Related Experiment Video
Updated: Sep 17, 2025
Fundus Photography as a Convenient Tool to Study Microvascular Responses to Cardiovascular Disease Risk Factors in Epidemiological Studies
Published on: October 22, 2014
Novel biomarker panel combined with imaging parameters for predicting cardiovascular complications in diabetic
Jiaxing Guo1, Zhongchen Zhang2
1Internal Medicine Department, Shandong University Hospital, Jinan, No.91 Shanda North Road, 250100, Shandong Province, China.
Insights
A new multimodal model combining biomarkers and imaging significantly improves cardiovascular risk prediction in diabetes patients. This approach offers better clinical utility than existing methods for identifying high-risk individuals.
Area of Science:
- Cardiology
- Endocrinology
- Biomarkers and Imaging
Background:
- Diabetes mellitus (DM) patients have a high risk of cardiovascular disease (CVD).
- Current risk tools are suboptimal for diabetic populations.
- Novel approaches are needed to improve CVD risk prediction in DM.
Purpose of the Study:
- To develop and validate a multimodal diagnostic model for predicting cardiovascular complications in diabetic patients.
- Integrate novel biomarkers (sST2, GDF-15) with imaging parameters (coronary artery calcium score [CACS], carotid plaque characteristics).
- Evaluate the clinical utility of this integrated model.
Main Methods:
- Retrospective cohort study of 600 adults with type 2 diabetes.
- Collected laboratory biomarkers (sST2, GDF-15, HbA1c), imaging parameters (CACS, carotid plaque ulceration), and clinical data.
- Used LASSO regression for predictor selection and logistic regression for model construction, validated with ROC, NRI, IDI, and DCA.
Main Results:
- The final model included sST2, GDF-15, CACS, ulcerated carotid plaques, HbA1c, and SGLT2i use.
- The multimodal model achieved an AUC of 0.811, outperforming biomarker-only (0.774) and imaging-only (0.735) models.
- Demonstrated significant improvements in reclassification (NRI=0.52) and discrimination (IDI=0.09) with superior clinical net benefit.
Conclusions:
- Integrating biomarkers and imaging parameters significantly enhances cardiovascular risk prediction in diabetic patients.
- The developed multimodal model offers improved clinical utility for risk stratification.
- Further multicenter prospective studies are recommended to confirm generalizability and cost-effectiveness.
Background:
Patients with diabetes mellitus (DM) face a significantly elevated risk of cardiovascular disease (CVD). However, existing risk stratification tools (e.g., Framingham Risk Score) perform suboptimally in diabetic populations. Traditional biomarkers (e.g., hs-CRP, NT-proBNP) and single-modality imaging parameters (e.g., coronary artery calcium score) have limitations, necessitating a multimodal approach to enhance predictive accuracy.
Objective:
To develop and validate a multimodal diagnostic model integrating novel biomarkers (sST2, GDF-15) and imaging parameters (coronary artery calcium score [CACS], carotid plaque characteristics) for predicting cardiovascular complications in diabetic patients and to evaluate its clinical utility.
Methods:
This single-center retrospective cohort study included 600 adults with type 2 diabetes (2015-2023) and no baseline cardiovascular disease. Laboratory biomarkers (sST2, GDF-15, HbA1c), imaging parameters (CACS, carotid plaque ulceration), and clinical variables were collected. Predictors were selected via LASSO regression, and a multimodal logistic regression model was constructed. Model performance was assessed using ROC curves (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).
Results:
The final model incorporated sST2, GDF-15, CACS, ulcerated carotid plaques, HbA1c, and SGLT2i use. In the validation cohort, the multimodal model achieved an AUC of 0.811 (95% CI: 0.73-0.83), outperforming biomarker-only (AUC = 0.774) and imaging-only models (AUC = 0.735). NRI and IDI were 0.52 (p < 0.001) and 0.09 (p < 0.001), respectively. DCA demonstrated superior clinical net benefit for the combined model across threshold probabilities (10-30%), with a net benefit of 0.32 at 20% risk threshold. Sensitivity and subgroup analyses confirmed model stability.
Conclusion:
The integration of biomarkers and imaging parameters significantly improves cardiovascular risk prediction in diabetic patients, offering enhanced clinical utility. Future multicenter prospective studies are strongly warranted to validate generalizability and cost-effectiveness.
More Related Videos
08:02Author Spotlight: Enhanced Quantification of Cardiovascular Calcification Progression for Longitudinal Micro PET/CT Studies in Small Research Animals
Published on: November 15, 2024
07:25Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia
Published on: September 22, 2020
Related Concept Videos
01:28Imaging Studies for Cardiovascular System V: CT
01:21Imaging Studies for Cardiovascular System IV: CMRI
01:25Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT
01:20Imaging Studies for Cardiovascular System III: X-Ray
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
01:20Blood Studies for Cardiovascular System I: Cardiac Biomarkers
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
01:21Peripheral Arterial Disease II: Clinical Manifestations and Diagnostic Evaluation