Evidence map›Paper›PMID 42752803›Full record

ArticleDiabetes care2026

Beyond Traditional Glycemic Measures: CGM Glycemic Profiles Reveal Associations With Cardiometabolic Risk in Individuals Without Diabetes.

Bahar Bakhshi, Honghuang Lin, Naznin Sultana, Elizabeth Healey, Havelah Queen, Ge Gao, Sophie E Claudel, Erika T Minetti, Gary F Mitchell, Joanne M Murabito and 7 more

Abstract read
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In one paragraph

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

0numbers the graph read from it
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

17 authors.

Bahar BakhshiSection of Endocrinology, Diabetes, Nutrition, and Weight Management, Department of Medicine, Boston University Chobanian and Avedisian School of Medicine (BUCASM) and Boston Medical Center, Boston, MA.ORCID 0000-0003-3748-1429
Honghuang LinDepartment of Medicine, University of Massachusetts Chan Medical School, Worcester, MA.
Naznin SultanaDepartment of Biostatistics, Boston University School of Public Health, Boston, MA.
Elizabeth HealeyBoston Children's Hospital, Harvard Medical School, Boston, MA.
Havelah QueenDepartment of Biostatistics, Boston University School of Public Health, Boston, MA.
Ge GaoDepartment of Biostatistics, Boston University School of Public Health, Boston, MA.
Sophie E ClaudelSection of Nephrology, Department of Medicine, BUCASM and Boston Medical Center, Boston, MA.
Erika T MinettiSection of Endocrinology, Diabetes, Nutrition, and Weight Management, Department of Medicine, Boston University Chobanian and Avedisian School of Medicine (BUCASM) and Boston Medical Center, Boston, MA.
Gary F MitchellCardiovascular Engineering, Inc., Needham, MA.
Joanne M MurabitoSection of General Internal Medicine, Department of Medicine, BUCASM and Boston Medical Center, Boston, MA.
Donald M Lloyd-JonesFramingham Heart Study, Boston University and National Heart, Lung, and Blood Institute, Framingham, MA.
Michael C CheneySection of Endocrinology, Diabetes, Nutrition, and Weight Management, Department of Medicine, Boston University Chobanian and Avedisian School of Medicine (BUCASM) and Boston Medical Center, Boston, MA.
Devin W SteenkampSection of Endocrinology, Diabetes, Nutrition, and Weight Management, Department of Medicine, Boston University Chobanian and Avedisian School of Medicine (BUCASM) and Boston Medical Center, Boston, MA.
Matthew NayorFramingham Heart Study, Boston University and National Heart, Lung, and Blood Institute, Framingham, MA.
Vanessa XanthakisDepartment of Biostatistics, Boston University School of Public Health, Boston, MA.ORCID 0000-0002-7352-621X
Maura E WalkerFramingham Heart Study, Boston University and National Heart, Lung, and Blood Institute, Framingham, MA.
Nicole L SpartanoSection of Endocrinology, Diabetes, Nutrition, and Weight Management, Department of Medicine, Boston University Chobanian and Avedisian School of Medicine (BUCASM) and Boston Medical Center, Boston, MA.ORCID 0000-0002-0724-8629

Funding

FRAMINGHAM HEART STUDY - YEAR 5 EXAM75N92019D00031 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI RAMACHANDRAN, VASAN · 2019 to 2024
$29.8M
RESEARCH TRAINING IN PEDIATRIC EMERGENCY MEDICINET32HD040128 · NICHD · CHILDREN'S HOSPITAL BOSTON · PI Kenneth D. Mandl · 2001 to 2026
$5.0M
Metabolic Responses to an Oral Mixed Meal Tolerance Test: Intra-individual changes, correlates, and prognostic significanceR01HL156975 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI NAYOR, MATTHEW G. · 2021 to 2025
$3.3M
Continuous glucose monitoring: determinants and prediction of diabetes mellitus development in the Framingham Heart StudyR01DK129305 · NIDDK · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Nicole L Spartano · 2022 to 2026
$2.0M
American Heart Association 26PRE1564902NHLBI NIH HHS 75N92019D00031NHLBI NIH HHS HHSN268201500001INHLBI NIH HHS N01-HC25195NHLBI NIH HHS R01HL156975NICHD NIH HHS T32HD040128NIDDK NIH HHS R01DK129305
6 · The paper itself

Abstract

objectiveFasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) may not fully capture the heterogeneity underlying dysglycemia or its associations with cardiometabolic risk factors (CMRFs). We cross-sectionally investigated whether continuous glucose monitoring (CGM) measures and glycemic profiles could reveal 10-year cardiovascular disease (CVD) risk and individual CMRFs among individuals without diabetes. RESEARCH DESIGN AND

methodsWe included 1,356 participants without prevalent diabetes or CVD from the Framingham Heart Study, who wore a blinded Dexcom G6 Pro CGM for up to 10 days. CGM glycemic profiles were characterized using 1) binary classification of glycemic burden and variability and 2) unsupervised clustering of CGM measures. We performed multivariable-adjusted linear and logistic regression models to examine associations of CGM measures and profiles with 10-year CVD risk estimates (PREVENT equations) and individual CMRFs.

resultsParticipants' mean was age 59.3 years; 55.8% were women, and 92.6% were non-Hispanic White. CGM measures were positively associated with 10-year CVD risk estimates and CMRFs. For example, 1 SD higher % time >140 mg/dL (15.4%) was associated with 2% higher 10-year CVD risk estimates and 21-26% higher odds of hypertension and dyslipidemia, even after adjusting for FPG. Glycemic profiles representing higher CGM dysglycemia revealed discordance with traditionally defined glycemic status (FPG and HbA1c). Although 48.1-84.2% of individuals with traditionally defined prediabetes were included in CGM profiles representing higher dysglycemia, such profiles were associated with 6-7% higher 10-year CVD risk estimates, in addition to worse CMRFs.

conclusionsGlycemic measures reflective of continuous glucose dynamics may provide additional insights into cardiometabolic risk beyond traditional measures.

Indexed as

Blood GlucoseCardiovascular DiseasesAgedCardiometabolic Risk FactorsContinuous Glucose MonitoringCross-Sectional StudiesFemaleGlycated HemoglobinHumansMaleMiddle AgedBlood GlucoseGlycated Hemoglobin

Identifiers

PMID42752803

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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 OpenQuestion graph.