Investigation of the possible cause of over-estimation of human aldosterone in plasma, using a unique, non-synthetic human aldosterone-free matrix
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Sheree Stoner
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Peter E. Hickman
Abstract
Objectives
Aldosterone is over-estimated by direct immunoassay when compared to assay by LC-MS/MS. We have investigated the mechanism for this over-estimation, using dichloromethane (DCM) extraction and a unique matrix of non-synthetic aldosterone-free plasma (ALFP).
Methods
Samples used were from patients with normal estimated Glomerular Filtration Rate (eGFR). All samples were measured by direct DiaSorin immunoassay, followed by extraction using dichloromethane (DCM) and LC-MS/MS.
Post-DCM samples were reconstituted in DiaSorin Liaison Endocrinology Diluent (DED), which is effectively a human serum albumin matrix, or in a unique matrix of non-synthetic human aldosterone-free plasma (ALFP). Patient plasma samples were selected to cover a wide range of aldosterone concentrations.
Results
Aldosterone direct immunoassay over-estimated aldosterone concentration in all samples, compared to LC-MS/MS. Post-DCM extraction and reconstitution with DED, aldosterone results matched well with LC-MS/MS results. Reconstitution with our unique ALFP plasma matrix rather than the DED, resulted in an over-estimation of aldosterone by approximately 50 pmol/L in all samples.
Conclusions
In this investigation, we have demonstrated that the most likely source of interference in the direct DiaSorin aldosterone immunoassay is an endogenous protein commonly found in patient samples. This protein probably interferes with the immunoassay by binding to the tracer.
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Research ethics: The local Institutional Review Board deemed the study exempt from review.
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Informed consent: Our institutional Ethics Committee approved all studies as a quality improvement exercise that did not require informed consent.
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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: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
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