TYPE Original Research PUBLISHED 22 July 2022 DOI 10.3389/fpsyt.2022.869023 Differential trajectories of OPEN ACCESS tobacco smoking in people at ultra-high risk for psychosis: EDITED BY Peter James Kelly, University of Wollongong, Australia REVIEWED BY Kyle Minor, Associations with clinical Purdue University Indianapolis, United States Ana González Pinto, outcomes Hospital Universitario Alava, Spain Laura Robinson, University of Wollongong, Australia Frederike Schirmbeck1*, Els van der Ven2 , Lindy-Lou Boyette3 , *CORRESPONDENCE Philip McGuire4 , Lucia R. Valmaggia5 , Matthew J. Kempton4 , Frederike Schirmbeck n.f.schirmbeck@amsterdamumc.nl Mark van der Gaag6 , Anita Riecher-Rössler7 , SPECIALTY SECTION Neus Barrantes-Vidal8,9 , Barnaby Nelson10,11 , This article was submitted to Addictive Disorders, Marie-Odile Krebs12 , Stephan Ruhrmann13 , Gabriele Sachs14 , a section of the journal Bart P. F. Rutten15 , Merete Nordentoft16 , Frontiers in Psychiatry EU-GEI High Risk Study Group, Lieuwe de Haan1 and RECEIVED 03 February 2022 ACCEPTED 27 June 2022 Jentien M. Vermeulen1 PUBLISHED 22 July 2022 1 Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands, CITATION 2 Department of Clinical, Neuro- and Developmental Psychology, Vrije Universiteit Amsterdam, Schirmbeck F, van der Ven E, Amsterdam, Netherlands, 3 Department of Clinical Psychology, University of Amsterdam, Boyette L, McGuire P, Valmaggia LR, Amsterdam, Netherlands, 4 Department of Psychosis Studies, Institute of Psychiatry, Psychology and Kempton MJ, van der Gaag M, Neuroscience, King’s College London, London, United Kingdom, 5 Department of Psychology, Riecher-Rössler A, Barrantes-Vidal N, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, Nelson B, Krebs M, Ruhrmann S, United Kingdom, 6 Department of Clinical, Neuro- and Developmental Psychology, Amsterdam Sachs G, Rutten BPF, Nordentoft M, Public Mental Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands, Group EHRS, de Haan L and 7 Medical Faculty, University of Basel, Basel, Switzerland, 8 Departament de Psicologia Clínica i de la Vermeulen JM (2022) Differential Salut, Universitat Autònoma de Barcelona, Barcelona, Spain, 9 Fundació Sanitària Sant Pere Claver, trajectories of tobacco smoking in Spanish Mental Health Research Network (CIBERSAM), Spain, 10 Orygen, Parkville, VIC, Australia, people at ultra-high risk for psychosis: 11 Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia, 12 University Associations with clinical outcomes. of Paris, GHU-Paris, Sainte-Anne, C’JAAD, Inserm U1266, Institut de Psychiatrie (CNRS 3557), Paris, Front. Psychiatry 13:869023. France, 13 Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, doi: 10.3389/fpsyt.2022.869023 University of Cologne, Cologne, Germany, 14 Department of Psychiatry and Psychotherapy, Medical COPYRIGHT University of Vienna, Vienna, Austria, 15 Department of Psychiatry and Neuropsychology, School for © 2022 Schirmbeck, van der Ven, Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, Netherlands, 16 Boyette, McGuire, Valmaggia, Mental Health Center Copenhagen, Department of Clinical Medicine, Copenhagen University Kempton, van der Gaag, Hospital, Copenhagen, Denmark Riecher-Rössler, Barrantes-Vidal, Nelson, Krebs, Ruhrmann, Sachs, Rutten, Nordentoft, Group, de Haan Objective: People at ultra-high risk (UHR) for psychosis have a high prevalence and Vermeulen. This is an open-access article distributed under the terms of of tobacco smoking, and rates are even higher among the subgroup that the Creative Commons Attribution later develop a psychotic disorder. However, the longitudinal relationship License (CC BY). The use, distribution or reproduction in other forums is between the course of tobacco smoking and clinical outcomes in UHR permitted, provided the original subjects is unknown. author(s) and the copyright owner(s) are credited and that the original Methods: We investigated associations between tobacco smoking and clinical publication in this journal is cited, in outcomes in a prospective study of UHR individuals (n = 324). Latent accordance with accepted academic class mixed model analyses were used to identify trajectories of smoking practice. No use, distribution or reproduction is permitted which does severity. Mixed effects models were applied to investigate associations not comply with these terms. between smoking trajectory class and the course of attenuated psychotic symptoms (APS) and affective symptoms, as assessed using the CAARMS. Frontiers in Psychiatry 01 frontiersin.org Schirmbeck et al. 10.3389/fpsyt.2022.869023 Results: We identified four different classes of smoking trajectory: (i) Persistently High (n = 110), (ii) Decreasing (n = 29), (iii) Persistently Low (n = 165) and (iv) Increasing (n = 20). At two-year follow-up, there had been a greater increase in APS in the Persistently High class than for both the Persistently Low (ES = 9.77, SE = 4.87, p = 0.046) and Decreasing (ES = 18.18, SE = 7.61, p = 0.018) classes. There were no differences between smoking classes in the incidence of psychosis. There was a greater reduction in the severity of emotional disturbance and general symptoms in the Decreasing class than in the High (ES = −10.40, SE = 3.41, p = 0.003; ES = −22.36, SE = 10.07, p = 0.027), Increasing (ES = −11.35, SE = 4.55, p = 0.014; ES = −25.58, SE = 13.17, p = 0.050) and Low (ES = −11.38, SE = 3.29, p = 0.001; ES = −27.55, SE = 9.78, p = 0.005) classes, respectively. Conclusions: These findings suggests that in UHR subjects persistent tobacco smoking is associated with an unfavorable course of psychotic symptoms, whereas decrease in the number of cigarettes smoked is associated with improvement in affective symptoms. Future research into smoking cessation interventions in the early stages of psychoses is required to shine light on the potential of modifying smoking behavior and its relation to clinical outcomes. KEYWORDS ultra-high risk, psychosis, tobacco, smoking, affective symptoms, trajectories Introduction prospective cohort study found specifically early onset and heavy smoking as risk factors for affective symptoms later The prevalence of tobacco smoking is much higher in in life (14). Accordingly, another study in UHR individuals patients with psychosis (61.6%) (1), and individuals at ultra-high found a larger number of cigarettes smoked per day associated risk for psychosis (UHR) (up to 53%) (2, 3) than in the general with more severe general symptoms including anxiety and population (25.9%) (4). In addition to an increased risk for depression (2). somatic morbidity and mortality, tobacco smoking is associated To the best of our knowledge, different prospective patterns with an increased incidence of psychotic disorders (5, 6) and a of smoking behavior and possible differential associations higher level of symptoms in patients with a psychotic disorder with symptomatic outcome have not yet been investigated (7–9). In the general population and UHR samples, some studies in UHR populations. Applying advanced methods to detect have found an association between tobacco smoking and severity trajectories of tobacco smoking as a possible modifiable risk of subclinical or attenuated psychotic symptoms (10–12), while factor could contribute to the efforts of prevention. We other studies have not (2, 3). The cross-sectional nature of therefore aimed to identify 2-year trajectories of tobacco most studies and categorical approach on tobacco smoking smoking behavior in UHR individuals who were recruited to leaves differences in the severity and course unrecognized. the multicenter European Gene-Environment Interactions (EU- Investigating different long-term trajectories of tobacco smoking GEI) study. Second, we sought to examine sociodemographic and their associations with clinical outcomes in UHR individuals and clinical characteristics associated with identified trajectory may help to identify subgroups in whom the effects of tobacco classes. Finally, we aimed to examine associations between smoking may be particularly detrimental and are therefore most trajectories and the course of attenuated psychotic symptoms suitable for clinical interventions aimed at reducing tobacco (APS), including the risk of transition to psychosis, as well use. It is possible that not all tobacco users are equally at risk as associations between trajectory class and the course of for psychotic symptom exacerbation but that heavy users or emotional disturbance and general symptoms as assessed those who increase their use are at higher risk of poor clinical with the Comprehensive Assessment of At-Risk Mental outcomes. In this line, one prospective study from the Northern States (CAARMS). We hypothesized that more unfavorable Finland Birth Cohort 1986 found a greater risk for subsequent tobacco smoking trajectories would be associated with a more psychosis in the heaviest smoking category (13). Regarding negative course of symptoms and increased risk for transition symptomatic outcome other than psychotic symptoms, a recent to psychosis. Frontiers in Psychiatry 02 frontiersin.org Schirmbeck et al. 10.3389/fpsyt.2022.869023 Methods the disability score of the General Assessment of Functioning Scale (GAF-d) (21). The GAF proved to be a reliable and valid Study design and participants measure of psychiatric disturbances (22, 23). Cannabis use was measured with the Cannabis Experience Questionnaire (CEQ) Data were collected as part of EU-GEI study, from May asking participants whether or not they currently use cannabis. 2010 to April 2015 (15). The study methodology has previously The experience of childhood trauma was assessed with the been described in detail elsewhere (16). In short, the study had Childhood Trauma Questionnaire (CTQ) (24) a 25-item self- a naturalistic, prospective design, consisting of a baseline and report questionnaire assessing traumatic events before the age two or three follow-up assessments, depending on the outcome of 17 including emotional abuse, physical abuse, sexual abuse, measure. Subjects were recruited from 11 mental healthcare emotional neglect, and physical neglect. Good reliability and institutions in London, Amsterdam, The Hague, Vienna, Basel, validity of the CTQ has been reported in the general population Cologne, Melbourne, Kortenberg, Paris, Barcelona and São (25), as well as in patients with psychotic disorders (26). Paulo. The study protocol was approved by the Medical Ethics Attenuated psychotic and affective symptoms were assessed Committees at each participating sites. EU-GEI was conducted with the CAARMS (17), a semi-structured interview with a in accordance with the Declaration of Helsinki. total of 27 items, clustered in seven subscales. For the current Typical age of participants was 18–35 years but not restricted study the following three subscales were used: APS included to due to variation between sites in the age at which persons items measuring unusual thought content, non-bizarre ideas, are accepted by clinical services. Subjects were eligible for the perceptual abnormalities and disorganized speech. Emotional study if they met criteria of the CAARMS (17) for the UHR disturbance included items measuring subjective emotional state classified into one or more of the following three groups: disturbance, observed blunted and observed inappropriate (1) GRD: schizotypal personality disorder or having a first affect. General symptoms included symptoms of depression, degree relative with a psychotic disorder and experiencing a anxiety, obsessive compulsive disorder, mania, suicidality and significant decline in or chronic low psychosocial functioning, self-harm, mood swings, dissociative symptoms and impaired (2) APS: having positive psychotic symptoms that do not reach tolerance to normal stress. the threshold levels for psychosis (3) BLIPS: an experience of Symptom severity was operationalized by summing a recent brief psychotic episode which remitted within a week intensity∗ frequency scores of the corresponding items (27, 28). without use of antipsychotic medications. Psychometric features Good reliability and prognostic validity of the CAARMS of the UHR state have been described elsewhere (18). Exclusion has been reported (17). The prospective course of CAARMS criteria were an intelligence quotient (IQ) below 60 and the positive, emotional disturbance and general symptoms was prior experience of a psychotic episode of more than 1 week as assessed at baseline, 1 and 2 years follow up, in addition to assessed by the CAARMS. the risk of transition defined as the development of psychotic disorder according to the CAARMS (29). Assessment Covariates Participants were invited for face-to-face follow-up meetings A-priori selected potential confounders based on previous at baseline, and 6 months (limited data as this assessment was literature (2, 30) including age, gender, socioeconomic status as introduced later in the course of the study), 12 months and assessed with education in years and current employment status, 24 months after baseline. Information regarding transition to childhood trauma, and cannabis use. psychosis were followed up for 2 years using available clinical records, in case face-to-face meetings were not possible. Tobacco smoking was assessed with the Composite Statistical analysis International Diagnostic Interview (CIDI) (19). The CIDI defines smokers as people who smoked daily during at least 1 Latent class mixed model analysis (LCMM) was used to month over the past 12 months. In addition, participants were empirically identify and visualize clusters of participants with asked how many cigarettes they smoked per day in the time similar trajectories of tobacco smoking over time within one frame they smoked the most during the past months. Studies sample. For reporting of study design and analyses we followed have confirmed good test-retest and interrater reliability of the state-of-the-art guidelines (GRoLTS checklist) (31). CIDI as well as good agreement of CIDI diagnosis with routine Missing values at baseline were replaced applying multiple clinical diagnosis and applied checklists (20). Sociodemographic imputation procedure to be able to include participants with and clinical characteristics at baseline included age, gender, at least one assessment. With maximum likelihood (ML) ethnicity, education in years, current employment status, IQ estimation LCMM then makes use of all available data, and medication use. General functioning was assessed with regardless of intermittent missing data and/or later dropout. Frontiers in Psychiatry 03 frontiersin.org Schirmbeck et al. 10.3389/fpsyt.2022.869023 Subject and time were used to infer latent class trajectories Kaplan–Meier functions with R version 3.6.2. All other analyses of cigarettes smoked per day. The actual individual time of were performed using SPSS version 26. measurement (days since baseline) was used to account for possible deviation around the planned assessment date. The maximum observational period was set to <1,000 days to avoid Results including large outlying values (>2SD). Unconditional LCMM were used to describe the Sample characteristics “raw” latent trajectories of smoking without imposing any conditions/predictors on the model. Starting with a one-class Of the 345 CHR-P individuals participating in EU-GEI, 324 model, we fitted models with increasing numbers of classes provided data on the number of cigarettes smoked per day. until we reached the inflection point of the Akaike information Of these 324 individuals, 39 (12.0%) were assessed with the criterion (AIC) and Bayesian information criterion (BIC). The CIDI and CAARMS at 6-months follow-up, 174 (53.7%) at 1 AIC can be used to identify the point at which the benefits of year and 127 (39.2%) at 2 years follow-up, respectively. Median improved model fit are outweighed by the cost of the model in follow-up period in days was 196 (range 21–272) for 6 months, terms of its complexity and thus helps to prevent overfitting of 380 days (range 187–580) for 1-year, and 757 days (min = the data. In addition, we also examined the somewhat stricter 535 and max = 993) for 2-year assessments. See flow-chart Bayesian information criterion, and the log-likelihood (LL). Supplementary Figure 1. The latter is a measure of goodness of model fit regardless Data regarding missingness at baseline, and comparisons of model complexity. Finally, posterior probabilities of class between dropouts and completers at 1-year are presented membership for each patient were computed using the Bayes as Supplementary Sections 2, 3. Comparing completers and theorem (32). According to the GRoLTS checklist the final dropouts at 1-year follow-up showed no significant differences model was selected based on both statistical (log-likelihood, in number of cigarettes smoked per day, age, gender, current AIC, BIC) and clinical (class size, distinctness of class-specific employment, GAF disability scores, experienced childhood trajectories, likelihood of class membership based on posterior trauma and current cannabis use at baseline. Dropouts had a probabilities) considerations. lower IQ (t = 3.380, p = 0.001), less years of education (t = According to the standard Three-Step Method (31), 4.057, p < 0.001) and were more likely to have an ethnic minority unconditional trajectories were identified as described above background (X = 6.521, p = 0.011). (step 1) and class membership was saved and merged with the Overall, 13 (4.0%) of the 324 participants who were included original data (step 2). To examine associations between baseline in our study completed all four assessments, 103 (31.8%) three, characteristics with most likely trajectory class membership chi- 95 (29.3%) two and 113 (34.9%) one assessment. Attrition within square test and analyses of variance (ANOVA) were conducted the analysis sample seemed mostly at random as the number of for categorical and continuous variables, respectively (step 3). assessments was not associated with tobacco smoking, CAARMS To examine associations between longitudinal outcome outcome, trajectory class membership, gender, ethnicity, current in APS and affective symptoms in relation to trajectories employment, cannabis use, GAF, trauma. Participants with one of smoking, mixed effects models were applied. The model or two assessments were significantly younger compared to included fixed effects for time (as categorical), most likely class those who completed three assessments. membership (based on the LCMM as reported above), their two- way interaction, a random intercept and an autoregressive error covariance structure to account for within-subject correlation Trajectories of smoking behavior over time. Pre-specified contrasts were tested from the model with the low and decreasing trajectory class as reference for A 4-class model was selected for smoking trajectories as the sequential follow-up assessments. Analyses were controlled for associated BIC was the lowest among the tested models (see a priori selected covariates. Table 1). For this 4-class model, mean class probabilities were Associations between trajectory class and risk to transition moderate to high (0.78- 0.95), suggesting individuals had a 78– to psychotic disorders within the 2-year follow-up interval was 95% probability to be correctly assigned to one of the four assessed using Cox proportional hazard regression analyses latent classes. after assessing the proportional hazards assumption. The overall After visual inspection of the identified trajectories, the cumulative risk of psychosis onset for individuals with different smoking classes were labeled as: (i) Persistently High (n = trajectories was plotted with the Kaplan–Meier cumulative event 110), (ii) Decreasing (n = 29), (iii) Persistently Low (n = 165) function and 95% confidence intervals (CI) (33). and (iv) Increasing (n = 20), see Figure 1. Individuals in the LCMM was conducted using the lcmm R package (34), cox Persistently High smoking trajectory class smoked on average proportional hazard regression analyses were analyzed using 15.23 (SD = 8.34) cigarettes per day across time points, patients survival R package (35) and survminer R package (33) to plot in the Low smoking trajectory class smoked no cigarettes or a Frontiers in Psychiatry 04 frontiersin.org Schirmbeck et al. 10.3389/fpsyt.2022.869023 TABLE 1 Model Fit Parameters for LCMM of numbers of cigarettes smoked with One to Five Classes. Number of classes Number of parameters AIC BIC Max log-likelihood Posterior probability Sample size per class 1 11 3794.156 3835.744 −1886.078 2 14 3618.716 3671.647 −1795.358 0.98 137 / 187 3 17 3585.048 3649.321 −1775.524 0.85–0.98 43 / 173 / 108 4 20 3528.232 3603.847 −1744.116 0.78–0.95 110 / 29 / 165 / 20 5 23 3573.718 3660.675 −1763.859 0.59–0.92 43 / 0 / 11 / 173 / 97 AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; LCMM, Latent Class MixedModeling. The bold values indicate the statistically significant results (p ≥ 0.05). FIGURE 1 Model estimated class-specific mean predicted trajectories of tobacco smoking with 95% confidence intervals. consistently low number (mean of 0.24 (0.84) and maximum smoking class reported less current cannabis use compared to of 5 cigarettes per day). For observed individual courses of all other trajectory classes. cigarettes smoked per day by most likely trajectory membership see Supplementary Figures 2A–D. Prospective outcome associated with trajectory class Trajectory class membership and baseline As only a small subgroup of participants provided characteristics CAARMS data at 6 months follow-up (see flow-chart, Supplement Figure 1), we only included baseline, 1 and 2- Comparisons between trajectory classes on baseline years follow-up data of APS, emotional disturbance and characteristics are presented in Table 2. Classes did not general symptoms in the analyses on smoking trajectory and significantly differ in gender, ethnicity, years of education, GAF clinical outcomes. disability scores, IQ score or medication use. The Persistently Although the overall trajectory class by time interaction High and Increasing class was older compared to the Persistently effect for APS was not significant (F = 1.677, p = 0.127), Low smoking class. In term of cannabis use, subjects in the Low pre-specified contrasts with the Low and Decreasing trajectory Frontiers in Psychiatry 05 frontiersin.org Schirmbeck et al. 10.3389/fpsyt.2022.869023 TABLE 2 Baseline information on sociodemographic and clinical variables by trajectory class. Class 1 Class 2 Class 3 Class 4 Group Pairwise (persistent high) (decreasing) (persistent low) (increasing) comparisons comparisons N = 110 N = 29 N = 165 N = 20 Age 23.17 (5.22) 22.00 (5.69) 21.74 (4.61) 24.35 (5.09) F = 2.964, p = 0.032 High > Low, Inc > Low Gender (% male) 58.2 55.2 48.5 65.0 X = 3.731, p = 0.292 Ethnicity (% white) 73.6 65.5 70.9 60.0 X = 1.94, p = 0.585 Years of education 13.98 (3.19) 13.93 (2.00) 14.42 (3.18) 15.40 (2.60) F = 1.485, p = 0.219 Now paid work or 49.1 51.7 64.0 70.0 X = 7.631, p = 0.054 student ( % yes) GAF disability 53.83 (10.90) 55.07 (11.75) 57.19 (13.50) 53.90 (12.34) F = 1.781, p = 0.151 Cannabis use (% yes) 39.1 37.9 12.7 35.0 X = 28.308, p < 0.001 High > Low, Inc > Low, Dec > Low Childhood trauma 9.73 (3.09) 10.87 (3.58) 9.71 (3.11) 9.67 (3.14) F = 1.840, p = 0.146 IQ 95.94 (16.01) 96.22 (19.79) 100.66 (17.40) 99.89 (14.57) F = 2.098, p = 0.101 Medication* 26 (28.6) 5 (20.8) 43 (31.9) 6 (37.5) X = 1.695, p = 0.638 Antidepressants/ Mood stabilizers Anxiolytics 11 (12.1) 0 13 (9.6) 2 (12.5) X = 3.290, p = 0.349 Antipsychotics 11 (12.1) 1 (4.2) 12 (8.9) 3 (18.8) X = 2.850, p = 0.415 SD, standard deviation; Cigs/day, number of cigarettes per day; GAF, global assessment of functioning. *Information from a subsample of n = 266. The bold values indicate the statistically significant results (p ≥ 0.05). classes as reference, revealed a significant increase in APS in classes. Within the 2-year period, 53 (16.8%) UHR individuals the High trajectory class compared to the Low trajectory class transitioned to psychosis. Transition occurred in 15 (14.3%) (ES = 9.770, SE = 4.873, p = 0.046) and Decreasing trajectory individuals from the Persistently High smoking class, 5 (25.0%) class (ES = 18.182, SE = 7.612, p = 0.018) at 2-years follow- from the Increasing, 5 (17.8%) from the Decreasing and 28 up, respectively (Table 3). A significant overall interaction effect (17.6%) form the Persistently Low class. The median time was found for CAARMS emotional disturbance (F = 2.308 to transition was 220.5 days (25th−75th percentiles 122– p = 0.035). Pre-specified contrasts showed more decrease in 398). The last transition was observed at 779 days when 28 the Decreasing trajectory class at 2 years compared with the individuals were still at-risk. Cox proportional hazard regression High (ES = −10.396, SE = 3.414, p = 0.003), Increasing (ES analyses showed no increased cumulative risk to develop a = −11.347, SE = 4.551, p = 0.014) and Low class (ES = psychotic disorder in the High HR = 0.84 (95%CI:0.45–1.6, −11.378, SE = 3.290, p = 0.001) (Table 4). No significant overall p = 0.593), Decreasing HR = 0.75 (95%CI:0.29–1.9, p = interaction effect was found for CAARMS general symptoms 0.556) or Increasing 1.25 (95%CI:0.48–3.3, p = 0.647) trajectory (F = 1.494 p = 0.180). Pre-specified contrasts showed more class compared to the Low class, while again controlling a decrease in the Decreasing trajectory group at 2 years compared priori defined covariates. The corresponding Kaplan–Meier with the High (ES = −22.356, SE = 10.074, p = 0.027), cumulative risk of psychosis curves are depicted in Figure 3. Increasing (ES = −25.582, SE = 13.169, p = 0.050) and Low smoking class (ES = −27.553, SE = 9.783, p = 0.005) (Table 5). Model estimated means for CAARMS APS, emotional Discussion disturbance and general symptoms by trajectory class are presented in Figures 2A–C, respectively. To our knowledge, this is the first study investigating differential trajectories of tobacco smoking in UHR individuals. Our findings show a clustering around four distinct trajectory Transition classes, with the majority of participants (84%) reporting either Transition to psychosis data within the 1,000 days’ persistently high (34%) or persistently low (51%) tobacco timeframe was available in 312 participants of the current smoking across the 2 year assessment period. Smaller subgroups sample, who were assigned to one of the four smoking trajectory showed a longitudinal decrease (9%) or an increase (6%) Frontiers in Psychiatry 06 frontiersin.org Schirmbeck et al. 10.3389/fpsyt.2022.869023 TABLE 3 Results of mixed model analyses of the effect of trajectory class membership on attenuated positive symptoms (APS). Outcome Fixed effects Estimate SE p-value 95% CI APS Reference low smokingclass Intercept 30.574 7.541 0.000 15.735 45.412 Trajectoryclass High −3.329 2.764 0.229 −8.760 2.102 Decreasing 7.835 4.349 0.072 −0.710 16.381 Increasing 1.157 5.215 0.824 −9.088 11.403 Time 1 year −12.691 2.616 0.000 −17.838 −7.542 2 years −17.189 3.135 0.000 −23.358 −11.019 Class*Time High*1 year −0.092 3.949 0.981 −7.865 7.679 High*2 years 9.770 4.873 0.046 0.179 19.360 Decreasing*1 year 1.508 5.849 0.797 −10.004 13.021 Decreasing*2 years −8.412 7.328 0.252 −22.838 6.014 Increasing*1 year 3.652 6.877 0.596 −9.887 17.191 Increasing*2 years 10.324 8.132 0.206 −5.697 26.345 Reference decreasingclass Intercept 38.409 8.402 0.000 21.882 54.936 Trajectoryclass High −11.164 4.46 0.013 −19.935 −2.393 Low −7.835 4.349 0.072 −16.381 0.710 Increasing −6.678 6.300 0.290 −19.056 5.699 Time 1 year −11.181 5.232 0.033 −21.481 −0.881 2 years −25.601 6.627 0.000 −38.650 −12.552 Class*Time High*1 year −1.601 6.013 0.790 −13.437 10.234 High*2 years 18.182 7.612 0.018 3.196 33.168 Low*1year −1.508 5.849 0.797 −13.021 10.004 Low *2 years 8.412 7.328 0.252 −6.014 22.838 Increasing*1 year 2.143 8.233 0.795 −14.065 18.352 Increasing*2 years 18.736 10.012 0.062 −0.985 38.457 The bold values indicate the statistically significant results (p ≥ 0.05). in number of cigarettes smoked. The High and Increasing which limits comparability. In line with our finding of a larger trajectory class was older and reported more cannabis use when reduction of emotional symptoms in the decreasing smoking compared to the Low trajectory class. Identified trajectory classes class, a previous general population study found smoking did not significantly differ on any other sociodemographic or cessation associated with a decrease in depressive symptoms clinical characteristics at baseline. and increased resilience over a two-year period (36). A recent Regarding associations between trajectory class membership meta-analysis showed that smoking discontinuation led to an and the course of symptoms, a persistently high level of improvement of mental health symptoms, also in psychiatric tobacco smoking was associated with an unfavorable course of patients (37). Although our results suggest an unfavorable APS severity at 2-years follow up: in contrast the Persistently course of APS severity at the last assessment in the Persistently Low and Decreasing trajectory classes showed a continuous High smoking class, we did not find an increased risk for decrease in APS severity over time, the Persistently High transition, as has previously been reported for the heaviest smoking class showed increasing severity at 2-years follow-up smoking category in The Northern Finland Birth Cohort 1986 (see Figure 2A). Although no increased risk for transition was study (13). found in the Persistently High or Increasing smoking trajectory Regarding associations between baseline characteristics class, interpretation of this finding is limited by the small and smoking trajectory class membership, associations with transition numbers per class. Furthermore, results show a larger age and cannabis use are in line with earlier research in the decrease in emotional disturbance and general symptoms in the general population (38, 39). A prospective investigation Decreasing trajectory class compared to all other classes (see of first episode psychosis patients found cannabis use Figures 2B,C). to be associated with lower smoking cessation rates, Noteworthy, we can only compare our results with studies specifically in female smokers (40). We also found higher conducted in psychiatric patients or the general population, cannabis use in the Persistently High and Increasing class, Frontiers in Psychiatry 07 frontiersin.org Schirmbeck et al. 10.3389/fpsyt.2022.869023 TABLE 4 Results of mixed model analyses of the effect of trajectory class membership on emotional disturbances. Outcome Fixed effects Parameter Estimate SE p-value 95% CI Emotional Reference low smoking class Intercept 7,123 3,640 0.051 −0.044 14,290 Trajectoryclass High 1,333 1,303 0.307 −1,228 3,895 Decreasing 7,653 2,047 0.000 3,629 11,677 Increasing −1,821 2,456 0.459 −6,648 3,006 Time 1 year −5,923 1,388 0.000 −8,660 −3,186 2 years −5,405 1,442 0.000 −8,254 −2,555 Class*Time High*1 year 1,906 2,094 0.364 −2,220 6,033 High*2 years −,982 2,228 0.660 −5,385 3,420 Decreasing*1 year −1,265 3,240 0.697 −7,653 5,123 Decreasing*2 years −11,378 3,290 0.001 −17,883 −4,873 Increasing*1 year 0.063 3,556 0.986 −6,951 7,077 Increasing*2 years −,031 3,745 0.993 −7,440 7,378 Reference decreasing class Intercept 14,776 4,034 0.000 6,837 22,716 Trajectory class High −6,320 2,101 0.003 −10,449 −2,191 Low −7,653 2,047 0.000 −11,677 −3,629 Increasing −9,474 2,966 0.001 −15,304 −3,645 1 year −7,188 2,927 0.015 −12,960 −1,416 2 years −16,783 2,958 0.000 −22,633 −10,932 High*1 year 3,171 3,324 0.341 −3,382 9,725 High*2 years 10,396 3,414 0.003 3,645 17,146 Low*1year 1,265 3,240 0.697 −5,123 7,653 Low *2 years 11,378 3,290 0.001 4,873 17,883 Increasing*1 year 1,328 4,394 0.763 −7,337 9,993 Increasing*2 years 11,347 4,550 0.014 2,345 20,348 The bold values indicate the statistically significant results (p ≥ 0.05). however not in the Decreasing class compared to the such as nicotine-induced elevated dopamine release (6, 43) Low smoking trajectory class. Due to small samples sizes and shared genetic vulnerability (44). On the behavioral level, we were unable to investigate possible moderating effects both maladaptive coping and misattribution are thought to of gender. play a key role in the relationship between smoking and A previous study showed cannabis use to be a possible symptoms. Smoking may represent a maladaptive strategy mediating factor between adolescent smoking trajectory and of trying to cope with the stress of experienced symptoms, adult mental health (14). In another study, authors directly potentially resulting into even higher levels of symptoms (45). compared the effect of patterns of cigarette and cannabis use Smokers may misattribute the relief of withdrawal symptoms on subsequent psychotic experiences in a prospective cohort such as irritability, anxiety, and depression after smoking to study and found an almost 2-fold increased risk in early- the perception that smoking has psychological benefits, which onset cigarette-only users and an almost 4-fold increased risk also makes them less likely to stop smoking (46). A growing in early-onset cannabis users, compared with non-users (41). body of evidence showed that smoking is not effective to In contrast to a previous study, we did not find childhood alleviate symptoms but stopping smoking is associated with trauma to be associated with an unfavorable smoking course. improvement of mental health in both the general population as Yoon et al. found that adolescents with early childhood trauma clinical samples, arguing against the self-medication hypothesis were 2 to 3 times more likely to show increase in smoking (7, 37, 47). So far, most research suggests a bidirectional behavior compared to the persistently low smoking trajectory relation between smoking and symptoms. Experienced stress class (42). and related emotional distress may heighten the risk of smoking Different non-mutually exclusive mechanisms have been initiation, progression, maintenance, cessation avoidance, and proposed to explain the link between tobacco smoking and relapse (48). Conversely, smoking and associated withdrawal mental health symptoms, including biological explanations symptoms cause stress and emotional disturbances. Lastly, those Frontiers in Psychiatry 08 frontiersin.org Schirmbeck et al. 10.3389/fpsyt.2022.869023 TABLE 5 Results of mixed model analyses of the effect of trajectory class membership on general symptoms. Outcome Fixed effects Parameter Estimate SE p-value 95% CI General Reference low smoking class Intercept 25,909 10,202 ,012 5,833 45,984 Trajectoryclass High 5,588 3,658 ,127 −1,602 12,777 Decreasing 8,787 5,749 ,127 −2,510 20,085 Increasing 5,943 6,894 ,389 −7,605 19,489 Time 1 year −17,557 3,404 ,000 −24,268 −10,866 2 years −14,768 4,136 ,000 −22,893 −6,642 Class*Time High*1 year 0,219 5,094 ,966 −9,810 10,248 High*2 years −5,197 6,304 ,410 −17,584 7,189 Decreasing*1 year −4,602 7,767 ,554 −19,896 10,690 Decreasing*2 years −27,553 9,784 ,005 −46,778 −8,329 Increasing*1 year 6,197 8,469 ,465 −10,481 22,876 Increasing*2 years −1,971 10,575 ,852 −22,753 18,810 Reference decreasing class Intercept 34,697 11,314 ,002 12,440 56,953 Trajectory class High −3,199 5,897 ,588 −14,787 8,388 Low −8,787 5,749 ,127 −20,085 2,510 Increasing −2,845 8,325 ,733 −19,204 13,514 1 year −22,169 6,956 ,002 −35,927 −8,412 2 years −42,321 8,869 ,000 −59,748 −24,893 High*1 year 4,821 7,952 ,545 −10,837 20,479 High*2 years 22,356 10,075 ,027 2,560 42,152 Low*1year 4,602 7,767 ,554 −10,691 19,896 Low *2 years 27,553 9,78 ,005 8,32 46,778 Increasing*1 year 10,799 10,437 ,302 −9,756 31,356 Increasing*2 years 25,582 13,169 ,050 −,296 51,460 The bold values indicate the statistically significant results (p ≥ 0.05). with more severe symptoms might have difficulties in stopping and psychotic or affective experiences. Second, the relatively smoking or decreasing the number of cigarettes smoked per small number of individuals assigned to the increasing and day (49, 50). decreasing trajectory class, in combination with considerable loss to follow-up during the course of the study, limits the reliability of the assessed associations between identified Limitations trajectories and prospective outcome. Although sensitivity analyses (see Supplementary Section 5) resulted in comparable Our results should be interpreted in the light of several tobacco smoking trajectory classes, careful interpretation is limitations. First, from a temporality point of view there warranted and there is a need for replication with prospective was no information available on whether smoking initiation data of a larger sample. Third, loss to follow-up might took place before or after the occurrence of first psychotic further have influenced our findings as dropouts showed experiences, precluding causal interpretations. A large cohort lower IQ, less years of education and were more likely to study investigating longitudinal classes of tobacco use in have an ethnic minority background compared to completers. minors showed that specifically early-onset tobacco use was These differences limit the generalizability of findings. Fourth, correlated with subsequent onset of psychotic experiences generalizability is also limited to help-seeking UHR individuals. (41). In order to determine causal interrelations between Fifth, information on other potential confounders affecting the tobacco smoking and the course of symptoms, future studies course of psychopathology such as the effect of medication should seek to assess tobacco smoking in the daily life use was only available in a subgroup of participants and of UHR individuals. This would allow the investigation of therefore not included in the analyses. In the subgroup with moment-to-moment associations between smoking behavior known medication status, no significant differences between Frontiers in Psychiatry 09 frontiersin.org Schirmbeck et al. 10.3389/fpsyt.2022.869023 FIGURE 2 (A) Model estimated means and 1-standard errors of APS scores predicted by most likely trajectory class membership and assessment time. (B) Model estimated means and 1-standard errors of emotional disturbance scores predicted by most likely trajectory class membership and assessment time. (C) Model estimated means and 1-standard errors of general symptom scores predicted by most likely trajectory class membership and assessment time. identified trajectory class membership were found (see Table 2). and a positive interrelation between reduction in tobacco use Unfortunately, information on psychological interventions and an improvement in affective symptoms over time. More during the course of the study was not available at all sites. research is needed to understand possible covariation and causal To account for between-trajectory differences in cannabis use interactions. Although a causal direction cannot be established we controlled for this variable in subsequent analyses, however and bidirectional interrelations are most probable in the current possible interacting effects of these substances on clinical study, smoking cessation interventions in this vulnerable group outcome are worth investigating in larger samples in the future. should receive more attention. UHR individuals experience less intense and frequent symptoms than individuals with established psychosis and it might be easier in this phase to Conclusion and clinical implications quit smoking. Early intervention smoking cessation programs should therefore be offered when UHR individuals present to Findings showed interrelations between a persistently high psychiatric services. Current findings suggest that differentiating level of tobacco use and an unfavorable course of APS severity UHR individuals based on patterns of smoking behavior might Frontiers in Psychiatry 10 frontiersin.org Schirmbeck et al. 10.3389/fpsyt.2022.869023 Joachim Klosterkötter14 , Iris Lasser16 , Bernadette Winklbaur16 , Philippe A. Delespaul17,18 . 1 Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom; 2 Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands; 3 Icahn School of Medicine at Mount Sinai, department of Psychiatry, New York, NY, United States; 4 Parnassia Group, Psychosis Research Institute, The Hague, Netherlands; 5 Orygen, Parkville, VIC, Australia. 6 Medical Faculty, University of Basel, Basel, Switzerland; 7 Depto Psiquiatria, Escola Paulista de Medicina, LiNC-Lab Interdisciplinar Neuroci ncias Cl nicas, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil; 8 Depto Psiquiatria, Escola Paulista de Medicina, Universidade Federal de São Paulo–UNIFESP, Brazil; 9 CONACYT-Dirección de Investigaciones Epidemiológicas y FIGURE 3 Cumulative event Kaplan–Meier function for risk of Psicosociales, Instituto Nacional de Psiquiatría Ramón de la development of psychotic disorders in 312 ultra-high risk (UHR) individuals stratified for smoking trajectory class. Fuente Muñiz, México, Mexico; 10 Departament de Psicologia Clínica i de la Salut, Universitat Autònoma de Barcelona, Barcelona, Spain; 11 Department of Psychology, University of Illinois at Urbana-Champaign, United States; 12 University Paris contribute to identifying subgroups with a higher risk for an Descartes, Hôpital Sainte-Anne, C’JAAD, Service Hospitalo- unfavorable outcome. Universitaire, Inserm U894, Institut de Psychiatrie (CNRS 3557) Paris, France; 13 Mental Health Center Copenhagen and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Data availability statement Research, CINS, Mental Health Center Glostrup, Mental Health Services in the Capital Region of Copenhagen, University The original contributions presented in the study are of Copenhagen, Copenhagen, Denmark; 14 Department of included in the article/Supplementary Materials, further Psychiatry and Psychotherapy, Faculty of Medicine and inquiries can be directed to the corresponding author. University Hospital, University of Cologne, Cologne, Germany; 15 Psyberlin, Berlin, Germany; 16 Department of Psychiatry and Ethics statement Psychotherapy, Medical University of Vienna, Vienna, Austria; 17 Department of Psychiatry and Neuropsychology, School The studies involving human participants were reviewed for Mental Health and Neuroscience, Maastricht University and approved by Local Medisch Ethische Toetsingscommissie Medical Centre, Maastricht, Netherlands; 18 Mondriaan Mental (METC), University Medical Center Amsterdam, Location Health Trust, Heerlen, Netherlands. AMC, Amsterdam, the Netherlands (NL32721.018.10). The patients/participants provided their written informed consent to Author contributions participate in this study. FS and JV: literature search, figures, data analysis, data interpretation, and writing. EV and L-LB: literature search, EU-GEI high risk study group figures, data interpretation, and writing. PM, LV, MK, MG, AR- R, NB-V, BN, M-OK, SR, GS, BR, MN, and LH: conception Maria Calem1 , Stefania Tognin1 , Gemma Modinos1 , and design of the cohort study and critical feedback. All authors Sara Pisani1 , Emily P. Hedges1 , Eva Velthorst2,3 , Tamar contributed to the article and approved the submitted version. C. Kraan2 , Daniella S. van Dam2 , Nadine Burger4 , Athena Politis5 , Joanne Goodall5 , Stefan Borgwardt6 , Erich Studerus6 , Ary Gadelha7 , Elisa Brietzke8 , Graccielle Asevedo7 , Elson Funding Asevedo7 , Andre Zugman7 , Tecelli Domínguez-Martínez9 , Manel Monsonet10 , Lidia Hinojosa10 , Paula Cristóbal10 , The European Network of National Schizophrenia Networks Thomas R. Kwapil11 , Mathilde Kazes12 , Claire Daban12 , Julie Studying Gene-Environment Interactions (EU-GEI) Project Bourgin12 , Olivier Gay12 , Célia Mam-Lam-Fook12 , Dorte was funded by grant agreement HEALTH-F2-2010-241909 Nordholm13 , Lasse Randers13 , Kristine Krakauer13 , Louise (Project EU-GEI) from the European Community’s Seventh Birkedal Glenthøj13 , Dominika Gebhard14 , Julia Arnhold15 , Framework Programme. Additional support was provided by Frontiers in Psychiatry 11 frontiersin.org Schirmbeck et al. 10.3389/fpsyt.2022.869023 a Medical Research Council Fellowship to M Kempton (grant Publisher’s note MR/J008915/1). BN was supported by an NHMRC Senior Research Fellowship (1137687). All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the Acknowledgments reviewers. 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