Evidence map›Paper›PMID 40413580›Full record

ArticleJournal of diabetes science and technology2025

Temporal Glycemic Patterns in Type 1 and Type 2 Diabetes: Insights From Extended Continuous Glucose Monitoring.

Tomoki Okuno, Lucas Sort, Bowen Zhang, Kerry Zhou, Matthew Kitchen, Victor Li, Donald R Miller, Gregory J Norman, Peter Reaven, Jin J Zhou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of diabetes science and technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Tomoki OkunoDepartment of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0009-0006-9884-0906
Lucas SortCentrale Supélec, CNRS, Laboratoire des Signaux et Systèmes, Université Paris-Saclay, Gif-Sur-Yvette, France.
Bowen ZhangDepartment of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0009-0007-9941-5669
Kerry ZhouPortola High School, Irvine, CA, USA.
Matthew KitchenGeffen Academy at UCLA, Los Angeles, CA, USA.
Victor LiBronx High School of Science, Bronx, NY, USA.
Donald R MillerDepartment of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, MA, USA.
Gregory J NormanDexcom, Inc, San Diego CA, USA.ORCID 0000-0001-7989-9597
Peter ReavenPhoenix VA Health Care System, Phoenix, AZ, USA.
Jin J ZhouDepartment of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0000-0001-7983-0274

Funding

NHGRI NIH HHS R01 HG006139
6 · The paper itself

Abstract

backgroundAchieving optimal glycemic control for persons with diabetes remains difficult. Real-world continuous glucose monitoring (CGM) data can illuminate previously underrecognized glycemic fluctuations. We aimed to characterize glucose trajectories in individuals with Type 1 and Type 2 diabetes, and to examine how baseline glycemic control, CGM usage frequency, and regional differences shape these patterns.

methodsWe linked Dexcom CGM data (2015-2020) with Veterans Health Administration electronic health records, identifying 892 Type 1 and 1716 Type 2 diabetes patients. Analyses focused on the first three years of CGM use, encompassing over 2.1 million glucose readings. We explored temporal trends in average daily glucose and time-in-range values.

resultsBoth Type 1 and Type 2 cohorts exhibited a gradual rise in mean daily glucose over time, although higher CGM usage frequency was associated with lower overall glucose or attenuated increases. Notable weekly patterns emerged: Sundays consistently showed the highest glucose values, whereas Wednesdays tended to have the lowest. Seasonally, glycemic control deteriorated from October to February and rebounded from April to August, with more pronounced fluctuations in the Northeast compared to the Southwest U.S.

conclusionsOur findings underscore the importance of recognizing day-of-week and seasonal glycemic variations in diabetes management. Tailoring interventions to account for these real-world fluctuations may enhance patient engagement, optimize glycemic control, and ultimately improve health outcomes.

Indexed as

continuous glucose monitoringglycemic controlglycemic patterntime in rangetype 1 diabetestype 2 diabetes

Identifiers

PMID40413580
PMCPMC12104196

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.