Accuracy of Continuous Glucose Monitoring Measurements in Normo-Glycemic Individuals
Publication Date
October 07, 2015
Journal
PLOS ONE
Authors
Abimbola A. Akintola, Raymond Noordam, Steffy W. Jansen, Anton J. De Craen, et al
Volume
10
Issue
10
Pages
e0139973
DOI
https://dx.plos.org/10.1371/journal.pone.0139973
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0139973
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/26445499
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4596806
Europe PMC
http://europepmc.org/abstract/MED/26445499
Web of Science
000362510600114
Scopus
84948664931
Mendeley
http://www.mendeley.com/research/accuracy-continuous-glucose-monitoring-measurements-normoglycemic-individuals
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Mendeley | Further Information

{"title"=>"Accuracy of continuous glucose monitoring measurements in normo-glycemic individuals", "type"=>"journal", "authors"=>[{"first_name"=>"Abimbola A.", "last_name"=>"Akintola", "scopus_author_id"=>"55651787800"}, {"first_name"=>"Raymond", "last_name"=>"Noordam", "scopus_author_id"=>"56602691600"}, {"first_name"=>"Steffy W.", "last_name"=>"Jansen", "scopus_author_id"=>"54895539300"}, {"first_name"=>"Anton J.", "last_name"=>"De Craen", "scopus_author_id"=>"7004695294"}, {"first_name"=>"Bart E.", "last_name"=>"Ballieux", "scopus_author_id"=>"6602973980"}, {"first_name"=>"Christa M.", "last_name"=>"Cobbaert", "scopus_author_id"=>"34569310800"}, {"first_name"=>"Simon P.", "last_name"=>"Mooijaart", "scopus_author_id"=>"7801636685"}, {"first_name"=>"Hanno", "last_name"=>"Pijl", "scopus_author_id"=>"7006457351"}, {"first_name"=>"Rudi G.", "last_name"=>"Westendorp", "scopus_author_id"=>"7103236448"}, {"first_name"=>"Diana", "last_name"=>"Van Heemst", "scopus_author_id"=>"6602350356"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"doi"=>"10.1371/journal.pone.0139973", "issn"=>"19326203", "sgr"=>"84948664931", "scopus"=>"2-s2.0-84948664931", "pui"=>"607096707", "pmid"=>"26445499"}, "id"=>"289cd158-9b79-34b6-b0fe-75917abc3ea6", "abstract"=>"BACKGROUND: The validity of continuous glucose monitoring (CGM) is well established in diabetic patients. CGM is also increasingly used for research purposes in normo-glycemic individuals, but the CGM validity in such individuals is unknown. We studied the accuracy of CGM measurements in normo-glycemic individuals by comparing CGM-derived versus venous blood-derived glucose levels and measures of glycemia and glycemic variability.\\n\\nMETHODS: In 34 healthy participants (mean age 65.7 years), glucose was simultaneously measured every 10 minutes, via both an Enlite® CGM sensor, and in venous blood sampled over a 24-hour period. Validity of CGM-derived individual glucose measurements, calculated measures of glycemia over daytime (09:00h-23:00h) and nighttime (23:00h-09:00h), and calculated measures of glycemic variability (e.g. 24h standard deviation [SD]) were assessed by Pearson correlation coefficients, mean absolute relative difference (MARD) and paired t-tests.\\n\\nRESULTS: The median correlation coefficient between CGM and venous glucose measurements per participant was 0.68 (interquartile range: 0.40-0.78), and the MARD was 17.6% (SD = 17%). Compared with venous sampling, the calculated measure of glycemia during daytime was 0.22 mmol/L higher when derived from CGM, but no difference was observed during nighttime. Most measures of glycemic variability were lower with CGM than with venous blood sampling (e.g., 24h SD: 1.07 with CGM and 1.26 with venous blood; p-value = 0.004).\\n\\nCONCLUSION: In normo-glycemic individuals, CGM-derived glucose measurements had good agreement with venous glucose levels. However, the measure of glycemia was higher during the day and most measures of glycemic variability were lower when derived from CGM.", "link"=>"http://www.mendeley.com/research/accuracy-continuous-glucose-monitoring-measurements-normoglycemic-individuals", "reader_count"=>17, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Researcher"=>4, "Student > Ph. D. Student"=>6, "Student > Postgraduate"=>1, "Student > Master"=>2, "Other"=>1, "Student > Bachelor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Researcher"=>4, "Student > Ph. D. Student"=>6, "Student > Postgraduate"=>1, "Student > Master"=>2, "Other"=>1, "Student > Bachelor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>1, "Medicine and Dentistry"=>9, "Agricultural and Biological Sciences"=>1, "Veterinary Science and Veterinary Medicine"=>1, "Sports and Recreations"=>1, "Psychology"=>1, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>9}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>1}, "Unspecified"=>{"Unspecified"=>1}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>1}}, "reader_count_by_country"=>{"New Zealand"=>1, "Japan"=>1, "Russia"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2348289"], "description"=>"<p>Data presented as the mean (SE) glucose level every 10 minutes. In red, the continuous glucose monitoring measurement data. In blue, the venous blood glucose measurement data.</p>", "links"=>[], "tags"=>["venous glucose levels", "venous blood sampling", "glycemia", "Pearson correlation coefficients", "Continuous Glucose Monitoring Measurements", "individual", "venous glucose measurements", "cgm", "glycemic variability", "MARD", "sd", "glycemic variability.MethodsIn 34", "venous blood"], "article_id"=>1566984, "categories"=>["Biological Sciences"], "users"=>["Abimbola A. Akintola", "Raymond Noordam", "Steffy W. Jansen", "Anton J. de Craen", "Bart E. Ballieux", "Christa M. Cobbaert", "Simon P. Mooijaart", "Hanno Pijl", "Rudi G. Westendorp", "Diana van Heemst"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0139973.g001", "stats"=>{"downloads"=>6, "page_views"=>34, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Venous_and_continuous_glucose_monitoring_CGM_derived_glucose_during_24h_period_/1566984", "title"=>"Venous- and continuous glucose monitoring (CGM)- derived glucose during 24h period.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-07 02:52:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/2348290"], "description"=>"<p>Bar chart showing the distribution of the Pearson correlations between paired CGM and venous glucose measurements determined for each of the 34 participants. Dashed line represents the median per-person Pearson correlation.</p>", "links"=>[], "tags"=>["venous glucose levels", "venous blood sampling", "glycemia", "Pearson correlation coefficients", "Continuous Glucose Monitoring Measurements", "individual", "venous glucose measurements", "cgm", "glycemic variability", "MARD", "sd", "glycemic variability.MethodsIn 34", "venous blood"], "article_id"=>1566985, "categories"=>["Biological Sciences"], "users"=>["Abimbola A. Akintola", "Raymond Noordam", "Steffy W. Jansen", "Anton J. de Craen", "Bart E. Ballieux", "Christa M. Cobbaert", "Simon P. Mooijaart", "Hanno Pijl", "Rudi G. Westendorp", "Diana van Heemst"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0139973.g002", "stats"=>{"downloads"=>5, "page_views"=>42, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Per_person_Pearson_correlations_coefficients_between_venous_and_continuous_glucose_monitoring_CGM_derived_glucose_levels_/1566985", "title"=>"Per-person Pearson correlations coefficients between venous- and continuous glucose monitoring (CGM)- derived glucose levels.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-07 02:52:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/2348292"], "description"=>"<p>Each dot represents one paired (CGM and venous) glucose measurement (N = 4,523 data points derived from 34 participants). The bias of the measurements (represented as the solid lines) and the ± 1.96 SD (dotted lines) are presented for the measurements obtained (A) over 24 hours, (B) during the day (09.00h–23.00h), and (C) during the night (23.00h–09.00h).</p>", "links"=>[], "tags"=>["venous glucose levels", "venous blood sampling", "glycemia", "Pearson correlation coefficients", "Continuous Glucose Monitoring Measurements", "individual", "venous glucose measurements", "cgm", "glycemic variability", "MARD", "sd", "glycemic variability.MethodsIn 34", "venous blood"], "article_id"=>1566987, "categories"=>["Biological Sciences"], "users"=>["Abimbola A. Akintola", "Raymond Noordam", "Steffy W. Jansen", "Anton J. de Craen", "Bart E. Ballieux", "Christa M. Cobbaert", "Simon P. Mooijaart", "Hanno Pijl", "Rudi G. Westendorp", "Diana van Heemst"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0139973.g003", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Bland_Altman_plots_of_individual_glucose_measurements_/1566987", "title"=>"Bland-Altman plots of individual glucose measurements.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-10-07 02:52:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/2348293"], "description"=>"<p>Data represent mean with standard deviation unless stated otherwise.</p><p>Characteristics of the study population.</p>", "links"=>[], "tags"=>["venous glucose levels", "venous blood sampling", "glycemia", "Pearson correlation coefficients", "Continuous Glucose Monitoring Measurements", "individual", "venous glucose measurements", "cgm", "glycemic variability", "MARD", "sd", "glycemic variability.MethodsIn 34", "venous blood"], "article_id"=>1566988, "categories"=>["Biological Sciences"], "users"=>["Abimbola A. Akintola", "Raymond Noordam", "Steffy W. Jansen", "Anton J. de Craen", "Bart E. Ballieux", "Christa M. Cobbaert", "Simon P. Mooijaart", "Hanno Pijl", "Rudi G. Westendorp", "Diana van Heemst"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0139973.t001", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characteristics_of_the_study_population_/1566988", "title"=>"Characteristics of the study population.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-10-07 02:52:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/2348294"], "description"=>"<p>Values are mean (SD) unless otherwise indicated. Abbreviations: CGM, continuous glucose monitoring; SD, standard deviation; SE, standard error; SD<i>ws</i>1and SD<i>ws</i>4, standard deviation within time series of respectively 1 and 4 hours; CONGA1 and CONGA4, continuous overlapping net glycemic action over respectively 1 and 4 hours; and MAGE, mean amplitude of glycemic excursions.</p><p>Comparison of estimates of glycemia and glycemic variability.</p>", "links"=>[], "tags"=>["venous glucose levels", "venous blood sampling", "glycemia", "Pearson correlation coefficients", "Continuous Glucose Monitoring Measurements", "individual", "venous glucose measurements", "cgm", "glycemic variability", "MARD", "sd", "glycemic variability.MethodsIn 34", "venous blood"], "article_id"=>1566989, "categories"=>["Biological Sciences"], "users"=>["Abimbola A. Akintola", "Raymond Noordam", "Steffy W. Jansen", "Anton J. de Craen", "Bart E. Ballieux", "Christa M. Cobbaert", "Simon P. Mooijaart", "Hanno Pijl", "Rudi G. Westendorp", "Diana van Heemst"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0139973.t002", "stats"=>{"downloads"=>3, "page_views"=>27, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_estimates_of_glycemia_and_glycemic_variability_/1566989", "title"=>"Comparison of estimates of glycemia and glycemic variability.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-10-07 02:52:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/2348295", "https://ndownloader.figshare.com/files/2348296", "https://ndownloader.figshare.com/files/2348297"], "description"=>"<div><p>Background</p><p>The validity of continuous glucose monitoring (CGM) is well established in diabetic patients. CGM is also increasingly used for research purposes in normo-glycemic individuals, but the CGM validity in such individuals is unknown. We studied the accuracy of CGM measurements in normo-glycemic individuals by comparing CGM-derived versus venous blood-derived glucose levels and measures of glycemia and glycemic variability.</p><p>Methods</p><p>In 34 healthy participants (mean age 65.7 years), glucose was simultaneously measured every 10 minutes, via both an Enlite<sup>®</sup> CGM sensor, and in venous blood sampled over a 24-hour period. Validity of CGM-derived individual glucose measurements, calculated measures of glycemia over daytime (09:00h-23:00h) and nighttime (23:00h-09:00h), and calculated measures of glycemic variability (e.g. 24h standard deviation [SD]) were assessed by Pearson correlation coefficients, mean absolute relative difference (MARD) and paired t-tests.</p><p>Results</p><p>The median correlation coefficient between CGM and venous glucose measurements per participant was 0.68 (interquartile range: 0.40–0.78), and the MARD was 17.6% (SD = 17%). Compared with venous sampling, the calculated measure of glycemia during daytime was 0.22 mmol/L higher when derived from CGM, but no difference was observed during nighttime. Most measures of glycemic variability were lower with CGM than with venous blood sampling (e.g., 24h SD: 1.07 with CGM and 1.26 with venous blood; p-value = 0.004).</p><p>Conclusion</p><p>In normo-glycemic individuals, CGM-derived glucose measurements had good agreement with venous glucose levels. However, the measure of glycemia was higher during the day and most measures of glycemic variability were lower when derived from CGM.</p></div>", "links"=>[], "tags"=>["venous glucose levels", "venous blood sampling", "glycemia", "Pearson correlation coefficients", "Continuous Glucose Monitoring Measurements", "individual", "venous glucose measurements", "cgm", "glycemic variability", "MARD", "sd", "glycemic variability.MethodsIn 34", "venous blood"], "article_id"=>1566990, "categories"=>["Biological Sciences"], "users"=>["Abimbola A. Akintola", "Raymond Noordam", "Steffy W. Jansen", "Anton J. de Craen", "Bart E. Ballieux", "Christa M. Cobbaert", "Simon P. Mooijaart", "Hanno Pijl", "Rudi G. Westendorp", "Diana van Heemst"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0139973.s001", "https://dx.doi.org/10.1371/journal.pone.0139973.s002", "https://dx.doi.org/10.1371/journal.pone.0139973.s003"], "stats"=>{"downloads"=>2, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Accuracy_of_Continuous_Glucose_Monitoring_Measurements_in_Normo_Glycemic_Individuals_/1566990", "title"=>"Accuracy of Continuous Glucose Monitoring Measurements in Normo-Glycemic Individuals", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-10-07 02:52:52"}

PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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