| Autor: |
Tort‐Merino, Adrià, Sarto, Jordi, Esteller, Diana, Tarnanas, Ioannis, Bügler, Maximilian, Harms, Robbert, Iulita, M. Florencia, Santuccione, Antonella, Ruiz‐García, Raquel, Naranjo, Laura, Martínez, Neus Falgàs, Borrego‐Écija, Sergi, Guillén, Núria, Fernandez‐Villullas, Guadalupe, Val‐Guardiola, Andrea, Juncà‐Parella, Jordi, Bosch, Beatriz, Lladó, Albert, Sanchez‐Valle, Raquel, Balasa, Mircea |
| Zdroj: |
Alzheimer's & Dementia: The Journal of the Alzheimer's Association; Dec2023 Supplement 18, Vol. 19, p1-3, 3p |
| Abstrakt: |
Background: The application of blood‐based biomarkers for the identification of Alzheimer's disease (AD) and the development of novel digital technologies as cognitive screening tests are critical to moving toward a reliable, more accessible early diagnosis. Our aim was to evaluate the diagnostic performance of a machine learning‐based cognitive assessment known as Altoida's digital neuro‐signature (DNS) in patients with non‐degenerative mild cognitive impairment (ndMCI) and MCI due to AD (prodromal AD) and its association with CSF and plasma biomarkers. Method: Altoida's MCI‐DNS is a 10‐minute cognitive test battery evaluating activities of daily living via motoric and augmented reality tasks. The test consists of placing and finding virtual objects in a real environment and its final score is obtained by weighting multi‐modal digital data features, such as hands' micro‐movements, speed, reaction times, or navigation trajectories. We included 81 participants, classified according to their clinical status and CSF AD biomarker profile as: cognitively unimpaired controls, CTR (n = 10; age = 68.5±5.9; MMSE = 29.4±1.1), ndMCI (n = 25; age = 67.6±7.2; MMSE = 26.9±1.9) and prodromal AD (n = 46; age = 70.8±4.9; MMSE = 24.3±3.3). We further investigated a subsample of participants classified according to their plasma pTau181 levels as measured by SiMoA [cutoff = 1.37 pg/mL (Sarto et al., 2022)]: pTau181 negative (n = 27; age = 68.5±5.9; MMSE = 26.9±2.7) or pTau181 positive (n = 30; age = 70.3±5.5; MMSE = 24.1±3.7). Result: Significant differences were found in MCI‐DNS scores between CTR group and ndMCI (F = 23.5; p<0.001) and prodromal AD (F = 114.4; p<0.001) groups. Also, ndMCI showed higher MCI‐DNS scores than the prodromal AD group (F = 4.53; p<0.05, Fig. 1). ROC curves showed an excellent diagnostic accuracy of the MCI‐DNS in the discrimination between CTR vs. ndMCI (AUC = 0.879) and CTR vs. prodromal AD (AUC = 0.975) (Fig. 1). Further analyses showed differences in MCI‐DNS scores between CSF Aβ42 negative and CSF Aβ42 positive (F = 18.9; p<0.001; Fig. 2), as well as between plasma pTau181 negative and plasma pTau181 positive (F = 6.16; p<0.01; Fig. 2). Finally, MCI DNS scores significantly correlated with CSF Aβ42, CSF Aβ42/pTau ratio, CSF neurofilament‐light chain (NfL) and plasma pTau181 concentrations (Fig. 3). Conclusion: Altoida's MCI‐DNS test allows excellent discrimination between CTR and patients with MCI. MCI‐DNS scores significantly correlate with CSF AD core biomarkers, biomarkers of neurodegeneration and blood‐based biomarkers (i.e., plasma pTau181). [ABSTRACT FROM AUTHOR] |
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