Abstract 4348378: Episodes of Higher Glucose Are Associated with Paroxysmal Atrial Fibrillation Occurrences During Continuous Glucose and Heart Rhythm Monitoring in Older Adults with Diabetes: the Atherosclerosis Risk in Communities (ARIC) Study

S Shengyuan Luo (Johns Hopkins University, Baltimore, Maryland, United States) J Joseph Sartini (Johns Hopkins University, Baltimore, Maryland, United States) D Dan Wang M Mary Rooney (Johns Hopkins University, Baltimore, Maryland, United States) J Jung-Im Shin (Johns Hopkins Bloomberg School of Public Health, Baltimore) A Amelia Wallace (JH Bloomberg Sch. of Public Health, Baltimore, Maryland, United States) A Anum Minhas (Johns Hopkins University, Baltimore, Maryland, United States) E Elsayed Soliman (Wake Forest School of Medicine, Winston-Salem, North Carolina, United States) P Pamela Lutsey (University of Minnesota, Minneapolis, Minnesota, United States) J Justin Echouffo (Johns Hopkins Hospital, Baltimore, Maryland, United States) S Scott Zeger (Johns Hopkins University, Baltimore, Maryland, United States) L Lin Yee Chen E Elizabeth Selvin (Johns Hopkins Bloomberg School of Public Health, Baltimore)

Abstract

Background: Cardiac arrhythmias, particularly atrial fibrillation (AF), are highly prevalent in diabetes due, in part, to cardiac autonomic neuropathy. More frequent AF may contribute to negative cardiovascular outcomes such as stroke and heart failure. It is unknown if episodes of higher glucose are associated with paroxysmal AF occurrences. Hypothesis: Episodes of higher glucose levels are associated with higher risks of paroxysmal AF onset. Methods: We invited Atherosclerosis Risk in Communities participants with diabetes at visits 9 and 10 (2021-2023) to undergo 14 days of concurrent continuous glucose monitoring (CGM) and “patch” electrocardiographic (ECG) monitoring regardless of arrhythmia history. Among them, we modelled the odds of episodic AF onset, defined as the transition from sinus rhythm to an AF episode (>30 seconds of irregularly irregular rhythm without P-waves), based on same-hour glucose using multilevel logistic regression, and 4-hour CGM data preceding AF onset using generalized scalar-on-function regression. Results: Of 211 CGM-ECG study participants, AF was detected in 18. Ten individuals with at least one episodic AF onset (mean age 81 years, 5 females, 3 Black adults) formed the analytic sample where a total of 129 days of ECG data and 11,453 glucose measurements were analyzed. Among individual monitoring periods, the number of AF episodes ranged from 1 to 159, and the median CGM time below (<70 mg/dl), in (70-180 mg/dl), and above (>180 mg/dl) range was 2%, 65%, and 32%, respectively. Using multilevel logistic regression dividing data into distinct one-hour intervals and accounting for diurnal patterns and participant random effects, we found a 4% higher odds of AF onset per 10 mg/dl higher same-hour mean glucose (odds ratio [OR] 1.04, 95% confidence interval [CI] 1.00-1.09, p =0.04; Figure 1A ). Generalized scalar-on-function regression with participant random effects and adjustment for time of day found a potentiating association between AF onset and glucose elevations accumulated over 4 preceding hours (per 10 mg/dl consistent elevation over 4 hours, integrated OR 1.17, 95% CI 1.11-1.24, p <0.001; Figure 1B ). Conclusion: Among older, US community-dwelling adults with diabetes, the risk of a paroxysmal AF episode was preceded by higher glucose levels for up to 4 hours as measured by CGM. This finding highlights the importance of continuous monitoring with wearable devices and timely detection of hyperglycemia as a marker of AF risk.

Article Details

Journal Circulation
Volume / Issue Vol. 152, Issue Suppl_3
Published November 04, 2025
ISSN 0009-7322
Publisher Lippincott Williams & Wilkins

Journal Info

Circulation

Lippincott Williams & Wilkins

ISSN: 0009-7322 Health Sciences

Authors (13)

S

Shengyuan Luo

Johns Hopkins University, Baltimore, Maryland, United States

J

Joseph Sartini

Johns Hopkins University, Baltimore, Maryland, United States

D

Dan Wang

M

Mary Rooney

Johns Hopkins University, Baltimore, Maryland, United States

J

Jung-Im Shin

Johns Hopkins Bloomberg School of Public Health, Baltimore

A

Amelia Wallace

JH Bloomberg Sch. of Public Health, Baltimore, Maryland, United States

A

Anum Minhas

Johns Hopkins University, Baltimore, Maryland, United States

E

Elsayed Soliman

Wake Forest School of Medicine, Winston-Salem, North Carolina, United States

P

Pamela Lutsey

University of Minnesota, Minneapolis, Minnesota, United States

J

Justin Echouffo

Johns Hopkins Hospital, Baltimore, Maryland, United States

S

Scott Zeger

Johns Hopkins University, Baltimore, Maryland, United States

L

Lin Yee Chen

E

Elizabeth Selvin

Johns Hopkins Bloomberg School of Public Health, Baltimore