Robust parametric modeling of Alzheimer's disease progression

Quantitative characterization of disease progression using longitudinal data can provide long-term predictions for the pathological stages of individuals. This work studies the robust modeling of Alzheimer's disease progression using parametric methods. The proposed method linearly maps the individual's age to a disease progression score (DPS) and jointly fits constrained generalized logistic functions to the longitudinal dynamics of biomarkers as functions of the DPS using M-estimation. Robustness of the estimates is quantified using bootstrapping via Monte Carlo resampling, and the estimated inflection points of the fitted functions are used to temporally order the modeled biomarkers in the disease course. Kernel density estimation is applied to the obtained DPSs for clinical status classification using a Bayesian classifier. Different M-estimators and logistic functions, including a novel type proposed in this study, called modified Stannard, are evaluated on the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for robust modeling of volumetric MRI and PET biomarkers, CSF measurements, as well as cognitive tests. The results show that the modified Stannard function fitted using the logistic loss achieves the best modeling performance with an average normalized MAE of 0.991 across all biomarkers and bootstraps. Applied to the ADNI test set, this model achieves a multiclass AUC of 0.934 in clinical status classification. The obtained results for the proposed model outperform almost all state-of-the-art results in predicting biomarker values and classifying clinical status. Finally, the experiments show that the proposed model, trained using abundant ADNI data, generalizes well to data from the National Alzheimer's Coordinating Center (NACC) with an average normalized MAE of 1.182 and a multiclass AUC of 0.929

Current advances in systems and integrative biology

Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result. This latter approach foregoes assumptions which predict how a biological system should react to an altered microenvironment within a cellular context, across a tissue or impacting on distant organs. Additionally, re-use of existing data by systematic data mining and re-stratification, one of the cornerstones of integrative systems biology, is also gaining attention. While tremendous efforts using a systems methodology have already yielded excellent results, it is apparent that a lack of suitable analytic tools and purpose-built databases poses a major bottleneck in applying a systematic workflow. This review addresses the current approaches used in systems analysis and obstacles often encountered in large-scale data analysis and integration which tend to go unnoticed, but have a direct impact on the final outcome of a systems approach. Its wide applicability, ranging from basic research, disease descriptors, pharmacological studies, to personalized medicine, makes this emerging approach well suited to address biological and medical questions where conventional methods are not ideal

Quantification of white matter cellularity and damage in preclinical and early symptomatic Alzheimer\u27s disease

Interest in understanding the roles of white matter (WM) inflammation and damage in the pathophysiology of Alzheimer disease (AD) has been growing significantly in recent years. However, in vivo magnetic resonance imaging (MRI) techniques for imaging inflammation are still lacking. An advanced diffusion-based MRI method, neuro-inflammation imaging (NII), has been developed to clinically image and quantify WM inflammation and damage in AD. Here, we employed NII measures in conjunction with cerebrospinal fluid (CSF) biomarker classification (for β-amyloid (Aβ) and neurodegeneration) to evaluate 200 participants in an ongoing study of memory and aging. Elevated NII-derived cellular diffusivity was observed in both preclinical and early symptomatic phases of AD, while disruption of WM integrity, as detected by decreased fractional anisotropy (FA) and increased radial diffusivity (RD), was only observed in the symptomatic phase of AD. This may suggest that WM inflammation occurs earlier than WM damage following abnormal Aβ accumulation in AD. The negative correlation between NII-derived cellular diffusivity and CSF Aβ42 level (a marker of amyloidosis) may indicate that WM inflammation is associated with increasing Aβ burden. NII-derived FA also negatively correlated with CSF t-tau level (a marker of neurodegeneration), suggesting that disruption of WM integrity is associated with increasing neurodegeneration. Our findings demonstrated the capability of NII to simultaneously image and quantify WM cellularity changes and damage in preclinical and early symptomatic AD. NII may serve as a clinically feasible imaging tool to study the individual and composite roles of WM inflammation and damage in AD. Keywords: Inflammation, White matter damage, Diffusion basis spectrum imaging, Neuro-inflammation imaging, Cerebrospinal fluid, Preclinical Alzheimer disease, Early symptomatic Alzheimer disease, Magnetic resonance imagin

Potential Alzheimer\u27s Disease Plasma Biomarkers

In this series of studies, we examined the potential of a variety of blood-based plasma biomarkers for the identification of Alzheimer\u27s disease (AD) progression and cognitive decline. With the end goal of studying these biomarkers via mixture modeling, we began with a literature review of the methodology. An examination of the biomarkers with demographics and other health factors found evidence of minimal risk of confounding along the causal pathway from biomarkers to cognitive performance. Further study examined the usefulness of linear combinations of biomarkers, achieved via partial least squares (PLS) analysis, as predictors of various cognitive assessment scores and clinical cognitive diagnosis. The identified biomarker linear combinations were not effective at predicting cognitive outcomes. The final study of our biomarkers utilized mixture modeling through the extension of group-based trajectory modeling (GBTM). We modeled five biomarkers, covering a range of functions within the body, to identify distinct trajectories over time. Final models showed statistically significant differences in baseline risk factors and cognitive assessments between developmental trajectories of the biomarker outcomes. This course of study has added valuable information to the field of plasma biomarker research in relation to Alzheimer’s disease and cognitive decline

Fast and accurate modelling of longitudinal and repeated measures neuroimaging data

Despite the growing importance of longitudinal data in neuroimaging, the standard analysis methods make restrictive or unrealistic assumptions (e.g., assumption of Compound Symmetry—the state of all equal variances and equal correlations—or spatially homogeneous longitudinal correlations). While some new methods have been proposed to more accurately account for such data, these methods are based on iterative algorithms that are slow and failure-prone. In this article, we propose the use of the Sandwich Estimator (SwE) method which first estimates the parameters of interest with a simple Ordinary Least Square model and second estimates variances/covariances with the “so-called” SwE which accounts for the within-subject correlation existing in longitudinal data. Here, we introduce the SwE method in its classic form, and we review and propose several adjustments to improve its behaviour, specifically in small samples. We use intensive Monte Carlo simulations to compare all considered adjustments and isolate the best combination for neuroimaging data. We also compare the SwE method to other popular methods and demonstrate its strengths and weaknesses. Finally, we analyse a highly unbalanced longitudinal dataset from the Alzheimer's Disease Neuroimaging Initiative and demonstrate the flexibility of the SwE method to fit within- and between-subject effects in a single model. Software implementing this SwE method has been made freely available at http://warwick.ac.uk/tenichols/SwE

Progression Modeling of Cognitive Disease Using Temporal Data Mining: Research Landscape, Gaps and Solution Design

Dementia is a cognitive disorder whose diagnosis and progression monitoring is very difficult due to a very slow onset and progression. It is difficult to detect whether cognitive decline is due to ageing process or due to some form of dementia as MRI scans of the brain cannot reliably differentiate between ageing related volume loss and pathological changes. Laboratory tests on blood or CSF samples have also not proved very useful. Alzheimer�s disease (AD) is recognized as the most common cause of dementia. Development of sensitive and reliable tool for evaluation in terms of early diagnosis and progression monitoring of AD is required. Since there is an absence of specific markers for predicting AD progression, there is a need to learn more about specific attributes and their temporal relationships that lead to this disease and determine progression from mild cognitive impairment to full blown AD. Various stages of disease and transitions from one stage to the have be modelled based on longitudinal patient data. This paper provides a critical review of the methods to understand disease progression modelling and determine factors leading to progression of AD from initial to final stages. Then the design of a machine learning based solution is proposed to handle the gaps in current research

Obstructive sleep apnea severity affects amyloid burden in cognitively normal elderly a longitudinal study

Recent evidence suggests that Obstructive Sleep Apnea (OSA) may be a risk factor for developing Mild Cognitive Impairment and Alzheimer’s disease. However, how sleep apnea affects longitudinal risk for Alzheimer’s disease is less well understood.Postprint (author's final draft

Cost-utility analysis in Alsheimer´s disease

Alzheimer s disease (AD) is a neurodegenerative disorder causing dementia, a syndrome of gradual loss of cognitive function causing impairment in social and occupational functioning. This leads to substantial loss in quality of life and premature death for persons with the disease, associated suffering for their families and large costs to society. In parallel to the aging of the population the world prevalence is predicted to threefold within the next 40 years, creating a challenge for researchers and decision-makers to make better treatments available. Further, improved methods for economic evaluation in AD are needed to identify the optimal treatment strategies. The overall objective of this thesis is to explore the application of cost-utility analysis in AD and address key methodological challenges and data needs. In paper I, prediction functions for simulating disease progression and economic endpoints in a decision-analytic model were estimated. Three year follow-up data from the Swedish Alzheimer Treatment Study (SATS) on the natural course of AD of 435 patients commencing treatment with donepezil and their care setting and costs of care was analyzed. A simplified model in which cognition (representing the underlying course of disease) and the ability to perform activities of daily living (ADL) (representing patient care need) was assumed to predict the provision of care. According to the estimated statistical functions, cognition was found to be the key predictor of ADL-ability which itself was the main predictor of care setting and costs of care. In paper II, we used contingent valuation methods to elicit caregivers willingness-to-pay (WTP) for reductions in patient care need. In total, 517 caregivers of AD patients in four countries (Spain, Sweden, UK and US) were interviewed. The mean WTP for a one hour reduction per day was estimated at between £59 and £144 per month depending on country. The income of the caregiver was the only consistently significant determinant of WTP across all countries. In paper III, we assessed predictors of the costs of care of 1,222 AD patients in four countries (Spain, Sweden, UK and US), both residing in the community and in residential care settings. Cognition, ADL-ability, behavioural symptoms and costs of care (RUD-Lite) were assessed via a patient and caregiver interview. Cost estimates ranged between £1,000 to £5,000 per patient and month, increasing with disease severity and higher in residential care settings. ADL-ability was the most important predictor of costs but part of the variation was also explained by cognition and behavioural symptoms. In paper IV, the key components and drivers of costs of care in a clinical trial sample of 2,744 mild to moderate AD patients were identified. Costs were assessed with RUD-Lite at baseline and every 6 months over the 18 months trial. Informal care constituted 82-86 percent of total costs, whereas community care and patient accommodation constituted an equal share of 12-16 percent. Informal care also had the strongest correlation with disease severity measures including cognition, ADL-ability, global function and behavioural symptoms. In conclusion, cognition, ADL-ability and behavioural symptoms are all important indicators of care need in AD and should be considered in economic modelling. Caregivers have a substantial willingness to pay for reductions in care need. Informal care is the key cost component in clinical trials in mild to moderate AD. Health utility estimates of AD patients are highly dependent on the methodology including choice of instrument, respondent and utility tariffs

Transcriptome meta-analysis reveals a central role for sex steroids in the degeneration of hippocampal neurons in Alzheimer’s disease

BACKGROUND: Alzheimer’s disease is the most prevalent form of dementia. While a number of transcriptomic studies have been performed on the brains of Alzheimer’s specimens, no clear picture has emerged on the basis of neuronal transcriptional alterations linked to the disease. Therefore we performed a meta-analysis of studies comparing hippocampal neurons in Alzheimer’s disease to controls. RESULTS: Homeostatic processes, encompassing control of gene expression, apoptosis, and protein synthesis, were identified as disrupted during Alzheimer’s disease. Focusing on the genes carrying out these functions, a protein-protein interaction network was produced for graph theory and cluster exploration. This approach identified the androgen and estrogen receptors as key components and regulators of the disrupted homeostatic processes. CONCLUSIONS: Our systems biology approach was able to identify the importance of the androgen and estrogen receptors in not only homeostatic cellular processes but also the role of other highly central genes in Alzheimer’s neuronal dysfunction. This is important due to the controversies and current work concerning hormone replacement therapy in postmenopausal women, and possibly men, as preventative approaches to ward off this neurodegenerative disorder