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. 2022 Oct 13;14(1):155.
doi: 10.1186/s13195-022-01094-5.

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

Alberto Benussi  1   2 , Valentina Cantoni  3 , Jasmine Rivolta  1 , Silvana Archetti  4 , Anna Micheli  5 , Nicholas Ashton  6   7   8   9 , Henrik Zetterberg  6   10   11   12   13 , Kaj Blennow  6   10 , Barbara Borroni  14   15
Affiliations

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

Alberto Benussi et al. Alzheimers Res Ther. .

Abstract

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-Tau181) concentrations, as well as amyloid β42 to 40 ratio (Aβ1-42/1-40) 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-Tau181 levels between the groups, but not for the Aβ1-42/Aβ1-40 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-Tau181, 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 Aβ1-42/Aβ1-40 ratio, p-Tau181, 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.

Keywords: Alzheimer’s disease; Amyloid; Biomarkers; Frontotemporal dementia; GFAP; Neurofilament light; Serum; Transcranial magnetic stimulation; p-tau.

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Conflict of interest statement

HZ has served at scientific advisory boards and/or as a consultant for Abbvie, Alector, Annexon, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Novo Nordisk, Pinteon Therapeutics, Red Abbey Labs, Passage Bio, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave; has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen, and Roche; and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program (outside submitted work). KB has served as a consultant at advisory boards or at data monitoring committees for Abcam, Axon, BioArctic, Biogen, JOMDD/Shimadzu. Julius Clinical, Lilly, MagQu, Novartis, Roche Diagnostics, and Siemens Healthineers and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program. BB has served at scientific boards for Denali, Wave, Alector, and Aviadobio.

Figures

Fig. 1
Fig. 1
Serum biomarker concentrations and neurophysiological measures in participants by clinical diagnosis. A PLasma NfL. B GFAP. C Aβ1–42/Aβ1–40 ratio. D p-Tau181. E average SICI. F ICF. G LICI. H SAI values in participants by clinical diagnosis. HC, healthy controls; AD, Alzheimer’s disease; FTLD, frontotemporal lobar degeneration; NfL, neurofilament light; GFAP, glial fibrillary acidic protein; SICI, average short-interval intracortical inhibition (1, 2, 3 ms); ICF, average intracortical facilitation (7, 10, 15 ms); LICI, average long-interval intracortical inhibition (50, 100, 150 ms); SAI, average short-latency afferent inhibition (0, + 4 ms) expressed as the ratio of the unconditioned motor evoked potential (MEP). Bar graphs represent the median values, and error bars represent the interquartile range. *p < 0.050; **p < 0.010; ***p < 0.001 after multiple-comparisons corrected post hoc tests
Fig. 2
Fig. 2
ROC curve analysis. ROC curves for differentiating A “cases” vs “controls” and B AD vs FTLD patients. ROC, receiver operating characteristic; AUC, area under the curve; HC, healthy controls; AD, Alzheimer’s disease; FTLD, frontotemporal lobar degeneration; NfL, neurofilament light; GFAP, glial fibrillary acidic protein; SICI, average short-interval intracortical inhibition (1, 2, 3 ms); ICF, average intracortical facilitation (7, 10, 15 ms); LICI, average long-interval intracortical inhibition (50, 100, 150 ms); SAI, average short-latency afferent inhibition (0, + 4 ms) expressed as the ratio of the unconditioned motor evoked potential (MEP)
Fig. 3
Fig. 3
Model selection process and performance of predicting “cases” vs “controls”. The logistic regression model selection process with the best model fit by data-driven selection with the lowest AIC. The parsimonious model shows the model that had a similar performance (ΔAIC < 2) with as few significant predictors as possible. In subsequent models, predictors were removed in a stepwise procedure. Comparisons between AUCs were performed using DeLong statistics, *p < 0.05, **p < 0.010, ***p < 0.001 compared to the best model fit. ROC curves combining different models are plotted on the right side of the figure. AUC, area under the curve; AIC, Akaike Information Criterion; HC, healthy controls; NfL, neurofilament light; GFAP, glial fibrillary acidic protein; SICI, average short-interval intracortical inhibition (1, 2, 3 ms); ICF, average intracortical facilitation (7, 10, 15 ms); SAI, average short-latency afferent inhibition (0, + 4 ms) expressed as the ratio of the unconditioned motor evoked potential (MEP)
Fig. 4
Fig. 4
Model selection process and performance of predicting AD vs FTLD. The logistic regression model selection process with the best model fit by data-driven selection with the lowest AIC. The parsimonious model shows the model that had a similar performance (ΔAIC < 2) with as few significant predictors as possible. In subsequent models, predictors were removed in a stepwise procedure. Comparisons between AUCs were performed using DeLong statistics, *p < 0.05, **p < 0.010, ***p < 0.001 compared to the best model fit (ref). ROC curves combining different models are plotted on the right side of the figure. AUC, area under the curve; AIC, Akaike Information Criterion; AD, Alzheimer’s disease; FTLD, frontotemporal lobar degeneration; NfL, neurofilament light; GFAP, glial fibrillary acidic protein; SICI, average short-interval intracortical inhibition (1, 2, 3 ms); ICF, average intracortical facilitation (7, 10, 15 ms); SAI, average short-latency afferent inhibition (0, + 4 ms) expressed as the ratio of the unconditioned motor evoked potential (MEP)
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