Diana Schendel’s research while affiliated with Drexel University and other places

Eating disorders (EDs) commonly co-occur with other psychiatric and neurodevelopmental disorders including attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD); however, the pattern of family history and genetic overlap among them requires clarification. This study investigated the diagnostic, familial, and genetic associations of EDs with ADHD and ASD. The nationwide population-based cohort study included all individuals born in Denmark, 1981–2008, linked to their siblings and cousins. Cox regression was used to estimate associations between EDs and ADHD or ASD, and mediation analysis was used to assess the effects of intermediate mood or anxiety disorders. Polygenic scores (PGSs) were used to investigate the genetic association between anorexia nervosa (AN) and ADHD or ASD. Significantly increased risk for any ED was observed following an ADHD or ASD diagnosis. Mediation analysis suggested that intermediate mood or anxiety disorders could account for 44%–100% of the association between ADHD or ASD and ED. Individuals with a full sibling or maternal half sibling with ASD had increased risk of AN compared to those with siblings without ASD. A positive association was found between ASD-PGS and AN risk whereas a negative association was found between AN-PGS and ADHD. In this study, positive phenotypic associations between EDs and ADHD or ASD, mediation by mood or anxiety disorder, and genetic associations between ASD-PGS and AN and between AN-PGS and ADHD were observed. These findings could guide future research in the development of new treatments that can mitigate the development of EDs among individuals with ADHD or ASD.

The relatively few conditions and family member types (e.g., sibling, parent) considered in investigations of family health history in autism spectrum disorder (ASD, or autism) limits understanding of the role of family history in autism etiology. For more comprehensive understanding and hypothesis‐generation, we produced an open‐source catalog of autism associations with family histories of mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions. All live births in Denmark, 1980–2012, of Denmark‐born parents (1,697,231 births), and their 3‐generation family members were followed through April 10, 2017 for each of 90 diagnoses (including autism), emigration or death. Adjusted hazard ratios (aHR) were estimated via Cox regression for each diagnosis‐family member type combination, adjusting for birth year, sex, birth weight, gestational age, parental ages at birth, and number of family member types of index person; aHRs also calculated for sex‐specific co‐occurrence of each disorder. We obtained 6462 individual family history aHRS across autism overall (26,840 autistic persons; 1.6% of births), by sex, and considering intellectual disability (ID); and 350 individual co‐occurrence aHRS. Results are cataloged in interactive heat maps and down‐loadable data files: https://ncrr-au.shinyapps.io/asd-riskatlas/ and interactive graphic summaries: https://public.tableau.com/app/profile/diana.schendel/viz/ASDPlots_16918786403110/e-Figure5 . While primarily for reference material or use in other studies (e.g., meta‐analyses), results revealed considerable breadth and variation in magnitude of familial health history associations with autism by type of condition, family member type, sex of the family member, side of the family, sex of the index person, and ID status, indicative of diverse genetic, familial, and nongenetic autism etiologic pathways. Careful attention to sources of autism likelihood in family health history, aided by our open data resource, may accelerate understanding of factors underlying neurodiversity.

Importance Small, geographically limited studies report that people with intellectual and developmental disabilities (IDD) have increased risk for serious pregnancy-related and birth-related challenges, including preeclampsia, preterm birth, and increased anxiety and depression, than their peers. United States–based population-level data among people with IDD are lacking. Objectives To identify perinatal and postpartum outcomes among a national, longitudinal sample of people with IDD enrolled in public health insurance, compare subgroups of people with IDD, and compare outcomes among people with IDD with those of peers without IDD. Design, Setting, and Participants This retrospective cohort study used national Medicaid claims from January 1, 2008, to December 31, 2019, for 55 440 birthing people with IDD and a random sample of 438 557 birthing people without IDD. Medicaid funds almost half of all births and is the largest behavioral health insurer in the US, covering a robust array of services for people with IDD. Statistical analysis was performed from July 2023 to June 2024. Exposure People who had a documented birth in Medicaid during the study years. Main Outcome and Measures Perinatal outcomes were compared across groups using univariate and multivariate logistic regression. The probability of postpartum anxiety and depression was estimated using Kaplan-Meier and Cox proportional hazards regression. Results The study sample included 55 440 birthing people with IDD (including 41 854 with intellectual disabilities [ID] and 13 586 with autism; mean [SD] age at first delivery, 24.9 [6.7] years) and a random sample of 438 557 birthing people without IDD (mean [SD] age at first delivery, 26.4 [6.3] years). People with IDD were younger at first observed delivery, had a lower prevalence of live births (66.6% vs 76.7%), and higher rates of obstetric conditions (gestational diabetes, 10.3% vs 9.9%; gestational hypertension, 8.7% vs 6.1%; preeclampsia, 6.1% vs 4.4%) and co-occurring physical conditions (heart failure, 1.4% vs 0.4%; hyperlipidemia, 5.3% vs 1.7%; ischemic heart disease, 1.5% vs 0.4%; obesity, 16.3% vs 7.4%) and mental health conditions (anxiety disorders, 27.9% vs 6.5%; depressive disorders, 32.1% vs 7.5%; posttraumatic stress disorder, 9.5% vs 1.2%) than people without IDD. The probability of postpartum anxiety (adjusted hazard ratio [AHR], 3.2 [95% CI, 2.9-3.4]) and postpartum depression (AHR, 2.4 [95% CI, 2.3-2.6]) was significantly higher among autistic people compared with people with ID only and people without IDD. Conclusions and Relevance In this retrospective cohort study, people with IDD had a younger mean age at first delivery, had lower prevalence of live births, and had poor obstetric, mental health, and medical outcomes compared with people without IDD, pointing toward a need for clinician training and timely delivery of maternal health care. Results highlight needed reproductive health education, increasing clinician knowledge, and expanding Medicaid to ensure access to care for people with IDD.

Background Testing etiologic heterogeneity – whether a disorder subtype is more or less impacted by a risk factor, is important for understanding causal pathways and optimizing statistical power. The study of mental health disorders especially benefits from strategic sub-categorization because these disorders are heterogenous and frequently co-occur. Existing methods to quantify etiologic heterogeneity are not appropriate for non-competing events in an open cohort of variable-length follow-up. Thus, we developed a new method. Methods We estimated risks from urban residence, maternal smoking during pregnancy, and parental psychiatric history, with subtypes defined by the presence or absence of a co-diagnosis: autism alone, attention deficit hyperactivity disorder (ADHD) alone, and joint diagnoses of autism+ADHD. To calculate the risk of a single diagnosis (e.g. autism alone), we subtracted the risk for autism+ADHD from the risk for autism overall. We tested the equivalency of average risk ratios over time, using a Wald-type test and bootstrapped standard errors. Results Urban residence was most strongly linked with autism+ADHD and least with ADHD only; maternal smoking was associated with ADHD only but not autism only; and parental psychiatric history exhibited similar associations with all subgroups. Conclusions Our method allowed the calculation of appropriate p values to test strength of association, informing etiologic heterogeneity wherein two of these three risk factors exhibited different impacts across diagnostic subtypes. The method used all available data, avoided neurodevelopmental outcome misclassification, exhibited robust statistical precision, and is applicable to similar heterogeneous complex conditions using common diagnostic data with variable follow-up.

Eating disorders (EDs) commonly co-occur with other psychiatric and neurodevelopmental disorders including attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD); however, the pattern of family history and genetic overlap among them requires clarification. This study investigated the diagnostic, familial, and genetic associations of EDs with ADHD and ASD. The nationwide population-based cohort study included all individuals born in Denmark, 1981–2008, linked to their siblings and cousins. Cox regression was used to estimate associations between EDs and ADHD or ASD, and mediation analysis was used to assess the effects of intermediate mood or anxiety disorders. Polygenic scores (PGSs) were used to investigate the genetic association between anorexia nervosa (AN) and ADHD or ASD. Significantly increased risk for any ED was observed following an ADHD [hazard ratio = 1.97, 95% confidence interval = 1.75–2.22] or ASD diagnosis [2.82, 2.48–3.19]. Mediation analysis suggested that intermediate mood or anxiety disorders could account for 44–100% of the association between ADHD or ASD and ED. Individuals with a full sibling or maternal halfsibling with ASD had increased risk of AN [1.54, 1.33–1.78; 1.45, 1.08–1.94] compared to those with siblings without ASD. A positive association was found between ASD-PGS and AN risk [1.06, 1.02–1.09]. In this study, positive phenotypic associations between EDs and ADHD or ASD, mediation by mood or anxiety disorder, and a genetic association between ASD-PGS and AN were observed. These findings could guide future research in the development of new treatments that can mitigate the development of EDs among individuals with ADHD or ASD.

The relatively few conditions and family members investigated in autism family health history limits etiologic understanding. For more comprehensive understanding and hypothesis-generation we produced an open- source catalogue of autism associations with family histories of mental, neurologic, cardiometabolic, birth defect, asthma, allergy, and autoimmune conditions. All live births in Denmark, 1980-2012, of Denmark-born parents (1,697,231 births), and their 3-generation family members were followed through April 10, 2017 for each of 90 diagnoses (including autism), emigration or death. Adjusted hazard ratios (aHR) were estimated via Cox regression for each diagnosis-family member type combination, adjusting for birth year, sex, birth weight, gestational age, parental ages at birth, and number of family member types of index person; aHRs also calculated for sex-specific co-occurrence of each disorder. We obtained 6,462 individual family history aHRS across autism overall (26,840 autistic persons; 1.6% of births), by sex, and considering intellectual disability (ID); and 350 individual co-occurrence aHRS. Results are catalogued in interactive heat maps and down- loadable data files: https://ncrr-au.shinyapps.io/asd-riskatlas/ and interactive graphic summaries: https://public.tableau.com/views/ASDPlots_16918786403110/e-Figure5 . While primarily for reference material or use in other studies (e.g., meta-analyses), results revealed considerable breadth and variation in magnitude of familial health history associations with autism by type of condition, family member type, sex of the family member, side of the family, sex of the index person, and ID status, indicative of diverse genetic, familial, and non-genetic autism etiologic pathways. Careful attention to sources of autism likelihood in family health history, aided by our open data resource, may accelerate understanding of factors underlying neurodiversity. Lay summary We calculated the likelihood that a person will be diagnosed with autism if they had a specific family member (e.g, a parent, sibling, grandparent) with a specific mental, neurologic, cardiometabolic, birth defect, asthma, allergy, or autoimmune condition - over 6,000 separate estimates based on 26,840 autistic persons. Results are catalogued in interactive figures and down-loadable data files: https://ncrr-au.shinyapps.io/asd-riskatlas/ and interactive graphic summaries: https://public.tableau.com/views/ASDPlots_16918786403110/e-Figure5 . The st udy of autism family health history - which varies widely by condition, family member type, sex of the family member, side of the family, sex of the index person, intellectual disability status - may advance understanding of factors underlying neurodiversity.

Background: Testing etiologic heterogeneity, whether a disorder subtype is more or less impacted by a risk factor, is important toward understanding causal pathways and optimizing statistical power. The study of mental health disorders especially benefits from strategic sub-categorization because these disorders are heterogenous and frequently co-occur. Existing methods to quantify etiologic heterogeneity are not appropriate for non-competing events in an open cohort of variable-length follow-up. Thus, we developed a new method. Methods: We estimated risks from urban residence, maternal smoking during pregnancy, and parental psychiatric history, with subtypes defined by the presence or absence of a co-diagnosis: autism alone, attention deficit hyperactivity disorder (ADHD) alone, and joint diagnoses of autism+ADHD. To calculate the risk of a single diagnosis (e.g. autism alone), we subtracted the risk for autism+ADHD from the risk for autism overall. We tested the equivalency of average risk ratios over time, using a Wald-type test and bootstrapped standard errors. Results: Urban residence was most strongly linked with autism+ADHD and least with ADHD only; maternal smoking was associated with ADHD only but not autism only; and parental psychiatric history exhibited similar associations with all subgroups. Conclusions: Our method allowed the calculation of appropriate p values to test strength of association, showing etiologic heterogeneity wherein 2 of these 3 risk factors exhibited different impacts across diagnostic subtypes. The method used all available data, avoided neurodevelopmental outcome misclassification, exhibited robust statistical precision, and is applicable to similar heterogeneous complex conditions using common diagnostic data with variable follow-up.

While the concept of big data has emerged over the past decade as a hot topic in nearly all areas of scientific inquiry, it has rarely been discussed in the context of autism research. In this commentary we describe aspects of big data that are relevant to autism research and methodological issues such as confounding and data error that can hamper scientific investigation. Although big data studies can have transformative impact, bigger is not always better, and big data require the same methodological considerations and interdisciplinary collaboration as "small data" to extract useful scientific insight.