Alzheimer's disease as a multistage process: an analysis from a population-based cohort study

Redox dysregulation as a driver for DNA damage and its relationship to neurodegenerative diseases

Redox homeostasis refers to the balance between the production of reactive oxygen species (ROS) as well as reactive nitrogen species (RNS), and their elimination by antioxidants. It is linked to all important cellular activities and oxidative stress is a result of imbalance between pro-oxidants and antioxidant species. Oxidative stress perturbs many cellular activities, including processes that maintain the integrity of DNA. Nucleic acids are highly reactive and therefore particularly susceptible to damage. The DNA damage response detects and repairs these DNA lesions. Efficient DNA repair processes are therefore essential for maintaining cellular viability, but they decline considerably during aging. DNA damage and deficiencies in DNA repair are increasingly described in age-related neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis and Huntington's disease. Furthermore, oxidative stress has long been associated with these conditions. Moreover, both redox dysregulation and DNA damage increase significantly during aging, which is the biggest risk factor for neurodegenerative diseases. However, the links between redox dysfunction and DNA damage, and their joint contributions to pathophysiology in these conditions, are only just emerging. This review will discuss these associations and address the increasing evidence for redox dysregulation as an important and major source of DNA damage in neurodegenerative disorders. Understanding these connections may facilitate a better understanding of disease mechanisms, and ultimately lead to the design of better therapeutic strategies based on preventing both redox dysregulation and DNA damage.

Forecasting prevalence and mortality of Alzheimer's disease using the partitioning models

OBJECTIVES

Health forecasting is an important aspect of ensuring that the health system can effectively respond to the changing epidemiological environment. Common models for forecasting Alzheimer's disease and related dementias (AD/ADRD) are based on simplifying methodological assumptions, applied to limited population subgroups, or do not allow analysis of medical interventions. This study uses 5 %-Medicare data (1991-2017) to identify, partition, and forecast age-adjusted prevalence and incidence-based mortality of AD as well as their causal components.

METHODS

The core underlying methodology is the partitioning analysis that calculates the relative impact each component has on the overall trend as well as intertemporal changes in the strength and direction of these impacts. B-spline functions estimated for all parameters of partitioning models represent the basis for projections of these parameters in future.

RESULTS

Prevalence of AD is predicted to be stable between 2017 and 2028 primarily due to a decline in the prevalence of pre-AD-diagnosis stroke. Mortality, on the other hand, is predicted to increase. In all cases the resulting patterns come from a trade-off of two disadvantageous processes: increased incidence and disimproved survival. Analysis of health interventions demonstrates that the projected burden of AD differs significantly and leads to alternative policy implications.

DISCUSSION

We developed a forecasting model of AD/ADRD risks that involves rigorous mathematical models and incorporation of the dynamics of important determinative risk factors for AD/ADRD risk. The applications of such models for analyses of interventions would allow for predicting future burden of AD/ADRD conditional on a specific treatment regime.

Sporadic, late-onset, and multistage diseases

Multistage disease processes are often characterized by a linear relationship between the log of incidence rates and the log of age. Examples include sequences of somatic mutations, that can cause cancer, and have recently been linked with a range of non-malignant diseases. Using a Weibull distribution to model diseases that occur through an ordered sequence of stages, and another model where stages can occur in any order, we characterized the age-related onset of disease in UK Biobank data. Despite their different underlying assumptions, both models accurately described the incidence of over 450 diseases, demonstrating that multistage disease processes cannot be inferred from this data alone. The parametric models provided unique insights into age-related disease, that conventional studies of relative risks cannot. The rate at which disease risk increases with age was used to distinguish between "sporadic" diseases, with an initially low and slowly increasing risk, and "late-onset" diseases whose negligible risk when young rapidly increases with age. "Relative aging rates" were introduced to quantify how risk factors modify age-related risk, finding the effective age-at-risk of sporadic diseases is strongly modified by common risk factors. Relative aging rates are ideal for risk-stratification, allowing the identification of ages with equivalent-risk in groups with different exposures. Most importantly, our results suggest that a substantial burden of sporadic diseases can be substantially delayed or avoided by early lifestyle interventions.

The common incidence-age multistep model of neurodegenerative diseases revisited: wider general age range of incidence corresponds to fewer disease steps

Background

Previously, we collected age-stratified incidence data of 404 epidemiological datasets of 10 neurodegenerative diseases (NDs), namely Amyotrophic Lateral Sclerosis (ALS), Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), Fronto Temporal Dementia (FTD), Dementia with Lewy Bodies (DLB), Parkinsonism (PDM), Parkinson’s disease with Dementia (PDD), Creutzfeldt–Jakob disease (CJD), and Multiple Sclerosis (MS). We tested whether each ND follows a multistep model, found the number of steps necessary for the onset of each ND, found the number of common steps with other NDs and the number of specific steps of each ND, and built a parsimony tree of the genealogy of the NDs. The tree disclosed three groups of NDs: the stem NDs with less than 3 steps; the trunk NDs with 5–7 steps; and the crown NDs with more than 7 steps.

Methods

We made a multidimensional reduction of the previously collected age-stratified incidence epidemiological data of the 10 NDs. We studied the general range of incidence of the 10 NDs using the age- and sex-stratified incidence data. First, we calculated the log of the incidence versus the log of the age for each ND. Next, we calculated the age intervals of the spread of the incidence of each ND. We calculated the regression of the steps obtained with the multistep model versus the age of incidence of the NDs.

Results

We found that the number of steps of the NDs is inversely correlated with the age of incidence of the NDs, and calculated the number of years required for a single step for each ND. Based on these results, we extended the genealogy tree model of the NDs to account for the time needed for a ND step to occur.

Conclusion

The extended genealogy tree disclosed three groups of NDs according to the estimated time needed for a step to occur: the stem ND, HD, with 32.5 years/step, the trunk NDs ALS, FTD, PD and CJD, with 6.7–13.7 years/step; and the crown NDs PDM, PDD, AD and DLB, with 2.3–3.8 years/step. Thus, the NDs cluster into three groups according to both the number of steps and the number of years for a step to occur.

Analysis of Time Trends in Alzheimer's Disease and Related Dementias Using Partitioning Approach

BACKGROUND

Understanding the dynamics of epidemiologic trends in Alzheimer's disease (AD) and related dementias (ADRD) and their epidemiologic causes is vital to providing important insights into reducing the burden associated with these conditions.

OBJECTIVE

To model the time trends in age-adjusted AD/ADRD prevalence and incidence-based mortality (IBM), and identify the main causes of the changes in these measures over time in terms of interpretable epidemiologic quantities.

METHODS

Trend decomposition was applied to a 5%sample of Medicare beneficiaries between 1991 and 2017.

RESULTS

Prevalence of AD was increasing between 1992 and 2011 and declining thereafter, while IBM increased over the study period with a significant slowdown in its rate of growth from 2011 onwards. For ADRD, prevalence and IBM increased through 2014 prior to taking a downwards turn. The primary determinant responsible for declines in prevalence and IBM was the deceleration in the increase and eventual decrease in incidence rates though changes in relative survival began to affect the overall trends in prevalence/IBM in a noticeable manner after 2008. Other components showed only minor effects.

CONCLUSION

The prevalence and IBM of ADRD is expected to continue to decrease. The directions of these trends for AD are not clear because AD incidence, the main contributing component, is decreasing but at a decreasing rate suggesting a possible reversal. Furthermore, emerging treatments may contribute through their effects on survival. Improving ascertainment of AD played an important role in trends of AD/ADRD over the 1991-2009/10 period but this effect has exhausted itself by 2017.

An Effective Brain Imaging Biomarker for AD and aMCI: ALFF in Slow-5 Frequency Band

BACKGROUND

As a potential brain imaging biomarker, amplitude of low frequency fluctuation (ALFF) has been used as a feature to distinguish patients with Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) from normal controls (NC). However, it remains unclear whether the frequency-dependent pattern of ALFF alterations can effectively distinguish the different phases of the disease.

METHODS

In the present study, 52 AD and 50 aMCI patients were enrolled together with 43 NC in total. The ALFF values were calculated in the following three frequency bands: classical (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) for the three different groups. Subsequently, the local functional abnormalities were employed as features to examine the effect of classification among AD, aMCI and NC using a support vector machine (SVM).

RESULTS

We found that the among-group differences of ALFF in the different frequency bands were mainly located in the left hippocampus (HP), right HP, bilateral posterior cingulate cortex (PCC) and bilateral precuneus (PCu), left angular gyrus (AG) and left medial prefrontal cortex (mPFC). When the local functional abnormalities were employed as features, we identified that the ALFF in the slow-5 frequency band showed the highest accuracy to distinguish among the three groups.

CONCLUSION

These findings may deepen our understanding of the pathogenesis of AD and suggest that slow-5 frequency band may be helpful to explore the pathogenesis and distinguish the phases of this disease.

Genealogy of the neurodegenerative diseases based on a meta-analysis of age-stratified incidence data

While the central common feature of the neurodegenerative diseases (NDs) is the accumulation of misfolded proteins, they share other pathogenic mechanisms. However, we miss an explanation for the onset of the NDs. The mechanisms through which genetic mutations, present from conception are expressed only after several decades of life are unknown. We aim to find clues on the complexity of the disease onset trigger of the different NDs expressed in the number of steps of factors related to a disease. We collected brain autopsies on diseased patients with NDs, and found a dynamic increase of the ND multimorbidity with the advance of age. Together with the observation that the NDs accumulate multiple misfolded proteins, and the same misfolded proteins are involved in more than one ND, motivated us to propose a model for a genealogical tree of the NDs. To collect the dynamic data needed to build the tree, we used a Big-data approach that searched automatically epidemiological datasets for age-stratified incidence of NDs. Based on meta-analysis of over 400 datasets, we developed an algorithm that checks whether a ND follows a multistep model, finds the number of steps necessary for the onset of each ND, finds the number of common steps with other NDs and the number of specific steps of each ND, and builds with these findings a parsimony tree of the genealogy of the NDs. The tree discloses three types of NDs: the stem NDs with less than 3 steps; the trunk NDs with 5 to 6 steps; and the crown NDs with more than 7 steps. The tree provides a comprehensive understanding of the relationship across the different NDs, as well as a mathematical framework for dynamic adjustment of the genealogical tree of the NDs with the appearance of new epidemiological studies and the addition of new NDs to the model, thus setting the basis for the search for the identity and order of these steps. Understanding the complexity, or number of steps, of factors related to disease onset trigger is important prior deciding to study single factors for a multiple steps disease.

Objectives, design and main findings until 2020 from the Rotterdam Study

The Rotterdam Study is an ongoing prospective cohort study that started in 1990 in the city of Rotterdam, The Netherlands. The study aims to unravel etiology, preclinical course, natural history and potential targets for intervention for chronic diseases in mid-life and late-life. The study focuses on cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. Since 2016, the cohort is being expanded by persons aged 40 years and over. The findings of the Rotterdam Study have been presented in over 1700 research articles and reports. This article provides an update on the rationale and design of the study. It also presents a summary of the major findings from the preceding 3 years and outlines developments for the coming period.

Predisposition to Alzheimer's and Age-Related Brain Pathologies by PM2.5 Exposure: Perspective on the Roles of Oxidative Stress and TRPM2 Channel

Accumulating epidemiological evidence supports that chronic exposure to ambient fine particular matters of <2.5 μm (PM2.5) predisposes both children and adults to Alzheimer’s disease (AD) and age-related brain damage leading to dementia. There is also experimental evidence to show that PM2.5 exposure results in early onset of AD-related pathologies in transgenic AD mice and development of AD-related and age-related brain pathologies in healthy rodents. Studies have also documented that PM2.5 exposure causes AD-linked molecular and cellular alterations, such as mitochondrial dysfunction, synaptic deficits, impaired neurite growth, neuronal cell death, glial cell activation, neuroinflammation, and neurovascular dysfunction, in addition to elevated levels of amyloid β (Aβ) and tau phosphorylation. Oxidative stress and the oxidative stress-sensitive TRPM2 channel play important roles in mediating multiple molecular and cellular alterations that underpin AD-related cognitive dysfunction. Documented evidence suggests critical engagement of oxidative stress and TRPM2 channel activation in various PM2.5-induced cellular effects. Here we discuss recent studies that favor causative relationships of PM2.5 exposure to increased AD prevalence and AD- and age-related pathologies, and raise the perspective on the roles of oxidative stress and the TRPM2 channel in mediating PM2.5-induced predisposition to AD and age-related brain damage.

Multi-stage models for the failure of complex systems, cascading disasters, and the onset of disease

Complex systems can fail through different routes, often progressing through a series of (rate-limiting) steps and modified by environmental exposures. The onset of disease, cancer in particular, is no different. Multi-stage models provide a simple but very general mathematical framework for studying the failure of complex systems, or equivalently, the onset of disease. They include the Armitage-Doll multi-stage cancer model as a particular case, and have potential to provide new insights into how failures and disease, arise and progress. A method described by E.T. Jaynes is developed to provide an analytical solution for a large class of these models, and highlights connections between the convolution of Laplace transforms, sums of random variables, and Schwinger/Feynman parameterisations. Examples include: exact solutions to the Armitage-Doll model, the sum of Gamma-distributed variables with integer-valued shape parameters, a clonal-growth cancer model, and a model for cascading disasters. Applications and limitations of the approach are discussed in the context of recent cancer research. The model is sufficiently general to be used in many contexts, such as engineering, project management, disease progression, and disaster risk for example, allowing the estimation of failure rates in complex systems and projects. The intended result is a mathematical toolkit for applying multi-stage models to the study of failure rates in complex systems and to the onset of disease, cancer in particular.