Three components of glucose dynamics – value, variability, and autocorrelation – are independently associated with coronary plaque vulnerability

Impaired glucose homeostasis leads to many complications, with coronary artery disease (CAD) being a major contributor to healthcare costs. However, current CAD screening methods lack efficacy. Here, we predicted CAD using easy-to-measure indices, including continuous glucose monitoring (CGM)-derived indices. We found that CGM-derived indices, particularly ADRR and AC\_Var, exhibited stronger predictive capabilities for CAD compared to commonly used diabetes diagnostic indices such as fasting blood glucose (FBG), hemoglobin A1C (HbA1c), and plasma glucose level at 120 min during oral glucose tolerance tests (PG120). Factor analysis identified three distinct components underlying glucose dynamics – value, variability, and autocorrelation – each independently associated with CAD. Remarkably, ADRR was influenced by the first two components, and AC\_Var was influenced by the third component. FBG, HbA1c, and PG120 were influenced only by the value component, making them insufficient for CAD prediction. CGM-derived indices reflecting the three components can outperform traditional diabetes diagnostic methods in CAD prediction. (150/150 words) ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (JP21H04759), CREST, the Japan Science and Technology Agency (JST) (JPMJCR2123), and The Uehara Memorial Foundation. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The retrospective observational study was approved by the ethics committee of Kobe University Graduate School of Medicine (UMIN000018326; Kobe, Japan). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The CGM data that support the findings of this study are available from the GitHub repository ([https://github.com/HikaruSugimoto/CGM\_regression\_app][1]). [1]: https://github.com/HikaruSugimoto/CGM_regression_app