Investigating the neurodevelopmental correlates of early adolescent-onset emotional problems.

BACKGROUND: Emotional problems (EPs) increase sharply after mid-adolescence. Earlier EPs are associated with poorer long-term outcomes, and their underlying mechanisms may differ to later-onset EPs. Given an established relationship between ADHD, autism, and later depression, we aimed to examine associations between neurodevelopmental conditions and correlates and early adolescent-onset EPs. METHODS: Adolescents in two UK population cohorts, Avon Longitudinal Study of Parents and Children (ALSPAC) and Millennium Cohort Study (MCS), were included. Individuals scoring >6 on the Strengths and Difficulties Questionnaire (SDQ) emotional problems subscale between ages 11-14 were defined as having early adolescent-onset EP, whilst those scoring >6 for the first time at 16-25 were defined as having later-onset EP. We tested associations between early adolescent-onset EP (total cases = 887, controls = 19,582) and ICD-10/DSM-5 neurodevelopmental conditions and known correlates, including: sex, birth complications, low cognitive ability, special educational needs (SEND), and epilepsy. Analyses were conducted separately in ALSPAC and MCS then meta-analysed. RESULTS: In the meta-analysis of both cohorts, early adolescent-onset EPs were associated with female sex and greater likelihood of low cognitive ability, SEND, autism, ADHD, and reading difficulties. Compared to later-onset EP, early adolescent-onset EPs were associated with male sex, low cognitive ability, SEND, epilepsy, ASD, ADHD, and reading difficulties. LIMITATIONS: A clinical definition of depression/anxiety was available only in ALSPAC, instead we primarily defined EP via questionnaires, which capture a broader phenotype. CONCLUSIONS: Individuals with early adolescent-onset EP are likely to have a co-occurring neurodevelopmental condition. Clinicians should consider assessing for neurodevelopmental conditions in young adolescents with EPs.

The impact of targeted local outreach clinics to improve COVID-19 vaccine uptake: controlled interrupted time series in South West England.

BACKGROUND: Outreach clinics were part of efforts to maximise uptake in COVID-19 vaccination. METHODS: We used controlled interrupted time series, matching on age, sex, deprivation and vaccination eligibility date, to determine the effect of outreach clinics on time to first COVID-19 vaccine, using a population-based electronic health record database of 914,478 people, from December 2020 to December 2021; people living within 1 mile of each outreach clinics were exposed. RESULTS: 50% of 288,473 exposed citizens were white British, and 71% were aged 0-49 years. There was no evidence for an overall statistically significant increase in cumulative percentage vaccinated due to the outreach clinic at 6 weeks, with an overall pooled effect estimate of -0.07% (95% CI: -1.15%, 1.02%). The pooled estimate for increased cumulative vaccine uptake varied slightly depending on how the analysis was stratified; by ethnic group it was - 0.12% (95% CI: -0.90%, 0.66%); by age group it was - 0.06% (95% CI: -0.41%, 0.28%); and by deprivation it was 0.03% (95% CI: -0.74%, 0.79%). CONCLUSIONS: Living within a mile of an outreach clinic was not associated with higher vaccine uptake. Evaluation of future outreach clinics should consider the relative importance of travel amongst other barriers to accessing vaccines.

Evaluation and comparison of nine growth and development-based measures of pubertal timing.

BACKGROUND: Pubertal timing is heritable, varies between individuals, and has implications for life-course health. There are many different indicators of pubertal timing, and how they relate to each other is unclear. Our aim was to quantitatively compare nine indicators of pubertal timing. METHODS: We used data from questionnaires and height, weight, and bone measurements from ages 7-17 y in a population-based cohort of 4267 females and 4251 males to compare nine growth and development-based indicators of pubertal timing. We summarise age of each indicator, their phenotypic and genetic correlations, and how they relate to established genetic risk score (GRS) for puberty timing, and phenotypic childhood body composition measures. RESULTS: We show that pubic hair in males (mean: 12.6 y) and breasts in females (11.5 y) are early indicators of puberty, and voice breaking (14.2 y) and menarche (12.7 y) are late indicators however, there is substantial variation between individuals in pubertal age. All indicators show evidence of positive phenotypic intercorrelations (e.g., r = 0.49: male genitalia and pubic hair ages), and positive genetic intercorrelations. An age at menarche GRS positively associates with all other pubertal age indicators (e.g., difference in female age at peak height velocity per SD higher GRS: 0.24 y, 95%CI: 0.21 to 0.26), as does an age at voice breaking GRS (e.g., difference in age at male axillary hair: 0.11 y, 0.07 to 0.15). Higher childhood fat mass and lean mass associated with earlier puberty timing. CONCLUSIONS: Our findings provide insights into the measurements of the timing of pubertal growth and development and illustrate value of various pubertal timing indicators in life-course research.

Age of puberty varies between individuals and can affect a person's future health. We obtained information from 8500 British children as they progressed through puberty. We compared nine measures of pubertal timing. We found that the appearance of pubic hair in boys and breasts in girls are early indicators of puberty, and that voice change and onset of menstruation are late indicators. However, there was also substantial variability between individuals in age of puberty. All puberty measures were correlated with each other and related to an individual's adult body mass index, as well as to their childhood muscle and fat mass. Our findings are useful information for health care workers and researchers who are interested in assessing and studying puberty.

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.

Use of genetic correlations to examine selection bias.

Observational studies are rarely representative of their target population because there are known and unknown factors that affect an individual's choice to participate (the selection mechanism). Selection can cause bias in a given analysis if the outcome is related to selection (conditional on the other variables in the model). Detecting and adjusting for selection bias in practice typically requires access to data on nonselected individuals. Here, we propose methods to detect selection bias in genetic studies by comparing correlations among genetic variants in the selected sample to those expected under no selection. We examine the use of four hypothesis tests to identify induced associations between genetic variants in the selected sample. We evaluate these approaches in Monte Carlo simulations. Finally, we use these approaches in an applied example using data from the UK Biobank (UKBB). The proposed tests suggested an association between alcohol consumption and selection into UKBB. Hence, UKBB analyses with alcohol consumption as the exposure or outcome may be biased by this selection.

Epigenetic Aging and Racialized, Economic, and Environmental Injustice: NIMHD Social Epigenomics Program.

Importance: Epigenetic age acceleration is associated with exposure to social and economic adversity and may increase the risk of premature morbidity and mortality. However, no studies have included measures of structural racism, and few have compared estimates within or across the first and second generation of epigenetic clocks. Objective: To determine whether epigenetic age acceleration is positively associated with exposures to diverse measures of racialized, economic, and environmental injustice measured at different levels and time periods. Design, Setting, and Participants: This cross-sectional study used data from the My Body My Story (MBMS) study between August 8, 2008, and December 31, 2010, and examination 5 of the Multi-Ethnic Atherosclerosis Study (MESA) from April 1, 2010, to February 29, 2012. In the MBMS, DNA extraction was performed in 2021; linkage of structural measures to the MBMS and MESA, in 2022. US-born individuals were randomly selected from 4 community health centers in Boston, Massachusetts (MBMS), and 4 field sites in Baltimore, Maryland; Forsyth County, North Carolina; New York City, New York; and St Paul, Minnesota (MESA). Data were analyzed from November 13, 2021, to August 31, 2023. Main Outcomes and Measures: Ten epigenetic clocks (6 first-generation and 4 second-generation), computed using DNA methylation data (DNAm) from blood spots (MBMS) and purified monocytes (MESA). Results: The US-born study population included 293 MBMS participants (109 men [37.2%], 184 women [62.8%]; mean [SD] age, 49.0 [8.0] years) with 224 Black non-Hispanic and 69 White non-Hispanic participants and 975 MESA participants (492 men [50.5%], 483 women [49.5%]; mean [SD] age, 70.0 [9.3] years) with 229 Black non-Hispanic, 191 Hispanic, and 555 White non-Hispanic participants. Of these, 140 (11.0%) exhibited accelerated aging for all 5 clocks whose estimates are interpretable on the age (years) scale. Among Black non-Hispanic MBMS participants, epigenetic age acceleration was associated with being born in a Jim Crow state by 0.14 (95% CI, 0.003-0.27) SDs and with birth state conservatism by 0.06 (95% CI, 0.01-0.12) SDs, pooling across all clocks. Low parental educational level was associated with epigenetic age acceleration, pooling across all clocks, for both Black non-Hispanic (0.24 [95% CI, 0.08-0.39] SDs) and White non-Hispanic (0.27 [95% CI, 0.03-0.51] SDs) MBMS participants. Adult impoverishment was positively associated with the pooled second-generation clocks among the MESA participants (Black non-Hispanic, 0.06 [95% CI, 0.01-0.12] SDs; Hispanic, 0.07 [95% CI, 0.01-0.14] SDs; White non-Hispanic, 0.05 [95% CI, 0.01-0.08] SDs). Conclusions and Relevance: The findings of this cross-sectional study of MBMS and MESA participants suggest that epigenetic age acceleration was associated with racialized and economic injustice, potentially contributing to well-documented inequities in premature mortality. Future research should test the hypothesis that epigenetic accelerated aging may be one of the biological mechanisms underlying the well-documented elevated risk of premature morbidity and mortality among social groups subjected to racialized and economic injustice.

Protein Identification for Stroke Progression via Mendelian Randomization in Million Veteran Program and UK Biobank.

BACKGROUND: Individuals who have experienced a stroke, or transient ischemic attack, face a heightened risk of future cardiovascular events. Identification of genetic and molecular risk factors for subsequent cardiovascular outcomes may identify effective therapeutic targets to improve prognosis after an incident stroke. METHODS: We performed genome-wide association studies for subsequent major adverse cardiovascular events (MACE; ncases=51 929; ncontrols=39 980) and subsequent arterial ischemic stroke (AIS; ncases=45 120; ncontrols=46 789) after the first incident stroke within the Million Veteran Program and UK Biobank. We then used genetic variants associated with proteins (protein quantitative trait loci) to determine the effect of 1463 plasma protein abundances on subsequent MACE using Mendelian randomization. RESULTS: Two variants were significantly associated with subsequent cardiovascular events: rs76472767 near gene RNF220 (odds ratio, 0.75 [95% CI, 0.64-0.85]; P=3.69×10-8) with subsequent AIS and rs13294166 near gene LINC01492 (odds ratio, 1.52 [95% CI, 1.37-1.67]; P=3.77×10-8) with subsequent MACE. Using Mendelian randomization, we identified 2 proteins with an effect on subsequent MACE after a stroke: CCL27 ([C-C motif chemokine 27], effect odds ratio, 0.77 [95% CI, 0.66-0.88]; adjusted P=0.05) and TNFRSF14 ([tumor necrosis factor receptor superfamily member 14], effect odds ratio, 1.42 [95% CI, 1.24-1.60]; adjusted P=0.006). These proteins are not associated with incident AIS and are implicated to have a role in inflammation. CONCLUSIONS: We found evidence that 2 proteins with little effect on incident stroke appear to influence subsequent MACE after incident AIS. These associations suggest that inflammation is a contributing factor to subsequent MACE outcomes after incident AIS and highlights potential novel targets.

Non-linear Mendelian randomization: detection of biases using negative controls with a focus on BMI, Vitamin D and LDL cholesterol.

Mendelian randomisation (MR) is an established technique in epidemiological investigation, using the principle of random allocation of genetic variants at conception to estimate the causal linear effect of an exposure on an outcome. Extensions to this technique include non-linear approaches that allow for differential effects of the exposure on the outcome depending on the level of the exposure. A widely used non-linear method is the residual approach, which estimates the causal effect within different strata of the non-genetically predicted exposure (i.e. the "residual" exposure). These "local" causal estimates are then used to make inferences about non-linear effects. Recent work has identified that this method can lead to estimates that are seriously biased, and a new method-the doubly-ranked method-has been introduced as a possibly more robust approach. In this paper, we perform negative control outcome analyses in the MR context. These are analyses with outcomes onto which the exposure should have no predicted causal effect. Using both methods we find clearly biased estimates in certain situations. We additionally examined a situation for which there are robust randomised controlled trial estimates of effects-that of low-density lipoprotein cholesterol (LDL-C) reduction onto myocardial infarction, where randomised trials have provided strong evidence of the shape of the relationship. The doubly-ranked method did not identify the same shape as the trial data, and for LDL-C and other lipids they generated some highly implausible findings. Therefore, we suggest there should be extensive simulation and empirical methodological examination of performance of both methods for NLMR under different conditions before further use of these methods. In the interim, use of NLMR methods needs justification, and a number of sanity checks (such as analysis of negative and positive control outcomes, sensitivity analyses excluding removal of strata at the extremes of the distribution, examination of biological plausibility and triangulation of results) should be performed.

Regression discontinuity design for the study of health effects of exposures acting early in life.

Regression discontinuity design (RDD) is a quasi-experimental approach to study the causal effect of an exposure on later outcomes by exploiting the discontinuity in the exposure probability at an assignment variable cut-off. With the intent of facilitating the use of RDD in the Developmental Origins of Health and Disease (DOHaD) research, we describe the main aspects of the study design and review the studies, assignment variables and exposures that have been investigated to identify short- and long-term health effects of early life exposures. We also provide a brief overview of some of the methodological considerations for the RDD identification using an example of a DOHaD study. An increasing number of studies investigating the effects of early life environmental stressors on health outcomes use RDD, mostly in the context of education, social and welfare policies, healthcare organization and insurance, and clinical management. Age and calendar time are the mostly used assignment variables to study the effects of various early life policies and programs, shock events and guidelines. Maternal and newborn characteristics, such as age, birth weight and gestational age are frequently used assignment variables to study the effects of the type of neonatal care, health insurance, and newborn benefits, while socioeconomic measures have been used to study the effects of social and welfare programs. RDD has advantages, including intuitive interpretation, and transparent and simple graphical representation. It provides valid causal estimates if the assumptions, relatively weak compared to other non-experimental study designs, are met. Its use to study health effects of exposures acting early in life has been limited to studies based on registries and administrative databases, while birth cohort data has not been exploited so far using this design. Local causal effect around the cut-off, difficulty in reaching high statistical power compared to other study designs, and the rarity of settings outside of policy and program evaluations hamper the widespread use of RDD in the DOHaD research. Still, the assignment variables' cut-offs for exposures applied in previous studies can be used, if appropriate, in other settings and with additional outcomes to address different research questions.

Depressive symptoms in adolescence and adult educational and employment outcomes: a structured life course analysis.

BACKGROUND: Depression is a common mental health disorder that often starts during adolescence, with potentially important future consequences including 'Not in Education, Employment or Training' (NEET) status. METHODS: We took a structured life course modeling approach to examine how depressive symptoms during adolescence might be associated with later NEET status, using a high-quality longitudinal data resource. We considered four plausible life course models: (1) an early adolescent sensitive period model where depressive symptoms in early adolescence are more associated with later NEET status relative to exposure at other stages; (2) a mid adolescent sensitive period model where depressive symptoms during the transition from compulsory education to adult life might be more deleterious regarding NEET status; (3) a late adolescent sensitive period model, meaning that depressive symptoms around the time when most adults have completed their education and started their careers are the most strongly associated with NEET status; and (4) an accumulation of risk model which highlights the importance of chronicity of symptoms. RESULTS: Our analysis sample included participants with full information on NEET status (N = 3951), and the results supported the accumulation of risk model, showing that the odds of NEET increase by 1.015 (95% CI 1.012-1.019) for an increase of 1 unit in depression at any age between 11 and 24 years. CONCLUSIONS: Given the adverse implications of NEET status, our results emphasize the importance of supporting mental health during adolescence and early adulthood, as well as considering specific needs of young people with re-occurring depressed mood.

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.

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.

A framework for conducting time-varying genome-wide association studies: An application to body mass index across childhood in six multiethnic cohorts.

Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in genome-wide association studies (GWASs). Using childhood body mass index (BMI) as an example, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound. GWAS on each of the estimated phenotypes identified 28 genome-wide significant variants at 13 loci across the 12 estimated phenotypes, one of which was novel (in DAOA) and had not been previously associated with childhood or adult BMI. Genetic studies of changes in human traits over time could uncover novel biological mechanisms influencing quantitative traits.

A tool to assess risk of bias in non-randomized follow-up studies of exposure effects (ROBINS-E).

BACKGROUND: Observational epidemiologic studies provide critical data for the evaluation of the potential effects of environmental, occupational and behavioural exposures on human health. Systematic reviews of these studies play a key role in informing policy and practice. Systematic reviews should incorporate assessments of the risk of bias in results of the included studies. OBJECTIVE: To develop a new tool, Risk Of Bias In Non-randomized Studies - of Exposures (ROBINS-E) to assess risk of bias in estimates from cohort studies of the causal effect of an exposure on an outcome. METHODS AND RESULTS: ROBINS-E was developed by a large group of researchers from diverse research and public health disciplines through a series of working groups, in-person meetings and pilot testing phases. The tool aims to assess the risk of bias in a specific result (exposure effect estimate) from an individual observational study that examines the effect of an exposure on an outcome. A series of preliminary considerations informs the core ROBINS-E assessment, including details of the result being assessed and the causal effect being estimated. The assessment addresses bias within seven domains, through a series of 'signalling questions'. Domain-level judgements about risk of bias are derived from the answers to these questions, then combined to produce an overall risk of bias judgement for the result, together with judgements about the direction of bias. CONCLUSION: ROBINS-E provides a standardized framework for examining potential biases in results from cohort studies. Future work will produce variants of the tool for other epidemiologic study designs (e.g. case-control studies). We believe that ROBINS-E represents an important development in the integration of exposure assessment, evidence synthesis and causal inference.

Protein identification for stroke progression via Mendelian Randomization in Million Veteran Program and UK Biobank.

Background: Individuals who have experienced a stroke, or transient ischemic attack, face a heightened risk of future cardiovascular events. Identification of genetic and molecular risk factors for subsequent cardiovascular outcomes may identify effective therapeutic targets to improve prognosis after an incident stroke. Methods: We performed genome-wide association studies (GWAS) for subsequent major adverse cardiovascular events (MACE) (Ncases=51,929, Ncntrl=39,980) and subsequent arterial ischemic stroke (AIS) Ncases=45,120, Ncntrl=46,789) after first incident stroke within the Million Veteran Program and UK Biobank. We then used genetic variants associated with proteins (pQTLs) to determine the effect of 1,463 plasma protein abundances on subsequent MACE using Mendelian randomization (MR). Results: Two variants were significantly associated with subsequent cardiovascular events: rs76472767 (OR=0.75, 95% CI = 0.64-0.85, p= 3.69×10-08) with subsequent AIS and rs13294166 (OR=1.52, 95% CI = 1.37-1.67, p=3.77×10-08) with subsequent MACE. Using MR, we identified 2 proteins with an effect on subsequent MACE after a stroke: CCL27 (effect OR= 0.77, 95% CI = 0.66-0.88, adj. p=0.05), and TNFRSF14 (effect OR=1.42, 95% CI = 1.24-1.60, adj. p=0.006). These proteins are not associated with incident AIS and are implicated to have a role in inflammation. Conclusions: We found evidence that two proteins with little effect on incident stroke appear to influence subsequent MACE after incident AIS. These associations suggest that inflammation is a contributing factor to subsequent MACE outcomes after incident AIS and highlights potential novel targets.

Using the global randomization test as a Mendelian randomization falsification test for the exclusion restriction assumption.

Mendelian randomization may give biased causal estimates if the instrument affects the outcome not solely via the exposure of interest (violating the exclusion restriction assumption). We demonstrate use of a global randomization test as a falsification test for the exclusion restriction assumption. Using simulations, we explored the statistical power of the randomization test to detect an association between a genetic instrument and a covariate set due to (a) selection bias or (b) horizontal pleiotropy, compared to three approaches examining associations with individual covariates: (i) Bonferroni correction for the number of covariates, (ii) correction for the effective number of independent covariates, and (iii) an r2 permutation-based approach. We conducted proof-of-principle analyses in UK Biobank, using CRP as the exposure and coronary heart disease (CHD) as the outcome. In simulations, power of the randomization test was higher than the other approaches for detecting selection bias when the correlation between the covariates was low (r2 < 0.1), and at least as powerful as the other approaches across all simulated horizontal pleiotropy scenarios. In our applied example, we found strong evidence of selection bias using all approaches (e.g., global randomization test p < 0.002). We identified 51 of the 58 CRP genetic variants as horizontally pleiotropic, and estimated effects of CRP on CHD attenuated somewhat to the null when excluding these from the genetic risk score (OR = 0.96 [95% CI: 0.92, 1.00] versus 0.97 [95% CI: 0.90, 1.05] per 1-unit higher log CRP levels). The global randomization test can be a useful addition to the MR researcher's toolkit.

Two sample Mendelian Randomisation using an outcome from a multilevel model of disease progression.

Identifying factors that are causes of disease progression, especially in neurodegenerative diseases, is of considerable interest. Disease progression can be described as a trajectory of outcome over time-for example, a linear trajectory having both an intercept (severity at time zero) and a slope (rate of change). A technique for identifying causal relationships between one exposure and one outcome in observational data whilst avoiding bias due to confounding is two sample Mendelian Randomisation (2SMR). We consider a multivariate approach to 2SMR using a multilevel model for disease progression to estimate the causal effect an exposure has on the intercept and slope. We carry out a simulation study comparing a naïve univariate 2SMR approach to a multivariate 2SMR approach with one exposure that effects both the intercept and slope of an outcome that changes linearly with time since diagnosis. The simulation study results, across six different scenarios, for both approaches were similar with no evidence against a non-zero bias and appropriate coverage of the 95% confidence intervals (for intercept 93.4-96.2% and the slope 94.5-96.0%). The multivariate approach gives a better joint coverage of both the intercept and slope effects. We also apply our method to two Parkinson's cohorts to examine the effect body mass index has on disease progression. There was no strong evidence that BMI affects disease progression, however the confidence intervals for both intercept and slope were wide.

Multiple imputation strategies for missing event times in a multi-state model analysis.

In clinical studies, multi-state model (MSM) analysis is often used to describe the sequence of events that patients experience, enabling better understanding of disease progression. A complicating factor in many MSM studies is that the exact event times may not be known. Motivated by a real dataset of patients who received stem cell transplants, we considered the setting in which some event times were exactly observed and some were missing. In our setting, there was little information about the time intervals in which the missing event times occurred and missingness depended on the event type, given the analysis model covariates. These additional challenges limited the usefulness of some missing data methods (maximum likelihood, complete case analysis, and inverse probability weighting). We show that multiple imputation (MI) of event times can perform well in this setting. MI is a flexible method that can be used with any complete data analysis model. Through an extensive simulation study, we show that MI by predictive mean matching (PMM), in which sampling is from a set of observed times without reliance on a specific parametric distribution, has little bias when event times are missing at random, conditional on the observed data. Applying PMM separately for each sub-group of patients with a different pathway through the MSM tends to further reduce bias and improve precision. We recommend MI using PMM methods when performing MSM analysis with Markov models and partially observed event times.

Investigation of genetic determinants of cognitive change in later life.

Cognitive decline is a major health concern and identification of genes that may serve as drug targets to slow decline is important to adequately support an aging population. Whilst genetic studies of cross-sectional cognition have been carried out, cognitive change is less well-understood. Here, using data from the TOMMORROW trial, we investigate genetic associations with cognitive change in a cognitively normal older cohort. We conducted a genome-wide association study of trajectories of repeated cognitive measures (using generalised estimating equation (GEE) modelling) and tested associations with polygenic risk scores (PRS) of potential risk factors. We identified two genetic variants associated with change in attention domain scores, rs534221751 (p = 1 × 10-8 with slope 1) and rs34743896 (p = 5 × 10-10 with slope 2), implicating NCAM2 and CRIPT/ATP6V1E2 genes, respectively. We also found evidence for the association between an education PRS and baseline cognition (at >65 years of age), particularly in the language domain. We demonstrate the feasibility of conducting GWAS of cognitive change using GEE modelling and our results suggest that there may be novel genetic associations for cognitive change that have not previously been associated with cross-sectional cognition. We also show the importance of the education PRS on cognition much later in life. These findings warrant further investigation and demonstrate the potential value of using trial data and trajectory modelling to identify genetic variants associated with cognitive change.