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Oganesova Z, Pemberton J, Brown A. Innovative solution or cause for concern? The use of continuous glucose monitors in people not living with diabetes: A narrative review. Diabet Med 2024; 41:e15369. [PMID: 38925143 DOI: 10.1111/dme.15369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 05/14/2024] [Accepted: 05/15/2024] [Indexed: 06/28/2024]
Abstract
AIMS Continuous glucose monitors (CGMs) have expanded their scope beyond indicated uses for diabetes management and are gaining traction among people not living with diabetes (PNLD). CGMs track in time glucose levels and are proposed as tools for the early detection of abnormal glucose and a potential solution for its normalisation through behavioural change, particularly, diet personalisation and motivation of physical activity. This becomes relevance given the growing incidence of metabolic conditions, such as type 2 diabetes mellitus (T2DM). Clinical guidelines, however, do not recommend CGMs in contexts outside type 1 diabetes (T1DM) or insulin-treated T2DM. Therefore, there is a visible disconnect between the indicated and real-world usage of these medical devices. While the commercial market for CGMs in PNLD is expanding rapidly, a comprehensive and evidence-based evaluation of the devices' utility in this population has not been done. Therefore, this review aims to formulate a working model for CGM utility in PNLD as proposed by the 'health and wellness' market that advertises and distributes it to these individuals. METHODS We aim to critically analyse the available research addressing components of the working model, that is (1) detection of abnormal glucose; (2) behavioural change, and (3) metabolic health improvement. RESULTS We find a lack of consistent and high-quality evidence to support the utility of CGMs for these purposes. We identify significantly under-reserved areas including clinical benchmarks and scoring procedures for CGM measures, device acceptability, and potential adverse effects of CGMs on eating habits in PNLD. We also raise concerns about the robustness of available CGM research. CONCLUSION In the face of these research gaps, we urge for the commercial claims suggesting the utility of the device in PNLD to be labelled as misleading. We argue that there is a regulatory inadequacy that fuels 'off-label' CGM distribution and calls for the strengthening of post-market clinical follow-up oversight for CGMs. We hope this will help to avert the continued misinformation risk to PNLD and 'off-label' exacerbation of health disparities.
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Affiliation(s)
- Zhanna Oganesova
- Centre for Obesity Research, University College London, London, UK
| | | | - Adrian Brown
- Centre for Obesity Research, University College London, London, UK
- National Institute for Health Research Biomedical Research Centre, University College London Hospital, London, UK
- Bariatric Centre for Weight Management and Metabolic Surgery, University College London Hospital NHS Trust, London, UK
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Berube LT, Popp CJ, Curran M, Hu L, Pompeii ML, Barua S, Bernstein E, Salcedo V, Li H, St-Jules DE, Segal E, Bergman M, Williams NJ, Sevick MA. Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study: study protocol for a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes. Trials 2024; 25:506. [PMID: 39049121 PMCID: PMC11271038 DOI: 10.1186/s13063-024-08337-w] [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: 05/28/2024] [Accepted: 07/11/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND The Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study is a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes (T2D). The study aims to test the efficacy of a personalized behavioral approach for dietary management of moderately controlled T2D, versus a standardized behavioral intervention that uses one-size-fits-all dietary recommendations, versus a usual care control (UCC). The primary outcome will compare the impact of each intervention on the mean amplitude of glycemic excursions (MAGE). METHODS Eligible participants are between 21 and 80 years of age diagnosed with moderately controlled T2D (HbA1c: 6.0 to 8.0%) and managed on lifestyle alone or lifestyle plus metformin. Participants must be willing and able to attend virtual counseling sessions and log meals into a dietary tracking smartphone application (DayTwo), and wear a continuous glucose monitor (CGM) for up to 12 days. Participants are randomized with equal allocation (n = 255, n = 85 per arm) to one of three arms: (1) Personalized, (2) Standardized, or (3) UCC. Measurements occur at 0 (baseline), 3, and 6 months. All participants receive isocaloric energy and macronutrient targets to meet Mediterranean diet guidelines, in addition to 14 intervention contacts over 6 months (4 weekly then 10 biweekly) to cover diabetes self-management education. The first 4 UCC intervention contacts are delivered via synchronous videoconferences followed by educational video links. Participants in Standardized receive the same educational content as those in the UCC arm, following the same schedule. However, all intervention contacts are conducted via synchronous videoconferences, paired with Social Cognitive Theory (SCT)-based behavioral counseling, plus dietary self-monitoring of planned meals using a mobile app that provides real-time feedback on calories and macronutrients. Participants in the Personalized arm receive all elements of the Standardized intervention, in addition to real-time feedback on predicted post-prandial glycemic response (PPGR) to meals and snacks logged into the mobile app. DISCUSSION The DiaTeleMed Study aims to address an important gap in the current landscape of precision nutrition by determining the contributions of behavioral counseling and personalized nutrition recommendations on glycemic control in individuals with T2D. The fully remote methodology of the study allows for scalability and innovative delivery of personalized dietary recommendations at a population level. TRIAL REGISTRATION ClinicalTrials.gov NCT05046886. Registered on September 16, 2021.
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Affiliation(s)
- Lauren T Berube
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA.
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA.
| | - Collin J Popp
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
| | - Margaret Curran
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
| | - Lu Hu
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
| | - Mary Lou Pompeii
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
| | - Souptik Barua
- Division of Precision Medicine, Department of Medicine, New York University Langone Health, New York, NY, USA
| | - Emma Bernstein
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
| | - Vanessa Salcedo
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
| | - Huilin Li
- Division of Biostatistics, Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
| | - David E St-Jules
- Department of Nutrition, University of Nevada, Reno, 1664 N. Virginia Street, Reno, NV, 89557, USA
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
| | - Michael Bergman
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
- Department of Medicine, New York University Langone Health, New York, NY, USA
- Holman Division of Endocrinology, Diabetes and Metabolism, Manhattan VA Medical Center, 423 East 23rd Street, New York, NY, 10010, USA
| | - Natasha J Williams
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
| | - Mary Ann Sevick
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
- Department of Population Health, New York University Langone Health, 180 Madison Ave, New York, NY, 10016, USA
- Department of Medicine, New York University Langone Health, New York, NY, USA
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3
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Zahalka SJ, Galindo RJ, Shah VN, Low Wang CC. Continuous Glucose Monitoring for Prediabetes: What Are the Best Metrics? J Diabetes Sci Technol 2024; 18:835-846. [PMID: 38629784 PMCID: PMC11307227 DOI: 10.1177/19322968241242487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
BACKGROUND Continuous glucose monitoring (CGM) has transformed the care of type 1 and type 2 diabetes, and there is potential for CGM to also become influential in prediabetes identification and management. However, to date, we do not have any consensus guidelines or high-quality evidence to guide CGM goals and metrics for use in prediabetes. METHODS We searched PubMed for all English-language articles on CGM use in nonpregnant adults with prediabetes published by November 1, 2023. We excluded any articles that included subjects with type 1 diabetes or who were known to be at risk for type 1 diabetes due to positive islet autoantibodies. RESULTS Based on the limited data available, we suggest possible CGM metrics to be used for individuals with prediabetes. We also explore the role that glycemic variability (GV) plays in the transition from normoglycemia to prediabetes. CONCLUSIONS Glycemic variability indices beyond the standard deviation and coefficient of variation are emerging as prominent identifiers of early dysglycemia. One GV index in particular, the mean amplitude of glycemic excursion (MAGE), may play a key future role in CGM metrics for prediabetes and is highlighted in this review.
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Affiliation(s)
- Salwa J. Zahalka
- Division of Endocrinology, Metabolism
and Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Viral N. Shah
- Division of Endocrinology and
Metabolism, Indiana University, Indianapolis, IN, USA
| | - Cecilia C. Low Wang
- Division of Endocrinology, Metabolism
and Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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Berube LT, Popp CJ, Curran M, Hu L, Pompeii ML, Barua S, Bernstein E, Salcedo V, Li H, St-Jules DE, Segal E, Bergman M, Williams NJ, Sevick MA. Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study: study protocol for a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes. RESEARCH SQUARE 2024:rs.3.rs-4492352. [PMID: 38978573 PMCID: PMC11230484 DOI: 10.21203/rs.3.rs-4492352/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background The Diabetes Telemedicine Mediterranean Diet (DiaTeleMed) Study is a fully remote randomized clinical trial evaluating personalized dietary management in individuals with type 2 diabetes (T2D). The study aims to test the efficacy of a personalized behavioral approach for dietary management of moderately-controlled T2D, versus a standardized behavioral intervention that uses one-size-fits-all dietary recommendations, versus a usual care control (UCC). The primary outcome will compare the impact of each intervention on the mean amplitude of glycemic excursions (MAGE). Methods Eligible participants are between 21 to 80 years of age diagnosed with moderately-controlled T2D (HbA1c: 6.0-8.0%), and managed on lifestyle alone or lifestyle plus metformin. Participants must be willing and able to attend virtual counseling sessions and log meals into a dietary tracking smartphone application (DayTwo), and wear a continuous glucose monitor (CGM) for up to 12 days. Participants are randomized with equal allocation (n = 255, n = 85 per arm) to one of three arms: 1) Personalized, 2) Standardized, or 3) UCC. Measurements occur at 0 (baseline), 3, and 6 months. All participants receive isocaloric energy and macronutrients targets to meet Mediterranean diet guidelines plus 14 intervention contacts over 6 months (4 weekly then 10 biweekly) to cover diabetes self-management education. The first 4 UCC intervention contacts are delivered via synchronous videoconferences followed by educational video links. Participants in Standardized receive the same education content as UCC on the same schedule. However, all intervention contacts are conducted via synchronous videoconferences, paired with Social Cognitive Theory (SCT)-based behavioral counseling, plus dietary self-monitoring of planned meals using a mobile app that provides real-time feedback on calories and macronutrients. Participants in the Personalized arm receive all elements of the Standardized intervention, plus real-time feedback on predicted post-prandial glycemic response (PPGR) to meals and snacks logged into the mobile app. Discussion The DiaTeleMed study will address an important gap in the current landscape of precision nutrition by determining the contributions of behavioral counseling and personalized nutrition recommendations on glycemic control in individuals with T2D. The fully remote methodology of the study allows for scalability and innovative delivery of personalized dietary recommendations at a population level. Trial registration The DiaTeleMed Study is registered with ClinicalTrials.gov (Identifier: NCT05046886).
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Affiliation(s)
| | | | | | - Lu Hu
- New York University Grossman School of Medicine
| | | | | | | | | | - Huilin Li
- New York University Grossman School of Medicine
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Hjort A, Iggman D, Rosqvist F. Glycemic variability assessed using continuous glucose monitoring in individuals without diabetes and associations with cardiometabolic risk markers: A systematic review and meta-analysis. Clin Nutr 2024; 43:915-925. [PMID: 38401227 DOI: 10.1016/j.clnu.2024.02.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 02/06/2024] [Accepted: 02/13/2024] [Indexed: 02/26/2024]
Abstract
BACKGROUND & AIMS Continuous glucose monitoring (CGM) provides data on short-term glycemic variability (GV). GV is associated with adverse outcomes in individuals with diabetes. Whether GV is associated with cardiometabolic risk in individuals without diabetes is unclear. We systematically reviewed the literature to assess whether GV is associated with cardiometabolic risk markers or outcomes in individuals without diabetes. METHODS Searches were performed in PubMed/Medline, Embase and Cochrane from inception through April 2022. Two researchers were involved in study selection, data extraction and quality assessment. Studies evaluating GV using CGM for ≥24 h were included. Studies in populations with acute and/or critical illness were excluded. Both narrative synthesis and meta-analyzes were performed, depending on outcome. RESULTS Seventy-one studies were included; the majority were cross-sectional. Multiple measures of GV are higher in individuals with compared to without prediabetes and GV appears to be inversely associated with beta cell function. In contrast, GV is not clearly associated with insulin sensitivity, fatty liver disease, adiposity, blood lipids, blood pressure or oxidative stress. However, GV may be positively associated with the degree of atherosclerosis and cardiovascular events in individuals with coronary disease. CONCLUSION GV is elevated in prediabetes, potentially related to beta cell dysfunction, but less clearly associated with obesity or traditional risk factors. GV is associated with coronary atherosclerosis development and may predict cardiovascular events and type 2 diabetes. Prospective studies are warranted, investigating the predictive power of GV in relation to incident disease. GV may be an important risk measure also in individuals without diabetes.
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Affiliation(s)
- Anna Hjort
- Department of Biology and Biological Engineering, Division of Food and Nutrition Science, Chalmers University of Technology, Kemivägen 10, 41296 Gothenburg, Sweden.
| | - David Iggman
- Center for Clinical Research Dalarna, Uppsala University, Nissers väg 3, 79182 Falun, Sweden; Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Husargatan 3, BMC, Box 564, 75122 Uppsala, Sweden.
| | - Fredrik Rosqvist
- Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Husargatan 3, BMC, Box 564, 75122 Uppsala, Sweden.
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Rizos EC, Kanellopoulou A, Filis P, Markozannes G, Chaliasos K, Ntzani EE, Tzamouranou A, Tentolouris N, Tsilidis KK. Difference on Glucose Profile From Continuous Glucose Monitoring in People With Prediabetes vs. Normoglycemic Individuals: A Matched-Pair Analysis. J Diabetes Sci Technol 2024; 18:414-422. [PMID: 36715208 PMCID: PMC10973849 DOI: 10.1177/19322968221123530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Comprehensive characteristics of the glycemic profile for prediabetes derived by continuous glucose monitoring (CGM) are unknown. We evaluate the difference of CGM profiles between individuals with prediabetes and normoglycemic individuals, including the response to oral glucose tolerance test (OGTT). METHODS Individuals with prediabetes matched for age, sex, and BMI with normoglycemic individuals were instructed to use professional CGM for 1 week. OGTT was performed on the second day. The primary outcomes were percentages of glucose readings time below range (TBR): <54 or <70 mg/dL, time in range (TIR): 70 to 180 mg/dL, and time above range (TAR): >180 or >250 mg/dL. Area under the curve (AUC) was calculated following the OGTT. Glucose variability was depicted by coefficient of variation (CV), SD, and mean amplitude of glucose excursion (MAGE). Wilcoxon sign-ranked test, McNemar mid P-test and linear regression models were employed. RESULTS In all, 36 participants (median age 51 years; median body mass index [BMI] = 26.4 kg/m2) formed 18 matched pairs. Statistically significant differences were observed for 24-hour time in range (TIR; median 98.5% vs. 99.9%, P = .013), time above range (TAR) >180 mg/dl (0.4% vs. 0%, P = .0062), and 24-hour mean interstitial glucose (113.8 vs. 108.8 mg/dL, P = .0038) between people with prediabetes compared to normoglycemic participants. Statistically significant differences favoring the normoglycemic group were found for glycemic variability indexes (median CV 15.2% vs. 11.9%, P = .0156; median MAGE 44.3 vs. 33.3 mg/dL, P = 0.0043). Following OGTT, the AUC was significantly lower in normoglycemic compared to the prediabetes group (median 18615.3 vs. 16370.0, P = .0347 for total and 4666.5 vs. 2792.7, P = .0429 for incremental 2-hour post OGTT). CONCLUSION Individuals with prediabetes have different glucose profiles compared to normoglycemic individuals. CGM might be helpful in individuals with borderline glucose values for a more accurate reclassification.
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Affiliation(s)
- Evangelos C. Rizos
- Department of Internal Medicine, University Hospital of Ioannina, Ioannina, Greece
- School of Medicine, European University Cyprus, Nicosia, Cyprus
| | - Afroditi Kanellopoulou
- Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece
| | - Panagiotis Filis
- Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece
| | - Georgios Markozannes
- Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece
| | - Konstantinos Chaliasos
- Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece
| | - Evangelia E. Ntzani
- Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece
- Center for Evidence-Based Medicine, Department of Health Services, Policy and Practice, School of Public Health, Brown University, Providence, RI, USA
| | - Athina Tzamouranou
- Pharmacy Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Nikolaos Tentolouris
- First Department of Propaedeutic and Internal Medicine, Diabetes Centre, Medical School, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
| | - Konstantinos K. Tsilidis
- Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, Greece
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
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Barua S, Glantz N, Larez A, Bevier W, Sabharwal A, Kerr D. A probabilistic computation framework to estimate the dawn phenomenon in type 2 diabetes using continuous glucose monitoring. Sci Rep 2024; 14:2915. [PMID: 38316854 PMCID: PMC10844336 DOI: 10.1038/s41598-024-52461-1] [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: 04/29/2023] [Accepted: 01/18/2024] [Indexed: 02/07/2024] Open
Abstract
In type 2 diabetes (T2D), the dawn phenomenon is an overnight glucose rise recognized to contribute to overall glycemia and is a potential target for therapeutic intervention. Existing CGM-based approaches do not account for sensor error, which can mask the true extent of the dawn phenomenon. To address this challenge, we developed a probabilistic framework that incorporates sensor error to assign a probability to the occurrence of dawn phenomenon. In contrast, the current approaches label glucose fluctuations as dawn phenomena as a binary yes/no. We compared the proposed probabilistic model with a standard binary model on CGM data from 173 participants (71% female, 87% Hispanic/Latino, 54 ± 12 years, with either a diagnosis of T2D for six months or with an elevated risk of T2D) stratified by HbA1c levels into normal but at risk for T2D, with pre-T2D, or with non-insulin-treated T2D. The probabilistic model revealed a higher dawn phenomenon frequency in T2D [49% (95% CI 37-63%)] compared to pre-T2D [36% (95% CI 31-48%), p = 0.01] and at-risk participants [34% (95% CI 27-39%), p < 0.0001]. While these trends were also found using the binary approach, the probabilistic model identified significantly greater dawn phenomenon frequency than the traditional binary model across all three HbA1c sub-groups (p < 0.0001), indicating its potential to detect the dawn phenomenon earlier across diabetes risk categories.
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Affiliation(s)
- Souptik Barua
- Division of Precision Medicine, Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA.
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA.
| | - Namino Glantz
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
- Santa Barbara County Education Office, Santa Barbara, CA, USA
| | - Arianna Larez
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | - Wendy Bevier
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | - Ashutosh Sabharwal
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - David Kerr
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
- Center for Health Systems Research, Sutter Health, Santa Barbara, CA, USA
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8
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Visser MM, Gillard P. Best practices in collecting and reporting continuous glucose monitoring data in research settings. Nat Metab 2024; 6:189-191. [PMID: 38360954 DOI: 10.1038/s42255-024-00973-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Affiliation(s)
- Margaretha M Visser
- Department of Endocrinology, University Hospitals Leuven - KU Leuven, Leuven, Belgium.
| | - Pieter Gillard
- Department of Endocrinology, University Hospitals Leuven - KU Leuven, Leuven, Belgium
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Pai A, Santiago R, Glantz N, Bevier W, Barua S, Sabharwal A, Kerr D. Multimodal digital phenotyping of diet, physical activity, and glycemia in Hispanic/Latino adults with or at risk of type 2 diabetes. NPJ Digit Med 2024; 7:7. [PMID: 38212415 PMCID: PMC10784546 DOI: 10.1038/s41746-023-00985-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 12/04/2023] [Indexed: 01/13/2024] Open
Abstract
Digital phenotyping refers to characterizing human bio-behavior through wearables, personal devices, and digital health technologies. Digital phenotyping in populations facing a disproportionate burden of type 2 diabetes (T2D) and health disparities continues to lag compared to other populations. Here, we report our study demonstrating the application of multimodal digital phenotyping, i.e., the simultaneous use of CGM, physical activity monitors, and meal tracking in Hispanic/Latino individuals with or at risk of T2D. For 14 days, 36 Hispanic/Latino adults (28 female, 14 with non-insulin treated T2D) wore a continuous glucose monitor (CGM) and a physical activity monitor (Actigraph) while simultaneously logging meals using the MyFitnessPal app. We model meal events and daily digital biomarkers representing diet, physical activity choices, and corresponding glycemic response. We develop a digital biomarker for meal events that differentiates meal events into normal and elevated categories. We examine the contribution of daily digital biomarkers of elevated meal event count and step count on daily time-in-range 54-140 mg/dL (TIR54-140) and average glucose. After adjusting for step count, a change in elevated meal event count from zero to two decreases TIR54-140 by 4.0% (p = 0.003). An increase in 1000 steps in post-meal step count also reduces the meal event glucose response by 641 min mg/dL (p = 0.0006) and reduces the odds of an elevated meal event by 55% (p < 0.0001). The proposed meal event digital biomarkers may provide an opportunity for non-pharmacologic interventions for Hispanic/Latino adults facing a disproportionate burden of T2D.
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Affiliation(s)
- Amruta Pai
- Electrical and Computer Engineering, Rice University, Houston, TX, USA.
| | - Rony Santiago
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | - Namino Glantz
- Santa Barbara County Education Office, Children & Family Resource Services, Santa Barbara, CA, USA
| | - Wendy Bevier
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | - Souptik Barua
- Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | | | - David Kerr
- Sutter Center for Health Systems Research, Santa Barbara, CA, USA
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10
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Sato Imuro SE, Sabharwal A, Conneely C, Glantz N, Bevier W, Barua S, Pai A, Larez A, Kerr D. Temporal changes in bio-behavioral and glycemic outcomes following a produce prescription program among predominantly Hispanic/Latino adults with or at risk of type 2 diabetes. Heliyon 2023; 9:e18440. [PMID: 37533982 PMCID: PMC10391944 DOI: 10.1016/j.heliyon.2023.e18440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 07/15/2023] [Accepted: 07/18/2023] [Indexed: 08/04/2023] Open
Abstract
In the United States (U.S.), consumption of fresh vegetables and fruits is below recommended levels. Enhancing access to nutritious food through food prescriptions has been recognized as a promising approach to combat diet-related illnesses. However, the effectiveness of this strategy at a large scale remains untested, particularly in marginalized communities where food insecurity rates and the prevalence of health conditions such as type 2 diabetes (T2D) are higher compared to the background population. This study evaluated the impact of a produce prescription program for predominantly Hispanic/Latino adults living with or at risk of T2D. A total of 303 participants enrolled in a 3-month observational cohort received 21 medically prescribed portions/week of fresh produce. A subgroup of 189 participants used continuous glucose monitoring (CGM) to assess the relationship between CGM profile changes and HbA1c level changes. For 247 participants completing the study (76% female, 84% Hispanic/Latino, 32% with T2D, age 56·6 ± 11·9 years), there was a reduction in weight (-1·1 [-1·6 to -0·6] lbs., p < 0.001), waist circumference (-0·4 [-1·0 to 0·6] cm, p = 0·007) and systolic blood pressure (SBP) for participants with baseline SBP >120 mmHg (-4·2 [-6·8 to -1·8] mmHg, p = 0·001). For participants with an HbA1c ≥ 7·0% at baseline, HbA1c fell significantly (-0·5 [-0·9 to -0·1] %, p = 0·01). There were also improvements in food security (p < 0·0001), self-reported ratings of sleep, mood, pain (all p < 0·001), and measures of depression (p < 0·0001), anxiety (p = 0·045), and stress (p = 0·002) (DASS-21). There was significant correlation (r = 0·8, p = 0·001) between HbA1c change and the change in average glucose for participants with worsening HbA1c, but not for participants with an improvement in HbA1c. In conclusion, medical prescription of fresh produce is associated with significant improvements in cardio-metabolic and psycho-social risk factors for Hispanic/Latino adults with or at risk of T2D.
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Affiliation(s)
| | | | - Casey Conneely
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | - Namino Glantz
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | - Wendy Bevier
- Santa Barbara County Education Office, Children & Family Resource Services, Santa Barbara, CA, USA
| | - Souptik Barua
- Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Amruta Pai
- Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Arianna Larez
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | - David Kerr
- Diabetes Technology Society, Burlingame, CA, USA
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11
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Kharmats AY, Popp C, Hu L, Berube L, Curran M, Wang C, Pompeii ML, Li H, Bergman M, St-Jules DE, Segal E, Schoenthaler A, Williams N, Schmidt AM, Barua S, Sevick MA. A randomized clinical trial comparing low-fat with precision nutrition-based diets for weight loss: impact on glycemic variability and HbA1c. Am J Clin Nutr 2023; 118:443-451. [PMID: 37236549 PMCID: PMC10447469 DOI: 10.1016/j.ajcnut.2023.05.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 05/16/2023] [Accepted: 05/23/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Recent studies have demonstrated considerable interindividual variability in postprandial glucose response (PPGR) to the same foods, suggesting the need for more precise methods for predicting and controlling PPGR. In the Personal Nutrition Project, the investigators tested a precision nutrition algorithm for predicting an individual's PPGR. OBJECTIVE This study aimed to compare changes in glycemic variability (GV) and HbA1c in 2 calorie-restricted weight loss diets in adults with prediabetes or moderately controlled type 2 diabetes (T2D), which were tertiary outcomes of the Personal Diet Study. METHODS The Personal Diet Study was a randomized clinical trial to compare a 1-size-fits-all low-fat diet (hereafter, standardized) with a personalized diet (hereafter, personalized). Both groups received behavioral weight loss counseling and were instructed to self-monitor diets using a smartphone application. The personalized arm received personalized feedback through the application to reduce their PPGR. Continuous glucose monitoring (CGM) data were collected at baseline, 3 mo and 6 mo. Changes in mean amplitude of glycemic excursions (MAGEs) and HbA1c at 6 mo were assessed. We performed an intention-to-treat analysis using linear mixed regressions. RESULTS We included 156 participants [66.5% women, 55.7% White, 24.1% Black, mean age 59.1 y (standard deviation (SD) = 10.7 y)] in these analyses (standardized = 75, personalized = 81). MAGE decreased by 0.83 mg/dL per month for standardized (95% CI: 0.21, 1.46 mg/dL; P = 0.009) and 0.79 mg/dL per month for personalized (95% CI: 0.19, 1.39 mg/dL; P = 0.010) diet, with no between-group differences (P = 0.92). Trends were similar for HbA1c values. CONCLUSIONS Personalized diet did not result in an increased reduction in GV or HbA1c in patients with prediabetes and moderately controlled T2D, compared with a standardized diet. Additional subgroup analyses may help to identify patients who are more likely to benefit from this personalized intervention. This trial was registered at clinicaltrials.gov as NCT03336411.
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Affiliation(s)
- Anna Y Kharmats
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Collin Popp
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Lu Hu
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Lauren Berube
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States.
| | - Margaret Curran
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Chan Wang
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Mary Lou Pompeii
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Huilin Li
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Michael Bergman
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States; Division of Endocrinology, Diabetes and Metabolism, New York University Grossman School of Medicine, New York, NY, United States
| | - David E St-Jules
- Department of Nutrition, University of Nevada, Reno, Reno, NV, United States
| | - Eran Segal
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
| | - Antoinette Schoenthaler
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Natasha Williams
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Ann Marie Schmidt
- Diabetes Research Program, Department of Medicine, New York University Langone Health, New York, NY, United States
| | - Souptik Barua
- Division of Precision Medicine, Department of Medicine, New York University Langone Health, New York, NY, United States
| | - Mary Ann Sevick
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States; Division of Endocrinology, Diabetes and Metabolism, New York University Grossman School of Medicine, New York, NY, United States
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12
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Alawode O, Humble S, Herrick CJ. Food insecurity, SNAP participation and glycemic control in low-income adults with predominantly type 2 diabetes: a cross-sectional analysis using NHANES 2007-2018 data. BMJ Open Diabetes Res Care 2023; 11:e003205. [PMID: 37220963 PMCID: PMC10230897 DOI: 10.1136/bmjdrc-2022-003205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 04/25/2023] [Indexed: 05/25/2023] Open
Abstract
INTRODUCTION Diabetes, characterized by elevated blood glucose levels, affects 13% of US adults, 95% of whom have type 2 diabetes (T2D). Social determinants of health (SDoH), such as food insecurity, are integral to glycemic control. The Supplemental Nutrition Assistance Program (SNAP) aims to reduce food insecurity, but it is not clear how this affects glycemic control in T2D. This study investigated the associations between food insecurity and other SDoH and glycemic control and the role of SNAP participation in a national socioeconomically disadvantaged sample. RESEARCH DESIGN AND METHODS Adults with likely T2D and income <185% of the federal poverty level (FPL) were identified using cross-sectional National Health and Nutrition Examination Survey (NHANES) data (2007-2018). Multivariable logistic regression assessed the association between food insecurity, SNAP participation and glycemic control (defined by HbA1c 7.0%-8.5% depending on age and comorbidities). Covariates included demographic factors, clinical comorbidities, diabetes management strategies, and healthcare access and utilization. RESULTS The study population included 2084 individuals (90% >40 years of age, 55% female, 18% non-Hispanic black, 25% Hispanic, 41% SNAP participants, 36% low or very low food security). Food insecurity was not associated with glycemic control in the adjusted model (adjusted OR (aOR) 1.181 (0.877-1.589)), and SNAP participation did not modify the effect of food insecurity on glycemic control. Insulin use, lack of health insurance, and Hispanic or another race and ethnicity were among the strongest associations with poor glycemic control in the adjusted model. CONCLUSIONS For low-income individuals with T2D in the USA, health insurance may be among the most critical predictors of glycemic control. Additionally, SDoH associated with race and ethnicity plays an important role. SNAP participation may not affect glycemic control because of inadequate benefit amounts or lack of incentives for healthy purchases. These findings have implications for community engaged interventions and healthcare and food policy.
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Affiliation(s)
- Oluwatobi Alawode
- Department of Obstetrics and Gynecology, Meharry Medical College, Nashville, Tennessee, USA
| | - Sarah Humble
- Department of Surgery, Division of Public Health Sciences, Washington University School of Medicine in Saint Louis, St Louis, Missouri, USA
| | - Cynthia J Herrick
- Department of Surgery, Division of Public Health Sciences, Washington University School of Medicine in Saint Louis, St Louis, Missouri, USA
- Department of Medicine, Division of Endocrinology, Metabolism, and Lipid Research, Washington University in St Louis, St Louis, Missouri, USA
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13
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Dingena CF, Holmes MJ, Campbell MD, Cade JE, Scott EM, Zulyniak MA. Observational assessments of the relationship of dietary and pharmacological treatment on continuous measures of dysglycemia over 24 hours in women with gestational diabetes. Front Endocrinol (Lausanne) 2023; 14:1065985. [PMID: 36777347 PMCID: PMC9909093 DOI: 10.3389/fendo.2023.1065985] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 01/17/2023] [Indexed: 01/27/2023] Open
Abstract
Objectives Studies that use continuous glucose monitoring (CGM) to monitor women with gestational diabetes (GDM), highlight the importance of managing dysglycemia over a 24-hour period. However, the effect of current treatment methods on dysglycemia over 24-hrs are currently unknown. This study aimed to characterise CGM metrics over 24-hrs in women with GDM and the moderating effect of treatment strategy. Methods Retrospective analysis of CGM data from 128 women with GDM in antenatal diabetes clinics. CGM was measured for 7-days between 30-32 weeks gestation. Non-parametric tests were used to evaluate differences of CGM between periods of day (morning, afternoon, evening, and overnight) and between treatment methods (i.e., diet alone or diet+metformin). Exploratory analysis in a subgroup of 34 of participants was performed to investigate the association between self-reported macronutrient intake and glycaemic control. Results Glucose levels significantly differed during the day (i.e., morning to evening; P<0.001) and were significantly higher (i.e., mean blood glucose and area under the curve [AUC]) and more variable (i.e., SD and CV) than overnight glucose levels. Morning showed the highest amount of variability (CV; 8.4% vs 6.5%, P<0.001 and SD; 0.49 mmol/L vs 0.38 mmol/L, P<0.001). When comparing treatment methods, mean glucose (6.09 vs 5.65 mmol/L; P<0.001) and AUC (8760.8 vs 8115.1 mmol/L.hr; P<0.001) were significantly higher in diet+metformin compared to diet alone. Finally, the exploratory analysis revealed a favourable association between higher protein intake (+1SD or +92 kcal/day) and lower mean glucose (-0.91 mmol/L p, P=0.02) and total AUC (1209.6 mmol/L.h, P=0.021). Conclusions Glycemia varies considerably across a day, with morning glycemia demonstrating greatest variability. Additionally, our work supports that individuals assigned to diet+metformin have greater difficulty managing glycemia and results suggest that increased dietary protein may assist with management of dysglycemia. Future work is needed to investigate the benefit of increased protein intake on management of dysglycemia.
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Affiliation(s)
- Cassy F. Dingena
- Nutritional Epidemiology, School of Food Science and Nutrition, University of Leeds, Leeds, United Kingdom
| | - Melvin J. Holmes
- Nutritional Epidemiology, School of Food Science and Nutrition, University of Leeds, Leeds, United Kingdom
| | - Matthew D. Campbell
- School of Nursing and Health Sciences, Institute of Health Sciences and Wellbeing, University of Sunderland, Sunderland, United Kingdom
| | - Janet E. Cade
- Nutritional Epidemiology, School of Food Science and Nutrition, University of Leeds, Leeds, United Kingdom
| | - Eleanor M. Scott
- Department of Clinical and Population Science, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | - Michael A. Zulyniak
- Nutritional Epidemiology, School of Food Science and Nutrition, University of Leeds, Leeds, United Kingdom
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14
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Liang Z. Mining associations between glycemic variability in awake-time and in-sleep among non-diabetic adults. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:1026830. [DOI: 10.3389/fmedt.2022.1026830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 10/12/2022] [Indexed: 11/06/2022] Open
Abstract
It is often assumed that healthy people have the genuine ability to maintain tight blood glucose regulation. However, a few recent studies revealed that glucose dysregulation such as hyperglycemia may occur even in people who are considered normoglycemic by standard measures and were more prevalent than initially thought, suggesting that more investigations are needed to fully understand the within-day glucose dynamics of healthy people. In this paper, we conducted an analysis on a multi-modal dataset to examine the relationships between glycemic variability when people were awake and that when they were sleeping. The interstitial glucose levels were measured with a wearable continuous glucose monitoring (CGM) technology FreeStyle Libre 2 at every 15 min interval. In contrast to the traditional single-time-point measurements, the CGM data allow the investigation into the temporal patterns of glucose dynamics at high granularity. Sleep onset and offset timestamps were recorded daily with a Fitbit Charge 3 wristband. Our analysis leveraged the sleep data to split the glucose readings into segments of awake-time and in-sleep, instead of using fixed cut-off time points as has been done in existing literature. We combined repeated measure correlation analysis and quantitative association rules mining, together with an original post-filtering method, to identify significant and most relevant associations. Our results showed that low overall glucose in awake time was strongly correlated to low glucose in subsequent sleep, which in turn correlated to overall low glucose in the next day. Moreover, both analysis techniques identified significant associations between the minimal glucose reading in sleep and the low blood glucose index the next day. In addition, the association rules discovered in this study achieved high confidence (0.75–0.88) and lift (4.1–11.5), which implies that the proposed post-filtering method was effective in selecting quality rules.
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15
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Barua S, A Wierzchowska-McNew R, Deutz NEP, Sabharwal A. Discordance between postprandial plasma glucose measurement and continuous glucose monitoring. Am J Clin Nutr 2022; 116:1059-1069. [PMID: 35776949 DOI: 10.1093/ajcn/nqac181] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 06/27/2022] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND There has been growing interest in studying postprandial glucose responses using continuous glucose monitoring (CGM) in nondiabetic individuals. Accurate measurement of glucose responses to meals can facilitate applications such as precision nutrition and early detection of diabetes. OBJECTIVES We aimed to quantify the discordance between simultaneous postprandial glucose measurements made using plasma and CGM. METHODS We studied 10 nondiabetic older adults who randomly consumed 9 predefined meals of varying macronutrient compositions. Glucose was measured for 8 h after the meal by the CGM, blood samples for plasma collection were taken regularly, and plasma glucose was quantified using gold-standard laboratory measurement and a fingerstick blood glucose meter. The primary outcome measured was the mean absolute relative difference (MARD) of CGM and fingerstick measurements relative to the gold standard. Secondary outcomes were Bland-Altman statistics, Clarke Error Grid, and time in range metrics. Additional subgroup analyses were performed by stratifying the postprandial glucose measurements based on the macronutrient composition of the meals. RESULTS When compared against the gold-standard postprandial glucose measurements, the fingerstick meter was more accurate (MARD: 8.0%; 95% CI: 7.6%, 8.6%) than the CGM (MARD: 13.7%; 95% CI: 13.1%, 14.3%; P < 0.0001). After the meals, Bland-Altman analysis demonstrated that the CGM underestimated the 8-h gold-standard glucose rise by 12.8 mg/dL on average (P < 0.0001), whereas the fingerstick meter did so by 5.5 mg/dL on average (P < 0.0001). The CGM underestimated the time spent in the 70-180 mg/dL range (P = 0.002) and overestimated the time spent <70 mg/dL (P < 0.0001) compared with the other 2 methods. CONCLUSIONS We discovered discordance between gold standard, fingerstick, and CGM in measuring plasma glucose concentrations after a meal. Consequently, emerging applications of CGM in healthy individuals, such as precision nutrition and diabetes onset prediction, may need to account for these discordances.This trial was registered at clinicaltrials.gov as NCT04928872.
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Affiliation(s)
- Souptik Barua
- Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Raven A Wierzchowska-McNew
- Center for Translational Research in Aging and Longevity, Texas A&M University, College Station, TX, USA
| | - Nicolaas E P Deutz
- Center for Translational Research in Aging and Longevity, Texas A&M University, College Station, TX, USA
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16
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Dabbagh Z, McKee MD, Pirraglia PA, Clements KM, Liu F, Amante DJ, Shukla P, Gerber BS. The Expanding Use of Continuous Glucose Monitoring in Type 2 Diabetes. Diabetes Technol Ther 2022; 24:510-515. [PMID: 35231190 DOI: 10.1089/dia.2021.0536] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Zakery Dabbagh
- Department of Population and Quantitative Health Sciences, UMass Chan Medical School, Worcester, Massachusetts, USA
| | - M Diane McKee
- Department of Family Medicine and Community Health, UMass Chan Medical School, Worcester, Massachusetts, USA
| | - Paul A Pirraglia
- Division of General Medicine and Community Health, UMass Chan Medical School-Baystate, Springfield, Massachusetts, USA
| | - Karen M Clements
- Commonwealth Medicine, UMass Chan Medical School, Worcester, Massachusetts, USA
| | - Feifan Liu
- Department of Population and Quantitative Health Sciences, UMass Chan Medical School, Worcester, Massachusetts, USA
| | - Daniel J Amante
- Department of Population and Quantitative Health Sciences, UMass Chan Medical School, Worcester, Massachusetts, USA
| | - Prateek Shukla
- Department of Medicine, UMass Chan Medical School, Worcester, Massachusetts, USA
| | - Ben S Gerber
- Department of Population and Quantitative Health Sciences, UMass Chan Medical School, Worcester, Massachusetts, USA
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17
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Barua S, Sabharwal A, Glantz N, Conneely C, Larez A, Bevier W, Kerr D. The northeast glucose drift: Stratification of post-breakfast dysglycemia among predominantly Hispanic/Latino adults at-risk or with type 2 diabetes. EClinicalMedicine 2022; 43:101241. [PMID: 34988413 PMCID: PMC8703234 DOI: 10.1016/j.eclinm.2021.101241] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 11/16/2021] [Accepted: 11/29/2021] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND There is minimal experience in continuous glucose monitoring (CGM) among underserved racial/ethnic minority populations with or at risk of type 2 diabetes (T2D), and therefore a lack of CGM-driven insight for these individuals. We analyzed breakfast-related CGM profiles of free-living, predominantly Hispanic/Latino individuals at-risk of T2D, with pre-T2D, or with non-insulin treated T2D. METHODS Starting February 2019, 119 participants in Santa Barbara, CA, USA, (93 female, 87% Hispanic/Latino [predominantly Mexican-American], age 54·4 [±12·1] years), stratified by HbA1c levels into (i) at-risk of T2D, (ii) with pre-T2D, and (iii) with non-insulin treated T2D, wore blinded CGMs for two weeks. We compared valid CGM profiles from 106 of these participants representing glucose response to breakfast using four parameters. FINDINGS A "northeast drift" was observed in breakfast glucose responses comparing at-risk to pre-T2D to T2D participants. T2D participants had a significantly higher pre-breakfast glucose level, glucose rise, glucose incremental area under the curve (all p < 0·0001), and time to glucose peak (p < 0·05) compared to pre-T2D and at-risk participants. After adjusting for demographic and clinical covariates, pre-breakfast glucose and time to peak (p < 0·0001) were significantly associated with HbA1c. The model predicted HbA1c within (0·55 ± 0·67)% of true laboratory HbA1c values. INTERPRETATION For predominantly Hispanic/Latino adults, the average two-week breakfast glucose response shows a progression of dysglycemia from at-risk of T2D to pre-T2D to T2D. CGM-based breakfast metrics have the potential to predict HbA1c levels and monitor diabetes progression. FUNDING US Department of Agriculture (Grant #2018-33800-28404), a seed grant from the industry board fees of the NSF Engineering Research Center for Precise Advanced Technologies and Health Systems for Underserved Populations (PATHS-UP) (Award #1648451), and the Elsevier foundation.
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Affiliation(s)
- Souptik Barua
- Electrical and Computer Engineering, Rice University, Houston, TX, United States
| | - Ashutosh Sabharwal
- Electrical and Computer Engineering, Rice University, Houston, TX, United States
| | - Namino Glantz
- Sansum Diabetes Research Institute, Santa Barbara, California, United States
| | - Casey Conneely
- Sansum Diabetes Research Institute, Santa Barbara, California, United States
| | - Arianna Larez
- Sansum Diabetes Research Institute, Santa Barbara, California, United States
| | - Wendy Bevier
- Sansum Diabetes Research Institute, Santa Barbara, California, United States
| | - David Kerr
- Sansum Diabetes Research Institute, Santa Barbara, California, United States
- Corresponding author.
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