Validity of continuous glucose monitoring for categorizing glycemic responses to diet: implications for use in personalized nutrition.
Jordi Merino, Inbar Linenberg, Kate M Bermingham, Sajaysurya Ganesh, Elco Bakker, Linda M Delahanty, Andrew T Chan, Joan Capdevila Pujol, Jonathan Wolf, Haya Al Khatib, Paul W Franks, Tim D Spector, Jose M Ordovas, Sarah E Berry, Ana M Valdes
Author Information
Jordi Merino: Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. ORCID
Inbar Linenberg: Zoe Ltd, London, United Kingdom.
Kate M Bermingham: Department of Nutritional Sciences, King's College London, London, United Kingdom.
Sajaysurya Ganesh: Zoe Ltd, London, United Kingdom.
Elco Bakker: Zoe Ltd, London, United Kingdom.
Linda M Delahanty: Department of Medicine, Harvard Medical School, Boston, MA, USA.
Andrew T Chan: Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. ORCID
Joan Capdevila Pujol: Zoe Ltd, London, United Kingdom.
Jonathan Wolf: Zoe Ltd, London, United Kingdom. ORCID
Haya Al Khatib: Zoe Ltd, London, United Kingdom.
Paul W Franks: Department of Clinical Sciences, Lund University, Malmö, Sweden.
Tim D Spector: Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom.
Jose M Ordovas: Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA, USA. ORCID
Sarah E Berry: Department of Nutritional Sciences, King's College London, London, United Kingdom. ORCID
Ana M Valdes: School of Medicine, University of Nottingham, Nottingham, United Kingdom.
BACKGROUND: Continuous glucose monitor (CGM) devices enable characterization of individuals' glycemic variation. However, there are concerns about their reliability for categorizing glycemic responses to foods that would limit their potential application in personalized nutrition recommendations. OBJECTIVES: We aimed to evaluate the concordance of 2 simultaneously worn CGM devices in measuring postprandial glycemic responses. METHODS: Within ZOE PREDICT (Personalised Responses to Dietary Composition Trial) 1, 394 participants wore 2 CGM devices simultaneously [n = 360 participants with 2 Abbott Freestyle Libre Pro (FSL) devices; n = 34 participants with both FSL and Dexcom G6] for ≤14 d while consuming standardized (n = 4457) and ad libitum (n = 5738) meals. We examined the CV and correlation of the incremental area under the glucose curve at 2 h (glucoseiAUC0-2 h). Within-subject meal ranking was assessed using Kendall τ rank correlation. Concordance between paired devices in time in range according to the American Diabetes Association cutoffs (TIRADA) and glucose variability (glucose CV) was also investigated. RESULTS: The CV of glucoseiAUC0-2 h for standardized meals was 3.7% (IQR: 1.7%-7.1%) for intrabrand device and 12.5% (IQR: 5.1%-24.8%) for interbrand device comparisons. Similar estimates were observed for ad libitum meals, with intrabrand and interbrand device CVs of glucoseiAUC0-2 h of 4.1% (IQR: 1.8%-7.1%) and 16.6% (IQR: 5.5%-30.7%), respectively. Kendall τ rank correlation showed glucoseiAUC0-2h-derived meal rankings were agreeable between paired CGM devices (intrabrand: 0.9; IQR: 0.8-0.9; interbrand: 0.7; IQR: 0.5-0.8). Paired CGMs also showed strong concordance for TIRADA with a intrabrand device CV of 4.8% (IQR: 1.9%-9.8%) and an interbrand device CV of 3.2% (IQR: 1.1%-6.2%). CONCLUSIONS: Our data demonstrate strong concordance of CGM devices in monitoring glycemic responses and suggest their potential use in personalized nutrition.This trial was registered at clinicaltrials.gov as NCT03479866.
