Evolution of genetic testing supports precision medicine for caring Alzheimer's disease patients

Research Participants' Perspectives on Precision Diagnostics for Alzheimer's Disease

BACKGROUND

Understanding research participants' responses to learning Alzheimer's disease (AD) risk information is important to inform clinical implementation of precision diagnostics given rapid advances in disease modifying therapies.

OBJECTIVE

We assessed participants' perspectives on the meaning of their amyloid positron emission tomography (PET) imaging results for their health, self-efficacy to understand their results, psychological impact of learning their results, experience receiving their results from the clinical team, and interest in genetic testing for AD risk.

METHODS

We surveyed individuals who were being clinically evaluated for AD and received PET imaging six weeks after the return of results. We analyzed responses to close-ended survey items by PET result using Fisher's exact test and qualitatively coded open-ended responses.

RESULTS

A total of 88 participants completed surveys, most of whom had mild cognitive impairment due to AD (38.6%), AD (28.4%), or were cognitively unimpaired (21.6%). Participants subjectively understood their results (25.3% strongly agreed, 41.8% agreed), which could help them plan (16.5% strongly agreed, 49.4% agreed). Participants with a negative PET result (n = 25) reported feelings of relief (Fisher's exact p < 0.001) and happiness (p < 0.001) more frequently than those with a positive result. Most participants felt that they were treated respectfully and were comfortable voicing concerns during the disclosure process. Genetic testing was anticipated to be useful for medical care decisions (48.2%) and to inform family members about AD risk (42.9%).

CONCLUSIONS

Participants had high subjective understanding and self-efficacy around their PET results and did not experience negative psychological effects. Interest in genetic testing was high.

Biomarkers of diagnosis, prognosis, pathogenesis, response to therapy: Convergence or divergence? Lessons from Alzheimer's disease and synucleinopathies

Alzheimer's disease (AD) is the most common disorder associated with cognitive impairment. Recent observations emphasize the pathogenic role of multiple factors inside and outside the central nervous system, supporting the notion that AD is a syndrome of many etiologies rather than a "heterogeneous" but ultimately unifying disease entity. Moreover, the defining pathology of amyloid and tau coexists with many others, such as α-synuclein, TDP-43, and others, as a rule, not an exception. Thus, an effort to shift our AD paradigm as an amyloidopathy must be reconsidered. Along with amyloid accumulation in its insoluble state, β-amyloid is becoming depleted in its soluble, normal states, as a result of biological, toxic, and infectious triggers, requiring a shift from convergence to divergence in our approach to neurodegeneration. These aspects are reflected-in vivo-by biomarkers, which have become increasingly strategic in dementia. Similarly, synucleinopathies are primarily characterized by abnormal deposition of misfolded α-synuclein in neurons and glial cells and, in the process, depleting the levels of the normal, soluble α-synuclein that the brain needs for many physiological functions. The soluble to insoluble conversion also affects other normal brain proteins, such as TDP-43 and tau, accumulating in their insoluble states in both AD and dementia with Lewy bodies (DLB). The two diseases have been distinguished by the differential burden and distribution of insoluble proteins, with neocortical phosphorylated tau deposition more typical of AD and neocortical α-synuclein deposition peculiar to DLB. We propose a reappraisal of the diagnostic approach to cognitive impairment from convergence (based on clinicopathologic criteria) to divergence (based on what differs across individuals affected) as a necessary step for the launch of precision medicine.

Understanding the Intricate Role of Exosomes in Pathogenesis of Alzheimer's Disease

Alzheimer's disease causes loss of memory and deterioration of mental abilities is utmost predominant neurodegenerative disease accounting 70-80% cases of dementia. The appearance of plaques of amyloid-β and neurofibrillary tangles in the brain post-mortems of Alzheimer's patients established them as key participants in the etiology of Alzheimer's disease. Exosomes exist as extracellular vesicles of nano-size which are present throughout the body. Exosomes are known to spread toxic hyperphosphorylated tau and amyloid-β between the cells and are linked to the loss of neurons by inducing apoptosis. Exosomes have progressed from cell trashcans to multifunctional organelles which are involved in various functions like internalisation and transmission of macromolecules such as lipids, proteins, and nucleic acids. This review covers current findings on relationship of exosomes in biogenesis and angiogenesis of Alzheimer's disease and functions of exosomes in the etiology of AD. Furthermore, the roles of exosomes in development, diagnosis, treatment, and its importance as therapeutic targets and biomarkers for Alzheimer's disease have also been highlighted.

A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer's Disease Spectrum

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD.

OBJECTIVE

To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Alzheimer's Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion.

METHODS

We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables.

RESULTS

The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects.

CONCLUSION

Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex.