DEFINE-T2D Investigator and Staff Spotlights

Alexis (Lekki) Wood, PhD, FAHA

Dr. Wood is an Associate Professor at the Baylor College of Medicine USDA Children’s Nutrition Research Center. Her research uses multi-omics data to answer research questions focused on how diet and other lifestyle factors increase the risk of chronic disease. She started her career out as a teacher for children with special educational needs but realized that we simply did not have enough information on how disease presentation responds to different environments, prompting her to complete her PhD in Statistical Genetics. In DEFINE-T2D, she is one of the MPIs at the MGH-Manning site and past Co-Chair of the Steering Committee. She is also a project Co-Lead (with Dr. Jerry Rotter) for a project that uses multi-omics data to cluster people with T2D to examine their disease progression and prognosis (“Clustering of type 2 diabetes and prediabetes”). This project (collaborates extensively with another Consortium project, “Phenotypic clustering: replication and de novo”). She brings both her interests in disease development and expertise in using longitudinal multi-omics data in cohort studies to the Consortium. In terms of future directions, Dr. Wood would like to see DEFINE-T2D incorporate questions on how lifestyle factors, such as what we eat and how we sleep, modify the T2D subgroup we are assigned to and/or how they modify the disease trajectories of subgroups.

Josep M. Mercader, PhD

Dr. Mercader is an Assistant Professor of Medicine at the Harvard Medical School, and an Associate Member of the Broad Institute of MIT and Harvard and the Massachusetts General Hospital Diabetes Unit. He is a geneticist whose research centers on discovering genetic variation associated with T2D and related traits, characterizing the function of these variants, and translating genetic findings into clinical applications. His work places particular emphasis on studying diverse ancestries to reduce health disparities in precision medicine. Dr. Mercader has led large-scale genetic analyses, including genome-wide association studies and whole-exome association studies, to identify novel genetic variants linked to type 2 diabetes, with a particular focus on diverse populations. He has also contributed to characterizing the molecular mechanisms underlying these genetic associations by integrating functional genomics data and analyses of regulatory variation in pancreatic islets. Through a series of international collaborations, his team has worked to improve variant identification in diverse populations, refine polygenic risk scores for non-European populations, generate and integrate genomic resources for the functional follow-up of genetic variants, and develop a framework for improving the interpretation of rare variants in monogenic diabetes genes. Within the DEFINE-T2D Consortium, Dr. Mercader serves as one of the MPIs of the MGH-Udler site. He also co-leads, together with Dr. Maggie Ng, the Consortium project titled, “Polygenic score clustering of type 2 diabetes,” in which the group implements novel approaches that use genetics to identify distinct subgroups of type 2 diabetes patients.

Shahab Razavi, PhD

Dr. Razavi is a Staff Scientist at the Vanderbilt University Medical Center, working closely with the Gamazon laboratory. He holds a B.Sc. and two M.Sc. degrees in computer science, and a Ph.D. in theoretical physics. His transition into medicine came when he joined Dr. Gamazon's laboratory as a postdoctoral fellow. His research in genetics now centers on developing computational frameworks and applying neural networks and artificial intelligence to medical and genomic data. Within DEFINE-T2D, Dr. Razavi’s work centers on clustering methods that group individuals into subtypes and track them longitudinally over the course of the study. He is one of several staff scientists contributing to data analysis and framework development for the consortium. He is involved in the Analysis Working Group and is contributing analyses to two Consortium projects: “Polygenic score clustering of type 2 diabetes” and “Phenotypic clustering: replication and de novo.” Across all of these, his role is to develop new analytical frameworks for the datasets and to uncover trends that offer fresh perspectives on how genetics and phenotypes are related. In terms of future directions, he'd welcome opportunities to apply AI/ML methods more broadly across the Consortium's projects, particularly where multi-omic data integration could help our understanding of T2D subtypes.

Micheal Bancks, PhD

Dr. Bancks is an Associate Professor of Epidemiology and Prevention at the Wake Forest University School of Medicine. In his research, he is interested in answering epidemiological and clinical questions on heterogeneity of T2D, including prevention, disease development, response to interventions, and complications. He was interested in human health sciences from an early age and then learned about public health during the last year of his undergraduate studies. This and reading a couple of books on the topic sparked his interest in epidemiology, leading him to pursue a Master's in Public Health and then a PhD in Epidemiology. In DEFINE-T2D, Dr. Bancks is an MPI of the Wake Forest site, Co-Chair of the Steering Committee, and Co-Lead of the Consortium project, “Phenotypic clustering: replication and de novo”, focusing on the phenotypic clustering of individuals with T2D across several cohorts. He is also involved with other clustering projects, to which he brings his expertise in epidemiological methods and study design. In terms of future directions, Dr. Bancks would like to see how social and environmental factors influence the etiological heterogeneity of T2D, so that we might be able to develop interventions to act on those pathways.

Kristen Boyle, PhD

Dr. Boyle is a Professor of Pediatrics in the Section of Nutrition and Co-Director for the Basic Science Research Track in the Lifecourse Epidemiology of Adiposity & Diabetes (LEAD) Center at the University of Colorado Anschutz, with expertise in obesity and insulin resistance, molecular metabolism, stem cell biology, and developmental programming. Her research investigates how metabolic disease risk is established before clinical symptoms appear. Using infant stem cells and molecular metabolic phenotyping, the Boyle laboratory studies how prenatal exposures shape cellular metabolism, metabolic flexibility, and developmental trajectories that may contribute to the heterogeneity of obesity and type 2 diabetes later in life. Dr. Boyle’s interest in science grew from a fascination with understanding how we metabolize the foods we eat. Her early investigations in adults with obesity and type 2 diabetes raised questions regarding the influences of early-life biology on later-life health outcomes, ultimately leading her to study the cellular origins of metabolic disease. As a member of the DEFINE-T2D Biostatistics Research Core Internal Advisory Board, she provides expertise in cellular metabolism and molecular phenotyping, bringing a lifecourse lens to the Consortium's efforts to define biologically meaningful subtypes of type 2 diabetes. She is also a member of the Phenotypes working group, bringing deep interest in metabolic phenotyping and early biomarker discovery. In the future, Dr. Boyle would like to understand how metabolic heterogeneity may distinguish disease subtypes before development of clinical diabetes.

DEFINE-T2D Investigator and Staff Spotlights from previous months

Dr. Kakar is a Postdoctoral Research Associate at the University of Colorado Anschutz and an investigator in the DEFINE-T2D Biostatistics Research Center. She studies why individuals with obesity and type 2 diabetes follow different disease trajectories by integrating genetic and omics data to identify distinct biological processes underlying metabolic risk. More specifically, her work focuses on polygenic and multi-omics risk scores, and predictors of cardiometabolic complications. She trained as a physician in India and later transitioned into genetic epidemiology to understand the biological heterogeneity she observed in clinical settings. She decided to pursue research during medical school, when caring for patients with similar diagnoses, but vastly different outcomes made her want to investigate the underlying mechanisms. She received her PhD in Genetic Epidemiology from the University of Louisville. In DEFINE-T2D, she is the Project Manager for the Phenotype Working Group and the EHR Subgroup, and she also supports the Steering Committee through administrative coordination and project management responsibilities. Scientifically, she contributes to the Phenotypic clustering of individuals with type 2 diabetes project. She is also interested in genetic clustering, multi-omics integration, and cardiometabolic complications, as well as incorporating AI and machine learning within DEFINE T2D to integrate genetics, multi-omics, and EHR data.

Dr. Anna Bellatorre is a Research Associate at the Colorado School of Public Health and is the Associate Director for Data Operations at the Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center at CU Anschutz. Her main research questions focus on the causes and consequences of diabetes, social determinants of health, and diabetes surveillance. She has been involved in several national studies of diabetes, including SEARCH for Diabetes in Youth, DiCAYA Network, and others. Dr. Bellatorre has a somewhat unusual background with degrees in Economics, Applied Sociology, Medical Sociology and Survey Research and Methodology as well as postdoctoral training in Epidemiology. She decided to become a scientist in her early twenties when she was nearing the end of her undergraduate program and felt a deeper commitment to lifelong learning and the pursuit of academia as a career. In the Biostatistics Research Center (BRC), she is a key resource for IRB, data acquisition, and data security matters as well as one of several project managers and data analysts. She is a member of the Publications and Presentations Committee and is currently working with the Y2 Opportunity Fund based on the Look AHEAD trial. She is a strong advocate for studies of social determinants of health and hopes that DEFINE-T2D will incorporate these important factors into the Consortium projects.

Dr. Eric R. Gamazon is a tenured member of the faculty of the Division of Genetic Medicine at Vanderbilt University Medical Center. He is a Life Member of Clare Hall, University of Cambridge. His research centers on functional genomics, machine learning/AI, and molecular biology, with a primary emphasis on human metabolism, diabetes, and cardiometabolic disorders. He utilizes genomic, single-cell/spatial transcriptomics, and multi-omics (proteomics, metabolomics) to investigate disease mechanisms, therapeutic targets, and genome function. His lab also leverages induced pluripotent stems cells and differentiated cell types to model disease and uncover the molecular and cellular mechanisms underlying genetic associations with disease. Trained in genomics and mathematics, Dr. Gamazon's research bridges computational modeling and translational genomics. He committed to a career in scientific research after being exposed to computational genomics at the University of Chicago. Within the DEFINE-T2D Consortium, Dr. Gamazon serves as co-chair the Steering Committee and co-convener of the EHR subgroup of the Phenotypes Working Group. In the past, Dr. Gamazon has served as a co-convener of the Omics Working Group. Looking forward, he is interested in seeing the Consortium further expand its integration of AI and other emerging high-impact opportunities, including leveraging precision phenotyping across multiple levels of biological organization. Finally, Dr. Gamazon is invested in mentorship, as the the long-term success of the Consortium will depend on investing in people as much as infrastructure.

Dr. Iain Konigsberg is a Senior Research Scientist in the Department of Biomedical Informatics at the University of Colorado Anschutz. He is a computational geneticist whose research leverages genetics, multi-omics technologies, and large-scale biobank data to uncover the biological mechanisms underlying complex diseases. He is especially interested in identifying biomarkers that can predict disease progression and provide insights into dynamic biological changes over time. Dr. Konigsberg earned his PhD in Human Medical Genetics and Genomics after developing an interest in genetics during high school. He was drawn to the field by its potential to improve the lives of people affected by serious diseases and quickly discovered a passion for discovery-driven, data-intensive research. As a member of the DEFINE-T2D Biostatistics Research Center, Dr. Konigsberg provides both analytical and administrative support to Consortium investigators and projects. He is a member of the Omics Working Group and the Publications and Presentations Committee. He co-leads a Consortium project, Harmonizing T2D-Associated Metabolites Across Metabolomics Platforms, which seeks to harmonize data across diverse metabolomics platforms. Dr. Konigsberg also contributes analytically to projects on diabetes subtyping, multi-omics predictors of incident diabetes, and metabolomics-based proxies of key type 2 diabetes traits. His expertise in computational biology, statistical genetics, and multi-omics integration helps advance collaborative efforts across the Consortium. Looking to the future, Dr. Konigsberg is interested in opportunities to look at the connections between diabetes, metabolic dysregulation, and chronic respiratory disease.

Kirk Smith is a computational biologist and staff scientist at Massachusetts General Hospital's Center for Genomic Medicine and a affiliated researcher at The Broad Institute, as well as a member of the Udler research team. His research is focused on studying the heterogeneity of type 2 diabetes and other related complex diseases. Kirk studied Mechanical Engineering as an undergraduate at Virginia Tech, he then pursued graduate studies for Biomedical Engineering at the University of Michigan, thinking that he wanted to pursue a career in biomedical device design. However, during his graduate studies he became much more interested in the field of computational biology, which eventually led him to his current position at The Broad Institute. As a data scientist, his role primarily involves programming and data engineering. He is currently working on several DEFINE-T2D projects, all related to either phenotypic or genetic clustering of patients. These projects fit perfectly in both his interests and skill set, as he has done several phenotypic and genetic clustering projects in the past, also related to type 2 diabetes. For future directions of DEFINE-T2D, Kirk would like for Consortium projects to start incorporating more sophisticated machine-learning and/or deep learning techniques.

Dr. Kristina M. Utzschneider is a board-certified endocrinologist at the VA Puget Sound, Director of the VA Diabetes Care Program, and an Associate Professor of Medicine and Metabolism, Endocrinology and Nutrition at the University of Washington. Her research focuses on preserving beta-cell function through interventions such as diet, medications, and weight loss, and on identifying type 2 diabetes subtypes to better understand disease mechanisms, personalize treatment, and predict future complications. Dr. Utzschneider’s interest in medicine began early in life and was reinforced by hands-on exposure to physiology and neuroscience during elementary school. Research experiences throughout college and medical school further shaped her career path, particularly studies investigating hormonal responses to hypoglycemia. Within DEFINE-T2D, Dr. Utzschneider serves as one of the principal investigators for the MGH–Udler site. She is actively involved in the Phenotype Working Group, including the Electronic Health Record (EHR) subgroup, as well as the Publications & Presentations Committee. She is a member of one of the core DEFINE-T2D projects, clustering of T2D patients prior to disease onset, which seeks to define diabetes subgroups using metabolomics data collected before disease onset. Her contributions to the Consortium draw on both her scientific expertise in physiology underlying type 2 diabetes and her clinical experience caring for individuals with diverse forms of diabetes. Through DEFINE-T2D, she is particularly interested in advancing approaches to applying diabetes subtyping to the continuum of disease, including prediabetes to be able to define the risk and trajectory of progression earlier.

Kyle Salmon, MSPH is a Project Manager with the Biostatistics Research Center. From a young age, Kyle was interested in health, nutrition, science, math, and had a passion to help others. During the beginning of her undergraduate education, she discovered Public Health Nutrition and knew that was the career path for her. After graduating from Syracuse University with a B.S. in Public Health and Nutrition, she received her MSPH in Human Nutrition at Johns Hopkins University. She has worked for a number of public health agencies including a U.S. Army Public Health Center, the Maryland Department of Health, and now in the Lifecourse Epidemiology of Adiposity & Diabetes (LEAD) Center in the Colorado School of Public Health, where she focuses on prevention of chronic diseases from a public health nutrition lens. In DEFINE-T2D, Kyle helps manage the Analysis Working Group and the Publications & Presentations Committee to ensure they run effectively and efficiently.

Ms. Lana Olson is a Senior Biostatistician at Atrium Health Wake Forest Baptist whose research focuses on identifying type 2 diabetes subtypes through phenotypic clustering. Her work builds upon prior diabetes subtyping studies and seeks to validate and extend these approaches across diverse DEFINE-T2D cohorts. Drawn to science through her interest in applying quantitative methods to clinically relevant problems, Ms. Olson pursued training in biostatistics and now specializes in the use of statistical approaches to advance biomedical research. She is particularly motivated by opportunities to translate complex data into insights that can improve patient care. Within the DEFINE-T2D Consortium, Ms. Olson contributes statistical expertise, including the development of sharable programming code to promote reproducibility. She is a member of one of the core DEFINE-T2D projects, phenotypic clustering of individuals with type 2 diabetes, where she applies her biostatistical expertise to advance diabetes subtyping using clinical variables. Looking ahead, Ms. Olson is interested in exploring how type 2 diabetes subtype patterns compare across individual cohorts vs. in combined analyses. She believes these efforts can provide important insights into the diversity of type 2 diabetes and help inform more personalized approaches to treatment.

Dr. Espeland is a Professor in the Divisions of Gerontology and Geriatric Medicine and Public Health Sciences. He received his PhD in Statistics from the University of Rochester. He has been a faculty member at Wake Forest School of Medicine since 1986, including 11 years of serving as Chair of the Department of Biostatistics. His growth as a scientist has been nurtured by opportunities to collaborate with remarkable cross-disciplinary teams for many years. He shifted his primary academic appointment at Wake Forest from biostatistics to gerontology, sparked by his interest in cognitive aging as part of the leadership team for the Women’s Health Initiative Memory Study. His current research interest continues to be in aging and cognitive decline, focusing specifically on the roles that T2D and obesity play in accelerating these processes and asking whether the processes may be slowed by behavioral interventions. He has been working with the Look AHEAD research program with Dr. Lynne Wagenknecht since its inception and has helped to transition it into a study of aging, leading ancillary studies to the program that focused on brain MRI and cognitive aging. He is a fellow of the American Statistical Association, the Society for Clinical Trials, and the American Association for the Advancement of Science. In DEFINE-T2D, he co-leads the Publications and Presentations Committee. Scientifically, he is interested in how aging-related outcomes might differ among T2D subtypes identified in DEFINE-T2D.

Dr. Mary Rooney is an Assistant Research Professor at the Bloomberg School of Public Health at Johns Hopkins University. She is an epidemiologist whose research seeks to identify more precise ways to predict diabetes risk and progression using both traditional clinical measures and novel biomarkers, supporting earlier, more personalized prevention strategies. Dr. Rooney's path to epidemiology began during her undergraduate studies at the University of Illinois Urbana-Champaign. While deciding between biology and the social sciences, she discovered epidemiology and was immediately drawn to its multidisciplinary nature, as well as its emphasis on prevention and population health. Within the DEFINE-T2D Consortium, Dr. Rooney contributes her expertise through the Omics and Phenotype Working Groups and is leading a DEFINE-T2D project on prediabetes clustering using clinical variables across multiple cohorts. Her work in DEFINE-T2D dovetails with a NIDDK K01 award that Dr. Rooney received in 2025, which aims to refine risk stratification for diabetes and complications among people with prediabetes using clinical and novel omics measurements.

Dr. Shuey is an Assistant Professor of Clinical Pharmacology and Genetics at Vanderbilt University. Her research program incorporates genetic epidemiology, pharmacology, and informatics to 1) elucidate and contextualize factors that influence complex disease risk; 2) predict medication response and profile adverse-event risk; and 3) develop evidence-based prioritization strategies for drug discovery and repurposing efforts. She knew that she wanted to work in the medical field since childhood, but it wasn’t until college that she realized she could make scientific research into a career. Dr. Shuey received her Bachelors in Molecular and Cellular Biology with minors in Anthropology and Science Education from the University of Arizona. During her undergraduate studies, she completed a thesis on Y-STR assay development for human lineage tracing and worked in a virology lab. She then received her Master's in evolutionary genetics and worked in a Biosafety Level III laboratory at Northern Arizona University. As the final move of her scientific career so far, she moved to Nashville to get a PhD from Vanderbilt University where she studied Pharmacology with a dissertation project focused heavily on electronic health record (EHR) phenotyping and medication response extraction. She continued this work as a postdoctoral fellow at Vanderbilt Medical Center and then joined the faculty of Vanderbilt Health. In DEFINE-T2D, Dr. Shuey leads the EHR Working Group and oversees data harmonization efforts as well as the development of standard operating procedures for variable extraction and validation. In addition, she is an investigator on the Polygenic score clustering of T2D and prediabetes project. She has an interest in sex-differences, maternal health, and multi-omics, and is looking for new collaborations in this space. In DEFINE-T2D, she hopes to do more work to understand diabetes across the life-course and the implications of sex hormone fluctuations.

Dr. Nichole (Palmer) Allred is a Professor of Biochemistry at the Wake Forest School of Medicine. She is a molecular geneticist who holds a PhD in Biochemistry and Molecular Biology. Her research focuses on understanding the genomic architecture of diabetes towards prediction and treatment of disease. Combining omics-derived data with quantitative, intermediate phenotypes provides the opportunity to advance our understanding of disease. In addition, it also creates the opportunity to understand how these sophisticated physiological phenotypes can be modeled using less invasive omics data to enhance power and expand impact. In DEFINE-T2D, Dr. Allred is a Multiple Principal Investigator (MPI) for the Wake Forest study site along with Drs. Bancks and Hsu. She is a co-convener of the Omics working group which aligns well with her research interest in using omics technologies to understand the genetic architecture of cardiometabolic disease. With a broad interest in quantitative intermediate phenotypes of glucose homeostasis, she would like to see more metabolic phenotyping and wearable technologies used in DEFINE-T2D to disentangle the heterogeneity underlying diabetes.

Dr. Qi is a Professor of Epidemiology and Population Health as well as Associate Director for Center for Population Cohorts at the Albert Einstein College of Medicine. He is one of the MPIs of the MGH Manning DEFINE-T2D site. His major research interest is to identify novel molecule signatures (e.g., metabolites and proteins) and related pathways which may help better understand the pathophysiology of cardiometabolic diseases, especially type 2 diabetes. Dr. Qi is a molecular epidemiologist with a background in genetics, biomarkers, and nutrition. His interest in nature and the life sciences started in elementary school, but he committed fully to his scientific career after completing his PhD training and his postdoctoral training at the Harvard School of Public Health. In DEFINE-T2D, he is involved in multiple projects, including Project 1 “Phenotypic clustering of individuals with type 2 diabetes”, Project 2 “Polygenic score clustering of T2D and prediabetes”, and Project 3 “Clustering of T2D patients prior to disease onset”, as well as the Omics and Analysis working groups, where he brings his expertise in T2D epidemiology, genetics and multi-omics. He looks forward to seeing novel AI and machine learning approaches applied to T2D subtyping in DEFINE-T2D projects.

Dr. Qing Pan is a Professor of Statistics in the Milken Institute School of Public Health at the George Washington University. She completed her Ph.D. in 2007 in Biostatistics at the University of Michigan in Ann Arbor. She has over 20 years of experience coordinating large-scale multicenter clinical studies, including the Diabetes Prevention Program Outcome Studies (DPPOS), the Antibacterial Resistance Leadership Group, and PATHWEIGH Pragmatic Weight Management in Primary Care. As the lead statistician in these trials, she participates in proposal preparation, data management, statistical analysis, conference presentations, and paper publication on various mobility and mortality outcomes as well as high-dimensional “omics” data. She has also served as PI or co-I for multiple projects funded by NIH, Gates Foundation, FDA, and US Department of Justice. Her main interests are applying cutting-edge computational tools to modern clinical research. She brings in her expertise in the DPPOS study to DEFINE-T2D through one of the Massachusetts General Hospital (MGH) study sites (MPIs: Udler, Mercader, Utzschneider).

Dr. Ravi Shah is a Professor of Medicine and the Gottlieb C. Friesinger II Endowed Chair in Cardiovascular Medicine. He serves as the co-director of the Vanderbilt Diabetes Research Center and Director of Clinical and Translational Cardiovascular Science in the Division of Cardiovascular Medicine. Dr. Shah got his MD from Harvard in 2007 followed by internship, residency and fellowship training at the Massachusetts General Hospital. He is a physician scientist focused on the metabolic underpinnings of CVD. His group is interested in why certain patients with metabolic illnesses—specifically obesity and diabetes—develop CVD. Research questions his group tries to answer are:

(1) How do specific molecular biomarkers that reflect metabolic dysfunction (e.g., metabolites and proteins in the blood) predict CVD or related cardiometabolic diseases? and
(2) Can these “biomarkers” be used to uncover potential mechanisms of CVD and related diseases?

In DEFINE-T2D, Dr. Shah is a co-investigator at the Vanderbilt site (MPIs Ng and Gamazon), where his group brings expertise in CVD phenotypes, metabolomics and proteomics, and epidemiologic and machine learning approaches.  He is interested in seeing the progress in the phenotyping space, including lifestyle and environment (the “exposome”), wearable data, imaging, and other more granular data on dysglycemia in DEFINE-T2D. He also thinks that harnessing artificial intelligence approaches to analyze these rich phenotype data will give our community unique insights into diabetes heterogeneity and will provide an anchor for molecular genetic discovery.

Dr. Srinivasan is an Associate Professor of Pediatrics at the University of California San Francisco. She is a physician-scientist whose work aims to improve outcomes for youth with type 2 diabetes by developing more personalized approaches to treatment. Her research centers on understanding the risk factors for T2D development in youth, including understudied populations such as Asian American and Native Hawaiian and Pacific Islander groups. She also studies variability in T2D medication response, disease progression, and the use of real-world electronic health record data to inform clinical decision-making for youth with type 2 diabetes. As a practicing pediatric endocrinologist, she cares for children and adolescents with endocrine disorders and diabetes. Receiving outstanding mentorship and the opportunity to work on meaningful research questions during her pediatric endocrinology fellowship shaped her path as a physician-scientist. In DEFINE-T2D, she serves as a co-investigator on the MGH Udler site representing the Progress in Diabetes Genetics in Youth (ProDiGY) Consortium, which brings together genetic and clinical data from the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) and SEARCH for Diabetes in Youth studies. She is a Co-Convener of the Phenotype Working Group, bringing her expertise in diabetes physiology and working with large, multi-study datasets to support phenotype harmonization efforts across the Consortium. In future DEFINE-T2D work, she would love to see more projects focused on early-onset type 2 diabetes and increased inclusion of understudied racial and ethnic populations.

Dr. Swapan Kumar (SK) Das is an Associate Professor in the Section on Endocrinology and Metabolism in the Department of Internal Medicine at the Wake Forest University School of Medicine. His research focuses on understanding the genetic, epigenetic, and molecular mechanisms that drive insulin resistance, obesity, and type 2 diabetes. He is especially interested in how these mechanisms differ across populations and contribute to heterogeneity in diabetes risk and progression. A major goal of his work is to use integrative genomics, multi-omics, and systems biology to identify pathways that can inform precision prevention and treatment of type 2 diabetes. Following completion of B.Sc., M.Sc. and Ph.D. studies at the University of Calcutta, he obtained research training in the United States on physiological genomics of complex diseases at the Internal Medicine-Endocrinology and Metabolism department of University of Arkansas for Medical Science. He was drawn to science early in high school because he was fascinated by how genetic variation influences health and disease. In DEFINE-T2D, Dr. Das contributes expertise in human molecular genetics, integrative genomics, and diabetes heterogeneity, particularly in areas related to the biological pathways underlying prediabetes and type 2 diabetes subtypes. He is a member of the Omics Working Group and an investigator on the Clustering of Type 2 Diabetes and Prediabetes project, which is led by Drs. Jerome Rotter and Alexis Wood. For future directions of the Consortium, Dr. Das would be especially interested in seeing continued emphasis on development of methods for disease subtyping via multi-tissue multi-omics integration, type 2 diabetes-related gene regulation in diverse and underrepresented populations, and the mechanisms that link molecular heterogeneity to long-term clinical outcomes.

Dr. Tamar Sofer is an Associate Professor and Director of Bioinformatics at the Cardiovascular institute of the Beth Israel Deaconess Medical Center. She develops and uses statistical methods to harness the power of “omics” to understand phenotypes. She is particularly interested in studying metabolic and cardiovascular disease in understudied populations. Dr. Sofer obtained her PhD in Biostatistics from Harvard, where she worked on theoretical problems, followed by a postdoctoral fellowship focused on causal and semiparametric inference. During her training, she realized that she wanted to answer research questions beyond math and methodology. She joined the Genetic Analysis Center and the Biostatistics Department at the University of Washington in Seattle where she worked closely with collaborators on projects aiming to understand the genetic underpinnings of various health and disease phenotypes. It is only there when she realized how excited she became when methods she developed helped answer questions that can be investigated using real data, and improve the lives of real people — rather than theoretical data and people. In the DEFINE-T2D consortium, she serves as a co-convener of the Analysis Working Group. She is currently working on two projects: one is on harmonizing metabolomics data between the two largest sources of metabolomics data available across cohort datasets, the second is a benchmarking study using simulation data for methodology for clustering of individuals based on omics data to identify subtypes of type 2 diabetes. In the future, Dr. Sofer would like to see projects studying contribution of sleep disorders, such as obstructive sleep apnea (OSA) to heterogeneity of T2D.

Dr. Xue Zhong is a computational biologist and biostatistician whose research focuses on understanding disease heterogeneity through the integration of genomics, electronic health records (EHRs), and multi-omics data. Working at the intersection of precision medicine and data science, she uses large-scale EHR-linked biobanks to investigate complex diseases. She is particularly interested in developing predictive models that connect type 2 diabetes heterogeneity with treatment response and other clinically actionable outcomes. Currently a Research Assistant Professor in the Division of Genetic Medicine and Clinical Pharmacology in the Department of Medicine at Vanderbilt University Medical Center, Dr. Zhong integrates genomic data, molecular profiles, and clinical records to answer meaningful questions about human health and disease. Within the DEFINE-T2D Consortium, Dr. Zhong contributes to the analytical design and implementation of projects focused on genetics-based diabetes subtyping, including one of the core DEFINE-T2D projects, polygenic score clustering of T2D and prediabetes, and both the Analysis and Phenotype Working Groups. She has also contributed to the development of methods for EHR-based phenotyping, longitudinal data analysis, and deep learning to identify incident type 2 diabetes cases using diverse datasets, including EHR data. Looking toward the future, Dr. Zhong is interested in advancing precision medicine approaches that move beyond broad diabetes subtype classifications. She believes that individualized prediction models, powered by artificial intelligence and deep learning, may offer a more effective path toward understanding disease progression, predicting treatment response, and tailoring care to the unique characteristics of each patient.

Dr. Zsu-Zsu Chen is an Assistant Professor of Medicine at the Harvard Medical School and Staff Physician Scientist at the Beth Israel Deaconess Medical Center (BIDMC). She is a clinician scientist, with an MD from the University of Alabama at Birmingham (UAB) and an MPH in Quantitative Methods at the Harvard T.H. Chan School of Public Health. She decided to become a clinician scientist during her research years as an endocrinology fellow at BIDMC. Her research focuses on elucidating causal disease pathways and their interactions with adapted behaviors, environmental exposures, and therapeutic treatments through the lens of the plasma circulatome with the goal of helping to optimize prevention and treatment strategies in type 2 diabetes. She has recently been focused on understanding how repeated proteomics and metabolomic measurements may help identify novel circulating T2D biomarkers that may point to distinct biology compared to associations found with a single measurement. Additionally, she has been studying how circulating small molecule and protein T2D biomarkers may reveal why the age of onset of T2D appears to be associated with different rates of T2D progression to long term micro- and macrovascular complications. She is a DEFINE-T2D co-investigator through one of the Massachusetts General Hospital (MGH) study sites (MPIs: Manning, Qi, Rotter, Wood). She is a member of the Omics and Phenotyping working group and is involved in multiple ongoing DEFINE-T2D projects. In addition to her current work in DEFINE-T2D, she is interested in improving T2D and long-term micro/macrovascular complications.

Tools & Resources

CU Campuses

CU Anschutz

DEFINE-T2D Investigator and Staff Spotlights

Alexis (Lekki) Wood, PhD, FAHA

Josep M. Mercader, PhD

Shahab Razavi, PhD

Micheal Bancks, PhD

Kristen Boyle, PhD

DEFINE-T2D Investigator and Staff Spotlights from previous months

DEFINE-T2D Investigator and Staff Spotlights

Alexis (Lekki) Wood, PhD, FAHA

Josep M. Mercader, PhD

Shahab Razavi, PhD

Micheal Bancks, PhD

Kristen Boyle, PhD

DEFINE-T2D Investigator and Staff Spotlights from previous months

Aastha Kakar, PhD, MBBS

Anna Bellatorre, PhD

Eric Gamazon, PhD

Iain Konigsberg, PhD

Kirk Smith, MS

Kristina Utzschneider, MD

Kyle Salmon, MSPH

Lana Olson, MS

Mark Espeland, PhD

Mary Rooney, PhD, MPH

Megan Shuey, PhD

Nichole (Palmer) Allred, PhD

Qibin Qi, PhD

Qing Pan, PhD

Ravi Shah, PhD

Shylaya (Shy) Srinivasan, MD, MAS

Swapan Das, PhD

Tamar Sofer, PhD

Xue Zhong, PhD

Zsu-Zsu Chen, PhD