Evidence map›Paper›PMID 42348255›Full record

ArticleClinical chemistry2026

Patterns of One-Year Change in HbA1c and Continuous Glucose Monitoring (CGM) Metrics in Older Adults with Type 2 Diabetes.

Tianyu Cao, Michael Fang, Amelia S Wallace, Natalie Daya Malek, Arielle Valint, Dan Wang, Justin B Echouffo-Tcheugui, Scott L Zeger, Elizabeth Selvin

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In one paragraph

Article in Clinical chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Tianyu CaoThe Welch Center for Prevention, Epidemiology and Clinical Research and the Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0001-7908-2886
Michael FangThe Welch Center for Prevention, Epidemiology and Clinical Research and the Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0003-2849-1780
Amelia S WallaceThe Welch Center for Prevention, Epidemiology and Clinical Research and the Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Natalie Daya MalekThe Welch Center for Prevention, Epidemiology and Clinical Research and the Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Arielle ValintCollaborative Studies Coordinating Center, University of North Carolina Gillings School of Public Health, Chapel Hill, NC, United States.
Dan WangThe Welch Center for Prevention, Epidemiology and Clinical Research and the Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Justin B Echouffo-TcheuguiThe Welch Center for Prevention, Epidemiology and Clinical Research and the Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0002-8460-1617
Scott L ZegerDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Elizabeth SelvinThe Welch Center for Prevention, Epidemiology and Clinical Research and the Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0002-3539-2070

Funding

Glucose instability and neurocognitive outcomes in older adultsR01AG074044 · JOHNS HOPKINS UNIVERSITY · 2025 to 2025
$697k
Glucose patterns and cardiac arrhythmias in older adults with diabetesR01HL158022 · JOHNS HOPKINS UNIVERSITY · 2025 to 2025
$683k
Disparities in the management and prognosis of type 1 diabetesK01DK138273 · JOHNS HOPKINS UNIVERSITY · 2025 to 2025
$157k
HHS R01 AG074044HHS R01 DK128837HHS R01 HL158022NHLBI NIH HHSNHLBI NIH HHS K23 HL153774NHLBI NIH HHS K24 HL152440NHLBI NIH HHS R01 HL158022NIA NIH HHS R01 AG074044NIDDK NIH HHS K01 DK138273NIDDK NIH HHS K24 DK106414NIDDK NIH HHS R01 DK128837NIH HHS
6 · The paper itself

Abstract

backgroundHow changes in hemoglobin A1c (HbA1c) and continuous glucose monitoring (CGM) metrics track together over time is poorly understood, particularly in type 2 diabetes. We investigated the patterns of change in HbA1c and CGM metrics among older adults with type 2 diabetes.

methodsWe analyzed data from 88 Atherosclerosis Risk in Communities (ARIC) study participants (baseline age, 82 years; 28% Black race and 42% women) who had HbA1c and 14-days of CGM assessed by standardized protocols at visit 9 (2021-22) and visit 10 (2023). HbA1c, CGM mean glucose, and time in range (TIR, 70-180 mg/dL) were compared across visits. Discordance was defined as having a different direction or magnitude of change, based on an absolute HbA1c change of 0.5% and the corresponding changes in CGM metrics derived from linear mixed-effect models.

resultsOver a median of 1.6 (IQR, 1.3-1.8) years, HbA1c, CGM mean glucose, and TIR showed moderate to strong correlations (r ∼0.5 to 0.7) across visits, and HbA1c had the lowest within-person variability (CVw = 8.4%). Approximately one-third of the participants had discordant changes between HbA1c and CGM metrics, with percentage agreement of 68.2% between HbA1c and CGM mean glucose, and 67.0% between HbA1c and TIR. Similar results were found in subgroups by sex, race, diabetes medication use, and after excluding participants with reduced kidney function.

conclusionsAmong older adults with type 2 diabetes, long-term changes in HbA1c and CGM metrics are frequently discordant. This suggests the complementary nature of using HbA1c and CGM together to monitor glucose control.

Identifiers

PMID42348255
PMCPMC13308177

What Socratic holds

Textmetadata
LicenceTDM
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Registered trials

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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.