BUSCADOR RECOLECTA

This trial was registered at clinicaltrials.gov as NCT03479866., [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., Supported by Zoe Ltd; TwinsUK is funded by the Wellcome Trust, Medical Research Council, Versus Arthritis, European Union Horizon 2020, Chronic Disease Research Foundation (CDRF), Zoe Global Ltd and the National Institute for Health Research (NIHR) Clinical Research Network (CRN) and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London. SEB was supported in part by Biotechnology and Biological Sciences Research Council grant BB/NO12739/1. ATC was supported by a Stuart and Suzanne Steele MGH Research Scholar Award and R35 CA253185. PWF was supported in part by European Research Council grant CoG-2015_681742_NASCENT, the Swedish Research Council, the Novo Nordisk Foundation, and a Swedish Foundation for Strategic Research IRC award. AMV was supported by the National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre. TwinsUK is funded by the Wellcome Trust, MRC, European Union, Chronic Disease Research Foundation, Zoe Ltd, and the NIHR-funded BioResource, Clinical Research Facility, and Biomedical Research Centre based at Guy's and St Thomas’ NHS Foundation Trust in partnership with King's College London. JM was supported by American Diabetes Association grant #7-21-JDFM-005 and NIH grant P30 DK40561., Peer reviewed