The current status of serum insulin measurements and the need for standardization
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Curt Rohlfing
Abstract
Objectives
Insulin assays are used to assess insulin resistance and to aid in the diagnosis of conditions such as insulinoma and various forms of hypoglycemia. However, discrepancies among commercial assays limit their clinical and research utility. This study evaluates the current comparability of the most widely used insulin assays.
Methods
Forty serum samples, including those from healthy individuals and patients with type 1 or type 2 diabetes, were analyzed by nine manufacturers using 12 commercial immunoassays. Results from each assay were compared both to the isotope dilution mass spectrometry (IDMS) method and across the different immunoassays.
Results
Intra-assay repeatability was excellent (ICCs>0.99), but substantial inter-assay variability was observed. Differences relative to LC-MS ranged from −298.2 to +302.6 pmol/L. Several assays overestimated insulin concentrations at low levels and underestimated them at higher levels. Only one assay method showed full agreement with the IDMS method.
Conclusions
Despite all methods claiming traceability to the WHO 66/304 standard, significant variability persists among insulin assays. These findings highlight the urgent need for insulin assay standardization using commutable certified reference materials.
Funding source: National Institute of Health
Award Identifier / Grant number: 5U01DK133106-03
Award Identifier / Grant number: U01 DK137097
Acknowledgments
We would like to acknowledge the following manufacturers for their participation in our study: Abbott Laboratories, Beckman Coulter Inc., DiaSorin Deutschland GmbH, QuidelOrtho, Fujirebio Inc., Siemens Healthineers, Tosoh Corporation, Mercodia AB, and Roche Diagnostics GmbH.
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Research ethics: The protocols were approved by the University of Missouri Health Sciences Institutional Review Board (#2031722).
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Informed consent: Informed consent was obtained from all individuals included in this study.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: Not applicable.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: This work was supported by NIH [Grant Number 5U01DK133106-03, U01 DK137097].
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Data availability: Not applicable.
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