Evidence map›Paper›PMID 39638855›Full record

ArticleScientific reports2024

Monitoring individualized glucose levels predicts risk for bradycardia in type 2 diabetes patients with chronic kidney disease: a pilot study.

Pejman Farhadi Ghalati, Moein E Samadi, Marlo Verket, Paul Balfanz, Dirk Müller-Wieland, Stephan Jonas, Andreas Napp, Christoph Wanner, Markus Ketteler, Athina Vassiliadou and 8 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. 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

18 authors.

Pejman Farhadi Ghalati *Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany.
Moein E Samadi *Institute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany.
Marlo VerketDepartment of Internal Medicine I, University Hospital RWTH, Aachen, Germany.
Paul BalfanzDepartment of Internal Medicine I, University Hospital RWTH, Aachen, Germany.
Dirk Müller-WielandDepartment of Internal Medicine I, University Hospital RWTH, Aachen, Germany.
Stephan JonasInstitute for Digital Medicine, University Clinic Bonn, Bonn, Germany.
Andreas NappDepartment of Internal Medicine I, University Hospital RWTH, Aachen, Germany.
Christoph WannerDepartment of Internal Medicine I, University Hospital Würzburg, Würzburg, Germany.
Markus KettelerDepartment of Nephrology, Coburg, Germany.
Athina VassiliadouDepartment of Nephrology and Diabetology, Kliniken Maria-Hilf GmbH, Mönchengladbach, Germany.
Stefan HeidenreichKFH Nephrology Center, Aachen, Germany.
Thomas DesernoPeter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, Germany.
Gudrun HetzelDepartment of Nephrology, Düsseldorf, Germany.
Danilo FliserDepartment of Internal Medicine IV, Saarland University Medical Centre, Homburg, Germany.
Malte KelmDepartment of Cardiology, Pulmonology and Vascular Medicine, HHU Düsseldorf, Düsseldorf, Germany.
Jürgen FloegeDepartment of Internal Medicine II, University Hospital Aachen, RWTH Aachen University, Aachen, Germany.
Nikolaus MarxDepartment of Internal Medicine I, University Hospital RWTH, Aachen, Germany.
Andreas SchuppertInstitute for Computational Biomedicine, RWTH Aachen University, Aachen, Germany. aschuppert@ukaachen.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patients with diabetes mellitus (DM) and chronic kidney disease (CKD) exhibit an elevated risk for cardiac arrhythmias, such as bradycardia, which may potentially lead to sudden cardiac death (SCD). While hypoglycemia, defined as a critical drop in glucose levels below the normal range, has long been associated with adverse cardiovascular events, recent studies have highlighted the need for a comprehensive reevaluation of its direct impact on cardiovascular outcomes, particularly in high-risk populations such as those with DM and CKD. In this study, we investigated the association between glucose levels and bradycardia by simultaneously monitoring interstitial glucose (IG) and ECG for 7 days in insulin-treated patients with DM and CKD. We identified bradycardia episodes in 19 of 85 patients (22%) and associated these episodes with personalized low, medium, and high relative glucose levels. Our analysis revealed a significant increase in bradycardia frequency during periods of lowest relative glucose, particularly between 06:00-09:00 and 12:00-15:00. Furthermore, leveraging a Random Forests classifier, we achieved a promising area under the curve (AUC) of 0.94 for predicting bradyarrhythmias using glucose levels and heart rate variability features. Contrary to previous findings, only 4% of bradycardia episodes in our study population occurred at glucose levels of 70 mg/dL or lower, with 28% observed at levels exceeding 180 mg/dL. Our findings not only highlight the strong correlation between relative glucose levels, heart rate parameters, and bradycardia onset but also emphasize the need for a more personalized definition of hypoglycemia to understand its relationship with bradyarrhythmias in high-risk DM and CKD patient populations.

Indexed as

Blood GlucoseBradycardiaDiabetes Mellitus, Type 2Renal Insufficiency, ChronicAgedElectrocardiographyFemaleHeart RateHumansHypoglycemiaMaleMiddle AgedPilot ProjectsRisk FactorsBlood GlucoseBradycardiaChronic kidney diseaseDiabetes mellitusGlucose monitoringHypoglycemiaMachine learningPersonalized medicine

Identifiers

PMID39638855
PMCPMC11621348

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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.