Continuous glucose monitoring and intrapersonal variability in fasting glucose.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
08 Apr 2024
08 Apr 2024
Historique:
received:
19
11
2023
accepted:
04
03
2024
medline:
9
4
2024
pubmed:
9
4
2024
entrez:
8
4
2024
Statut:
aheadofprint
Résumé
Plasma fasting glucose (FG) levels play a pivotal role in the diagnosis of prediabetes and diabetes worldwide. Here we investigated FG values using continuous glucose monitoring (CGM) devices in nondiabetic adults aged 40-70 years. FG was measured during 59,565 morning windows of 8,315 individuals (7.16 ± 3.17 days per participant). Mean FG was 96.2 ± 12.87 mg dl
Identifiants
pubmed: 38589602
doi: 10.1038/s41591-024-02908-9
pii: 10.1038/s41591-024-02908-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.
Références
Khan, M. A. B. et al. Epidemiology of type 2 diabetes—global burden of disease and forecasted trends. J. Epidemiol. Glob. Health 10, 107–111 (2020).
doi: 10.2991/jegh.k.191028.001
pubmed: 32175717
pmcid: 7310804
Danaei, G. et al. National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants. Lancet 378, 31–40 (2011).
doi: 10.1016/S0140-6736(11)60679-X
pubmed: 21705069
American Diabetes Association. Standards of medical care in diabetes—2022 abridged for primary care providers. Clin. Diabetes 40, 10–38 (2022).
Norberg, M. et al. A combination of HbA1c, fasting glucose and BMI is effective in screening for individuals at risk of future type 2 diabetes: OGTT is not needed. J. Intern. Med. 260, 263–271 (2006).
doi: 10.1111/j.1365-2796.2006.01689.x
pubmed: 16918824
Rasmussen, S. S. et al. Short-term reproducibility of impaired fasting glycaemia, impaired glucose tolerance and diabetes the ADDITION study, DK. Diabetes Res. Clin. Pract. 80, 146–152 (2008).
doi: 10.1016/j.diabres.2007.11.003
pubmed: 18082284
Mooy, J. M. et al. Intra-individual variation of glucose, specific insulin and proinsulin concentrations measured by two oral glucose tolerance tests in a general Caucasian population: the Hoorn study. Diabetologia 39, 298–305 (1996).
doi: 10.1007/BF00418345
pubmed: 8721775
Brohall, G., Behre, C. -J., Hulthe, J., Wikstrand, J. & Fagerberg, B. Prevalence of diabetes and impaired glucose tolerance in 64-year-old Swedish women: experiences of using repeated oral glucose tolerance tests. Diabetes Care 29, 363–367 (2006).
doi: 10.2337/diacare.29.02.06.dc05-1229
pubmed: 16443888
Feskens, E. J., Bowles, C. H. & Kromhout, D. Intra- and interindividual variability of glucose tolerance in an elderly population. J. Clin. Epidemiol. 44, 947–953 (1991).
doi: 10.1016/0895-4356(91)90058-H
pubmed: 1890437
Balion, C. M. et al. Reproducibility of impaired glucose tolerance (IGT) and impaired fasting glucose (IFG) classification: a systematic review. Clin. Chem. Lab. Med. 45, 1180–1185 (2007).
doi: 10.1515/CCLM.2007.505
pubmed: 17635074
Freckmann, G., Pleus, S., Grady, M., Setford, S. & Levy, B. Measures of accuracy for continuous glucose monitoring and blood glucose monitoring devices. J. Diabetes Sci. Technol. 13, 575–583 (2019).
doi: 10.1177/1932296818812062
pubmed: 30453761
Facchinetti, A. Continuous glucose monitoring sensors: past, present and future algorithmic challenges. Sensors 16, 2093 (2016).
doi: 10.3390/s16122093
pubmed: 27941663
pmcid: 5191073
Sacks, D. B. et al. Guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Clin. Chem. 69, 808–868 (2023).
doi: 10.1093/clinchem/hvad080
pubmed: 37473453
Battelino, T. et al. Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabetes Endocrinol. 11, 42–57 (2023).
doi: 10.1016/S2213-8587(22)00319-9
pubmed: 36493795
Keshet, A. et al. CGMap: characterizing continuous glucose monitor data in thousands of non-diabetic individuals. Cell Metab. 35, 758–769.e3 (2023).
doi: 10.1016/j.cmet.2023.04.002
pubmed: 37080199
Shilo, S. et al. 10K: a large-scale prospective longitudinal study in Israel. Eur. J. Epidemiol. 36, 1187–1194 (2021).
doi: 10.1007/s10654-021-00753-5
pubmed: 33993378
Shah, V. N. et al. Continuous glucose monitoring profiles in healthy nondiabetic participants: a multicenter prospective study. J. Clin. Endocrinol. Metab. 104, 4356–4364 (2019).
doi: 10.1210/jc.2018-02763
pubmed: 31127824
pmcid: 7296129
Jarvis, P. R. E., Cardin, J. L., Nisevich-Bede, P. M. & McCarter, J. P. Continuous glucose monitoring in a healthy population: understanding the post-prandial glycemic response in individuals without diabetes mellitus. Metab. Clin. Exp. 146, 155640 (2023).
doi: 10.1016/j.metabol.2023.155640
pubmed: 37356796
Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).
doi: 10.1016/j.cell.2015.11.001
pubmed: 26590418
Alberti, K. G. M. M. & Zimmet, P. Z. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO consultation. Diabet. Med. 15, 539–553 (1998).
doi: 10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S
pubmed: 9686693
Inzucchi, S. E. Diagnosis of diabetes. N. Engl. J. Med. 367, 542–550 (2012).
doi: 10.1056/NEJMcp1103643
pubmed: 22873534
Kim, J. A. et al. Impact of visit-to-visit fasting plasma glucose variability on the development of type 2 diabetes: a nationwide population-based cohort study. Diabetes Care 41, 2610–2616 (2018).
doi: 10.2337/dc18-0802
pubmed: 30254081
Chang, T. et al. Highly integrated watch for noninvasive continual glucose monitoring. Microsyst. Nanoeng. 8, 25 (2022).
doi: 10.1038/s41378-022-00355-5
pubmed: 35310514
pmcid: 8866463
Hu, Y. et al. Combined use of fasting plasma glucose and glycated hemoglobin A1c in the screening of diabetes and impaired glucose tolerance. Acta Diabetol. 47, 231–236 (2010).
doi: 10.1007/s00592-009-0143-2
pubmed: 19760291
Gao, L. et al. Association between carotid intima-media thickness and fasting blood glucose level: a population-based cross-sectional study among low-income adults in rural China. J. Diabetes Investig. 8, 788–797 (2017).
doi: 10.1111/jdi.12639
pubmed: 28160451
pmcid: 5668475
Rodbard, D. Characterizing accuracy and precision of glucose sensors and meters. J. Diabetes Sci. Technol. 8, 980–985 (2014).
doi: 10.1177/1932296814541810
pubmed: 25037194
pmcid: 4455380
Pleus, S. et al. Rate-of-change dependence of the performance of two cgm systems during induced glucose swings. J. Diabetes Sci. Technol. 9, 801–807 (2015).
doi: 10.1177/1932296815578716
pubmed: 25852074
pmcid: 4525645
Leelarathna, L. & Wilmot, E. G. Flash forward: a review of flash glucose monitoring. Diabet. Med. 35, 472–482 (2018).
doi: 10.1111/dme.13584
pubmed: 29356072
Tsoukas, M. et al. Accuracy of FreeStyle Libre in adults with type 1 diabetes: the effect of sensor age. Diabetes Technol. Ther. 22, 203–207 (2020).
doi: 10.1089/dia.2019.0262
pubmed: 31613140
Yalamanchali, S. et al. Diagnosis of obstructive sleep apnea by peripheral arterial tonometry: meta-analysis. JAMA Otolaryngol. Head. Neck Surg. 139, 1343–1350 (2013).
doi: 10.1001/jamaoto.2013.5338
pubmed: 24158564
DRSplus TrueColor high-resolution confocal fundus camera. iCare https://www.icare-world.com/product/icare-drsplus/ (2024).
Zhou, Y. et al. Automorph: automated retinal vascular morphology quantification via a deep learning pipeline. Transl. Vis. Sci. Technol. 11, 12 (2022).
doi: 10.1167/tvst.11.7.12
pubmed: 35833885
pmcid: 9290317
Levey, A. S. et al. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of diet in renal disease study group. Ann. Intern. Med. 130, 461–470 (1999).
doi: 10.7326/0003-4819-130-6-199903160-00002
pubmed: 10075613
Seabold, S. & Perktold, J. Statsmodels: econometric and statistical modeling with Python. In Proc. 9th Python in Science Conference 92–96 (SciPy, 2010).
Vallat, R. Pingouin: statistics in Python. JOSS 3, 1026 (2018).
doi: 10.21105/joss.01026