{"paper_id":"f37eb5ee-1c87-4e6a-9b20-139e11aefef5","body_text":"Article\nTwenty-Five and Up (25Up) Study: A New Wave of the Brisbane\nLongitudinal Twin Study\nBrittany L. Mitchell1,2,*, Adrian I. Campos1,3,*, Miguel E. Rentería1,2,3, Richard Parker1, Lenore Sullivan1, Kerrie McAloney1,\nBaptiste Couvy-Duchesne4,5, Sarah E. Medland 1, Nathan A. Gillespie 6, Jan Scott 7,8, Brendan P. Zietsch 9, Penelope\nA. Lind1, Nicholas G. Martin 1 and Ian B. Hickie 7\n1Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia, 2Institute of Health and Biomedical\nInnovation, Queensland University of Technology, Brisbane, QLD, Australia, 3Faculty of Medicine, The University of Queensland, Brisbane QLD, Australia,\n4Institute for Molecular Bioscience, The University of Queensland, Brisbane QLD, Australia, 5Queensland Brain Institute, The University of Queensland, Brisbane\nQLD Australia, 6Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA, 7Brain and Mind Centre,\nUniversity of Sydney, Sydney, NSW, Australia, 8Institute of Neuroscience, Newcastle University, Newcastle, UK and 9School of Psychology, The University of\nQueensland, Brisbane QLD, Australia\nAbstract\nThe aim of the 25 and Up (25Up) study was to assess a wide range of psychological and behavioral risk factors behind mental illness in a large\ncohort of Australian twins and their non-twin siblings. Participants had already been studied longitudinally from the age of 12 and most\nrecently in the 19Up study (mean age = 26.1 years, SD = 4.1, range = 20–39). This subsequent wave follows up these twins several years later\nin life (mean age = 29.7 years, SD = 2.2, range = 22–44). The resulting data set enables additional detailed investigations of genetic pathways\nunderlying psychiatric illnesses in the Brisbane Longitudinal Twin Study (BLTS). Data were collected between 2016 and 2018 from 2540 twins\nand their non-twin siblings (59% female, including 341 monozygotic complete twin-pairs, 415 dizygotic complete pairs and 1028 non-twin\nsiblings and singletons). Participants were from South-East Queensland, Australia, and the sample was of predominantly European ancestry.\nThe 25Up study collected information on 20 different mental disorders, including depression, anxiety, substance use, psychosis, bipolar and\nattention-deficit hyper-activity disorder, as well as general demographic information such as occupation, education level, number of children,\nself-perceived IQ and household environment. In this article, we describe the prevalence, comorbidities and age of onset for all 20 examined\ndisorders. The 25Up study also assessed general and physical health, including physical activity, sleep patterns, eating behaviors, baldness,\nacne, migraines and allergies, as well as psychosocial items such as suicidality, perceived stress, loneliness, aggression, sleep–wake cycle, sexual\nidentity and preferences, technology and internet use, traumatic life events, gambling and cyberbullying. In addition, 25Up assessed female\nhealth traits such as morning sickness, breastfeeding and endometriosis. Furthermore, given that the 25Up study is an extension of previous\nBLTS studies, 86% of participants have already been genotyped. This rich resource will enable the assessment of epidemiological risk factors, as\nwell as the heritability and genetic correlations of mental conditions.\nKeywords: Mental health; cohort study; longitudinal; genetics\n(Received 20 April 2019; accepted 13 May 2019)\nThe Twenty-Five Up Study (25Up: Can we predict who will develop\nmental disorders? A long-term study of adolescent twins) ran from\n2016 to 2018 and assessed a wide range of mental health and behav-\nioral disorders in a young Australian cohort consisting of twins and\ntheir non-twin siblings. This study is an extension of its predecessor,\nthe 19Up study, and the Brisbane Longitudinal Twin Study (BLTS;\nCouvy-Duchesne et al.,2018;G i l l e s p i ee ta l . ,2013), which examined\nmental health and alcohol and substance use (Gillespie et al., 2009)\nin a large sample of young twin adults (mean age = 26.1, range\n= 19.7–38.6). 25Up aimed to assess individuals who were slightly\nolder than those in 19Up, with the similar overall aim of collecting\ndata that will help shed light on the risk factors and pathways\ninvolved in the development of affective disorders. These studies re-\npresent a large collection of a wide array of phenotypic data, includ-\ning psychological and environmental variables of relevance for\nmental health and disorders. Some of these include personality\ndimensions and psychological symptoms, mental disorders that\nmeet the Diagnostic and Statistical Manual of Mental Disorders\n(5th ed., DSM-5; American Psychiatric Association, 2013) and five\ndiagnostic criteria: alcohol and substance use and misuse, migraine,\nsleep behaviour, as well as neurobiological correlates (neuroimag-\ning) and genome-wide genotyping ( Figure 1 ). Notably, the mean\nage increase between the individuals participating in the 19Up\nand 25Up studies was 3.6 years (Supplementary Figure 1).\nAuthor for correspondence: Brittany L. Mitchell, Email: Brittany.mitchell@\nqimrberghofer.edu.au\n*Authors contributed equally.\nCite this article: Mitchell BL, Campos AI, Rentería ME, Parker R, Sullivan L,\nMcAloney K, Couvy-Duchesne B, Medland SE, Gillespie NA, Scott J, Zietsch BP,\nLind PA, Martin NG, and Hickie IB. (2019) Twenty-Five and Up (25Up) Study: A New\nWave of the Brisbane Longitudinal Twin Study. Twin Research and Human Genetics\n22: 154 –163, https://doi.org/10.1017/thg.2019.27\n© The Author(s) 2019.\nTwin Research and Human Genetics (2019), 22, 154 –163\ndoi:10.1017/thg.2019.27\nhttps://doi.org/10.1017/thg.2019.27 Published online by Cambridge University Press\n\nThe 25Up study was structured so as to collect further psycho-\nlogical, behavioral and subclinical assessments, with the aim of\nusing these to understand the factors involved in the development\nand progression of mental health disorders from late teenage years\nto early adulthood. This new wave of the BLTS will allow us to gain\ninsight into the genetic basis of behavioral phenotypes, mental\nhealth and comorbidities. Finally, as with the 19Up study, data col-\nlection was also designed to contribute to twin and genetic consor-\ntia in psychiatry, personality and behavior genetics. The aim of the\npresent article is to describe the key characteristics of the cohort\nand give examples of important themes (e.g. comorbidity and pri-\nmacy of onset in dual diagnoses) that can be examined in future in\nmore detailed analyses of the cohort data, and also to highlight the\nopportunities for new collaborations that could make use of the\nrich 25Up data set.\nMethods\nStudy Contact\nBetween February 2016 and October 2018, 3785 individuals were\napproached via email to participate in the 25Up study, which\nentailed a detailed, three-part, self-report online survey (TFU1,\nTFU2, and TFU3). There was a 67.1% response rate (2540 individ-\nuals) for completing the first part of the survey (TFU1) and a 62%\nresponse rate (2343 individuals) for completing all three parts of\nthe survey (TFU1, TFU2, and TU3). Follow-up was conducted\nby a phone call 1 –2 weeks after the initial contact email.\nSupplementary Figure 2 depicts the variation in participant\nresponse.\nPart 1 of the survey (TFU1, N = 2540) assessed general health,\nmedical and treatment history as well as lifetime diagnoses. TFU2\n(N = 2484) expanded on TFU1, assessing other physical character-\nistics such as baldness, skin tone and acne, as well as a multitude of\nconditions and behavioral characteristics. TFU3 ( N = 2343) was\nthe final part of the survey, containing questions addressing sexual\nidentity, quality of romantic relationships, sexual and romantic\npreferences. For further information regarding the specific items\naddressed in each section, the instruments used to assess these\nitems and number of participants answering each section, refer\nto Table 1 .\nStatistical Analyses\nIn this study, we focus on describing the prevalence, risk factors\n(i.e. age and sex), comorbidity and primacy of onset of self-\nreported mental health variables. A study assessing specific diag-\nnoses using the relevant questionnaires is outside the scope of this\ncohort description. Age and sex effects were assessed by means of\nlogistic regression modeling in Python using the statsmodels mod-\nule. The variables of interest were included as covariates and their\nsignificance was assessed using Wald tests.\nDisorder Clustering and Comorbidity Analysis\nAll pairwise tetrachoric correlations ( ρ) were calculated using\nR (3.1.1) and the psych package (v1.4.3). Briefly, the presence or\nabsence of a disorder was coded as a binary vector, and all disorders\nwere compared by computing their tetrachoric correlation coeffi-\ncient. Next, a hierarchical clustering was performed using Ward ’s\nminimum intracluster variance objective function (Ward, 1963)\nwith a distance metric calculated as 1 − ρ. Mixed-effects logistic\nregressions were used to calculate the increase in risk on a second\ndisorder given the occurrence of a given disorder. The function\nglmer from the R library lme was used, including age and sex as\nfixed effects, and the family ID as grouping factor for the random\neffects variance –covariance structure. For each pair of disorders,\none was modeled as an outcome variable, while the other one\nwas used as a predictor. The p-values of the association of one dis-\norder with another were subjected to a Bonferroni multiple testing\ncorrection.\nFig. 1. Summary of the BLTS data collection. Longitudinal: vitamin D; infections (anti-\nbodies); neuroticism junior Eysenck personality questionnaire (JEPQ) Neuroticism –\nExtraversion–Openness inventory (NEO); psychiatric signs (SPHERE). Cross-sectional:\nhair cortisol; cognition (verbal, performance IQ, working memory, and information\nprocessing); binocular rivalry (rivalry rate); brain imaging (multimodal magnetic res-\nonance imaging); substance use (alcohol, tobacco, and recreational drugs); sleep pat-\nterns (actigraphy); psychiatric diagnoses (Composite International Diagnostic\nInterview); life events/social support/relationships (e.g. early home environment, fam-\nily relationships, traumatic events, socioeconomic factors). Note: Sample size is only\nindicative as some of the waves are still recruiting new participants. Figure adapted\nfrom Couvy-Duchesne et al. ( 2018) and Gillespie et al. ( 2013).\nTwin Research and Human Genetics 155\nhttps://doi.org/10.1017/thg.2019.27 Published online by Cambridge University Press\n\nFig. 2. Kaplan–Meier curves stratified by sex. Kaplan–Meier curves depict the self-reported age of onset for three disorders (depression, anorexia and bipolar disorder,\nrespectively) in the 25Up cohort.\nTable 1. Variables examined in the different surveys of the 25Up study\nTFU1 Instrument\nApproximate\nN\nDemographics Young and Well Cooperative\nResearch Centre (YAW CRC)\n2540\nGeneral health and\nwell-being\nQIMR 16Up 682 –2535\nPuberty Perceived pubertal timing\n(Cance et al., 2012)\n1310–2476\nCognition Self-perceived 2481\nMedical history National Comorbidity Survey (NCS)\nscreener\n1949\nMorning sickness Nausea and Vomiting of Pregnancy\n(NVP) Short form\n1777\nBreast feeding NVP short form 550\nPregnancy-related\ndepression\nscreen 300 –655\nMental health Global Assessment of Functioning\n(GAF)\n2489\nMental health NCS Screener 2489\nMental health Treatment history 874\nAlcohol/substances Treatment history 843\nFamily mental\nhealth\nYAW CRC 2379\nLifetime\nexperiences —\nanxiety disorders\nNCS screener 2455\nPanic disorder NCS screener 2493\nSocial phobia NCS screener 1563\nSpecific phobia NCS screener 2493\nSeparation anxiety\ndisorder\nNCS screener 2370\nMajor Depressive\nDisorder (MDD)\nNCS screener 2500\nMania–hypomania NCS screener 2445\nIrritable depression NCS screener 2488\nOppositional\ndefiance disorder\nNCS screener 2487\nConduct disorder NCS screener 2513\nADHD NCS screener 2298\nIntermitent\nexplosive disorder\nNCS screener 2512\n(Continued)\nTable 1. (Continued )\nTFU1 Instrument\nApproximate\nN\nCurrent mental\nhealth\nBMRI 2536\nCurrent mental\nhealth\nKessler 10 2538\nDay functioning Composite International Diagnostic\nInterview (CIDI)\n2529\nPersonality NEO TIPI Scale 2536\nHypo mania\nscreener\nFive-item self-report 2402\nPsychosis screener Self-rated scale based on CIDI 2513\nFunctional\nimpairment\nGAF 1442\nFunctional\nimpairment\nSocial and Occupational\nFunctioning Assessment Scale\n1442\nSuicidality Self-harm/past month/lifetime 2531\nAlcohol or other\nsubstance misuse\nFrom QIMR 16Up 2369\nPhysical health\nand activity\nschedule\nAustralian Bureau of Statistics\nNational Survey\n2200\nSleep–wake cycle BMRI from 19Up 2489\nEating behaviors YAW CRC 2465\nBody image YAW CRC 2534\nRelationships and\nsocial networking\nQIMR 16Up 2514\nTFU2 Instrument\nApproximate\nN\nDemographics\nbeing a twin\nNA 2141\nPhysical\nphenotypes —\nPart 1\nHWB 1490\nPhysical\nphenotypes —\nPart 2\nHWB 2468\nMigraines HWB 2434\nMigraines —\nfemales\nHWB 566\nAsthma eczema\nallergies\nHWB 2403\nEndometriosis HWB 1497\n(Continued)\n156 Brittany L. Mitchell et al.\nhttps://doi.org/10.1017/thg.2019.27 Published online by Cambridge University Press\n\nPower Analysis\nPhenotype simulations (of continuous traits) and power analyses\nwere performed using the powerFun, MASS and OpenMx R libra-\nries. Multivariate data were simulated using the mvrnorm function\nspecifying a variance –covariance structure determined by a linear\ncombination of varying values for the additive genetic (A) and\ncommon environmental (C) components. The sample sizes of\nthese simulated distributions were identical to the number of\ntwin-pairs available in the 25Up cohort. OpenMx was used to fit\nan ACE model to the simulated data. The significance of the A\nand C variance components was assessed using a log likelihood\nratio test (the mxCompare function of OpenMx) comparing the\nfull model to a model only including the other two components\n(e.g. ACE vs. CE). This procedure was repeated 100 times, and\npower was estimated by counting the number of iterations in which\nthe studied component was rejected. A similar procedure was used\nto simulate and assess the power to detect a genetic correlation.\nTwo phenotypes were simulated with a specified underlying\ngenetic correlation, and a bivariate ACE model was used to assess\nthe significance of the genetic correlation. The results are available\nin the Supplementary Figure 3 .\nResults\nCohort Description\nOf the 3785 individuals invited to participate in the study, 62% of\nthe twins and non-twin siblings provided complete data. Overall,\nfemales were slightly over-represented among the 25Up respon-\ndents, comprising 52% of the invited population but 59.5% of\nactual ascertained participants ( Table 2). Survey completion rates\nwere high and, of the participants who had completed TFU1, 2484\n(97.8%) completed the second section of the survey (TFU2), and\n2343 (92.2%) completed the third part (TFU3). Females tended\nto complete all sections of the survey more often than males, with\n95.3% of females completing all three parts compared to 87.7% of\nmales. The greatest dropout for men was between completing Part\n2 (96.3%) and Part 3 (87.7%) of the survey. The mean age of all\nparticipants was 29.7 (SD = 4.2, range = 22–44; Supplementary\nFigure 2), consisting of 341 complete monozygotic pairs, 415 dizy-\ngotic pairs, 125 MZ singletons, 269 DZ singletons and 634 siblings.\nTwins and non-twin siblings did not differ in maximum educa-\ntional attainment level ( p = .57), but nontwin individuals were\nolder (30.5 vs. 29.4, p = .001), more likely to be married (62%\nvs. 55%, p = .001) and less likely to have children compared with\nco-twins (49.6% vs. 57.5%, p = .001). Ethnically, the cohort reflects\nthe population structure of families with twins in Queensland at\nthe time of recruitment, with a majority of participants having\nEuropean ancestry and minorities of predominantly Asian ances-\ntry (Gillespie et al., 2013).\nAll participants had been invited to complete previous BLTS\n(Gillespie et al., 2013; Wright & Martin, 2004) studies ( Figure 1).\nTherefore, variables such as height, weight, personality, psychiatric\nsigns, sleep patterns, migraine and blood samples (hematological\nand immunological measures: e.g. antibodies markers of infections,\nvitamin D) were collected longitudinally in the BLTS, with up to five\ntime points for some phenotypes (Figure 1). A noteworthy example\nis the assessment of personality traits using the Neuroticism –\nExtraversion–Openness (NEO) Personality Inventory-related scales\n(Costa & McRae, 1992). Although some cohorts present different\nversions of the NEO (due to updates and study design changes),\nthe overall constructs measured should remain highly isomorphic,\nTable 1. (Continued )\nTFU1 Instrument\nApproximate\nN\nPhysical\nPhenotypes —\nPart 3\nHWB 2449\nCurrent mental\nhealth\nPerceived stress scale 2464\nCurrent mental\nhealth\nBorderline, autism and loneliness\n(PAI/BOR and SRS)\n2460\nCurrent mental\nhealth\nAdult ADHD Self-Report 2440\nCurrent mental\nhealth\nBuss Perry Aggression Questionnaire 2432\nSleep–wake cycle Pittsburgh Sleep Quality\nAssessment)\n2424\nSleep apnea\nscreen\nMaislin et al., 1995 2348\nCaffeine and\ngeneral sleep\nquestions\nNA 2413\nSleep–wake cycle Insomnia Severity Index 2379\nEating behavior\nand anorexia\nnervosa\nFrom QIMR 16Up 2394\nSocial networking\nand relationships\nPBI 2321\nSocial networking\nand relationships\nKessler perceived social support 2241\nStressful life events List of threatening\nexperiences\n2301\nTechnology use YAW CRC 2405\nGames and\ngambling\nProblem gambling severity index 1942\nCyberbullying and\nsexting\nNA 2160\nTFU3 Instrument\nApproximate\nN\nDemographics NA 2331\nRomantic\npreferences\nDesigned by Zietsch (from 19Up) 2324\nRomantic\npreferences\nFluid gender identity based on\nMulti-GIQ (Joel et al., 2014)\n2295\nRomantic\npreferences —\nfemales\nContraceptives 1430\nSNR-disgust Three domain disgust scale 2280\nSociosexuality NA 2290\nSelf-rated physical\nattractiveness\nNA 2286\nAttraction NA 2254\nRelationships NA 1736\nPartner section Cognition and self-report IQ,\neducation level, SPHERE, height and\nweight and eye color\n9\nNote: TFU1, TFU2 and TFU3 refer to the three parts of the online questionnaire. Approximate\nN represents the average of not null respondents for representative (not follow-up) questions\nof each section.\nTwin Research and Human Genetics 157\nhttps://doi.org/10.1017/thg.2019.27 Published online by Cambridge University Press\n\nFig. 3. Disorder comorbidity within the 25Up study. Lower\ntriangle depicts a hierarchical clustering (Ward ’s method)\nof the disorders based on their self-reported lifetime co-\noccurrence (tetrachoric correlations). Upper triangle por-\ntrays lifetime comorbidity odds ratio (ordered based on\nthe clustering of the lower triangle). Note: *p < .05 after\nmultiple testing correction ( α < .000146).\nTable 2. Demographics of the 25Up cohort\nTotal Females Males\nCompleted Part 1 (TFU1) 2540 (100.0%) 1523 (100.0%) 1017 (100.0%)\nCompleted Part 2 (TFU2) 2484 (97.8%) 1505 (98.8%) 979 (96.3%)\nCompleted Part 3 (TFU3) 2343 (92.2%) 1451 (95.3%) 892 (87.7%)\nAge (SD; range) 29.7 (4.2; 22 –44] 29.4 (4.3; 22 –44) 29.9 (4.2; 22 –41)\nSingle 679 (26.7%) 393 (25.8%) 286 (28.1%)\nMarried 1502 (59.1%) 916 (60.1%) 586 (57.6%)\nRelationship 296 (11.7%) 175 (11.5%) 121 (11.9%)\nSeparated but married 35 (1.4%) 22 (1.4%) 13 (1.3%)\nDivorced 23 (0.9%) 12 (0.8%) 11 (1.1%)\nWidowed 0 (0.0%) 0 (0.0%) 0 (0.0%)\nNo formal education 10 (0.4%) 6 (0.4%) 4 (0.4%)\nPrimary school 0 (0.0%) 0 (0.0%) 0 (0.0%)\nJunior high school 36 (1.4%) 10 (0.7%) 26 (2.6%)\nSenior high school 259 (10.2%) 156 (10.2%) 103 (10.1%)\nCertificate or diploma 679 (26.7%) 358 (23.5%) 321 (31.6%)\nDegree 1,054 (41.5%) 680 (44.6%) 374 (36.8%)\nPostgraduate diploma, masters or PhD 500 (19.7%) 312 (20.5%) 188 (18.5%)\n158 Brittany L. Mitchell et al.\nhttps://doi.org/10.1017/thg.2019.27 Published online by Cambridge University Press\n\nas we would expect with biometrical phenotypes (such as height)\nand other behavioral instruments such as the Somatic and\nPsychological HEalth REport (SPHERE; Hickie et al., 2001)( a l s o\nused to assess mental health on most of the BLTS). In addition,\ngenome-wide single nucleotide polymorphism genotypes are cur-\nrently available for 86% ( N = 2205) of participants.\nFindings to Date\nThe 25Up study has collected information on 20 different psychi-\natric or affective disorders (see Table 3 ) and a range of lifestyle,\nhealth and behavioural traits ( Table 1). Overall, ∼20% of the par-\nticipants self-reported a lifetime major mental health problem\naffecting their everyday life. This estimate is consistent with esti-\nmates for the Australian population (Department of Health,\n2009). Among the disorders examined, general anxiety (16%,\nN = 402) and depression (17.3%, N = 436) were the most prevalent\ndiagnoses. Following these, panic, substance use, sleep, and post\ntraumatic stress disorders (PTSD) had the highest prevalence in\nthe 25Up cohort (3.3%, 2.9%, 2.5% and 2.4%, respectively;Table 3).\nSex Differences\nThe differences in response rates and fallout rates between males\nand females motivated the assessment of whether self-reported life-\ntime prevalence of mental health problems was associated with sex\nin this cohort. Both depression and general anxiety were far more\nprevalent in females than males (11.6% vs. 5%, p < .001 and 15.3%\nvs. 3.4%, p < .001, respectively). PTSD, obsessive-compulsive dis-\norder (OCD), panic disorder, general eating disorders and bulimia\nand anorexia showed significant sex effects, all having a higher\nprevalence in females ( Table 3 ). In addition, PTSD was the only\ndisorder to show a nominally significant increased prevalence with\nage (p = .006), although this did not survive correcting for multiple\ntesting, but would be consistent with a higher probability for the\noccurrence of a traumatic event as time passes. No other\ndifferences reached statistical significance in this cohort ( Table 3).\nAge of Onset\nThe age of onset of the examined self-reported phenotypes (disor-\nders) was not significantly different between males and females. In\nthe case of depression, females had a slightly earlier age of onset\n(18.8 years vs. 20.5 years, p ≤ .001; Figure 2 and Supplementary\nFigure 4 ). The youngest mean age of onset was for autistic spec-\ntrum disorders, including Asperger syndrome (mean = 5.5 years,\nSD = 6.5), while the oldest mean age of onset recorded in this\ncohort was for psychosis (mean = 24.4 years, SD = 5.9). Age of\nonset estimates was not available for alcohol dependence and mis-\nuse as only the age at alcohol drinking initiation (mean = 16.0\nyears) was collected. Notably, the mean age of onset of anorexia,\nbulimia and eating disorders were all during adolescence ( ∼16\nyears), while the mean age of onset for other disorders was mostly\naround young adulthood ( Table 4 and Supplementary Figure 4 ).\nDisorder Comorbidity\nThere is a known overlap between affective, anxiety and substance\nuse disorders (Kessler et al., 1996; Merikangas et al., 1996; Regier\net al., 1990). Within the 25Up study, evidence of the relationships\nbetween the 20 disorders was observed through hierarchical clus-\ntering. Self-reported history of psychosis and schizophrenia\nTable 3. Lifetime prevalence of self-reported mental health disorders in the 25Up study\nN Prevalence \n(%)*\nMale Prevalence  \n(%)\nFemale Prevalence  \n(%) OR —95%C.I.— Sex pvalue Age pvalue\nAnyMHP 519 20.6 18.6 21.7 0.030 0.002\nADD ADHD 32 1.3 1.5 1.1 0.360 0.060\nAnorexia 37 1.5 0.3 2.2 0.001 0.190\nAnxiety 402 16.0 8.9 20.5 0.000 0.140\nAu/g415sm 13 0.5 0.9 0.3 0.040 0.410\nBipolar Disorder 28 1.1 1.2 1.1 0.740 0.730\nBulimia 27 1.1 0.1 1.7 0.005 0.310\nConduct Disorder 2 0.1 0.1 0.1 0.750 0.690\nDepression 436 17.3 12.9 20.0 0.000 0.290\nDrug addic/g415on 38 1.5 1.3 1.6 0.460 0.840\nEa/g415ng disorder 57 2.3 0.1 3.7 0.000 0.210\nMemory disorder 12 0.5 0.6 0.4 0.500 0.650\nNarcolepsy 1 0.0 0.1 0.0 1.000 0.380\nOCD 45 1.8 0.7 2.5 0.002 0.510\nPMS 28 1.1 0.0 1.8 1.000 0.460\nPTSD 61 2.4 1.4 3.1 0.005 0.004\nPanic 83 3.3 1.5 4.5 0.000 0.330\nPsychosis 12 0.5 0.5 0.5 0.920 0.830\nSUD 73 2.9 3.0 2.8 0.910 0.190\nSchizophrenia 9 0.4 0.4 0.3 0.790 0.880\nSleep disorder 63 2.5 2.2 2.7 0.290 0.290\nNominally significant P values are highlighted in bold. Analyses were performed by using a logis/g415c regression accoun/g415ng simultaneously for the effects of sex (females as a \nreference) and age. OR - odds ra/g415o, C.I.- 95% confidence intervals. MHP- mental health problem. ADD/ADHD- A/g425en/g415on deficit disorder/A/g425en/g415on deficit and hyperca/g415vity \ndisorder.  OCD- obsessive compulsive disorder. PMS - premenstrual syndrome. PTSD - post trauma/g415c stress disorder. SUD- Alcohol and substance misuse. , MHP- mental health \nproblem. *Prevalences calculated based only on not null values (par/g415cipants that responded to the sec/g415on (N=2516)\nTwin Research and Human Genetics 159\nhttps://doi.org/10.1017/thg.2019.27 Published online by Cambridge University Press\n\nclustered together, as did anorexia, bulimia and eating disorders.\nAdditional clusters were attention deficit and hypercativity disor-\nder (ADHD) and autism, as well as depression, anxiety, PTSD and\npanic disorder (Figure 3). In order to quantify the increased risk of\na condition, given the presence of a second condition (while cor-\nrecting for the effects of age, sex and relatedness), we used a mixed-\neffects logistic regression approach (see Methods). All significant\nassociations between the disorders studied were positive while\nnone of the negative associations (i.e. decreased risk) reached stat-\nistical significance. Individuals who were rated as positive for an\nalcohol and other substance misuse were more likely to score pos-\nitive for memory disorder and depression ( p < .05 after multiple\ntesting correction). Furthermore, a significant risk increase was\nalso detected between psychosis and schizophrenia and between\ndepression and a variety of other comorbid disorders, including\nPTSD, sleep disorder and premenstrual syndrome (PMS) among\nothers ( Figure 3 ). Interestingly, of those participants (both males\nand females) reporting substance misuse ~50% reported also hav-\ning another mental health disorder, the majority reported that the\nsubstance abuse disorder followed the mental condition (Figure 4).\nThe 25Up Cohort Will Enable Longitudinal Analyses\nThe unique strength of the 25Up study is that it is the latest wave in\na longitudinal study spanning more than 20 years. This allows for\nunparalleled analysis of the dynamic nature of mental health var-\niables as individuals progress through adolescence and into young\nadulthood. For example, when comparing the lifetime prevalence\nof self-reported psychotic symptoms (CIDI Psychosis Screener;\nScott et al., 2006) in the previous 19Up cohort to those in 25Up,\nwe found that, as expected, the prevalence for most symptoms\nhas increased in the 25Up. However, there were instances where\nthe lifetime prevalence decreased in the 25Up cohort, pointing\nto possible recall bias. Nonetheless, with those that increased,\nthe increase was heterogeneous, that is, the prevalence of some\nsymptoms did not change significantly, whereas others doubled\n(Figure 5 ). The extent to which this heterogeneity is caused by\nrecall bias or other factors might be studied in the future.\nNotably, the BLTS includes several potential isomorphic instru-\nments (such as depression and personality) that will enable genetic\nand environmental longitudinal analyses.\nDiscussion\nHere we described the demographics and self-reported history of\nmental disorders in the 25Up cohort. Our findings are consistent\nwith previous mental illness prevalence estimates in Australia\n(Lawrence et al., 2016; Liddell et al., 2016), and observations of\nwomen having a higher prevalence of PTSD (Galea et al., 2005;\nGavranidou & Rosner, 2003), panic disorder (Crowe et al., 1983;\nWeissman et al., 1997) eating disorders (Mitchison & Hay, 2014),\ndepression (Weissman & Klerman, 1977) and anxiety disorders\n(Bandelow & Michaelis,2015). We detected a significant association\nbetween sex and OCD prevalence, an observation not previously\nmade in adults (Karno et al., 1988;L ´opez-Solà et al., 2014), but only\nin individuals with an age of onset before or during adolescence\n(Grant, 2014). We also identified a nominal association of PTSD\nwith age, although a plausible explanation of this effect could be\nrelated to a higher probability of a stressful event with age, but this\nassociation did not survive multiple testing corrections.\nThere is consistent evidence for a broad distinction between\nexternalizing and internalizing disorders (Cosgrove et al., 2011;\nKrueger et al., 1998). Nonetheless, our findings suggest high levels\nof lifetime comorbidity between affective and substance use disor-\nders. This is consistent with recent studies detecting a genetic over-\nlap between psychological distress, somatic distress, affective\ndisorders and substance use (Chang et al., 2018) and with the\nself-medication hypothesis suggesting substance misuse as a\ncoping mechanism (Marshall, 1994; Myrick & Brady, 2003).\nNotably, we identified a high comorbidity between externalizing\nFig. 4. Most participants reported a mental disorder prior to substance abuse. Bar\nplots depicting the number of participants reporting precedence of either a mental\nor a substance abuse disorder. Only participants that reported both type of conditions\nresponded to this question ( n = 66).\nTable 4. Disorder age of onset in the 25Up study\nTrait Males Females p-value\nADD/ADHD 9.91 (3.2) 10.4 (6.09) 0.8\nAnorexia 23.5 (6.5) 16.6 (5.1) 0.1\nAnxiety 19.2 (6.7) 18.4 (6.4) 0.4\nAutism/Asperger 4.0 (4.53) 8.5 (8.5) 0.5\nBipolar disorder 21.6 (5.5) 19.5 (4.3) 0.3\nBulimia 30.0 (0.0) 16.4 (2.2) NA\nConduct disorder 6.0 (0.0) 11.0 (0.0) NA\nDepression 20.5 (6.6) 18.8 (5.6) 0.01\nDrug addiction 18.1 (2.8) 18.5 (3.3) 0.7\nEating disorder 29.0 (0.0) 16.5 (4.1) NAN\nMemory disorder 19.5 (4.0) 22.8 (8.8) 0.6\nNarcolepsy NA NA NA\nOCD 20.7 (4.5) 16.8 (6.6) 0.2\nPMS NA 17.5 (5.8) NA\nPTSD 22.8 (5.4) 22.5 (6.5) 0.9\nPanic 22.2 (6.2) 19.7 (7.1) 0.2\nPsychosis 23.8 (3.1) 24.8 (7.4) 0.8\nSchizophrenia 19.7 (2.4) 20.8 (6.6) 0.8\nSleep disorder 21.8 (6.0) 20.9 (8.0) 0.7\nAlcohol and substance misuse 20.0 (4.6) 18.6 (4.1) 0.2\nNote: ADD/ADHD = attention deficit disorder/attention-deficit hyper-activity disorder;\nOCD = obsessive-compulsive disorder; PMS = premenstrual syndrome; PTSD = post\ntraumatic stress disorder.\n160 Brittany L. Mitchell et al.\nhttps://doi.org/10.1017/thg.2019.27 Published online by Cambridge University Press\n\nand internalizing disorders such as depression and substance\nmisuse or OCD and attention deficit disorder (ADD)/ADHD.\nFurthermore, we detected expected comorbidity of co-occurring\ndiseases and symptoms such as eating disorders and anorexia\n(Thornton et al., 2010), drug addiction and alcohol misuse, psy-\nchosis and schizophrenia (National Collaborating Centre for\nMental Health 2014) and depression and anxiety (Gorman,\n1996). Depression was the disorder with the highest comorbidity,\nhaving a significant association with around half of the conditions.\nAltogether, these observations are consistent with the high genetic\ncorrelation between psychiatric disorders and the overlap in diag-\nnostic criteria (Anttila et al., 2018; Bulik-Sullivan et al., 2015).\nFurthermore, these findings suggest the self-reported lifetime of\nmental health conditions on the 25Up to be valid and reliable.\nWe also analyzed whether there were sex differences in the age\nof onset of psychiatric and substance abuse disorders. Our findings\nindicated a nominally significant difference in the age of onset of\ndepression, which is consistent with previous observations on ado-\nlescents (Avenevoli et al., 2015). Notably, the mean age of onset of\neating disorders was during adolescence, as previously reported\n(Hudson et al., 2007; Volpe et al., 2016).\nStrengths and Limitations\nThe 25Up study represents a large effort to characterize and under-\nstand the genetic, environmental and behavioral factors associated\nwith mental health in young adults transitioning from adolescence.\nMore than 50 different mental health and lifestyle variables, such as\ntechnology use, sociosexuality and substance use, were assessed in\n(on average) ~2100 twins, making this a rich and valuable data set.\nThe preceding work on the BLTS cohort has started to shed light\nonto the genetic and neurobiological etiology of substance abuse\n(Chang et al., 2018; Gillespie et al., 2018; Schmaal et al., 2016).\nWe expect that this follow-up of the cohort will drive further\ncross-sectional and longitudinal genetic epidemiology studies of\nhuman behaviour and mental health.\nThe sample size of the 25Up study (341 complete monozygotic\ntwin-pairs and 415 dizygotic pairs) should allow the detection of\nheritability estimates ≥30% and common environmental\ninfluences >30% with at least 80% power (Martin et al., 1978;\nVisscher et al., 2008). Furthermore, the 25Up cohort provides at\nleast 80% power to detect genetic correlations as low as r\ng = .3\n(considering a heritability for each trait >20%) in line with cohorts\nwith similar sizes and power simulations. The existence of longi-\ntudinal biometric and mental health data could enable novel family\nand epidemiological studies to be performed. The longitudinal\nnature of the BLTS has enabled the discovery that lifetime progres-\nsion of self-reported psychotic symptoms is heterogeneous. Future\nstudies assessing the genetic and environmental factors accounting\nfor this heterogeneity across development could result in new\nintervention and prevention strategies.\nSome limitations of the 25Up study must be noted. Although\nclinical, lifestyle and demographic variables were assessed through\nestablished instruments, they were obtained through online sur-\nveys and therefore all responses are subject to the possible biases\nand accuracy of self-report questionnaires. Notably, recall bias\n(e.g. not remembering a depressive episode) and subjectivity\n(e.g. when rating their physical and mental health) should be con-\nsidered and corrected for, before conducting analyses and espe-\ncially before comparing with other population-based studies. It\nis also important to note that all analyses presented in this article\nwere based on self-reported lifetime medical history data and\ntherefore are not necessarily in accordance with observations made\nwhen using other criteria, such as theDSM-5. Additionally, caution\nmust be taken when trying to ascertain specific disorders from the\ninstruments used in this study. For example, with schizophrenia,\nself-reported and CIDI-based ascertainment might not match\nthe DSM-5 diagnostic criteria.\nFig. 5. Comparison of lifetime prevalence of any psychotic\nsymptoms in the 19Up and 25Up cohort studies. Bar plots\ndepict the prevalence and 95% confidence intervals (1000\nbootstrap pseudo replications) of psychotic symptoms in\nthe 19Up and 25Up cohorts stratified by sex. Results are\ndepicted only for participants with available data for both\ndata sets ( n = 2319; M = males, F = females).\nTwin Research and Human Genetics 161\nhttps://doi.org/10.1017/thg.2019.27 Published online by Cambridge University Press\n\nFurthermore, genetic and epidemiological analyses using the\ntwins from the 25Up cohort will inevitably assume the cohort to\nbe representative of the overall (age-matched) population. It is well\ndocumented that the liability to twinning has a genetic component\n(Mbarek et al., 2016; Painter et al., 2010) and could therefore be\ngenetically correlated with other traits (Laisk et al., 2018).\nMoreover, environmental differences between twins and nontwins\n(such as twins being treated more similar than siblings) are also a\npossible source of bias. Nonetheless, both of these limitations can\nbe circumvented by including the large number of siblings who are\npart of the 25Up cohort in the analyses.\nConclusion\nThe 25Up cohort represents a rich data set that will enable analyses\nof the epidemiology, heritability and genetic correlations of well-\nbeing and mental health variables. The overall prevalence of mental\ndisorders and the prevalence rates for lifetime comorbidities were in\nline with previous studies. As an update of the BLTS, it represents a\nunique opportunity for longitudinal studies aiming at further under-\nstanding the etiology and heterogeneity underlying the progression\nof affective, mood and substance use disorders from young- to mid-\nadulthood. Here, we exemplified this by comparing item-level\nprevalence of psychotic items across the 19Up and 25Up cohorts,\nidentifying a heterogeneous increase that might be explained by\ndynamic (age-dependent) genetic effects. Moreover, the inclusion\nof surveys focused on the usage of technological devices (e.g. internet\nand mobile phone usage) will enable unprecedented analysis of their\nrelationship with mental health. We anticipate the 25Up cohort to be\nan attractive resource to boost collaboration, ultimately propelling\nscientific discovery.\nSupplementary Material. To view supplementary material for this article,\nplease visit https://doi.org/10.1017/thg.2019.27.\nAcknowledgments. This project was funded by NHMRC Project Grant\nAPP1069141 to IBH and NGM. We also thank David Smyth and Scott\nGordon for IT support. In particular, we thank the twins and their families\nfor their participation in our research. AIC is supported by a research training\nscholarship (RTP) granted by The University of Queensland (UQ).\nReferences\nAmerican Psychiatric Association. (2013). Diagnostic and statistical manual of\nmental disorders (5th ed.). Washington, DC.\nAnttila, V., Bulik-Sullivan, B., Finucane, H. 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