Latent trajectory classes for alcohol-related blackouts from age 15 to 19 in ALSPAC.

Alcohol-related blackouts (ARBs) are anterograde amnesias related to heavy alcohol intake seen in about 50% of drinkers. Although a major determinant of ARBs relates to blood alcohol concentrations, additional contributions come from genetic vulnerabilities and possible impacts of cannabis use disorders (CUDs). We evaluated relationships of genetics and cannabis use to latent class trajectories of ARBs in 829 subjects from the Collaborative Study of the Genetics of Alcoholism (COGA). The number of ARBs experienced every 2 years from subjects with average ages of 18 to 25 were entered into a latent class growth analysis in Mplus, and resulting class membership was evaluated in light of baseline characteristics, including CUDs. Correlations of number of ARBs across assessments were also compared for sibling pairs versus unrelated subjects. Latent class growth analysis identified ARB-based Classes 1 (consistent low = 42.5%), 2 (moderate low = 28.3%), 3 (moderate high = 22.9%), and 4 (consistent high = 6.3%). A multinomial logistic regression analysis within latent class growth analysis revealed that baseline CUDs related most closely to Classes 3 and 4. The number of ARBs across time correlated .23 for sibling pairs and -.10 for unrelated subjects. Baseline CUDs related to the most severe latent ARB course over time, even when considered along with other trajectory predictors, including baseline alcohol use disorders and maximum number of drinks. Data indicated significant roles for genetic factors for alcohol use disorder patterns over time. Future research is needed to improve understanding of how cannabis adds to the ARB risk and to find genes that contribute to risks for ARBs among drinkers.

Young people who self-harm are at an increased risk of suicide. Furthering our understanding of the risk factors for self-harm is essential for identifying high-risk groups, which can be used to inform the design of preventative interventions. This study used the Avon Longitudinal Study of Parents and Children (ALSPAC) and applied latent class analysis to the risk factors for self-harm at ages 13 and 17. Longitudinal associations between the latent classes and self-harm at ages 17 and 20 were examined. Cross-cohort comparisons were conducted between this study and a previous study using Irish data. At age 13 there was a low risk group, a peer problems group, and substance use group, similar for the two cohort studies, and a family conflict group, which was the least similar group to its matching group in the Irish study. All of these age 13 high-risk groups had approximately twice the relative risk (between 1.3 and 2.5) for self-harm at age 17 compared to the low risk group. The age 17 models were very similar across the two cohorts, each with a low risk group, a depression and high substance use group, a depression and low substance use group, and a substance use group. The relative risk of self-harm at age 20 for these high-risk groups compared the low risk group ranged from 3.6 to 8.0. These groups could help identify those at risk of self-harm and inform the design of prevention programmes to reduce self-harm behaviour in young people.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00787-025-02702-z.

There is a growing realisation that anxiety symptoms and disorders during pregnancy are associated with various negative outcomes. The aims of this study were to identify latent classes of anxiety symptom trajectories during pregnancy, compare anxiety levels between pregnancy trimesters and ascertain the predictors of anxiety symptom trajectories. Two hundred pregnant women in their first trimester who attended obstetric clinics at the local hospital were recruited. Three self-report questionnaires assessing anxiety levels were administered at three time points during pregnancy. Latent class growth analysis was used to identify anxiety symptom trajectories, and logistic regression analysis was performed to ascertain the predictors of latent class membership. Data for analyses were available for 188 women. Two anxiety symptom trajectories were identified: a 'high-anxiety' trajectory (13.3% of participating women) and 'low-anxiety' trajectory (86.7%). This finding was consistent across all the anxiety measures used in the study. Overall, anxiety levels gradually decreased during pregnancy, with a significant decrease between the first and second trimesters on some measures. Past mental disorders and significant stressors in the preceding 6 months predicted membership in the 'high-anxiety' trajectory group. These findings support an early assessment of anxiety in pregnant women. If untreated, a high level of anxiety in the first trimester is likely to persist throughout pregnancy, although it may decrease somewhat. An early recognition of pathological anxiety during pregnancy allows its timely treatment and prevention of unfavourable outcomes.

Many alcohol research questions require methods that take a person-centered approach because the interest is in finding heterogeneous groups of individuals, such as those who are susceptible to alcohol dependence and those who are not. A person-centered focus also is useful with longitudinal data to represent heterogeneity in developmental trajectories. In alcohol, drug, and mental health research the recognition of heterogeneity has led to theories of multiple developmental pathways. This paper gives a brief overview of new methods that integrate variable- and person-centered analyses. Methods discussed include latent class analysis, latent transition analysis, latent class growth analysis, growth mixture modeling, and general growth mixture modeling. These methods are presented in a general latent variable modeling framework that expands traditional latent variable modeling by including not only continuous latent variables but also categorical latent variables. Four examples that use the National Longitudinal Survey of Youth (NLSY) data are presented to illustrate latent class analysis, latent class growth analysis, growth mixture modeling, and general growth mixture modeling. Latent class analysis of antisocial behavior found four classes. Four heavy drinking trajectory classes were found. The relationship between the latent classes and background variables and consequences was studied. Person-centered and variable-centered analyses typically have been seen as different activities that use different types of models and software. This paper gives a brief overview of new methods that integrate variable- and person-centered analyses. The general framework makes it possible to combine these models and to study new models serving as a stimulus for asking research questions that have both person- and variable-centered aspects.

ObjectiveThere is overlap between an autistic and hyperactive-inattentive symptomatology when studied cross-sectionally. This study is the first to examine the longitudinal pattern of association between social-communication deficits and hyperactive-inattentive symptoms in the general population, from childhood through adolescence. We explored the interrelationship between trajectories of co-occurring symptoms, and sought evidence for shared prenatal/perinatal risk factors.MethodStudy participants were 5,383 singletons of white ethnicity from the Avon Longitudinal Study of Parents and Children (ALSPAC). Multiple measurements of hyperactive-inattentive traits (Strengths and Difficulties Questionnaire) and autistic social-communication impairment (Social Communication Disorder Checklist) were obtained between 4 and 17 years. Both traits and their trajectories were modeled in parallel using latent class growth analysis (LCGA). Trajectory membership was subsequently investigated with respect to prenatal/perinatal risk factors.ResultsLCGA analysis revealed two distinct social-communication trajectories (persistently impaired versus low-risk) and four hyperactive-inattentive trait trajectories (persistently impaired, intermediate, childhood-limited and low-risk). Autistic symptoms were more stable than those of attention-deficit/hyperactivity disorder (ADHD) behaviors, which showed greater variability. Trajectories for both traits were strongly but not reciprocally interlinked, such that the majority of children with a persistent hyperactive-inattentive symptomatology also showed persistent social-communication deficits but not vice versa. Shared predictors, especially for trajectories of persistent impairment, were maternal smoking during the first trimester, which included familial effects, and a teenage pregnancy.ConclusionsOur longitudinal study reveals that a complex relationship exists between social-communication and hyperactive-inattentive traits. Patterns of association change over time, with corresponding implications for removing exclusivity criteria for ASD and ADHD, as proposed for DSM-5.

EFL students' syntactic complexity development in argumentative writing：A latent class growth analysis (LCGA) approach

These analyses use data from a 40-year prospective study to extend information into the sixth and seventh decades of life regarding latent trajectory classes of cannabis use and predictors of those classes. Data from the San Diego Prospective Study were analyzed for 329 men of European and Hispanic ethnicity who had used cannabis at about age 23 at study entry (Time 1) and who were interviewed about every 5 years through about age 60 to 70. Latent classes of cannabis use trajectories were evaluated using latent class growth analyses, baseline predictors of class membership were determined, and significant predictors of each class were established using logistic regression analyses. Four latent classes were identified ranging from 12.5% with cannabis use at every follow-up to 25.8% with no use after Time 1. Eight of 14 Time 1 predictors differed significantly across the trajectory classes, including five (age, marital status, religious identity, intensity of cannabis use, and sensation seeking) that significantly contributed to regression analyses when all significant predictors were considered together. Forty-two percent of participants continued using cannabis long-term, including one in eight who used it at every follow-up. Predictors of continued use and identification of those most likely to stop required gathering information on a range of demographic, prior substance use, and personality characteristics. Considering the potential enhanced dangers of cannabis use in later life, the high rate of continued use over four decades implies that clinicians should ask all older patients about recent cannabis use, especially if they had used it in their 20s.

‘Asthma’ is now recognised as an umbrella term that includes a heterogeneous group of phenotypes whose aetiology and prognosis vary. This heterogeneity is even more significant in asthma diagnosed before...

BackgroundSchizophrenia is associated with a higher prevalence of cannabis use and cigarette use. However, it is unknown to what extent these associations are due to a shared genetic aetiology. We therefore aim to examine how schizophrenia genetic risk associates with patterns of cigarette and cannabis use in adolescence.MethodsWe analysed repeated measures of cigarette and cannabis use during adolescence in a sample of 5,300 individuals in the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort who had at least 3 measures of cigarette and cannabis use between ages 14–19 years. Cigarette and cannabis use data were summarised using longitudinal latent class analysis to identify longitudinal classes of substance use, and associations between polygenic scores for schizophrenia and resulting classes were assessed.ResultsThe schizophrenia polygenic score based on single nucleotide polymorphisms (SNPs) meeting a discovery sample threshold of p ≤ 0.05 was associated with late onset cannabis use as compared to non-use (OR = 1.20; 95% CI = 1.05, 1.37) but not with early onset or cigarette only use latent classes. This association persisted after excluding the CHRNA5-CHRNA3-CHRNB4 nicotinic receptor gene cluster (OR = 1.25; 95% CI = 1.08, 1.44), a locus which has previously been found to strongly associate with schizophrenia.DiscussionThis study found that genetic risk of schizophrenia (as captured by polygenic scores) is associated with late-onset cannabis use but not with other smoking phenotypes in adolescence in ALSPAC. Possible explanations for these results are that schizophrenia and cannabis use have a shared genetic aetiology or that biological risk of schizophrenia leads to cannabis use through secondary mechanisms. These secondary mechanisms may include stress of childhood behavioural problems occurring as a result of biological processes underling schizophrenia. Future analyses involving mediation models may shed some light on factors influencing patterns of substance use in individuals with a high genetic liability for schizophrenia.

Objective: The aim of this study was to use prospective data from the Avon Longitudinal Study of Parents and Children (ALSPAC) to examine association between trajectories of early childhood developmental skills and psychotic experiences (PEs) in early adolescence.Method: This study examined data from n = 6790 children from the ALSPAC cohort who participated in a semi-structured interview to assess PEs at age 12. Child development was measured using parental report at 6, 18, 30, and 42 months of age using a questionnaire of items adapted from the Denver Developmental Screening Test – II. Latent class growth analysis was used to generate trajectories over time for measures of fine and gross motor development, social, and communication skills. Logistic regression was used to investigate associations between developmental trajectories in each of these early developmental domains and PEs at age 12.Results: The results provided evidence that decline rather than enduringly poor social (adjusted OR = 1.28, 95% CI = 1.10–1.92, p = 0.044) and communication skills (adjusted OR 1.12, 95% CI = 1.03–1.22, p = 0.010) is predictive of suspected or definite PEs in early adolescence, than those with stable and/or improving skills. Motor skills did not display the same pattern of association; although gender specific effects provided evidence that only declining pattern of fine motor skills was associated with suspected and definite PEs in males compared to females (interaction OR = 1.47, 95% CI = 1.09–1.97, p = 0.012).Conclusion: Findings suggest that decline rather than persistent impairment in social and communication skills were most predictive of PEs in early adolescence. Findings are discussed in terms of study’s strengths, limitations, and clinical implications.

Specific language impairment (SLI) is a common developmental disorder characterized by difficulties in language acquisition despite otherwise normal development and in the absence of any obvious explanatory factors. We performed a high-density screen of SLI1, a region of chromosome 16q that shows highly significant and consistent linkage to nonword repetition, a measure of phonological short-term memory that is commonly impaired in SLI. Using two independent language-impaired samples, one family-based (211 families) and another selected from a population cohort on the basis of extreme language measures (490 cases), we detected association to two genes in the SLI1 region: that encoding c-maf-inducing protein (CMIP, minP = 5.5 × 10−7 at rs6564903) and that encoding calcium-transporting ATPase, type2C, member2 (ATP2C2, minP = 2.0 × 10−5 at rs11860694). Regression modeling indicated that each of these loci exerts an independent effect upon nonword repetition ability. Despite the consistent findings in language-impaired samples, investigation in a large unselected cohort (n = 3612) did not detect association. We therefore propose that variants in CMIP and ATP2C2 act to modulate phonological short-term memory primarily in the context of language impairment. As such, this investigation supports the hypothesis that some causes of language impairment are distinct from factors that influence normal language variation. This work therefore implicates CMIP and ATP2C2 in the etiology of SLI and provides molecular evidence for the importance of phonological short-term memory in language acquisition.

ABSTRACTPurpose: We aimed to investigate substance use patterns and identify predictors of homogeneous subgroups of adolescent substance users.Methods: We analyzed nationally representative secondary data collected from Korean adolescents (N = 72,435). To investigate substance use patterns, we conducted latent class analysis using seven behaviors linked to alcohol, cigarette, and e-cigarette use. After choosing the best latent class model, we investigated predictors of latent class membership (LCM) for substance use, using demographics and mental health conditions.Results: A four-latent class model best fit the data. Non-users (74%) had low likelihoods of reporting lifetime and current use of alcohol, cigarette, and e-cigarette. Experimenters (10%) had high likelihoods of reporting lifetime alcohol and cigarette use. Current drinkers (10%) had high likelihoods of reporting lifetime and current alcohol use. Multi-substance users (6%) had high likelihoods of reporting lifetime and current use of alcohol and cigarettes, lifetime e-cigarette use, and current binge drinking. Additionally, demographics (gender, grades, socioeconomic status, co-residence with family members, and grade point average) and mental health conditions (depression, suicidal ideation, and subjective unhappiness) successfully predicted LCM.Conclusions: In developing interventions for addressing substance-related issues, health professionals should focus on adolescent substance use patterns and take into account factors predicting LCM.

Previous research has demonstrated a graded relationship between the number of Adverse Childhood Experiences reported (an ACE score) and child outcomes. However, ACE scores lack specificity and ignore the patterning of adversities, which are informative for interventions. The aim of the present study was to explore the clustering of ACEs and whether this clustering differs by gender or is predicted by poverty. Data on 8,572 participants of the Avon Longitudinal Study of Parents and Children (ALSPAC) were used. ALSPAC is a regionally representative prenatal cohort of children born between 1991 and 1992 in the Avon region of South-West England. ACEs included parental divorce, death of a close family member, interparental violence, parental mental health problems, parental alcohol misuse, parental drug use, parental convictions, and sexual, emotional, and physical abuse, between birth and 19 years. Latent class analysis was used to derive ACE clusters and associations between poverty, gender, and the derived classes tested using multinomial logistic regression. Five latent classes were identified: “Low ACEs” (55%), “Parental separation and mother’s mental health problems” (18%), “Parental mental health problems, convictions and separation” (15%), “Abuse and mental health problems” (6%), and “Poly adversity” (6%). Death of a close family member and sexual abuse did not cluster with other adversities. The clustering did not differ by gender. Poverty was strongly related to both individual ACEs and clusters. These findings demonstrate that ACEs cluster in specific patterns and that poverty is strongly related to this. Therefore, reducing child poverty might be one strategy for reducing ACEs.

Although subgroups of adolescent problem behaviors (PBs) may exist and have different characteristics, most available studies have focused on exploring a single PB. Thus, we aimed to investigate latent classes of adolescent PBs and to identify important predictors of latent class membership. We analyzed nationally representative secondary data—Waves 4 and 5 of the Korean Children & Youth Panel Survey—obtained from the 2010 cohort of seventh graders and their parents. Specifically, using seven PBs (e.g., daily smoking, monthly drinking, and sexual intercourse) measured in Wave 5, we conducted a latent class analysis (LCA) to identify the model that best fit the data. In the next step, we conducted an LCA with covariates to investigate Wave-4 predictors of latent class membership. In our study, a three-latent-class model best fit the data: the Low Risk class (78%) characterized by low probabilities of engagement in all PBs, Non-Habitual Alcohol Use class (14%), and Habitual Cigarette and Alcohol Use class (7%). In addition, successful predictors of latent class membership included gender, parental education, friendships, relationships with teachers, parental affection, abuse inflicted by parents, and aggression. Health professionals should develop interventions tailored to each homogeneous subgroups of PBs in order to obtain more effective outcomes. Additionally, when developing these interventions, they should consider multilevel characteristics (e.g., individual, peer, and parental factors) that differentiate these subgroups.

This study proposed a three-stage measurement model utilizing the Latent Growth Curve Modeling and Latent Class Growth Analysis. The measurement model was illustrated using repeated data collected through a four-week prospective study tracking the subjective well-being of volunteer college students (n=154). Firstly, several unconditional growth models were estimated to define the model providing a better representation of individual growth trajectories. Secondly, several conditional growth models were formulated to test the usefulness of covariate variables hypothesized to explain observed variance in growth factors. Finally, latent class models were estimated to explore different latent trajectory classes further. Results showed that students' subjective well-being changed over time, and the rate of this change, as well as its covariates, were not constant for the entire sample. This study clearly illustrates how a longitudinal measurement approach can enhance the scope of findings and the depth of inferences when repeated measurements are available.