Validation of a novel and accurate ApoE4 assay for automated chemistry analyzers.
Sergio VeigaAndrés Rodríguez-MartínGuillermo Garcia-RibasIgnacio ArribasMiriam Menacho-RománMiguel Calero LaraPublished in: Scientific reports (2020)
The allele ε4 of the apolipoprotein E gene (APOE ε4) is the major genetic risk factor for non-dominantly inherited Alzheimer's Disease (AD). Current techniques for APOE ε4 carriers identification show good accuracy but have several disadvantages that limit its implementation in a clinical laboratory. These include the need for sample preprocessing, poor automation, low throughput, requirement of additional equipment, and high cost. We followed ISO 13485 guidelines to validate the e4Risk test, a new latex-enhanced immunoturbidimetric blood assay for apolipoprotein E4 (ApoE4) determination in human plasma samples. The test showed high performance in terms of lot to lot variability, precision, interferences, reagents stability, prozone, and detectability. Furthermore, diagnostic accuracy is almost equal (99%) to the gold standard, APOE ε4 genotyping by polymerase chain reaction (PCR). Furthermore, we demonstrated that the e4Risk test can be adapted to any clinical chemistry analyzer, including the high throughput analyzers present in most hospitals and clinical laboratories. The e4Risk test versatility, low cost, and easiness provides an excellent solution for APOE ε4 carriers identification using the same blood sample drawn for biochemical diagnostic work-up of AD patients, which can have important advantages for patient stratification in clinical trials, preventative strategies for AD, and clinical assessment of risk for brain amyloidosis.
Keyphrases
- high throughput
- cognitive decline
- high fat diet
- clinical trial
- low cost
- genome wide
- end stage renal disease
- primary care
- machine learning
- chronic kidney disease
- type diabetes
- mild cognitive impairment
- gene expression
- metabolic syndrome
- newly diagnosed
- copy number
- randomized controlled trial
- insulin resistance
- prognostic factors
- mass spectrometry
- deep learning
- transcription factor
- functional connectivity
- subarachnoid hemorrhage
- clinical practice
- breast cancer risk
- genome wide identification