Harmonization of CSF and imaging biomarkers in Alzheimer's disease: Need and practical applications for genetics studies and preclinical classification.

In Alzheimer's disease (AD) research, cerebrospinal fluid (CSF) Amyloid beta (Aß), Tau and pTau are the most accepted and well validated biomarkers. Several methods and platforms exist to measure those biomarkers, leading to challenges in combining data across studies. Thus, there is a need to identify methods that harmonize and standardize these values. We used a Z-score based approach to harmonize CSF and amyloid imaging data from multiple cohorts and compared GWAS results using this approach with currently accepted methods. We also used a generalized mixture model to calculate the threshold for biomarker-positivity. Based on our findings, our normalization approach performed as well as meta-analysis and did not lead to any spurious results. In terms of dichotomization, cutoffs calculated with this approach were very similar to those reported previously. These findings show that the Z-score based harmonization approach can be applied to heterogeneous platforms and provides biomarker cut-offs consistent with the classical approaches without requiring any additional data.

Examining changes in sexual lifestyles in Britain between 1990-2010: a latent class analysis approach.

BACKGROUND: Understanding sexual lifestyles and how they change over time is important for determining the likelihood of sexual health outcomes. Standard descriptive and regression methods are limited in their ability to capture multidimensional concepts such as sexual lifestyles. Latent Class Analysis (LCA) is a mixture modelling method that generates a categorical latent variable to derive homogenous groups from a heterogeneous population. Our study investigates (1) the potential of LCA to assess change over time in sexual lifestyles and (2) how quantifying this change using LCA compares to previous findings using standard approaches. METHODS: Probability-sampled data from three rounds of the National Survey of Sexual Attitudes and Lifestyle (Natsal) were used, restricted to sexually active participants (i.e., those reporting sexual partners in the past year) aged 16-44 years (N1990 = 11,738; N2000 = 9,690; N2010 = 8,397). An LCA model was built from four variables: number of sexual partners (past year), number of partners without a condom (past year), age at first sex and self-perceived HIV risk. Covariates included age, ethnicity, educational attainment, same-sex attraction, and marital status. Multinomial regression analyses and Chi-Squared tests were used to investigate change over time in the size of each class. RESULTS: We successfully used a LCA approach to examine change in sexual lifestyle over time. We observed a statistically significant increase between 1990 and 2010 in the proportion of men (χ2 = 739.49, p < 0.01) and women (χ2 = 1270.43, p < 0.01) in a latent class associated with reporting 2 or more partners in the last year, relatively high probabilities of reporting condomless sex partners, greater self-perceived HIV risk, and a high probability of first sex before age 16 years, increasing from 19.5% to 31.1% (men) and 9.9% to 22.1% (women). CONCLUSION: Our results indicate the viability of LCA models to assess change over time for complex behavioural phenomena. They align with previous findings, namely changing sexual lifestyles in Britain in recent decades, partnership number driving class assignment, and significant sex differences in sexual lifestyles. This approach can be used to extend previous LCA models (e.g., to investigate the impact of COVID-19 on sexual lifestyles) and to support empirical evidence of change over time, facilitating more nuanced public health policy.

Parental domestic violence and abuse, mental ill-health, and substance misuse and the impact on child mental health: a secondary data analysis using the UK Millennium Cohort Study.

BACKGROUND: Parental domestic violence and abuse (DVA), mental ill-health (MH), and substance misuse (SU) can have a negative impact on both parents and children. However, it remains unclear if and how parental DVA, MH, and SU cluster and the impacts this clustering might have. We examined how parental DVA, MH, and SU cluster during early childhood, the demographic/contextual profiles of these clusters, and how these clusters relate to child MH trajectories. METHODS: We examined data from 15,377 families in the UK Millennium Cohort Study. We used: (1) latent class analysis to create groups differentially exposed to parental DVA, MH, and SU at age three; (2) latent growth curve modelling to create latent trajectories of child MH from ages 3-17; and (3) a case-weight approach to relate latent classes to child MH trajectories. RESULTS: We identified three latent classes: high-frequency alcohol use (11.9%), elevated adversity (3.5%), and low-level adversity (84.6%). Children in the elevated adversity class had higher probabilities of being from low-socioeconomic backgrounds and having White, younger parents. Children exposed to elevated adversity displayed worse MH at age three (intercept = 2.274; p < 0.001) compared the low-level adversity (intercept = 2.228; p < 0.001) and high-frequency alcohol use class (intercept = 2.068; p < 0.001). However, latent growth factors (linear and quadratic terms) of child MH did not differ by latent class. CONCLUSIONS: Parental DVA, MH, and SU cluster during early childhood and this has a negative impact on children's MH at age three, leading to similar levels of poor MH across time. Intervening early to prevent the initial deterioration, using a syndemic-approach is essential.

Differentiating 'pure' and comorbid self-identified burnout: Diagnostic and management implications.

OBJECTIVE: A previous study identified categorically differing scores on the Sydney Burnout Measure (SBM) between individuals with self-identified burnout with (n = 354) or without (n = 188) a history of mental illness. The current study examined whether the SBM's validity held in both scenarios. METHOD: The factorial structure and scores on the SBM measure were compared between the two groups. RESULTS: Similar underlying symptom constructs were identified. The group with a mental illness history had higher general factor scores, suggesting more severe burnout. The group without such a history (and thus a 'purer' burnout state) had higher scores on the empathy loss factor, suggesting its greater specificity to burnout than to other psychological states. CONCLUSIONS: Burnout appears to be experienced similarly by those with and without a mental illness history as measured by the SBM.

The association between trajectories of change in social functioning and psychological treatment outcome in university students: a growth mixture model analysis.

BACKGROUND: The transition to university and resultant social support network disruption can be detrimental to the mental health of university students. As the need for mental health support is becoming increasingly prevalent in students, identification of factors associated with poorer outcomes is a priority. Changes in social functioning have a bi-directional relationship with mental health, however it is not clear how such measures may be related to effectiveness of psychological treatments. METHODS: Growth mixture models were estimated on a sample of 5221 students treated in routine mental health services to identify different trajectories of change in self-rated impairment in social leisure activities and close relationships during the course of treatment. Multinomial regression explored associations between trajectory classes and treatment outcomes. RESULTS: Five trajectory classes were identified for social leisure activity impairment while three classes were identified for close relationship impairment. In both measures most students remained mildly impaired. Other trajectories included severe impairment with limited improvement, severe impairment with delayed improvement, and, in social leisure activities only, rapid improvement, and deterioration. Trajectories of improvement were associated with positive treatment outcomes while trajectories of worsening or stable severe impairment were associated with negative treatment outcomes. CONCLUSIONS: Changes in social functioning impairment are associated with psychological treatment outcomes in students, suggesting that these changes may be associated with treatment effectiveness as well as recovery experiences. Future research should seek to establish whether a causal link exists to understand whether integrating social support within psychological treatment may bring additional benefit for students.

Psychotic experiences and negative symptoms from adolescence to emerging adulthood: developmental trajectories and associations with polygenic scores and childhood characteristics.

BACKGROUND: Psychotic experiences and negative symptoms (PENS) are common in non-clinical populations. PENS are associated with adverse outcomes, particularly when they persist. Little is known about the trajectories of PENS dimensions in young people, nor about the precursory factors associated with these trajectories. METHODS: We conducted growth mixture modelling of paranoia, hallucinations, and negative symptoms across ages 16, 17, and 22 in a community sample (N = 12 049-12 652). We then described the emergent trajectory classes through their associations with genome-wide polygenic scores (GPS) for psychiatric and educational phenotypes, and earlier childhood characteristics. RESULTS: Three trajectory classes emerged for paranoia, two for hallucinations, and two for negative symptoms. Across PENS, GPS for clinical help-seeking, major depressive disorder, and attention deficit hyperactivity disorder were associated with increased odds of being in the most elevated trajectory class (OR 1.07-1.23). Lower education GPS was associated with the most elevated trajectory class for hallucinations and negative symptoms (OR 0.77-0.91). Conversely for paranoia, higher education GPS was associated with the most elevated trajectory class (OR 1.25). Trajectory class associations were not significant for schizophrenia, obsessive-compulsive disorder, bipolar disorder, or anorexia GPS. Emotional/behaviour problems and life events in childhood were associated with increased odds of being in the most elevated trajectory class across PENS. CONCLUSIONS: Our results suggest latent heterogeneity in the development of paranoia, hallucinations, and negative symptoms in young people that is associated with specific polygenic scores and childhood characteristics.

Association between high levels of comorbid anxiety and depressive symptoms and decreased likelihood of birth without intervention: A longitudinal prospective cohort study.

OBJECTIVE: To assess the association between trajectories of comorbid anxiety and depressive (CAD) symptoms assessed in each pregnancy trimester and physiological birth. DESIGN: Large longitudinal prospective cohort study with recruitment between January 2013 and September 2014. SETTING: Primary care, in the Netherlands. POPULATION: Dutch-speaking pregnant women with gestational age at birth ≥37 weeks, and without multiple pregnancy, severe psychiatric disorder or chronic disease history. METHODS: Pregnancy-specific anxiety and depressive symptoms were measured prospectively in each trimester of pregnancy using the negative affect subscale of the Tilburg Pregnancy Distress Scale and Edinburgh (Postnatal) Depression Scale. Data on physiological birth were obtained from obstetric records. Multivariate growth mixture modelling was performed in MPLUS to determine longitudinal trajectories of CAD symptoms. Multiple logistic regression analysis was used to examine the association between trajectories and physiological birth. MAIN OUTCOME MEASURES: Trajectories of CAD symptoms and physiological birth. RESULTS: Seven trajectories (classes) of CAD symptoms were identified in 1682 women and subsequently merged into three groups: group 1-persistently low levels of symptoms (reference class 1; 79.0%), group 2-intermittently high levels of symptoms (classes 3, 6 and 7; 11.2%), and group 3-persistently high levels of symptoms (classes 2, 4 and 5; 9.8%). Persistently high levels of CAD symptoms (group 3) were associated with a lower likelihood of physiological birth (odds ratio 0.67, 95% confidence interval 0.47-0.95, P = 0.027) compared with the reference group (persistently low levels of symptoms), after adjusting for confounders. CONCLUSIONS: This study is the first showing evidence that persistently high CAD levels, assessed in each pregnancy trimester, are associated with a lower likelihood of physiological birth.

Characterizing the individual course of health-related quality of life after subarachnoid haemorrhage: Latent growth mixture modelling.

BACKGROUND: Survivors of aneurysmal subarachnoid haemorrhage (SAH) show heterogeneous profiles of health-related quality of life (HrQoL). The aim of this study was to characterize individual differences in the course of HrQoL following SAH using latent growth mixture modelling (LGMM). METHODS: A longitudinal study with 113 incident cases of aneurysmal SAH was performed in order to evaluate clinical outcome (Hunt and Hess scale, Barthel-Index, Beck Depression Inventory) and HrQoL data (EQ-5D) at baseline, 6 and 12 months. The heterogeneity in HrQoL courses after SAH was analysed using LGMM. RESULTS: Four subgroups (classes) of different patterns of HrQoL course after SAH were identified. Two of these classes (1 and 3) comprised patients with considerably reduced initial HrQoL, which was associated with more severe symptoms of SAH. Class 1 showing the worst EQ5D-index values during the entire study period. Class 3 experiencing a considerable improvement in HrQoL values. In comparison to classes 1 and 3, class 2 and 4 were characterized by less severe SAH and better functional outcome. An important difference in the disease course between classes 2 and 4 was a temporary increase in depression scores at the 6-month time point in class 4, which was associated with a considerable reduction in HrQoL.The specific clinical parameters characterizing differences between classes, such as severity of SAH, functional outcome, cognitive impairment and post-stroke depression, were identified and the influence of their potential improvement on HrQoL was estimated. CONCLUSION: By means of LGMM we could classify the course of HrQoL after SAH in four different patterns, which are relevant for the clinical decisions. Clinical parameters, which can be modified in order to improve the course of HrQoL were identified and could help to develop individual therapeutic strategies for the rehabilitation after SAH.

Prevalence and predictors of ICD-11 posttraumatic stress disorder and complex PTSD in young people.

OBJECTIVE: The prevalence, construct validity, risk factors and psychopathological correlates associated with ICD-11 posttraumatic stress disorder (PTSD) and complex PTSD (CPTSD) as measured by the International Trauma Questionnaire for Children and Adolescents (ITQ-CA) were assessed in a sample of young people from Northern Ireland. METHOD: Participants were trauma-exposed 11-19-year-olds (N = 507) who participated in the Northern Ireland Youth Wellbeing Prevalence Survey (YWS-NI, 2020). Factor mixture modelling (FMM) was used to test the latent structure of the ITQ-CA. Risk-factors and psychopathological correlates associated with latent class membership, and ICD-11diagnostic status, were also investigated. RESULTS: More participants met the ITQ-CA criteria for CPTSD (3.4%, n = 44) than PTSD (1.5%, n = 19). A second-order FMM comprising a 'partial-PTSD class', a 'CPTSD class', a 'DSO class' and a 'low symptom endorsement class' was the best-fitting model. Younger age and cumulative trauma were risk factors for all trauma classes. Female gender and two or more violent traumas were significant predictors of the 'PTSD' and 'CPTSD' classes, while single sexual trauma was a significant predictor of the 'DSO' and 'CPTSD' classes. Two or more sexual traumas was a unique predictor of 'CPTSD class', while two or more vicarious traumas was a unique predictor of 'DSO class'. The 'CPTSD' class displayed the most notable comorbidity. CONCLUSIONS: Findings indicate that CPTSD may be more prevalent than PTSD in children and young people. Support for the ICD-11 conceptualisation of CPTSD as representing a unique diagnostic construct was supported using FMM, with findings indicating trauma symptom class-specific risk profiles.

Estimation of invasive Group B Streptococcus disease risk in young infants from case-control serological studies.

BACKGROUND: Group B Streptococcus (GBS) infections are a major cause of invasive disease (IGbsD) in young infants and cause miscarriage and stillbirths. Immunization of pregnant women against GBS in addition to intrapartum antibiotic prophylaxis could prevent disease. Establishing accurate serological markers of protection against IGbsD could enable use of efficient clinical trial designs for vaccine development and licensure, without needing to undertake efficacy trials in prohibitively large number of mother-infant dyads. The association of maternal naturally acquired serotype-specific anti-capsular antibodies (IgG) against serotype-specific IGbsD in their infants has been studied in case-control studies. The statistical models used so far to estimate IGbsD risk from these case-control studies assumed that the antibody concentrations measured sharing the same disease status are sampled from the same population, not allowing for differences between mothers colonised by GBS and mothers also potentially infected (e.g urinary tract infection or chorioamnionitis) by GBS during pregnancy. This distinction is relevant as infants born from infected mothers with occult medical illness may be exposed to GBS prior to the mother developing antibodies measured in maternal or infant sera. METHODS: Unsupervised mixture model averaging (MMA) is proposed and applied here to accurately estimate infant IGbsD risk from case-control study data in presence or absence of antibody concentration subgroups potentially associated to maternal GBS carriage or infection. MMA estimators are compared to non-parametric disease risk estimators in simulation studies and by analysis of two published GBS case-control studies. RESULTS: MMA provides more accurate relative risk estimates under a broad range of data simulation scenarios and more accurate absolute disease risk estimates when the proportion of IGbsD cases with high antibody levels is not ignorable. MMA estimates of the relative and absolute disease risk curves are more amenable to clinical interpretation compared to non-parametric estimates with no detectable overfitting of the data. Antibody concentration thresholds predictive of protection from infant IGbsD estimated by MMA from maternal and infant sera are consistent with non-parametric estimates. CONCLUSIONS: MMA is a flexible and robust method for design, accurate analysis and clinical interpretation of case-control studies estimating relative and absolute IGbsD risk from antibody concentrations measured at or after birth.

Latent profiles of autism symptoms in children and adolescents with Down syndrome.

BACKGROUND: Down syndrome (DS) is associated with elevated rates of autism spectrum disorder (ASD) and autism symptomatology. To better characterise heterogeneity in ASD symptomatology in DS, profiles of caregiver-reported ASD symptoms were modelled for children and adolescents with DS. METHODS: Participants (n = 125) were recruited through several multi-site research studies on cognition and language in DS. Using the Social Responsiveness Scale-2 (SRS-2; Constantino and Gruber 2012), two latent profile analyses (LPA) were performed, one on the broad composite scores of social communication and interaction and restricted interests and repetitive behaviour, and a second on the four social dimensions of social communication, social motivation, social awareness, and social cognition. RESULTS: A three-profile model was the best fit for both analyses, with each analysis yielding a low ASD symptom profile, an elevated or mixed ASD symptom profile and a high ASD symptom profile. Associations were observed between profile probability scores and IQ, the number of co-occurring biomedical conditions reported, sex, and SRS-2 form. CONCLUSIONS: Characterising heterogeneity in ASD symptom profiles can inform more personalised supports in this population, and implications for potential therapeutic approaches for individuals with DS are discussed.

When pregnancy and pandemic coincide: changes in stress and anxiety over the course of pregnancy.

BACKGROUND: Pregnant women experienced high levels of perceived stress and anxiety at the onset of the COVID-19 pandemic. However, the course of stress and anxiety in individual pregnant women during the pandemic is unknown. METHODS: Participants were 1,087 women ≤20 weeks pregnant in April-May 2020 (T1) at recruitment into the Stony Brook COVID-19 Pregnancy Experiences (SB-COPE) Study, with additional assessments in July-August 2020 (T2) and October 2020 (T3). Growth mixture models conditioned on covariates were used to identify patterns of change over time in pandemic-related stress (originating from feeling unprepared for birth and fearing perinatal infection), pregnancy-specific stress, and anxiety symptoms. RESULTS: A uniform pattern of change (i.e. one-class solution) in stress perceptions was observed over time. Participants showed the same functional form of decreases in all three types of stress perceptions over the course of their pregnancy and as the pandemic persisted. Initial level of stress did not predict change over time. Anxiety symptoms had a two-class solution in which 25% of participants had high and convex patterns of anxiety, and 75% had low levels with concave patterns. DISCUSSION: Stress perceptions and anxiety patterns of change over the course of pregnancy during the COVID-19 pandemic were different. Therefore, to evaluate the well-being of pregnant women during a global health crisis, it is important to assess both stress perceptions and emotional stress responses (i.e. anxiety). Screening for anxiety symptoms in early pregnancy would be valuable as symptoms may not spontaneously decrease even when stressful conditions improve.

mixtur: An R package for designing, analysing, and modelling continuous report visual short-term memory studies.

Visual short-term memory (vSTM) is often measured via continuous-report tasks whereby participants are presented with stimuli that vary along a continuous dimension (e.g., colour) with the goal of memorising the stimulus features. At test, participants are probed to recall the feature value of one of the memoranda in a continuous manner (e.g., by clicking on a colour wheel). The angular deviation between the participant response and the true feature value provides an estimate of recall precision. Two prominent models of performance on such tasks are the two- and three-component mixture models (Bays et al., Journal of Vision, 9(10), Article 7, 2009; Zhang and Luck, Nature, 453(7192), 233-235, 2008). Both models decompose participant responses into probabilistic mixtures of: (1) responses to the true target value based on a noisy memory representation; (2) random guessing when memory fails. In addition, the three-component model proposes (3) responses to a non-target feature value (i.e., binding errors). Here we report the development of mixtur, an open-source package written for the statistical programming language R that facilitates the fitting of the two- and three-component mixture models to continuous report data. We also conduct simulations to develop recommendations for researchers on trial numbers, set sizes, and memoranda similarity, as well as parameter recovery and model recovery. In the Discussion, we discuss how mixtur can be used to fit the slots and the slots-plus-averaging models, as well as how mixtur can be extended to fit explanatory models of visual short-term memory. It is our hope that mixtur will lower the barrier of entry for utilising mixture modelling.

Prospective predictors of decline v. stability in mild cognitive impairment with Lewy bodies or Alzheimer's disease.

BACKGROUND: Mild cognitive impairment (MCI) may gradually worsen to dementia, but often remains stable for extended periods of time. Little is known about the predictors of decline to help explain this variation. We aimed to explore whether this heterogeneous course of MCI may be predicted by the presence of Lewy body (LB) symptoms in a prospectively-recruited longitudinal cohort of MCI with Lewy bodies (MCI-LB) and Alzheimer's disease (MCI-AD). METHODS: A prospective cohort (n = 76) aged ⩾60 years underwent detailed assessment after recent MCI diagnosis, and were followed up annually with repeated neuropsychological testing and clinical review of cognitive status and LB symptoms. Latent class mixture modelling identified data-driven sub-groups with distinct trajectories of global cognitive function. RESULTS: Three distinct trajectories were identified in the full cohort: slow/stable progression (46%), intermediate progressive decline (41%) and a small group with a much faster decline (13%). The presence of LB symptomology, and visual hallucinations in particular, predicted decline v. a stable cognitive trajectory. With time zeroed on study end (death, dementia or withdrawal) where available (n = 39), the same subgroups were identified. Adjustment for baseline functioning obscured the presence of any latent classes, suggesting that baseline function is an important parameter in prospective decline. CONCLUSIONS: These results highlight some potential signals for impending decline in MCI; poorer baseline function and the presence of probable LB symptoms - particularly visual hallucinations. Identifying people with a rapid decline is important but our findings are preliminary given the modest cohort size.

Growth mixture models: a case example of the longitudinal analysis of patient-reported outcomes data captured by a clinical registry.

BACKGROUND: An assumption in many analyses of longitudinal patient-reported outcome (PRO) data is that there is a single population following a single health trajectory. One approach that may help researchers move beyond this traditional assumption, with its inherent limitations, is growth mixture modelling (GMM), which can identify and assess multiple unobserved trajectories of patients' health outcomes. We describe the process that was undertaken for a GMM analysis of longitudinal PRO data captured by a clinical registry for outpatients with atrial fibrillation (AF). METHODS: This expository paper describes the modelling approach and some methodological issues that require particular attention, including (a) determining the metric of time, (b) specifying the GMMs, and (c) including predictors of membership in the identified latent classes (groups or subtypes of patients with distinct trajectories). An example is provided of a longitudinal analysis of PRO data (patients' responses to the Atrial Fibrillation Effect on QualiTy-of-Life (AFEQT) Questionnaire) collected between 2008 and 2016 for a population-based cardiac registry and deterministically linked with administrative health data. RESULTS: In determining the metric of time, multiple processes were required to ensure that "time" accounted for both the frequency and timing of the measurement occurrences in light of the variability in both the number of measures taken and the intervals between those measures. In specifying the GMM, convergence issues, a common problem that results in unreliable model estimates, required constrained parameter exploration techniques. For the identification of predictors of the latent classes, the 3-step (stepwise) approach was selected such that the addition of predictor variables did not change class membership itself. CONCLUSIONS: GMM can be a valuable tool for classifying multiple unique PRO trajectories that have previously been unobserved in real-world applications; however, their use requires substantial transparency regarding the processes underlying model building as they can directly affect the results and therefore their interpretation.

In search of the optimum structural model for Internet Gaming Disorder.

BACKGROUND: Internet gaming Disorder (IGD) constitutes a recently proposed clinical disorder (American Psychiatric Association, Diagnostic and statistical manual of mental disorders, 2013). The present study examined if IGD is best conceptualized as categorical (present/absent), or dimensional (severity ranging from low to high), or both (i.e., hybrid of categorical/dimensional). METHODS: Ratings of the nine DSM-5 IGD symptoms, as presented in the Internet Gaming Disorder Scale 9-Short Form (Pontes & Griffiths, Comput Hum Behav 45:137-143, 2015), from 738 gamers, aged 17 to 72 years, were collected. Confirmatory factor analysis (CFA), latent class analysis (LCA), and factor mixture modelling analysis (FMMA) procedures were applied to determine the optimum IGD model. RESULTS: Although the findings showed most support for a FFMA model with two classes and one factor, there was also good statistical and substantive support for the one-factor CFA model, and the LCA model with three classes. CONCLUSION: It was concluded that while the optimum structure of IGD is most likely to be a hybrid model (i.e., concurrently categorical and dimensional), a uni-dimensional model and/or a three-class categorical model are also plausible.

The combined self- and parent-rated SDQ score profile predicts care use and psychiatric diagnoses.

The Strengths and Difficulties Questionnaire (SDQ) is widely used, based on evidence of its value for screening. This evidence primarily regards the single informant total difficulties scale and separate difficulties subscales. We assessed to what degree adolescents' SDQ profiles that combined all self- and parent-rated subscales were associated with use of care and psychiatric diagnoses, and examined the added value thereof over using only a single informant and the total scale. Cluster analysis was used to identify common SDQ profiles based on self- and parent-reports among adolescents aged 12-17 in mental healthcare (n = 4282), social care (n = 124), and the general population (n = 1293). We investigated associations of the profiles with 'care use' and 'DSM-IV diagnoses', depending on gender. We identified six common SDQ profiles: five profiles with varying types and severities of reported difficulties, pertaining to 95% of adolescents in care, and one without difficulties, pertaining to 55% of adolescents not in care. The types of reported difficulties in the profiles matched DSM-IV diagnoses for 88% of the diagnosed adolescents. The SDQ profiles were found to be more useful for predicting care use and diagnoses than SDQ scores reported by the adolescent as single informant and the total difficulties scale. The latter indicated the presence of problems among 42-63% of the adolescents in care, missing a substantial number of adolescents with reported emotional difficulties and borderline problem severity. These findings advocate the use of combined self- and parent-rated SDQ score profiles for screening.

The impact of physical activity on healthy ageing trajectories: evidence from eight cohort studies.

BACKGROUND: Research has suggested the positive impact of physical activity on health and wellbeing in older age, yet few studies have investigated the associations between physical activity and heterogeneous trajectories of healthy ageing. We aimed to identify how physical activity can influence healthy ageing trajectories using a harmonised dataset of eight ageing cohorts across the world. METHODS: Based on a harmonised dataset of eight ageing cohorts in Australia, USA, Mexico, Japan, South Korea, and Europe, comprising 130,521 older adults (Mage = 62.81, SDage = 10.06) followed-up up to 10 years (Mfollow-up = 5.47, SDfollow-up = 3.22), we employed growth mixture modelling to identify latent classes of people with different trajectories of healthy ageing scores, which incorporated 41 items of health and functioning. Multinomial logistic regression modelling was used to investigate the associations between physical activity and different types of trajectories adjusting for sociodemographic characteristics and other lifestyle behaviours. RESULTS: Three latent classes of healthy ageing trajectories were identified: two with stable trajectories with high (71.4%) or low (25.2%) starting points and one with a high starting point but a fast decline over time (3.4%). Engagement in any level of physical activity was associated with decreased odds of being in the low stable (OR: 0.18; 95% CI: 0.17, 0.19) and fast decline trajectories groups (OR: 0.44; 95% CI: 0.39, 0.50) compared to the high stable trajectory group. These results were replicated with alternative physical activity operationalisations, as well as in sensitivity analyses using reduced samples. CONCLUSIONS: Our findings suggest a positive impact of physical activity on healthy ageing, attenuating declines in health and functioning. Physical activity promotion should be a key focus of healthy ageing policies to prevent disability and fast deterioration in health.

Semi-automatic segmentation from intrinsically-registered 18F-FDG-PET/MRI for treatment response assessment in a breast cancer cohort: comparison to manual DCE-MRI.

OBJECTIVES: To investigate the reliability of simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI)-derived biomarkers using semi-automated Gaussian mixture model (GMM) segmentation on PET images, against conventional manual tumor segmentation on dynamic contrast-enhanced (DCE) images. MATERIALS AND METHODS: Twenty-four breast cancer patients underwent PET/MRI (following 18F-fluorodeoxyglucose (18F-FDG) injection) at baseline and during neoadjuvant treatment, yielding 53 data sets (24 untreated, 29 treated). Two-dimensional tumor segmentation was performed manually on DCE-MRI images (manual DCE) and using GMM with corresponding PET images (GMM-PET). Tumor area and mean apparent diffusion coefficient (ADC) derived from both segmentation methods were compared, and spatial overlap between the segmentations was assessed with Dice similarity coefficient and center-of-gravity displacement. RESULTS: No significant differences were observed between mean ADC and tumor area derived from manual DCE segmentation and GMM-PET. There were strong positive correlations for tumor area and ADC derived from manual DCE and GMM-PET for untreated and treated lesions. The mean Dice score for GMM-PET was 0.770 and 0.649 for untreated and treated lesions, respectively. DISCUSSION: Using PET/MRI, tumor area and mean ADC value estimated with a GMM-PET can replicate manual DCE tumor definition from MRI for monitoring neoadjuvant treatment response in breast cancer.

Modelling changes in anxiety and depression during low-intensity cognitive behavioural therapy: An application of growth mixture models.

OBJECTIVES: Research largely supports the clinical effectiveness of low-intensity cognitive behavioural therapy (LICBT) for mild-to-moderate anxiety and depression, delivered by psychological well-being practitioners (PWPs). Knowledge regarding the predictors of treatment response, however, is relatively limited. The primary aim of this study was to model the heterogeneity in longitudinal changes in anxiety and depression throughout LICBT provided by PWPs in Northern Ireland (NI), and to explore associations between pre-treatment variables and differences in treatment response. METHODS: Growth mixture modelling (GMM) techniques were employed to examine changes in psychological status in clients (N = 253) over the first six sessions of treatment, to identify divergent early response trajectories. A series of pre-treatment variables were used to predict class membership using chi-square tests and binary logistic regression models. RESULTS: There was one class representing improvement and one representing no improvement for both anxiety and depression. Class membership was predictive of treatment outcome. Pre-treatment variables associated with less improvement included unemployment, risk of suicide, neglect of self or others, using medication, receiving previous or concurrent treatments, a longer duration of difficulties, and comorbidities. CONCLUSIONS: Findings indicate most of the sample populated an 'improvers' class for both depression and anxiety. Pre-treatment variables identified as predictive of poor treatment response may need to be considered by practitioners in potential triage referral decision policies, supporting cost-effective and efficient services. Further research around predictors of clinical outcome is recommended. PRACTITIONER POINTS: Most of the sample belonged to an 'improvers' class. Several pre-treatment variables predicted poor treatment response (unemployment, suicide risk, neglect, medication, previous or concurrent treatments, longer duration of difficulties, and comorbidities). Few studies have utilized GMM to determine predictors of outcome following LICBT Regarding pre-treatment variables, the possibility of self-report bias cannot be excluded. The time period was relatively short, although represented the optimum number of sessions recommended for LICBT. The lack of a control group and random allocation were the main limitations.