The role of body image dissatisfaction in the relationship between body size and disordered eating and self-harm: complimentary Mendelian randomization and mediation analyses.

Disordered eating and self-harm commonly co-occur in young people suggesting potential for shared underlying causes. Body image dissatisfaction (BID) has been recognised as a psychological correlate of body size, associated with both disordered eating and self-harm. However, the investigation into etiological pathways early in the lifecourse to provide detail on how body size and BID may foster disordered eating and self-harm remains largely unexplored. Employing data from two large population-based cohorts, the UK Biobank and the Avon Longitudinal Study of Parents And Children (ALSPAC), we conducted bidirectional Mendelian randomization (MR) to determine the causal direction of effect between genetically predicted prepubertal body size and two measures of BID indicating (i) desire to be smaller, and (ii) desire to be larger. We then used multivariable regression followed by counterfactual mediation analyses. Bidirectional MR indicated robust evidence that increased genetically predicted prepubertal body size increased desire to be smaller and decreased desire to be larger. Evidence for the reverse causal direction was negligible. These findings remained very similar across sensitivity analyses. In females and males, multivariable regression analyses demonstrated that being overweight increased the risk of disordered eating (risk ratio (RR), 95% confidence interval (CI): 1.19, 1.01 to 1.40 and 1.98, 1.28 to 3.05, respectively) and self-harm (RR, 95% CI: 1.35, 1.04 to 1.77 and 1.55, 0.86 to 2.81, respectively), while being underweight was protective against disordered eating (RR, 95% CI: 0.57, 0.40 to 0.81 and 0.81, 0.38 to 1.73, respectively). There was weak evidence of an increase in the risk of self-harm among underweight individuals. Mediation analyses indicated that the relationship between being overweight and subsequent disordered eating was largely mediated by the desire to be smaller. Our research carries important public health implications, suggesting distinct risk profiles for self-harm and disordered eating in relation to weight and body image. In addition, a better understanding of genetically predicted prepubertal BID may be valuable in the prevention and treatment of disordered eating and self-harm in adolescence.

Analyses Using Multiple Imputation Need to Consider Missing Data in Auxiliary Variables.

Auxiliary variables are used in multiple imputation (MI) to reduce bias and increase efficiency. These variables may often themselves be incomplete. We explored how missing data in auxiliary variables influenced estimates obtained from MI. We implemented a simulation study with three different missing data mechanisms for the outcome. We then examined the impact of increasing proportions of missing data and different missingness mechanisms for the auxiliary variable on bias of an unadjusted linear regression coefficient and the fraction of missing information. We illustrate our findings with an applied example in the Avon Longitudinal Study of Parents and Children. We found that where complete records analyses were biased, increasing proportions of missing data in auxiliary variables, under any missing data mechanism, reduced the ability of MI including the auxiliary variable to mitigate this bias. Where there was no bias in the complete records analysis, inclusion of a missing not at random auxiliary variable in MI introduced bias of potentially important magnitude (up to 17% of the effect size in our simulation). Careful consideration of the quantity and nature of missing data in auxiliary variables needs to be made when selecting them for use in MI models.

Childhood adversities characterize the heterogeneity in the brain pattern of individuals during neurodevelopment.

BACKGROUND: Several factors shape the neurodevelopmental trajectory. A key area of focus in neurodevelopmental research is to estimate the factors that have maximal influence on the brain and can tip the balance from typical to atypical development. METHODS: Utilizing a dissimilarity maximization algorithm on the dynamic mode decomposition (DMD) of the resting state functional MRI data, we classified subjects from the cVEDA neurodevelopmental cohort (n = 987, aged 6-23 years) into homogeneously patterned DMD (representing typical development in 809 subjects) and heterogeneously patterned DMD (indicative of atypical development in 178 subjects). RESULTS: Significant DMD differences were primarily identified in the default mode network (DMN) regions across these groups (p < 0.05, Bonferroni corrected). While the groups were comparable in cognitive performance, the atypical group had more frequent exposure to adversities and faced higher abuses (p < 0.05, Bonferroni corrected). Upon evaluating brain-behavior correlations, we found that correlation patterns between adversity and DMN dynamic modes exhibited age-dependent variations for atypical subjects, hinting at differential utilization of the DMN due to chronic adversities. CONCLUSION: Adversities (particularly abuse) maximally influence the DMN during neurodevelopment and lead to the failure in the development of a coherent DMN system. While DMN's integrity is preserved in typical development, the age-dependent variability in atypically developing individuals is contrasting. The flexibility of DMN might be a compensatory mechanism to protect an individual in an abusive environment. However, such adaptability might deprive the neural system of the faculties of normal functioning and may incur long-term effects on the psyche.

Using instruments for selection to adjust for selection bias in Mendelian randomization.

Selection bias is a common concern in epidemiologic studies. In the literature, selection bias is often viewed as a missing data problem. Popular approaches to adjust for bias due to missing data, such as inverse probability weighting, rely on the assumption that data are missing at random and can yield biased results if this assumption is violated. In observational studies with outcome data missing not at random, Heckman's sample selection model can be used to adjust for bias due to missing data. In this paper, we review Heckman's method and a similar approach proposed by Tchetgen Tchetgen and Wirth (2017). We then discuss how to apply these methods to Mendelian randomization analyses using individual-level data, with missing data for either the exposure or outcome or both. We explore whether genetic variants associated with participation can be used as instruments for selection. We then describe how to obtain missingness-adjusted Wald ratio, two-stage least squares and inverse variance weighted estimates. The two methods are evaluated and compared in simulations, with results suggesting that they can both mitigate selection bias but may yield parameter estimates with large standard errors in some settings. In an illustrative real-data application, we investigate the effects of body mass index on smoking using data from the Avon Longitudinal Study of Parents and Children.

Multiple imputation using auxiliary imputation variables that only predict missingness can increase bias due to data missing not at random.

BACKGROUND: Epidemiological and clinical studies often have missing data, frequently analysed using multiple imputation (MI). In general, MI estimates will be biased if data are missing not at random (MNAR). Bias due to data MNAR can be reduced by including other variables ("auxiliary variables") in imputation models, in addition to those required for the substantive analysis. Common advice is to take an inclusive approach to auxiliary variable selection (i.e. include all variables thought to be predictive of missingness and/or the missing values). There are no clear guidelines about the impact of this strategy when data may be MNAR. METHODS: We explore the impact of including an auxiliary variable predictive of missingness but, in truth, unrelated to the partially observed variable, when data are MNAR. We quantify, algebraically and by simulation, the magnitude of the additional bias of the MI estimator for the exposure coefficient (fitting either a linear or logistic regression model), when the (continuous or binary) partially observed variable is either the analysis outcome or the exposure. Here, "additional bias" refers to the difference in magnitude of the MI estimator when the imputation model includes (i) the auxiliary variable and the other analysis model variables; (ii) just the other analysis model variables, noting that both will be biased due to data MNAR. We illustrate the extent of this additional bias by re-analysing data from a birth cohort study. RESULTS: The additional bias can be relatively large when the outcome is partially observed and missingness is caused by the outcome itself, and even larger if missingness is caused by both the outcome and the exposure (when either the outcome or exposure is partially observed). CONCLUSIONS: When using MI, the naïve and commonly used strategy of including all available auxiliary variables should be avoided. We recommend including the variables most predictive of the partially observed variable as auxiliary variables, where these can be identified through consideration of the plausible casual diagrams and missingness mechanisms, as well as data exploration (noting that associations with the partially observed variable in the complete records may be distorted due to selection bias).

Systematic Review of Clinical Prediction Models for the Risk of Emergency Caesarean Births.

BACKGROUND: Globally, caesarean births (CB), including emergency caesareans births (EmCB), are rising. It is estimated that nearly a third of all births will be CB by 2030. OBJECTIVES: Identify and summarise the results from studies developing and validating prognostic multivariable models predicting the risk of EmCBs. Ultimately understanding the accuracy of their development, and whether they are operationalised for use in routine clinical practice. SEARCH STRATEGY: Studies were identified using databases: MEDLINE, CINAHL, Cochrane Central and Scopus with a search strategy tailored to models predicting EmCBs. SELECTION CRITERIA: Prospective studies developing and validating clinical prediction models, with two or more covariates, to predict risk of EmCB. DATA COLLECTION AND ANALYSIS: Data were extracted onto a proforma using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). RESULTS: In total, 8083 studies resulted in 56 unique prediction modelling studies and seven validating studies, with a total of 121 different predictors. Frequently occurring predictors included maternal height, maternal age, parity, BMI and gestational age. PROBAST highlighted 33 studies with low overall bias, and these all internally validated their model. Thirteen studies externally validated; only eight of these were graded an overall low risk of bias. Six models offered applications that could be readily used, but only one provided enough time to offer a planned caesarean birth (pCB). These well-refined models have not been recalibrated since development. Only one model, developed in a relatively low-risk population, with data collected a decade ago, remains useful at 36 weeks for arranging a pCB. CONCLUSION: To improve personalised clinical conversations, there is a pressing need for a model that accurately predicts the timely risk of an EmCB for women across diverse clinical backgrounds. TRIAL REGISTRATION: PROSPERO registration number: CRD42023384439.

Exploring the associations between adverse childhood experiences (ACEs) and adolescent cancer risk behaviours in the ALSPAC cohort.

BACKGROUND: Some modifiable risk factors for cancer originate during adolescence. While there is evidence indicating relationships between adverse childhood experiences and health risk behaviours generally, little is known about how childhood adversity influences the engagement of adolescents in cancer risk behaviours. This study aimed to determine the relationship between adverse childhood experiences and adolescent cancer risk behaviours. METHODS: Data were collected prospectively from birth to age 18 years on children born to mothers enrolled into the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort study. Multivariable linear regression models assessed relationships of a composite exposure measure comprised of adverse childhood experiences (total number of childhood adversities experienced from early infancy until age 9 years) with multiple cancer risk behaviours. The latter was expressed as a single continuous score for tobacco smoking, alcohol consumption, obesity, unsafe sex, and physical inactivity, at ages 11, 14, 16 and 18 years. Analysis was carried out on the complete case and imputation samples of 1,368 and 7,358 participants respectively. RESULTS: All adolescent cancer risk behaviours increased in prevalence as the adolescents grew older, except for obesity. Each additional adverse childhood experience was associated with a 0.25 unit increase in adolescent cancer risk behaviour (95% CI 0.16-0.34; p < 0.001). Individually, parental substance misuse (ß 0.64, 95% CI 0.25-1.03, p < 0.001) and parental separation (ß 0.56, 95% CI 0.27-0.86, p < 0.001) demonstrated the strongest evidence of association with engagement in adolescent cancer risk behaviour. CONCLUSION: Childhood adversity was associated with a greater degree of engagement in adolescent cancer risk behaviours. This finding demonstrates the need for targeted primary and secondary prevention interventions that reduce engagement across multiple cancer risk behaviours for children and adolescents who have experienced adversity in childhood, such as parental substance misuse and separation, and reduce exposure to adversity.

Mental health problems, stressful life events and new-onset urinary incontinence in primary school-age children: a prospective cohort study.

Emotional/behaviour problems and exposure to stressful life events are thought to contribute to new onset of urinary incontinence (UI) amongst children who have attained bladder control. However, very few prospective studies have examined these associations. We assessed whether mental health problems and stressful life events were associated with subsequent new onset in UI using multivariable logistic regression in a prospective UK cohort (n = 6408). Mothers provided information on their child's symptoms of common mental disorders (Development and Wellbeing Assessment, 7 years), stressful life events (7-8 years) and wetting (day and night, 9 years). There was strong evidence that separation anxiety symptoms were associated with new-onset UI in the fully adjusted model (OR (95% CI) = 2.08 (1.39, 3.13), p < 0.001). Social anxiety, attention-deficit hyperactivity disorder and oppositional defiant disorder symptoms were associated with new-onset UI, but these associations attenuated following adjustment for child developmental level and earlier emotional/behaviour problems. There was weak evidence for a sex interaction with stressful life events (p = 0.065), such that females experiencing more stressful life events were at higher risk of new-onset UI (fully adjusted model OR (95% CI) = 1.66 (1.05, 2.61), p = 0.029), but there was no association in males (fully adjusted model OR (95% CI) = 0.87 (0.52, 1.47), p = 0.608). These results suggest that separation anxiety and stressful life events in girls may lead to an increase in UI.

Sleep duration, sleep problems and developmental trajectories of urinary incontinence: a prospective cohort study.

To examine if preschool sleep duration and sleep problems are associated with urinary incontinence (UI) at primary school-age. We used multinomial logistic regression to examine the association of child sleep duration/problems (3½ years) with UI trajectories (4-9 years) in 8751 (4507 boys, 4244 girls) from the Avon Longitudinal Study of Parents and Children. We adjusted for sex, socioeconomic indicators, mothers' emotional/practical/financial support, developmental delay, stressful life events, temperament, and emotional/behaviour problems. Preschool children who slept more than 8½ hours per night had a decreased probability of UI at school-age. There was a 33% reduction in odds of daytime wetting per additional hour of sleep (odds ratio [OR] = 0.67, 95% confidence interval [CI] 0.52-0.86). Sleep problems were associated with increased odds of UI e.g., getting up after being put to bed was associated with daytime wetting (OR = 2.20, 95% CI 1.43-3.39); breathing problems whilst sleeping were associated with delayed bladder control (OR = 1.68, 95% CI 1.12-2.52), and night-time waking was associated with persistent (day and night) wetting (OR = 1.53, 95% CI 1.16-2.00). Waking during the night and waking up early in the morning were associated with reduced odds of bedwetting at school-age (OR = 0.76, 95% CI 0.61-0.96 and OR = 0.80, 95% CI 0.64-0.99 respectively). Preschool children who sleep for longer have a lower likelihood of UI at school-age, whilst those with sleep problems are more likely to experience daytime wetting and combined (day and night) wetting, but not bedwetting alone. Short sleep duration and sleep problems in early childhood could be indicators of future problems attaining and maintaining bladder control.

Emotional problems across development: examining measurement invariance across childhood, adolescence and early adulthood.

Emotional problems (anxiety, depression) are prevalent in children, adolescents and young adults with varying ages at onset. Studying developmental changes in emotional problems requires repeated assessments using the same or equivalent measures. The parent-rated Strengths and Difficulties Questionnaire is commonly used to assess emotional problems in childhood and adolescence, but there is limited research about whether it captures a similar construct across these developmental periods. Our study addressed this by investigating measurement invariance in the scales' emotional problems subscale (SDQ-EP) across childhood, adolescence and early adulthood. Data from two UK population cohorts were utilised: the Millennium Cohort Study (ages 3-17 years) and the Avon Longitudinal Study of Parents and Children (4-25 years). In both samples we observed weak (metric) measurement invariance by age, suggesting that the parent-rated SDQ-EP items contribute to the underlying construct of emotional problems similarly across age. This supports the validity of using the subscale to rank participants on their levels of emotional problems in childhood, adolescence and early adulthood. However strong (scalar) measurement invariance was not observed, suggesting that the same score may correspond to different levels of emotional problems across developmental periods. Comparisons of mean parent-rated SDQ-EP scores across age may therefore not be valid.

Counterfactual Mediation Analysis with a Latent Class Exposure.

Latent classes are a useful tool in developmental research, however there are challenges associated with embedding them within a counterfactual mediation model. We develop and test a new method "updated pseudo class draws (uPCD)" to examine the association between a latent class exposure and distal outcome that could easily be extended to allow the use of any counterfactual mediation method. UPCD extends an existing group of methods (based on pseudo class draws) that assume that the true values of the latent class variable are missing, and need to be multiply imputed using class membership probabilities. We simulate data based on the Avon Longitudinal Study of Parents and Children, examine performance for existing techniques to relate a latent class exposure to a distal outcome ("one-step," "bias-adjusted three-step," "modal class assignment," "non-inclusive pseudo class draws," and "inclusive pseudo class draws") and compare bias in parameter estimates and their precision to uPCD when estimating counterfactual mediation effects. We found that uPCD shows minimal bias when estimating counterfactual mediation effects across all levels of entropy. UPCD performs similarly to recommended methods (one-step and bias-adjusted three-step), but provides greater flexibility and scope for incorporating the latent grouping within any commonly-used counterfactual mediation approach.

Examining the role of maternal religiosity in offspring mental health using latent class analysis in a UK prospective cohort study.

BACKGROUND: Previous research has examined the role of parental religious belief in offspring mental health, but has revealed inconsistent results, and suffered from a number of limitations. The aim of this study is to examine the prospective relationship between maternal religiosity and offspring mental health and psychosocial outcomes. METHODS: We used latent classes of religious belief (Highly religious, Moderately religious, Agnostic, Atheist) in mothers from the Avon Longitudinal Study of Parents and Children from 1990, and examined their association with parent-reported mental health outcomes and self-reported psychosocial outcomes in their children at age 7-8 (n = 6079 for mental health outcomes and n = 5235 for psychosocial outcomes). We used inverse probability weighted multivariable logistic regression analysis adjusted for maternal mental health, adverse childhood experience, and socioeconomic variables. RESULTS: There was evidence for a greater risk of internalising problems among the offspring of the Highly religious and Moderately religious classes [e.g. for depression; OR 1.40. 95% CI (1.07-1.85), OR 1.48, 95% CI (1.17-1.87)], and greater risk of externalising problems in the offspring of the Atheist class [e.g. for ADHD; OR 1.41, 95% CI (1.08-1.85)], compared to the offspring of the Agnostic class. CONCLUSIONS: These novel findings provide evidence associations between maternal religiosity and offspring mental health differ when examined using a person-centred approach, compared to the previously used variable-centred approaches. Our findings also suggest that differences may exist in the relationship between religious (non)belief and mental health variables when comparing the UK and US.

Autism, autistic traits and multiple risk behaviours in adolescence: a longitudinal birth cohort study.

BACKGROUND: Multiple risk behaviours (MRBs), typically beginning in adolescence, are associated with increased risk of adverse health and social outcomes. The association between autism and MRBs is little understood. METHODS: Data were from the Avon Longitudinal Study of Parents and Children, an UK-based longitudinal, birth cohort study. Exposures were diagnosed autism and four autistic traits: social communication difficulties, pragmatic language, repetitive behaviours and reduced sociability. Outcomes were participation in up to 14 risk behaviours, including alcohol consumption, smoking, risky sexual behaviours and physical inactivity. Outcome data were collected at ages approximately 12, 14, 16 and 18. RESULTS: Up to 4300 participants were included in latent basis growth curve analyses with adjustment for confounders. Social communication difficulties were associated with an above average level of MRBs engagement at ~12 years (mean difference ß 0.26; 95% CI 0.13-0.40), and above average rate of engagement from ages ~12-18 (ß 0.08; 95% CI 0.02-0.13). Repetitive behaviours were associated with above average levels of engagement in MRBs at ~12 years (ß 0.24; 95% CI 0.09-0.38). Contrastingly, reduced sociability was associated with a reduced rate of engagement in MRBs from ages ~12-18 (ß -0.06; 95% CI -0.11 to -0.02). In sex-specific analyses, persisting differences in MRB engagement patterns from ages ~12-18 were observed in males with social communication difficulties and females with reduced sociability temperament. CONCLUSIONS: Having elevated levels of some autistic traits appear to have differentiated effects on MRB engagement patterns. These findings could reflect difficulties fitting in and/or coping mechanisms relating to difficulties with fitting in.

Developmental trajectories of autistic social traits in the general population.

BACKGROUND: Autistic people show diverse trajectories of autistic traits over time, a phenomenon labelled 'chronogeneity'. For example, some show a decrease in symptoms, whilst others experience an intensification of difficulties. Autism spectrum disorder (ASD) is a dimensional condition, representing one end of a trait continuum that extends throughout the population. To date, no studies have investigated chronogeneity across the full range of autistic traits. We investigated the nature and clinical significance of autism trait chronogeneity in a large, general population sample. METHODS: Autistic social/communication traits (ASTs) were measured in the Avon Longitudinal Study of Parents and Children using the Social and Communication Disorders Checklist (SCDC) at ages 7, 10, 13 and 16 (N = 9744). We used Growth Mixture Modelling (GMM) to identify groups defined by their AST trajectories. Measures of ASD diagnosis, sex, IQ and mental health (internalising and externalising) were used to investigate external validity of the derived trajectory groups. RESULTS: The selected GMM model identified four AST trajectory groups: (i) Persistent High (2.3% of sample), (ii) Persistent Low (83.5%), (iii) Increasing (7.3%) and (iv) Decreasing (6.9%) trajectories. The Increasing group, in which females were a slight majority (53.2%), showed dramatic increases in SCDC scores during adolescence, accompanied by escalating internalising and externalising difficulties. Two-thirds (63.6%) of the Decreasing group were male. CONCLUSIONS: Clinicians should note that for some young people autism-trait-like social difficulties first emerge during adolescence accompanied by problems with mood, anxiety, conduct and attention. A converse, majority-male group shows decreasing social difficulties during adolescence.

Co-development of attention deficit hyperactivity disorder and autistic trait trajectories from childhood to early adulthood.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) and autism, defined as traits or disorders, commonly co-occur. Developmental trajectories of ADHD and autistic traits both show heterogeneity in onset and course, but little is known about how symptom trajectories co-develop into adulthood. METHODS: Using data from a population cohort, the Avon Longitudinal Study of Parents and Children, we examined correlations between ADHD and autistic traits across development, using the Social Communication Disorders Checklist and ADHD subscale of the Strengths and Difficulties Questionnaire. We modelled joint developmental trajectories of parent-reported ADHD and autistic traits between 4 and 25 years, then characterised trajectory classes based on sociodemographic, perinatal, psychopathology, cognition and social functioning variables and tested for associations with neurodevelopmental/psychiatric polygenic scores (PGS). RESULTS: Three classes of trajectories were identified; a typically developing majority with low-stable ADHD-autistic traits (87%), a male-predominant subgroup with child/adolescent-declining traits (6%) and a subgroup with late-emerging traits (6%). ADHD-autistic trait correlations were greatest in young adulthood for the two nontypically developing classes. There were higher rates of emotional and conduct problems, low IQ, childhood seizures and poor social functioning in the declining and late-emerging classes compared to the low-stable class. Emotional, conduct and peer problems were more prevalent during childhood in the childhood/adolescent-declining class compared to other classes, but were more prevalent in young adulthood in the late-emerging class. Neurodevelopmental/psychiatric PGS also differed: both nontypically developing classes showed elevated ADHD PGS compared to the low-stable group, and the late-emerging group additionally showed elevated schizophrenia PGS and decreased executive function PGS, whereas the declining group showed elevated broad depression PGS. CONCLUSIONS: Distinct patterns of ADHD-autism co-development are present across development in the general population, each with different characterising factors and genetic signatures as indexed by PGS.

Emotional dysregulation in childhood and disordered eating and self-harm in adolescence: prospective associations and mediating pathways.

BACKGROUND: Emotional dysregulation may be a risk factor for disordered eating and self-harm in young people, but few prospective studies have assessed these associations long-term, or considered potential mediators. We examined prospective relationships between childhood emotional dysregulation and disordered eating and self-harm in adolescence; and social cognition, emotional recognition, and being bullied as mediators. METHODS: We analysed Avon Longitudinal Study of Parents and Children data on 3,453 males and 3,481 females. We examined associations between emotional dysregulation at 7 years and any disordered eating and any self-harm at 16 years with probit regression models. We also assessed whether social cognition (7 years), emotional recognition (8 years) and bullying victimisation (11 years) mediated these relationships. RESULTS: Emotional dysregulation at age 7 years was associated with disordered eating [fully adjusted probit B (95% CI) = 0.082 (0.029, 0.134)] and self-harm [fully adjusted probit B (95% CI) = 0.093 (0.036, 0.150)] at age 16 years. There was no evidence of sex interactions or difference in effects between self-harm and disordered eating. Mediation models found social cognition was a key pathway to disordered eating (females 51.2%; males 27.0% of total effect) and self-harm (females 15.7%; males 10.8% of total effect). Bullying victimisation was an important pathway to disordered eating (females 17.1%; males 10.0% of total effect), but only to self-harm in females (15.7% of total effect). Indirect effects were stronger for disordered eating than self-harm. CONCLUSIONS: In males and females, emotional dysregulation in early childhood is associated with disordered eating and self-harm in adolescence and may be a useful target for prevention and treatment. Mediating pathways appeared to differ by sex and outcome, but social cognition was a key mediating pathway for both disordered eating and self-harm.

Developing and validating a prediction model of adolescent major depressive disorder in the offspring of depressed parents.

BACKGROUND: Parental depression is common and is a major risk factor for depression in adolescents. Early identification of adolescents at elevated risk of developing major depressive disorder (MDD) in this group could improve early access to preventive interventions. METHODS: Using longitudinal data from 337 adolescents at high familial risk of depression, we developed a risk prediction model for adolescent MDD. The model was externally validated in an independent cohort of 1,384 adolescents at high familial risk. We assessed predictors at baseline and MDD at follow-up (a median of 2-3 years later). We compared the risk prediction model to a simple comparison model based on screening for depressive symptoms. Decision curve analysis was used to identify which model-predicted risk score thresholds were associated with the greatest clinical benefit. RESULTS: The MDD risk prediction model discriminated between those adolescents who did and did not develop MDD in the development (C-statistic = .783, IQR (interquartile range) = .779, .778) and the validation samples (C-statistic = .722, IQR = -.694, .741). Calibration in the validation sample was good to excellent (calibration intercept = .011, C-slope = .851). The MDD risk prediction model was superior to the simple comparison model where discrimination was no better than chance (C-statistic = .544, IQR = .536, .572). Decision curve analysis found that the highest clinical utility was at the lowest risk score thresholds (0.01-0.05). CONCLUSIONS: The developed risk prediction model successfully discriminated adolescents who developed MDD from those who did not. In practice, this model could be further developed with user involvement into a tool to target individuals for low-intensity, selective preventive intervention.

Age-specific effects of weight-based body size on fracture risk in later life: a lifecourse Mendelian randomisation study.

Musculoskeletal conditions, including fractures, can have severe and long-lasting consequences. Higher body mass index in adulthood is widely acknowledged to be protective for most fracture sites. However, sources of bias induced by confounding factors may have distorted previous findings. Employing a lifecourse Mendelian randomisation (MR) approach by using genetic instruments to separate effects at different life stages, this investigation aims to explore how prepubertal and adult body size independently influence fracture risk in later life.Using data from a large prospective cohort, univariable and multivariable MR were conducted to simultaneously estimate the effects of age-specific genetic proxies for body size (n = 453,169) on fracture risk (n = 416,795). A two-step MR framework was additionally applied to elucidate potential mediators. Univariable and multivariable MR indicated strong evidence that higher body size in childhood reduced fracture risk (OR, 95% CI: 0.89, 0.82 to 0.96, P = 0.005 and 0.76, 0.69 to 0.85, P = 1 × 10- 6, respectively). Conversely, higher body size in adulthood increased fracture risk (OR, 95% CI: 1.08, 1.01 to 1.16, P = 0.023 and 1.26, 1.14 to 1.38, P = 2 × 10- 6, respectively). Two-step MR analyses suggested that the effect of higher body size in childhood on reduced fracture risk was mediated by its influence on higher estimated bone mineral density (eBMD) in adulthood.This investigation provides novel evidence that higher body size in childhood reduces fracture risk in later life through its influence on increased eBMD. From a public health perspective, this relationship is complex since obesity in adulthood remains a major risk factor for co-morbidities. Results additionally indicate that higher body size in adulthood is a risk factor for fractures. Protective effect estimates previously observed are likely attributed to childhood effects.

Methodological approaches, challenges, and opportunities in the application of Mendelian randomisation to lifecourse epidemiology: A systematic literature review.

Diseases diagnosed in adulthood may have antecedents throughout (including prenatal) life. Gaining a better understanding of how exposures at different stages in the lifecourse influence health outcomes is key to elucidating the potential benefits of disease prevention strategies. Mendelian randomisation (MR) is increasingly used to estimate causal effects of exposures across the lifecourse on later life outcomes. This systematic literature review explores MR methods used to perform lifecourse investigations and reviews previous work that has utilised MR to elucidate the effects of factors acting at different stages of the lifecourse. We conducted searches in PubMed, Embase, Medline and MedRXiv databases. Thirteen methodological studies were identified. Four studies focused on the impact of time-varying exposures in the interpretation of "standard" MR techniques, five presented methods for repeat measures of the same exposure, and four described methodological approaches to handling multigenerational exposures. A further 127 studies presented the results of an applied research question. Over half of these estimated effects in a single generation and were largely confined to the exploration of questions regarding body composition. The remaining mostly estimated maternal effects. There is a growing body of research focused on the development and application of MR methods to address lifecourse research questions. The underlying assumptions require careful consideration and the interpretation of results rely on select conditions. Whilst we do not advocate for a particular strategy, we encourage practitioners to make informed decisions on how to approach a research question in this field with a solid understanding of the limitations present and how these may be affected by the research question, modelling approach, instrument selection, and data availability.

Stratification of adolescents across mental phenomena emphasizes the importance of transdiagnostic distress: a replication in two general population cohorts.

Characterizing patterns of mental phenomena in epidemiological studies of adolescents can provide insight into the latent organization of psychiatric disorders. This avoids the biases of chronicity and selection inherent in clinical samples, guides models of shared aetiology within psychiatric disorders and informs the development and implementation of interventions. We applied Gaussian mixture modelling to measures of mental phenomena from two general population cohorts: the Avon Longitudinal Study of Parents and Children (ALSPAC, n = 3018) and the Neuroscience in Psychiatry Network (NSPN, n = 2023). We defined classes according to their patterns of both positive (e.g. wellbeing and self-esteem) and negative (e.g. depression, anxiety, and psychotic experiences) phenomena. Subsequently, we characterized classes by considering the distribution of diagnoses and sex split across classes. Four well-separated classes were identified within each cohort. Classes primarily differed by overall severity of transdiagnostic distress rather than particular patterns of phenomena akin to diagnoses. Further, as overall severity of distress increased, so did within-class variability, the proportion of individuals with operational psychiatric diagnoses. These results suggest that classes of mental phenomena in the general population of adolescents may not be the same as those found in clinical samples. Classes differentiated only by overall severity support the existence of a general, transdiagnostic mental distress factor and have important implications for intervention.