To identify blood-based DNA methylation signatures that distinguish high-risk prediabetes clusters, emphasizing the significance of these clusters in predicting diabetes risk.
Key Findings:
More than 120,000 differentially methylated CpG sites were identified across clusters, highlighting the complexity of prediabetes.
1,557 CpG sites were selected as stable predictors of cluster membership with over 95% classification accuracy in the discovery cohort, indicating strong predictive power.
In the independent replication cohort, methylation-based partitioning reproduced high-risk cluster identities with 92% accuracy, confirming the reliability of the findings.
Cluster-specific markers were enriched in genes related to TGF-β receptor, MAPK signaling, and Wnt pathways, suggesting potential biological mechanisms.
1,512 of the 1,557 CpG sites correlated with anthropometric or metabolic traits, particularly insulin sensitivity and glucose levels, underscoring their clinical relevance.
Interpretation:
The findings suggest that peripheral blood epigenetic profiling can effectively stratify metabolic risk in prediabetes, potentially replacing more invasive testing methods and improving accessibility to risk assessment.
Limitations:
The study population was limited to individuals of Central European ancestry, which may affect generalizability.
Longitudinal validation is needed to determine predictive value for incident type 2 diabetes or complications, and potential biases or confounding factors should be considered.
Conclusion:
The identification of blood-borne biomarkers for prediabetes could facilitate broader population screening and risk stratification without extensive clinical testing, significantly impacting public health strategies.
More than 80% of women who were partially up to date reported a wellness visit in the prior year, suggesting missed opportunities for screening engagement in primary care.
