Maternal risk factors vary between subpopulations of children with autism spectrum disorder
Autism ResearchGenevieve Grivas, Richard E Frye, Juergen Hahn
2022
Previous work identified three subgroups of children with ASD based upon co-occurring conditions (COCs) diagnosed during the first 5 years of life. This work examines prenatal risk factors, given by maternal medical claims, for each of the three subgroups: children with a High-Prevalence of COCs, children with mainly developmental delay and seizures (DD/Seizure COCs), and children with a Low-Prevalence of COCs. While some risk factors are shared by all three subgroups, the majority of the factors identified for each subgroup were unique; infections, anti-inflammatory and other complex medications were associated with the High-Prevalence COCs group; immune deregulatory conditions such as asthma and joint disorders were associated with the DD/Seizure COCs group; and overall pregnancy complications were associated with the Low-Prevalence COCs group. Thus, we have found that the previously identified subgroups of children with ASD have distinct associated prenatal risk factors. As such, this work supports subgrouping children with ASD based upon COCs, which may provide a framework for elucidating some of the heterogeneity associated with ASD.
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Towards a Multivariate Biomarker-Based Diagnosis of Autism Spectrum Disorder: Review and Discussion of Recent Advancements
Seminars in Pediatric NeurologyTroy Vargason, Genevieve Grivas, Kathryn L Hollowood-Jones, Juergen Hahn
2020
An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.
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Clustering of co-occurring conditions in autism spectrum disorder during early childhood: A retrospective analysis of medical claims data
Autism ResearchTroy Vargason, Richard E Frye, Deborah L McGuinness, Juergen Hahn
2019
Individuals with autism spectrum disorder (ASD) are frequently affected by co-occurring medical conditions (COCs), which vary in severity, age of onset, and pathophysiological characteristics. The presence of COCs contributes to significant heterogeneity in the clinical presentation of ASD between individuals and a better understanding of COCs may offer greater insight into the etiology of ASD in specific subgroups while also providing guidance for diagnostic and treatment protocols. This study retrospectively analyzed medical claims data from a private United States health plan between years 2000 and 2015 to investigate patterns of COC diagnoses in a cohort of 3,278 children with ASD throughout their first 5 years of enrollment compared to 279,693 children from the general population without ASD diagnoses (POP cohort).
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Multivariate techniques enable a biochemical classification of children with autism spectrum disorder versus typically-developing peers: A comparison and validation study
Bioengineering & Translational MedicineDaniel P Howsmon, Troy Vargason, Robert A Rubin, Leanna Delhey, Marie Tippett, Shannon Rose, Sirish C Bennuri, John C Slattery, Stepan Melnyk, S Jill James, Richard E Frye, Juergen Hahn
2018
Autism spectrum disorder (ASD) is a developmental disorder which is currently only diagnosed through behavioral testing. Impaired folate-dependent one carbon metabolism (FOCM) and transsulfuration (TS) pathways have been implicated in ASD, and recently a study involving multivariate analysis based upon Fisher Discriminant Analysis returned very promising results for predicting an ASD diagnosis. This article takes another step toward the goal of developing a biochemical diagnostic for ASD by comparing five classification algorithms on existing data of FOCM/TS metabolites, and also validating the classification results with new data from an ASD cohort.
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Classification and adaptive behavior prediction of children with autism spectrum disorder based upon multivariate data analysis of markers of oxidative stress and DNA methylation
PLoS Computational BiologyDaniel P Howsmon, Uwe Kruger, Stepan Melnyk, S Jill James, Juergen Hahn
2017
The number of diagnosed cases of Autism Spectrum Disorders (ASD) has increased dramatically over the last four decades; however, there is still considerable debate regarding the underlying pathophysiology of ASD. This lack of biological knowledge restricts diagnoses to be made based on behavioral observations and psychometric tools. However, physiological measurements should support these behavioral diagnoses in the future in order to enable earlier and more accurate diagnoses.
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Modeling regulatory mechanisms in IL‐6 signal transduction in hepatocytes
Biotechnology and BioengineeringAbhay Singh, Arul Jayaraman, Juergen Hahn
2006
Cytokines like interleukin‐6 (IL‐6) play an important role in triggering the acute phase response of the body to injury or inflammation. Signaling by IL‐6 involves two pathways: Janus‐associated kinases (JAK) and signal transducers and activators of transcription (STAT 3) are activated in the first pathway while the second pathway involves the activation of mitogen‐activated protein kinases (MAPK). While it is recognized that both pathways play a major role in IL‐6 signal transduction, a majority of studies have focused on signaling through either one of the pathways...
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An improved method for nonlinear model reduction using balancing of empirical gramians
Computers & Chemical EngineeringJuergen Hahn, Thomas F Edgar
2002
Nonlinear model predictive control has become increasingly popular in the chemical process industry. Highly accurate models can now be simulated with modern dynamic simulators combined with powerful optimization algorithms. However, computational requirements grow with the complexity of the models...
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Automatic control in microelectronics manufacturing: Practices, challenges, and possibilities
AutomaticaThomas F Edgar, Stephanie W Butler, W Jarrett Campbell, Carlos Pfeiffer, Christopher Bode, Sung Bo Hwang, KS Balakrishnan, Juergen Hahn
2000
Advances in modeling and control will be required to meet future technical challenges in microelectronics manufacturing. The implementation of closed-loop control on key unit operations has been limited due to a dearth of suitable in situ measurements, variations in process equipment and wafer properties, limited process understanding, non-automated operational practices, and lack of trained personnel. This paper reviews the state-of-the-art for process control in semiconductor processing, and covers the key unit operations of lithography, plasma etching, thin film deposition, rapid thermal processing, and chemical–mechanical planarization...
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