Dataset Information

The Influence of Baseline Alzheimer's Disease Severity on Cognitive Decline and CSF Biomarkers in the NILVAD Trial.

ABSTRACT: We examined the effects of a dihydropyridine calcium channel blocker nilvadipine with anti-inflammatory properties on cognition and cerebrospinal fluid (CSF) biomarkers by baseline Alzheimer's disease (AD) severity. Exploratory analyses were performed on the dataset (n = 497) of a phase III randomized placebo-controlled trial to examine the response to nilvadipine in AD subjects stratified by baseline AD severity into very mild (MMSE ? 25), mild (MMSE 20-24) and moderate AD (MMSE < 20). The outcome measures included total and subscale scores of the Alzheimer's Disease Assessment Scale Cognitive 12 (ADAS-Cog 12), the Clinical Dementia Rating Scale sum of boxes (CDR-sb) and the AD composite score (ADCOMS). Cerebrospinal fluid biomarkers A?38, A?40, A?42, neurofilament light chain (NFL), neurogranin, YKL-40, total tau and P181 tau (ptau) were measured in a subset of samples (n = 55). Regression analyses were adjusted for confounders to specifically examine the influence of nilvadipine and baseline AD severity on cognitive outcomes over 78-weeks. Compared to their respective placebo-controls, nilvadipine-treated, very mild AD subjects showed less decline, whereas moderate AD subjects showed a greater cognitive decline on the ADAS-Cog 12 test and the ADCOMS. A lower decline was observed after nilvadipine treatment for a composite memory trait in very mild AD subjects and a composite language trait in mild AD subjects. Cerebrospinal fluid A?42/A?40 ratios were increased in mild AD and decreased in moderate AD patients treated with nilvadipine, compared to their respective controls. Among moderate AD subjects, levels of ptau, total tau, neurogranin and YKL-40 increased in subjects treated with nilvadipine compared to placebo. These studies suggest that baseline AD severity influenced the treatment outcome in the NILVAD trial and that future clinical trials of nilvadipine should be restricted to mild and very mild AD patients. Trial Registration: NCT02017340 Registered 20 December 2013, https://clinicaltrials.gov/ct2/show/NCT02017340 EUDRACT Reference Number 2012-002764-27 Registered 04 February 2013, https://www.clinicaltrialsregister.eu/ctr-search/search?query=2012-002764-27.

SUBMITTER: Abdullah L

PROVIDER: S-EPMC7067750 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Influence of Baseline Alzheimer's Disease Severity on Cognitive Decline and CSF Biomarkers in the NILVAD Trial.

Abdullah Laila L Crawford Fiona F Tsolaki Magda M Börjesson-Hanson Anne A Olde Rikkert Marcel M Pasquier Florence F Wallin Anders A Kennelly Sean S Ait-Ghezala Ghania G Paris Daniel D Hendrix Suzanne S Blennow Kaj K Lawlor Brian B Mullan Michael M

Frontiers in neurology 20200306

We examined the effects of a dihydropyridine calcium channel blocker nilvadipine with anti-inflammatory properties on cognition and cerebrospinal fluid (CSF) biomarkers by baseline Alzheimer's disease (AD) severity. Exploratory analyses were performed on the dataset (<i>n</i> = 497) of a phase III randomized placebo-controlled trial to examine the response to nilvadipine in AD subjects stratified by baseline AD severity into very mild (MMSE ≥ 25), mild (MMSE 20-24) and moderate AD (MMSE < 20). T ...[more]

PMID: 32210906

Similar Datasets

Project description:BackgroundCrucial to the success of clinical trials targeting early Alzheimer's disease (AD) is recruiting participants who are more likely to progress over the course of the trials. We hypothesize that a combination of plasma and structural MRI biomarkers, which are less costly and non-invasive, is predictive of longitudinal progression measured by atrophy and cognitive decline in early AD, providing a practical alternative to PET or cerebrospinal fluid biomarkers.MethodsLongitudinal T1-weighted MRI, cognitive (memory-related test scores and clinical dementia rating scale), and plasma measurements of 245 cognitively normal (CN) and 361 mild cognitive impairment (MCI) patients from ADNI were included. Subjects were further divided into β-amyloid positive/negative (Aβ+/Aβ-)] subgroups. Baseline plasma (p-tau181 and neurofilament light chain) and MRI-based structural medial temporal lobe subregional measurements and their association with longitudinal measures of atrophy and cognitive decline were tested using stepwise linear mixed effect modeling in CN and MCI, as well as separately in the Aβ+/Aβ- subgroups. Receiver operating characteristic (ROC) analyses were performed to investigate the discriminative power of each model in separating fast and slow progressors (first and last terciles) of each longitudinal measurement.ResultsA total of 245 CN (35.0% Aβ+) and 361 MCI (53.2% Aβ+) participants were included. In the CN and MCI groups, both baseline plasma and structural MRI biomarkers were included in most models. These relationships were maintained when limited to the Aβ+ and Aβ- subgroups, including Aβ- CN (normal aging). ROC analyses demonstrated reliable discriminative power in identifying fast from slow progressors in MCI [area under the curve (AUC): 0.78-0.93] and more modestly in CN (0.65-0.73).ConclusionsThe present data support the notion that plasma and MRI biomarkers, which are relatively easy to obtain, provide a prediction for the rate of future cognitive and neurodegenerative progression that may be particularly useful in clinical trial stratification and prognosis. Additionally, the effect in Aβ- CN indicates the potential use of these biomarkers in predicting a normal age-related decline.

Project description:In this paper, we explore the utility of resting-state EEG measures as potential biomarkers for the detection and assessment of cognitive decline in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Neurophysiological biomarkers of AD derived from EEG and FDG-PET, once characterized and validated, would expand the set of existing diagnostic molecular biomarkers of AD pathology with associated biomarkers of disease progression and neural dysfunction. Since symptoms of AD often begin to appear later in life, successful identification of EEG-based biomarkers must account for age-related neurophysiological changes that occur even in healthy individuals. To this end, we collected EEG data from individuals with AD (n = 26), MCI (n = 53), and cognitively normal healthy controls stratified by age into three groups: 18-40 (n = 129), 40-60 (n = 62) and 60-90 (= 55) years old. For each participant, we computed power spectral density at each channel and spectral coherence between pairs of channels. Compared to age matched controls, in the AD group, we found increases in both spectral power and coherence at the slower frequencies (Delta, Theta). A smaller but significant increase in power of slow frequencies was observed for the MCI group, localized to temporal areas. These effects on slow frequency spectral power opposed that of normal aging observed by a decrease in the power of slow frequencies in our control groups. The AD group showed a significant decrease in the spectral power and coherence in the Alpha band consistent with the same effect in normal aging. However, the MCI group did not show any significant change in the Alpha band. Overall, Theta to Alpha ratio (TAR) provided the largest and most significant differences between the AD group and controls. However, differences in the MCI group remained small and localized. We proposed a novel method to quantify these small differences between Theta and Alpha bands' power using empirically derived distributions of spectral power across the time domain as opposed to averaging power across time. We defined Power Distribution Distance Measure (PDDM) as a distance measure between probability distribution functions (pdf) of Theta and Alpha power. Compared to average TAR, using PDDF enhanced the statistical significance, the effect size, and the spatial distribution of significant effects in the MCI group. We designed classifiers for differentiating individual MCI and AD participants from age-matched controls. The classification performance measured by the area under ROC curve after cross-validation were AUC = 0.85 and AUC = 0.6, for AD and MCI classifiers, respectively. Posterior probability of AD, TAR, and the proposed PDDM measure were all significantly correlated with MMSE score and neuropsychological tests in the AD group.

Dataset Information

The Influence of Baseline Alzheimer's Disease Severity on Cognitive Decline and CSF Biomarkers in the NILVAD Trial.

Publications

The Influence of Baseline Alzheimer's Disease Severity on Cognitive Decline and CSF Biomarkers in the NILVAD Trial.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets