okey, S. A., Marohnic, S. D. \u0026amp; Corbin, W. R. Prospective Effects of UPPS-P Impulsivity and Typical Drinking Context on Future Drinking Behavior. \u003cem\u003eJ. Stud. Alcohol Drugs\u003c/em\u003e. \u003cb\u003e83\u003c/b\u003e (2), 212\u0026ndash;222 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayes, A. F. \u0026amp; Preacher, K. J. Statistical mediation analysis with a multicategorical independent variable. \u003cem\u003eBr. J. Math. Stat. Psychol.\u003c/em\u003e \u003cb\u003e67\u003c/b\u003e (3), 451\u0026ndash;470 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFritz, M. S. \u0026amp; Mackinnon, D. P. Required sample size to detect the mediated effect. \u003cem\u003ePsychol. Sci.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e (3), 233\u0026ndash;239 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. Arlington, VA, American Psychiatric Association, (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa, Y. J. et al. Reliability and validity of DSM-IV and DSM-5 methamphetamine use disorder diagnoses using the Chinese Version of the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA). \u003cem\u003eDrug Alcohol Depend.\u003c/em\u003e ; (2021). 229(Pt B):109047.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatton, J. H., Stanford, M. S. \u0026amp; Barratt, E. S. Factor structure of the Barratt impulsiveness scale. \u003cem\u003eJ. Clin. Psychol.\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e (6), 768\u0026ndash;774 (1995).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAn, J., Phillips, M. R. \u0026amp; Conner, K. R. Validity of proxy-based reports of impulsivity and aggression in Chinese research on suicidal behavior. \u003cem\u003eCrisis\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e (3), 137\u0026ndash;142 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, C. L., Lin, H. H. \u0026amp; Wang, H. H. The psychometric properties of the Chinese version of the Fagerstrom Test for Nicotine Dependence. \u003cem\u003eAddict. Behav.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e (12), 2324\u0026ndash;2327 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFagerstrom, K., Russ, C., Yu, C. R., Yunis, C. \u0026amp; Foulds, J. The Fagerstrom Test for Nicotine Dependence as a predictor of smoking abstinence: a pooled analysis of varenicline clinical trial data. \u003cem\u003eNicotine Tob. Res.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (12), 1467\u0026ndash;1473 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Q., Babor, T. F., Hao, W. \u0026amp; Chen, X. The Chinese translations of Alcohol Use Disorders Identification Test (AUDIT) in China: a systematic review. \u003cem\u003eAlcohol Alcohol\u003c/em\u003e. \u003cb\u003e46\u003c/b\u003e (4), 416\u0026ndash;423 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDybek, I. et al. The reliability and validity of the Alcohol Use Disorders Identification Test (AUDIT) in a German general practice population sample. \u003cem\u003eJ. Stud. Alcohol\u003c/em\u003e. \u003cb\u003e67\u003c/b\u003e (3), 473\u0026ndash;481 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, H. Y. Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. \u003cem\u003eRestor. Dent. Endod\u003c/em\u003e. \u003cb\u003e38\u003c/b\u003e (1), 52\u0026ndash;54 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoon, J. H. et al. Assessment of demand for methamphetamine and cigarettes among individuals with methamphetamine use disorder. \u003cem\u003eExp. Clin. Psychopharmacol.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e (4), 334\u0026ndash;344 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShoptaw, S. et al. Frequency of reported methamphetamine use linked to prevalence of clinical conditions, sexual risk behaviors, and social adversity in diverse men who have sex with men in Los Angeles. \u003cem\u003eDrug Alcohol Depend.\u003c/em\u003e \u003cb\u003e232\u003c/b\u003e, 109320 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBujarski, S. et al. The relationship between methamphetamine and alcohol use in a community sample of methamphetamine users. \u003cem\u003eDrug Alcohol Depend.\u003c/em\u003e \u003cb\u003e142\u003c/b\u003e, 127\u0026ndash;132 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarmony, Z. R., Alderson, E. M., Garcia-Carachure, I., Bituin, L. D. \u0026amp; Crawford, C. A. Effects of nicotine exposure on oral methamphetamine self-administration, extinction, and drug-primed reinstatement in adolescent male and female rats. \u003cem\u003eDrug Alcohol Depend.\u003c/em\u003e \u003cb\u003e209\u003c/b\u003e, 107927 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCardenas, A. \u0026amp; Lotfipour, S. Age- and Sex-Dependent Nicotine Pretreatment Effects on the Enhancement of Methamphetamine Self-administration in Sprague-Dawley Rats. \u003cem\u003eNicotine Tob. Res.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e (8), 1186\u0026ndash;1192 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan, J. et al. Cross-talks between gut microbiota and tobacco smoking: a two-sample Mendelian randomization study. \u003cem\u003eBMC Med.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (1), 163 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamamoto, T., Kimura, T., Tamakoshi, A. \u0026amp; Matsumoto, T. Variables associated with methamphetamine use within the past year and sex differences among patients with methamphetamine use disorder: A cross-sectional study in Japan. \u003cem\u003eAm. J. Addict.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e (2), 134\u0026ndash;141 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoallem, N. R., Courtney, K. E. \u0026amp; Ray, L. A. The relationship between impulsivity and methamphetamine use severity in a community sample. \u003cem\u003eDrug Alcohol Depend.\u003c/em\u003e \u003cb\u003e187\u003c/b\u003e, 1\u0026ndash;7 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, T. et al. Virtual Digital Psychotherapist App-Based Treatment in Patients With Methamphetamine Use Disorder (Echo-APP): Single-Arm Pilot Feasibility and Efficacy Study. \u003cem\u003eJMIR Mhealth Uhealth\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, e40373 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalevich, E. C., Wein, N. D. \u0026amp; Flory, J. D. Cigarette smoking and measures of impulsivity in a college sample. \u003cem\u003eSubst. Abus\u003c/em\u003e. \u003cb\u003e34\u003c/b\u003e (3), 256\u0026ndash;262 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRezvanfard, M., Ekhtiari, H., Mokri, A., Djavid, G. \u0026amp; Kaviani, H. Psychological and behavioral traits in smokers and their relationship with nicotine dependence level. \u003cem\u003eArch. Iran. Med.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (5), 395\u0026ndash;405 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMittal, A. et al. Impulsivity-Related Personality Traits as Predictors of E-Cigarette Use among Young Adults over Time. \u003cem\u003eSubst. Use Misuse\u003c/em\u003e. \u003cb\u003e57\u003c/b\u003e (7), 1007\u0026ndash;1013 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlory, J. D. \u0026amp; Manuck, S. B. Impulsiveness and cigarette smoking. \u003cem\u003ePsychosom. Med.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e (4), 431\u0026ndash;437 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, C. Y., Hung, C. C., Ho, Y. J. \u0026amp; Fang, S. C. Trait Impulsivity as a Mediator Between Early Cigarette Smoking Initiation and Addiction Severity in Patients with Methamphetamine Use Disorder. \u003cem\u003eInt. J. Ment Health Ad\u003c/em\u003e. \u003cb\u003e22\u003c/b\u003e (1), 279\u0026ndash;298 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBechara, A. Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. \u003cem\u003eNat. Neurosci.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (11), 1458\u0026ndash;1463 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoltin, R. W., Luba, R., Chen, Y., Wang, Y. \u0026amp; Evans, S. M. Impulsivity in cocaine users compared to matched controls: Effects of sex and preferred route of cocaine use. \u003cem\u003eDrug Alcohol Depend.\u003c/em\u003e \u003cb\u003e226\u003c/b\u003e, 108840 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerges, A., Littlefield, A. K., Arriaza, T. \u0026amp; Alvarado, M. E. Impulsivity facets and substance use initiation: A comparison of two models of impulsivity. \u003cem\u003eAddict. Behav.\u003c/em\u003e \u003cb\u003e88\u003c/b\u003e, 61\u0026ndash;66 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBertin, L., Benca-Bachman, C. E., Kogan, S. M. \u0026amp; Palmer, R. H. C. Examining the differential effects of latent impulsivity factors on substance use outcomes in African American men. \u003cem\u003eAddict. Behav.\u003c/em\u003e \u003cb\u003e117\u003c/b\u003e, 106847 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoutts, J. J. \u0026amp; Hayes, A. F. Questions of value, questions of magnitude: An exploration and application of methods for comparing indirect effects in multiple mediator models. \u003cem\u003eBehav. Res. Methods\u003c/em\u003e. \u003cb\u003e55\u003c/b\u003e (7), 3772\u0026ndash;3785 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu, H. et al. An association between BDNF Val66Met polymorphism and impulsivity in methamphetamine abusers. \u003cem\u003eNeurosci. Lett.\u003c/em\u003e \u003cb\u003e582\u003c/b\u003e, 16\u0026ndash;20 (2014).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"methamphetamine use disorder, nicotine dependence, trait impulsivity, attentional impulsivity, mediation analysis","lastPublishedDoi":"10.21203/rs.3.rs-8615424/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8615424/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMethamphetamine use disorder (MUD) lacks effective treatments. MUD, nicotine dependence, alcohol dependence, and trait impulsivity may be correlated, however, the precise magnitude of the correlation remains unclear between these four aspects. This study aimed to construct comprehensive models to analyze the mediating role of trait impulsivity on nicotine/alcohol dependence and MUD severity.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study included 1230 participants from five drug rehabilitation centers in China. After semi-structured interviews and standardized tests, 163 participants were divided into three groups based on MUD severity. Trait impulsivity was evaluated using the Chinese version of Barratt Impulsiveness Scale, 11th version.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mediation analyses revealed that the positive direct effect of nicotine dependence on MUD severity was 0.319 (95% CI: 0.231, 0.413), and the positive indirect effect of nicotine dependence on MUD severity via trait impulsivity was 0.079 (95% CI: 0.040, 0.126), accounting for 19.85% of the total effect. However, the parallel multiple mediation analysis indicated that the indirect effects of nicotine dependence on MUD severity via motor impulsivity and non-planning impulsivity were invalid (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The positive indirect effect of nicotine dependence on MUD severity via attentional impulsivity (AI) alone was 0.064 (95% CI: 0.027, 0.109), accounting for 16.08% of the total effect.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eTrait impulsivity, especially AI, mediated the positive effects of nicotine dependence on MUD severity in a Chinese population. In the context of the causalities, high levels of nicotine dependence and trait impulsivity can accelerate the progression of severe MUD. Smoking cessation and impulsivity reduction may help prevent MUD.\u003c/p\u003e","manuscriptTitle":"Attentional impulsivity in methamphetamine use disorder: Assessing its mediating role between nicotine dependence and methamphetamine use disorder severity in the Chinese population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-04 09:41:10","doi":"10.21203/rs.3.rs-8615424/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-02T04:46:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-01T15:20:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-22T01:09:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246500716945890788960872779332632174799","date":"2026-02-05T17:34:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291442251508956152086508130925537694595","date":"2026-02-02T12:54:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-02T09:21:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-21T10:10:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-17T11:28:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-17T11:27:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-16T05:02:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c086f809-100b-4e5d-8fa3-452202536478","owner":[],"postedDate":"February 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62164208,"name":"Health sciences/Diseases"},{"id":62164209,"name":"Health sciences/Medical research"},{"id":62164210,"name":"Biological sciences/Neuroscience"},{"id":62164211,"name":"Biological sciences/Psychology"},{"id":62164212,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-04-16T10:24:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-04 09:41:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8615424","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8615424","identity":"rs-8615424","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}