Evidence mapPaperPMID 40768053Full record

ArticleDiabetes care2025

Continuous Glucose Monitoring Metrics Predict All-Cause Mortality in Diabetes: A Real-world Long-term Study.

Tomoki Okuno, Sharon A Macwan, Gregory J Norman, Donald R Miller, Peter D Reaven, Jin J Zhou

Abstract read
In one paragraph

Article in Diabetes care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Trial
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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

6 authors.

Tomoki OkunoDepartment of Biostatistics, University of California Los Angeles, Los Angeles, CA.ORCID 0009-0006-9884-0906
Sharon A MacwanPhoenix VA Health Care System, Phoenix, AZ.
Gregory J NormanDexcom, Inc., San Diego, CA.
Donald R MillerDepartment of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, MA.
Peter D ReavenPhoenix VA Health Care System, Phoenix, AZ.ORCID 0000-0001-8923-6690
Jin J ZhouDepartment of Biostatistics, University of California Los Angeles, Los Angeles, CA.ORCID 0000-0001-7983-0274

Funding

Transforming Precision Medicine: Dynamic Learning and Prediction of Disease Progression in Massive, Diverse, and Multimodal CohortsR01DK142026 · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2025 to 2025
$563k
BLRD VA I01 BX006126Dexcom, Inc.National Science Foundation DMS-2054253National Science Foundation I01BX006126-01National Science Foundation IIS-2205441NHGRI NIH HHS R01 HG006139NIDDK NIH HHS R01 DK142026NIDDK NIH HHS R01DK142026NIH HHS R01HG006139
6 · The paper itself

Abstract

objectiveInvestigate the association between continuous glucose monitoring (CGM)-derived glucose metrics and all-cause mortality in patients with type 1 or type 2 diabetes (T1D or T2D). RESEARCH DESIGN AND

methodsWe analyzed data from 2,752 adults (≥21 years old) with diabetes (65% T2D) from the Veterans Affairs Healthcare System who received Dexcom CGM between 2015 and 2020. All participants had ≥10 days of CGM data over landmark (LM) periods (14 days, 3 months, and 6 months) merged with electronic health records. All-cause mortality was assessed over 5 years from CGM initiation. Cox models evaluated associations between mortality and CGM metrics: mean glucose (MG, mg/dL), time in range (TIR, %), time above range (TAR, %), coefficient of variation (CV), and glycemic risk index (GRI, %).

resultsMean age at CGM initiation was 64 years, and median CGM use was nearly 3 years. There were 407 deaths. In separate multivariable Cox models (adjusting for mortality-related variables), higher MG, TAR, CV, and GRI and lower TIR during the 6-month LM were associated with 5-year mortality (hazard ratios: MG 1.18, TAR 1.20, GRI 1.23, CV 1.18, and TIR 0.83; all P ≤ 0.01) and those associations remained significant after adjusting for LM HbA1c. Results were similar with shorter CGM LM observation windows. The association between CV and mortality was independent of other CGM metrics and appeared strongest in those with lower HbA1c levels.

conclusionsCGM-derived metrics were associated with all-cause mortality in patients with diabetes and may better capture long-term risk associated with glucose fluctuations and periods of hypo- and hyperglycemia than HbA1c.

Indexed as

Blood GlucoseBlood Glucose Self-MonitoringDiabetes Mellitus, Type 1Diabetes Mellitus, Type 2AdultAgedContinuous Glucose MonitoringFemaleGlycated HemoglobinHumansMaleMiddle AgedProportional Hazards ModelsBlood GlucoseGlycated Hemoglobin

Identifiers

PMID40768053
PMCPMC12451845

What Socratic holds

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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.