Multiplex assessment of a panel of 16 serum molecules for the differential diagnosis of Alzheimer's disease

Gloria Biella, Massimo Franceschi, Francesca De Rino, Annalisa Davin, Giacomo Giacalone, Paola Brambilla, Panagiotis Bountris, Maria Haritou, Giuseppe Magnani, Filippo Martinelli Boneschi, Gianluigi Forloni, Diego Albani

Research output: Contribution to journalArticlepeer-review


One of the current challenge in Alzheimer's disease (AD) is the identification of reliable biomarkers that might improve diagnostic accuracy, possibly correlating with the disease progression and patient's response to therapy. As the clinically validated AD biomarkers evaluate cerebrospinal fluid (CSF) parameters, the need for less invasive diagnostic markers is well evident. To this respect, blood circulating cytokines or growth factors have provided some encouraging results, even though no clinically validated to date. In 2007 Ray et al suggested a panel of 18 circulating molecules that might increase AD diagnostic accuracy. In an attempt of replicating their data, we designed a multiplex fluorimetric assay comprising 16 independent analytes and covering 15 out of the 18 described proteins. We collected serum samples from three diagnostic groups: probable AD (n=33), matched healthy controls (CNT, n=23) and non AD demented (NAD, n=14). After correction for age, we found an increased level of EGF-1 in AD in comparison to CNT and NAD, while an increase of TRAIL-R4 was found in NAD. However, evaluation of specificity/ sensitivity by ROC curve analysis gave weak evidence of diagnostic accuracy (area under the curve = 0.63 and 0.66 for EGF and TRAIL-R4, respectively). Finally, we tried to find a diagnostic classifier by a multivariate algorithm. We found indication of diagnostic evidence for AD only, while NAD samples did not show a diagnostic pattern.

Original languageEnglish
Pages (from-to)40-45
Number of pages6
JournalAmerican Journal of Neurodegenerative Diseases
Issue number1
Publication statusPublished - 2013


  • Alzheimer's disease
  • Artificial neural networks
  • Diagnosis
  • EGF-1
  • Machine learning
  • Multiplex analysis
  • Multivariate classifier
  • Peripheral biomarkers

ASJC Scopus subject areas

  • Clinical Neurology
  • Neurology


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