Mild cognitive impairment. Part 2: Biological markers for diagnosis and prediction of dementia in Alzheimer's disease

Healthy mitochondrial DNA in balanced mitochondrial dynamics: A potential marker for neuro‑aging prediction (Review)

The mitochondrial genome or mitochondrial DNA (mtDNA) is released as a response to cellular stress. In mitochondrial biogenesis, active communication between the mitochondria genome and nucleus is associated with the mtDNA profile that affects the mitochondrial quality. The present review aimed to assess the molecular mechanism and potential roles of mitochondria in neuro-aging, including the importance of evaluating the health status of mtDNA via mitochondrial dynamics. The normal condition of mitochondria, defined as mitochondrial dynamics, includes persistent changes in morphology due to fission and fusion events and autophagy-mitophagy in the mitochondrial quality control process. The calculated copy number of mtDNA in the mitochondria genome represents cellular health, which can be affected by a long-term imbalance between the production and accumulation of reactive oxygen species in the neuroendocrine system, which leads to an abnormal function of mitochondria and mtDNA damage. Mitochondria health is a new approach to discovering a potential indicator for the health status of the nervous system and several types of neurodegenerative disorders. Mitochondrial dynamics is a key contributor to predicting neuro-aging development, which affects the self-renewal and differentiation of neurons in cell metabolism. Neuro-aging is associated with uncontrolled mitochondrial dynamics, which generates age-associated diseases via various mechanisms and signaling routes that lead to the mtDNA damage that has been associated with neurodegeneration. Future studies on the strategic positioning of mtDNA health profile are needed to detect early neurodegenerative disorders.

Predicting Conversion from Subjective Cognitive Decline to Mild Cognitive Impairment and Alzheimer's Disease Dementia Using Ensemble Machine Learning

BACKGROUND

Subjective cognitive decline (SCD) may represent a preclinical stage of Alzheimer's disease (AD). Predicting progression of SCD patients is of great importance in AD-related research but remains a challenge.

OBJECTIVE

To develop and implement an ensemble machine learning (ML) algorithm to identify SCD subjects at risk of conversion to mild cognitive impairment (MCI) or AD.

METHODS

Ninety-nine SCD patients were included. Thirty-two progressed to MCI/AD, while 67 remained stable. To minimize the effect of class imbalance, both classes were balanced, and sensitivity was taken as evaluation metric. Bagging and boosting ML models were developed by using socio-demographic and clinical information, Mini-Mental State Examination and Geriatric Depression Scale (GDS) scores (feature-set 1a); socio-demographic characteristics and neuropsychological tests scores (feature-set 1b) and regional magnetic resonance imaging grey matter volumes (feature-set 2). The most relevant variables were combined to find the best model.

RESULTS

Good prediction performances were obtained with feature-sets 1a and 2. The most relevant variables (variable importance exceeding 20%) were: Age, GDS, and grey matter volumes measured in four cortical regions of interests. Their combination provided the optimal classification performance (highest sensitivity and specificity) ensemble ML model, Extreme Gradient Boosting with over-sampling of the minority class, with performance metrics: sensitivity = 1.00, specificity = 0.92 and area-under-the-curve = 0.96. The median values based on fifty random train/test splits were sensitivity = 0.83 (interquartile range (IQR) = 0.17), specificity = 0.77 (IQR = 0.23) and area-under-the-curve = 0.75 (IQR = 0.11).

CONCLUSION

A high-performance algorithm that could be translatable into practice was able to predict SCD conversion to MCI/AD by using only six predictive variables.

Brain simulation augments machine-learning-based classification of dementia

Introduction

Computational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning (ML) and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimer's disease (AD).

Methods

We enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local amyloid beta (Aβ) positron emission tomography (PET) with altered excitability. We use PET and magnetic resonance imaging (MRI) data from 33 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for ML classification.

Results

The combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1-score empirical 64.34% vs. combined 74.28%). Informative features showed high biological plausibility regarding the AD-typical spatial distribution.

Discussion

The cause-and-effect implementation of local hyperexcitation caused by Aβ can improve the ML-driven classification of AD and demonstrates TVB's ability to decode information in empirical data using connectivity-based brain simulation.

Potential Fluid Biomarkers for the Diagnosis of Mild Cognitive Impairment

Mild cognitive impairment (MCI) is characterized by a level of cognitive impairment that is lower than normal for a person’s age, but a higher function than that that observed in a demented person. MCI represents a transitional state between normal aging and dementia disorders, especially Alzheimer’s disease (AD). Much effort has been made towards determining the prognosis of a person with MCI who will convert to AD. It is now clear that cerebrospinal fluid (CSF) levels of Aβ40, Aβ42, total tau and phosphorylated tau are useful for predicting the risk of progression from MCI to AD. This review highlights the advantages of the current blood-based biomarkers in MCI, and discusses some of these challenges, with an emphasis on recent studies to provide an overview of the current state of MCI.

Fluid and imaging biomarkers for Alzheimer's disease: Where we stand and where to head to

There is increasing evidence that a number of potentially informative biomarkers for Alzheimer disease (AD) can improve the accuracy of diagnosing this form of dementia, especially when used as a panel of diagnostic assays and interpreted in the context of neuroimaging and clinical data. Moreover, by combining the power of CSF biomarkers with neuroimaging techniques to visualize Aβ deposits (or neurodegenerative lesions), it might be possible to better identify individuals at greatest risk for developing MCI and converting to AD. The objective of this article was to review recent progress in selected imaging and chemical biomarkers for prediction, early diagnosis and progression of AD. We present our view point of a scenario that places CSF and imaging markers on the verge of general utility based on accuracy levels that already match (or even surpass) current clinical precision.

A Clinically-Translatable Machine Learning Algorithm for the Prediction of Alzheimer's Disease Conversion in Individuals with Mild and Premild Cognitive Impairment

BACKGROUND

Available therapies for Alzheimer's disease (AD) can only alleviate and delay the advance of symptoms, with the greatest impact eventually achieved when provided at an early stage. Thus, early identification of which subjects at high risk, e.g., with MCI, will later develop AD is of key importance. Currently available machine learning algorithms achieve only limited predictive accuracy or they are based on expensive and hard-to-collect information.

OBJECTIVE

The current study aims to develop an algorithm for a 3-year prediction of conversion to AD in MCI and PreMCI subjects based only on non-invasively and effectively collectable predictors.

METHODS

A dataset of 123 MCI/PreMCI subjects was used to train different machine learning techniques. Baseline information regarding sociodemographic characteristics, clinical and neuropsychological test scores, cardiovascular risk indexes, and a visual rating scale for brain atrophy was used to extract 36 predictors. Leave-pair-out-cross-validation was employed as validation strategy and a recursive feature elimination procedure was applied to identify a relevant subset of predictors.

RESULTS

16 predictors were selected from all domains excluding sociodemographic information. The best model resulted a support vector machine with radial-basis function kernel (whole sample: AUC = 0.962, best balanced accuracy = 0.913; MCI sub-group alone: AUC = 0.914, best balanced accuracy = 0.874).

CONCLUSIONS

Our algorithm shows very high cross-validated performances that outperform the vast majority of the currently available algorithms, and all those which use only non-invasive and effectively assessable predictors. Further testing and optimization in independent samples will warrant its application in both clinical practice and clinical trials.

Calcium Dysregulation in Alzheimer's Disease: A Target for New Drug Development

Alzheimer's disease (AD) is a devastating neurodegenerative disorder and the most common cause of dementia among aged people whose population is rapidly increasing. AD not only seriously affects the patient's physical health and quality of life, but also adds a heavy burden to the patient's family and society. It is urgent to understand AD pathogenesis and develop the means of prevention and treatment. AD is a chronic devastating neurodegenerative disease without effective treatment. Current approaches for management focus on helping patients relieve or delay the symptoms of cognitive dysfunction. The calcium ion (Ca²⁺) is an important second messenger in the function and structure of nerve cell circuits in the brain such as neuronal growth, exocytosis, as well as in synaptic and cognitive function. Increasing numbers of studies suggested that disruption of intracellular Ca²⁺ homeostasis, especially the abnormal and excessive Ca²⁺ release from the endoplasmic reticulum (ER) via the ryanodine receptor (RYR), plays important roles in orchestrating the dynamic of the neuropathology of AD and associated memory loss, cognitive dysfunction. Dantrolene, a known antagonist of the RYR and a clinically available drug to treat malignant hyperthermia, can ameliorate the abnormal Ca²+ release from the RYR in AD and the subsequent pathogenesis, such as increased β-secretase and γ-secretase activities, production of Amyloid-β 42 (Aβ 42) and its oligomer, impaired autophagy, synapse dysfunction, and memory loss. However, more studies are needed to confirm the efficacy and safety repurposing dantrolene as a therapeutic drug in AD.

Predicting neurocognitive function with hippocampal volumes and DTI metrics in patients with Alzheimer's dementia and mild cognitive impairment

INTRODUCTION

Cognitive performance in patients with Alzheimer's dementia (AD) and mild cognitive impairment (MCI) has been reported to be related to hippocampal atrophy and microstructural changes in white matter (WM). We aimed to predict the neurocognitive functions of patients with MCI or AD using hippocampal volumes and diffusion tensor imaging (DTI) metrics via partial least squares regression (PLSR).

METHODS

A total of 148 elderly female subjects were included: AD (n = 49), MCI (n = 66), and healthy controls (n = 33). Twenty-four hippocampal subfield volumes and the average values for fractional anisotropy (FA) and mean diffusivity (MD) of 48 WM tracts were used as predictors, CERAD-K total scores, scores of CERAD-K 7 cognitive subdomains and K-GDS were used as dependent variables in PLSR.

RESULTS

Regarding MCI patients, DTI metrics such as the MD values of the left retrolenticular part of the internal capsule and left fornix (cres)/stria terminalis were significant predictors, while hippocampal subfield volumes, like the left CA1 and hippocampal tail, were main contributors to cognitive function in AD patients, although global FA/MD values were also strong predictors. The 10-fold cross-validation and stricter 300-iteration tests proved that global cognition measured by the CERAD-K total scores and the scores of several CERAD-K subdomains can be reliably predicted using the PLSR models.

CONCLUSIONS

Our findings indicate different structural contributions to cognitive function in MCI and AD patients, implying that diffuse WM microstructural changes may precede hippocampal atrophy during the AD neurodegenerative process.

Tau imaging in neurodegenerative diseases

Aggregated tau protein is a major neuropathological substrate central to the pathophysiology of neurodegenerative diseases such as Alzheimer's disease (AD), frontotemporal dementia, progressive supranuclear palsy, corticobasal degeneration and chronic traumatic encephalopathy. In AD, it has been shown that the density of hyperphosphorylated tau tangles correlates closely with neuronal dysfunction and cell death, unlike β-amyloid. Until now, diagnostic and pathologic information about tau deposition has only been available from invasive techniques such as brain biopsy or autopsy. The recent development of selective in-vivo tau PET imaging ligands including [(18)F]THK523, [(18)F]THK5117, [(18)F]THK5105 and [(18)F]THK5351, [(18)F]AV1451(T807) and [(11)C]PBB3 has provided information about the role of tau in the early phases of neurodegenerative diseases, and provided support for diagnosis, prognosis, and imaging biomarkers to track disease progression. Moreover, the spatial and longitudinal relationship of tau distribution compared with β - amyloid and other pathologies in these diseases can be mapped. In this review, we discuss the role of aggregated tau in tauopathies, the challenges posed in developing selective tau ligands as biomarkers, the state of development in tau tracers, and the new clinical information that has been uncovered, as well as the opportunities for improving diagnosis and designing clinical trials in the future.

A correlativity study of plasma APL1β28 and clusterin levels with MMSE/MoCA/CASI in aMCI patients

Amnestic mild cognitive impairment (aMCI) is a sub-clinical condition characterized by memory deficits that are not severe enough to affect daily functioning. Here we investigated two potential biomarkers found in the cerebrospinal fluid of AD patients, APLP1-derived Aβ-like peptides 28 (APL1β28) and clusterin plasma levels, in terms of their relationship to cognitive function, as reflected in the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA) and the Cognitive Assessment Screening Instrument (CASI) in aMCI patients. Forty-seven aMCI patients and thirty-five age- and gender-matched healthy adult controls were recruited for this study. Using the ELISA method, we found that the mean concentrations of both APL1β28 and clusterin were not significantly different between the control and aMCI groups. The APL1β28 levels were positively correlated with clusterin and that both were negatively correlated with the MMSE scores of the aMCI patients. Clusterin levels were negatively correlated with the MoCA and CASI scores of the aMCI patients. Using multivariate analysis, the correlation between clusterin and MMSE/MoCA/CASI was independent of other AD risk factors including age, education, sex, body mass index and ApoE genotype. The data presented here demonstrate that plasma clusterin levels reflect cognitive function in aMCI patients.

Neuroproteomic profiling of human body fluids

Analysis of protein expression and abundance provides a possibility to extend the current knowledge on disease-associated processes and pathways. The human brain is a complex organ and dysfunction or damage can give rise to a variety of neurological diseases. Although many proteins potentially reflecting disease progress are originating from brain, the scarce availability of human tissue material has lead to utilization of body fluids such as cerebrospinal fluid and blood in disease-related research. Within the most common neurological disorders, much effort has been spent on studying the role of a few hallmark proteins in disease pathogenesis but despite extensive investigation, the signatures they provide seem insufficient to fully understand and predict disease progress. In order to expand the view the field of neuroproteomics has lately emerged alongside developing technologies, such as affinity proteomics and mass spectrometry, for multiplexed and high-throughput protein profiling. Here, we provide an overview of how such technologies have been applied to study neurological disease and we also discuss some important considerations concerning discovery of disease-associated profiles.

Neuropsychiatric symptoms in the prodromal stages of dementia

PURPOSE OF REVIEW

To critically discuss the neuropsychiatric symptoms in the prodromal stages of dementia in order to improve the early clinical diagnosis of cognitive and functional deterioration.

RECENT FINDINGS

Current criteria for cognitive syndrome, including Alzheimer's disease, comprise the neuropsychiatric symptoms in addition to cognitive and functional decline. Although there is growing evidence that neuropsychiatric symptoms may precede the prodromal stages of dementia, these manifestations have received less attention than traditional clinical hallmarks such as cognitive and functional deterioration. Depression, anxiety, apathy, irritability, agitation, sleep disorders, among other symptoms, have been hypothesized to represent a prodromal stage of dementia or, at least, they increase the risk for conversion from minor neurocognitive disorder to major neurocognitive disorder. Longitudinal investigations have provided increased evidence of progression to dementia in individuals with minor neurocognitive disorder when neuropsychiatric symptoms also were present.

SUMMARY

Although neuropsychiatric symptoms are strongly associated with a higher risk of cognitive and functional deterioration, frequently the clinician does not acknowledge these conditions as increasing the risk of dementia. When the clinician accurately diagnoses neuropsychiatric symptoms in the prodromal stage of dementia, he could early establish appropriate treatment and, may be, delay the beginning of clinical and functional deterioration.