Dataset Information

A New Analysis Tool for Continuous Glucose Monitor Data.

ABSTRACT:

Background

With the development of continuous glucose monitoring systems (CGMS), detailed glycemic data are now available for analysis. Yet analysis of this data-rich information can be formidable. The power of CGMS-derived data lies in its characterization of glycemic variability. In contrast, many standard glycemic measures like hemoglobin A1c (HbA1c) and self-monitored blood glucose inadequately describe glycemic variability and run the risk of bias toward overreporting hyperglycemia. Methods that adjust for this bias are often overlooked in clinical research due to difficulty of computation and lack of accessible analysis tools.

Methods

In response, we have developed a new R package rGV, which calculates a suite of 16 glycemic variability metrics when provided a single individual's CGM data. rGV is versatile and robust; it is capable of handling data of many formats from many sensor types. We also created a companion R Shiny web app that provides these glycemic variability analysis tools without prior knowledge of R coding. We analyzed the statistical reliability of all the glycemic variability metrics included in rGV and illustrate the clinical utility of rGV by analyzing CGM data from three studies.

Results

In subjects without diabetes, greater glycemic variability was associated with higher HbA1c values. In patients with type 2 diabetes mellitus (T2DM), we found that high glucose is the primary driver of glycemic variability. In patients with type 1 diabetes (T1DM), we found that naltrexone use may potentially reduce glycemic variability.

Conclusions

We present a new R package and accompanying web app to facilitate quick and easy computation of a suite of glycemic variability metrics.

SUBMITTER: Olawsky E

PROVIDER: S-EPMC9631526 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

A New Analysis Tool for Continuous Glucose Monitor Data.

Olawsky Evan E Zhang Yuan Y Eberly Lynn E LE Helgeson Erika S ES Chow Lisa S LS

Journal of diabetes science and technology 20210720 6

<h4>Background</h4>With the development of continuous glucose monitoring systems (CGMS), detailed glycemic data are now available for analysis. Yet analysis of this data-rich information can be formidable. The power of CGMS-derived data lies in its characterization of glycemic variability. In contrast, many standard glycemic measures like hemoglobin A1c (HbA1c) and self-monitored blood glucose inadequately describe glycemic variability and run the risk of bias toward overreporting hyperglycemia. ...[more]

PMID: 34282646

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Project description:BackgroundGestational Diabetes Mellitus (GDM) incidence and adverse outcomes have increased globally. The validity of the oral glucose tolerance test (OGTT) for GDM diagnosis has long been questioned, with no suitable substitute reported yet. Continuous Glucose Monitoring (CGM) is potentially a more acceptable and comprehensive test. The aim of this study was to assess the Freestyle Libre Pro 2 acceptability as a diagnostic test for GDM, then triangulating its results with OGTT results as well as risk factors and sonographic features of GDM.MethodsWomen wore the CGM device for 7 days at 24-28 weeks, undergoing the OGTT before CGM removal. CGM/OGTT acceptability as well as GDM risk factors evaluation occurred via three online surveys. CGM distribution/variability/time in range parameters, combined in a CGM Score of Variability (CGMSV), were triangulated with OGTT results and a risk-factor-based Total Risk Score (TRS). In a subgroup, GDM ultrasound features (as modified Ultrasound Gestational Diabetes Score - m-UGDS) were also incorporated.ResultsOf 107 women recruited, 87 (81%) were included: 74 (85%) with negative OGTT (NGT) and 13 (15%) positive (GDM). No significant difference was found between NGT and GDM in terms of demographics (apart from family history of diabetes mellitus), CGM parameters and perinatal outcomes. Women considered CGM significantly more acceptable than OGTT (81% versus 27% rating 5/5, p < 0.001). Of the 55 NGT with triangulation data, 28 were considered 'true negative' (TRS concordant with OGTT and CGMSV): of these 4/5 evaluated at ultrasound had m-UGDS below the cut-off. Five women were considered 'false negative' (negative OGTT with both TRS and CGMSV above the respective cut-offs). Triangulation identified also six 'false positive' women (positive OGTT but TRS and CGM both below the cut-offs). Only one woman for each of the last two categories had m-UGDS evaluated, with discordant results.ConclusionsCGM represents a more acceptable alternative for GDM diagnosis to the OGTT. CGM triangulation analysis suggests OGTT screening may result in both false positives and negatives. Further research including larger cohorts of patients, and additional triangulation elements (such as GDM biomarkers/outcomes and expanded m-UGDS) is needed to explore CGM potential for GDM diagnosis.

Dataset Information

A New Analysis Tool for Continuous Glucose Monitor Data.

Background

Methods

Results

Conclusions

Publications

A New Analysis Tool for Continuous Glucose Monitor Data.

Similar Datasets

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