Category: Diabetes/Prediabetes/Hypoglycemia

Monitor: 16

16 - CARDIOMETABOLIC DISEASE STAGING PREDICTS INCIDENT DIABETES IN A LARGE AND DIVERSE POPULATION: RACIAL AND GENDER DIFFERENCES

Thursday, Apr 25
12:00 PM – 12:30 PM

Objective :

The prevention of type-2 diabetes (T2DM) has become imperative to stem the rising rates of this disease, particularly among black Americans; however, the at-risk pool is large and a clinically meaningful metric for risk stratification to guide interventions remains a challenge. The purpose of this study is to predict diabetes risk from nationally sampled data from white and black American adults ≥ 45 years.


Methods :

The population-based cohort, the REasons for Geographic and Racial Differences in Stroke (REGARDS) (2003-2007), was observed through 2013-2016. A sex and race stratified cardiometabolic score using data regularly assessed in the primary care setting was selected from a previously validated Cardiometabolic Risk Score to assess the association between T2DM in a sample of 12,123 black and white men and women using a series of logistic regressions, accounting for blood glucose, blood pressure, HDL-cholesterol, waist circumference, triglycerides, and age. External validation was performed using 9,712 participants from Atherosclerotic Risk in Communities (ARIC) (1987-1989), observed through 1996-1998. Discrimination was assessed with area under the receiver operating characteristic curves and C-statistics.


Results :

In the REGARDS cohort, there were 1,614 incident cases of diabetes. C-statistics ranged from 0.72 (95% CI, 0.72 to 0.75) for black men to 0.79 (95% CI, 0.79 to 0.81) for white women. Externally validated models performed as well as or better than the derivation model; C-statistics ranged from 0.75 (95% CI, 0.75 to 0.80) (black men) to 0.83 (95% CI, 0.83 to 0.86) (white women). Cardiometabolic risks differed more by race than sex (p=0.0001).


Discussion :

In the current study, we present novel findings: 1) a practical and robust cardiometabolic disease staging (CMDS) risk score for future diabetes based on age and the presence of Metabolic Syndrome traits that is specific for black and white men and women; 2) the first risk prediction tool for black individuals derived from a large scientific US sample and validated for black men and women in a second cohort; and 3) CMDS is unique in incorporating our observation that incident T2DM is not a linear function of age. The high AUC values highlight the metabolic syndrome and its central pathological mechanism, insulin resistance, in the pathogenesis of T2DM.


Conclusion : The weighted CMDS score has high model discrimination using available clinical information, and can be used to quantify race- and sex-specific T2DM risk providing a new powerful predictive tool. This score can be used for T2DM prevention efforts by allowing clinicians to target high risk individuals in a manner that could be used to optimize outcomes.

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Lua Wilkinson

Postdoc
University of Alabama, Birmingham
Birmingham, Alabama

Dr. Lua Wilkinson majored in cultural anthropology and dietetics at the University of Northern Colorado, and completed her dietetic internship at UCSF medical center in 2006. Lua received a Master’s of Arts in medical anthropology from the University of Colorado, Denver in 2010. Lua received her PhD in Nutrition Science in 2016 from Cornell University. Lua has worked at Children’s Hospital Colorado and New York Presbyterian Hospital as a Registered Dietitian, where she developed individual and group programming for children with obesity-related co-morbidities, eating disorders, neonatal complications and congenital heart disease. In 2010, she was awarded a Fulbright and moved to China, where she researched infant feeding behavior among migrant women for two years. She continued work internationally with maternal and child nutrition, and has carried out research in India, China, Papua New Guinea, and the United States. She speaks Chinese fluently, and Oriya with working proficiency.

The overarching theme of Dr. Wilkinson’s research program focuses on the identification of factors associated with the translation of nutrition interventions. She has worked on a range of social, behavioral, clinical, and quantitative projects related to the implementation of nutrition interventions, but her primary publications and research activities are centered around meaningful clinical applications to nutrition, metabolism, and insulin resistance

Tapan Mehta

Associate Professor
School of Health Professions, University of Alabama at Birmingham, Alabama

Dr. Mehta is a health services researcher and a data scientist with several years of experience in biostatistics and data mining. He is an Associate Professor in the Department of Health Services Administration at the University of Alabama at Birmingham (UAB) and his training included PhD in Biostatistics and Masters in Electrical Engineering. He directs the statistical analysis and design core of the UAB/Lakeshore Research Collaborative, a collaboration between UAB School of Health Profession and Lakeshore Foundation, in the area of disability and rehabilitation sciences. His research projects include from large randomized controlled trials related to telehealth to analysis of large existing datasets. Dr. Mehta’s research interest span from clinical, epidemiological and health services related scientific inquiries in topics related to obesity, cardiometabolic conditions, disability, and rehabilitation. One of his recent line of research is in the area of healthcare quality improvement in obesity and diabetes care.

Dr. Mehta is a co-investigator, site PI, and lead statistician to several research studies funded by a variety of funding agencies such as NIH, NIDDILR and PCORI. Dr. Mehta has a strong publication record with over 30 peer-reviewed articles including several first-author articles. He has published in highly competitive journals such as New England Journal of Medicine, and Obesity Reviews. He has been involved in leading and serving on several national society committees such as serving on the Finance Committee and Public Affairs committee of The Obesity Society. He is also serving as the Chair of the University-wide Faculty Senate Research Committee, which is focused on strengthening the research environment at UAB.

Fangjian Guo

Assistant Professor
The University of Texas Medical Branch

I am currently Assistant Professor in the Department of Obstetrics And Gynecology at University of Texas Medical Branch, and an expert in Population Health and Health Promotion. My research involves health service research, cancer prevention, obesity, diabetes, and CVD. My research findings has been published in high-impact medical journals, such as JACC, Annals of Internal Medicine, JCEM, Diabetes, Diabetes Care, and AJPH. I have also served as a peer reviewer for high-impact journals.

W. Timothy Garvey

Professor
University of Alabama at Birmingham
Birmingham, Alabama

Dr. Garvey is the Butterworth Professor of Medicine in the Department of Nutrition Sciences at the University of Alabama at Birmingham. He obtained his MD degree from St. Louis University, completed residency training in Internal Medicine at Barnes Hospital, Washington University, and was a clinical fellow in Endocrinology and Metabolism at the University of Colorado Health Sciences Center and University of California, San Diego School of Medicine. He subsequently held faculty posts at UAB, University of California San Diego School of Medicine, Indiana University School of Medicine, and Medical University of South Carolina where he directed the Endocrinology Division.

Dr. Garvey has achieved international recognition for his research in the metabolic, molecular, and genetic pathogenesis of insulin resistance, Type 2 Diabetes, and obesity. His studies have involved the cellular and molecular biology of cell and animal models, metabolic investigations of human subjects on metabolic research wards, and the genetic basis of diseases in Gullah-speaking African Americans. The Garvey laboratory has made important observations regarding the pathogenesis of human insulin resistance and has been a principle contributor to our understanding of the role of the glucose transport system and glucose transporter proteins. He has identified gene families that contribute to insulin resistance in human muscle insulin using cDNA microarray, e.g., NR4A orphan nuclear receptors and the tribbles gene family, and has elucidated the role of adiponectin in cardiometabolic disease. He has contributed to the training of clinicians, basic scientists, and physician scientists. Since 1987, his laboratory has been supported by the NIH, Department of Veterans Affairs, AHA, JDFI, ADA, and other agencies.

Dr. Garvey’s career has been distinguished by a second impactful sphere of activity involving our understanding of obesity as a disease, and in advancing medical models to improve the care of patients with obesity. Dr. Garvey was the chief architect of the Complications-Centric Model for Care of the Overweight/Obese Patient. Much of his work in this area has occurred as a board member in the American Association of Clinical Endocrinologists and chair of the AACE Obesity Committee. Dr. Garvey was a lead author in the AACE Position Statement designating Obesity as a disease (Endocrine Practice 18:642, 2012); chair of the AACE Obesity Consensus Conference held in Washington DC in 2014; lead author on the evidence-based AACE obesity management guidelines (Endocr Pract. 22(7):842, 2016); and co-author of the new medical diagnostic term for obesity – Adiposity-Based Chronic Disease. This obesity treatment algorithm emphasizes the use of weight loss therapy to treat obesity-related complications as the primary goal of treatment, as opposed to reductions in BMI per se. Further, Dr. Garvey developed Cardiometabolic Disease Staging (Obesity 22:110, 2014) that allows clinicians to quantify risk for Type 2 Diabetes and cardiovascular disease mortality, when deciding upon the intensity of weight loss therapy for their patients. Thus, Dr. Garvey is a national leader in the development of medical models for the management of obesity and diabetes prevention.