About

Arjun Athreya is a faculty member at the University of Illinois Urbana-Champaign, affiliated with the Grainger College of Engineering and the Department of Bioengineering. His research focuses on health analytics, with an emphasis on developing algorithms and technologies for detecting and treating health issues. He is involved in projects that explore the potential of machine learning and data-driven approaches to personalize treatment strategies, including the use of algorithms to individualize treatment for depression and improve prognoses for mental health conditions. He is guided by Iyer in his research endeavors and collaborates with institutions such as the Mayo Clinic to advance precision medicine. His work contributes to the broader field of bioengineering by integrating computational methods with biomedical applications, aiming to enhance health outcomes through innovative technological solutions.

Research topics

• Computer Science
• Medicine
• Psychology
• Internal medicine
• Psychiatry
• Data science
• Physical therapy

Selected publications

• The validation of measured and self-reported sleep duration and perceived sleep quality: an empirical study with three generations of smartwatches

  Frontiers in Psychiatry · 2026-04-20

  articleOpen access

  Introduction: Sleep is routinely assessed in the management of mental health conditions. Wearable technologies like smartwatches offer a non-intrusive method to quantitatively measure sleep. However, there are limited empirical benchmarks for sleep duration and sleep quality measured by wearables against user reports. This study aims to evaluate the concordance between user-reported and smartwatch-measured hours of sleep and sleep quality. Methods: Participants were recruited from two decentralized digital health well-being studies and completed a 7-day sleep diary while simultaneously wearing their smartwatch to sleep (November 7, 2023 - June 30, 2024). Participants self-reported sleep timestamps and perceived sleep quality using the Sleep Quality Scale. Sleep timestamps and quality were also derived from their smartwatches (Garmin Vívoactive 4 2019, Garmin Venu 2 Plus 2022, and Garmin Venu 3/3S 2023). Statistical analyses included paired t-tests, equipercentile linking, and chi-square tests to assess agreement between smartwatch and self-reported sleep parameters. Exploratory analyses established the difference between reported and recorded sleep duration in healthcare shift workers. Results: From 841 sleep instances reported by 130 participants wearing three different generation smartwatches, the mean difference in sleep duration between smartwatch-recorded and participant-reported was 21.22 (Garmin Vívoactive), 11.67 (Garmin Venu 2 Plus), and 6.58 (Garmin Venu 3/3S) minutes, respectively. There were statistically significant between-group differences in mean sleep durations assessed by participant self-report vs. Vívoactive 4 smartwatches, but not self-report vs. Venu 2 Plus or Venu 3/3S smartwatches. Equipercentile linking revealed concordance between smartwatch sleep scores and self-reported sleep quality using the Sleep Quality Scale (SQS) between 4 and 7, with disagreements observed at the SQS ranges from 0-4 and 7-10. Conclusions: These results suggest that wearables can reliably measure sleep duration, and future research warrants improvements in algorithms that estimate sleep quality with validations across different wearable vendors.

  PublisherOA PDFDOI

• Cardiac Biomarkers as Potential Indicators of Neuroimaging Brain Changes in Children and Young Adults with Psychiatric Disorders: A Systematic Review

  Journal of Child and Adolescent Psychopharmacology · 2026-05-05

  article

  BACKGROUND: Cardiac biomarkers are recognized as potential indicators for brain function, emotion regulation, and psychiatric risk. While research in adults has suggested associations between cardiac variables of autonomic function and neural changes, little is known about these relationships in children and young adults with psychiatric conditions. This systematic review aimed to examine the evidence linking cardiac biomarkers with structural, functional, and connectivity-based brain changes in this population. METHODS: A systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Multiple databases were searched for studies examining associations between any cardiac biomarker and neuroimaging findings in individuals under 21 years old with psychiatric diagnoses. After screening and full-text review, 11 eligible studies were included. RESULTS: The included studies investigating a range of psychiatric conditions and cardiac biomarkers were associated with brain changes across three domains: structure, connectivity, and neural responses. Associations were most consistent between higher vagal tone and structural and functional integrity of emotion-regulating regions such as the prefrontal cortex, amygdala, and insula. However, findings were heterogeneous and potentially moderated by symptom severity or environmental stressors. CONCLUSIONS: This review supports the potential of cardiac biomarkers, particularly high-frequency heart rate variability and root mean square of the successive differences, as proxies of brain changes in youth with psychiatric disorders. While promising, current evidence is too limited and variable for clinical applications. Future research should prioritize large-scale, longitudinal studies using harmonized protocols and wearable technologies to validate these indices as translational tools in child and adolescent psychiatry.

  PublisherDOI

• Compositional and functional differences of gut microbiome and metabolome inform pathogenesis of cholestatic liver disease

  Gut Microbes · 2026-04-13

  articleOpen access

  = 278, respectively) were analyzed by shotgun sequencing and ultrahigh-resolution mass spectrometry. Comparisons were conducted with covariate-adjusted linear models. The gut microbiomes of patients with PSC and PBC were characterized by reduced diversity and increased abundance of pathobionts and virulence factors, coupled with altered microbial metabolism, including a reduction of short-chain fatty acids and B-vitamins. Untargeted stool metabolomics supported these results. Patients were stratified into groups using their microbial signatures, and each group had distinct patterns of microbiome-related changes. Cox regression analysis revealed that pathogenic microbial species were predictive of hepatic decompensation, whereas beneficial species had a protective effect. Based on previous groundwork and our new results, microbiome-based interventions such as probiotics, short-chain fatty acid supplementation, and phage therapy represent promising therapeutic options for cholestatic liver diseases.

  PublisherDOI

• Hemoglobin A1c Target in Kidney Transplant Recipients with Type 2 Diabetes Mellitus

  American Journal of Transplantation · 2025-08-01

  articleOpen access
  PublisherOA PDFDOI

• Comprehensive Characterization of Antidepressant Pharmacogenetics: A Systematic Review of Studies in Major Depressive Disorder

  Clinical and Translational Science · 2025-06-01 · 6 citations

  reviewOpen accessSenior authorCorresponding

  Pharmacogenetics is a promising strategy to facilitate individualized care for patients with Major Depressive Disorder (MDD). Research is ongoing to identify the optimal genetic markers for predicting outcomes to antidepressant therapies. The primary aim of this systematic review was to summarize antidepressant pharmacogenetic studies to enhance understanding of the genes, variants, datatypes/methodologies, and outcomes investigated in the context of MDD. The secondary aim was to identify clinical genetic panels indicated for antidepressant prescribing and summarize their genes and variants. Screening of N = 5793 articles yielded N = 390 for inclusion, largely comprising adult (≥ 18 years) populations. Top-studied variants identified in the search were discussed and compared with those represented on the N = 34 clinical genetic panels that were identified. Summarization of articles revealed sources of heterogeneity across studies and low rates of replicability of pharmacogenetic associations. Heterogeneity was present in outcome definitions, treatment regimens, and differential inclusion of mediating variables in analyses. Efficacy outcomes (i.e., response, remission) were studied at greater frequency than adverse-event outcomes. Studies that used advanced analytical approaches, such as machine learning, to integrate variants with complimentary biological datatypes were fewer in number but achieved higher rates of significant associations with treatment outcomes than candidate variant approaches. As large biological datasets become more prevalent, machine learning will be an increasingly valuable tool for parsing the complexity of antidepressant response. This review provides valuable context and considerations surrounding pharmacogenetic associations in MDD which will help inform future research and translation efforts for guiding antidepressant care.

  PublisherOA PDFDOI

• CMV T-Cell Immunity Assay Based Guidance to Interrupt Prophylaxis in Intermediate Risk Kidney Transplant Patients

  American Journal of Transplantation · 2025-08-01

  articleOpen access
  PublisherOA PDFDOI

• Trajectories of Irritability Improvement in Depressed Adolescents Treated With 1 Hz and 10 Hz Transcranial Magnetic Stimulation

  The Journal of Clinical Psychiatry · 2025-04-08 · 2 citations

  article

  Irritability is a debilitating, transdiagnostic symptom in adolescents spanning internalizing and externalizing disorders, and early reduction in irritability with antidepressant treatments has been seen as a positive prognostic sign in depression recovery. There are substantial knowledge gaps regarding how transcranial magnetic stimulation (TMS) treatments impact irritability. diagnosis of major depressive disorder (MDD) undergoing treatment with 2 different doses of TMS. Participants aged 12-18 years (N = 41) underwent 6 weeks of treatment (30 sessions) in a double-blind, randomized trial of 1 Hz vs. 10 Hz TMS for the treatment of MDD. The clinical trial was conducted from September 24, 2018, through March 3, 2023. A linear mixed model was used to assess the change in irritability (assessed with item 8 on the Children's Depression Rating Scale Revised) throughout the treatment course, and a logistic regression was implemented to examine the relationship between early (week 4) irritability improvements and a posttreatment Clinical Global Impressions-Improvement (CGI-I) score. = .0231). These results suggest that irritability is an important correlate of disease severity and predictor of treatment response for MDD in adolescents, replicating similar results found in trials using antidepressant medications. Future research should focus on incorporating assessments of irritability into clinical decision-making and intervention discovery for transdiagnostic symptoms of irritability in youth and adolescents. Data used in this secondary analysis came from ClinicalTrials.gov identifier: NCT03363919.

  PublisherDOI

• Individualized Medicine in the Era of Artificial Intelligence

  Mayo Clinic Proceedings · 2025-10-01 · 1 citations

  reviewOpen access
  PublisherOA PDFDOI

• "Immune Predictors of BK Viremia Clearance: Role of BK Virus-Specific T-Cell Immunity in Kidney Transplant Recipients"

  American Journal of Transplantation · 2025-08-01

  article
  PublisherDOI

• 3.61 Effect of Wearable, Enhanced Parent-Child Interaction Therapy on Parenting Skills

  Journal of the American Academy of Child & Adolescent Psychiatry · 2025-10-01

  article
  PublisherDOI

Recent grants

• FW-HTF-RM: Introducing Patient-Specific Therapy Profiles in Electronic Health Records for Guiding Treatment Selection in the Era of Genomic Medicine

  NSF · $1.4M · 2020–2026

Frequent coauthors

• Paul E. Croarkin

  WinnMed

  57 shared

• William V. Bobo

  Mayo Clinic in Florida

  52 shared

• Richard M. Weinshilboum

  Novel (United States)

  37 shared

• Liewei Wang

  36 shared

• A. John Rush

  Duke University

  27 shared

• Michelle Skime

  Mayo Clinic

  25 shared

• Magdalena Romanowicz

  WinnMed

  21 shared

• Caroline W. Grant

  Mayo Clinic

  21 shared

Labs

• Everitt LabPI

Education

• Ph.D., Bioengineering

  University of Illinois Urbana-Champaign

  2009

• M.S., Bioengineering

  University of Illinois Urbana-Champaign

  2005

• B.S., Bioengineering

  University of Illinois Urbana-Champaign

  2003