An Assessment of Clinical Continuous Glucose Monitoring Targets for Older and High-Risk People Living with Type 1 Diabetes
Aim: To assess relationships between continuous glucose monitoring (CGM) time in range (TIR), 70–180 mg/dL, time below range (TBR), 180 mg/dL, and glucose coefficient of variation (CV) in relation to currently recommended clinical CGM targets for older people, which recommend reduced TIR and TBR tar...
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| Veröffentlicht in: | Diabetes technology & therapeutics 2023-02, Vol.25 (2), p.18-115 |
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| creator | O'Neal, David N Cohen, Ohad Vogrin, Sara Vigersky, Robert A Jenkins, Alicia J |
| description | Aim:
To assess relationships between continuous glucose monitoring (CGM) time in range (TIR), 70–180 mg/dL, time below range (TBR), 180 mg/dL, and glucose coefficient of variation (CV) in relation to currently recommended clinical CGM targets for older people, which recommend reduced TIR and TBR targets relative to the general type 1 diabetes population.
Methods:
We conducted a post hoc analysis using the JDRF Australia Adult Hybrid Closed Loop trial database examining correlations in 120 adults with type 1 diabetes of 3 weeks masked CGM (Guardian Sensor 3; Medtronic) metrics (
n
= 61 on multiple daily injections, 59 on non-CGM augmented pumps) using manual insulin dosing at baseline and at 26-weeks, with 50% randomized to automated insulin dosing (AID).
Results:
Correlations between baseline TIR and TAR were strong (
r
= −0.966;
P
|
| doi_str_mv | 10.1089/dia.2022.0350 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2730645858</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2771359818</sourcerecordid><originalsourceid>FETCH-LOGICAL-c365t-67e5a3b4f1f000c702aec5b5baac29dc2a2fa73007baf56173b7062c82ad37a23</originalsourceid><addsrcrecordid>eNqFkU1v1DAQhi0EoqVw7BVZ4sIliz_qODmuFmiRFhWh5RxNnMnWxWtvbQfU_vo62raHXnryyHrm1bx6CDnlbMFZ034ZLCwEE2LBpGKvyDFXSleNauXreRasarmqj8i7lK4ZY1oK_pYcyVpyxZv2mNwtPV2mhCnt0GcaRrpy1lsDjq6Cz9ZPYUr03E0mJKQ_g7c5ROu3dANxiznRMUR66QaMFPxAL-z2qvpt01_6C8PeIV3bfzP93-YrurndI-X0q4UeM6b35M0ILuGHh_eE_Pn-bbO6qNaX5z9Wy3VlZK1yVWtUIPuzkY-lgNFMABrVqx7AiHYwAsQIWpZuPYyq5lr2mtXCNAIGqUHIE_L5kLuP4WbClLudTQadA4-lXCfKcn2mGtUU9NMz9DpM0ZfrCqW5VG3DZ6o6UCaGlCKO3T7aHcTbjrNultIVKd0spZulFP7jQ-rU73B4oh8tFEAegPkbvHcWe4z5hdh7BuaYng</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2771359818</pqid></control><display><type>article</type><title>An Assessment of Clinical Continuous Glucose Monitoring Targets for Older and High-Risk People Living with Type 1 Diabetes</title><source>MEDLINE</source><source>Alma/SFX Local Collection</source><creator>O'Neal, David N ; Cohen, Ohad ; Vogrin, Sara ; Vigersky, Robert A ; Jenkins, Alicia J</creator><creatorcontrib>O'Neal, David N ; Cohen, Ohad ; Vogrin, Sara ; Vigersky, Robert A ; Jenkins, Alicia J ; Australian JDRF Closed-Loop Research Group ; on behalf of the Australian JDRF Closed-Loop Research Group</creatorcontrib><description><![CDATA[Aim:
To assess relationships between continuous glucose monitoring (CGM) time in range (TIR), 70–180 mg/dL, time below range (TBR), <70 mg/dL, time above range (TAR), >180 mg/dL, and glucose coefficient of variation (CV) in relation to currently recommended clinical CGM targets for older people, which recommend reduced TIR and TBR targets relative to the general type 1 diabetes population.
Methods:
We conducted a post hoc analysis using the JDRF Australia Adult Hybrid Closed Loop trial database examining correlations in 120 adults with type 1 diabetes of 3 weeks masked CGM (Guardian Sensor 3; Medtronic) metrics (
n
= 61 on multiple daily injections, 59 on non-CGM augmented pumps) using manual insulin dosing at baseline and at 26-weeks, with 50% randomized to automated insulin dosing (AID).
Results:
Correlations between baseline TIR and TAR were strong (
r
= −0.966;
P
< 0.0001), weak for TBR (
r
= 0.363;
P
< 0.0001), and glucose CV (
r
= 0.037;
P
= 0.687) while moderate between CV and TBR (
r
= 0.726;
P
< 0.0001). Associations were similar for participants aged >60 years (
n
= 15) versus younger subjects. Correlations of changes in (Δ) TIR with ΔTAR over 26 weeks were strong (
r
= −0.945;
P
< 0.001) and correlations for ΔTBR were weak (
r
= 0.025;
P
= 0.802). ΔCV did not significantly correlate with ΔTAR (
r
= −0.064;
P
= 0.526) but did with ΔTBR (
r
= 0.770;
P
= <0.001).
Conclusions:
Changes in TIR are not associated with changes in TBR. Thus, we recommend that for older AID users whilst TBR targets should be prioritized to reduce hypoglycemia-related risk, TBR should be addressed independently of TIR. Clinical Trial Registratrion number: (ACTRN12617000520336).]]></description><identifier>ISSN: 1520-9156</identifier><identifier>EISSN: 1557-8593</identifier><identifier>DOI: 10.1089/dia.2022.0350</identifier><identifier>PMID: 36315189</identifier><language>eng</language><publisher>United States: Mary Ann Liebert, Inc., publishers</publisher><subject>Adult ; Aged ; Blood Glucose ; Blood Glucose Self-Monitoring ; Diabetes ; Diabetes Mellitus, Type 1 - drug therapy ; Disease management ; Glucose monitoring ; Health risks ; Humans ; Hypoglycemia ; Hypoglycemic Agents - therapeutic use ; Insulin - therapeutic use ; Older people ; Original Articles</subject><ispartof>Diabetes technology & therapeutics, 2023-02, Vol.25 (2), p.18-115</ispartof><rights>2023, Mary Ann Liebert, Inc., publishers</rights><rights>Copyright Mary Ann Liebert, Inc. Feb 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c365t-67e5a3b4f1f000c702aec5b5baac29dc2a2fa73007baf56173b7062c82ad37a23</citedby><cites>FETCH-LOGICAL-c365t-67e5a3b4f1f000c702aec5b5baac29dc2a2fa73007baf56173b7062c82ad37a23</cites><orcidid>0000-0002-3546-3385 ; 0000-0003-4337-1795 ; 0000-0003-0583-3717 ; 0000-0002-0870-4032 ; 0000-0002-1597-9421</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36315189$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>O'Neal, David N</creatorcontrib><creatorcontrib>Cohen, Ohad</creatorcontrib><creatorcontrib>Vogrin, Sara</creatorcontrib><creatorcontrib>Vigersky, Robert A</creatorcontrib><creatorcontrib>Jenkins, Alicia J</creatorcontrib><creatorcontrib>Australian JDRF Closed-Loop Research Group</creatorcontrib><creatorcontrib>on behalf of the Australian JDRF Closed-Loop Research Group</creatorcontrib><title>An Assessment of Clinical Continuous Glucose Monitoring Targets for Older and High-Risk People Living with Type 1 Diabetes</title><title>Diabetes technology & therapeutics</title><addtitle>Diabetes Technol Ther</addtitle><description><![CDATA[Aim:
To assess relationships between continuous glucose monitoring (CGM) time in range (TIR), 70–180 mg/dL, time below range (TBR), <70 mg/dL, time above range (TAR), >180 mg/dL, and glucose coefficient of variation (CV) in relation to currently recommended clinical CGM targets for older people, which recommend reduced TIR and TBR targets relative to the general type 1 diabetes population.
Methods:
We conducted a post hoc analysis using the JDRF Australia Adult Hybrid Closed Loop trial database examining correlations in 120 adults with type 1 diabetes of 3 weeks masked CGM (Guardian Sensor 3; Medtronic) metrics (
n
= 61 on multiple daily injections, 59 on non-CGM augmented pumps) using manual insulin dosing at baseline and at 26-weeks, with 50% randomized to automated insulin dosing (AID).
Results:
Correlations between baseline TIR and TAR were strong (
r
= −0.966;
P
< 0.0001), weak for TBR (
r
= 0.363;
P
< 0.0001), and glucose CV (
r
= 0.037;
P
= 0.687) while moderate between CV and TBR (
r
= 0.726;
P
< 0.0001). Associations were similar for participants aged >60 years (
n
= 15) versus younger subjects. Correlations of changes in (Δ) TIR with ΔTAR over 26 weeks were strong (
r
= −0.945;
P
< 0.001) and correlations for ΔTBR were weak (
r
= 0.025;
P
= 0.802). ΔCV did not significantly correlate with ΔTAR (
r
= −0.064;
P
= 0.526) but did with ΔTBR (
r
= 0.770;
P
= <0.001).
Conclusions:
Changes in TIR are not associated with changes in TBR. Thus, we recommend that for older AID users whilst TBR targets should be prioritized to reduce hypoglycemia-related risk, TBR should be addressed independently of TIR. Clinical Trial Registratrion number: (ACTRN12617000520336).]]></description><subject>Adult</subject><subject>Aged</subject><subject>Blood Glucose</subject><subject>Blood Glucose Self-Monitoring</subject><subject>Diabetes</subject><subject>Diabetes Mellitus, Type 1 - drug therapy</subject><subject>Disease management</subject><subject>Glucose monitoring</subject><subject>Health risks</subject><subject>Humans</subject><subject>Hypoglycemia</subject><subject>Hypoglycemic Agents - therapeutic use</subject><subject>Insulin - therapeutic use</subject><subject>Older people</subject><subject>Original Articles</subject><issn>1520-9156</issn><issn>1557-8593</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkU1v1DAQhi0EoqVw7BVZ4sIliz_qODmuFmiRFhWh5RxNnMnWxWtvbQfU_vo62raHXnryyHrm1bx6CDnlbMFZ034ZLCwEE2LBpGKvyDFXSleNauXreRasarmqj8i7lK4ZY1oK_pYcyVpyxZv2mNwtPV2mhCnt0GcaRrpy1lsDjq6Cz9ZPYUr03E0mJKQ_g7c5ROu3dANxiznRMUR66QaMFPxAL-z2qvpt01_6C8PeIV3bfzP93-YrurndI-X0q4UeM6b35M0ILuGHh_eE_Pn-bbO6qNaX5z9Wy3VlZK1yVWtUIPuzkY-lgNFMABrVqx7AiHYwAsQIWpZuPYyq5lr2mtXCNAIGqUHIE_L5kLuP4WbClLudTQadA4-lXCfKcn2mGtUU9NMz9DpM0ZfrCqW5VG3DZ6o6UCaGlCKO3T7aHcTbjrNultIVKd0spZulFP7jQ-rU73B4oh8tFEAegPkbvHcWe4z5hdh7BuaYng</recordid><startdate>20230201</startdate><enddate>20230201</enddate><creator>O'Neal, David N</creator><creator>Cohen, Ohad</creator><creator>Vogrin, Sara</creator><creator>Vigersky, Robert A</creator><creator>Jenkins, Alicia J</creator><general>Mary Ann Liebert, Inc., publishers</general><general>Mary Ann Liebert, Inc</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>K9.</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-3546-3385</orcidid><orcidid>https://orcid.org/0000-0003-4337-1795</orcidid><orcidid>https://orcid.org/0000-0003-0583-3717</orcidid><orcidid>https://orcid.org/0000-0002-0870-4032</orcidid><orcidid>https://orcid.org/0000-0002-1597-9421</orcidid></search><sort><creationdate>20230201</creationdate><title>An Assessment of Clinical Continuous Glucose Monitoring Targets for Older and High-Risk People Living with Type 1 Diabetes</title><author>O'Neal, David N ; Cohen, Ohad ; Vogrin, Sara ; Vigersky, Robert A ; Jenkins, Alicia J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c365t-67e5a3b4f1f000c702aec5b5baac29dc2a2fa73007baf56173b7062c82ad37a23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Blood Glucose</topic><topic>Blood Glucose Self-Monitoring</topic><topic>Diabetes</topic><topic>Diabetes Mellitus, Type 1 - drug therapy</topic><topic>Disease management</topic><topic>Glucose monitoring</topic><topic>Health risks</topic><topic>Humans</topic><topic>Hypoglycemia</topic><topic>Hypoglycemic Agents - therapeutic use</topic><topic>Insulin - therapeutic use</topic><topic>Older people</topic><topic>Original Articles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>O'Neal, David N</creatorcontrib><creatorcontrib>Cohen, Ohad</creatorcontrib><creatorcontrib>Vogrin, Sara</creatorcontrib><creatorcontrib>Vigersky, Robert A</creatorcontrib><creatorcontrib>Jenkins, Alicia J</creatorcontrib><creatorcontrib>Australian JDRF Closed-Loop Research Group</creatorcontrib><creatorcontrib>on behalf of the Australian JDRF Closed-Loop Research Group</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>Diabetes technology & therapeutics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>O'Neal, David N</au><au>Cohen, Ohad</au><au>Vogrin, Sara</au><au>Vigersky, Robert A</au><au>Jenkins, Alicia J</au><aucorp>Australian JDRF Closed-Loop Research Group</aucorp><aucorp>on behalf of the Australian JDRF Closed-Loop Research Group</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Assessment of Clinical Continuous Glucose Monitoring Targets for Older and High-Risk People Living with Type 1 Diabetes</atitle><jtitle>Diabetes technology & therapeutics</jtitle><addtitle>Diabetes Technol Ther</addtitle><date>2023-02-01</date><risdate>2023</risdate><volume>25</volume><issue>2</issue><spage>18</spage><epage>115</epage><pages>18-115</pages><issn>1520-9156</issn><eissn>1557-8593</eissn><abstract><![CDATA[Aim:
To assess relationships between continuous glucose monitoring (CGM) time in range (TIR), 70–180 mg/dL, time below range (TBR), <70 mg/dL, time above range (TAR), >180 mg/dL, and glucose coefficient of variation (CV) in relation to currently recommended clinical CGM targets for older people, which recommend reduced TIR and TBR targets relative to the general type 1 diabetes population.
Methods:
We conducted a post hoc analysis using the JDRF Australia Adult Hybrid Closed Loop trial database examining correlations in 120 adults with type 1 diabetes of 3 weeks masked CGM (Guardian Sensor 3; Medtronic) metrics (
n
= 61 on multiple daily injections, 59 on non-CGM augmented pumps) using manual insulin dosing at baseline and at 26-weeks, with 50% randomized to automated insulin dosing (AID).
Results:
Correlations between baseline TIR and TAR were strong (
r
= −0.966;
P
< 0.0001), weak for TBR (
r
= 0.363;
P
< 0.0001), and glucose CV (
r
= 0.037;
P
= 0.687) while moderate between CV and TBR (
r
= 0.726;
P
< 0.0001). Associations were similar for participants aged >60 years (
n
= 15) versus younger subjects. Correlations of changes in (Δ) TIR with ΔTAR over 26 weeks were strong (
r
= −0.945;
P
< 0.001) and correlations for ΔTBR were weak (
r
= 0.025;
P
= 0.802). ΔCV did not significantly correlate with ΔTAR (
r
= −0.064;
P
= 0.526) but did with ΔTBR (
r
= 0.770;
P
= <0.001).
Conclusions:
Changes in TIR are not associated with changes in TBR. Thus, we recommend that for older AID users whilst TBR targets should be prioritized to reduce hypoglycemia-related risk, TBR should be addressed independently of TIR. Clinical Trial Registratrion number: (ACTRN12617000520336).]]></abstract><cop>United States</cop><pub>Mary Ann Liebert, Inc., publishers</pub><pmid>36315189</pmid><doi>10.1089/dia.2022.0350</doi><tpages>98</tpages><orcidid>https://orcid.org/0000-0002-3546-3385</orcidid><orcidid>https://orcid.org/0000-0003-4337-1795</orcidid><orcidid>https://orcid.org/0000-0003-0583-3717</orcidid><orcidid>https://orcid.org/0000-0002-0870-4032</orcidid><orcidid>https://orcid.org/0000-0002-1597-9421</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1520-9156 |
| ispartof | Diabetes technology & therapeutics, 2023-02, Vol.25 (2), p.18-115 |
| issn | 1520-9156 1557-8593 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2730645858 |
| source | MEDLINE; Alma/SFX Local Collection |
| subjects | Adult Aged Blood Glucose Blood Glucose Self-Monitoring Diabetes Diabetes Mellitus, Type 1 - drug therapy Disease management Glucose monitoring Health risks Humans Hypoglycemia Hypoglycemic Agents - therapeutic use Insulin - therapeutic use Older people Original Articles |
| title | An Assessment of Clinical Continuous Glucose Monitoring Targets for Older and High-Risk People Living with Type 1 Diabetes |
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