1
|
Klein KR, Abrahamsen TJ, Kahkoska AR, Alexander GC, Chute CG, Haendel M, Hong SS, Mehta H, Moffitt R, Stürmer T, Kvist K, Buse JB. Association of Premorbid GLP-1RA and SGLT-2i Prescription Alone and in Combination with COVID-19 Severity. Diabetes Ther 2024; 15:1169-1186. [PMID: 38536629 PMCID: PMC11043305 DOI: 10.1007/s13300-024-01562-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 03/04/2024] [Indexed: 04/26/2024] Open
Abstract
INTRODUCTION People with type 2 diabetes are at heightened risk for severe outcomes related to COVID-19 infection, including hospitalization, intensive care unit admission, and mortality. This study was designed to examine the impact of premorbid use of glucagon-like peptide-1 receptor agonist (GLP-1RA) monotherapy, sodium-glucose cotransporter-2 inhibitor (SGLT-2i) monotherapy, and concomitant GLP1-RA/SGLT-2i therapy on the severity of outcomes in individuals with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. METHODS Utilizing observational data from the National COVID Cohort Collaborative through September 2022, we compared outcomes in 78,806 individuals with a prescription of GLP-1RA and SGLT-2i versus a prescription of dipeptidyl peptidase 4 inhibitors (DPP-4i) within 24 months of a positive SARS-CoV-2 PCR test. We also compared concomitant GLP-1RA/SGLT-2i therapy to GLP-1RA and SGLT-2i monotherapy. The primary outcome was 60-day mortality, measured from the positive test date. Secondary outcomes included emergency room (ER) visits, hospitalization, and mechanical ventilation within 14 days. Using a super learner approach and accounting for baseline characteristics, associations were quantified with odds ratios (OR) estimated with targeted maximum likelihood estimation (TMLE). RESULTS Use of GLP-1RA (OR 0.64, 95% confidence interval [CI] 0.56-0.72) and SGLT-2i (OR 0.62, 95% CI 0.57-0.68) were associated with lower odds of 60-day mortality compared to DPP-4i use. Additionally, the OR of ER visits and hospitalizations were similarly reduced with GLP1-RA and SGLT-2i use. Concomitant GLP-1RA/SGLT-2i use showed similar odds of 60-day mortality when compared to GLP-1RA or SGLT-2i use alone (OR 0.92, 95% CI 0.81-1.05 and OR 0.88, 95% CI 0.76-1.01, respectively). However, lower OR of all secondary outcomes were associated with concomitant GLP-1RA/SGLT-2i use when compared to SGLT-2i use alone. CONCLUSION Among adults who tested positive for SARS-CoV-2, premorbid use of either GLP-1RA or SGLT-2i is associated with lower odds of mortality compared to DPP-4i. Furthermore, concomitant use of GLP-1RA and SGLT-2i is linked to lower odds of other severe COVID-19 outcomes, including ER visits, hospitalizations, and mechanical ventilation, compared to SGLT-2i use alone. Graphical abstract available for this article.
Collapse
Affiliation(s)
- Klara R Klein
- Division of Endocrinology and Metabolism, Department of Medicine, University of North Carolina School of Medicine, Campus Box #7172, 8072 Burnett Womack, 160 Dental Circle, Chapel Hill, NC, 27599, USA.
| | | | - Anna R Kahkoska
- Division of Endocrinology and Metabolism, Department of Medicine, University of North Carolina School of Medicine, Campus Box #7172, 8072 Burnett Womack, 160 Dental Circle, Chapel Hill, NC, 27599, USA
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - G Caleb Alexander
- Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Division of General Internal Medicine, Johns Hopkins Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Melissa Haendel
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, USA
| | - Stephanie S Hong
- Division of General Internal Medicine, Johns Hopkins Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Hemalkumar Mehta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Til Stürmer
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - John B Buse
- Division of Endocrinology and Metabolism, Department of Medicine, University of North Carolina School of Medicine, Campus Box #7172, 8072 Burnett Womack, 160 Dental Circle, Chapel Hill, NC, 27599, USA
| |
Collapse
|
2
|
Cristello Sarteau A, Ercolino G, Muthukkumar R, Fruik A, Mayer-Davis EJ, Kahkoska AR. Nutritional Status, Dietary Intake, and Nutrition-Related Interventions Among Older Adults With Type 1 Diabetes: A Systematic Review and Call for More Evidence Toward Clinical Guidelines. Diabetes Care 2024:dci230099. [PMID: 38687466 DOI: 10.2337/dci23-0099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 03/08/2024] [Indexed: 05/02/2024]
Abstract
There is an emerging population of older adults (≥65 years) living with type 1 diabetes. Optimizing health through nutrition during this life stage is challenged by multiple and ongoing changes in diabetes management, comorbidities, and lifestyle factors. There is a need to understand nutritional status, dietary intake, and nutrition-related interventions that may maximize well-being throughout the life span in type 1 diabetes, in addition to nutrition recommendations from clinical guidelines and consensus reports. Three reviewers used Cochrane guidelines to screen original research (January 1993-2023) and guidelines (2012-2023) in two databases (MEDLINE and CENTRAL) to characterize nutrition evidence in this population. We found limited original research explicitly focused on nutrition and diet in adults ≥65 years of age with type 1 diabetes (six experimental studies, five observational studies) and meta-analyses/reviews (one scoping review), since in the majority of analyses individuals ≥65 years of age were combined with those age ≥18 years, with diverse diabetes durations, and also individuals with type 1 and type 2 diabetes were combined. Further, existing clinical guidelines (n = 10) lacked specificity and evidence to guide clinical practice and self-management behaviors in this population. From a scientific perspective, little is known about nutrition and diet among older adults with type 1 diabetes, including baseline nutrition status, dietary intake and eating behaviors, and the impact of nutrition interventions on key clinical and patient-oriented outcomes. This likely reflects the population's recent emergence and unique considerations. Addressing these gaps is foundational to developing evidence-based nutrition practices and guidelines for older adults living with type 1 diabetes.
Collapse
Affiliation(s)
- Angelica Cristello Sarteau
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Gabriella Ercolino
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Rashmi Muthukkumar
- Division of Endocrinology and Metabolism, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Angela Fruik
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Elizabeth J Mayer-Davis
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Anna R Kahkoska
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Division of Endocrinology and Metabolism, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Center for Aging and Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| |
Collapse
|
3
|
Sy S, Sinclair A, Munshi M, Kahkoska AR, Weinstock R, Cukierman-Yaffe T. Use of Technologies at the Advanced Age. Diabetes Technol Ther 2024; 26:S172-S186. [PMID: 38441458 DOI: 10.1089/dia.2024.2511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Affiliation(s)
- Sarah Sy
- Division of Geriatric Medicine, Department of Medicine, University of British Columbia, Vancouver, Canada
| | - Alan Sinclair
- Foundation of Diabetes Research in Older People (fDROP), London, UK
- King's College, London, UK
| | - Medha Munshi
- Joslin Diabetes Center, Boston, MA, USA
- Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Anna R Kahkoska
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Division of Endocrinology and Metabolism, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Center for Aging and Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ruth Weinstock
- Department of Medicine, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Tali Cukierman-Yaffe
- Division of Endocrinology, Diabetes and Metabolism, Sheba Medical Center, Ramat Gan, Israel
- Department of Epidemiology and Preventive Medicine, School of Public Health, Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
| |
Collapse
|
4
|
Freeman NLB, Muthukkumar R, Weinstock RS, Wickerhauser MV, Kahkoska AR. Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a post-hoc analysis of a case-control study. BMJ Open Diabetes Res Care 2024; 12:e003748. [PMID: 38413176 PMCID: PMC10900355 DOI: 10.1136/bmjdrc-2023-003748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/30/2024] [Indexed: 02/29/2024] Open
Abstract
INTRODUCTION Severe hypoglycemia (SH) in older adults (OAs) with type 1 diabetes is associated with profound morbidity and mortality, yet its etiology can be complex and multifactorial. Enhanced tools to identify OAs who are at high risk for SH are needed. This study used machine learning to identify characteristics that distinguish those with and without recent SH, selecting from a range of demographic and clinical, behavioral and lifestyle, and neurocognitive characteristics, along with continuous glucose monitoring (CGM) measures. RESEARCH DESIGN AND METHODS Data from a case-control study involving OAs recruited from the T1D Exchange Clinical Network were analyzed. The random forest machine learning algorithm was used to elucidate the characteristics associated with case versus control status and their relative importance. Models with successively rich characteristic sets were examined to systematically incorporate each domain of possible risk characteristics. RESULTS Data from 191 OAs with type 1 diabetes (47.1% female, 92.1% non-Hispanic white) were analyzed. Across models, hypoglycemia unawareness was the top characteristic associated with SH history. For the model with the richest input data, the most important characteristics, in descending order, were hypoglycemia unawareness, hypoglycemia fear, coefficient of variation from CGM, % time blood glucose below 70 mg/dL, and trail making test B score. CONCLUSIONS Machine learning may augment risk stratification for OAs by identifying key characteristics associated with SH. Prospective studies are needed to identify the predictive performance of these risk characteristics.
Collapse
Affiliation(s)
- Nikki L B Freeman
- Department of Surgery, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA
| | - Rashmi Muthukkumar
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Ruth S Weinstock
- Department of Medicine, SUNY Upstate Medical University, Syracuse, New York, USA
| | - M Victor Wickerhauser
- Department of Mathematics, Washington University in St Louis, St Louis, Missouri, USA
| | - Anna R Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Division of Endocrinology and Metabolism, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| |
Collapse
|
5
|
Cho H, She J, De Marchi D, El-Zaatari H, Barnes EL, Kahkoska AR, Kosorok MR, Virkud AV. Machine Learning and Health Science Research: Tutorial. J Med Internet Res 2024; 26:e50890. [PMID: 38289657 PMCID: PMC10865203 DOI: 10.2196/50890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 11/30/2023] [Accepted: 12/21/2023] [Indexed: 02/01/2024] Open
Abstract
Machine learning (ML) has seen impressive growth in health science research due to its capacity for handling complex data to perform a range of tasks, including unsupervised learning, supervised learning, and reinforcement learning. To aid health science researchers in understanding the strengths and limitations of ML and to facilitate its integration into their studies, we present here a guideline for integrating ML into an analysis through a structured framework, covering steps from framing a research question to study design and analysis techniques for specialized data types.
Collapse
Affiliation(s)
- Hunyong Cho
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Jane She
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Daniel De Marchi
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Helal El-Zaatari
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Edward L Barnes
- Division of Gastroenterology and Hepatology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Center for Gastrointestinal Biology and Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Anna R Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Division of Endocrinology and Metabolism, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Center for Aging and Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Michael R Kosorok
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Arti V Virkud
- Kidney Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| |
Collapse
|
6
|
Sarteau AC, Muthukkumar R, Smith C, Busby-Whitehead J, Lich KH, Pratley RE, Thambuluru S, Weinstein J, Weinstock RS, Young LA, Kahkoska AR. Supporting the 'lived expertise' of older adults with type 1 diabetes: An applied focus group analysis to characterize barriers, facilitators, and strategies for self-management in a growing and understudied population. Diabet Med 2024; 41:e15156. [PMID: 37278610 PMCID: PMC11002954 DOI: 10.1111/dme.15156] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 05/11/2023] [Accepted: 05/24/2023] [Indexed: 06/07/2023]
Abstract
INTRODUCTION There is a growing number of older adults (≥65 years) who live with type 1 diabetes. We qualitatively explored experiences and perspectives regarding type 1 diabetes self-management and treatment decisions among older adults, focusing on adopting care advances such as continuous glucose monitoring (CGM). METHODS Among a clinic-based sample of older adults ≥65 years with type 1 diabetes, we conducted a series of literature and expert informed focus groups with structured discussion activities. Groups were transcribed followed by inductive coding, theme identification, and inference verification. Medical records and surveys added clinical information. RESULTS Twenty nine older adults (age 73.4 ± 4.5 years; 86% CGM users) and four caregivers (age 73.3 ± 2.9 years) participated. Participants were 58% female and 82% non-Hispanic White. Analysis revealed themes related to attitudes, behaviours, and experiences, as well as interpersonal and contextual factors that shape self-management and outcomes. These factors and their interactions drive variability in diabetes outcomes and optimal treatment strategies between individuals as well as within individuals over time (i.e. with ageing). Participants proposed strategies to address these factors: regular, holistic needs assessments to match people with effective self-care approaches and adapt them over the lifespan; longitudinal support (e.g., education, tactical help, sharing and validating experiences); tailored education and skills training; and leveraging of caregivers, family, and peers as resources. CONCLUSIONS Our study of what influences self-management decisions and technology adoption among older adults with type 1 diabetes underscores the importance of ongoing assessments to address dynamic age-specific needs, as well as individualized multi-faceted support that integrates peers and caregivers.
Collapse
Affiliation(s)
| | - Rashmi Muthukkumar
- School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
| | - Cambray Smith
- Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
| | - Jan Busby-Whitehead
- Division of Geriatric Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
- UNC Center for Aging and Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
| | - Kristen Hassmiller Lich
- Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
| | | | - Sirisha Thambuluru
- Division of Endocrinology and Metabolism, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
| | - Joshua Weinstein
- Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
| | | | - Laura A. Young
- Division of Endocrinology and Metabolism, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
| | - Anna R. Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
- UNC Center for Aging and Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
- Division of Endocrinology and Metabolism, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514
| |
Collapse
|
7
|
Smith C, Cristello Sarteau A, Qu X, Noe V, Young LA, Hassmiller Lich K, Kahkoska AR. A conceptual model of the continuous glucose monitoring integration process for older adults with diabetes developed using participatory systems science methods. Diabetes Res Clin Pract 2024; 207:111053. [PMID: 38097112 PMCID: PMC10958737 DOI: 10.1016/j.diabres.2023.111053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 11/21/2023] [Accepted: 12/09/2023] [Indexed: 01/13/2024]
Abstract
AIMS Continuous glucose monitoring (CGM) use remains low in older adults. We aimed to develop a conceptual model of CGM integration among older adults with type 1 and type 2 diabetes. METHODS We previously engaged older adults with type 1 diabetes using participatory system science methods to develop a model of the system of factors that shape CGM integration. To validate and expand the model, we conducted semi-structured interviews with 17 older adults with type 1 and type 2 diabetes and 3 caregivers. Vignettes representing each integration phase were used to elicit outcomes and strategies to support CGM use. Data were analyzed using team-based causal loop diagraming. RESULTS The model includes six phases spanning (1) CGM uptake; (2) device set-up; acquisition of (3) belief in oneself to use CGM effectively; (4) belief that CGM is preferable to blood glucose monitoring; (5) belief in future CGM benefits CGM; and (6) development of a sense of reliance on CGM. Causal loop diagrams visualize factors and feedback loops shaping outcomes at each phase. Participants proposed support strategies spanning clinical, educational, and behavioral interventions. CONCLUSIONS The model underscores the complex transition of learning new technology and provides opportunities for tailored support for older adults.
Collapse
Affiliation(s)
- Cambray Smith
- University of North Carolina at Chapel Hill, Department of Health Policy and Management, United States
| | | | - Xiaorui Qu
- University of North Carolina at Chapel Hill, Department of Nutrition, United States
| | - Violet Noe
- University of North Carolina at Chapel Hill, Department of Nutrition, United States
| | - Laura A Young
- University of North Carolina at Chapel Hill, School of Medicine Division of Endocrinology and Metabolism, United States
| | - Kristen Hassmiller Lich
- University of North Carolina at Chapel Hill, Department of Health Policy and Management, United States
| | - Anna R Kahkoska
- University of North Carolina at Chapel Hill, Department of Nutrition, United States; University of North Carolina at Chapel Hill, School of Medicine Division of Endocrinology and Metabolism, United States; Center for Aging and Health, University of North Carolina at Chapel Hill, United States.
| |
Collapse
|
8
|
Zhang J, Wei X, Liu W, Wang Y, Kahkoska AR, Zhou X, Zheng H, Zhang W, Sheng T, Zhang Y, Liu Y, Ji K, Xu Y, Zhang P, Xu J, Buse JB, Wang J, Gu Z. Week-long normoglycaemia in diabetic mice and minipigs via a subcutaneous dose of a glucose-responsive insulin complex. Nat Biomed Eng 2023:10.1038/s41551-023-01138-7. [PMID: 38057427 DOI: 10.1038/s41551-023-01138-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 10/17/2023] [Indexed: 12/08/2023]
Abstract
Glucose-responsive formulations of insulin can increase its therapeutic index and reduce the burden of its administration. However, it has been difficult to develop single-dosage formulations that can release insulin in both a sustained and glucose-responsive manner. Here we report the development of a subcutaneously injected glucose-responsive formulation that nearly does not trigger the formation of a fibrous capsule and that leads to week-long normoglycaemia and negligible hypoglycaemia in mice and minipigs with type 1 diabetes. The formulation consists of gluconic acid-modified recombinant human insulin binding tightly to poly-L-lysine modified by 4-carboxy-3-fluorophenylboronic acid via glucose-responsive phenylboronic acid-diol complexation and electrostatic attraction. When the insulin complex is exposed to high glucose concentrations, the phenylboronic acid moieties of the polymers bind rapidly to glucose, breaking the complexation and reducing the polymers' positive charge density, which promotes the release of insulin. The therapeutic performance of this long-acting single-dose formulation supports its further evaluation and clinical translational studies.
Collapse
Affiliation(s)
- Juan Zhang
- National Key Laboratory of Advanced Drug Delivery and Release Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Jinhua Institute of Zhejiang University, Jinhua, China
- Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Xiangqian Wei
- National Key Laboratory of Advanced Drug Delivery and Release Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Jinhua Institute of Zhejiang University, Jinhua, China
- Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Wei Liu
- National Key Laboratory of Advanced Drug Delivery and Release Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Jinhua Institute of Zhejiang University, Jinhua, China
- Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yanfang Wang
- National Key Laboratory of Advanced Drug Delivery and Release Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Jinhua Institute of Zhejiang University, Jinhua, China
- Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Anna R Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xianchi Zhou
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization of Ministry of Education, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, China
| | - Huimin Zheng
- National Key Laboratory of Advanced Drug Delivery and Release Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Wentao Zhang
- National Key Laboratory of Advanced Drug Delivery and Release Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Jinhua Institute of Zhejiang University, Jinhua, China
- Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Tao Sheng
- National Key Laboratory of Advanced Drug Delivery and Release Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Jinhua Institute of Zhejiang University, Jinhua, China
- Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yang Zhang
- National Key Laboratory of Advanced Drug Delivery and Release Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Jinhua Institute of Zhejiang University, Jinhua, China
- Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yun Liu
- National Key Laboratory of Advanced Drug Delivery and Release Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Jinhua Institute of Zhejiang University, Jinhua, China
- Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Kangfan Ji
- National Key Laboratory of Advanced Drug Delivery and Release Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Jinhua Institute of Zhejiang University, Jinhua, China
- Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yichen Xu
- National Key Laboratory of Advanced Drug Delivery and Release Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Department of Pharmacy, Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Peng Zhang
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization of Ministry of Education, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, China
| | - Jianchang Xu
- National Key Laboratory of Advanced Drug Delivery and Release Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
- Jinhua Institute of Zhejiang University, Jinhua, China
- Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - John B Buse
- Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Jinqiang Wang
- National Key Laboratory of Advanced Drug Delivery and Release Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
- Jinhua Institute of Zhejiang University, Jinhua, China.
- Department of Pharmacy, Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China.
| | - Zhen Gu
- National Key Laboratory of Advanced Drug Delivery and Release Systems, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
- Jinhua Institute of Zhejiang University, Jinhua, China.
- Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
- MOE Key Laboratory of Macromolecule Synthesis and Functionalization of Ministry of Education, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, China.
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
- Liangzhu Laboratory, Hangzhou, China.
| |
Collapse
|
9
|
Ratzki-Leewing A, Black JE, Kahkoska AR, Ryan BL, Zou G, Klar N, Timcevska K, Harris SB. Severe (level 3) hypoglycaemia occurrence in a real-world cohort of adults with type 1 or 2 diabetes mellitus (iNPHORM, United States). Diabetes Obes Metab 2023; 25:3736-3747. [PMID: 37700692 PMCID: PMC10958739 DOI: 10.1111/dom.15268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 08/17/2023] [Accepted: 08/18/2023] [Indexed: 09/14/2023]
Abstract
AIMS Among adults with insulin- and/or secretagogue-treated diabetes in the United States, very little is known about the real-world descriptive epidemiology of iatrogenic severe (level 3) hypoglycaemia. Addressing this gap, we collected primary, longitudinal data to quantify the absolute frequency of events as well as incidence rates and proportions. MATERIALS AND METHODS iNPHORM is a US-wide, 12-month ambidirectional panel survey (2020-2021). Adults with type 1 diabetes mellitus (T1DM) or insulin- and/or secretagogue-treated type 2 diabetes mellitus (T2DM) were recruited from a probability-based internet panel. Participants completing ≥1 follow-up questionnaire(s) were analysed. RESULTS Among 978 respondents [T1DM 17%; mean age 51 (SD 14.3) years; male: 49.6%], 63% of level 3 events were treated outside the health care system (e.g. by family/friend/colleague), and <5% required hospitalization. Following the 12-month prospective period, one-third of individuals reported ≥1 event(s) [T1DM 44.2% (95% CI 36.8%-51.8%); T2DM 30.8% (95% CI 28.7%-35.1%), p = .0404, α = 0.0007]; and the incidence rate was 5.01 (95% CI 4.15-6.05) events per person-year (EPPY) [T1DM 3.57 (95% CI 2.49-5.11) EPPY; T2DM 5.29 (95% CI 4.26-6.57) EPPY, p = .1352, α = 0.0007]. Level 3 hypoglycaemia requiring non-transport emergency medical services was more common in T2DM than T1DM (p < .0001, α = 0.0016). In total, >90% of events were experienced by <15% of participants. CONCLUSIONS iNPHORM is one of the first long-term, prospective US-based investigations on level 3 hypoglycaemia epidemiology. Our results underscore the importance of participant-reported data to ascertain its burden. Events were alarmingly frequent, irrespective of diabetes type, and concentrated in a small subsample.
Collapse
Affiliation(s)
- Alexandria Ratzki-Leewing
- Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Jason E. Black
- Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Anna R. Kahkoska
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Bridget L. Ryan
- Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Guangyong Zou
- Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Robarts Research Institute, Western University, London, Ontario, Canada
| | - Neil Klar
- Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Kristina Timcevska
- Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Stewart B. Harris
- Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Department of Medicine/Division of Endocrinology, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| |
Collapse
|
10
|
Allen CG, Olstad DL, Kahkoska AR, Guan Y, Ramos PS, Steinberg J, Staras SAS, Lumpkins CY, Milko LV, Turbitt E, Rahm AK, Saylor KW, Best S, Hatch A, Santangelo I, Roberts MC. Extending an Antiracism Lens to the Implementation of Precision Public Health Interventions. Am J Public Health 2023; 113:1210-1218. [PMID: 37651661 PMCID: PMC10568499 DOI: 10.2105/ajph.2023.307386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/06/2023] [Indexed: 09/02/2023]
Abstract
Precision public health holds promise to improve disease prevention and health promotion strategies, allowing the right intervention to be delivered to the right population at the right time. Growing concerns underscore the potential for precision-based approaches to exacerbate health disparities by relying on biased data inputs and recapitulating existing access inequities. To achieve its full potential, precision public health must focus on addressing social and structural drivers of health and prominently incorporate equity-related concerns, particularly with respect to race and ethnicity. In this article, we discuss how an antiracism lens could be applied to reduce health disparities and health inequities through equity-informed research, implementation, and evaluation of precision public health interventions. (Am J Public Health. 2023;113(11):1210-1218. https://doi.org/10.2105/AJPH.2023.307386).
Collapse
Affiliation(s)
- Caitlin G Allen
- Caitlin G. Allen and Ashley Hatch are with the Department of Public Health Sciences, College of Medicine, and Paula S. Ramos is with the Departments of Medicine and Public Health Sciences, Medical University of South Carolina, Charleston. Dana Lee Olstad is with the Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Anna R. Kahkoska is with the Department of Nutrition, Laura V. Milko is with the Department of Genetics, and Megan C. Roberts is with the Eshelman School of Pharmacy, University of North Carolina, Chapel Hill. Yue Guan and Isabella Santangelo are with the Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA. Julia Steinberg is with The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia. Stephanie A. S. Staras is with the Department of Health Outcome and Biomedical Informatics, College of Medicine, and Institute for Child Health Policy, University of Florida, Gainesville. Crystal Y. Lumpkins is with the Department of Communication, Huntsman Cancer Institute, University of Utah, Salt Lake City. Erin Turbitt is with the Graduate School of Health, University of Technology Sydney, Ultimo, NSW, Australia. Alanna K. Rahm is with the Department of Genomic Health, Geisinger Medical Center, Danville, PA. Katherine W. Saylor is with the Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia. Stephanie Best is with the Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Dana Lee Olstad
- Caitlin G. Allen and Ashley Hatch are with the Department of Public Health Sciences, College of Medicine, and Paula S. Ramos is with the Departments of Medicine and Public Health Sciences, Medical University of South Carolina, Charleston. Dana Lee Olstad is with the Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Anna R. Kahkoska is with the Department of Nutrition, Laura V. Milko is with the Department of Genetics, and Megan C. Roberts is with the Eshelman School of Pharmacy, University of North Carolina, Chapel Hill. Yue Guan and Isabella Santangelo are with the Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA. Julia Steinberg is with The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia. Stephanie A. S. Staras is with the Department of Health Outcome and Biomedical Informatics, College of Medicine, and Institute for Child Health Policy, University of Florida, Gainesville. Crystal Y. Lumpkins is with the Department of Communication, Huntsman Cancer Institute, University of Utah, Salt Lake City. Erin Turbitt is with the Graduate School of Health, University of Technology Sydney, Ultimo, NSW, Australia. Alanna K. Rahm is with the Department of Genomic Health, Geisinger Medical Center, Danville, PA. Katherine W. Saylor is with the Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia. Stephanie Best is with the Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Anna R Kahkoska
- Caitlin G. Allen and Ashley Hatch are with the Department of Public Health Sciences, College of Medicine, and Paula S. Ramos is with the Departments of Medicine and Public Health Sciences, Medical University of South Carolina, Charleston. Dana Lee Olstad is with the Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Anna R. Kahkoska is with the Department of Nutrition, Laura V. Milko is with the Department of Genetics, and Megan C. Roberts is with the Eshelman School of Pharmacy, University of North Carolina, Chapel Hill. Yue Guan and Isabella Santangelo are with the Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA. Julia Steinberg is with The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia. Stephanie A. S. Staras is with the Department of Health Outcome and Biomedical Informatics, College of Medicine, and Institute for Child Health Policy, University of Florida, Gainesville. Crystal Y. Lumpkins is with the Department of Communication, Huntsman Cancer Institute, University of Utah, Salt Lake City. Erin Turbitt is with the Graduate School of Health, University of Technology Sydney, Ultimo, NSW, Australia. Alanna K. Rahm is with the Department of Genomic Health, Geisinger Medical Center, Danville, PA. Katherine W. Saylor is with the Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia. Stephanie Best is with the Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Yue Guan
- Caitlin G. Allen and Ashley Hatch are with the Department of Public Health Sciences, College of Medicine, and Paula S. Ramos is with the Departments of Medicine and Public Health Sciences, Medical University of South Carolina, Charleston. Dana Lee Olstad is with the Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Anna R. Kahkoska is with the Department of Nutrition, Laura V. Milko is with the Department of Genetics, and Megan C. Roberts is with the Eshelman School of Pharmacy, University of North Carolina, Chapel Hill. Yue Guan and Isabella Santangelo are with the Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA. Julia Steinberg is with The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia. Stephanie A. S. Staras is with the Department of Health Outcome and Biomedical Informatics, College of Medicine, and Institute for Child Health Policy, University of Florida, Gainesville. Crystal Y. Lumpkins is with the Department of Communication, Huntsman Cancer Institute, University of Utah, Salt Lake City. Erin Turbitt is with the Graduate School of Health, University of Technology Sydney, Ultimo, NSW, Australia. Alanna K. Rahm is with the Department of Genomic Health, Geisinger Medical Center, Danville, PA. Katherine W. Saylor is with the Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia. Stephanie Best is with the Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Paula S Ramos
- Caitlin G. Allen and Ashley Hatch are with the Department of Public Health Sciences, College of Medicine, and Paula S. Ramos is with the Departments of Medicine and Public Health Sciences, Medical University of South Carolina, Charleston. Dana Lee Olstad is with the Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Anna R. Kahkoska is with the Department of Nutrition, Laura V. Milko is with the Department of Genetics, and Megan C. Roberts is with the Eshelman School of Pharmacy, University of North Carolina, Chapel Hill. Yue Guan and Isabella Santangelo are with the Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA. Julia Steinberg is with The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia. Stephanie A. S. Staras is with the Department of Health Outcome and Biomedical Informatics, College of Medicine, and Institute for Child Health Policy, University of Florida, Gainesville. Crystal Y. Lumpkins is with the Department of Communication, Huntsman Cancer Institute, University of Utah, Salt Lake City. Erin Turbitt is with the Graduate School of Health, University of Technology Sydney, Ultimo, NSW, Australia. Alanna K. Rahm is with the Department of Genomic Health, Geisinger Medical Center, Danville, PA. Katherine W. Saylor is with the Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia. Stephanie Best is with the Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Julia Steinberg
- Caitlin G. Allen and Ashley Hatch are with the Department of Public Health Sciences, College of Medicine, and Paula S. Ramos is with the Departments of Medicine and Public Health Sciences, Medical University of South Carolina, Charleston. Dana Lee Olstad is with the Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Anna R. Kahkoska is with the Department of Nutrition, Laura V. Milko is with the Department of Genetics, and Megan C. Roberts is with the Eshelman School of Pharmacy, University of North Carolina, Chapel Hill. Yue Guan and Isabella Santangelo are with the Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA. Julia Steinberg is with The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia. Stephanie A. S. Staras is with the Department of Health Outcome and Biomedical Informatics, College of Medicine, and Institute for Child Health Policy, University of Florida, Gainesville. Crystal Y. Lumpkins is with the Department of Communication, Huntsman Cancer Institute, University of Utah, Salt Lake City. Erin Turbitt is with the Graduate School of Health, University of Technology Sydney, Ultimo, NSW, Australia. Alanna K. Rahm is with the Department of Genomic Health, Geisinger Medical Center, Danville, PA. Katherine W. Saylor is with the Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia. Stephanie Best is with the Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Stephanie A S Staras
- Caitlin G. Allen and Ashley Hatch are with the Department of Public Health Sciences, College of Medicine, and Paula S. Ramos is with the Departments of Medicine and Public Health Sciences, Medical University of South Carolina, Charleston. Dana Lee Olstad is with the Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Anna R. Kahkoska is with the Department of Nutrition, Laura V. Milko is with the Department of Genetics, and Megan C. Roberts is with the Eshelman School of Pharmacy, University of North Carolina, Chapel Hill. Yue Guan and Isabella Santangelo are with the Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA. Julia Steinberg is with The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia. Stephanie A. S. Staras is with the Department of Health Outcome and Biomedical Informatics, College of Medicine, and Institute for Child Health Policy, University of Florida, Gainesville. Crystal Y. Lumpkins is with the Department of Communication, Huntsman Cancer Institute, University of Utah, Salt Lake City. Erin Turbitt is with the Graduate School of Health, University of Technology Sydney, Ultimo, NSW, Australia. Alanna K. Rahm is with the Department of Genomic Health, Geisinger Medical Center, Danville, PA. Katherine W. Saylor is with the Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia. Stephanie Best is with the Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Crystal Y Lumpkins
- Caitlin G. Allen and Ashley Hatch are with the Department of Public Health Sciences, College of Medicine, and Paula S. Ramos is with the Departments of Medicine and Public Health Sciences, Medical University of South Carolina, Charleston. Dana Lee Olstad is with the Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Anna R. Kahkoska is with the Department of Nutrition, Laura V. Milko is with the Department of Genetics, and Megan C. Roberts is with the Eshelman School of Pharmacy, University of North Carolina, Chapel Hill. Yue Guan and Isabella Santangelo are with the Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA. Julia Steinberg is with The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia. Stephanie A. S. Staras is with the Department of Health Outcome and Biomedical Informatics, College of Medicine, and Institute for Child Health Policy, University of Florida, Gainesville. Crystal Y. Lumpkins is with the Department of Communication, Huntsman Cancer Institute, University of Utah, Salt Lake City. Erin Turbitt is with the Graduate School of Health, University of Technology Sydney, Ultimo, NSW, Australia. Alanna K. Rahm is with the Department of Genomic Health, Geisinger Medical Center, Danville, PA. Katherine W. Saylor is with the Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia. Stephanie Best is with the Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Laura V Milko
- Caitlin G. Allen and Ashley Hatch are with the Department of Public Health Sciences, College of Medicine, and Paula S. Ramos is with the Departments of Medicine and Public Health Sciences, Medical University of South Carolina, Charleston. Dana Lee Olstad is with the Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Anna R. Kahkoska is with the Department of Nutrition, Laura V. Milko is with the Department of Genetics, and Megan C. Roberts is with the Eshelman School of Pharmacy, University of North Carolina, Chapel Hill. Yue Guan and Isabella Santangelo are with the Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA. Julia Steinberg is with The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia. Stephanie A. S. Staras is with the Department of Health Outcome and Biomedical Informatics, College of Medicine, and Institute for Child Health Policy, University of Florida, Gainesville. Crystal Y. Lumpkins is with the Department of Communication, Huntsman Cancer Institute, University of Utah, Salt Lake City. Erin Turbitt is with the Graduate School of Health, University of Technology Sydney, Ultimo, NSW, Australia. Alanna K. Rahm is with the Department of Genomic Health, Geisinger Medical Center, Danville, PA. Katherine W. Saylor is with the Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia. Stephanie Best is with the Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Erin Turbitt
- Caitlin G. Allen and Ashley Hatch are with the Department of Public Health Sciences, College of Medicine, and Paula S. Ramos is with the Departments of Medicine and Public Health Sciences, Medical University of South Carolina, Charleston. Dana Lee Olstad is with the Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Anna R. Kahkoska is with the Department of Nutrition, Laura V. Milko is with the Department of Genetics, and Megan C. Roberts is with the Eshelman School of Pharmacy, University of North Carolina, Chapel Hill. Yue Guan and Isabella Santangelo are with the Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA. Julia Steinberg is with The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia. Stephanie A. S. Staras is with the Department of Health Outcome and Biomedical Informatics, College of Medicine, and Institute for Child Health Policy, University of Florida, Gainesville. Crystal Y. Lumpkins is with the Department of Communication, Huntsman Cancer Institute, University of Utah, Salt Lake City. Erin Turbitt is with the Graduate School of Health, University of Technology Sydney, Ultimo, NSW, Australia. Alanna K. Rahm is with the Department of Genomic Health, Geisinger Medical Center, Danville, PA. Katherine W. Saylor is with the Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia. Stephanie Best is with the Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Alanna K Rahm
- Caitlin G. Allen and Ashley Hatch are with the Department of Public Health Sciences, College of Medicine, and Paula S. Ramos is with the Departments of Medicine and Public Health Sciences, Medical University of South Carolina, Charleston. Dana Lee Olstad is with the Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Anna R. Kahkoska is with the Department of Nutrition, Laura V. Milko is with the Department of Genetics, and Megan C. Roberts is with the Eshelman School of Pharmacy, University of North Carolina, Chapel Hill. Yue Guan and Isabella Santangelo are with the Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA. Julia Steinberg is with The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia. Stephanie A. S. Staras is with the Department of Health Outcome and Biomedical Informatics, College of Medicine, and Institute for Child Health Policy, University of Florida, Gainesville. Crystal Y. Lumpkins is with the Department of Communication, Huntsman Cancer Institute, University of Utah, Salt Lake City. Erin Turbitt is with the Graduate School of Health, University of Technology Sydney, Ultimo, NSW, Australia. Alanna K. Rahm is with the Department of Genomic Health, Geisinger Medical Center, Danville, PA. Katherine W. Saylor is with the Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia. Stephanie Best is with the Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Katherine W Saylor
- Caitlin G. Allen and Ashley Hatch are with the Department of Public Health Sciences, College of Medicine, and Paula S. Ramos is with the Departments of Medicine and Public Health Sciences, Medical University of South Carolina, Charleston. Dana Lee Olstad is with the Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Anna R. Kahkoska is with the Department of Nutrition, Laura V. Milko is with the Department of Genetics, and Megan C. Roberts is with the Eshelman School of Pharmacy, University of North Carolina, Chapel Hill. Yue Guan and Isabella Santangelo are with the Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA. Julia Steinberg is with The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia. Stephanie A. S. Staras is with the Department of Health Outcome and Biomedical Informatics, College of Medicine, and Institute for Child Health Policy, University of Florida, Gainesville. Crystal Y. Lumpkins is with the Department of Communication, Huntsman Cancer Institute, University of Utah, Salt Lake City. Erin Turbitt is with the Graduate School of Health, University of Technology Sydney, Ultimo, NSW, Australia. Alanna K. Rahm is with the Department of Genomic Health, Geisinger Medical Center, Danville, PA. Katherine W. Saylor is with the Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia. Stephanie Best is with the Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Stephanie Best
- Caitlin G. Allen and Ashley Hatch are with the Department of Public Health Sciences, College of Medicine, and Paula S. Ramos is with the Departments of Medicine and Public Health Sciences, Medical University of South Carolina, Charleston. Dana Lee Olstad is with the Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Anna R. Kahkoska is with the Department of Nutrition, Laura V. Milko is with the Department of Genetics, and Megan C. Roberts is with the Eshelman School of Pharmacy, University of North Carolina, Chapel Hill. Yue Guan and Isabella Santangelo are with the Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA. Julia Steinberg is with The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia. Stephanie A. S. Staras is with the Department of Health Outcome and Biomedical Informatics, College of Medicine, and Institute for Child Health Policy, University of Florida, Gainesville. Crystal Y. Lumpkins is with the Department of Communication, Huntsman Cancer Institute, University of Utah, Salt Lake City. Erin Turbitt is with the Graduate School of Health, University of Technology Sydney, Ultimo, NSW, Australia. Alanna K. Rahm is with the Department of Genomic Health, Geisinger Medical Center, Danville, PA. Katherine W. Saylor is with the Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia. Stephanie Best is with the Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Ashley Hatch
- Caitlin G. Allen and Ashley Hatch are with the Department of Public Health Sciences, College of Medicine, and Paula S. Ramos is with the Departments of Medicine and Public Health Sciences, Medical University of South Carolina, Charleston. Dana Lee Olstad is with the Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Anna R. Kahkoska is with the Department of Nutrition, Laura V. Milko is with the Department of Genetics, and Megan C. Roberts is with the Eshelman School of Pharmacy, University of North Carolina, Chapel Hill. Yue Guan and Isabella Santangelo are with the Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA. Julia Steinberg is with The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia. Stephanie A. S. Staras is with the Department of Health Outcome and Biomedical Informatics, College of Medicine, and Institute for Child Health Policy, University of Florida, Gainesville. Crystal Y. Lumpkins is with the Department of Communication, Huntsman Cancer Institute, University of Utah, Salt Lake City. Erin Turbitt is with the Graduate School of Health, University of Technology Sydney, Ultimo, NSW, Australia. Alanna K. Rahm is with the Department of Genomic Health, Geisinger Medical Center, Danville, PA. Katherine W. Saylor is with the Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia. Stephanie Best is with the Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Isabella Santangelo
- Caitlin G. Allen and Ashley Hatch are with the Department of Public Health Sciences, College of Medicine, and Paula S. Ramos is with the Departments of Medicine and Public Health Sciences, Medical University of South Carolina, Charleston. Dana Lee Olstad is with the Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Anna R. Kahkoska is with the Department of Nutrition, Laura V. Milko is with the Department of Genetics, and Megan C. Roberts is with the Eshelman School of Pharmacy, University of North Carolina, Chapel Hill. Yue Guan and Isabella Santangelo are with the Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA. Julia Steinberg is with The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia. Stephanie A. S. Staras is with the Department of Health Outcome and Biomedical Informatics, College of Medicine, and Institute for Child Health Policy, University of Florida, Gainesville. Crystal Y. Lumpkins is with the Department of Communication, Huntsman Cancer Institute, University of Utah, Salt Lake City. Erin Turbitt is with the Graduate School of Health, University of Technology Sydney, Ultimo, NSW, Australia. Alanna K. Rahm is with the Department of Genomic Health, Geisinger Medical Center, Danville, PA. Katherine W. Saylor is with the Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia. Stephanie Best is with the Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Megan C Roberts
- Caitlin G. Allen and Ashley Hatch are with the Department of Public Health Sciences, College of Medicine, and Paula S. Ramos is with the Departments of Medicine and Public Health Sciences, Medical University of South Carolina, Charleston. Dana Lee Olstad is with the Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Anna R. Kahkoska is with the Department of Nutrition, Laura V. Milko is with the Department of Genetics, and Megan C. Roberts is with the Eshelman School of Pharmacy, University of North Carolina, Chapel Hill. Yue Guan and Isabella Santangelo are with the Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA. Julia Steinberg is with The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, Australia. Stephanie A. S. Staras is with the Department of Health Outcome and Biomedical Informatics, College of Medicine, and Institute for Child Health Policy, University of Florida, Gainesville. Crystal Y. Lumpkins is with the Department of Communication, Huntsman Cancer Institute, University of Utah, Salt Lake City. Erin Turbitt is with the Graduate School of Health, University of Technology Sydney, Ultimo, NSW, Australia. Alanna K. Rahm is with the Department of Genomic Health, Geisinger Medical Center, Danville, PA. Katherine W. Saylor is with the Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia. Stephanie Best is with the Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| |
Collapse
|
11
|
Young KG, McInnes EH, Massey RJ, Kahkoska AR, Pilla SJ, Raghavan S, Stanislawski MA, Tobias DK, McGovern AP, Dawed AY, Jones AG, Pearson ER, Dennis JM. Treatment effect heterogeneity following type 2 diabetes treatment with GLP1-receptor agonists and SGLT2-inhibitors: a systematic review. Commun Med (Lond) 2023; 3:131. [PMID: 37794166 PMCID: PMC10551026 DOI: 10.1038/s43856-023-00359-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/15/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND A precision medicine approach in type 2 diabetes requires the identification of clinical and biological features that are reproducibly associated with differences in clinical outcomes with specific anti-hyperglycaemic therapies. Robust evidence of such treatment effect heterogeneity could support more individualized clinical decisions on optimal type 2 diabetes therapy. METHODS We performed a pre-registered systematic review of meta-analysis studies, randomized control trials, and observational studies evaluating clinical and biological features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies, considering glycaemic, cardiovascular, and renal outcomes. After screening 5,686 studies, we included 101 studies of SGLT2-inhibitors and 75 studies of GLP1-receptor agonists in the final systematic review. RESULTS Here we show that the majority of included papers have methodological limitations precluding robust assessment of treatment effect heterogeneity. For SGLT2-inhibitors, multiple observational studies suggest lower renal function as a predictor of lesser glycaemic response, while markers of reduced insulin secretion predict lesser glycaemic response with GLP1-receptor agonists. For both therapies, multiple post-hoc analyses of randomized control trials (including trial meta-analysis) identify minimal clinically relevant treatment effect heterogeneity for cardiovascular and renal outcomes. CONCLUSIONS Current evidence on treatment effect heterogeneity for SGLT2-inhibitor and GLP1-receptor agonist therapies is limited, likely reflecting the methodological limitations of published studies. Robust and appropriately powered studies are required to understand type 2 diabetes treatment effect heterogeneity and evaluate the potential for precision medicine to inform future clinical care.
Collapse
Affiliation(s)
- Katherine G Young
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Exeter, UK
| | - Eram Haider McInnes
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Robert J Massey
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Anna R Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott J Pilla
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sridharan Raghavan
- Section of Academic Primary Care, US Department of Veterans Affairs Eastern Colorado Health Care System, Aurora, CO, USA
| | - Maggie A Stanislawski
- Department of Biomedical Informatics, School of Medicine, University of Colorado, Aurora, USA
| | - Deirdre K Tobias
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrew P McGovern
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Exeter, UK
| | - Adem Y Dawed
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Angus G Jones
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Exeter, UK
| | - Ewan R Pearson
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK.
| | - John M Dennis
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Exeter, UK.
| |
Collapse
|
12
|
Tobias DK, Merino J, Ahmad A, Aiken C, Benham JL, Bodhini D, Clark AL, Colclough K, Corcoy R, Cromer SJ, Duan D, Felton JL, Francis EC, Gillard P, Gingras V, Gaillard R, Haider E, Hughes A, Ikle JM, Jacobsen LM, Kahkoska AR, Kettunen JLT, Kreienkamp RJ, Lim LL, Männistö JME, Massey R, Mclennan NM, Miller RG, Morieri ML, Most J, Naylor RN, Ozkan B, Patel KA, Pilla SJ, Prystupa K, Raghavan S, Rooney MR, Schön M, Semnani-Azad Z, Sevilla-Gonzalez M, Svalastoga P, Takele WW, Tam CHT, Thuesen ACB, Tosur M, Wallace AS, Wang CC, Wong JJ, Yamamoto JM, Young K, Amouyal C, Andersen MK, Bonham MP, Chen M, Cheng F, Chikowore T, Chivers SC, Clemmensen C, Dabelea D, Dawed AY, Deutsch AJ, Dickens LT, DiMeglio LA, Dudenhöffer-Pfeifer M, Evans-Molina C, Fernández-Balsells MM, Fitipaldi H, Fitzpatrick SL, Gitelman SE, Goodarzi MO, Grieger JA, Guasch-Ferré M, Habibi N, Hansen T, Huang C, Harris-Kawano A, Ismail HM, Hoag B, Johnson RK, Jones AG, Koivula RW, Leong A, Leung GKW, Libman IM, Liu K, Long SA, Lowe WL, Morton RW, Motala AA, Onengut-Gumuscu S, Pankow JS, Pathirana M, Pazmino S, Perez D, Petrie JR, Powe CE, Quinteros A, Jain R, Ray D, Ried-Larsen M, Saeed Z, Santhakumar V, Kanbour S, Sarkar S, Monaco GSF, Scholtens DM, Selvin E, Sheu WHH, Speake C, Stanislawski MA, Steenackers N, Steck AK, Stefan N, Støy J, Taylor R, Tye SC, Ukke GG, Urazbayeva M, Van der Schueren B, Vatier C, Wentworth JM, Hannah W, White SL, Yu G, Zhang Y, Zhou SJ, Beltrand J, Polak M, Aukrust I, de Franco E, Flanagan SE, Maloney KA, McGovern A, Molnes J, Nakabuye M, Njølstad PR, Pomares-Millan H, Provenzano M, Saint-Martin C, Zhang C, Zhu Y, Auh S, de Souza R, Fawcett AJ, Gruber C, Mekonnen EG, Mixter E, Sherifali D, Eckel RH, Nolan JJ, Philipson LH, Brown RJ, Billings LK, Boyle K, Costacou T, Dennis JM, Florez JC, Gloyn AL, Gomez MF, Gottlieb PA, Greeley SAW, Griffin K, Hattersley AT, Hirsch IB, Hivert MF, Hood KK, Josefson JL, Kwak SH, Laffel LM, Lim SS, Loos RJF, Ma RCW, Mathieu C, Mathioudakis N, Meigs JB, Misra S, Mohan V, Murphy R, Oram R, Owen KR, Ozanne SE, Pearson ER, Perng W, Pollin TI, Pop-Busui R, Pratley RE, Redman LM, Redondo MJ, Reynolds RM, Semple RK, Sherr JL, Sims EK, Sweeting A, Tuomi T, Udler MS, Vesco KK, Vilsbøll T, Wagner R, Rich SS, Franks PW. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine. Nat Med 2023; 29:2438-2457. [PMID: 37794253 PMCID: PMC10735053 DOI: 10.1038/s41591-023-02502-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/14/2023] [Indexed: 10/06/2023]
Abstract
Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.
Collapse
Affiliation(s)
- Deirdre K Tobias
- Division of Preventative Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jordi Merino
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Abrar Ahmad
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - Catherine Aiken
- Department of Obstetrics and Gynaecology, The Rosie Hospital, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
| | - Jamie L Benham
- Departments of Medicine and Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Dhanasekaran Bodhini
- Department of Molecular Genetics, Madras Diabetes Research Foundation, Chennai, India
| | - Amy L Clark
- Division of Pediatric Endocrinology, Department of Pediatrics, Saint Louis University School of Medicine, SSM Health Cardinal Glennon Children's Hospital, St. Louis, MO, USA
| | - Kevin Colclough
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Rosa Corcoy
- CIBER-BBN, ISCIII, Madrid, Spain
- Institut d'Investigació Biomèdica Sant Pau (IIB SANT PAU), Barcelona, Spain
- Departament de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Sara J Cromer
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Daisy Duan
- Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jamie L Felton
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Ellen C Francis
- Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, NJ, USA
| | | | - Véronique Gingras
- Department of Nutrition, Université de Montréal, Montreal, Quebec, Quebec, Canada
- Research Center, Sainte-Justine University Hospital Center, Montreal, Quebec, Quebec, Canada
| | - Romy Gaillard
- Department of Pediatrics, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Eram Haider
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Alice Hughes
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Jennifer M Ikle
- Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | | | - Anna R Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jarno L T Kettunen
- Helsinki University Hospital, Abdominal Centre/Endocrinology, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Raymond J Kreienkamp
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Pediatrics, Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA
| | - Lee-Ling Lim
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Asia Diabetes Foundation, Hong Kong SAR, China
- Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jonna M E Männistö
- Departments of Pediatrics and Clinical Genetics, Kuopio University Hospital, Kuopio, Finland
- Department of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Robert Massey
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Niamh-Maire Mclennan
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Rachel G Miller
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mario Luca Morieri
- Metabolic Disease Unit, University Hospital of Padova, Padova, Italy
- Department of Medicine, University of Padova, Padova, Italy
| | - Jasper Most
- Department of Orthopedics, Zuyderland Medical Center, Sittard-Geleen, The Netherlands
| | - Rochelle N Naylor
- Departments of Pediatrics and Medicine, University of Chicago, Chicago, IL, USA
| | - Bige Ozkan
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Kashyap Amratlal Patel
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Scott J Pilla
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
- Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Katsiaryna Prystupa
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Sridharan Raghavan
- Section of Academic Primary Care, US Department of Veterans Affairs Eastern Colorado Health Care System, Aurora, CO, USA
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Mary R Rooney
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Martin Schön
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Diabetes Research and Metabolic Diseases (IDM), Helmholtz Center Munich, Neuherberg, Germany
- Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Zhila Semnani-Azad
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Magdalena Sevilla-Gonzalez
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Pernille Svalastoga
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | - Wubet Worku Takele
- Eastern Health Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Claudia Ha-Ting Tam
- Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Anne Cathrine B Thuesen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mustafa Tosur
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
- Division of Pediatric Diabetes and Endocrinology, Texas Children's Hospital, Houston, TX, USA
- Children's Nutrition Research Center, USDA/ARS, Houston, TX, USA
| | - Amelia S Wallace
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Caroline C Wang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jessie J Wong
- Stanford University School of Medicine, Stanford, CA, USA
| | | | - Katherine Young
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Chloé Amouyal
- Department of Diabetology, APHP, Paris, France
- Sorbonne Université, INSERM, NutriOmic team, Paris, France
| | - Mette K Andersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Maxine P Bonham
- Department of Nutrition, Dietetics and Food, Monash University, Melbourne, Victoria, Australia
| | - Mingling Chen
- Monash Centre for Health Research and Implementation, Monash University, Clayton, Victoria, Australia
| | - Feifei Cheng
- Health Management Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China
| | - Tinashe Chikowore
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Sian C Chivers
- Department of Women and Children's Health, King's College London, London, UK
| | - Christoffer Clemmensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Dana Dabelea
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Adem Y Dawed
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Aaron J Deutsch
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Laura T Dickens
- Section of Adult and Pediatric Endocrinology, Diabetes and Metabolism, Kovler Diabetes Center, University of Chicago, Chicago, IL, USA
| | - Linda A DiMeglio
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Pediatrics, Riley Hospital for Children, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Carmella Evans-Molina
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
- Richard L. Roudebush VAMC, Indianapolis, IN, USA
| | - María Mercè Fernández-Balsells
- Biomedical Research Institute Girona, IdIBGi, Girona, Spain
- Diabetes, Endocrinology and Nutrition Unit Girona, University Hospital Dr Josep Trueta, Girona, Spain
| | - Hugo Fitipaldi
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - Stephanie L Fitzpatrick
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Stephen E Gitelman
- University of California at San Francisco, Department of Pediatrics, Diabetes Center, San Francisco, CA, USA
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jessica A Grieger
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Robinson Research Institute, The University of Adelaide, Adelaide, South Australia, Australia
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nahal Habibi
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Robinson Research Institute, The University of Adelaide, Adelaide, South Australia, Australia
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Chuiguo Huang
- Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Arianna Harris-Kawano
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Heba M Ismail
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Benjamin Hoag
- Division of Endocrinology and Diabetes, Department of Pediatrics, Sanford Children's Hospital, Sioux Falls, SD, USA
- University of South Dakota School of Medicine, E Clark St, Vermillion, SD, USA
| | - Randi K Johnson
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - Angus G Jones
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Robert W Koivula
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Aaron Leong
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Gloria K W Leung
- Department of Nutrition, Dietetics and Food, Monash University, Melbourne, Victoria, Australia
| | | | - Kai Liu
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
| | - S Alice Long
- Center for Translational Immunology, Benaroya Research Institute, Seattle, WA, USA
| | - William L Lowe
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Robert W Morton
- Department of Pathology & Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton, Ontario, Canada
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark
| | - Ayesha A Motala
- Department of Diabetes and Endocrinology, Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Maleesa Pathirana
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Robinson Research Institute, The University of Adelaide, Adelaide, South Australia, Australia
| | - Sofia Pazmino
- Department of Chronic Diseases and Metabolism, Clinical and Experimental Endocrinologyó, KU Leuven, Leuven, Belgium
| | - Dianna Perez
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - John R Petrie
- School of Health and Wellbeing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Camille E Powe
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Obstetrics, Gynecology, and Reproductive Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Alejandra Quinteros
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
| | - Rashmi Jain
- Sanford Children's Specialty Clinic, Sioux Falls, SD, USA
- Department of Pediatrics, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA
| | - Debashree Ray
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Mathias Ried-Larsen
- Centre for Physical Activity Research, Rigshospitalet, Copenhagen, Denmark
- Institute for Sports and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark
| | - Zeb Saeed
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Vanessa Santhakumar
- Division of Preventative Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Kanbour
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
- AMAN Hospital, Doha, Qatar
| | - Sudipa Sarkar
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Gabriela S F Monaco
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Denise M Scholtens
- Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Elizabeth Selvin
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Wayne Huey-Herng Sheu
- Institute of Molecular and Genomic Medicine, National Health Research Institutes, Zhunan, Taiwan
- Divsion of Endocrinology and Metabolism, Taichung Veterans General Hospital, Taichung, Taiwan
- Division of Endocrinology and Metabolism, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Cate Speake
- Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA, USA
| | - Maggie A Stanislawski
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Nele Steenackers
- Department of Chronic Diseases and Metabolism, Clinical and Experimental Endocrinologyó, KU Leuven, Leuven, Belgium
| | - Andrea K Steck
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Norbert Stefan
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Diabetes Research and Metabolic Diseases (IDM), Helmholtz Center Munich, Neuherberg, Germany
- University Hospital of Tübingen, Tübingen, Germany
| | - Julie Støy
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | | | - Sok Cin Tye
- Sections on Genetics and Epidemiology, Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Marzhan Urazbayeva
- Division of Pediatric Diabetes and Endocrinology, Texas Children's Hospital, Houston, TX, USA
- Gastroenterology, Baylor College of Medicine, Houston, TX, USA
| | - Bart Van der Schueren
- Department of Chronic Diseases and Metabolism, Clinical and Experimental Endocrinologyó, KU Leuven, Leuven, Belgium
- Department of Endocrinology, University Hospitals Leuven, Leuven, Belgium
| | - Camille Vatier
- Sorbonne University, Inserm U938, Saint-Antoine Research Centre, Institute of Cardiometabolism and Nutrition, Paris, France
- Department of Endocrinology, Diabetology and Reproductive Endocrinology, Assistance Publique-Hôpitaux de Paris, Saint-Antoine University Hospital, National Reference Center for Rare Diseases of Insulin Secretion and Insulin Sensitivity (PRISIS), Paris, France
| | - John M Wentworth
- Royal Melbourne Hospital Department of Diabetes and Endocrinology, Parkville, Victoria, Australia
- Walter and Eliza Hall Institute, Parkville, Victoria, Australia
- University of Melbourne Department of Medicine, Parkville, Victoria, Australia
| | - Wesley Hannah
- Deakin University, Melbourne, Victoria, Australia
- Department of Epidemiology, Madras Diabetes Research Foundation, Chennai, India
| | - Sara L White
- Department of Women and Children's Health, King's College London, London, UK
- Department of Diabetes and Endocrinology, Guy's and St Thomas' Hospitals NHS Foundation Trust, London, UK
| | - Gechang Yu
- Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yingchai Zhang
- Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Shao J Zhou
- Robinson Research Institute, The University of Adelaide, Adelaide, South Australia, Australia
- School of Agriculture, Food and Wine, University of Adelaide, Adelaide, South Australia, Australia
| | - Jacques Beltrand
- Institut Cochin, Inserm U 10116, Paris, France
- Pediatric Endocrinology and Diabetes, Hopital Necker Enfants Malades, APHP Centre, Université de Paris, Paris, France
| | - Michel Polak
- Institut Cochin, Inserm U 10116, Paris, France
- Pediatric Endocrinology and Diabetes, Hopital Necker Enfants Malades, APHP Centre, Université de Paris, Paris, France
| | - Ingvild Aukrust
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Elisa de Franco
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Sarah E Flanagan
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Kristin A Maloney
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Andrew McGovern
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Janne Molnes
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Mariam Nakabuye
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Pål Rasmus Njølstad
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | - Hugo Pomares-Millan
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Michele Provenzano
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS-Azienda Ospedaliero-Universitaria di Bologna, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Cécile Saint-Martin
- Department of Medical Genetics, AP-HP Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
| | - Cuilin Zhang
- Global Center for Asian Women's Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Yeyi Zhu
- Kaiser Permanente Northern California Division of Research, Oakland, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Sungyoung Auh
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Russell de Souza
- Population Health Research Institute, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Andrea J Fawcett
- Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Clinical and Organizational Development, Chicago, IL, USA
| | | | - Eskedar Getie Mekonnen
- College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
- Global Health Institute, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Emily Mixter
- Department of Medicine and Kovler Diabetes Center, University of Chicago, Chicago, IL, USA
| | - Diana Sherifali
- Population Health Research Institute, Hamilton, Ontario, Canada
- School of Nursing, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Robert H Eckel
- Division of Endocrinology, Metabolism, Diabetes, University of Colorado, Aurora, CO, USA
| | - John J Nolan
- Department of Clinical Medicine, School of Medicine, Trinity College Dublin, Dublin, Ireland
- Department of Endocrinology, Wexford General Hospital, Wexford, Ireland
| | - Louis H Philipson
- Department of Medicine and Kovler Diabetes Center, University of Chicago, Chicago, IL, USA
| | - Rebecca J Brown
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Liana K Billings
- Division of Endocrinology, NorthShore University HealthSystem, Skokie, IL, USA
- Department of Medicine, Prtizker School of Medicine, University of Chicago, Chicago, IL, USA
| | - Kristen Boyle
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Tina Costacou
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - John M Dennis
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Jose C Florez
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Anna L Gloyn
- Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Maria F Gomez
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden
- Faculty of Health, Aarhus University, Aarhus, Denmark
| | - Peter A Gottlieb
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Siri Atma W Greeley
- Departments of Pediatrics and Medicine and Kovler Diabetes Center, University of Chicago, Chicago, IL, USA
| | - Kurt Griffin
- Department of Pediatrics, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA
- Sanford Research, Sioux Falls, SD, USA
| | - Andrew T Hattersley
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Irl B Hirsch
- University of Washington School of Medicine, Seattle, WA, USA
| | - Marie-France Hivert
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Department of Medicine, Universite de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Korey K Hood
- Stanford University School of Medicine, Stanford, CA, USA
| | - Jami L Josefson
- Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Soo Heon Kwak
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Lori M Laffel
- Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA
| | - Siew S Lim
- Eastern Health Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Ruth J F Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ronald C W Ma
- Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, China
| | | | | | - James B Meigs
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Shivani Misra
- Division of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Diabetes & Endocrinology, Imperial College Healthcare NHS Trust, London, UK
| | - Viswanathan Mohan
- Department of Diabetology, Madras Diabetes Research Foundation & Dr. Mohan's Diabetes Specialities Centre, Chennai, India
| | - Rinki Murphy
- Department of Medicine, Faculty of Medicine and Health Sciences, University of Auckland, Auckland, New Zealand
- Auckland Diabetes Centre, Te Whatu Ora Health New Zealand, Auckland, New Zealand
- Medical Bariatric Service, Te Whatu Ora Counties, Health New Zealand, Auckland, New Zealand
| | - Richard Oram
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Katharine R Owen
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Susan E Ozanne
- University of Cambridge, Metabolic Research Laboratories and MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, Cambridge, UK
| | - Ewan R Pearson
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Wei Perng
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Toni I Pollin
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Rodica Pop-Busui
- Department of Internal Medicine, Division of Metabolism, Endocrinology and Diabetes, University of Michigan, Ann Arbor, MI, USA
| | | | | | - Maria J Redondo
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
- Division of Pediatric Diabetes and Endocrinology, Texas Children's Hospital, Houston, TX, USA
| | - Rebecca M Reynolds
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Robert K Semple
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | | | - Emily K Sims
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arianne Sweeting
- Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
- Department of Endocrinology, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
| | - Tiinamaija Tuomi
- Helsinki University Hospital, Abdominal Centre/Endocrinology, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Miriam S Udler
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kimberly K Vesco
- Kaiser Permanente Northwest, Kaiser Permanente Center for Health Research, Portland, OR, USA
| | - Tina Vilsbøll
- Clinial Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Robert Wagner
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Department of Endocrinology and Diabetology, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Paul W Franks
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden.
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark.
| |
Collapse
|
13
|
Weinstein JM, Urick B, Pathak S, Fuller KA, Albright J, Stürmer T, Buse JB, Kahkoska AR. Impact of Continuous Glucose Monitoring Initiation on Emergency Health Services Utilization. Diabetes Care 2023; 46:e146-e147. [PMID: 37335978 PMCID: PMC10369120 DOI: 10.2337/dc23-0341] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 05/11/2023] [Indexed: 06/21/2023]
Affiliation(s)
- Joshua M. Weinstein
- Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Ben Urick
- Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Prime Therapeutics, LLC, Eagan, MN
| | - Shweta Pathak
- Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Kathryn A. Fuller
- Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | - Til Stürmer
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - John B. Buse
- Division of Endocrinology and Metabolism, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Anna R. Kahkoska
- Division of Endocrinology and Metabolism, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Center for Aging and Health, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC
| |
Collapse
|
14
|
Huang ES, Sinclair A, Conlin PR, Cukierman-Yaffe T, Hirsch IB, Huisingh-Scheetz M, Kahkoska AR, Laffel L, Lee AK, Lee S, Lipska K, Meneilly G, Pandya N, Peek ME, Peters A, Pratley RE, Sherifali D, Toschi E, Umpierrez G, Weinstock RS, Munshi M. The Growing Role of Technology in the Care of Older Adults With Diabetes. Diabetes Care 2023; 46:1455-1463. [PMID: 37471606 PMCID: PMC10369127 DOI: 10.2337/dci23-0021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 05/24/2023] [Indexed: 07/22/2023]
Abstract
The integration of technologies such as continuous glucose monitors, insulin pumps, and smart pens into diabetes management has the potential to support the transformation of health care services that provide a higher quality of diabetes care, lower costs and administrative burdens, and greater empowerment for people with diabetes and their caregivers. Among people with diabetes, older adults are a distinct subpopulation in terms of their clinical heterogeneity, care priorities, and technology integration. The scientific evidence and clinical experience with these technologies among older adults are growing but are still modest. In this review, we describe the current knowledge regarding the impact of technology in older adults with diabetes, identify major barriers to the use of existing and emerging technologies, describe areas of care that could be optimized by technology, and identify areas for future research to fulfill the potential promise of evidence-based technology integrated into care for this important population.
Collapse
Affiliation(s)
| | | | - Paul R. Conlin
- Harvard Medical School, Boston, MA
- Veteran Affairs Boston Healthcare System, Boston, MA
| | - Tali Cukierman-Yaffe
- Division of Endocrinology, Diabetes, and Metabolism, Ramat Gan, Israel
- Sheba Medical Centre, Ramat Gan, Israel
- Epidemiology Department, Sackler Faculty of Medicine, Herczeg Institute on Aging, Tel Aviv University, Tel Aviv, Israel
| | | | | | | | | | | | - Sei Lee
- University of California San Francisco, San Francisco, CA
| | | | - Graydon Meneilly
- University of British Columbia, Vancouver, British Columbia, Canada
| | - Naushira Pandya
- Department of Geriatrics, Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Ft. Lauderdale, FL
| | | | - Anne Peters
- University of Southern California, Los Angeles, CA
| | - Richard E. Pratley
- AdventHealth Diabetes Institute, AdventHealth Translational Research Institute, AdventHealth, Orlando, FL
| | | | | | | | | | | |
Collapse
|
15
|
Weinstein JM, Berkowitz SA, Pratley RE, Shah KS, Kahkoska AR. Statistically Adjusting for Wear Time in Randomized Trials of Continuous Glucose Monitors as a Complement to Intent-to-Treat and As-Treated Analyses: Application and Evaluation in Two Trials. Diabetes Technol Ther 2023; 25:457-466. [PMID: 36999890 PMCID: PMC10398732 DOI: 10.1089/dia.2023.0029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/01/2023]
Abstract
Background: Randomized trials of continuous glucose monitoring (CGM) often estimate treatment effects using standard intent-to-treat (ITT) analyses. We explored how adjusting for CGM-measured wear time could complement existing analyses by estimating the effect of receiving and using CGM 100% of the time. Methods: We analyzed data from two 6-month CGM trials spanning diverse ages, the Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) and CGM Intervention in Teens and Young Adults with Type 1 Diabetes (CITY) Studies. To adjust the ITT estimates for CGM use, as measured by wear time, we used an instrumental variable (IV) approach with the treatment assignment as an instrument. Outcomes included (1) time in range ([TIR] 70-180 mg/dL), time below range ([TBR] ≤70 mg/dL), and time above range ([TAR] ≥250 mg/dL). We estimated outcomes based on CGM use in the last 28 days of the trial and the full trial. Findings: In the WISDM study, the wear time rates over the 28-day window and full trial period were 93.1% (standard deviation [SD]: 20.4) and 94.5% (SD: 11.9), respectively. In the CITY study, the wear time rates over the 28-day window and full trial period were 82.2% (SD: 26.5) and 83.1% (SD: 21.5), respectively. IV-based estimates for the effect of CGM on TIR, TBR, and TAR suggested greater improvements in glycemic management than the ITT counterparts. The magnitude of the differences was proportional to the level of wear time observed in the trials. Interpretation: In trials of CGM use, the effect of variable wear time is non-negligible. By providing adherence-adjusted estimates, the IV approach may have additional utility for individual clinical decision-making.
Collapse
Affiliation(s)
- Joshua M. Weinstein
- Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Seth A. Berkowitz
- Division of General Medicine and Clinical Epidemiology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Kushal S. Shah
- Department of Biostatistics, and Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Anna R. Kahkoska
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Center for Aging and Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Division of Endocrinology and Metabolism, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA
| |
Collapse
|
16
|
Alexopoulos AS, Crowley MJ, Kahkoska AR. Diabetes Medication Changes in Older Adults With Type 2 Diabetes: Insights Into Physician Factors and Questions Ahead. Diabetes Care 2023; 46:1137-1139. [PMID: 37220268 DOI: 10.2337/dci23-0017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Affiliation(s)
- Anastasia-Stefania Alexopoulos
- Division of Endocrinology, Department of Medicine, Duke University, Durham, NC
- Durham Veterans Affairs Center of Innovation to Accelerate Discovery and Practice Transformation, Durham, NC
| | - Matthew J Crowley
- Division of Endocrinology, Department of Medicine, Duke University, Durham, NC
- Durham Veterans Affairs Center of Innovation to Accelerate Discovery and Practice Transformation, Durham, NC
| | - Anna R Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Division of Endocrinology and Metabolism, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Center for Aging and Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| |
Collapse
|
17
|
Kahkoska AR, Cristello Sarteau A, Crowley MJ. Delivering on the Promise of Technology to Augment Behavioral Interventions in Type 2 Diabetes. Diabetes Care 2023; 46:918-920. [PMID: 37185694 DOI: 10.2337/dci23-0009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Affiliation(s)
- Anna R Kahkoska
- 1Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC
- 2Division of Endocrinology and Metabolism, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | - Matthew J Crowley
- 3Center for Health Services Research in Primary Care, Durham Veterans Affairs Medical Center, Durham, NC
- 4Department of Medicine, Duke University, Durham, NC
| |
Collapse
|
18
|
Shirazi D, Haudenschild C, Lynch DH, Fanous M, Kahkoska AR, Jimenez D, Spangler H, Driesse T, Batsis JA. Obesity, multiple chronic conditions, and the relationship with physical function: Data from the national health and aging trends survey. Arch Gerontol Geriatr 2023; 107:104913. [PMID: 36565604 PMCID: PMC9975009 DOI: 10.1016/j.archger.2022.104913] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/17/2022] [Accepted: 12/18/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND The population of older adults living with multiple chronic conditions (MCC) continues to grow. MCC is independently associated with functional limitation and obesity. The aim of our study was to evaluate the association between obesity and MCC, and secondarily, the combined presence of obesity and functional limitations with MCC. METHODS We analyzed cross-sectional survey data from the National Health and Aging Trends Survey (NHATS) 2011 baseline data, a nationally representative Medicare beneficiary cohort of adults in the United States. We evaluated the coexistent prevalence of obesity and MCC overall, and by standard body mass index (BMI) categories. We then evaluated the prevalence of functional limitations (mobility, self-care, and household activities) and Fried-defined frailty status in persons with a BMI ≥ 30 kg/m2. Logistic regression was used to measure the association between MCC and BMI, and functional limitations and MCC among those with obesity. RESULTS In the 6,600 participants, the prevalence of concurrent obesity and MCC was 30.4%. Of those with obesity, the prevalence of MCC was 84.0%, and were more likely to have MCC (adjusted OR: 2.17, 95% CI 1.86, 2.54) compared to a normal BMI. Obesity and functional limitations or frailty were more likely have MCC than individuals with obesity alone. CONCLUSIONS We found that individuals with obesity is strongly associated with MCC and that functional limitations and frailty status have a greater association with having MCC than individuals with obesity without MCC. Future longitudinal analyses are needed to ascertain this relationship.
Collapse
Affiliation(s)
- Daniela Shirazi
- Division of Geriatric Medicine, University of North Carolina at Chapel Hill, 5017 Old Clinic Building, Chapel Hill, NC 27517, United States; California University of Science and Medicine, CA, United States
| | | | - David H Lynch
- Division of Geriatric Medicine, University of North Carolina at Chapel Hill, 5017 Old Clinic Building, Chapel Hill, NC 27517, United States
| | - Marco Fanous
- Division of Geriatric Medicine, University of North Carolina at Chapel Hill, 5017 Old Clinic Building, Chapel Hill, NC 27517, United States
| | - Anna R Kahkoska
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, United States
| | - Daniel Jimenez
- University of Miami Miller School of Medicine, MI, United States
| | - Hillary Spangler
- Division of Geriatric Medicine, University of North Carolina at Chapel Hill, 5017 Old Clinic Building, Chapel Hill, NC 27517, United States
| | - Tiffany Driesse
- Division of Geriatric Medicine, University of North Carolina at Chapel Hill, 5017 Old Clinic Building, Chapel Hill, NC 27517, United States
| | - John A Batsis
- Division of Geriatric Medicine, University of North Carolina at Chapel Hill, 5017 Old Clinic Building, Chapel Hill, NC 27517, United States; Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, United States.
| |
Collapse
|
19
|
Pathak S, Kearin K, Kahkoska AR, Fuller KA, Staats B, Albright J, Stürmer T, Buse JB, Urick BY. Impact of Expanding Access to Continuous Glucose Monitoring Systems Among Insulin Users with Type 1 or Type 2 Diabetes. Diabetes Technol Ther 2023; 25:169-177. [PMID: 36480256 PMCID: PMC10081703 DOI: 10.1089/dia.2022.0418] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background: Despite increased use of continuous glucose monitoring (CGM) systems, studies to quantify patterns of CGM use are limited. In December 2018, a policy change by a commercial insurer expanded coverage of CGM through the pharmacy benefit, creating an opportunity to evaluate the impact of this change on CGM utilization. Research Design and Methods: Pharmacy and medical claims from 2016 to 2020 were used to estimate the prevalence of CGM use among insulin users with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) before and after the policy change. Change in CGM use was assessed using an interrupted time series design. Results: At the beginning of the study period, 18.8% of T1DM patients and 1.2% of T2DM patients used CGM. Use rose to 30.5% and 6.6% in the quarter before the policy change. The policy resulted in an immediate 9.5% (P < 0.0001) and 2.8% (P < 0.0001) change in use and increased the rate of quarterly change by 0.5% (P = 0.002) and 0.8% (P < 0.0001). At the end of the study period, 58.2% and 14.9% of T1DM and T2DM patients used CGM. Conclusion: CGM use significantly increased after addition to the pharmacy benefit. Rate of change in CGM use was lower in T1DM compared to the T2DM population, but overall use remained higher among patients with T1DM. Increased CGM use in the population studied aligns with those whose clinical guidelines suggest would most likely benefit. Additional work is needed to evaluate the impact of this benefit change on health care spending and outcomes.
Collapse
Affiliation(s)
- Shweta Pathak
- Cecil G. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Kristina Kearin
- University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina, USA
| | - Anna R. Kahkoska
- Department of Nutrition, University of North Carolina Gillings School of Global Public Health, Chapel Hill, North Carolina, USA
| | - Kathryn A. Fuller
- University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina, USA
| | - Bradley Staats
- University of North Carolina Kenan-Flagler Business School, Chapel Hill, North Carolina, USA
| | - Joseph Albright
- BlueCross BlueShield North Carolina, Durham, North Carolina, USA
| | - Til Stürmer
- Department of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, North Carolina, USA
| | - John B. Buse
- Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA
| | - Benjamin Y. Urick
- University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina, USA
- Prime Therapeutics, LLC, Eagan, Minnesota, USA
| |
Collapse
|
20
|
Kahkoska AR, Smith C, Thambuluru S, Weinstein J, Batsis JA, Pratley R, Weinstock RS, Young LA, Hassmiller Lich K. "Nothing is linear": Characterizing the determinants and dynamics of CGM use in older adults with type 1 diabetes. Diabetes Res Clin Pract 2023; 196:110204. [PMID: 36509180 PMCID: PMC9974816 DOI: 10.1016/j.diabres.2022.110204] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 11/29/2022] [Accepted: 12/04/2022] [Indexed: 12/13/2022]
Abstract
AIMS Continuous glucose monitoring (CGM) can reduce hypoglycemia in older adults with type 1 diabetes (T1D). We aimed to characterize factors that influence effective use in this age group. METHODS Older adults with type T1D (age ≥ 65) and their caregivers participated in one of a series of parallel group model building workshops, a participatory approach to system dynamics involving drawing and scripted group activities. Data were synthesized in a qualitative model of the hypothesized system of factors producing distinct patterns of CGM use in older adults. The model was validated through virtual follow-up interviews. RESULTS Data were collected from 33 participants (four patient-caregiver dyads, mean age 73.8 ± 4.4 years [range 66-85 years]; 16 % non-CGM users, 79 % pump users). The system model delineates drivers of CGM uptake, drivers of ongoing CGM use, and feedback loops that either reinforce or counteract future CGM use. Participants emphasized the importance of different sets of feedback loops at different points in the duration of CGM use. CONCLUSIONS The holistic system model underscores that factors and feedback loops driving effective CGM use in older adults are both individualized and dynamic (e.g., changing over time), suggesting opportunities for staged and tailored age-specific education and support.
Collapse
Affiliation(s)
- Anna R Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Division of Endocrinology and Metabolism, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Center for Aging and Health, School of Medicine, University of North Carolina at Chapel Hill, NC, USA.
| | - Cambray Smith
- Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Sirisha Thambuluru
- Division of Endocrinology and Metabolism, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Joshua Weinstein
- Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - John A Batsis
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Division of Geriatric Medicine, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Center for Aging and Health, School of Medicine, University of North Carolina at Chapel Hill, NC, USA.
| | - Richard Pratley
- AdventHealth Translational Research Institute, Orlando, FL, USA.
| | | | - Laura A Young
- Division of Endocrinology and Metabolism, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Kristen Hassmiller Lich
- Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| |
Collapse
|
21
|
Kahkoska AR, Freeman NLB, Jones EP, Shirazi D, Browder S, Page A, Sperger J, Zikry TM, Yu F, Busby-Whitehead J, Kosorok MR, Batsis JA. Individualized interventions and precision health: Lessons learned from a systematic review and implications for analytics-driven geriatric research. J Am Geriatr Soc 2023; 71:383-393. [PMID: 36524627 PMCID: PMC10037848 DOI: 10.1111/jgs.18141] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 09/16/2022] [Accepted: 10/22/2022] [Indexed: 12/23/2022]
Abstract
Older adults are characterized by profound clinical heterogeneity. When designing and delivering interventions, there exist multiple approaches to account for heterogeneity. We present the results of a systematic review of data-driven, personalized interventions in older adults, which serves as a use case to distinguish the conceptual and methodologic differences between individualized intervention delivery and precision health-derived interventions. We define individualized interventions as those where all participants received the same parent intervention, modified on a case-by-case basis and using an evidence-based protocol, supplemented by clinical judgment as appropriate, while precision health-derived interventions are those that tailor care to individuals whereby the strategy for how to tailor care was determined through data-driven, precision health analytics. We discuss how their integration may offer new opportunities for analytics-based geriatric medicine that accommodates individual heterogeneity but allows for more flexible and resource-efficient population-level scaling.
Collapse
Affiliation(s)
- Anna R. Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Nikki L. B. Freeman
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Emily P. Jones
- Health Sciences Library, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Daniela Shirazi
- Department of Medicine, California University of Science and Medicine, Colton, California, USA
| | - Sydney Browder
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Annie Page
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - John Sperger
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Tarek M. Zikry
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Fei Yu
- School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Jan Busby-Whitehead
- Division of Geriatric Medicine, Department of Medicine, Center for Aging and Health, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Michael R. Kosorok
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States
| | - John A. Batsis
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Division of Geriatric Medicine, Department of Medicine, Center for Aging and Health, School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
| |
Collapse
|
22
|
Sarteau AC, Kahkoska AR, Crandell J, Igudesman D, Corbin KD, Kichler JC, Maahs DM, Muntis F, Pratley R, Seid M, Zaharieva D, Mayer-Davis E. More hypoglycemia not associated with increasing estimated adiposity in youth with type 1 diabetes. Pediatr Res 2023; 93:708-714. [PMID: 35729217 PMCID: PMC10958738 DOI: 10.1038/s41390-022-02129-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 04/08/2022] [Accepted: 05/17/2022] [Indexed: 11/09/2022]
Abstract
BACKGROUND Despite the widespread clinical perception that hypoglycemia may drive weight gain in youth with type 1 diabetes (T1D), there is an absence of published evidence supporting this hypothesis. METHODS We estimated the body fat percentage (eBFP) of 211 youth (HbA1c 8.0-13.0%, age 13-16) at baseline, 6, and 18 months of the Flexible Lifestyles Empowering Change trial using validated equations. Group-based trajectory modeling assigned adolescents to sex-specific eBFP groups. Using baseline 7-day blinded continuous glucose monitoring data, "more" vs. "less" percent time spent in hypoglycemia was defined by cut-points using sample median split and clinical guidelines. Adjusted logistic regression estimated the odds of membership in an increasing eBFP group comparing youth with more vs. less baseline hypoglycemia. RESULTS More time spent in clinical hypoglycemia (defined by median split) was associated with 0.29 the odds of increasing eBFP in females (95% CI: 0.12, 0.69; p = 0.005), and 0.33 the odds of stable/increasing eBFP in males (95% CI: 0.14, 0.78; p = 0.01). CONCLUSIONS Hypoglycemia may not be a major driver of weight gain in US youth with T1D and HbA1c ≥8.0. Further studies in different sub-groups are needed to clarify for whom hypoglycemia may drive weight gain and focus future etiological studies and interventions. IMPACT We contribute epidemiological evidence that hypoglycemia may not be a major driver of weight gain in US youth with type 1 diabetes and HbA1c ≥8.0% and highlight the need for studies to prospectively test this hypothesis rooted in clinical perception. Future research should examine the relationship between hypoglycemia and adiposity together with psychosocial, behavioral, and other clinical factors among sub-groups of youth with type 1 diabetes (i.e., who meet glycemic targets or experience a frequency/severity of hypoglycemia above a threshold) to further clarify for whom hypoglycemia may drive weight gain and progress etiological understanding of and interventions for healthy weight maintenance.
Collapse
Affiliation(s)
| | - Anna R Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jamie Crandell
- School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daria Igudesman
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Karen D Corbin
- Translational Research Institute, AdventHealth Orlando, Orlando, FL, USA
| | - Jessica C Kichler
- Department of Psychology, University of Windsor, Windsor, ON, Canada
| | - David M Maahs
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Diabetes Research Center and Health Research and Policy (Epidemiology), Stanford, CA, USA
| | - Frank Muntis
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Richard Pratley
- Translational Research Institute, AdventHealth Orlando, Orlando, FL, USA
| | - Michael Seid
- Cincinnati Children's Hospital Medical Center, University of Cincinnati Medical School, Cincinnati, OH, USA
| | - Dessi Zaharieva
- Stanford Diabetes Research Center and Health Research and Policy (Epidemiology), Stanford, CA, USA
| | - Elizabeth Mayer-Davis
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| |
Collapse
|
23
|
Everett EM, Wright D, Williams A, Divers J, Pihoker C, Liese AD, Bellatorre A, Kahkoska AR, Bell R, Mendoza J, Mayer-Davis E, Wisk LE. A Longitudinal View of Disparities in Insulin Pump Use Among Youth with Type 1 Diabetes: The SEARCH for Diabetes in Youth Study. Diabetes Technol Ther 2023; 25:131-139. [PMID: 36475821 PMCID: PMC9894603 DOI: 10.1089/dia.2022.0340] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Objective: To evaluate changes in insulin pump use over two decades in a national U.S. sample. Research Design and Methods: We used data from the SEARCH for Diabetes in Youth study to perform a serial cross-sectional analysis to evaluate changes in insulin pump use in participants <20 years old with type 1 diabetes by race/ethnicity and markers of socioeconomic status across four time periods between 2001 and 2019. Multivariable generalized estimating equations were used to assess insulin pump use. Temporal changes by subgroup were assessed through interactions. Results: Insulin pump use increased from 31.7% to 58.8%, but the disparities seen in pump use persisted and were unchanged across subgroups over time. Odds ratio for insulin pump use in Hispanic (0.57, confidence interval [95% CI] 0.45-0.73), Black (0.28, 95% CI 0.22-0.37), and Other race (0.49, 95% CI 0.32-0.76) participants were significantly lower than White participants. Those with ≤high school degree (0.39, 95% CI 0.31-0.47) and some college (0.68, 95% CI 0.58-0.79) had lower use compared to those with ≥bachelor's degree. Those with public insurance (0.84, 95% CI 0.70-1.00) had lower use than those with private insurance. Those with an annual household income <$25K (0.43, 95% CI 0.35-0.53), $25K-$49K (0.52, 95% CI 0.43-0.63), and $50K-$74K (0.79, 95% CI 0.66-0.94) had lower use compared to those with income ≥$75,000. Conclusion: Over the past two decades, there was no improvement in the racial, ethnic, and socioeconomic inequities in insulin pump use, despite an overall increase in use. Studies that evaluate barriers or test interventions to improve technology access are needed to address these persistent inequities.
Collapse
Affiliation(s)
- Estelle M. Everett
- Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles. California, USA
- Division of General Internal Medicine & Health Services Research, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles. California, USA
- VA Greater Los Angeles Healthcare System, Los Angeles. California, USA
| | - Davene Wright
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
| | | | - Jasmin Divers
- Division of Health Services Research, Department of Foundations of Medicine, New York University Long Island School of Medicine, Mineola, New York, USA
| | - Catherine Pihoker
- Department of Pediatrics, University of Washington, Seattle, Washington, USA
| | - Angela D. Liese
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA
| | - Anna Bellatorre
- University of Colorado Denver Lifecourse Epidemiology of Adiposity and Diabetes Center, Aurora, Colorado, USA
| | - Anna R. Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Ronny Bell
- Department of Social Sciences and Health Policy, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Jason Mendoza
- Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
- Department of Pediatrics, University of Washington, Seattle, Washington, USA
| | - Elizabeth Mayer-Davis
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Lauren E. Wisk
- Division of General Internal Medicine & Health Services Research, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles. California, USA
- Department of Health Policy and Management, Fielding School of Public Health, University of California, Los Angeles, Los Angeles. California, USA
| |
Collapse
|
24
|
Kahkoska AR, Shah KS, Kosorok MR, Miller KM, Rickels M, Weinstock RS, Young LA, Pratley RE. Estimation of a Machine Learning-Based Decision Rule to Reduce Hypoglycemia Among Older Adults With Type 1 Diabetes: A Post Hoc Analysis of Continuous Glucose Monitoring in the WISDM Study. J Diabetes Sci Technol 2023:19322968221149040. [PMID: 36629330 DOI: 10.1177/19322968221149040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
BACKGROUND The Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study demonstrated continuous glucose monitoring (CGM) reduced hypoglycemia over 6 months among older adults with type 1 diabetes (T1D) compared with blood glucose monitoring (BGM). We explored heterogeneous treatment effects of CGM on hypoglycemia by formulating a data-driven decision rule that selects an intervention (ie, CGM vs BGM) to minimize percentage of time <70 mg/dL for each individual WISDM participant. METHOD The precision medicine analyses used data from participants with complete data (n = 194 older adults, including those who received CGM [n = 100] and BGM [n = 94] in the trial). Policy tree and decision list algorithms were fit with 14 baseline demographic, clinical, and laboratory measures. The primary outcome was CGM-measured percentage of time spent in hypoglycemic range (<70 mg/dL), and the decision rule assigned participants to a subgroup reflecting the treatment estimated to minimize this outcome across all follow-up visits. RESULTS The optimal decision rule was found to be a decision list with 3 steps. The first step moved WISDM participants with baseline time-below range >1.35% and no detectable C-peptide levels to the CGM subgroup (n = 139), and the second step moved WISDM participants with a baseline time-below range of >6.45% to the CGM subgroup (n = 18). The remaining participants (n = 37) were left in the BGM subgroup. Compared with the BGM subgroup (n = 37; 19%), the group for whom CGM minimized hypoglycemia (n = 157; 81%) had more baseline hypoglycemia, a lower proportion of detectable C-peptide, higher glycemic variability, longer disease duration, and higher proportion of insulin pump use. CONCLUSIONS The decision rule underscores the benefits of CGM for older adults to reduce hypoglycemia. Diagnostic CGM and laboratory markers may inform decision-making surrounding therapeutic CGM and identify older adults for whom CGM may be a critical intervention to reduce hypoglycemia.
Collapse
Affiliation(s)
- Anna R Kahkoska
- Department of Nutrition, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Center for Aging and Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kushal S Shah
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michael R Kosorok
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Michael Rickels
- Rodebaugh Diabetes Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ruth S Weinstock
- Division of Endocrinology, Diabetes, and Metabolism, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Laura A Young
- Division of Endocrinology and Metabolism, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | |
Collapse
|
25
|
Affiliation(s)
- Anna R Kahkoska
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
| | - Nikki L B Freeman
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
| | - Kristen Hassmiller Lich
- Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill
| |
Collapse
|
26
|
Weinstein JM, Kahkoska AR, Berkowitz SA. Food Insecurity, Missed Workdays, And Hospitalizations Among Working-Age US Adults With Diabetes. Health Aff (Millwood) 2022; 41:1045-1052. [PMID: 35787082 PMCID: PMC9840294 DOI: 10.1377/hlthaff.2021.01744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Food insecurity is associated with poor clinical outcomes among adults with diabetes, but associations with nonclinical outcomes, such as missed work, have not been well characterized. Our objective was to assess the associations between food insecurity, health-related missed workdays, and overnight hospitalizations. We pooled National Health Interview Survey data from the period 2011-18 to analyze food insecurity among 13,116 US adults ages 18-65 who had diabetes. Experiencing food insecurity, compared with being food secure, was associated with increased odds of reporting any health-related missed workdays, more than twice the rate of health-related missed workdays, and increased odds of overnight hospitalization within the prior twelve months. There was no significant association between food insecurity and the number of nights spent hospitalized. These findings underscore the broad impacts of food insecurity on health and wellness for working-age adults with diabetes. When weighing the costs and benefits of proposed interventions to address food insecurity, policy makers should consider potential benefits related to productivity in addition to implications for health care use.
Collapse
Affiliation(s)
- Joshua M. Weinstein
- Department of Health Policy and Management, Gillings School
of Global Public Health, University of North Carolina at Chapel Hill
| | - Anna R. Kahkoska
- Department of Nutrition, Gillings School of Global Public
Health, University of North Carolina at Chapel Hill
| | - Seth A. Berkowitz
- Division of General Medicine and Clinical Epidemiology,
Department of Medicine, University of North Carolina at Chapel Hill School of
Medicine, Chapel Hill, NC
| |
Collapse
|
27
|
Abstract
This cross-sectional study investigates age, probability of continuous glucose monitoring use, and their association with glycemic control across the lifespan among US individuals with type 1 diabetes.
Collapse
Affiliation(s)
- Joshua M. Weinstein
- Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill
| | - Anna R. Kahkoska
- Department of Nutrition, University of North Carolina, Chapel Hill
| |
Collapse
|
28
|
Irwin A, Igudesman D, Crandell J, Kichler JC, Kahkoska AR, Burger K, Zaharieva DP, Addala A, Mayer-Davis EJ. Mindfulness, disordered eating, and impulsivity in relation to glycemia among adolescents with type 1 diabetes and suboptimal glycemia from the Flexible Lifestyles Empowering Change (FLEX) intervention trial. Pediatr Diabetes 2022; 23:516-526. [PMID: 35297136 PMCID: PMC9268578 DOI: 10.1111/pedi.13334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 02/28/2022] [Accepted: 03/14/2022] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVE To assess the relationship between mindfulness and glycemia among adolescents with type 1 diabetes (T1D) with suboptimal glycemia, and evaluate the potential mediation by ingestive behaviors, including disordered eating, and impulsivity. RESEARCH DESIGN AND METHODS We used linear mixed models for hemoglobin A1c (HbA1c) and linear regression for continuous glucose monitoring (CGM) to study the relationship of mindfulness [Child and Adolescent Mindfulness Measure (CAMM)] and glycemia in adolescents with T1D from the 18-month Flexible Lifestyles Empowering Change (FLEX) trial. We tested for mediation of the mindfulness-glycemia relationship by ingestive behaviors, including disordered eating (Diabetes Eating Problem Survey-Revised), restrained eating, and emotional eating (Dutch Eating Behavior Questionnaire); and impulsivity (total, attentional, and motor, Barrett Impulsiveness Scale). RESULTS At baseline, participants (n = 152) had a mean age of 14.9 ± 1.1 years and HbA1c of 9.4 ± 1.2% [79 ± 13 mmol/mol]. The majority of adolescents were non-Hispanic white (83.6%), 50.7% were female, and 73.0% used insulin pumps. From adjusted mixed models, a 5-point increase in mindfulness scores was associated with a -0.19% (95%CI -0.29, -0.08, p = 0.0006) reduction in HbA1c. We did not find statistically significant associations between mindfulness and CGM metrics. Mediation of the relationship between mindfulness and HbA1c by ingestive behaviors and impulsivity was not found to be statistically significant. CONCLUSIONS Among adolescents with T1D and suboptimal glycemia, increased mindfulness was associated with lower HbA1c levels. Future studies may consider mindfulness-based interventions as a component of treatment for improving glycemia among adolescents with T1D, though more data are needed to assess feasibility and efficacy.
Collapse
Affiliation(s)
- Ashley Irwin
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Daria Igudesman
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Jamie Crandell
- Department of Biostatistics and School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | | | - Anna R. Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Kyle Burger
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Dessi P. Zaharieva
- Department of Pediatrics, Division of Endocrinology, Stanford University, Stanford, CA 94305
| | - Ananta Addala
- Department of Pediatrics, Division of Endocrinology, Stanford University, Stanford, CA 94305
| | - Elizabeth J. Mayer-Davis
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599,Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| |
Collapse
|
29
|
Lynch DH, Petersen CL, Fanous MM, Spangler HB, Kahkoska AR, Jimenez D, Batsis JA. The relationship between multimorbidity, obesity and functional impairment in older adults. J Am Geriatr Soc 2022; 70:1442-1449. [PMID: 35113453 PMCID: PMC9106850 DOI: 10.1111/jgs.17683] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 12/15/2021] [Accepted: 01/07/2022] [Indexed: 01/31/2023]
Abstract
BACKGROUND Declining mortality rates and an aging population have contributed to increasing rates of multimorbidity (MM) in the United States. MM is strongly associated with a decline in physical function. Obesity is an important risk factor for the development of MM, and its prevalence continues to rise. Our study aimed to evaluate the associations between obesity, MM, and rates of functional limitations in older adults. METHODS We analyzed body mass index (BMI) and self-reported comorbidity data from 7261 individuals aged ≥60 years from the National Health and Nutrition Examination Surveys 2005-2014. Weight status was defined based on standard BMI categories. MM was defined as 2 or more comorbidities, while functional limitations were self-reported. Adjusted logistic regression quantified the association between standard BMI categories and MM. We also examined the difference in the prevalence of limitations between those with and without MM. RESULTS The overall proportion of individuals with concomitant MM and obesity was 27.0%. Compared to a normal BMI, older adults with obesity had higher odds of MM (Prevalence odds ratio 1.79, 95% CI 1.49, 2.12). Overall, 67.5% of patients with MM also reported a functional limitation, with rates of functional limitation increasing with increasing BMI. When evaluating functional limitations in those with MM by BMI class, 90% of patients classified as severely obese (BMI ≥40 kg/m2 ) with MM also had a concomitant functional limitation. CONCLUSIONS Compared to normal weight status, obesity is associated with an increased burden of MM and functional limitation among older adults. Our results underscore the importance of identifying and addressing obesity, MM, and functional limitation patterns and the need for evidence-based interventions that address all three conditions in this population.
Collapse
Affiliation(s)
- David H Lynch
- Division of Geriatric Medicine and Center for Aging and Health, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Curtis L Petersen
- The Dartmouth Institute for Health Policy, Dartmouth College, Hanover, New Hampshire, USA.,Quantitative Biomedical Sciences Program, Dartmouth Geisel School of Medicine, Hanover, New Hampshire, USA
| | - Marco M Fanous
- Division of Geriatric Medicine and Center for Aging and Health, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Hillary B Spangler
- Division of Geriatric Medicine and Center for Aging and Health, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Anna R Kahkoska
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Daniel Jimenez
- Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, Florida, USA
| | - John A Batsis
- Division of Geriatric Medicine and Center for Aging and Health, University of North Carolina, Chapel Hill, North Carolina, USA.,Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
| |
Collapse
|
30
|
Nolan JJ, Kahkoska AR, Semnani-Azad Z, Hivert MF, Ji L, Mohan V, Eckel RH, Philipson LH, Rich SS, Gruber C, Franks PW. ADA/EASD Precision Medicine in Diabetes Initiative: An International Perspective and Future Vision for Precision Medicine in Diabetes. Diabetes Care 2022; 45:261-266. [PMID: 35050364 PMCID: PMC8914425 DOI: 10.2337/dc21-2216] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 11/08/2021] [Indexed: 02/03/2023]
Affiliation(s)
- John J. Nolan
- Department of Clinical Medicine, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Anna R. Kahkoska
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC
| | - Zhila Semnani-Azad
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Marie-France Hivert
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, and Diabetes Unit, Massachusetts General Hospital, Boston, MA
| | - Linong Ji
- Peking University Diabetes Center, Peking University People’s Hospital, Beijing, China
| | - Viswanathan Mohan
- Dr. Mohan’s Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - Robert H. Eckel
- University of Colorado Anschutz College of Medicine, Aurora, CO
| | - Louis H. Philipson
- Departments of Medicine and Pediatrics, The University of Chicago, Chicago, IL
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA
| | | | - Paul W. Franks
- Lund University Diabetes Center, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Harvard T.H. Chan School of Public Health, Boston, MA
- Novo Nordisk Foundation, Copenhagen, Denmark
| |
Collapse
|
31
|
Kahkoska AR, Pokaprakarn T, Alexander GR, Crume TL, Dabelea D, Divers J, Dolan LM, Jensen ET, Lawrence JM, Marcovina S, Mottl AK, Pihoker C, Saydah SH, Kosorok MR, Mayer-Davis EJ. The Impact of Racial and Ethnic Health Disparities in Diabetes Management on Clinical Outcomes: A Reinforcement Learning Analysis of Health Inequity Among Youth and Young Adults in the SEARCH for Diabetes in Youth Study. Diabetes Care 2022; 45:108-118. [PMID: 34728528 PMCID: PMC8753766 DOI: 10.2337/dc21-0496] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 09/30/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To estimate difference in population-level glycemic control and the emergence of diabetes complications given a theoretical scenario in which non-White youth and young adults (YYA) with type 1 diabetes (T1D) receive and follow an equivalent distribution of diabetes treatment regimens as non-Hispanic White YYA. RESEARCH DESIGN AND METHODS Longitudinal data from YYA diagnosed 2002-2005 in the SEARCH for Diabetes in Youth Study were analyzed. Based on self-reported race/ethnicity, YYA were classified as non-White race or Hispanic ethnicity (non-White subgroup) versus non-Hispanic White race (White subgroup). In the White versus non-White subgroups, the propensity score models estimated treatment regimens, including patterns of insulin modality, self-monitored glucose frequency, and continuous glucose monitoring use. An analysis based on policy evaluation techniques in reinforcement learning estimated the effect of each treatment regimen on mean hemoglobin A1c (HbA1c) and the prevalence of diabetes complications for non-White YYA. RESULTS The study included 978 YYA. The sample was 47.5% female and 77.5% non-Hispanic White, with a mean age of 12.8 ± 2.4 years at diagnosis. The estimated population mean of longitudinal average HbA1c over visits was 9.2% and 8.2% for the non-White and White subgroup, respectively (difference of 0.9%). Within the non-White subgroup, mean HbA1c across visits was estimated to decrease by 0.33% (95% CI -0.45, -0.21) if these YYA received the distribution of diabetes treatment regimens of the White subgroup, explaining ∼35% of the estimated difference between the two subgroups. The non-White subgroup was also estimated to have a lower risk of developing diabetic retinopathy, diabetic kidney disease, and peripheral neuropathy with the White youth treatment regimen distribution (P < 0.05), although the low proportion of YYA who developed complications limited statistical power for risk estimations. CONCLUSIONS Mathematically modeling an equalized distribution of T1D self-management tools and technology accounted for part of but not all disparities in glycemic control between non-White and White YYA, underscoring the complexity of race and ethnicity-based health inequity.
Collapse
Affiliation(s)
- Anna R Kahkoska
- 1Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Teeranan Pokaprakarn
- 2Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - G Rumay Alexander
- 3School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Tessa L Crume
- 4Department of Epidemiology, Colorado School of Public Health, Aurora, CO
| | - Dana Dabelea
- 4Department of Epidemiology, Colorado School of Public Health, Aurora, CO.,5Department of Pediatrics, School of Medicine, University of Colorado, Aurora, CO
| | - Jasmin Divers
- 6Division of Health Services Research, Department of Foundations of Medicine, NYU Long Island School of Medicine, Mineola, NY
| | - Lawrence M Dolan
- 7Division of Endocrinology, Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Elizabeth T Jensen
- 8Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC
| | - Jean M Lawrence
- 9Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA
| | - Santica Marcovina
- 10Department of Medicine, Northwest Lipid Metabolism and Diabetes Research Laboratories, University of Washington, Seattle, WA
| | - Amy K Mottl
- 11Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | - Sharon H Saydah
- 13Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA
| | - Michael R Kosorok
- 2Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC.,14Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Elizabeth J Mayer-Davis
- 1Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC.,11Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC
| |
Collapse
|
32
|
Kahkoska AR, Sarteau AC, Igudesman D, Reboussin BA, Dabelea D, Dolan LM, Jensen E, Wadwa RP, Pihoker C, Mayer-Davis EJ. Association of Insulin Regimen and Estimated Body Fat Over Time among Youths and Young Adults with Type 1 Diabetes: The SEARCH for Diabetes in Youth Study. J Diabetes Res 2022; 2022:1054042. [PMID: 35127949 PMCID: PMC8816579 DOI: 10.1155/2022/1054042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 11/30/2021] [Accepted: 12/10/2021] [Indexed: 11/18/2022] Open
Abstract
AIMS To explore how changes in insulin regimen are associated with estimated adiposity over time among youths and young adults with type 1 diabetes and whether any associations differ according to sex. MATERIALS AND METHODS Longitudinal data were analyzed from youths and young adults with type 1 diabetes in the SEARCH for Diabetes in Youth study. Participants were classified according to insulin regimen categorized as exclusive pump ("pump only"), exclusive injections ("injections only"), injection-pump transition ("injections-pump"), or pump-injection transition ("pump-injections") for each follow-up visit completed. Estimated body fat percentage (eBFP) was calculated using validated equations. Sex-specific, linear mixed effects models examined the relationship between the insulin regimen group and change in eBFP during follow-up, adjusted for baseline eBFP, baseline insulin regimen, time-varying insulin dose, sociodemographic factors, and baseline HbA1c (≥9.0% vs. <9.0%). RESULTS The final sample included 284 females and 304 males, of whom 80% were non-Hispanic white with mean diagnosis age of 12.7 ± 2.4 years. In fully adjusted models for females, exclusive pump use over the study duration was associated with significantly greater increases in eBFP compared to exclusive use of injections (difference in rate of change = 0.023% increase per month, 95%CI = 0.01, 0.04). Injection-to-pump transitions and pump-to-injection transitions were also associated with greater increases in eBFP compared to exclusive use of injections (difference in rate of change = 0.02%, 95%CI = 0.004, 0.03, and 0.02%; 95%CI = 0.0001, 0.04, respectively). There was no relationship between the insulin regimen and eBFP among males. CONCLUSIONS Among females with type 1 diabetes, exclusive and partial pump use may have the unintended consequence of increasing adiposity over time compared to exclusive use of injections, independent of insulin dose.
Collapse
Affiliation(s)
- Anna R. Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Daria Igudesman
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Beth A. Reboussin
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Dana Dabelea
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Pediatrics, School of Medicine, University of Colorado, Aurora, CO, USA
| | - Lawrence M. Dolan
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Endocrinology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Elizabeth Jensen
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - R. Paul Wadwa
- Department of Pediatrics, School of Medicine, University of Colorado, Aurora, CO, USA
- Barbara Davis Center for Diabetes, University of Colorado School of Medicine, Aurora, CO, USA
| | - Catherine Pihoker
- Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Elizabeth J. Mayer-Davis
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| |
Collapse
|
33
|
Alexopoulos AS, Kahkoska AR, Pate V, Bradley MC, Niznik J, Thorpe C, Stürmer T, Buse J. Deintensification of Treatment With Sulfonylurea and Insulin After Severe Hypoglycemia Among Older Adults With Diabetes. JAMA Netw Open 2021; 4:e2132215. [PMID: 34726745 PMCID: PMC8564578 DOI: 10.1001/jamanetworkopen.2021.32215] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
IMPORTANCE Practice guidelines recommend deintensification of hypoglycemic agents among older adults with diabetes who are at high risk of hypoglycemia, yet real-world treatment deintensification practices are not well characterized. OBJECTIVE To examine the incidence of sulfonylurea and insulin deintensification after a hypoglycemia-associated emergency department (ED) visit or hospitalization among older adults with diabetes and to identify factors associated with deintensification of treatment. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study included a random sample of 20% of nationwide fee-for-service US Medicare beneficiaries aged 65 years and older with concurrent Medicare parts A, B, and D coverage between January 1, 2007, and December 31, 2017. Individuals with diabetes who had at least 1 hypoglycemia-associated ED visit or hospitalization were included. Data were analyzed from August 1, 2020, to August 1, 2021. EXPOSURES Baseline medication for the treatment of diabetes (sulfonylurea, insulin, or both). MAIN OUTCOMES AND MEASURES Incidence of treatment deintensification (yes or no) in the 100 days after a severe hypoglycemic episode requiring an ED visit or hospitalization, with treatment deintensification defined as (1) a decrease in sulfonylurea dose, (2) a change from long-acting to short-acting sulfonylurea (glipizide), (3) discontinuation of sulfonylurea, or (4) discontinuation of insulin based on pharmacy dispensing claims. RESULTS Among 76 278 distinct Medicare beneficiaries who had a hypoglycemia-associated ED visit or hospitalization, the mean (SD) age was 76.6 (7.6) years. Of 106 293 total hypoglycemic episodes requiring hospital attention, 69 084 (65.0%) occurred among women, 26 056 (24.5%) among Black individuals; 4761 (4.5%) among Hispanic individuals; 69 704 (65.6%) among White individuals; and 5772 (5.4%) among individuals of other races and ethnicities (comprising Asian, North American Native, unknown race or ethnicity, and unspecified race or ethnicity). A total of 32 074 episodes (30.2%) occurred among those receiving sulfonylurea only, 60 350 (56.8%) occurred among those receiving insulin only, and 13 869 (13.0%) occurred among those receiving both sulfonylurea and insulin. Treatment deintensification rates were highest among individuals receiving both sulfonylurea and insulin therapies at the time of their hypoglycemic episode (6677 episodes [48.1%]), followed by individuals receiving sulfonylurea only (14 192 episodes [44.2%]) and insulin only (14 495 episodes [24.0%]). Treatment deintensification rates increased between 2007 and 2017 (sulfonylurea only: from 41.4% to 49.7%; P < .001 for trend; insulin only: from 21.3% to 25.9%; P < .001 for trend; sulfonylurea and insulin: from 45.9% to 49.6%; P = .005 for trend). Lower socioeconomic status (as indicated by the receipt of low-income subsidies) was associated with lower odds of deintensification, regardless of baseline hypoglycemic regimen (sulfonylurea only: adjusted odds ratio [AOR], 0.74 [95% CI, 0.70-0.78]; insulin only: AOR, 0.71 [95% CI, 0.68-0.75]; sulfonylurea and insulin: AOR, 0.72 [95% CI, 0.66-0.78]). A number of patient factors were associated with higher odds of treatment deintensification: higher frailty (eg, ≥40% probability of needing assistance with activities of daily living among those receiving sulfonylurea and insulin: AOR, 1.50; 95% CI, 1.32-1.71), chronic kidney disease (eg, sulfonylurea and insulin: AOR, 1.29; 95% CI, 1.19-1.40), a history of falls (eg, sulfonylurea and insulin: AOR, 1.20; 95% CI, 1.09-1.33), and depression (eg, sulfonylurea and insulin: AOR, 1.11; 95% CI, 1.02-1.20). CONCLUSIONS AND RELEVANCE In this cohort study, deintensification of sulfonylurea and/or insulin therapy within 100 days after a hypoglycemia-associated ED visit or hospitalization occurred in fewer than 50% of older adults with diabetes; however, these deintensification rates may be increasing over time, and deintensification of insulin was likely underestimated because of challenges in capturing changes to insulin dosing using administrative claims data. These results suggest that greater efforts are needed to identify individuals at high risk of hypoglycemia to encourage appropriate treatment deintensification in accordance with current evidence.
Collapse
Affiliation(s)
- Anastasia-Stefania Alexopoulos
- Department of Medicine, Division of Endocrinology, Duke University, Durham, North Carolina
- Durham Veterans Affairs Center of Innovation to Accelerate Discovery and Practice Transformation, Durham, North Carolina
| | - Anna R. Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill
| | - Virginia Pate
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill
| | - Marie C. Bradley
- Division of Epidemiology, Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland
| | - Joshua Niznik
- Department of Medicine, Division of Geriatrics and Center for Aging and Health, University of North Carolina at Chapel Hill, Chapel Hill
- Division of Pharmaceutical Outcomes and Policy, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill
- Center of Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
| | - Carolyn Thorpe
- Division of Pharmaceutical Outcomes and Policy, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill
- Center of Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
| | - Til Stürmer
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill
| | - John Buse
- Department of Medicine, Division of Endocrinology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill
| |
Collapse
|
34
|
Liese AD, Reboussin BA, Kahkoska AR, Frongillo EA, Malik FS, Imperatore G, Saydah S, Bellatorre A, Lawrence JM, Dabelea D, Mendoza JA. Inequalities in Glycemic Control in Youth with Type 1 Diabetes Over Time: Intersectionality Between Socioeconomic Position and Race and Ethnicity. Ann Behav Med 2021; 56:461-471. [PMID: 34570884 DOI: 10.1093/abm/kaab086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND Racial/ethnic health inequities have been well-documented among youth and young adults with type 1 diabetes (T1D), yet little is known about how socioeconomic position (SEP) intersects with the risk marker of race/ethnicity to predict inequities in longitudinal glycemic control. PURPOSE To identify patterns of SEP, race/ethnicity, and clinical characteristics that differentiate hemoglobin A1c (HbA1c) trajectories among youth and young adults after T1D diagnosis. METHODS The SEARCH for Diabetes in Youth cohort includes youth with diabetes diagnosed from 2002 to 2006 and 2008 who were followed through 2015. We analyzed data from 1,313 youth and young adults with T1D with ≥3 HbA1c measures. Classification tree analysis identified patterns of baseline demographic, SEP, and clinical characteristic that best predicted HbA1c trajectories over an average of 8.3 years using group-based trajectory modeling. RESULTS Two HbA1c trajectories were identified: Trajectory 1 (77%) with lower baseline HbA1c and mild increases (from mean 7.4% to 8.4%) and Trajectory 2 (23%) with higher baseline HbA1c and major increases (from 8.5% to 11.2%). Race/ethnicity intersected with different SEP characteristics among non-Hispanic white (NHW) than in non-whites. Public health insurance predicted high-risk Trajectory 2 membership in non-whites, whereas parental education, household structure, diagnosis age and glucose checking frequency predicted membership for NHW youth and young adults. Two characteristics, race/ethnicity and parental education alone identified 80% of the Trajectory 2 members. CONCLUSIONS Race/ethnicity intersects with multiple SEP and clinical characteristics among youth and young adults with T1D, which is associated with particularly high risk of poor long-term glycemic control.
Collapse
Affiliation(s)
- Angela D Liese
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Beth A Reboussin
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Anna R Kahkoska
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Edward A Frongillo
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Faisal S Malik
- Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Giuseppina Imperatore
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, CDC, Atlanta, GA, USA
| | - Sharon Saydah
- Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, CDC, Atlanta, GA, USA
| | - Anna Bellatorre
- Department of Epidemiology and LEAD Center, Colorado School of Public Health, Aurora, CO, USA
| | - Jean M Lawrence
- Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA
| | - Dana Dabelea
- Department of Epidemiology and LEAD Center, Colorado School of Public Health, Aurora, CO, USA
| | - Jason A Mendoza
- Fred Hutchinson Cancer Research Center, University of Washington, and Seattle Children's Research Institute, Seattle, WA, USA
| |
Collapse
|
35
|
Kahkoska AR, Dabelea D. Diabetes in Youth: A Global Perspective. Endocrinol Metab Clin North Am 2021; 50:491-512. [PMID: 34399958 PMCID: PMC8374087 DOI: 10.1016/j.ecl.2021.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Diabetes is a common disease among pediatric populations in the United States and worldwide. The incidence of type 1 and type 2 diabetes is increasing, with disproportional increases in racial/ethnic subpopulations. As the prevalence of obesity continue to increase, type 2 diabetes now represents a major form of pediatric diabetes. The management of diabetes in youth centers on maintaining glycemic control to prevent acute and chronic complications. This article summarizes the epidemiology, etiology, management, and complications of type 1 and type 2 diabetes in youth, as well as future directions and opportunities.
Collapse
Affiliation(s)
- Anna R Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, McGavran-Greenberg Hall 2205A, Chapel Hill, NC 27599, USA.
| | - Dana Dabelea
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Colorado School of Public Health, University of Colorado School of Medicine, Anschutz Medical Campus, 13001 East 17th Avenue, Box B119, Room W3110, Aurora, CO 80045, USA
| |
Collapse
|
36
|
Freeman NLB, Sperger J, El-Zaatari H, Kahkoska AR, Lu M, Valancius M, Virkud AV, Zikry TM, Kosorok MR. Beyond Two Cultures: Cultural Infrastructure for Data-driven Decision Support. Obs Stud 2021; 7:77-94. [PMID: 35106520 PMCID: PMC8802367 DOI: 10.1353/obs.2021.0024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In the twenty years since Dr. Leo Breiman's incendiary paper Statistical Modeling: The Two Cultures was first published, algorithmic modeling techniques have gone from controversial to commonplace in the statistical community. While the widespread adoption of these methods as part of the contemporary statistician's toolkit is a testament to Dr. Breiman's vision, the number of high-profile failures of algorithmic models suggests that Dr. Breiman's final remark that "the emphasis needs to be on the problem and the data" has been less widely heeded. In the spirit of Dr. Breiman, we detail an emerging research community in statistics - data-driven decision support. We assert that to realize the full potential of decision support, broadly and in the context of precision health, will require a culture of social awareness and accountability, in addition to ongoing attention towards complex technical challenges.
Collapse
Affiliation(s)
- Nikki L B Freeman
- Department of Biostatistics, University of North Carolina at Chapel Hill
| | - John Sperger
- Department of Biostatistics, University of North Carolina at Chapel Hill
| | - Helal El-Zaatari
- Department of Biostatistics, University of North Carolina at Chapel Hill
| | - Anna R Kahkoska
- Department of Nutrition, University of North Carolina School of Medicine
| | - Minxin Lu
- Department of Biostatistics, University of North Carolina at Chapel Hill
| | - Michael Valancius
- Department of Biostatistics, University of North Carolina at Chapel Hill
| | - Arti V Virkud
- Department of Epidemiology, University of North Carolina at Chapel Hill
| | - Tarek M Zikry
- Department of Biostatistics, University of North Carolina at Chapel Hill
| | - Michael R Kosorok
- Department of Biostatistics, University of North Carolina at Chapel Hill
| |
Collapse
|
37
|
Kahkoska AR, Abrahamsen TJ, Alexander GC, Bennett TD, Chute CG, Haendel MA, Klein KR, Mehta H, Miller JD, Moffitt RA, Stürmer T, Kvist K, Buse JB. Association Between Glucagon-Like Peptide 1 Receptor Agonist and Sodium-Glucose Cotransporter 2 Inhibitor Use and COVID-19 Outcomes. Diabetes Care 2021; 44:1564-1572. [PMID: 34135013 PMCID: PMC8323175 DOI: 10.2337/dc21-0065] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 04/23/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To determine the respective associations of premorbid glucagon-like peptide-1 receptor agonist (GLP1-RA) and sodium-glucose cotransporter 2 inhibitor (SGLT2i) use, compared with premorbid dipeptidyl peptidase 4 inhibitor (DPP4i) use, with severity of outcomes in the setting of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. RESEARCH DESIGN AND METHODS We analyzed observational data from SARS-CoV-2-positive adults in the National COVID Cohort Collaborative (N3C), a multicenter, longitudinal U.S. cohort (January 2018-February 2021), with a prescription for GLP1-RA, SGLT2i, or DPP4i within 24 months of positive SARS-CoV-2 PCR test. The primary outcome was 60-day mortality, measured from positive SARS-CoV-2 test date. Secondary outcomes were total mortality during the observation period and emergency room visits, hospitalization, and mechanical ventilation within 14 days. Associations were quantified with odds ratios (ORs) estimated with targeted maximum likelihood estimation using a super learner approach, accounting for baseline characteristics. RESULTS The study included 12,446 individuals (53.4% female, 62.5% White, mean ± SD age 58.6 ± 13.1 years). The 60-day mortality was 3.11% (387 of 12,446), with 2.06% (138 of 6,692) for GLP1-RA use, 2.32% (85 of 3,665) for SGLT2i use, and 5.67% (199 of 3,511) for DPP4i use. Both GLP1-RA and SGLT2i use were associated with lower 60-day mortality compared with DPP4i use (OR 0.54 [95% CI 0.37-0.80] and 0.66 [0.50-0.86], respectively). Use of both medications was also associated with decreased total mortality, emergency room visits, and hospitalizations. CONCLUSIONS Among SARS-CoV-2-positive adults, premorbid GLP1-RA and SGLT2i use, compared with DPP4i use, was associated with lower odds of mortality and other adverse outcomes, although DPP4i users were older and generally sicker.
Collapse
Affiliation(s)
- Anna R Kahkoska
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | - G Caleb Alexander
- Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.,Division of General Internal Medicine, Johns Hopkins Medicine, Baltimore, MD
| | - Tellen D Bennett
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD
| | - Melissa A Haendel
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO
| | - Klara R Klein
- Division of Endocrinology and Metabolism, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC
| | - Hemalkumar Mehta
- Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Joshua D Miller
- Division of Endocrinology and Metabolism, Department of Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY
| | - Richard A Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Til Stürmer
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | - John B Buse
- Division of Endocrinology and Metabolism, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC .,NC Translational and Clinical Sciences Institute, University of North Carolina School of Medicine, Chapel Hill, NC
| | | |
Collapse
|
38
|
Wang J, Wang Z, Chen G, Wang Y, Ci T, Li H, Liu X, Zhou D, Kahkoska AR, Zhou Z, Meng H, Buse JB, Gu Z. Injectable Biodegradable Polymeric Complex for Glucose-Responsive Insulin Delivery. ACS Nano 2021; 15:4294-4304. [PMID: 33685124 PMCID: PMC8210813 DOI: 10.1021/acsnano.0c07291] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Insulin therapy is the central component of treatment for type 1 and advanced type 2 diabetes; however, its narrow therapeutic window is associated with a risk of severe hypoglycemia. A glucose-responsive carrier that demonstrates consistent and slow basal insulin release under a normoglycemic condition and accelerated insulin release in response to hyperglycemia in real-time could offer effective blood glucose regulation with reduced risk of hypoglycemia. Here, we describe a poly(l-lysine)-derived biodegradable glucose-responsive cationic polymer for constructing polymer-insulin complexes for glucose-stimulated insulin delivery. The effects of the modification degree of arylboronic acid in the synthesized cationic polymer and polymer-to-insulin ratio on the glucose-dependent equilibrated free insulin level and the associated insulin release kinetics have been studied. In addition, the blood glucose regulation ability of these complexes and the associated glucose challenge-triggered insulin release are evaluated in type 1 diabetic mice.
Collapse
Affiliation(s)
- Jinqiang Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China
- Department of Bioengineering, University of California, Los Angeles, California 90095, United States
- California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - Zejun Wang
- Department of Bioengineering, University of California, Los Angeles, California 90095, United States
- California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - Guojun Chen
- Department of Bioengineering, University of California, Los Angeles, California 90095, United States
- California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
- Department of Biomedical Engineering, and the Rosalind & Morris Goodman Cancer Research Center, McGill University, Montreal, Quebec H3G 0B1, Canada
| | - Yanfang Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Tianyuan Ci
- Department of Bioengineering, University of California, Los Angeles, California 90095, United States
- California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - Hongjun Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Department of Bioengineering, University of California, Los Angeles, California 90095, United States
- California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - Xiangsheng Liu
- California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
- Division of NanoMedicine, Department of Medicine, David Geffen School of Medicine, Los Angeles, California 90095, United States
| | - Daojia Zhou
- Department of Bioengineering, University of California, Los Angeles, California 90095, United States
- California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
| | - Anna R Kahkoska
- Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina 27599, United States
| | - Zhuxian Zhou
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education and Center for Bionanoengineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China
| | - Huan Meng
- Department of Bioengineering, University of California, Los Angeles, California 90095, United States
- Division of NanoMedicine, Department of Medicine, David Geffen School of Medicine, Los Angeles, California 90095, United States
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, California 90024, United States
| | - John B Buse
- Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina 27599, United States
| | - Zhen Gu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310027, China
- Department of Bioengineering, University of California, Los Angeles, California 90095, United States
- California NanoSystems Institute, University of California, Los Angeles, California 90095, United States
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, California 90024, United States
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, China
- Zhejiang Laboratory of Systems & Precision Medicine, Zhejiang University Medical Center, Hangzhou 311121, China
| |
Collapse
|
39
|
Travia KR, Kahkoska AR, Igudesman D, Souris KJ, Beasley C, Mayer-Davis EJ. Impact of Hurricane Matthew on Diabetes Self-Management and Outcomes. N C Med J 2021; 82:100-107. [PMID: 33649123 DOI: 10.18043/ncm.82.2.100] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
BACKGROUND Individuals with diabetes require extensive self-management. Little is known about how Hurricane Matthew (Matthew) or Hurricane Florence (Florence) impacted diabetes self-management and outcomes in Robeson County, North Carolina. METHODS Mixed methods were used to assess the impact of hurricanes on diabetes self-management and outcomes. Individuals with diabetes were recruited for focus groups to understand the perceived impact on diabetes self-management. Health care providers were recruited for parallel key informant interviews. Mean hemoglobin A1c (HbA1c) and frequency of diabetic ketoacidosis (DKA) from hospital data six months before and after Matthew were compared using Student t-tests. RESULTS A demographic breakdown of 34.25% white, 21.70% Black or African American, and 21.38% American Indian or Alaska Native was observed from focus groups. Qualitative results highlight a limited access to a balanced diet and medications. No significant differences were found between mean HbA1c values before and after Matthew (before Matthew: mean HbA1c 8.34 ± 1.87%; after Matthew: mean HbA1c 8.31 ± 1.93 %; P = .366). The period prevalence (PP) of DKA was higher after Matthew than before (before Matthew: 39 cases out of 4,025 visits, PP = .010; after Matthew: 87 cases out of 3,779 visits, PP = .023; P <.0001). LIMITATIONS Limitations include non-random sampling and limited sample sizes. Also, the cross-sectional panel approach did not follow the same individuals over time. CONCLUSIONS The period prevalence of DKA was higher in the six-month time period following Matthew compared to before the hurricane. Future interventions may improve outcomes via increased access to foods and medications recommended for those with diabetes.
Collapse
Affiliation(s)
- Kevin R Travia
- Clinical research assistant, Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Anna R Kahkoska
- University of North Carolina School of Medicine, Chapel Hill, North Carolina
| | - Daria Igudesman
- Doctoral student, Department of Nutrition, University of North Carolina School of Medicine, Chapel Hill, North Carolina
| | - Katherine J Souris
- Clinical research assistant, Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Cherry Beasley
- Distinguished professor of nursing, Department of Nursing, University of North Carolina at Pembroke, Pembroke, North Carolina
| | - Elizabeth J Mayer-Davis
- Distinguished professor of Nutrition and Medicine, chair, Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
| |
Collapse
|
40
|
Wang Z, Wang J, Kahkoska AR, Buse JB, Gu Z. Developing Insulin Delivery Devices with Glucose Responsiveness. Trends Pharmacol Sci 2021; 42:31-44. [PMID: 33250274 PMCID: PMC7758938 DOI: 10.1016/j.tips.2020.11.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 11/03/2020] [Accepted: 11/03/2020] [Indexed: 12/18/2022]
Abstract
Individuals with type 1 and advanced type 2 diabetes require daily insulin therapy to maintain blood glucose levels in normoglycemic ranges to prevent associated morbidity and mortality. Optimal insulin delivery should offer both precise dosing in response to real-time blood glucose levels as well as a feasible and low-burden administration route to promote long-term adherence. A series of glucose-responsive insulin delivery mechanisms and devices have been reported to increase patient compliance while mitigating the risk of hypoglycemia. This review discusses currently available insulin delivery devices, overviews recent developments towards the generation of glucose-responsive delivery systems, and provides commentary on the opportunities and barriers ahead regarding the integration and translation of current glucose-responsive insulin delivery designs.
Collapse
Affiliation(s)
- Zejun Wang
- Department of Bioengineering, University of California, Los Angeles, CA 90095, USA; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA 90095, USA
| | - Jinqiang Wang
- Department of Bioengineering, University of California, Los Angeles, CA 90095, USA; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA 90095, USA; College of Pharmaceutical Sciences, Zhejiang University, 310058 Hangzhou, China
| | - Anna R Kahkoska
- Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - John B Buse
- Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA.
| | - Zhen Gu
- Department of Bioengineering, University of California, Los Angeles, CA 90095, USA; Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA 90095, USA; College of Pharmaceutical Sciences, Zhejiang University, 310058 Hangzhou, China; California NanoSystems Institute, University of California, Los Angeles, CA 90095, USA.
| |
Collapse
|
41
|
Sutherland MW, Ma X, Reboussin BA, Mendoza JA, Bell BA, Kahkoska AR, Sauder KA, Lawrence JM, Pihoker C, Liese AD. Socioeconomic position is associated with glycemic control in youth and young adults with type 1 diabetes. Pediatr Diabetes 2020; 21:1412-1420. [PMID: 32902080 PMCID: PMC8054269 DOI: 10.1111/pedi.13112] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 08/28/2020] [Accepted: 09/02/2020] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE Health inequities persist in youth and young adults (YYA) with type 1diabetes in achieving optimal glycemic control. The purpose of this study was to assess the contribution of multiple indicators of social need to these inequities. RESEARCH DESIGN AND METHODS Two hundred and twenty two YYA withtype 1 diabetes enrolled in the SEARCH Food Insecurity Study in South Carolina and Washington between the years 2013 and 2015 were included. Latent class analysis was used to identify socioeconomic profiles based on household income, parental education, health insurance, household food insecurity, and food assistance. Profiles were evaluated in relation to glycemic control using multivariable linear and logistic regression, with HbA1c > 9%(75 mmol/mol) defined as high-risk glycemic control. RESULTS Two profiles were identified: a lower socioeconomic profile included YYA whose parents had lower income and/or education, and were more likely to be uninsured, receive food assistance, and be food insecure. A higher socioeconomic profile included YYA whose circumstances were opposite to those in the lower socioeconomic profile. Those with a lower socioeconomic profile were more likely to have high-risk glycemic control relative to those with a higher socioeconomic profile (OR = 2.24, 95%CI = 1.16-4.33). CONCLUSIONS Lower socioeconomic profiles are associated with high-risk glycemic control among YYA with type 1 diabetes. This supports recommendations that care providers of YYA with type 1 diabetes assess individual social needs in tailoring diabetes management plans to the social context of the patient.
Collapse
Affiliation(s)
- Melanie W. Sutherland
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC
| | - Xiaonan Ma
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC
| | - Beth A. Reboussin
- Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Jason A. Mendoza
- Department of Pediatrics and Nutritional Sciences Program, University of Washington; Cancer Prevention Program, Fred Hutchinson Cancer Research Center; and Seattle Children’s Research Institute, Seattle, WA
| | - Bethany A. Bell
- College of Social Work, University of South Carolina, Columbia, SC
| | - Anna R. Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Katherine A. Sauder
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO
| | - Jean M. Lawrence
- Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA
| | | | - Angela D. Liese
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC
| |
Collapse
|
42
|
Igudesman D, Crandell J, Zhong VW, Sarteau AC, Kahkoska AR, Corbin K, Pratley R, Kosorok MR, Maahs DM, Mayer-Davis EJ. Dietary intake on days with and without hypoglycemia in youth with type 1 diabetes: The Flexible Lifestyle Empowering Change trial. Pediatr Diabetes 2020; 21:1475-1484. [PMID: 32981192 PMCID: PMC9175139 DOI: 10.1111/pedi.13132] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 09/05/2020] [Accepted: 09/14/2020] [Indexed: 12/30/2022] Open
Abstract
OBJECTIVE To address a common perception that hypoglycemia is associated with increased dietary intake, we examined calorie and carbohydrate consumption on days with and without hypoglycemia among adolescents with type 1 diabetes (T1D). METHODS Days (N = 274) with 24-hour dietary recalls and continuous glucose monitoring were available for 122 adolescents with T1D in the Flexible Lifestyle Empowering Change trial (age 13-16 years, diabetes duration >1 year, hemoglobin A1c 8%-13%). Days with no hypoglycemia, clinical hypoglycemia (54-69 mg/dL) or clinically serious hypoglycemia (<54 mg/dL) were further split into night (12-5:59 am) and day (6 am-11:59 pm). Mixed models tested whether intake of calories or carbohydrates was greater on days with than without hypoglycemia. RESULTS Fifty-nine percent, 23% and 18% of days had no hypoglycemia, clinical hypoglycemia and clinically serious hypoglycemia, respectively. Intake of calories and carbohydrates was not statistically significantly different on days with clinical hypoglycemia (57.2 kcal [95% CI -126.7, 241.5]; 12.6 g carbohydrate [95% CI -12.7, 38.0]) or clinically serious hypoglycemia (-74.0 kcal [95% CI -285.9, 137.9]; (-7.8 g carbohydrate [95% CI -36.8, 21.1]), compared to days without hypoglycemia. Differences by day and night were not statistically significant. CONCLUSIONS Among adolescents with T1D, daily intake of calories and carbohydrates did not differ on days with and without hypoglycemia. It is possible that hypoglycemic episodes caused by undereating relative to insulin dosing, followed by overeating, leading to a net neutral difference. Given the post-hoc nature of these analyses, larger studies should be designed to prospectively test the hypoglycemia-diet relationship.
Collapse
Affiliation(s)
- Daria Igudesman
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Jamie Crandell
- School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Victor W. Zhong
- Division of Nutritional Sciences, Cornell University, Ithaca, NY 14853
| | | | - Anna R. Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Karen Corbin
- AdventHealth Translational Research Institute, Orlando, FL 32804
| | - Richard Pratley
- AdventHealth Translational Research Institute, Orlando, FL 32804
| | - Michael R. Kosorok
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - David M. Maahs
- Department of Pediatrics, Division of Endocrinology, Stanford University, Stanford, CA 94305
| | - Elizabeth J. Mayer-Davis
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599,Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| |
Collapse
|
43
|
Kahkoska AR, Betancourt RL, Kim LT. A Rare Case of Metastatic Adenocarcinoma Involving a Parathyroid Adenoma. Am Surg 2020; 88:2412-2413. [PMID: 32931303 DOI: 10.1177/0003134820951460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Anna R Kahkoska
- 2331 University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Renee L Betancourt
- 6797 Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lawrence T Kim
- Division of Surgical Oncology and Endocrine Surgery, Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| |
Collapse
|
44
|
Kahkoska AR, Geybels MS, Klein KR, Kreiner FF, Marx N, Nauck MA, Pratley RE, Wolthers BO, Buse JB. Validation of distinct type 2 diabetes clusters and their association with diabetes complications in the DEVOTE, LEADER and SUSTAIN-6 cardiovascular outcomes trials. Diabetes Obes Metab 2020; 22:1537-1547. [PMID: 32314525 PMCID: PMC7423751 DOI: 10.1111/dom.14063] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 04/01/2020] [Accepted: 04/12/2020] [Indexed: 02/06/2023]
Abstract
AIMS To validate the clusters of Swedish individuals with recent-onset diabetes at differential risk of complications, which were identified in a previous study, in three global populations with long-standing type 2 diabetes (T2D) who were at high cardiovascular risk, and to test for differences in the risk of major diabetes complications and survival endpoints. MATERIALS AND METHODS We assigned participants from recent global outcomes trials (DEVOTE [n = 7637], LEADER [n = 9340] and SUSTAIN-6 [n = 3297]) to the previously defined clusters according to age at diabetes diagnosis, baseline glycated haemoglobin (HbA1c) and body mass index (BMI). Outcomes were assessed using Kaplan-Meier analysis and log-rank tests. RESULTS The T2D clusters were consistently replicated across the three trial cohorts. The risk of major adverse cardiovascular events and cardiovascular death differed significantly, in all trials, across clusters over a median follow-up duration of 2.0, 3.8 and 2.1 years, respectively, and was highest for the cluster of participants with high HbA1c and low BMI (P < 0.05 in DEVOTE and LEADER). In LEADER and SUSTAIN-6, the risk of nephropathy differed across clusters (P < 0.0001 and P = 0.003, respectively). The risk of severe hypoglycaemia differed in DEVOTE (P = 0.006). CONCLUSIONS Previously identified clusters can be replicated in three geographically diverse cohorts of long-standing T2D and are associated with cluster-specific risk profiles for additional clinical and survival outcomes, providing further validation of the clustering methodology. The external validity and stability of clusters across cohorts provides a premise for future work to optimize the clustering approach to yield T2D subgroups with maximum predictive validity who may benefit from subtype-specific treatment paradigms.
Collapse
Affiliation(s)
- Anna R. Kahkoska
- School of MedicineUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | | | - Klara R. Klein
- School of MedicineUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | | | - Nikolaus Marx
- Department of Internal Medicine I, University Hospital AachenRWTH Aachen UniversityAachenGermany
| | - Michael A. Nauck
- Medical Department I, Diabetes Center Bochum‐HattingenSt. Josef‐Hospital, Ruhr‐University BochumBochumGermany
| | | | | | - John B. Buse
- School of MedicineUniversity of North CarolinaChapel HillNorth CarolinaUSA
| |
Collapse
|
45
|
Nguyen CT, Luckett DJ, Kahkoska AR, Shearrer GE, Spruijt-Metz D, Davis JN, Kosorok MR. Estimating individualized treatment regimes from crossover designs. Biometrics 2020; 76:778-788. [PMID: 31743424 PMCID: PMC7234899 DOI: 10.1111/biom.13186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 10/03/2019] [Accepted: 10/29/2019] [Indexed: 11/27/2022]
Abstract
The field of precision medicine aims to tailor treatment based on patient-specific factors in a reproducible way. To this end, estimating an optimal individualized treatment regime (ITR) that recommends treatment decisions based on patient characteristics to maximize the mean of a prespecified outcome is of particular interest. Several methods have been proposed for estimating an optimal ITR from clinical trial data in the parallel group setting where each subject is randomized to a single intervention. However, little work has been done in the area of estimating the optimal ITR from crossover study designs. Such designs naturally lend themselves to precision medicine since they allow for observing the response to multiple treatments for each patient. In this paper, we introduce a method for estimating the optimal ITR using data from a 2 × 2 crossover study with or without carryover effects. The proposed method is similar to policy search methods such as outcome weighted learning; however, we take advantage of the crossover design by using the difference in responses under each treatment as the observed reward. We establish Fisher and global consistency, present numerical experiments, and analyze data from a feeding trial to demonstrate the improved performance of the proposed method compared to standard methods for a parallel study design.
Collapse
Affiliation(s)
- Crystal T. Nguyen
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, U.S.A
| | - Daniel J. Luckett
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, U.S.A
| | - Anna R. Kahkoska
- Department of Nutrition, University of North Carolina, Chapel Hill, North Carolina, U.S.A
| | - Grace E. Shearrer
- Department of Nutrition, University of North Carolina, Chapel Hill, North Carolina, U.S.A
| | - Donna Spruijt-Metz
- Center of Economic and Social Research, University of Southern California, Los Angeles, California, U.S.A
| | - Jaimie N. Davis
- Department of Nutrition, University of Texas at Austin, Austin, Texas, U.S.A
| | - Michael R. Kosorok
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, U.S.A
| |
Collapse
|
46
|
Wang J, Wang Z, Yu J, Kahkoska AR, Buse JB, Gu Z. Glucose-Responsive Insulin and Delivery Systems: Innovation and Translation. Adv Mater 2020; 32:e1902004. [PMID: 31423670 PMCID: PMC7141789 DOI: 10.1002/adma.201902004] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 06/09/2019] [Indexed: 05/18/2023]
Abstract
Type 1 and advanced type 2 diabetes treatment involves daily injections or continuous infusion of exogenous insulin aimed at regulating blood glucose levels in the normoglycemic range. However, current options for insulin therapy are limited by the risk of hypoglycemia and are associated with suboptimal glycemic control outcomes. Therefore, a range of glucose-responsive components that can undergo changes in conformation or show alterations in intermolecular binding capability in response to glucose stimulation has been studied for ultimate integration into closed-loop insulin delivery or "smart insulin" systems. Here, an overview of the evolution and recent progress in the development of molecular approaches for glucose-responsive insulin delivery systems, a rapidly growing subfield of precision medicine, is presented. Three central glucose-responsive moieties, including glucose oxidase, phenylboronic acid, and glucose-binding molecules are examined in detail. Future opportunities and challenges regarding translation are also discussed.
Collapse
Affiliation(s)
- Jinqiang Wang
- Department of Bioengineering, University of California, Los Angeles, CA 90095, USA
- California NanoSystems Institute, University of California, Los Angeles, CA 90095, USA
| | - Zejun Wang
- Department of Bioengineering, University of California, Los Angeles, CA 90095, USA
- California NanoSystems Institute, University of California, Los Angeles, CA 90095, USA
| | | | - Anna R. Kahkoska
- Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - John B. Buse
- Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Zhen Gu
- Department of Bioengineering, University of California, Los Angeles, CA 90095, USA
- California NanoSystems Institute, University of California, Los Angeles, CA 90095, USA
- Zenomics Inc., Durham, NC 27709, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA 90095, USA
- Center for Minimally Invasive Therapeutics, University of California, Los Angeles, CA 90095, USA
| |
Collapse
|
47
|
Kahkoska AR, DeSelm TM, Young LA. Assessment of third-year medical students' comfort and preparedness for navigating challenging clinical scenarios with patients, peers, and supervisors. BMC Med Educ 2020; 20:71. [PMID: 32164733 PMCID: PMC7068976 DOI: 10.1186/s12909-020-1984-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 02/25/2020] [Indexed: 06/10/2023]
Abstract
BACKGROUND Medical training focuses heavily on clinical skills but lacks in training for navigating challenging clinical scenarios especially with regard to diversity issues. Our objective was to assess third-year medical students' preparedness to navigate such scenarios. METHODS A 24-item survey was administered electronically to third-year medical students describing a range of specific interactions with patients, peers, and "upper-levels" or superiors including residents and attendings, spanning subjects including gender, race/ethnicity, politics, age, sexual orientation/identity, disability, and religion. Students rated their level of comfort via a 5-point Likert scale ranging from 1 ("Very Uncomfortable") to 5 ("Very Comfortable"). Basic demographics were collected and data were summarized for trends. RESULTS Data were analyzed from 120 students (67% response rate, 54.2% female, 60.8% non-Hispanic white). Students reported lower comfort with peer and superiors compared to patient interactions (p < 0.0001). Students reported the highest comfort with sexual orientation/identity- and religion-related interactions (median (IQR): 3.3 (1.3) and 3.4 (10.0), respectively) and the lowest comfort with gender-, race/ethnicity-, and disability- related interactions (median (IQR): 2.3 (1.3), 2.0 (1.0), 2.5 (1.5), respectively). Males reported significantly higher median comfort levels for scenarios with upper-level, gender, and religion related interactions. Males were more likely to be completely comfortable versus females across the 24 scenarios, although multiple male response patterns showed evidence of a bimodal distribution. CONCLUSIONS Third-year medical students report generally inadequate comfort with navigating complex clinical scenarios, particularly with peers and supervisors and relating to gender-, race/ethnicity-, and disability-specific conflicts. There are differences across gender with regards to median comfort and distribution of scores suggesting that there is a subgroup of males report high/very high comfort with challenging clinical scenarios. Students may benefit from enhanced training modules and personalized toolkits for navigating these scenarios.
Collapse
Affiliation(s)
- Anna R Kahkoska
- Department of Nutrition, University of North Carolina School of Medicine, 135 Dauer Drive, Chapel Hill, NC, 27599, USA.
| | - Tracy M DeSelm
- Department of Emergency Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Laura A Young
- Division of Endocrinology, Diabetes and Metabolism, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| |
Collapse
|
48
|
Kahkoska AR, Nguyen CT, Jiang X, Adair LA, Agarwal S, Aiello AE, Burger KS, Buse JB, Dabelea D, Dolan LM, Imperatore G, Lawrence JM, Marcovina S, Pihoker C, Reboussin BA, Sauder KA, Kosorok MR, Mayer-Davis EJ. Characterizing the weight-glycemia phenotypes of type 1 diabetes in youth and young adulthood. BMJ Open Diabetes Res Care 2020; 8:8/1/e000886. [PMID: 32049631 PMCID: PMC7039605 DOI: 10.1136/bmjdrc-2019-000886] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 12/27/2019] [Accepted: 01/04/2020] [Indexed: 12/13/2022] Open
Abstract
INTRODUCTION Individuals with type 1 diabetes (T1D) present with diverse body weight status and degrees of glycemic control, which may warrant different treatment approaches. We sought to identify subgroups sharing phenotypes based on both weight and glycemia and compare characteristics across subgroups. RESEARCH DESIGN AND METHODS Participants with T1D in the SEARCH study cohort (n=1817, 6.0-30.4 years) were seen at a follow-up visit >5 years after diagnosis. Hierarchical agglomerative clustering was used to group participants based on five measures summarizing the joint distribution of body mass index z-score (BMIz) and hemoglobin A1c (HbA1c) which were estimated by reinforcement learning tree predictions from 28 covariates. Interpretation of cluster weight status and glycemic control was based on mean BMIz and HbA1c, respectively. RESULTS The sample was 49.5% female and 55.5% non-Hispanic white (NHW); mean±SD age=17.6±4.5 years, T1D duration=7.8±1.9 years, BMIz=0.61±0.94, and HbA1c=76±21 mmol/mol (9.1±1.9)%. Six weight-glycemia clusters were identified, including four normal weight, one overweight, and one subgroup with obesity. No cluster had a mean HbA1c <58 mmol/mol (7.5%). Cluster 1 (34.0%) was normal weight with the lowest HbA1c and comprised 85% NHW participants with the highest socioeconomic position, insulin pump use, dietary quality, and physical activity. Subgroups with very poor glycemic control (ie, ≥108 mmol/mol (≥12.0%); cluster 4, 4.4%, and cluster 5, 7.5%) and obesity (cluster 6, 15.4%) had a lower proportion of NHW youth, lower socioeconomic position, and reported decreased pump use and poorer health behaviors (overall p<0.01). The overweight subgroup with very poor glycemic control (cluster 5) showed the highest lipids and blood pressure (p<0.01). CONCLUSIONS There are distinct subgroups of youth and young adults with T1D that share weight-glycemia phenotypes. Subgroups may benefit from tailored interventions addressing differences in clinical care, health behaviors, and underlying health inequity.
Collapse
Affiliation(s)
- Anna R Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Crystal T Nguyen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Xiaotong Jiang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Linda A Adair
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Shivani Agarwal
- Center for Diabetes Translational Research, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Allison E Aiello
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Kyle S Burger
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - John B Buse
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Dana Dabelea
- Department of Epidemiology, Colorado School of Public Health, Aurora, Colorado, USA
- Department of Pediatrics, School of Medicine, University of Colorado, Aurora, Colorado, USA
| | - Lawrence M Dolan
- Division of Endocrinology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Giuseppina Imperatore
- Division of Diabetes Translation, Centers of Disease Control and Prevention, Atlanta, Georgia
| | - Jean Marie Lawrence
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, Southern California, USA
| | - Santica Marcovina
- Northwest Lipid Metabolism and Diabetes Research Laboratories, Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Catherine Pihoker
- Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Beth A Reboussin
- Department of Pediatrics, University of Washington, Seattle, Washington, USA
| | - Katherine A Sauder
- Department of Epidemiology, Colorado School of Public Health, Aurora, Colorado, USA
- Department of Pediatrics, School of Medicine, University of Colorado, Aurora, Colorado, USA
| | - Michael R Kosorok
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Elizabeth J Mayer-Davis
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| |
Collapse
|
49
|
Kahkoska AR, Nguyen CT, Adair LA, Aiello AE, Burger KS, Buse JB, Dabelea D, Dolan LM, Malik FS, Mottl AK, Pihoker C, Reboussin BA, Sauder KA, Kosorok MR, Mayer-Davis EJ. Longitudinal Phenotypes of Type 1 Diabetes in Youth Based on Weight and Glycemia and Their Association With Complications. J Clin Endocrinol Metab 2019; 104:6003-6016. [PMID: 31290977 PMCID: PMC6812733 DOI: 10.1210/jc.2019-00734] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 07/03/2019] [Indexed: 12/12/2022]
Abstract
CONTEXT Subclinical and clinical complications emerge early in type 1 diabetes (T1D) and may be associated with obesity and hyperglycemia. OBJECTIVE Test how longitudinal "weight-glycemia" phenotypes increase susceptibility to different patterns of early/subclinical complications among youth with T1D. DESIGN SEARCH for Diabetes in Youth observational study. SETTING Population-based cohort. PARTICIPANTS Youth with T1D (n = 570) diagnosed 2002 to 2006 or 2008. MAIN OUTCOME MEASURES Participants were clustered based on longitudinal body mass index z score and HbA1c from a baseline visit and 5+ year follow-up visit (mean diabetes duration: 1.4 ± 0.4 years and 8.2 ± 1.9 years, respectively). Logistic regression modeling tested cluster associations with seven early/subclinical diabetes complications at follow-up, adjusting for sex, race/ethnicity, age, and duration. RESULTS Four longitudinal weight-glycemia clusters were identified: The Referent Cluster (n = 195, 34.3%), the Hyperglycemia Only Cluster (n = 53, 9.3%), the Elevated Weight Only Cluster (n = 206, 36.1%), and the Elevated Weight With Increasing Hyperglycemia (EWH) Cluster (n = 115, 20.2%). Compared with the Referent Cluster, the Hyperglycemia Only Cluster had elevated odds of dyslipidemia [adjusted odds ratio (aOR) 2.22, 95% CI: 1.15 to 4.29], retinopathy (aOR 9.98, 95% CI: 2.49 to 40.0), and diabetic kidney disease (DKD) (aOR 4.16, 95% CI: 1.37 to 12.62). The EWH Cluster had elevated odds of hypertension (aOR 2.18, 95% CI: 1.19 to 4.00), dyslipidemia (aOR 2.36, 95% CI: 1.41 to 3.95), arterial stiffness (aOR 2.46, 95% CI: 1.09 to 5.53), retinopathy (aOR 5.11, 95% CI: 1.34 to 19.46), and DKD (aOR 3.43, 95% CI: 1.29 to 9.11). CONCLUSIONS Weight-glycemia phenotypes show different patterns of complications, particularly markers of subclinical macrovascular disease, even in the first decade of T1D.
Collapse
Affiliation(s)
- Anna R Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Correspondence and Reprint Requests: Anna R. Kahkoska, PhD, or Elizabeth J. Mayer-Davis, PhD, University of North Carolina at Chapel Hill, 245 Rosenau Drive, Chapel Hill, North Carolina 27599. E-mail: or
| | - Crystal T Nguyen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Linda A Adair
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Allison E Aiello
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Kyle S Burger
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - John B Buse
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Dana Dabelea
- Department of Epidemiology, Colorado School of Public Health, Aurora, Colorado
- Department of Pediatrics, School of Medicine, University of Colorado, Aurora, Colorado
| | - Lawrence M Dolan
- Division of Endocrinology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
| | - Faisal S Malik
- Department of Pediatrics, University of Washington, Seattle, Washington
| | - Amy K Mottl
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Catherine Pihoker
- Department of Pediatrics, University of Washington, Seattle, Washington
| | - Beth A Reboussin
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston Salem, North Carolina
| | - Katherine A Sauder
- Department of Epidemiology, Colorado School of Public Health, Aurora, Colorado
- Department of Pediatrics, School of Medicine, University of Colorado, Aurora, Colorado
| | - Michael R Kosorok
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, North Carolina
| | - Elizabeth J Mayer-Davis
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Correspondence and Reprint Requests: Anna R. Kahkoska, PhD, or Elizabeth J. Mayer-Davis, PhD, University of North Carolina at Chapel Hill, 245 Rosenau Drive, Chapel Hill, North Carolina 27599. E-mail: or
| |
Collapse
|
50
|
Addala A, Igudesman D, Kahkoska AR, Muntis FR, Souris KJ, Whitaker KJ, Pratley RE, Mayer-Davis E. The interplay of type 1 diabetes and weight management: A qualitative study exploring thematic progression from adolescence to young adulthood. Pediatr Diabetes 2019; 20:974-985. [PMID: 31392807 PMCID: PMC7196280 DOI: 10.1111/pedi.12903] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 08/04/2019] [Accepted: 08/05/2019] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND The impact of weight management in persons with type 1 diabetes (T1D) from childhood into adulthood has not been well described. The purpose of the study was to explore qualitative themes presented by young adults with T1D with respect to the dual management of weight and T1D. METHODS We analyzed focus group data from 17 young adults with T1D (65% female, age 21.7 ± 2.1 years, HbA1c 8.1% ± 1.5) via inductive qualitative analysis methods. Major themes were compared to themes presented by youth with T1D ages 13-16 years in previously published study in order to categorize thematic progression from early adolescence through adulthood. RESULTS Themes from young adults with T1D, when compared to those from youth were categorized as: (a) persistent and unchanged themes, (b) evolving themes, and (c) newly reported themes. Hypoglycemia and a sense of futility around exercise was an unchanged theme. Importance of insulin usage and a healthy relationship with T1D evolved to gather greater conviction. Newly reported themes are unique to integration of adulthood into T1D, such as family planning and managing T1D with work obligations. Young adults also reported negative experiences with providers in their younger years and desire for more supportive provider relationships. CONCLUSIONS Issues identified by youth regarding the dual management of T1D and weight rarely resolve, but rather, persist or evolve to integrate other aspects of young adulthood. Individualized and age-appropriate clinical support and practice guidelines are warranted to facilitate the dual management of weight and T1D in persons with T1D.
Collapse
Affiliation(s)
- Ananta Addala
- Department of Pediatric Endocrinology, Stanford University, Stanford, California
| | - Daria Igudesman
- Department of Nutrition, University of North Carolina, Chapel Hill, North Carolina
| | - Anna R. Kahkoska
- Department of Nutrition, University of North Carolina, Chapel Hill, North Carolina
| | - Franklin R. Muntis
- Department of Nutrition, University of North Carolina, Chapel Hill, North Carolina
| | - Katherine J. Souris
- Department of Nutrition, University of North Carolina, Chapel Hill, North Carolina
| | - Keri J. Whitaker
- AdventHealth Translational Research Institute for Metabolism and Diabetes, Orlando, Florida
| | - Richard E. Pratley
- AdventHealth Translational Research Institute for Metabolism and Diabetes, Orlando, Florida
| | | |
Collapse
|