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

Abstract

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).

Inflammatory plasma biomarkers in subjects with preclinical Alzheimer’s disease

“…Samples of 100 healthy male and female participants of 65 years of age and older were selected from the main study in health elderly [ 21 ]. All subjects were healthy volunteers without cognitive complaints who registered for participation voluntarily.…”

“…YKL-40 (Chitinase 3-like 1 [CHI3L1]) was measured in the plasma samples using the CHI3L1 Human ELISA Kit (Thermo Fisher) according to the manufacturer’s instructions. YKL-40 was measured previously in a larger sample and not for the sole purpose of this study [ 21 ]. Results of the 100 subjects selected for this study have been used in the analyses.…”

Neuroimmune contributions to Alzheimer’s disease: a focus on human data

“…CSF characterization is already an important component of clinical evaluations in AD, with validated biomarkers such as Aβ 1–42 , T-tau, and P-tau 181 , and an emerging set of immune-related biomarkers such as sTREM2, GFAP, YKL-40 and others are now being tested at reasonable scale so that we can begin to delineate exactly where, in the AD sequence of events, an individual marker is most relevant [ 255 ]. This study also illustrates the ongoing translation of markers from CSF to blood, which will allow studies to be pursued at much larger scale, although understanding in which context to deploy serum vs. CSF biomarkers remains a critical question [ 256 ]. Other reviews summarize this rapidly evolving field in more detail [ 257 ]; overall, no single immune response marker will be sufficient given the multiplicity of ways in which the immune system may be involved in AD.…”

Deep Learning of Speech Data for Early Detection of Alzheimer’s Disease in the Elderly

“…To satisfy the changing requirements, government agencies are planning to introduce a remote AD management system that will enable elderly people with reduced mobility to undergo dementia screening examinations at home [ 47 , 48 , 49 ]. Hence, there is a pressing need to develop measures with greater accuracy and efficiency for remote AD diagnostic screening [ 48 , 50 , 51 ]. Previously, remote dementia management systems were implemented over the phone [ 29 , 32 , 35 ].…”