Simulating effects of biomarker enrichment on Alzheimer's disease prevention trials: conceptual framework and example

Big Data Analytical Approaches to the NACC Dataset: Aiding Preclinical Trial Enrichment

BACKGROUND

Clinical trials increasingly aim to retard disease progression during presymptomatic phases of Mild Cognitive Impairment (MCI) and thus recruiting study participants at high risk for developing MCI is critical for cost-effective prevention trials. However, accurately identifying those who are destined to develop MCI is difficult. Collecting biomarkers is often expensive.

METHODS

We used only noninvasive clinical variables collected in the National Alzheimer's Coordinating Center (NACC) Uniform Data Sets version 2.0 and applied machine learning techniques to build a low-cost and accurate Mild Cognitive Impairment (MCI) conversion prediction calculator. Cross-validation and bootstrap were used to select as few variables as possible accurately predicting MCI conversion within 4 years.

RESULTS

A total of 31,872 unique subjects, 748 clinical variables, and additional 128 derived variables in NACC data sets were used. About 15 noninvasive clinical variables are identified for predicting MCI/aMCI/naMCI converters, respectively. Over 75% Receiver Operating Characteristic Area Under the Curves (ROC AUC) was achieved. By bootstrap we created a simple spreadsheet calculator which estimates the probability of developing MCI within 4 years with a 95% confidence interval.

CONCLUSIONS

We achieved reasonably high prediction accuracy using only clinical variables. The approach used here could be useful for study enrichment in preclinical trials where enrolling participants at risk of cognitive decline is critical for proving study efficacy, and also for developing a shorter assessment battery.

SPION Conjugated Curcumin Nano-Imaging Probe: Synthesis and Bio-Physical Evaluation

In this work, we investigated the loading and conjugation of Curcumin on oleic acid (OA) and citric acid (CA) functionalized iron oxide nanoparticles and its applications in improving contrast in MRI. Magnetic iron oxide nanoparticles (Fe₃O₄, MNPs) were synthesized using the co-precipitation method and characterized by XRD, DLS, FT-IR, VSM, and SEM. FT-IR results confirmed functionalization with oleic acid and citric acid. Curcumin was loaded and conjugated with the Nano-Systems and the amount of Curcumin loaded was quantified using spectrophotometry at 419 nm wavelength. The impact of solvent on the loading of Curcumin was studied. The wt% of loaded Curcumin was found to be 0.189 wt% using dimethylformamide (DMF) whereas using a combination of water-ethanol (15% v/v), this increased to 56.149 wt%. T2 relaxation time was determined using a 1.5 Tesla MRI machine; results showed that the MNPs reduced T2. Cytotoxicity of Nano-Systems (NS) in MTT assay showed that concentrations higher than 80 μg/mL (C_NS > 80 μg/mL) could lead to cancer cell death and low concentrations, up to 40 μg/mL (C_NS < 40 μg/mL) could be evaluated for diagnostic purposes.

Latent Classes of Neuropsychiatric Symptoms in NACC Controls and Conversion to Mild Cognitive Impairment or Dementia

BACKGROUND

A number of studies have linked neuropsychiatric symptoms to increase risk of dementia.

OBJECTIVE

To determine if risk of conversion to mild cognitive impairment or dementia among healthy controls varied as a function of their pattern of neuropsychiatric symptoms.

METHOD

We studied individuals in the National Alzheimer Coordinating Center dataset collected from 34 Alzheimer Disease Centers between 2005 and 2013. The analysis included 4,517 volunteers who were ≥60 years old, cognitively normal, and had complete Neuropsychiatric Inventory data at their baseline visit, and had at least one follow-up. We used latent class analysis to identify four classes based on patterns of NPI symptoms. We used a Cox proportional hazards model to determine if time to MCI or dementia varied by baseline latent class membership.

RESULTS

We identified four latent classes of neuropsychiatric symptoms: irritable, depressed, complex (depression, apathy, irritability, and nighttime behaviors) and asymptomatic. 873 participants converted to MCI or dementia. Hazard ratios for conversion by class were 1.76 (95% CI: 1.34, 2.33) for the irritable class, 3.20 (95% CI: 2.24, 4.58) for the complex class, and 1.90 (95% CI: 1.49, 2.43) for the depressed class, with the asymptomatic class as the reference.

CONCLUSIONS

Membership in all three symptomatic classes was associated with greater risk of conversion to MCI or dementia; the complex class had the greatest risk. Different patterns of neuropsychiatric symptoms may represent different underlying neuropathological pathways to dementia. Further work imaging and pathology research is necessary to determine if this is the case.

Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment

The mild cognitive impairment (MCI) stage of Alzheimer's disease (AD) may be optimal for clinical trials to test potential treatments for preventing or delaying decline to dementia. However, MCI is heterogeneous in that not all cases progress to dementia within the time frame of a trial and some may not have underlying AD pathology. Identifying those MCIs who are most likely to decline during a trial and thus most likely to benefit from treatment will improve trial efficiency and power to detect treatment effects. To this end, using multimodal, imaging-derived, inclusion criteria may be especially beneficial. Here, we present a novel multimodal imaging marker that predicts future cognitive and neural decline from [F-18]fluorodeoxyglucose positron emission tomography (PET), amyloid florbetapir PET, and structural magnetic resonance imaging, based on a new deep learning algorithm (randomized denoising autoencoder marker, rDAm). Using ADNI2 MCI data, we show that using rDAm as a trial enrichment criterion reduces the required sample estimates by at least five times compared with the no-enrichment regime and leads to smaller trials with high statistical power, compared with existing methods.

Five-year biomarker progression variability for Alzheimer's disease dementia prediction: Can a complex instrumental activities of daily living marker fill in the gaps?

Introduction

Biomarker progressions explain higher variability in cognitive decline than baseline values alone. This study examines progressions of established biomarkers along with a novel marker in a longitudinal cognitive decline.

Methods

A total of 215 subjects were used with a diagnosis of normal, mild cognitive impairment (MCI) or Alzheimer's disease (AD) at baseline. We calculated standardized biomarker progression rates and used them as predictors of outcome within 5 years.

Results

Early cognitive declines were more strongly explained by fluorodeoxyglucose-positron emission tomography, precuneus and medial temporal cortical thickness, and the complex instrumental activities of daily living (iADL) marker progressions. Using Cox proportional hazards model, we found that these progressions were a significant risk factor for conversion from both MCI to AD (adjusted hazard ratio 1.45; 95% confidence interval 1.20–1.93; P = 1.23 × 10⁻⁵) and cognitively normal to MCI (adjusted hazard ratio 1.76; 95% confidence interval 1.32–2.34; P = 1.55 × 10⁻⁵).

Discussion

Compared with standard biological biomarkers, complex functional iADL markers could also provide predictive information for cognitive decline during the presymptomatic stage. This has important implications for clinical trials focusing on prevention in asymptomatic individuals.

Use of High-Frequency In-Home Monitoring Data May Reduce Sample Sizes Needed in Clinical Trials

Background

Trials in Alzheimer’s disease are increasingly focusing on prevention in asymptomatic individuals. This poses a challenge in examining treatment effects since currently available approaches are often unable to detect cognitive and functional changes among asymptomatic individuals. Resultant small effect sizes require large sample sizes using biomarkers or secondary measures for randomized controlled trials (RCTs). Better assessment approaches and outcomes capable of capturing subtle changes during asymptomatic disease stages are needed.

Objective

We aimed to develop a new approach to track changes in functional outcomes by using individual-specific distributions (as opposed to group-norms) of unobtrusive continuously monitored in-home data. Our objective was to compare sample sizes required to achieve sufficient power to detect prevention trial effects in trajectories of outcomes in two scenarios: (1) annually assessed neuropsychological test scores (a conventional approach), and (2) the likelihood of having subject-specific low performance thresholds, both modeled as a function of time.

Methods

One hundred nineteen cognitively intact subjects were enrolled and followed over 3 years in the Intelligent Systems for Assessing Aging Change (ISAAC) study. Using the difference in empirically identified time slopes between those who remained cognitively intact during follow-up (normal control, NC) and those who transitioned to mild cognitive impairment (MCI), we estimated comparative sample sizes required to achieve up to 80% statistical power over a range of effect sizes for detecting reductions in the difference in time slopes between NC and MCI incidence before transition.

Results

Sample size estimates indicated approximately 2000 subjects with a follow-up duration of 4 years would be needed to achieve a 30% effect size when the outcome is an annually assessed memory test score. When the outcome is likelihood of low walking speed defined using the individual-specific distributions of walking speed collected at baseline, 262 subjects are required. Similarly for computer use, 26 subjects are required.

Conclusions

Individual-specific thresholds of low functional performance based on high-frequency in-home monitoring data distinguish trajectories of MCI from NC and could substantially reduce sample sizes needed in dementia prevention RCTs.

Prevention of sporadic Alzheimer's disease: lessons learned from clinical trials and future directions

Interventions that have even quite modest effects at the individual level could drastically reduce the future burden of dementia associated with Alzheimer's disease at the population level. In the past three decades, both pharmacological and lifestyle interventions have been studied for the prevention of cognitive decline or dementia in randomised controlled trials of individuals mostly aged older than 50-55 years with or without risk factors for Alzheimer's disease. Several trials testing the effects of physical activity, cognitive training, or antihypertensive interventions showed some evidence of efficacy on a primary cognitive endpoint. However, most of these trials had short follow-up periods, and further evidence is needed to confirm effectiveness and establish the optimum design or dose of interventions and ideal target populations. Important innovations in ongoing trials include the development of multidomain interventions, and the use of biomarker or genetic inclusion criteria. Challenges include the use of adaptive trial designs, the development of standardised, sensitive outcome measures, and the need for interventions that can be implemented in resource-poor settings.