Continuous glucose monitoring and 1-h plasma glucose identifies glycemic variability and dysglycemia in high-risk individuals with HbA1c &lt; 5.7%: a pilot study

Dorcely, Brenda; Sifonte, Eliud; Popp, Collin; Divakaran, Anjana; Katz, Karin; Musleh, Sarah; Jagannathan, Ram; Curran, Margaret; Sevick, Mary Ann; Alemán, José O.; Goldberg, Ira J.; Bergman, Michael

doi:10.1007/s12020-022-03109-5

Cited by 3 publications

(2 citation statements)

References 17 publications

(15 reference statements)

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“…13,14 In a previous study, both 1-h PG during the OGTT and CGM-derived GV indices identified individuals with dysglycemia despite having normal HbA1c. 15 In addition, both 1-h PG and CGM during an OGTT can detect elevated GV not captured by HbA1c. This prospective longitudinal cohort study aimed to examine GV in individuals with and without diabetes, and assess the effects of laparoscopic SG on glycemic status during acute weight loss at 6 weeks (W) and chronic weight loss at 6-month (M) post-SG by measuring: (1) 1-h PG during an OGTT, and (2) CGM-derived GV index: MAGE.…”

Section: Introductionmentioning

confidence: 99%

“…Elevated GV indices such as mean amplitude of glycemic excursions (MAGE), calculated from CGM‐derived interstitial glucose concentrations, are potential surrogate markers for early pancreatic β ‐cell dysfunction 13,14 . In a previous study, both 1‐h PG during the OGTT and CGM‐derived GV indices identified individuals with dysglycemia despite having normal HbA1c 15 . In addition, both 1‐h PG and CGM during an OGTT can detect elevated GV not captured by HbA1c.…”

Section: Introductionmentioning

confidence: 99%

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Continuous glucose monitoring captures glycemic variability in obesity after sleeve gastrectomy: A prospective cohort study

Dorcely,

DeBermont,

Gujral

et al. 2024

Obesity Science & Practice

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ObjectiveHbA1c is an insensitive marker for assessing real‐time dysglycemia in obesity. This study investigated whether 1‐h plasma glucose level (1‐h PG) ≥155 mg/dL (8.6 mmol/L) during an oral glucose tolerance test (OGTT) and continuous glucose monitoring (CGM) measurement of glucose variability (GV) better reflected dysglycemia than HbA1c after weight loss from metabolic and bariatric surgery.MethodsThis was a prospective cohort study of 10 participants with type 2 diabetes compared with 11 participants with non‐diabetes undergoing sleeve gastrectomy (SG). At each research visit; before SG, and 6 weeks and 6 months post‐SG, body weight, fasting lipid levels, and PG and insulin concentrations during an OGTT were analyzed. Mean amplitude of glycemic excursions (MAGE), a CGM‐derived GV index, was analyzed.ResultsThe 1‐h PG correlated with insulin resistance markers, triglyceride/HDL ratio and triglyceride glucose index in both groups before surgery. At 6 months, SG caused 22% weight loss in both groups. Despite a reduction in HbA1c by 3.0 ± 1.3% in the diabetes group (p < 0.01), 1‐h PG, and MAGE remained elevated, and the oral disposition index, which represents pancreatic β‐cell function, remained reduced in the diabetes group when compared to the non‐diabetes group.ConclusionsElevation of GV markers and reduced disposition index following SG‐induced weight loss in the diabetes group underscores persistent β‐cell dysfunction and the potential residual risk of diabetes complications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Continuous glucose monitoring captures glycemic variability in obesity after sleeve gastrectomy: A prospective cohort study

Dorcely,

DeBermont,

Gujral

et al. 2024

Obesity Science & Practice

Self Cite

View full text Add to dashboard Cite

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Remission of prediabetes via lifestyle intervention

Bergman,

Dorcely

2023

The Lancet Diabetes & Endocrinology

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Estimating Breakfast Characteristics Using Continuous Glucose Monitoring and Machine Learning in Adults With or at Risk of Type 2 Diabetes

Pai,

Barua,

Kim

et al. 2024

J Diabetes Sci Technol

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Background: Continuous glucose monitoring (CGM) systems allow detailed assessment of postprandial glucose responses (PPGR), offering new insights into food choices’ impact on dysglycemia. However, current approaches to analyze PPGR using a CGM require manual meal logging, limiting the scalability of CGM-driven applications like personalized nutrition and at-home diabetes risk assessment. Objective: We propose a machine learning (ML) framework to automatically identify and characterize breakfast-related PPGRs from CGM profiles in adults at risk of or living with noninsulin-treated type 2 diabetes (T2D). Methods: Our PPGR estimation framework uses a random forest ML algorithm trained on 15 adults without diabetes who wore a CGM for up to four weeks. The algorithm performance was evaluated on a held-out subset of the participants’ CGM data as well as on an external validation data set of 36 individuals at risk for or with noninsulin-treated T2D. Results: Our algorithm’s estimations of breakfast PPGRs displayed no statistically significant differences to annotated PPGRs, in terms of incremental area under the curve and glucose rise ( P > .05 for both data sets), while a small difference in prebreakfast glucose was found in the nondiabetes data set ( P = .005) but not in the validation T2D data set ( P = .18). Conclusions: We designed an ML framework to automatically estimate the timing of meal events from CGM data in individuals without diabetes and in individuals at risk or with T2D. This could provide a more scalable approach for analyzing postprandial glycemia, increasing the feasibility of CGM-based precision nutrition and diabetes risk assessment applications.

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Continuous glucose monitoring and 1-h plasma glucose identifies glycemic variability and dysglycemia in high-risk individuals with HbA1c < 5.7%: a pilot study

Cited by 3 publications

References 17 publications

Continuous glucose monitoring captures glycemic variability in obesity after sleeve gastrectomy: A prospective cohort study

Continuous glucose monitoring captures glycemic variability in obesity after sleeve gastrectomy: A prospective cohort study

Remission of prediabetes via lifestyle intervention

Estimating Breakfast Characteristics Using Continuous Glucose Monitoring and Machine Learning in Adults With or at Risk of Type 2 Diabetes

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