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Continuous glucose monitoring and intrapersonal variability in fasting glucose

An Author Correction to this article was published on 15 April 2024

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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−1, rising by 0.234 mg dl−1 per year with age. Intraperson, day-to-day variability expressed as FG standard deviation was 7.52 ± 4.31 mg dl−1. As there are currently no CGM-based criteria for diabetes diagnosis, we analyzed the potential implications of this variability on the classification of glycemic status based on current plasma FG-based diagnostic guidelines. Among 5,328 individuals who would have been considered to have normal FG based on the first FG measurement, 40% and 3% would have been reclassified as having glucose in the prediabetes and diabetes ranges, respectively, based on sequential measurements throughout the study. Finally, we revealed associations between mean FG and various clinical measures. Our findings suggest that careful consideration is necessary when interpreting FG as substantial intraperson variability exists and highlight the potential impact of using CGM data to refine glycemic status assessment.

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Fig. 1: Variability of FG.
Fig. 2: Estimating the misclassification rate of prediabetes and diabetes based on morning glucose measurements.
Fig. 3: Correlation of FG and glycemic variability with clinical measures.

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Data availability

The data that support the findings of this study originate from the 10K study. Reference data are available at Restrictions apply to the individual-level data and they are therefore not publicly available. The data can be accessed only by request to the authors. Data in this paper is part of the Human Phenotype Project (HHP) and is accessible to researchers from universities and other research institutions at

Code availability

Analyses in this study were performed using publicly available Python libraries, as detailed in Methods.

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We thank the members of the Segal group for fruitful discussions. E.S. is supported by the Crown Human Genome Center, the Larson Charitable Foundation New Scientist Fund, the Else Kröner Fresenius Foundation, the White Rose International Foundation, the Ben B. and Joyce E. Eisenberg Foundation, the Nissenbaum Family, M. Pinheiro de Andrade and V. Buchheim, M. Michels, A. Moussaieff and grants funded by the Minerva Foundation, with funding from the Federal German Ministry for Education and Research and by the European Research Council and the Israel Science Foundation. A.K. is partially supported by the Israeli Council for Higher Education via the Weizmann Data Science Research Center,

Author information

Authors and Affiliations



S.S. conceived the project, designed and conducted the analyses, interpreted the results and wrote the paper. A.K. designed and conducted the analyses, interpreted the results and wrote the paper. H.R. conducted the analyses and interpreted the results. A.G. conducted the analyses. Y.T.-B. and Y.A. interpreted the results. E.S. directed and supervised the project.

Corresponding author

Correspondence to Eran Segal.

Ethics declarations

Competing interests

H.R. is an employee in Pheno.AI, Ltd, a biomedical data science company from Tel Aviv, Israel. A.K. and E.S. are paid consultants to Pheno.AI, Ltd. The other authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks Jordi Merino and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Sonia Muliyil, in collaboration with the Nature Medicine team.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Distribution of number of days analyzed per participant.

Only days with valid CGM morning windows were included.

Extended Data Fig. 2 Distribution of hours prior to and following morning time windows.

(a) The mean timing of the last meal logged prior to the morning time window (Hours). A minimum of 8 hours was predefined. (b) The mean timing of the first meal logged following the morning time window (Hours).

Extended Data Fig. 3 Exclusion process.

The full exclusion process for (a) individuals in the study and (b) valid morning windows is presented. Overall this process resulted in 8,315 individuals with 61,339 morning windows.

Extended Data Fig. 4 Distribution of fasting glucose measurements.

the distribution of mean FG values (mg/dl) of the study participants.

Extended Data Fig. 5 Progression and of fasting glucose with age by sex.

Progression of the mean FG (mg/dl) with age (years) for Females (orange) and Males (blue). Dots show the distribution of the data points. Dotted black lines show the 3rd, 10th, 50th, 90th, and 97th percentiles obtained using Lowess regression. Robust regression equation is shown in black.

Extended Data Fig. 6 Percentage of reclassification of glycemic status per followup day for individuals considered to have normal FG at baseline.

The percentage of participants, from the individuals considered to have glucose values in the normal range, that were misclassified when using only the baseline FG values as opposed to using all the FG values available in the followup data to prediabetes (yellow), suspected diabetes (orange) and diabetes (red) (see also Supplementary Table 2). Individuals who had FG below 100 mg/dl (5.6 mmol/L) and between 100–125 mg/dL (5.6–6.9 mmol/L) were classified as normal or prediabetes respectively. Individuals with only one FG measurement ≥126 mg/dL (7.0 mmol/L) were classified as suspected diabetics and individuals with more than one FG measurement ≥126 mg/dL (7.0 mmol/L) were classified as diabetics.

Extended Data Fig. 7 Cohort ethnicity.

pie chart displays the distributions of the participant’s birth country, paternal birth country, and maternal birth country (N = 5,792 participants, who contributed this data at study initiation).

Extended Data Fig. 8 Variability of Intra-person, between mornings fasting glucose following exclusion of the first and last days of CGM usage for each participant.

The distribution of the standard deviation of mean FG values (mg/dl) between different mornings of the same participant is presented.

Extended Data Table 1 Correlations of Fasting Glucose Mean with clinical measures
Extended Data Table 2 Correlations of Fasting Glucose STD with clinical measures
Extended Data Table 3 Correlations of Fasting Glucose CV with clinical measures

Supplementary information

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Shilo, S., Keshet, A., Rossman, H. et al. Continuous glucose monitoring and intrapersonal variability in fasting glucose. Nat Med 30, 1424–1431 (2024).

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