To alleviate the interpretation of the core Alzheimer’s disease (AD) cerebrospinal fluid (CSF) biomarkers, amyloid β1–42 (Aβ42), total tau (T-tau), and phosphorylated tau (P-tau), the Erlangen Score (ES) interpretation algorithm has been proposed.
Objective:
In this study, we aim to assess the predictive properties of the ES algorithm on cognitive and neuroimaging outcomes in mild cognitive impairment (MCI).
Methods:
All MCI subjects with an available baseline CSF sample from ADNI-1 were included (n = 193), and assigned an ES between 0 and 4 based on their baseline CSF biomarker profile. Structural magnetic resonance imaging brain scans and MMSE and ADAS-Cog scores were collected at up to 7 times in follow-up examinations.
Results:
We observed strong and significant correlations between the ES at baseline and neuroimaging and cognitive results with patients with neurochemically probable AD (ES = 4) progressing significantly (p≤0.01) faster than those with a neurochemically improbable AD (ES = 0 or 1), and the subjects with neurochemically possible AD (ES = 2 or 3) in-between these two groups.
Conclusion:
This study further demonstrates the utility of the ES algorithm as a as a tool in predicting cognitive and imaging progression in MCI patients.
HenriquesAD, BenedetAL, CamargosEF, Rosa-NetoP, NobregaOT (2018) Fluid and imaging biomarkers for Alzheimer’s disease: Where we stand and where to head to. Exp Gerontol107, 169–177.
2.
FrisoniGB, BoccardiM, BarkhofF, BlennowK, CappaS, ChiotisK, DemonetJF, GaribottoV, GiannakopoulosP, GietlA, HanssonO, HerholzK, JackCRJr., NobiliF, NordbergA, SnyderHM, Ten KateM, VarroneA, AlbaneseE, BeckerS, BossuytP, CarrilloMC, CeramiC, DuboisB, GalloV, GiacobiniE, GoldG, HurstS, LonneborgA, LovbladKO, MattssonN, MolinuevoJL, MonschAU, MosimannU, PadovaniA, PiccoA, PorteriC, RatibO, Saint-AubertL, ScerriC, ScheltensP, SchottJM, SonniI, TeipelS, VineisP, VisserPJ, YasuiY, WinbladB (2017) Strategic roadmap for an early diagnosis of Alzheimer’s disease based on biomarkers. Lancet Neurol16, 661–676.
3.
LewczukP, RiedererP, O’BryantSE, VerbeekMM, DuboisB, VisserPJ, JellingerKA, EngelborghsS, RamirezA, ParnettiL, JackCRJr., TeunissenCE, HampelH, LleoA, JessenF, GlodzikL, de LeonMJ, FaganAM, MolinuevoJL, JansenWJ, WinbladB, ShawLM, AndreassonU, OttoM, MollenhauerB, WiltfangJ, TurnerMR, ZerrI, HandelsR, ThompsonAG, JohanssonG, ErmannN, TrojanowskiJQ, KaracaI, WagnerH, OecklP, van Waalwijk van DoornL, BjerkeM, KapogiannisD, KuiperijHB, FarottiL, LiY, GordonBA, EpelbaumS, VosSJB, KlijnCJM, Van NostrandWE, MinguillonC, SchmitzM, GalloC, Lopez MatoA, ThibautF, ListaS, AlcoleaD, ZetterbergH, BlennowK, KornhuberJ (2018) Cerebrospinal fluid and blood biomarkers for neurodegenerative dementias: An update of the Consensus of the Task Force on Biological Markers in Psychiatry of the World Federation of Societies of Biological Psychiatry. World J Biol Psychiatry19, 244–328.
4.
BarnettJH, LewisL, BlackwellAD, TaylorM (2014) Early intervention in Alzheimer’s disease: A health economic study of the effects of diagnostic timing. BMC Neurol14, 101.
5.
BlennowK, HampelH, WeinerM, ZetterbergH (2010) Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat Rev Neurol6, 131–144.
6.
SkillbackT, FarahmandBY, RosenC, MattssonN, NaggaK, KilanderL, ReligaD, WimoA, WinbladB, SchottJM, BlennowK, EriksdotterM, ZetterbergH (2015) Cerebrospinal fluid tau and amyloid-beta1–42 in patients with dementia. Brain138, 2716–2731.
7.
KuhlmannJ, AndreassonU, PanneeJ, BjerkeM, PorteliusE, LeinenbachA, BittnerT, KoreckaM, JenkinsRG, VandersticheleH, StoopsE, LewczukP, ShawLM, ZegersI, SchimmelH, ZetterbergH, BlennowK (2017) CSF Abeta1–42 - an excellent but complicated Alzheimer’s biomarker - a route to standardisation. Clin Chim Acta467, 27–33.
8.
LewczukP, ZimmermannR, WiltfangJ, KornhuberJ (2009) Neurochemical dementia diagnostics: A simple algorithm for interpretation of the CSF biomarkers. J Neural Transm (Vienna)116, 1163–1167.
9.
HanssonO, ZetterbergH, BuchhaveP, LondosE, BlennowK, MinthonL (2006) Association between CSF biomarkers and incipient Alzheimer’s disease in patients with mild cognitive impairment: A follow-up study. Lancet Neurol5, 228–234.
10.
ShawLM, VandersticheleH, Knapik-CzajkaM, FigurskiM, CoartE, BlennowK, SoaresH, SimonAJ, LewczukP, DeanRA, SiemersE, PotterW, LeeVM, TrojanowskiJQ (2011) Qualification of the analytical and clinical performance of CSF biomarker analyses in ADNI. Acta Neuropathol121, 597–609.
11.
LewczukP, KornhuberJ, ToledoJB, TrojanowskiJQ, Knapik-CzajkaM, PetersO, WiltfangJ, ShawLM (2015) Validation of the Erlangen score algorithm for the prediction of the development of dementia due to Alzheimer’s disease in pre-dementia subjects. J Alzheimers Dis48, 433–441.
12.
Filipek-GliszczynskaA, BarczakA, BudziszewskaM, MandeckaM, GabryelewiczT, BarcikowskaM (2018) The Erlangen score algorithm in the diagnosis and prediction of the progression from subjective cognitive decline and mild cognitive impairment to Alzheimer-type dementia. Folia Neuropathol56, 88–96.
13.
BaldeirasI, SantanaI, LeitaoMJ, VieiraD, DuroD, MroczkoB, KornhuberJ, LewczukP (2019) Erlangen score as a tool to predict progression from mild cognitive impairment to dementia in Alzheimer’s disease. Alzheimers Res Ther11, 2.
FischlB, SalatDH, BusaE, AlbertM, DieterichM, HaselgroveC, van der KouweA, KillianyR, KennedyD, KlavenessS, MontilloA, MakrisN, RosenB, DaleAM (2002) Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron33, 341–355.
17.
FischlB, van der KouweA, DestrieuxC, HalgrenE, SegonneF, SalatDH, BusaE, SeidmanLJ, GoldsteinJ, KennedyD, CavinessV, MakrisN, RosenB, DaleAM (2004) Automatically parcellating the human cerebral cortex. Cereb Cortex14, 11–22.
18.
JackCRJr., KnopmanDS, JagustWJ, PetersenRC, WeinerMW, AisenPS, ShawLM, VemuriP, WisteHJ, WeigandSD, LesnickTG, PankratzVS, DonohueMC, TrojanowskiJQ (2013) Tracking pathophysiological processes in Alzheimer’s disease: An updated hypothetical model of dynamic biomarkers. Lancet Neurol12, 207–216.
19.
HennemanWJ, SluimerJD, BarnesJ, van der FlierWM, SluimerIC, FoxNC, ScheltensP, VrenkenH, BarkhofF (2009) Hippocampal atrophy rates in Alzheimer disease: Added value over whole brain volume measures. Neurology72, 999–1007.
20.
LewczukP, MatzenA, BlennowK, ParnettiL, MolinuevoJL, EusebiP, KornhuberJ, MorrisJC, FaganAM (2017) Cerebrospinal fluid Abeta42/40 corresponds better than Abeta42 to amyloid PET in Alzheimer’s disease. J Alzheimers Dis55, 813–822.
21.
LewczukP, LelentalN, SpitzerP, MalerJM, KornhuberJ (2015) Amyloid-beta 42/40 cerebrospinal fluid concentration ratio in the diagnostics of Alzheimer’s disease: Validation of two novel assays. J Alzheimers Dis43, 183–191.
