Citations 2
Cardiovascular, Renal, and Hepatic Biomarkers Outside Guideline-Recommended Thresholds: Prevalence and Determinants in Type 1 Diabetes
Lucove, Jaime C. , CORNELIUS, EMILEE M., Nguyen, Huyen T., Shah, Viral N., Sherr, J L, Kudva, Yogish C., Carlson, Anders L., Wolf, Wendy A. , Shah, Viral N. , Sherr, Jennifer L , Kudva, Yogish C. , Carlson, Anders L.
Diabetes Technology & Therapeutics | May 8, 2026 | Volume: No volume available | Issue: No issue available | Pages: No page available
Introduction: The objective of the study was to evaluate the prevalence of cardiovascular, renal, and hepatic biomarkers that fall outside guideline-based thresholds in adults with type 1 diabetes (T1D), and to identify demographic and clinical factors associated with these biomarkers. Methods: This observational study linked data on 2,923 adults with T1D from the T1D Exchange Registry and NorstellaLinQ’s laboratory data (2022–2023). Prevalence of biomarkers outside guideline-recommended thresholds was calculated. Generalized estimating equations (GEE) were used to model associations with age, sex, race/ethnicity, insurance, T1D duration, body mass index (BMI), and hemoglobin A1c (HbA1c), while accounting for within-subject correlation due to repeated measures. Results: Elevated low-density lipoprotein (LDL) cholesterol (≥70 mg/dL, above the recommended goal for individuals with diabetes at high risk for atherosclerotic cardiovascular disease) was common (80.2%), as were overweight (33.8%) and obesity (30.3%). Additional findings included high total cholesterol (≥200 mg/dL, 22.6%), low high-density lipoprotein (HDL) cholesterol (<40 mg/dL males, <50 mg/dL females, 13.4%), elevated urinary albumin-to-creatinine ratio (≥30 mg/g, 13.0%), reduced estimated glomerular filtration rate (<60 mL/min/1.73 m 2 , 6.2%), and elevated fibrosis-4 index (FIB-4) (≥1.3, 14.4%). In adjusted GEE models, higher HbA1c was significantly associated with increased odds of abnormal cardiovascular and renal biomarkers across most measures. Male sex and BMI were linked to lower HDL cholesterol, while older age was strongly associated with elevated FIB-4. Conclusion: Obesity and elevated LDL cholesterol are highly prevalent in adults with T1D, indicating substantial cardiometabolic risk. The lack of outcome trials in this population underscores the need for studies to evaluate strategies to improve weight and lipid management and thereby reduce cardiovascular and renal risk in adults with T1D.
A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings
Klonoff, David C. , Wang, Jing , Rodbard, David , Kohn, Michael A. , Li, Chengdong , Liepmann, Dorian , Kerr, David , Ahn, David , Peters, Anne L. , Umpierrez, Guillermo E. , Xu, Nicole Y. , Nguyen, Kevin T. , Simonson, Gregg D. , Agus, Michael S. D. , Armaiz-Peña, Gustavo , Basu, Ananda , Battelino, Tadej , Yeshiwas, Sewagegn , Bequette, B. Wayne , Blevins, Thomas , Breton, Marc D. , Castle, Jessica R. , Close, Kelly L. , Drincic, Angela, Dungan, Kathleen , Ejskjær, Niels , Espinoza, Juan , Fleming, G. Alexander , Forlenza, Gregory P. , Freckmann, Guido , Galindo, Rodolfo J. , Gómez, Ana María , Gutow, Hanna A. , Hovorka, Roman , Ji, Linong , Joubert, Michaël , Koliwad, Suneil K. , Lal, Rayhan , Lansang, M. Cecilia , Lee, Wei-An , Leelarathna, Lalantha , Lind, Marcus , Mahoney, Katherine , Mankovsky, Boris , Masharani, Umesh , Mathioudakis, Nestoras , Mayorov, Alexander Yur'evich , Messler, Jordan , Mohan, Viswanathan , Nichols, James H. , O’Neal, David N. , Pasquel, Francisco J. , Philis‐Tsimikas, Athena , Phillip, Moshe , Rayman, Gerry , Russell, Steven J. , Spanakis, Elias K. , Waki, Kayo , Wallia, Amisha , Weinberg, Melissa E. , Wolpert, Howard , Zilbermint, Mihail , Kovatchev, Boris , Seley, Jane Jeffrie , Xu, Nicole Y , Nguyen, Kevin T , Simonson, Gregg , Agus, Michael S D , Al-Sofiani, Mohammed E , Armaiz-Pena, Gustavo , Bailey, Timothy S , Basu, Ananda , Battelino, Tadej , Bekele, Sewagegn Yeshiwas , Benhamou, Pierre-Yves , Bequette, B Wayne , Blevins, Thomas , Breton, Marc D , Castle, Jessica R , Chase, James Geoffrey , Chen, Kong , Choudhary, Pratik , Clements, Mark A , Close, Kelly L , Cook, Curtiss B , Doyle, Francis J , Drincic, Angela , Dungan, Kathleen M , Edelman, Steven V , Ejskjaer, Niels , Espinoza, Juan , Fleming, G Alexander , Forlenza, Gregory P , Freckmann, Guido , Galindo, Rodolfo J , Gomez, Ana Maria , Gutow, Hanna A , Heinemann, Lutz , Hirsch, Irl B , Hoang, Thanh D , Hovorka, Roman , Jendle, Johan H , Ji, Linong , Joshi, Shashank R , Joubert, Michael , Koliwad, Suneil K , Lal, Rayhan A , Lansang, M Cecilia , Lee, Wei-An Andy , Leelarathna, Lalantha , Leiter, Lawrence A , Lind, Marcus , Litchman, Michelle L , Mader, Julia K , Mahoney, Katherine M , Mankovsky, Boris , Masharani, Umesh , Mathioudakis, Nestoras N , Mayorov, Alexander , Messler, Jordan , Miller, Joshua D , Mohan, Viswanathan , Nichols, James H , Nørgaard, Kirsten , O'Neal, David N , Pasquel, Francisco J , Philis-Tsimikas, Athena , Pieber, Thomas , Phillip, Moshe , Polonsky, William H , Pop-Busui, Rodica , Rayman, Gerry , Rhee, Eun-Jung , Russell, Steven J , Shah, Viral N , Sherr, Jennifer L , Sode, Koji , Spanakis, Elias K , Wake, Deborah J , Waki, Kayo , Wallia, Amisha , Weinberg, Melissa E , Wolpert, Howard , Wright, Eugene E , Zilbermint, Mihail , Kovatchev, Boris , Kohn, Michael , Umpierrez, Guillermo E. , Peters, Anne L , Ahn, David T , Liepmann, Dorian , Bailey, Timothy S. , Chen, Kong Y. , Choudhary, Pratik , Cook, Curtiss B. , Danne, Thomas , Doyle, Francis J. , Heinemann, Lutz , Hirsch, Irl B. , Leiter, Lawrence A. , Mader, Julia K. , Pop‐Busui, Rodica , Shah, Viral N. , Sode, Koji , Wright, Eugene E.
Journal of Diabetes Science and Technology | March 29, 2022 | Volume: 17 | Issue: 5 | Pages: 1226-1242
Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.