C-Peptide in Type 2 Diabetes: An Underused Blood Test That Could Guide Treatment Decisions

This review found that C-peptide — a byproduct released when the pancreas makes insulin — is underused in managing type 2 diabetes. While it's well established as a diagnostic tool for type 1 diabetes, the evidence suggests C-peptide levels can also predict how well patients respond to different diabetes medications and help forecast disease progression in type 2 diabetes. The review highlights a gap between C-peptide's potential clinical value in T2D and how little it's actually used in practice.

The authors conducted a narrative review of published literature examining C-peptide levels in type 2 diabetes. They synthesized evidence on how C-peptide changes over the course of T2D, its relationship to drug response, its role in treatment strategy decisions, and its ability to predict future outcomes.

Type 2 diabetes treatment is often trial-and-error — patients try medications and switch if they don't work. If C-peptide testing could predict which drugs will work best for a given patient, it would save time, money, and the health consequences of poorly controlled blood sugar. This review makes the case that a simple, already-available blood test is being overlooked as a tool for personalizing diabetes care.

Precision medicine — matching the right treatment to the right patient — is a major goal in diabetes care. Most type 2 diabetes patients are started on the same first-line drug regardless of their individual biology. C-peptide could be a simple, inexpensive way to stratify patients early, potentially identifying who would benefit most from insulin sensitizers vs. secretagogues vs. insulin itself. This aligns with a broader push toward biomarker-guided treatment in metabolic disease.

As a narrative review, this paper identifies gaps and synthesizes trends rather than providing quantitative meta-analytic evidence. The authors acknowledge that C-peptide's utility in T2D has not been extensively studied, meaning much of their argument rests on limited data and the need for future research rather than strong existing evidence.