Loading…
Continuous Glucose Monitoring for Prediabetes: What Are the Best Metrics?
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...
Saved in:
Published in: | Journal of diabetes science and technology 2024-07, Vol.18 (4), p.835-846 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | cdi_FETCH-LOGICAL-c292t-d8c2d3341832d3ed0b89049903f13cb8175d1c1fd4114653777323630bac93173 |
container_end_page | 846 |
container_issue | 4 |
container_start_page | 835 |
container_title | Journal of diabetes science and technology |
container_volume | 18 |
creator | Zahalka, Salwa J. Galindo, Rodolfo J. Shah, Viral N. Low Wang, Cecilia C. |
description | 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. |
doi_str_mv | 10.1177/19322968241242487 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_3040322177</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sage_id>10.1177_19322968241242487</sage_id><sourcerecordid>3040322177</sourcerecordid><originalsourceid>FETCH-LOGICAL-c292t-d8c2d3341832d3ed0b89049903f13cb8175d1c1fd4114653777323630bac93173</originalsourceid><addsrcrecordid>eNp9kLFOwzAQhi0EoqXwACzII0uKz3ZjmwW1FZRKrWAAMUaJ47Sp0rjYzsDb46qFBYnpTqfvft19CF0DGQIIcQeKUapSSTlQTrkUJ6i_nyUMiDg99nughy683xAyiow4Rz0mU6qE5H00n9o21G1nO49nTaetN3hp2zpYV7crXFmHX50p67wwwfh7_LHOAx47g8Pa4InxAS9NcLX2D5forMobb66OdYDenx7fps_J4mU2n44XiaaKhqSUmpaMcZAsVlOSQirClSKsAqYLCWJUgoaq5AA8HTEhBKMsZaTItWIg2ADdHnJ3zn528YJsW3ttmiZvTfwiY4STqCX6iSgcUO2s985U2c7V29x9ZUCyvcHsj8G4c3OM74qtKX83fpRFYHgAfL4y2cZ2ro3v_pP4Da3VdjQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3040322177</pqid></control><display><type>article</type><title>Continuous Glucose Monitoring for Prediabetes: What Are the Best Metrics?</title><source>Sage Journals Online</source><creator>Zahalka, Salwa J. ; Galindo, Rodolfo J. ; Shah, Viral N. ; Low Wang, Cecilia C.</creator><creatorcontrib>Zahalka, Salwa J. ; Galindo, Rodolfo J. ; Shah, Viral N. ; Low Wang, Cecilia C.</creatorcontrib><description>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.</description><identifier>ISSN: 1932-2968</identifier><identifier>EISSN: 1932-3107</identifier><identifier>DOI: 10.1177/19322968241242487</identifier><identifier>PMID: 38629784</identifier><language>eng</language><publisher>Los Angeles, CA: SAGE Publications</publisher><subject>Blood Glucose - analysis ; Blood Glucose Self-Monitoring ; Continuous Glucose Monitoring ; Humans ; Prediabetic State - blood ; Prediabetic State - diagnosis</subject><ispartof>Journal of diabetes science and technology, 2024-07, Vol.18 (4), p.835-846</ispartof><rights>2024 Diabetes Technology Society</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c292t-d8c2d3341832d3ed0b89049903f13cb8175d1c1fd4114653777323630bac93173</cites><orcidid>0000-0002-3827-7107 ; 0000-0001-8557-5417 ; 0000-0002-9295-3225</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925,79364</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38629784$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zahalka, Salwa J.</creatorcontrib><creatorcontrib>Galindo, Rodolfo J.</creatorcontrib><creatorcontrib>Shah, Viral N.</creatorcontrib><creatorcontrib>Low Wang, Cecilia C.</creatorcontrib><title>Continuous Glucose Monitoring for Prediabetes: What Are the Best Metrics?</title><title>Journal of diabetes science and technology</title><addtitle>J Diabetes Sci Technol</addtitle><description>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.</description><subject>Blood Glucose - analysis</subject><subject>Blood Glucose Self-Monitoring</subject><subject>Continuous Glucose Monitoring</subject><subject>Humans</subject><subject>Prediabetic State - blood</subject><subject>Prediabetic State - diagnosis</subject><issn>1932-2968</issn><issn>1932-3107</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kLFOwzAQhi0EoqXwACzII0uKz3ZjmwW1FZRKrWAAMUaJ47Sp0rjYzsDb46qFBYnpTqfvft19CF0DGQIIcQeKUapSSTlQTrkUJ6i_nyUMiDg99nughy683xAyiow4Rz0mU6qE5H00n9o21G1nO49nTaetN3hp2zpYV7crXFmHX50p67wwwfh7_LHOAx47g8Pa4InxAS9NcLX2D5forMobb66OdYDenx7fps_J4mU2n44XiaaKhqSUmpaMcZAsVlOSQirClSKsAqYLCWJUgoaq5AA8HTEhBKMsZaTItWIg2ADdHnJ3zn528YJsW3ttmiZvTfwiY4STqCX6iSgcUO2s985U2c7V29x9ZUCyvcHsj8G4c3OM74qtKX83fpRFYHgAfL4y2cZ2ro3v_pP4Da3VdjQ</recordid><startdate>202407</startdate><enddate>202407</enddate><creator>Zahalka, Salwa J.</creator><creator>Galindo, Rodolfo J.</creator><creator>Shah, Viral N.</creator><creator>Low Wang, Cecilia C.</creator><general>SAGE Publications</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-3827-7107</orcidid><orcidid>https://orcid.org/0000-0001-8557-5417</orcidid><orcidid>https://orcid.org/0000-0002-9295-3225</orcidid></search><sort><creationdate>202407</creationdate><title>Continuous Glucose Monitoring for Prediabetes: What Are the Best Metrics?</title><author>Zahalka, Salwa J. ; Galindo, Rodolfo J. ; Shah, Viral N. ; Low Wang, Cecilia C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c292t-d8c2d3341832d3ed0b89049903f13cb8175d1c1fd4114653777323630bac93173</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Blood Glucose - analysis</topic><topic>Blood Glucose Self-Monitoring</topic><topic>Continuous Glucose Monitoring</topic><topic>Humans</topic><topic>Prediabetic State - blood</topic><topic>Prediabetic State - diagnosis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zahalka, Salwa J.</creatorcontrib><creatorcontrib>Galindo, Rodolfo J.</creatorcontrib><creatorcontrib>Shah, Viral N.</creatorcontrib><creatorcontrib>Low Wang, Cecilia C.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of diabetes science and technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zahalka, Salwa J.</au><au>Galindo, Rodolfo J.</au><au>Shah, Viral N.</au><au>Low Wang, Cecilia C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Continuous Glucose Monitoring for Prediabetes: What Are the Best Metrics?</atitle><jtitle>Journal of diabetes science and technology</jtitle><addtitle>J Diabetes Sci Technol</addtitle><date>2024-07</date><risdate>2024</risdate><volume>18</volume><issue>4</issue><spage>835</spage><epage>846</epage><pages>835-846</pages><issn>1932-2968</issn><eissn>1932-3107</eissn><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.</abstract><cop>Los Angeles, CA</cop><pub>SAGE Publications</pub><pmid>38629784</pmid><doi>10.1177/19322968241242487</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-3827-7107</orcidid><orcidid>https://orcid.org/0000-0001-8557-5417</orcidid><orcidid>https://orcid.org/0000-0002-9295-3225</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1932-2968 |
ispartof | Journal of diabetes science and technology, 2024-07, Vol.18 (4), p.835-846 |
issn | 1932-2968 1932-3107 |
language | eng |
recordid | cdi_proquest_miscellaneous_3040322177 |
source | Sage Journals Online |
subjects | Blood Glucose - analysis Blood Glucose Self-Monitoring Continuous Glucose Monitoring Humans Prediabetic State - blood Prediabetic State - diagnosis |
title | Continuous Glucose Monitoring for Prediabetes: What Are the Best Metrics? |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T05%3A01%3A02IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Continuous%20Glucose%20Monitoring%20for%20Prediabetes:%20What%20Are%20the%20Best%20Metrics?&rft.jtitle=Journal%20of%20diabetes%20science%20and%20technology&rft.au=Zahalka,%20Salwa%20J.&rft.date=2024-07&rft.volume=18&rft.issue=4&rft.spage=835&rft.epage=846&rft.pages=835-846&rft.issn=1932-2968&rft.eissn=1932-3107&rft_id=info:doi/10.1177/19322968241242487&rft_dat=%3Cproquest_cross%3E3040322177%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c292t-d8c2d3341832d3ed0b89049903f13cb8175d1c1fd4114653777323630bac93173%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=3040322177&rft_id=info:pmid/38629784&rft_sage_id=10.1177_19322968241242487&rfr_iscdi=true |