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Diego Robles Mazzotti, Ph.D.

Diego Robles Mazzotti portrait
Assistant Professor, Medical Informatics
droblesmazzotti@kumc.edu

Professional Background

Dr. Diego Mazzotti is an Assistant Professor in the Division of Medical Informatics, Department of Internal Medicine at the University of Kansas Medical Center. Dr. Mazzotti received his Ph.D. in Psychobiology at the Federal University of São Paulo, Brazil and a Certificate in Biomedical Informatics from the University of Pennsylvania Perelman School of Medicine. Dr. Mazzotti also served as a Research Scientist at the Center for Applied Genomics, Children’s Hospital of Philadelphia and took a faculty position as a Research Associate in Sleep Medicine at the University of Pennsylvania Perelman School of Medicine. He also worked as a Bioinformatics Consultant in several projects spanning many human complex disorders.

Education and Training
  • BS, Genetics, Federal University of São Paulo, São Paulo, São Paulo
  • PhD, Psychobiology, Federal University of São Paulo, São Paulo, São Paulo
  • Other, Biomedical Informatics, University of Pennsylvania, Philadelphia, PA
  • Post Doctoral Fellowship, Sleep Medicine, Federal University of São Paulo, São Paulo, São Paulo
Professional Affiliations
  • American Medical Informatics Association, Member, 2020 - Present
  • Sleep Apnea Global Interdisciplinary Consortium, Big Data Working Group, Chair, 2020 - Present
  • Sleep Research Society, Sleep Research Network Task Force, Member, 2020 - Present
  • American Heart Association, Member, 2019 - Present
  • American Thoracic Society, Member, 2019 - Present
  • American Academy of Sleep Medicine, Member, 2017 - Present
  • Sleep Research Society, Member, 2017 - Present

Research

Overview

Dr. Mazzotti current research interests focus on the application of innovative methods to the analysis of high-dimensional physiological, behavioral, genetic and epidemiological data in sleep and sleep disorders, to understand how they can be translated into clinical knowledge and into applications that can advance healthcare. Such methods include supervised and unsupervised machine learning, data integration and harmonization and development of tools that facilitate clinical research with the potential to impact clinical care. To achieve these goals, Dr. Mazzotti aims to establish a solid multidisciplinary research program in the interface between Biomedical Informatics and Sleep Medicine, particularly in the following areas: novel analytical approaches to obstructive sleep apnea phenotyping, predictive modeling and clinical decision support of cardiovascular outcomes using sleep physiological markers, clinical sleep data integration for health outcomes research using electronic health records and elucidating the genetic basis of sleep and sleep disorders in humans.

Publications
  • Mazzotti, D., R, Keenan, B., T, Lim, D., C, Gottlieb, D., J, Kim, J, Pack, A., I. 2019. Symptom Subtypes of Obstructive Sleep Apnea Predict Incidence of Cardiovascular Outcomes.. American journal of respiratory and critical care medicine, 200 (4), 493-506
  • Jones, S., E, van Hees, V., T, Mazzotti, D., R, Marques-Vidal, P, Sabia, S, van der Spek, A, Dashti, H., S, Engmann, J, Kocevska, D, Tyrrell, J, Beaumont, R., N, Hillsdon, M, Ruth, K., S, Tuke, M., A, Yaghootkar, H, Sharp, S., A, Ji, Y, Harrison, J., W, Freathy, R., M, Murray, A, Luik, A., I, Amin, N, Lane, J., M, Saxena, R, Rutter, M., K, Tiemeier, H, Kutalik, Z, Kumari, M, Frayling, T., M, Weedon, M., N, Gehrman, P., R, Wood, A., R. 2019. Genetic studies of accelerometer-based sleep measures yield new insights into human sleep behaviour.. Nature communications, 10 (1), 1585
  • Jones, S., E, Lane, J., M, Wood, A., R, van Hees, V., T, Tyrrell, J, Beaumont, R., N, Jeffries, A., R, Dashti, H., S, Hillsdon, M, Ruth, K., S, Tuke, M., A, Yaghootkar, H, Sharp, S., A, Jie, Y, Thompson, W., D, Harrison, J., W, Dawes, A, Byrne, E., M, Tiemeier, H, Allebrandt, K., V, Bowden, J, Ray, D., W, Freathy, R., M, Murray, A, Mazzotti, D., R, Gehrman, P., R, Lawlor, D., A, Frayling, T., M, Rutter, M., K, Hinds, D., A, Saxena, R, Weedon, M., N. 2019. Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms.. Nature communications, 10 (1), 343
  • Veatch, O., J, Bauer, C., R, Keenan, B., T, Josyula, N., S, Mazzotti, D., R, Bagai, K, Malow, B., A, Robishaw, J., D, Pack, A., I, Pendergrass, S., A. 2020. Characterization of genetic and phenotypic heterogeneity of obstructive sleep apnea using electronic health records.. BMC medical genomics, 13 (1), 105
  • Rizzatti, F., G, Mazzotti, D., R, Mindel, J, Maislin, G, Keenan, B., T, Bittencourt, L, Chen, N., H, Cistulli, P., A, McArdle, N, Pack, F., M, Singh, B, Sutherland, K, Benediktsdottir, B, Fietze, I, Gislason, T, Lim, D., C, Penzel, T, Sanner, B, Han, F, Li , Q., Y, Schwab, R, Tufik, S, Pack, A., I, Magalang, U., J. 2020. Defining Extreme Phenotypes of OSA Across International Sleep Centers.. Chest
  • Lim, D., C, Mazzotti, D., R, Sutherland, K, Mindel, J., W, Kim, J, Cistulli, P., A, Magalang, U., J, Pack, A., I, de Chazal, P, Penzel, T. 2020. Reinventing polysomnography in the age of precision medicine.. Sleep medicine reviews, 52, 101313
  • Mazzotti, D., R, Lim, D., C, Sutherland, K, Bittencourt, L, Mindel, J., W, Magalang, U, Pack, A., I, de Chazal, P, Penzel, T. 2018. Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity.. Physiological measurement, 39 (9), 09TR01
  • Sutherland, K, Keenan, B., T, Bittencourt, L, Chen, N., H, Gislason, T, Leinwand, S, Magalang, U., J, Maislin, G, Mazzotti, D., R, McArdle, N, Mindel, J, Pack, A., I, Penzel, T, Singh, B, Tufik, S, Schwab, R., J, Cistulli, P., A. 2019. A Global Comparison of Anatomic Risk Factors and Their Relationship to Obstructive Sleep Apnea Severity in Clinical Samples.. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 15 (4), 629-639
  • Fernando, R., C, Mazzotti, D., R, Azevedo, H, Sandes, A., F, Rizzatti, E., G, de Oliveira, M., B, Alves VLF, Eugênio AIP, de Carvalho, F, Dalboni, M., A, Martins, D., C, Colleoni GWB. 2019. Transcriptome Analysis of Mesenchymal Stem Cells from Multiple Myeloma Patients Reveals Downregulation of Genes Involved in Cell Cycle Progression, Immune Response, and Bone Metabolism.. Scientific reports, 9 (1), 1056
  • van Hees, V., T, Sabia, S, Jones, S., E, Wood, A., R, Anderson, K., N, Kivimäki, M, Frayling, T., M, Pack, A., I, Bucan, M, Trenell, M., I, Mazzotti, D., R, Gehrman, P., R, Singh-Manoux, B., A, Weedon, M., N. 2018. Estimating sleep parameters using an accelerometer without sleep diary.. Scientific reports, 8 (1), 12975