The biological variation, index of individuality, and reference change value for hematological and blood chemical analytes in ferrets (Mustela putorius furo)

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BackgroundUnderstanding biological variation is important when establishing or using currently published reference intervals (RIs) to evaluate hematological and blood chemical analysis results. Population based RIs may not be sensitive enough to identify clinically significant changes in individuals when the intraindividual variation is lower than the interindividual variation. This is the first study with the aim to investigate the biological variation of hematological and biochemical analytes and then to calculate the reference change values (RCVs) in ferrets. MethodsThis retrospective study analyzed blood test results of 13 ferrets that have been tested every year for 7 years. Intraindividual and interindividual coefficients of variation for each analyte were calculated using restricted maximum likelihood that is suitable for unbalanced design since five ferrets died earlier, before determining the index of individuality (IoI) and RCV. ResultsNo analytes had IoI lower than 0.6. The IoI of mean corpuscular hemoglobin (MCH), calcium, blood urea nitrogen (BUN), alanine aminotransferase (ALT), alkaline phosphatase (ALP), total protein, albumin, and globulin were between 0.6 and 1.4. The IoI of red blood cell count, hemoglobin, hematocrit, mean corpuscular volume, MCH concentration (MCHC), white blood cell count, lymphocytes, monocytes, neutrophils, eosinophils, basophils, sodium, potassium, phosphate, glucose, creatinine and amylase were higher than 1.4. In this study, the RCVs varied from 6.87% (sodium) to 391.46% (basophils). Conclusions and clinical relevanceThe results of this study indicate that population based RIs are appropriate to evaluate most analytes in this study. Population based RIs should be used with caution to evaluate MCH, calcium, BUN, ALT, ALP, total protein, albumin, and globulin. The hematological and blood chemical RCVs calculated in this study can assist in diagnosing diseases, and monitoring and evaluating the performance of long-term treatments.