Personalized medicine in diabetology: why C-peptide standardization is essential for translating research into clinical practice

Abstract Personalized medicine has advanced in diabetology over the past decade. Diabetes is diagnosed based on measures of glycaemia, i.e. glycated hemoglobin (HbA 1c ) and glucose, and the classification distinguishes type 1, type 2, gestational diabetes, and specific forms. Especially type 2 diabetes has clinically long been recognized as a heterogeneous metabolic disorder driven by diverse pathophysiological mechanisms. Data-driven approaches have identified distinct sub-phenotypes, offering a more nuanced understanding of diabetes and prediabetes heterogeneity. Identified subgroups differ in clinical characteristics, the risk of disease progression and developing long term complications. Implementing pathophysiology-based classification and emerging therapeutic decision tools requires additional laboratory biomarkers to estimate beta-cell function and insulin resistance. Fasting C-peptide has emerged as the most informative and broadly applicable biomarker and analytical comparability between laboratories has become a prerequisite for translating research findings into guidelines and clinical practice. Efforts to standardize C-peptide measurement have shown that results from different laboratories and assay manufacturers vary widely, even when using the same WHO reference material. Studies consistently demonstrate that recalibrating assays with serum-based, matrix-appropriate reference samples – rather than pure reference reagents – greatly improves agreement across methods. Although a full reference measurement system is now available, significant variability persists and broad implementation remains incomplete despite compelling evidence supporting its effectiveness. The remaining challenge is therefore no longer the development of appropriate reference materials or analytical procedures, but their consistent implementation across manufacturers. Addressing this final step is essential for enabling personalized diabetes care and will ultimately benefit both patients and manufacturers by improving clinical decision-making.