51 research outputs found
Matrix metalloproteinase 10 is linked to the risk of progression to dementia of the Alzheimer’s type
No full textAbstract Alzheimer’s disease has a long asymptomatic phase that offers a substantial time window for intervention. Using this window of opportunity will require early diagnostic and prognostic biomarkers to detect Alzheimer’s disease pathology at predementia stages, thus allowing identification of patients who will most probably progress to dementia of the Alzheimer’s type and benefit from specific disease-modifying therapies. Consequently, we searched for CSF proteins associated with disease progression along with the clinical disease staging. We measured the levels of 184 proteins in CSF samples from 556 subjective cognitive decline and mild cognitive impairment patients from three independent memory clinic longitudinal studies (Spanish ACE, n = 410; German DCN, n = 93; German Mannheim, n = 53). We evaluated the association between protein levels and clinical stage, and the effect of protein levels on the progression from mild cognitive impairment to dementia of the Alzheimer’s type. Mild cognitive impairment subjects with increased CSF level of matrix metalloproteinase 10 (MMP-10) showed a higher probability of progressing to dementia of the Alzheimer’s type and a faster cognitive decline. CSF MMP-10 increased the prediction accuracy of CSF amyloid-β 42 (Aβ42), phospho-tau 181 (P-tau181) and total tau (T-tau) for conversion to dementia of the Alzheimer’s type. Including MMP-10 to the [A/T/(N)] scheme improved considerably the prognostic value in mild cognitive impairment patients with abnormal Aβ42, but normal P-tau181 and T-tau, and in mild cognitive impairment patients with abnormal Aβ42, P-tau181 and T-tau. MMP-10 was correlated with age in subjects with normal Aβ42, P-tau181 and T-tau levels. Our findings support the use of CSF MMP-10 as a prognostic marker for dementia of the Alzheimer’s type and its inclusion in the [A/T/(N)] scheme to incorporate pathologic aspects beyond amyloid and tau. CSF level of MMP-10 may reflect ageing and neuroinflammation
Matrix metalloproteinase 10 is linked to the risk of progression to dementia of the Alzheimer's type
No full textAlzheimer's disease has a long asymptomatic phase that offers a substantial time window for intervention. Using this window of opportunity will require early diagnostic and prognostic biomarkers to detect Alzheimer's disease pathology at predementia stages, thus allowing identification of patients who will most probably progress to dementia of the Alzheimer's type and benefit from specific disease-modifying therapies. Consequently, we searched for CSF proteins associated with disease progression along with the clinical disease staging. We measured the levels of 184 proteins in CSF samples from 556 subjective cognitive decline and mild cognitive impairment patients from three independent memory clinic longitudinal studies (Spanish ACE, n = 410; German DCN, n = 93; German Mannheim, n = 53). We evaluated the association between protein levels and clinical stage, and the effect of protein levels on the progression from mild cognitive impairment to dementia of the Alzheimer's type. Mild cognitive impairment subjects with increased CSF level of matrix metalloproteinase 10 (MMP-10) showed a higher probability of progressing to dementia of the Alzheimer's type and a faster cognitive decline. CSF MMP-10 increased the prediction accuracy of CSF amyloid-β 42 (Aβ42), phospho-Tau 181 (P-Tau181) and total tau (T-Tau) for conversion to dementia of the Alzheimer's type. Including MMP-10 to the [A/T/(N)] scheme improved considerably the prognostic value in mild cognitive impairment patients with abnormal Aβ42, but normal P-Tau181 and T-Tau, and in mild cognitive impairment patients with abnormal Aβ42, P-Tau181 and T-Tau. MMP-10 was correlated with age in subjects with normal Aβ42, P-Tau181 and T-Tau levels. Our findings support the use of CSF MMP-10 as a prognostic marker for dementia of the Alzheimer's type and its inclusion in the [A/T/(N)] scheme to incorporate pathologic aspects beyond amyloid and tau. CSF level of MMP-10 may reflect ageing and neuroinflammation.Fil: Martino Adami, Pamela Victoria. University Of Cologne; Alemania. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Orellana, Adelina. Instituto de Salud Carlos III; España. International University of Catalonia; EspañaFil: García, Pablo. International University of Catalonia; España. Instituto de Salud Carlos III; EspañaFil: Kleineidam, Luca. University Of Cologne; AlemaniaFil: Alarcón Martín, Emilio. International University of Catalonia; EspañaFil: Montrreal, Laura. International University of Catalonia; EspañaFil: Aguilera, Nuria. International University of Catalonia; EspañaFil: Espinosa, Ana. Fundació Ace; EspañaFil: Abdelnour, Carla. Fundació Ace; EspañaFil: Rosende Roca, Maitee. Fundació Ace; EspañaFil: Pablo Tartari, Juan. Fundació Ace; EspañaFil: Vargas, Liliana. Fundació Ace; EspañaFil: Mauleón, Ana. Fundació Ace; EspañaFil: Esteban De Antonio, Ester. Fundació Ace; EspañaFil: López Cuevas, Rogelio. Fundació Ace; EspañaFil: Dalmasso, Maria Carolina. University Of Cologne; Alemania. Universidad Nacional Arturo Jauretche. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos; ArgentinaFil: Campos Martin, Rafael. University Of Cologne; AlemaniaFil: Parveen, Kayenat. University Of Cologne; AlemaniaFil: Andrade Fuentes, Victor M. University Of Cologne; AlemaniaFil: Amin, Najaf. University of Oxford; Reino UnidoFil: Ahmad, Shahzad. Erasmus MC; Países BajosFil: Ikram, M. Arfan. Erasmus MC; Países BajosFil: Lewczuk, Piotr. Universitat Erlangen-Nuremberg; AlemaniaFil: Kornhuber, Johannes. Universitat Erlangen-Nuremberg; AlemaniaFil: Peters, Oliver. University Hospital Of Białystok; PoloniaFil: Frölich, Lutz. Ruprecht Karls Universitat Heidelberg.; AlemaniaFil: Rüther, Eckart. Universität Göttingen; AlemaniaFil: Wiltfang, Jens. Universität Göttingen; AlemaniaFil: Tarraga, Lluis. Fundació Ace; EspañaFil: Boada, Merce. Fundació Ace; Españ
Detect probably Alzheimer's disease dementia across subsections & language domains (He et al., 2023)
No full textBackground: Decline in language has emerged as a new potential biomarker for the early detection of Alzheimer’s disease (AD). It remains unclear how sensitive language measures are across different tasks, language domains, and languages, and to what extent changes can be reliably detected in early stages such as subjective cognitive decline (SCD) and mild cognitive impairment (MCI).
Method: Using a scene construction task for speech elicitation in a new Spanish/Catalan speaking cohort (N = 119), we automatically extracted features across seven domains, three acoustic (spectral, cepstral, and voice quality), one prosodic, and three from text (morpholexical, semantic, and syntactic). They were forwarded to a random forest classifier to evaluate the discriminability of participants with probable AD dementia, amnestic and nonamnestic MCI, SCD, and cognitively healthy controls. Repeated-measures analyses of variance and paired-samples Wilcoxon signed-ranks test were used to assess whether and how performance differs significantly across groups and linguistic domains.
Results: The performance scores of the machine learning classifier were generally satisfactorily high, with the highest scores over .9. Model performance was significantly different for linguistic domains (p p = .043), with speech features outperforming textual features, and voice quality performing best. High diagnostic classification accuracies were seen even within both cognitively healthy (controls vs. SCD) and MCI (amnestic and nonamnestic) groups.
Conclusion: Speech-based machine learning is powerful in detecting cognitive decline and probable AD dementia across a range of different feature domains, though important differences exist between these domains as well.
Supplemental Material S1. (A) The recruitment procedure, diagnostic criteria, neuropsychological battery, impact of the epidemic, and criteria for inclusion and exclusion. (B) Speech transcription instruction. (C) Feature list and definitions. (D) Detailed group-wise classifier performance from the random forest. (F) Post-hoc analysis of the RMANOVA tests. (F) Results and statistical comparison from Gradient Boosting.
He, R., Chapin, K., Al-Tamimi, J., Bel, N., Marquié, M., Rosende-Roca, M., Pytel, V., Tartari, J. P., Alegret, M., Sanabria, A., Ruiz, A., Boada, M., Valero, S., & Hinzen, W. (2023). Automated classification of cognitive decline and probable Alzheimer’s dementia across multiple speech and language domains. American Journal of Speech-Language Pathology. Advance online publication. https://doi.org/10.1044/2023_AJSLP-22-00403</p
Correction: Cut-off Scores of a Brief Neuropsychological Battery (NBACE) for Spanish Individual Adults Older than 44 Years Old.
No full textAlzheimer's disease risk variants modulate endophenotypes in mild cognitive impairment
No full textIntroduction We evaluated the effect of Alzheimer's disease (AD) susceptibility loci on endophenotypes closely related with AD pathology in patients with mild cognitive impairment (MCI). Methods We selected 1730 MCI patients from four independent data sets. Weighted polygenic risk scores (PGS) were constructed of 18 non‐apolipoprotein E (APOE) AD risk variants. In addition, we determined APOE genotype. AD endophenotypes were cognitive decline over time and cerebrospinal fluid (CSF) biomarkers (aβ, tau, ptau). Results PGS was modestly associated with cognitive decline over time, as measured by mini‐mental state examination (MMSE) (β ± SE:−0.24 ± 0.10; P = .012), and with CSF levels of tau and ptau (tau: 1.38 ± 0.36, P = 1.21 × 10−4; ptau: 1.40 ± 0.36, P = 1.02 × 10−4). Discussion In MCI, we observed a joint effect of AD susceptibility loci on nonamyloid endophenotypes, suggesting a link of these genetic loci with neuronal degeneration in general rather than with Alzheimer‐related amyloid deposition
Characteristics of participants.
No full text<p>HS: healthy subjects; MDS: mild dementia syndrome; MMSE: Mini-Mental State Examination.</p>1<p>Cohens’ d for t-tests in age and MMSE, and <sup>2</sup>Phi for Chi square in education and gender.</p>*<p>p<0.05,</p>**<p>p<0.005.</p
Sample sizes of each of the 6 conditions (3 age ranges by 2 educational levels).
No full text<p>HS: healthy subjects; MDS: mild dementia syndrome.</p
Cut-off scores for 6 conditions (3 age ranges by 2 educational levels), including sensitivity (SE) and specificity (SP) values.
No full text<p>15-BNT: the abbreviated Boston Naming Test with 15 items.</p
Comparison between Spanish and Catalan group performances on NBACE.
No full text<p>HS: healthy subjects; MDS: mild dementia syndrome; WMS-III: Wechsler Memory Scale, Third Edition; WAIS-III: Wechsler Adult Intelligence Scale, Third edition; 15-BNT: the abbreviated Boston Naming Test with 15 items; 15-OT: The 15-Objects test; SKT: Syndrom Kurtz Test; s: time in seconds;</p>#<p>Verbal learning WMS-III = 1<sup>st</sup>+2<sup>nd</sup>+3<sup>rd</sup>+4<sup>th</sup> trial scores.</p><p>Spanish group values are written in regular print and Catalan group values in <i>italics</i>. Values are mean (standard deviation).</p>‡<p>In a subsample of 246 HS and 271 MDS.</p>*<p>p<0.003 after Bonferroni’s correction.</p
Cut-off scores for 6 conditions (3 age ranges by 2 educational levels), including sensitivity (SE) and specificity (SP) values.
No full text<p>WMS-III: Wechsler Memory Scale, Third Edition.</p
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