A cross-sectional study in healthy elderly subjects aimed at development of an algorithm to increase identification of Alzheimer pathology for the purpose of clinical trial participation
| Autor: | Robert-Jan Doll, Geert Jan Groeneveld, Samantha Prins, Ahnjili Zhuparris, Ellen P. Hart |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0301 basic medicine
Preclinical AD medicine.medical_specialty Pathology Neurology Cross-sectional study Cognitive Neuroscience tau Proteins Neurosciences. Biological psychiatry. Neuropsychiatry Logistic regression 03 medical and health sciences 0302 clinical medicine Cerebrospinal fluid Alzheimer Disease medicine Humans RC346-429 Aged Aged 80 and over Intention-to-treat analysis Amyloid beta-Peptides Receiver operating characteristic business.industry Research Peptide Fragments Clinical trial Algorithm 030104 developmental biology Cross-Sectional Studies Alzheimer Biomarker (medicine) CSF Aβ Neurology (clinical) Neurology. Diseases of the nervous system business 030217 neurology & neurosurgery Algorithms Biomarkers RC321-571 |
| Zdroj: | Alzheimer’s Research & Therapy, Vol 13, Iss 1, Pp 1-11 (2021) Alzheimer's Research and Therapy ISSUE=13;TITLE=Alzheimer's Research and Therapy Alzheimer's Research & Therapy |
| ISSN: | 1758-9193 |
| Popis: | Background In the current study, we aimed to develop an algorithm based on biomarkers obtained through non- or minimally invasive procedures to identify healthy elderly subjects who have an increased risk of abnormal cerebrospinal fluid (CSF) amyloid beta42 (Aβ) levels consistent with the presence of Alzheimer’s disease (AD) pathology. The use of the algorithm may help to identify subjects with preclinical AD who are eligible for potential participation in trials with disease modifying compounds being developed for AD. Due to this pre-selection, fewer lumbar punctures will be needed, decreasing overall burden for study subjects and costs. Methods Healthy elderly subjects (n = 200; age 65–70 (N = 100) and age > 70 (N = 100)) with an MMSE > 24 were recruited. An automated central nervous system test battery was used for cognitive profiling. CSF Aβ1-42 concentrations, plasma Aβ1-40, Aβ1-42, neurofilament light, and total Tau concentrations were measured. Aβ1-42/1-40 ratio was calculated for plasma. The neuroinflammation biomarker YKL-40 and APOE ε4 status were determined in plasma. Different mathematical models were evaluated on their sensitivity, specificity, and positive predictive value. A logistic regression algorithm described the data best. Data were analyzed using a 5-fold cross validation logistic regression classifier. Results Two hundred healthy elderly subjects were enrolled in this study. Data of 154 subjects were used for the per protocol analysis. The average age of the 154 subjects was 72.1 (65–86) years. Twenty-four (27.3%) were Aβ positive for AD (age 65–83). The results of the logistic regression classifier showed that predictive features for Aβ positivity/negativity in CSF consist of sex, 7 CNS tests, and 1 plasma-based assay. The model achieved a sensitivity of 70.82% (± 4.35) and a specificity of 89.25% (± 4.35) with respect to identifying abnormal CSF in healthy elderly subjects. The receiver operating characteristic curve showed an AUC of 65% (± 0.10). Conclusion This algorithm would allow for a 70% reduction of lumbar punctures needed to identify subjects with abnormal CSF Aβ levels consistent with AD. The use of this algorithm can be expected to lower overall subject burden and costs of identifying subjects with preclinical AD and therefore of total study costs. Trial registration ISRCTN.org identifier: ISRCTN79036545 (retrospectively registered). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |