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Selvin E. The Glucose Management Indicator: Time to Change Course? Diabetes Care 2024; 47:906-914. [PMID: 38295402 PMCID: PMC11116920 DOI: 10.2337/dci23-0086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/01/2023] [Indexed: 02/02/2024]
Abstract
Laboratory measurement of hemoglobin A1c (HbA1c) has, for decades, been the standard approach to monitoring glucose control in people with diabetes. Continuous glucose monitoring (CGM) is a revolutionary technology that can also aid in the monitoring of glucose control. However, there is uncertainty in how best to use CGM technology and its resulting data to improve control of glucose and prevent complications of diabetes. The glucose management indicator, or GMI, is an equation used to estimate HbA1c based on CGM mean glucose. GMI was originally proposed to simplify and aid in the interpretation of CGM data and is now provided on all standard summary reports (i.e., average glucose profiles) produced by different CGM manufacturers. This Perspective demonstrates that GMI performs poorly as an estimate of HbA1c and suggests that GMI is a concept that has outlived its usefulness, and it argues that it is preferable to use CGM mean glucose rather than converting glucose to GMI or an estimate of HbA1c. Leaving mean glucose in its raw form is simple and reinforces that glucose and HbA1c are distinct. To reduce patient and provider confusion and optimize glycemic management, mean CGM glucose, not GMI, should be used as a complement to laboratory HbA1c testing in patients using CGM systems.
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Affiliation(s)
- Elizabeth Selvin
- Department of Epidemiology and the Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
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Sartini J, Fang M, Rooney MR, Selvin E, Coresh J, Zeger S. Glucose Color Index: Development and Validation of a Novel Measure of the Shape of Glycemic Variability. J Diabetes Sci Technol 2024:19322968241245654. [PMID: 38641966 DOI: 10.1177/19322968241245654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/21/2024]
Abstract
BACKGROUND Standard continuous glucose monitoring (CGM) metrics: mean glucose, standard deviation, coefficient of variation, and time in range, fail to capture the shape of variability in the CGM time series. This information could facilitate improved diabetes management. METHODS We analyzed CGM data from 141 adults with type 2 diabetes in the Hyperglycemic Profiles in Obstructive Sleep Apnea (HYPNOS) trial. Participants in HYPNOS wore CGM sensors for up to two weeks at two time points, three months apart. We calculated the log-periodogram for each time period, summarizing using disjoint linear models. These summaries were combined into a single value, termed the Glucose Color Index (GCI), using canonical correlation analysis. We compared the between-wear correlation of GCI with those of standard CGM metrics and assessed associations between GCI and diabetes comorbidities in 398 older adults with type 2 diabetes from the Atherosclerosis Risk in Communities (ARIC) study. RESULTS The GCI achieved a test-retest correlation of R = .75. Adjusting for standard CGM metrics, the GCI test-retest correlation was R = .55. Glucose Color Index was significantly associated (p < .05) with impaired physical functioning, frailty/pre-frailty, cardiovascular disease, chronic kidney disease, and dementia/mild cognitive impairment after adjustment for confounders. CONCLUSION We developed and validated the GCI, a novel CGM metric that captures the shape of glucose variability using the periodogram signal decomposition. Glucose Color Index was reliable within participants over a three-month period and associated with diabetes comorbidities. The GCI suggests a promising avenue toward the development of CGM metrics which more fully incorporate time series information.
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Affiliation(s)
- Joseph Sartini
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Michael Fang
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Mary R Rooney
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Elizabeth Selvin
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Josef Coresh
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Grossman School of Medicine, New York University, New York City, NY, USA
| | - Scott Zeger
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Sergazinov R, Leroux A, Cui E, Crainiceanu C, Aurora RN, Punjabi NM, Gaynanova I. A case study of glucose levels during sleep using multilevel fast function on scalar regression inference. Biometrics 2023; 79:3873-3882. [PMID: 37189239 DOI: 10.1111/biom.13878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 04/26/2023] [Indexed: 05/17/2023]
Abstract
Continuous glucose monitors (CGMs) are increasingly used to measure blood glucose levels and provide information about the treatment and management of diabetes. Our motivating study contains CGM data during sleep for 174 study participants with type II diabetes mellitus measured at a 5-min frequency for an average of 10 nights. We aim to quantify the effects of diabetes medications and sleep apnea severity on glucose levels. Statistically, this is an inference question about the association between scalar covariates and functional responses observed at multiple visits (sleep periods). However, many characteristics of the data make analyses difficult, including (1) nonstationary within-period patterns; (2) substantial between-period heterogeneity, non-Gaussianity, and outliers; and (3) large dimensionality due to the number of study participants, sleep periods, and time points. For our analyses, we evaluate and compare two methods: fast univariate inference (FUI) and functional additive mixed models (FAMMs). We extend FUI and introduce a new approach for testing the hypotheses of no effect and time invariance of the covariates. We also highlight areas for further methodological development for FAMM. Our study reveals that (1) biguanide medication and sleep apnea severity significantly affect glucose trajectories during sleep and (2) the estimated effects are time invariant.
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Affiliation(s)
- Renat Sergazinov
- Department of Statistics, Texas A&M University, College Station, Texas, USA
| | - Andrew Leroux
- Department of Biostatistics & Informatics, University of Colorado Anschutz Medical Campus, Colorado, USA
| | - Erjia Cui
- Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA
| | - Ciprian Crainiceanu
- Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA
| | - R Nisha Aurora
- New York University Grossman School of Medicine, New York, New York, USA
| | - Naresh M Punjabi
- Miller School of Medicine, University of Miami, Coral Gables, Florida, USA
| | - Irina Gaynanova
- Department of Statistics, Texas A&M University, College Station, Texas, USA
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Giampá SQC, Lorenzi-Filho G, Drager LF. Obstructive sleep apnea and metabolic syndrome. Obesity (Silver Spring) 2023; 31:900-911. [PMID: 36863747 DOI: 10.1002/oby.23679] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/05/2022] [Accepted: 12/05/2022] [Indexed: 03/04/2023]
Abstract
Metabolic syndrome (MS) is a heterogeneous condition associated with increased cardiovascular risk. There is growing evidence from experimental, translational, and clinical investigations that has suggested that obstructive sleep apnea (OSA) is associated with prevalent and incident components of MS and MS itself. The biological plausibility is supportive, primarily related to one of the main features of OSA, namely intermittent hypoxia: increased sympathetic activation with hemodynamic repercussions, increased hepatic glucose output, insulin resistance through adipose tissue inflammation, pancreatic β-cell dysfunction, hyperlipidemia through the worsening of fasting lipid profiles, and the reduced clearance of triglyceride-rich lipoproteins. Although there are multiple related pathways, the clinical evidence relies mainly on cross-sectional data preventing any causality assumptions. The overlapping presence of visceral obesity or other confounders such as medications challenges the ability to understand the independent contribution of OSA on MS. In this review, we revisit the evidence on how OSA/intermittent hypoxia could mediate adverse effects of MS parameters independent of adiposity. Particular attention is devoted to discussing recent evidence from interventional studies. This review describes the research gaps, the challenges in the field, perspectives, and the need for additional high-quality data from interventional studies addressing the impact of not only established but promising therapies for OSA/obesity.
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Affiliation(s)
- Sara Q C Giampá
- Graduate Program in Cardiology, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Geraldo Lorenzi-Filho
- Laboratório do Sono, Divisão de Pneumologia, Instituto do Coração (InCor), Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Luciano F Drager
- Unidade de Hipertensão, Instituto do Coração (InCor), Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
- Unidade de Hipertensão, Disciplina de Nefrologia, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
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Danyluk A, Hadigal S, Leey J. Obstructive Sleep Apnea as a Cause of Nocturnal Hyperglycemia: A Case Study. Clin Diabetes 2023; 41:579-582. [PMID: 37849510 PMCID: PMC10577509 DOI: 10.2337/cd22-0123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Affiliation(s)
| | - Susheela Hadigal
- Division of Pulmonary, Critical Care and Sleep Medicine, North Florida/South Georgia Veterans Health System, Gainesville, FL
| | - Julio Leey
- Division of Endocrinology, North Florida/South Georgia Veterans Health System, Gainesville, FL
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Fang M, Wang D, Rooney MR, Echouffo-Tcheugui JB, Coresh J, Aurora RN, Punjabi NM, Selvin E. Performance of the Glucose Management Indicator (GMI) in Type 2 Diabetes. Clin Chem 2023; 69:422-428. [PMID: 36738249 PMCID: PMC10073330 DOI: 10.1093/clinchem/hvac210] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 11/14/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND The glucose management indicator (GMI) is an estimated measure of hemoglobin A1c (HbA1c) recommended for the management of persons with diabetes using continuous glucose monitoring (CGM). However, GMI was derived primarily in young adults with type 1 diabetes, and its performance in patients with type 2 diabetes is poorly characterized. METHODS We conducted a prospective cohort study in 144 adults with obstructive sleep apnea and type 2 diabetes not using insulin (mean age: 59.4 years; 45.1% female). HbA1c was measured at the study screening visit. Participants simultaneously wore 2 CGM sensors (Dexcom G4 and Abbott Libre Pro) for up to 4 weeks (2 weeks at baseline and 2 weeks at the 3-month follow-up visit). GMI was calculated using all available CGM data for each sensor. RESULTS Median wear time was 27 days (IQR: 23-29) for the Dexcom G4 and 28 days (IQR: 24-29) for the Libre Pro. The mean difference between HbA1c and GMI was small (0.12-0.14 percentage points) (approximately 2 mmol/mol). However, the 2 measures were only moderately correlated (r = 0.68-0.71), and there was substantial variability in GMI at any given value of HbA1c (root mean squared error: 0.66-0.69 percentage points [7 to 8 mmol/mol]). Between 36% and 43% of participants had an absolute difference between HbA1c and GMI ≥0.5 percentage points (≥5 mmol/mol), and 9% to 18% had an absolute difference >1 percentage points (>11 mmol/mol). Discordance was higher in the Libre Pro than the Dexcom G4. CONCLUSIONS GMI may be an unreliable measure of glycemic control for patients with type 2 diabetes and should be interpreted cautiously in clinical practice.Clinicaltrials.gov Registration Number: NCT02454153.
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Affiliation(s)
- Michael Fang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Dan Wang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Mary R Rooney
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Justin B Echouffo-Tcheugui
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.,Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.,Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - R Nisha Aurora
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Naresh M Punjabi
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miller School of Medicine, Miami, FL, USA
| | - Elizabeth Selvin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Selvin E, Wang D, Rooney MR, Fang M, Echouffo-Tcheugui JB, Zeger S, Sartini J, Tang O, Coresh J, Aurora RN, Punjabi NM. Within-Person and Between-Sensor Variability in Continuous Glucose Monitoring Metrics. Clin Chem 2023; 69:180-188. [PMID: 36495162 PMCID: PMC9898170 DOI: 10.1093/clinchem/hvac192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 10/04/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND The within-person and between-sensor variability of metrics from different interstitial continuous glucose monitoring (CGM) sensors in adults with type 2 diabetes not taking insulin is unclear. METHODS Secondary analysis of data from 172 participants from the Hyperglycemic Profiles in Obstructive Sleep Apnea randomized clinical trial. Participants simultaneously wore Dexcom G4 and Abbott Libre Pro CGM sensors for up to 2 weeks at baseline and again at the 3-month follow-up visit. RESULTS At baseline (up to 2 weeks of CGM), mean glucose for both the Abbott and Dexcom sensors was approximately 150 mg/dL (8.3 mmol/L) and time in range (70180 mg/dL [3.910.0 mmol/L]) was just below 80. When comparing the same sensor at 2 different time points (two 2-week periods, 3 months apart), the within-person coefficient of variation (CVw) in mean glucose was 17.4 (Abbott) and 14.2 (Dexcom). CVw for percent time in range: 20.1 (Abbott) and 18.6 (Dexcom). At baseline, the Pearson correlation of mean glucose from the 2 sensors worn simultaneously was r 0.86, root mean squared error (RMSE), 13 mg/dL (0.7 mmol/L); for time in range, r 0.88, RMSE, 8 percentage points. CONCLUSIONS Substantial variation was observed within sensors over time and across 2 different sensors worn simultaneously on the same individuals. Clinicians should be aware of this variability when using CGM technology to make clinical decisions.ClinicalTrials.gov Identifier: NCT02454153.
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Affiliation(s)
- Elizabeth Selvin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | - Dan Wang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | - Mary R. Rooney
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | - Michael Fang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | - Justin B. Echouffo-Tcheugui
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
- Division of Endocrinology, Diabetes & Metabolism, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Scott Zeger
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Joseph Sartini
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Olive Tang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
- Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | - R. Nisha Aurora
- Department of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Naresh M. Punjabi
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miller School of Medicine, Miami, FL, USA
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Schwenck J, Punjabi NM, Gaynanova I. bp: Blood pressure analysis in R. PLoS One 2022; 17:e0268934. [PMID: 36083882 PMCID: PMC9462781 DOI: 10.1371/journal.pone.0268934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 08/19/2022] [Indexed: 11/19/2022] Open
Abstract
Despite the world-wide prevalence of hypertension, there is a lack in open-source software for analyzing blood pressure data. The R package bp fills this gap by providing functionality for blood pressure data processing, visualization, and feature extraction. In addition to the comprehensive functionality, the package includes six sample data sets covering continuous arterial pressure data (AP), home blood pressure monitoring data (HBPM) and ambulatory blood pressure monitoring data (ABPM), making it easier for researchers to get started. The R package bp is publicly available on CRAN and at https://github.com/johnschwenck/bp.
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Affiliation(s)
- John Schwenck
- Department of Statistics, Texas A&M University, College Station, TX, United States of America
- * E-mail:
| | - Naresh M. Punjabi
- Miller School of Medicine, University of Miami, Miami, FL, United States of America
| | - Irina Gaynanova
- Department of Statistics, Texas A&M University, College Station, TX, United States of America
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Glucose profiles in obstructive sleep apnea and type 2 diabetes mellitus. Sleep Med 2022; 95:105-111. [DOI: 10.1016/j.sleep.2022.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/22/2022] [Accepted: 04/13/2022] [Indexed: 10/18/2022]
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