Individualized prediction models in ADHD: a systematic review and meta-regression.

There have been increasing efforts to develop prediction models supporting personalised detection, prediction, or treatment of ADHD. We overviewed the current status of prediction science in ADHD by: (1) systematically reviewing and appraising available prediction models; (2) quantitatively assessing factors impacting the performance of published models. We did a PRISMA/CHARMS/TRIPOD-compliant systematic review (PROSPERO: CRD42023387502), searching, until 20/12/2023, studies reporting internally and/or externally validated diagnostic/prognostic/treatment-response prediction models in ADHD. Using meta-regressions, we explored the impact of factors affecting the area under the curve (AUC) of the models. We assessed the study risk of bias with the Prediction Model Risk of Bias Assessment Tool (PROBAST). From 7764 identified records, 100 prediction models were included (88% diagnostic, 5% prognostic, and 7% treatment-response). Of these, 96% and 7% were internally and externally validated, respectively. None was implemented in clinical practice. Only 8% of the models were deemed at low risk of bias; 67% were considered at high risk of bias. Clinical, neuroimaging, and cognitive predictors were used in 35%, 31%, and 27% of the studies, respectively. The performance of ADHD prediction models was increased in those models including, compared to those models not including, clinical predictors (ß = 6.54, p = 0.007). Type of validation, age range, type of model, number of predictors, study quality, and other type of predictors did not alter the AUC. Several prediction models have been developed to support the diagnosis of ADHD. However, efforts to predict outcomes or treatment response have been limited, and none of the available models is ready for implementation into clinical practice. The use of clinical predictors, which may be combined with other type of predictors, seems to improve the performance of the models. A new generation of research should address these gaps by conducting high quality, replicable, and externally validated models, followed by implementation research.

Does Prospective Mental Imagery Predict Symptoms of Negative Affect and Anhedonia in Young People?

Adolescent depression is associated with unhelpful emotional mental imagery. Here, we investigated whether vividness of negative and positive prospective mental imagery predict negative affect and anhedonia in adolescents. 111 people from Israel completed measures of prospective mental imagery, negative affect, and anhedonia at two time-points approximately three months apart. Using three cross-lagged panel models, we showed once 'concurrent' (across-variable, within-time) and 'stability' paths (across-time, within-variable) were estimated, there were no significant cross-lag paths between: i) T1 prospective negative mental imagery and T8 negative affect (i.e. increased vividness of negative future imagery at Time 1 did not predict increased negative affect at Time 8); ii) T1 prospective positive mental imagery and T8 negative affect (i.e. reduced vividness of positive future imagery at Time 1 did not predict increased negative affect at Time 8); and iii) T1 prospective positive mental imagery and T8 anhedonia (i.e. reduced vividness of positive future imagery at Time 1 did not predict increased anhedonia at Time 8). Given high levels of attrition, future research should aim to explore these associations in a larger, more diverse population, as such data could inform on whether modifying earlier prospective mental imagery may influence later time/context-specific effects of prospective mental imagery on negative affect and anhedonia.

Low Occurrence of Colectomy With Long-Term (up to 4 Years) Golimumab Treatment in Patients With Moderate-to-Severe Active Ulcerative Colitis: Data From the PURSUIT Maintenance and Long-Term Extension Studies.

Background: This analysis evaluated the incidence of all-cause colectomies (total or partial) among patients with moderate-to-severe active ulcerative colitis (UC) in the golimumab (GLM) Program of Ulcerative Colitis Utilizing an Investigational Treatment (PURSUIT)-maintenance (-M) and long-term extension (-LTE) studies. Methods: Eligible PURSUIT-M trial participants completed a 6-week GLM induction trial without requiring colectomy. Responders to GLM induction were randomized 1:1:1 to GLM 50 mg, GLM 100 mg, or placebo (PBO) maintenance for up to 1 year, administered every 4 weeks (q4w). Nonresponders to GLM or PBO induction received GLM 100 mg; responders to PBO induction received PBO (each administered q4w for up to 1 year). Participants who completed PURSUIT-M were eligible to continue their treatment in the 3-year PURSUIT-LTE study. Results: A total of 60 (4.9%) colectomies were reported among the 1228 patients who enrolled in the 1-year PURSUIT-M study, which included 672 participants who continued into the 3-year PURSUIT-LTE LTE study (of which 666 were treated). The colectomy rate during the 3-year extension was lower than that observed during the maintenance phase of the study (9/666 [1.4%] compared to 51/1228 [4.2%]). The majority (43/60 [71.7%]) of the reported colectomies occurred in patients who had not responded to induction therapy and who tended to have had more severe disease characteristics at baseline. Conclusions: This retrospective evaluation of colectomy data from the PURSUIT-M and -LTE studies in patients with moderate-to-severe active UC demonstrated a low (<5%) occurrence of colectomy with long-term (up to 4 years) GLM treatment. PURSUIT-M (NCT00488631; EudraCT, 2006-003399-37).

Long-term golimumab persistence: Five-year treatment retention data pooled from pivotal Phase III clinical trials in patients with rheumatoid arthritis, psoriatic arthritis, and ankylosing spondylitis.

INTRODUCTION: Golimumab, a monoclonal antibody against tumor necrosis factor-α (TNF-α), is used widely for treatment of rheumatic diseases. Long-term persistence is an important factor influencing therapeutic benefit and is a surrogate measure of efficacy. We compared five-year golimumab treatment persistence across studies, indications, and lines of therapy using pooled data from pivotal golimumab Phase III clinical trials. METHODS: This post-hoc analysis evaluated use of golimumab administered subcutaneously (50 or 100 mg every four weeks) for up to five years in 2228 adult participants with rheumatoid arthritis (RA; GO-BEFORE, GO-AFTER, and GO-FORWARD studies), psoriatic arthritis (PsA; GO-REVEAL study), or ankylosing spondylitis (AS; GO-RAISE study). Retention rate differences were evaluated by study, indication, and line of therapy using log-rank tests, and probability of treatment persistence was estimated by Kaplan-Meier analysis. RESULTS: Golimumab retention rates at Year 5 were consistently high when used as 1st-line therapy (69.8%) and did not differ significantly across the three indications tested (p = 0.5106) or across 1st-line studies (p = 0.2327). Retention at Year 5 was better in participants using golimumab as 1st-line than in those using it as 2nd-line (41.6%) therapy. Participants on 2nd-line golimumab therapy had a longer disease duration (median 9.2 years versus 3.7 years) than those on 1st-line golimumab therapy. CONCLUSIONS: These data support the value of long-term golimumab therapy in patients with chronic, immune-mediated rheumatic diseases when used as 1st-line (RA, PsA, AS) or 2nd-line (RA) therapy. Key Points • Golimumab is a human monoclonal antibody directed against tumor necrosis factor-α (TNF-α) and is approved widely for the treatment of rheumatic autoimmune diseases. • We compared the probability of treatment persistence, or the time of continuous drug use, for golimumab across five Phase III studies spanning multiple rheumatic indications over five years. • Treatment persistence was favorable and did not differ significantly for participants with rheumatoid arthritis, psoriatic arthritis, and ankylosing spondylitis, but persistence was greater when golimumab was used as 1st-line than as 2nd-line biologic therapy.

Is Future Mental Imagery Associated with Reduced Impact of the COVID-19 Pandemic on Negative Affect and Anhedonic Symptoms in Young People?

Background: Difficulties with prospective mental images are associated with adolescent depression. Current treatments mainly focus on verbal techniques to reduce negative affect (e.g. low mood) rather than enhancing positive affect, despite anhedonia being present in adolescents. We investigated the concurrent relationships between the vividness of negative and positive prospective mental imagery and negative affect and positive affect; and examined whether negative and positive prospective mental imagery moderated the impact of recent stress (COVID-19-linked stress) on negative and positive affect. Methods: 2602 young people (12-25 years) completed the Prospective Imagery Task and self-reported on symptoms of negative affect, anhedonia and COVID-19 linked stress. Results: Elevated vividness of negative future mental imagery and reduced vividness of positive future mental imagery were associated with increased negative affect, whereas only reduced vividness of positive future imagery was associated with increased symptoms of anhedonia. Elevated vividness of negative future images amplified the association between COVID-19 linked stress and negative affect, while elevated vividness of positive future images attenuated the association between COVID-19 linked stress and anhedonia. Conclusions: Future mental imagery may be differentially associated with negative and positive affect, but this needs to be replicated in clinical populations to support novel adolescent psychological treatments. Supplementary Information: The online version contains supplementary material available at 10.1007/s10608-023-10352-1.

Patient-reported continuous clinical response to golimumab in adults with moderately to severely active ulcerative colitis: results from GO OBSERVE, a real-world European observational study.

BACKGROUND: In PURSUIT, golimumab (GLM) was efficacious in patients with moderate-to-severe ulcerative colitis (UC). We assessed whether remote monitoring of combined patient-reported Mayo stool frequency and rectal bleeding scores is an effective real-world outcome measure for assessing maintenance of GLM-induced clinical response. METHODS: This was a 54-week prospective, observational cohort study conducted at 43 European outpatient clinics in adults with moderate-to-severe UC who were biologic naïve or had received a maximum of one other biological therapy. Patients were treated according to European GLM UC label/local practice. Clinical response (based on partial or full Mayo score) was assessed at week 6, 10, or 14 of induction, depending on local practice. Investigators remotely monitored scores every 4 weeks. The primary endpoint was the proportion of induction responders in patient-reported continuous clinical response (pCCR) at week 54, defined as absence of UC flare based on combined patient-reported Mayo stool frequency and rectal bleeding scores every 4 weeks and full or partial Mayo score. A key secondary endpoint was the proportion of induction responders in clinical remission at week 54. RESULTS: Among 109 patients, 37 (34.0%) received at least two GLM induction doses and completed induction in clinical response (induction responders). At week 54, 15/37 (40.5%) induction responders were in pCCR, and 21/37 (56.8%) were in clinical remission. CONCLUSION: In daily clinical practice, regular remote monitoring of combined patient-reported Mayo stool frequency and rectal bleeding scores appears to be a meaningful real-world outcome measure for monitoring maintenance of GLM-induced clinical response in UC.

Clinical prediction models in psychiatry: a systematic review of two decades of progress and challenges.

Recent years have seen the rapid proliferation of clinical prediction models aiming to support risk stratification and individualized care within psychiatry. Despite growing interest, attempts to synthesize current evidence in the nascent field of precision psychiatry have remained scarce. This systematic review therefore sought to summarize progress towards clinical implementation of prediction modeling for psychiatric outcomes. We searched MEDLINE, PubMed, Embase, and PsychINFO databases from inception to September 30, 2020, for English-language articles that developed and/or validated multivariable models to predict (at an individual level) onset, course, or treatment response for non-organic psychiatric disorders (PROSPERO: CRD42020216530). Individual prediction models were evaluated based on three key criteria: (i) mitigation of bias and overfitting; (ii) generalizability, and (iii) clinical utility. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was used to formally appraise each study's risk of bias. 228 studies detailing 308 prediction models were ultimately eligible for inclusion. 94.5% of developed prediction models were deemed to be at high risk of bias, largely due to inadequate or inappropriate analytic decisions. Insufficient internal validation efforts (within the development sample) were also observed, while only one-fifth of models underwent external validation in an independent sample. Finally, our search identified just one published model whose potential utility in clinical practice was formally assessed. Our findings illustrated significant growth in precision psychiatry with promising progress towards real-world application. Nevertheless, these efforts have been inhibited by a preponderance of bias and overfitting, while the generalizability and clinical utility of many published models has yet to be formally established. Through improved methodological rigor during initial development, robust evaluations of reproducibility via independent validation, and evidence-based implementation frameworks, future research has the potential to generate risk prediction tools capable of enhancing clinical decision-making in psychiatric care.

Using a Loneliness Measure to Screen for Risk of Mental Health Problems: A Replication in Two Nationally Representative Cohorts.

BACKGROUND: Loneliness co-occurs alongside many mental health problems and is associated with poorer treatment outcomes. It could therefore be a phenomenon of interest to clinicians as an indicator of generalised risk for psychopathology. The present study tested whether a short measure of loneliness can accurately classify individuals who are at increased risk of common mental health problems. METHODS: Data were drawn from two nationally representative cohorts: the age-18 wave of the UK-based Environmental Risk (E-Risk) Longitudinal Twin Study and the age-38 wave of the New Zealand-based Dunedin Multidisciplinary Health and Development Study. In both cohorts, loneliness was assessed using the three-item UCLA Loneliness Scale, plus two stand-alone items about feeling alone and feeling lonely. Outcome measures consisted of diagnoses of depression and anxiety and self-reports of self-harm/suicide attempts, assessed via a structured interview. RESULTS: ROC curve analysis showed that the Loneliness Scale had fair accuracy in classifying individuals meeting criteria for all three outcomes, with a cut-off score of 5 (on a scale from 3 to 9) having the strongest empirical support. Both of the stand-alone items showed modest sensitivity and specificity but were more limited in their flexibility. The findings were replicated across the two cohorts, indicating that they are applicable both to younger and older adults. In addition, the accuracy of the loneliness scale in detecting mental health problems was comparable to a measure of poor sleep quality, a phenomenon which is often included in screening tools for depression and anxiety. CONCLUSIONS: These findings indicate that a loneliness measure could have utility in mental health screening contexts, as well as in research.

Developmental pathways from toddler difficult temperament to child generalized psychopathology and adult functioning.

BACKGROUND: Early difficult temperament and child mental health problems are consistently associated with impaired functioning in adulthood. We examined three potential pathways between difficult temperament in toddlerhood (age 2) and depressive symptoms (ages 21-23) and well-being (age 23): i) direct - early difficult temperament directly associates with these outcomes, ii) mediated - these direct effects are also mediated by a general psychopathology factor in late childhood/early adolescence (GPF; ages 7, 10,and 13), and iii) moderated-mediated - these mediated effects are also moderated by negative (age 42 months) and positive (age 33 months) parenting behaviors. METHODS: The analytic sample included 1892 mother-child dyads (33.4% male children) from the Avon Longitudinal Study of Parents and Children (ALSPAC). Mothers reported on their child's difficult temperament, negative parenting, positive parenting, and child's mental health symptoms. In adulthood, participants reported their own depressive symptoms and well-being (i.e. mental well-being, life satisfaction, happiness). RESULTS: First, early difficult temperament associated directly and positively with depressive symptoms, but negatively with well-being in adulthood. Second, the GPF in late childhood/early adolescence mediated these direct associations. Third, the mediated pathways were not moderated by negative or positive parenting. LIMITATIONS: i) low risk community sample, ii) early risks are based on maternal reports. CONCLUSIONS: Temperament is a risk factor for impaired psychosocial functioning in adulthood, manifested through increased susceptibility to psychopathology in childhood/adolescence. Although more research is needed to test their generalizability, these findings suggest that targeting early difficult temperament may alleviate the risk for later mental health difficulties and may increase general well-being.

Poor Individual Risk Classification From Adverse Childhood Experiences Screening.

INTRODUCTION: Adverse childhood experiences confer an increased risk for physical and mental health problems across the population, prompting calls for routine clinical screening based on reported adverse childhood experience exposure. However, recent longitudinal research has questioned whether adverse childhood experiences can accurately identify ill health at an individual level. METHODS: Revisiting data collected for the Adverse Childhood Experience Study between 1995 and 1997, this study derived approximate area under the curve estimates to test the ability of the retrospectively reported adverse childhood experience score to discriminate between adults with and without a range of common health risk factors and disease conditions. Furthermore, the classification accuracy of a recommended clinical definition for high-risk exposure (≥4 versus 0-3 adverse childhood experiences) was evaluated on the basis of sensitivity, specificity, positive and negative predictive values, and positive likelihood ratios. RESULTS: Across all health outcomes, the levels of discrimination for the continuous adverse childhood experience score ranged from very poor to fair (area under the curve=0.50-0.76). The binary classification of ≥4 versus 0-3 adverse childhood experiences yielded high specificity (true-negative detection) and negative predictive values (absence of ill health among low-risk adverse childhood experience groups). However, sensitivity (true-positive detection) and positive predictive values (presence of ill health among high-risk adverse childhood experience groups) were low, whereas positive likelihood ratios suggested only minimal-to-moderate increases in health risks among individuals reporting ≥4 adverse childhood experiences versus that among those reporting 0-3. CONCLUSIONS: These findings suggest that screening based on the adverse childhood experience score does not accurately identify those individuals at high risk of health problems. This can lead to both allocation of unnecessary interventions and lack of provision of necessary support.

Neonatal DNA methylation and childhood low prosocial behavior: An epigenome-wide association meta-analysis.

Low prosocial behavior in childhood has been consistently linked to later psychopathology, with evidence supporting the influence of both genetic and environmental factors on its development. Although neonatal DNA methylation (DNAm) has been found to prospectively associate with a range of psychological traits in childhood, its potential role in prosocial development has yet to be investigated. This study investigated prospective associations between cord blood DNAm at birth and low prosocial behavior within and across four longitudinal birth cohorts from the Pregnancy And Childhood Epigenetics (PACE) Consortium. We examined (a) developmental trajectories of "chronic-low" versus "typical" prosocial behavior across childhood in a case-control design (N = 2,095), and (b) continuous "low prosocial" scores at comparable cross-cohort time-points (N = 2,121). Meta-analyses were performed to examine differentially methylated positions and regions. At the cohort-specific level, three CpGs were found to associate with chronic low prosocial behavior; however, none of these associations was replicated in another cohort. Meta-analysis revealed no epigenome-wide significant CpGs or regions. Overall, we found no evidence for associations between DNAm patterns at birth and low prosocial behavior across childhood. Findings highlight the importance of employing multi-cohort approaches to replicate epigenetic associations and reduce the risk of false positive discoveries.

Population vs Individual Prediction of Poor Health From Results of Adverse Childhood Experiences Screening.

Importance: Adverse childhood experiences (ACEs) are well-established risk factors for health problems in a population. However, it is not known whether screening for ACEs can accurately identify individuals who develop later health problems. Objective: To test the predictive accuracy of ACE screening for later health problems. Design, Setting, and Participants: This study comprised 2 birth cohorts: the Environmental Risk (E-Risk) Longitudinal Twin Study observed 2232 participants born during the period from 1994 to 1995 until they were aged 18 years (2012-2014); the Dunedin Multidisciplinary Health and Development Study observed 1037 participants born during the period from 1972 to 1973 until they were aged 45 years (2017-2019). Statistical analysis was performed from May 28, 2018, to July 29, 2020. Exposures: ACEs were measured prospectively in childhood through repeated interviews and observations in both cohorts. ACEs were also measured retrospectively in the Dunedin cohort through interviews at 38 years. Main Outcomes and Measures: Health outcomes were assessed at 18 years in E-Risk and at 45 years in the Dunedin cohort. Mental health problems were assessed through clinical interviews using the Diagnostic Interview Schedule. Physical health problems were assessed through interviews, anthropometric measurements, and blood collection. Results: Of 2232 E-Risk participants, 2009 (1051 girls [52%]) were included in the analysis. Of 1037 Dunedin cohort participants, 918 (460 boys [50%]) were included in the analysis. In E-Risk, children with higher ACE scores had greater risk of later health problems (any mental health problem: relative risk, 1.14 [95% CI, 1.10-1.18] per each additional ACE; any physical health problem: relative risk, 1.09 [95% CI, 1.07-1.12] per each additional ACE). ACE scores were associated with health problems independent of other information typically available to clinicians (ie, sex, socioeconomic disadvantage, and history of health problems). However, ACE scores had poor accuracy in predicting an individual's risk of later health problems (any mental health problem: area under the receiver operating characteristic curve, 0.58 [95% CI, 0.56-0.61]; any physical health problem: area under the receiver operating characteristic curve, 0.60 [95% CI, 0.58-0.63]; chance prediction: area under the receiver operating characteristic curve, 0.50). Findings were consistent in the Dunedin cohort using both prospective and retrospective ACE measures. Conclusions and Relevance: This study suggests that, although ACE scores can forecast mean group differences in health, they have poor accuracy in predicting an individual's risk of later health problems. Therefore, targeting interventions based on ACE screening is likely to be ineffective in preventing poor health outcomes.

Assessing the content specificity of interpretation biases in community adolescents with persistent and interfering pain.

The tendency to select threatening over benign interpretations of ambiguous bodily sensations and cues characterises young people with chronic pain. However, previous studies disagree over whether these biases extend to nonbodily harm situations such as social evaluation. Understanding the content of these biases is crucial to the development of pain management strategies seeking to modify such biases. Two hundred forty-three young people aged 16 to 19 years completed an expanded version of the Adolescent Interpretation of Bodily Threat task. Using a factor-analytic approach, we removed items that did not consistently associate with bodily harm or social evaluation. Next, we examined whether the variance underlying negative and benign interpretations of bodily harm and social evaluation situations were best represented as a common factor (ie, one-factor model), 2 distinct factors (ie, 2-factor model), or one common and 2 distinct factors (ie, 2-factor bifactor model) in all adolescents. We then compared youth with and without persistent and impairing pain on factor scores derived from the best-fitting model. Although negative interpretations of bodily harm and social evaluation situations emerged as distinct factors, benign interpretations across situations were best captured by a common factor and 2 situation-specific factors (ie, bifactor model). Group comparisons showed that young people with moderate-to-high pain interference were more likely to endorse negative interpretations across all situations, and less likely to manifest a general benign interpretational style, than youth without interfering pain, although some of these group differences were explained by co-occurring anxiety and depressive symptoms. Replication of these findings is needed.

Developing an individualized risk calculator for psychopathology among young people victimized during childhood: A population-representative cohort study.

BACKGROUND: Victimized children are at greater risk for psychopathology than non-victimized peers. However, not all victimized children develop psychiatric disorders, and accurately identifying which victimized children are at greatest risk for psychopathology is important to provide targeted interventions. This study sought to develop and internally validate individualized risk prediction models for psychopathology among victimized children. METHODS: Participants were members of the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally-representative British birth cohort of 2,232 twins born in 1994-1995. Victimization exposure was measured prospectively between ages 5 and 12 years, alongside a comprehensive range of individual-, family-, and community-level predictors of psychopathology. Structured psychiatric interviews took place at age-18 assessment. Logistic regression models were estimated with Least Absolute Shrinkage and Selection Operator (LASSO) regularization to avoid over-fitting to the current sample, and internally validated using 10-fold nested cross-validation. RESULTS: 26.5% (n = 591) of E-Risk participants had been exposed to at least one form of severe childhood victimization, and 60.4% (n = 334) of victimized children met diagnostic criteria for any psychiatric disorder at age 18. Separate prediction models for any psychiatric disorder, internalizing disorders, and externalizing disorders selected parsimonious subsets of predictors. The three internally validated models showed adequate discrimination, based on area-under-the-curve estimates (range = =0.66-0.73), and good calibration. LIMITATIONS: External validation in wholly-independent data is needed before clinical implementation. CONCLUSIONS: Findings offer proof-of-principle evidence that prediction modeling can be useful in supporting identification of victimized children at greatest risk for psychopathology. This has the potential to inform targeted interventions and rational resource allocation.

Development of an individualized risk calculator for poor functioning in young people victimized during childhood: A longitudinal cohort study.

BACKGROUND: Childhood victimization elevates the average risk of developing functional impairment in adulthood. However, not all victimized children demonstrate poor outcomes. Although research has described factors that confer vulnerability or resilience, it is unknown if this knowledge can be translated to accurately identify the most vulnerable victimized children. OBJECTIVE: To build and internally validate a risk calculator to identify those victimized children who are most at risk of functional impairment at age 18 years. PARTICIPANTS: We utilized data from the Environmental Risk (E-Risk) Longitudinal Twin Study, a nationally-representative birth cohort of 2232 UK children born in 1994-95. METHODS: Victimization exposure was assessed repeatedly between ages 5 and 12 years along with a range of individual-, family- and community-level predictors. Functional outcomes were assessed at age 18 years. We developed and evaluated a prediction model for psychosocial disadvantage and economic disadvantage using the Least Absolute Shrinkage and Selection Operator (LASSO) regularized regression with nested 10-fold cross-validation. RESULTS: The model predicting psychosocial disadvantage following childhood victimization retained 12 of 22 predictors, had an area under the curve (AUC) of 0.65, and was well-calibrated within the range of 40-70% predicted risk. The model predicting economic disadvantage retained 10 of 22 predictors, achieved excellent discrimination (AUC = 0.80), and a high degree of calibration. CONCLUSIONS: Prediction modelling techniques can be applied to estimate individual risk for poor functional outcomes in young adulthood following childhood victimization. Such risk prediction tools could potentially assist practitioners to target interventions, which is particularly useful in a context of scarce resources.

Health and Functional Outcomes for Shared and Unique Variances of Interpersonal Callousness and Low Prosocial Behavior.

Previous factor-analytic studies identify significant comorbidity between interpersonal-callous (IC) traits and low prosocial behavior (LPB), which, in turn, is associated with high levels of childhood risk exposure and psychopathology. Longitudinal associations between IC, LPB, or their combination, and early-adult health and social functioning have not been investigated, however. Extending a previously-identified bifactor model within a prospective birth cohort, this study applied latent path analysis to test direct and indirect pathways (via adolescent delinquency, substance use, and physical activity) between these general and specific factors (age 13) and (i) emotional problems (age 18), (ii) physical health problems (age 18), and (iii) classification as 'not in education, employment, or training' (NEET; age 20). All models controlled for childhood adversity and IQ. Bifactor-specific estimates indicated that the residual IC factor did not reliably denote unique variance over and above a general factor (IC/LPB). IC/LPB itself was directly associated with NEET classification, while the residual LPB factor was associated with better emotional and physical health. IC/LPB also indirectly associated with emotional problems via greater adolescent delinquency, and with physical health problems via lower physical activity. In contrast, residual LPB variance was either non-significantly or negatively related to these adolescent domains. Findings indicate that the shared variance underlying IC and LPB confers an increased risk for poor health and functional outcomes in emerging adulthood, and highlight delinquency and physical inactivity as potential adolescent treatment targets that may mitigate the risk for health difficulties at high levels of this IC/LPB construct.

Shared and unique variances of interpersonal callousness and low prosocial behavior.

Although low prosocial behavior (LPB) items have been incorporated into youth measures of callousness, it remains unclear from current factor analytic findings whether callous traits and LPB are best operationalized as a common construct, or distinct dimensions. Using data from a population-representative birth cohort (N = 5,463), this study compared 4 latent factor structures for interpersonal callousness (IC; 6 items) and LPB (5 items) at age 13: (a) unidimensional; (b) two-factor; (c) higher-order (with 2 subfactors); and (d) bifactor (1 general and 2 specific residual factors). Alternative models distinguishing positively and negatively worded items were tested for comparative purposes. To assess the external validity of the factors that emerged from the best-fitting model, associations with early parenting styles and psychiatric comorbidities were examined. A bifactor model, achieving invariance for males and females, offered the best fit for these data. However, additional bifactor-specific indices suggested that the specific IC factor did not offer a unique contribution to the total variance over and above the general factor (IC/LPB). Of the remaining factors, IC/LPB was associated with higher levels of harsh parenting, externalizing and internalizing disorder, and social-cognitive difficulties, and lower levels of warm parenting. The LPB factor, meanwhile, was associated with greater social-cognitive difficulties and externalizing disorder, and lower maternal warmth, evoking a phenotype that may be more indicative of the autism spectrum than IC. These findings suggest that the shared variance underlying IC and LPB taps a severe psychiatric phenotype, while the residual variance for LPB may represent a distinct profile of social-cognitive dysfunction. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

No Pharmacokinetic Drug-Drug Interaction Between Prasugrel and Vorapaxar Following Multiple-Dose Administration in Healthy Volunteers.

Vorapaxar is a first-in-class antagonist of the protease-activated receptor-1, the primary thrombin receptor on human platelets, which mediates the downstream effects of thrombin in hemostasis and thrombosis. Prasugrel is a platelet inhibitor that acts as a P2Y12 receptor antagonist through an active metabolite, R-138727. This study investigated the interaction of these 2 platelet antagonists when coadministered. This was a randomized, open-label, multiple-dose study in 54 healthy volunteers consisting of a fixed-sequence crossover and a parallel group design. In sequence 1, 36 subjects received prasugrel 60 mg on day 1 and then prasugrel 10 mg once daily on days 2 to 7, followed by vorapaxar 40 mg and prasugrel 10 mg on day 8 and then vorapaxar 2.5 mg and prasugrel 10 mg orally once daily on days 9 to 28. In sequence 2, 18 subjects received vorapaxar 40 mg on day 1 and then vorapaxar 2.5 mg once daily on days 2 to 21. The geometric mean ratios (90% confidence intervals) for AUCτ and Cmax of coadministration/monotherapy for vorapaxar (0.93 ng·h/mL[0.85-1.02 ng·h/mL] and 0.95 ng/mL [0.86-1.05 ng/mL]) and R-138727 (0.91 ng·h/mL [0.85- 0.99 ng·h/mL] and 1.02 ng/mL [0.89-1.17 ng/mL]) were within prespecified bounds, demonstrating the absence of a pharmacokinetic interaction between vorapaxar and prasugrel. There was no specific safety or tolerability risk associated with multiple-dose coadministration of vorapaxar and prasugrel. In conclusion, in this study in healthy volunteers, there was no pharmacokinetic drug-drug interaction between vorapaxar and prasugrel. Multiple-dose coadministration of the 2 drugs was generally well tolerated.

Data linkage in medical science using the resource description framework: the AVERT model.

There is an ongoing challenge as to how best manage and understand 'big data' in precision medicine settings. This paper describes the potential for a Linked Data approach, using a Resource Description Framework (RDF) model, to combine multiple datasets with temporal and spatial elements of varying dimensionality. This "AVERT model" provides a framework for converting multiple standalone files of various formats, from both clinical and environmental settings, into a single data source. This data source can thereafter be queried effectively, shared with outside parties, more easily understood by multiple stakeholders using standardized vocabularies, incorporating provenance metadata and supporting temporo-spatial reasoning. The approach has further advantages in terms of data sharing, security and subsequent analysis. We use a case study relating to anti-Glomerular Basement Membrane (GBM) disease, a rare autoimmune condition, to illustrate a technical proof of concept for the AVERT model.