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Method Validation and Establishment of Reference Intervals for an Insulin-like Growth Factor-1 Chemiluminescent Immunoassay in Cats.

Arne GüssowSabine ThalmeierRuth GostelowJudith LangensteinGesine FoersterNatali BauerKatarina Hazuchova
Published in: Veterinary sciences (2023)
Previously, radioimmunoassay (RIA) has been the only assay to measure insulin-like growth factor-1 (IGF-1) to diagnose hypersomatotropism (HS). Due to radiation concerns, availability, and the cost of IGF-1 RIA, validation of assays for automated analysers such as a chemiluminescent immunoassay (CLIA) is needed. The aim of this study was to validate a CLIA for measurement of feline IGF-1 (IMMULITE 2000 ® XPi, Siemens Medical Solutions Diagnostics, Malvern, PA, USA) compared to IGF1 RIA, establish reference interval (RI), and determine a cut-off value for diagnosis of HS in diabetic cats. Validation of assay performance included precision, linearity, and recovery studies. Right-sided RI was determined using surplus serum of 50 healthy adult cats. Surplus serum samples of diabetic cats with known IGF-1 concentration with ( n = 32/68) and without HS ( n = 36/68) were used for method comparison with RIA. The cut-off for diagnosis of HS was established using receiver operating characteristic (ROC) analysis. The intra-assay coefficient of variation (CV) was ≤4.7%, and the inter-assay CV was ≤5.6% for samples with low, medium, and high IGF-1 concentration. Linearity was excellent (R 2 > 0.99). The correlation between CLIA and RIA was very high (r s = 0.97), with a mean negative bias for CLIA of 24.5%. The upper limit of RI was 670 ng/mL. ROC analysis showed an area under the curve of 0.94, with best cut-off for diagnosis of HS at 746 ng/mL (sensitivity, 84.4%; specificity, 97.2%). The performance of CLIA was good, and the RI and cut-off for HS diagnosis established in this study allow for CLIA to be used in routine work-up of diabetic cats.
Keyphrases
  • growth hormone
  • high throughput
  • pi k akt
  • binding protein
  • type diabetes
  • machine learning
  • wound healing
  • computed tomography
  • deep learning
  • young adults