A cotwin control study of associations between financial hardship and binge eating phenotypes during COVID-19.

BACKGROUND: COVID-19 was associated with significant financial hardship and increased binge eating (BE). However, it is largely unknown whether financial stressors contributed to BE during the pandemic. We used a longitudinal, cotwin control design that controls for genetic/environmental confounds by comparing twins in the same family to examine whether financial hardship during COVID-19 was associated with BE. METHODS: Female twins (N = 158; Mage  = 22.13) from the Michigan State University Twin Registry rated financial stressors (e.g., inability to afford necessities) daily for 49 consecutive days during COVID-19. We first examined whether financial hardship was associated with BE phenotypes across the full sample. We then examined whether cotwins who differed on financial hardship also differed in BE. RESULTS: Participants who experienced greater mean financial hardship across the study had significantly greater dimensional BE symptoms, and participants who experienced greater financial hardship on a given day reported significantly more emotional eating that day. These results were replicated in cotwin control analyses. Twins who experienced more financial hardship than their cotwin across the study reported greater dimensional BE symptoms than their cotwin, and participants who experienced more financial hardship than their cotwin on a given day reported greater emotional eating that day. Results were identical when restricting analyses to monozygotic twins, suggesting associations were not due to genetic confounds. CONCLUSIONS: Results suggest that BE-related symptoms may be elevated in women who experienced financial hardship during COVID-19 independent of potential genetic/environmental confounds. However, additional research in larger samples is needed. PUBLIC SIGNIFICANCE: Little is known regarding how financial difficulties during the COVID-19 pandemic may have contributed to increased binge eating (BE). We found preliminary evidence that financial hardship during COVID-19 may be associated with greater rates of BE-related symptoms even when comparing twins from the same family. While additional research is needed, results suggest that people who experienced financial hardship during COVID-19 may be at increased risk for BE.

Meaningful associations in the adolescent brain cognitive development study.

The Adolescent Brain Cognitive Development (ABCD) Study is the largest single-cohort prospective longitudinal study of neurodevelopment and children's health in the United States. A cohort of n = 11,880 children aged 9-10 years (and their parents/guardians) were recruited across 22 sites and are being followed with in-person visits on an annual basis for at least 10 years. The study approximates the US population on several key sociodemographic variables, including sex, race, ethnicity, household income, and parental education. Data collected include assessments of health, mental health, substance use, culture and environment and neurocognition, as well as geocoded exposures, structural and functional magnetic resonance imaging (MRI), and whole-genome genotyping. Here, we describe the ABCD Study aims and design, as well as issues surrounding estimation of meaningful associations using its data, including population inferences, hypothesis testing, power and precision, control of covariates, interpretation of associations, and recommended best practices for reproducible research, analytical procedures and reporting of results.

Science-based assessment of source materials for cell-based medicines: report of a stakeholders workshop.

Human pluripotent stem cells (hPSCs) have the potential to transform medicine. However, hurdles remain to ensure safety for such cellular products. Science-based understanding of the requirements for source materials is required as are appropriate materials. Leaders in hPSC biology, clinical translation, biomanufacturing and regulatory issues were brought together to define requirements for source materials for the production of hPSC-derived therapies and to identify other key issues for the safety of cell therapy products. While the focus of this meeting was on hPSC-derived cell therapies, many of the issues are generic to all cell-based medicines. The intent of this report is to summarize the key issues discussed and record the consensus reached on each of these by the expert delegates.

Determining the Quantitative Principles of T Cell Response to Antigenic Disparity in Stem Cell Transplantation.

Alloreactivity compromising clinical outcomes in stem cell transplantation is observed despite HLA matching of donors and recipients. This has its origin in the variation between the exomes of the two, which provides the basis for minor histocompatibility antigens (mHA). The mHA presented on the HLA class I and II molecules and the ensuing T cell response to these antigens results in graft vs. host disease. In this paper, results of a whole exome sequencing study are presented, with resulting alloreactive polymorphic peptides and their HLA class I and HLA class II (DRB1) binding affinity quantified. Large libraries of potentially alloreactive recipient peptides binding both sets of molecules were identified, with HLA-DRB1 generally presenting a greater number of peptides. These results are used to develop a quantitative framework to understand the immunobiology of transplantation. A tensor-based approach is used to derive the equations needed to determine the alloreactive donor T cell response from the mHA-HLA binding affinity and protein expression data. This approach may be used in future studies to simulate the magnitude of expected donor T cell response and determine the risk for alloreactive complications in HLA matched or mismatched hematopoietic cell and solid organ transplantation.

Extending Causality Tests with Genetic Instruments: An Integration of Mendelian Randomization with the Classical Twin Design.

Although experimental studies are regarded as the method of choice for determining causal influences, these are not always practical or ethical to answer vital questions in health and social research (e.g., one cannot assign individuals to a "childhood trauma condition" in studying the causal effects of childhood trauma on depression). Key to solving such questions are observational studies. Mendelian Randomization (MR) is an influential method to establish causality in observational studies. MR uses genetic variants to test causal relationships between exposures/risk factors and outcomes such as physical or mental health. Yet, individual genetic variants have small effects, and so, when used as instrumental variables, render MR liable to weak instrument bias. Polygenic scores have the advantage of larger effects, but may be characterized by horizontal pleiotropy, which violates a central assumption of MR. We developed the MR-DoC twin model by integrating MR with the Direction of Causation twin model. This model allows us to test pleiotropy directly. We considered the issue of parameter identification, and given identification, we conducted extensive power calculations. MR-DoC allows one to test causal hypotheses and to obtain unbiased estimates of the causal effect given pleiotropic instruments, while controlling for genetic and environmental influences common to the outcome and exposure. Furthermore, the approach allows one to employ strong instrumental variables in the form of polygenic scores, guarding against weak instrument bias, and increasing the power to detect causal effects of exposures on potential outcomes. Beside allowing to test pleiotropy directly, incorporating in MR data collected from relatives provide additional within-family data that resolve additional assumptions like random mating, the absence of the gene-environment interaction/covariance, no dyadic effects. Our approach will enhance and extend MR's range of applications, and increase the value of the large cohorts collected at twin/family registries as they correctly detect causation and estimate effect sizes even in the presence of pleiotropy.

Regime Switching Modeling of Substance Use: Time-Varying and Second-Order Markov Models and Individual Probability Plots.

A linear latent growth curve mixture model with regime switching is extended in 2 ways. Previously, the matrix of first-order Markov switching probabilities was specified to be time-invariant, regardless of the pair of occasions being considered. The first extension, time-varying transitions, specifies different Markov transition matrices between each pair of occasions. The second extension is second-order time-invariant Markov transition probabilities, such that the probability of switching depends on the states at the 2 previous occasions. The models are implemented using the R package OpenMx, which facilitates data handling, parallel computation, and further model development. It also enables the extraction and display of relative likelihoods for every individual in the sample. The models are illustrated with previously published data on alcohol use observed on 4 occasions as part of the National Longitudinal Survey of Youth, and demonstrate improved fit to the data.

Applying Multivariate Discrete Distributions to Genetically Informative Count Data.

We present a novel method of conducting biometric analysis of twin data when the phenotypes are integer-valued counts, which often show an L-shaped distribution. Monte Carlo simulation is used to compare five likelihood-based approaches to modeling: our multivariate discrete method, when its distributional assumptions are correct, when they are incorrect, and three other methods in common use. With data simulated from a skewed discrete distribution, recovery of twin correlations and proportions of additive genetic and common environment variance was generally poor for the Normal, Lognormal and Ordinal models, but good for the two discrete models. Sex-separate applications to substance-use data from twins in the Minnesota Twin Family Study showed superior performance of two discrete models. The new methods are implemented using R and OpenMx and are freely available.

Adjusted confidence intervals for a bounded parameter.

It is well known that the regular likelihood ratio test of a bounded parameter is not valid if the boundary value is being tested. This is the case for testing the null value of a scalar variance component. Although an adjusted test of variance component has been suggested to account for the effect of its lower bound of zero, no adjustment of its interval estimate has ever been proposed. If left unadjusted, the confidence interval of the variance may still contain zero when the adjusted test rejects the null hypothesis of a zero variance, leading to conflicting conclusions. In this research, we propose two ways to adjust the confidence interval of a parameter subject to a lower bound, one based on the Wald test and the other on the likelihood ratio test. Both are compatible to the adjusted test and parametrization-invariant. A simulation study and two examples are given in the framework of ACDE models in twin studies.

Differential age and sex effects in the assessment of major depression: a population-based twin item analysis of the DSM criteria.

A twin item factor analytic model was developed to test for the presence of noninvariant age, sex, and age by sex interaction effects on the individual DSM-III-R criteria for major depression (MD). Based on 1-year reports, six of the nine MD criteria and duration requirement were found to have covariate factor loading and/or threshold effects that significantly deviated from their corresponding factor level expectations. A significant age effect was found for the binary duration variable factor loading. The 'loss of interest', 'weight problems' and 'psychomotor problems' criteria all displayed forms of threshold only effects. 'Depressed mood', 'fatigue', and 'feeling worthless' had more complex patterns that included both factor loading and threshold effects. A significant factor age by sex interaction effect indicating an increasing female mean difference with age was found to be largely associated with the presence of differential threshold covariate effects. Disagreement between estimated factor scores and DSM-derived affected vs. unaffected classification was approximately 1.3%. Status on the duration requirement was found to be the one feature common to all discrepancies. The MD criteria set provided maximum information for calibrating MD factor scores in the scale region where discrepancies occurred. The dimensional modeling results are discussed in the broader context of epidemiological research and clinical assessment of major depression.

Estimating fetal and maternal genetic contributions to premature birth from multiparous pregnancy histories of twins using MCMC and maximum-likelihood approaches.

The analysis of genetic and environmental contributions to preterm birth is not straightforward in family studies, as etiology could involve both maternal and fetal genes. Markov Chain Monte Carlo (MCMC) methods are presented as a flexible approach for defining user-specified covariance structures to handle multiple random effects and hierarchical dependencies inherent in children of twin (COT) studies of pregnancy outcomes. The proposed method is easily modified to allow for the study of gestational age as a continuous trait and as a binary outcome reflecting the presence or absence of preterm birth. Estimation of fetal and maternal genetic factors and the effect of the environment are demonstrated using MCMC methods implemented in WinBUGS and maximum likelihood methods in a Virginia COT sample comprising 7,061 births. In summary, although the contribution of maternal and fetal genetic factors was supported using both outcomes, additional births and/or extended relationships are required to precisely estimate both genetic effects simultaneously. We anticipate the flexibility of MCMC methods to handle increasingly complex models to be of particular relevance for the study of birth outcomes.

VA intensive mental health case management in urban and rural areas: veteran characteristics and service delivery.

OBJECTIVE: The availability of mental health services in rural areas--particularly intensive services such as assertive community treatment (ACT)--has been of increasing concern and was the focus of this study. In recent decades the U.S. Department of Veterans Affairs (VA) has developed a national network of ACT-like programs called mental health intensive case management (MHICM), which have served veterans from diverse locations across the country, including urban and rural areas. METHODS: This study used rural-urban commuting area codes and national VA administrative data to compare characteristics of veterans and patterns of MHICM service delivery among veterans with mental illness living in large urban, large rural, small rural, and isolated rural communities. RESULTS: Among veterans enrolled in MHICM from FY 2000 to FY 2005 (N=5,221), 84% (N=4,373) resided in urban areas, 8% (N=421) in large cities, 6% (N=291) in small rural towns, and 3% (N=136) in isolated rural areas. MHICM participants who lived in rural areas had clinical problems broadly similar to those in urban areas, although more rural veterans were unemployed, disabled, received VA disability compensation, and had a payee or fiduciary. MHICM clients in smaller or isolated rural areas received slightly less frequent and less intensive contacts and less recovery-oriented services than those in large urban locations. CONCLUSIONS: These data highlight the need for intensive case management services in rural areas and note some challenges in providing them at the intensity and frequency observed in urban areas where travel distances and times are shorter.

Veterans Affairs Intensive Case Management for older veterans.

BACKGROUND: There is a growing need for information on evidence-based practices that may potentially address needs of elderly people with severe mental illness (SMI), and more specifically on community-based services such as assertive community treatment (ACT). METHODS: This study examines national evaluation data from fiscal year 2001-2005 from Veterans Affairs Mental Health Intensive Case Management (MHICM) program (N = 5,222), an ACT-based service model, to characterize the age distribution of participants and the distinctive needs, patterns of service delivery, and treatment outcomes for elderly veterans. RESULTS: Altogether, 24.8% of participants were 55-64 years; 7.4% 65-74 years; and 2.8% were older than 75. Veterans over 75 formed a distinct subgroup that had a later age of onset of primarily nonpsychotic illnesses without comorbid substance abuse and had experienced more limited lifetime hospital treatment than younger participants. Older veterans were less symptomatic and more satisfied with their social relationships than younger clients. They mostly live independently or in minimally restrictive housing, but they received less recovery-focused services and more crisis intervention and medical services. They thus do not appear to be young patients with SMI who have aged but rather constitute a distinct group with serious late-onset problems. It is possible that MHICM services keep them in the community and avoid costly nursing home placement while providing a respite service that reduces family burden. CONCLUSION: These data highlight the unique characteristics of older veterans receiving ACT-like services and the need to focus greater attention on recovery-oriented services as well as community support for this subgroup.

Flexible Mx specification of various extended twin kinship designs.

The extended twin kinship design allows the simultaneous testing of additive and nonadditive genetic, shared and individual-specific environmental factors, as well as sex differences in the expression of genes and environment in the presence of assortative mating and combined genetic and cultural transmission (Eaves et al., 1999). It also handles the contribution of these sources of variance to the (co)variation of multiple phenotypes. Keller et al. (2008) extended this comprehensive model for family resemblance to allow or a flexible specification of assortment and vertical transmission. As such, it provides a general framework which can easily be reduced to fit subsets of data such as twin-parent data, children-of-twins data, etc. A flexible Mx specification of this model that allows handling of these various designs is presented in detail and applied to data from the Virginia 30,000. Data on height, body mass index, smoking status, church attendance, and political affiliation were obtained from twins and their families. Results indicate that biases in the estimation of variance components depend both on the types of relative available for analysis, and on the underlying genetic and environmental architecture of the phenotype of interest.

Assessment of generalized anxiety disorder diagnostic criteria in the National Comorbidity Survey and Virginia Adult Twin Study of Psychiatric and Substance Use Disorders.

The authors investigated measurement properties of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, generalized anxiety disorder (GAD) criteria in the National Comorbidity Survey and the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders (VATSPSUD). The two studies used different widely used instruments. There were significant (p < .001) differences in measurement of GAD due to age, study, and age-study interaction on item thresholds and factor loadings of GAD, especially when different stem-probe structures of interviews were taken into account. Item thresholds were estimated to differ by as much as -.74 as a function of age and .40 as a function of study. Despite these differences, factor scores derived from symptom criteria strongly predicted categorical diagnostic outcomes based on symptom count. It is concluded that interview structure, especially the stem-probe format of structured interviews, and wording had significant effects on study findings; that future studies in psychiatric epidemiology should use common structured interviews as much as possible; and that factor scores can be used in conjunction with sum scores as cut points to retain the advantages of both dimensional and categorical classification.

Power calculations using exact data simulation: a useful tool for genetic study designs.

Statistical power calculations constitute an essential first step in the planning of scientific studies. If sufficient summary statistics are available, power calculations are in principle straightforward and computationally light. In designs, which comprise distinct groups (e.g., MZ & DZ twins), sufficient statistics can be calculated within each group, and analyzed in a multi-group model. However, when the number of possible groups is prohibitively large (say, in the hundreds), power calculations on the basis of the summary statistics become impractical. In that case, researchers may resort to Monte Carlo based power studies, which involve the simulation of hundreds or thousands of replicate samples for each specified set of population parameters. Here we present exact data simulation as a third method of power calculation. Exact data simulation involves a transformation of raw data so that the data fit the hypothesized model exactly. As in power calculation with summary statistics, exact data simulation is computationally light, while the number of groups in the analysis has little bearing on the practicality of the method. The method is applied to three genetic designs for illustrative purposes.

Assessment and etiology of attention deficit hyperactivity disorder and oppositional defiant disorder in boys and girls.

Attention deficit hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD) are more common in boys than girls. In this paper, we investigated whether the prevalence differences are attributable to measurement bias. In addition, we examined sex differences in the genetic and environmental influences on variation in these behaviors. Teachers completed the Conners Teacher Rating Scale-Revised:Short version (CTRS-R:S) in a sample of 800 male and 851 female 7-year-old Dutch twins. No sex differences in the factor structure of the CTRS-R:S were found, implying the absence of measurement bias. The heritabilities for both ADHD and ODD were high and were the same in boys and girls. However, partly different genes are expressed in boys and girls.

Methodological issues in the assessment of substance use phenotypes.

The measurement of behavior is inherently problematic, and this is especially true of substance use and abuse phenotypes. The contingent nature of many assessments, such that symptoms of abuse and dependence cannot be obtained from those who have not initiated substance use, presents special difficulties. Furthermore, it is not clear whether individual differences in liability to use, abuse and dependence are best characterized as dimensions or classes or a combination of both. This article outlines research designs and methods suitable for quantifying liability to substance use. The value of data collected from relatives is emphasized in this context, as they permit identification of models normally compromised because data on, e.g., substance dependence symptoms, are systematically missing in those who have not initiated use of the substance in question.

Discrete Latent Markov Models for Normally Distributed Response Data.

Van de Pol and Langeheine (1990) presented a general framework for Markov modeling of repeatedly measured discrete data. We discuss analogical single indicator models for normally distributed responses. In contrast to discrete models, which have been studied extensively, analogical continuous response models have hardly been considered. These models are formulated as highly constrained multinormal finite mixture models (McLachlan & Peel, 2000). The assumption of conditional independence, which is often postulated in the discrete models, may be relaxed in the normal-based models. In these models, the observed correlation between two variables may thus be due to the presence of two or more latent classes and the presence of within-class dependence. The latter may be subjected to structural equation modeling. In addition to presenting various normal-based Markov models, we demonstrate how these models, formulated as multinormal finite mixtures, may be fitted using the freely available program Mx (Neale, Boker, Xie, & Maes, 2002). To illustrate the application of some of the models, we report the analysis of data relating to the understanding of the conservation of continuous quantity (i.e., a Piagetian construct).

Preoperative cardiac assessment for patients with infrarenal abdominal aortic aneurysms: a survey of current practice by vascular surgeons in New South Wales and Australian Capital Territory.

BACKGROUND: The reported mortality rate following open elective repair of abdominal aortic aneurysm (AAA) varies between 0 and 12%. Much of the mortality and major morbidity is caused by cardiac events. The evidence regarding best practice for cardiac assessment and optimization of this patient group is unclear. The aim of the present study was to evaluate current practice of cardiac risk factor assessment by vascular surgeons in New South Wales (NSW) and Australian Capital Territory (ACT) for patients undergoing open elective repair of infrarenal AAA. METHODS: A postal questionnaire was sent to 46 surgeons in NSW and ACT identified as expressing a principal or major interest in vascular surgery. If no response was received within 3 weeks, a second questionnaire was sent and if no response was received after the second mailing, a telephone survey of non-responders was conducted. Data were collated regarding the importance of risk factors elicited by clinical history, preoperative investigation, referral for cardiological opinion, use of perioperative beta-blockade and the timing of aortic surgery in relation to coronary artery revascularization and acute myocardial infarction. RESULTS: The overall response rate was 87% (40/46) and the median (range) response time was 14 (4-109) days. Only 22 of 40 and 23 of 40 surgeons consider diabetes mellitus or renal impairment, respectively, to be important when assessing cardiac risk and 34 of 40 surgeons do not employ a validated risk index in preoperative assessment. Sixteen of 40 surgeons refer all patients needing AAA repair to a cardiologist, while 24 of 40 would initiate cardiac investigations themselves (either stress electrocardiography, scintigraphy or echocardiography). Seventeen surgeons always or usually commenced perioperative beta-blockade with wide variations in the commencement (1 to > 28 days preoperatively) and duration (< 1 week to > 28 days postoperatively) of treatment. The timing of AAA repair following coronary revascularization ranged from < 1 week to 6 months and delay in surgical repair of AAA following myocardial infarction ranged from < 1 week to > 6 months. CONCLUSION: Preoperative assessment of cardiac risk in patients for repair of AAA lacks consensus among vascular surgeons in NSW and ACT. The diversity of clinical practice may rest with the paucity of prospective trials published in the medical literature or the influence of local institutional facilities.

Impact of public support payments, intensive psychiatric community care, and program fidelity on employment outcomes for people with severe mental illness.

This study explored the relationship of public support payments, intensive psychiatric community care (IPCC), and fidelity of implementation to 1-year employment outcomes for 520 veterans with severe mental illness (SMI) in a clinical trial of IPCC. At study entry, 455 (87.5%) participants received public support. At 1 year, 46 (8.8%) participants met criteria to be classified as workers. A multivariate analysis indicated that baseline public support was significantly associated with a lower likelihood of employment, and baseline work was positively associated with employment at 1 year. IPCC patients were three times more likely to be working than control subjects, and a significant interaction favored well-implemented IPCC programs over others. This study points out not only the inhibiting effect of public support payment on employment but also the value of IPCC and the special importance of fidelity to program models for employment for people with SMI.