# A Review of Emerging Biomarkers Connecting Diabetes and Ischemic Stroke: Implications for Early Detection and Risk Stratification

## TL;DR

This review explores new biomarkers that connect diabetes with ischemic stroke, aiming to improve early detection and risk assessment.

## Contribution

The paper identifies and evaluates emerging biomarkers and multi-omics approaches for diabetes-related stroke risk stratification.

## Key findings

- Inflammatory and endothelial biomarkers are linked to atherogenesis and stroke risk in diabetes.
- Genetic and epigenetic markers provide insights into inherited and environmental stroke susceptibility.
- Multimarker panels and machine learning could enhance stroke prediction accuracy in diabetic patients.

## Abstract

Diabetes substantially increases the risk of ischemic stroke through complex metabolic, inflammatory, and vascular mechanisms, yet early identification of high‐risk individuals remains challenging. This narrative review synthesizes emerging circulating and genomic biomarkers that illuminate the pathways linking diabetes and ischemic stroke and evaluates their potential for early detection and precise risk stratification. Systematic searches of PubMed, Scopus, and Web of Science identified 141 relevant studies examining biomarkers, genetic and epigenetic factors, or risk prediction models in adults with diabetes. Evidence highlights several biomarker domains. Inflammatory markers such as high‐sensitivity C‐reactive protein, interleukin‐6, and tumor necrosis factor‐α indicate immune activation driving atherogenesis and plaque instability. Endothelial markers, including endothelin‐1, soluble vascular cell adhesion molecule‐1, and asymmetric dimethylarginine, reflect endothelial dysfunction and a prothrombotic state. Metabolic indicators, notably glycated hemoglobin, adipokines, and lipoprotein(a), capture cumulative glycemic burden, adipose signaling, and inherited atherothrombotic risk. Genetic and epigenetic measures, including polygenic risk scores, microRNAs, long noncoding RNAs, and DNA methylation, quantify inherited susceptibility and molecular imprints of the diabetic environment. Renal markers such as albuminuria and reduced eGFR reflect microvascular injury and consistently associate with stroke risk. Multimarker panels and multi‐omics integration using machine learning approaches show promise for improving predictive accuracy, though standardization, external validation, and demonstration of clinical utility are needed. Integrating these biomarkers with established clinical risk factors could transform stroke prevention in diabetes from reactive to proactive, enabling personalized, mechanism‐informed strategies for early detection and risk stratification.