Classification accuracy of blood-based and neurophysiological markers in the differential diagnosis of Alzheimer’s disease and frontotemporal lobar degeneration

BACKGROUND: In the last decade, non-invasive blood-based and neurophysiological biomarkers have shown great potential for the discrimination of several neurodegenerative disorders. However, in the clinical workup of patients with cognitive impairment, it will be highly unlikely that any biomarker will achieve the highest potential predictive accuracy on its own, owing to the multifactorial nature of Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD). METHODS: In this retrospective study, performed on 202 participants, we analysed plasma neurofilament light (NfL), glial fibrillary acidic protein (GFAP), and tau phosphorylated at amino acid 181 (p-Tau(181)) concentrations, as well as amyloid β42 to 40 ratio (Aβ(1–42)/(1–40)) ratio, using the ultrasensitive single-molecule array (Simoa) technique, and neurophysiological measures obtained by transcranial magnetic stimulation (TMS), including short-interval intracortical inhibition (SICI), intracortical facilitation (ICF), long-interval intracortical inhibition (LICI), and short-latency afferent inhibition (SAI). We assessed the diagnostic accuracy of combinations of both plasma and neurophysiological biomarkers in the differential diagnosis between healthy ageing, AD, and FTLD. RESULTS: We observed significant differences in plasma NfL, GFAP, and p-Tau(181) levels between the groups, but not for the Aβ(1–42)/Aβ(1–40) ratio. For the evaluation of diagnostic accuracy, we adopted a two-step process which reflects the clinical judgement on clinical grounds. In the first step, the best single biomarker to classify “cases” vs “controls” was NfL (AUC 0.94, p < 0.001), whilst in the second step, the best single biomarker to classify AD vs FTLD was SAI (AUC 0.96, p < 0.001). The combination of multiple biomarkers significantly increased diagnostic accuracy. The best model for classifying “cases” vs “controls” included the predictors p-Tau(181), GFAP, NfL, SICI, ICF, and SAI, resulting in an AUC of 0.99 (p < 0.001). For the second step, classifying AD from FTD, the best model included the combination of Aβ(1–42)/Aβ(1–40) ratio, p-Tau(181), SICI, ICF, and SAI, resulting in an AUC of 0.98 (p < 0.001). CONCLUSIONS: The combined assessment of plasma and neurophysiological measures may greatly improve the differential diagnosis of AD and FTLD.