Normal ageing of the brain: Histological and biological aspects

An Update on Parkinson's Disease and its Neurodegenerative Counterparts

INTRODUCTION

Neurodegenerative disorders are a group of diseases that cause nerve cell degeneration in the brain, resulting in a variety of symptoms and are not treatable with drugs. Parkinson's disease (PD), prion disease, motor neuron disease (MND), Huntington's disease (HD), spinal cerebral dyskinesia (SCA), spinal muscle atrophy (SMA), multiple system atrophy, Alzheimer's disease (AD), spinocerebellar ataxia (SCA) (ALS), pantothenate kinase-related neurodegeneration, and TDP-43 protein disorder are examples of neurodegenerative diseases. Dementia is caused by the loss of brain and spinal cord nerve cells in neurodegenerative diseases.

BACKGROUND

Even though environmental and genetic predispositions have also been involved in the process, redox metal abuse plays a crucial role in neurodegeneration since the preponderance of symptoms originates from abnormal metal metabolism.

METHOD

Hence, this review investigates several neurodegenerative diseases that may occur symptoms similar to Parkinson's disease to understand the differences and similarities between Parkinson's disease and other neurodegenerative disorders based on reviewing previously published papers.

RESULTS

Based on the findings, the aggregation of alpha-synuclein occurs in Parkinson's disease, multiple system atrophy, and dementia with Lewy bodies. Other neurodegenerative diseases occur with different protein aggregation or mutations.

CONCLUSION

We can conclude that Parkinson's disease, Multiple system atrophy, and Dementia with Lewy bodies are closely related. Therefore, researchers must distinguish among the three diseases to avoid misdiagnosis of Multiple System Atrophy and Dementia with Lewy bodies with Parkinson's disease symptoms.

Age-Related Alterations in Gray Matter Microstructure in Older People With Remitted Major Depression at Risk for Dementia

Background

Major depressive disorder (MDD) in late life is a risk factor for mild cognitive impairment (MCI) and Alzheimer's disease. However, studies of gray matter changes have produced varied estimates of which structures are implicated in MDD and dementia. Changes in gray matter volume and cortical thickness are macrostructural measures for the microstructural processes of free water accumulation and dendritic spine loss.

Methods

We conducted multishell diffusion imaging to assess gray matter microstructure in 244 older adults with remitted MDD (n = 44), MCI (n = 115), remitted MDD+MCI (n = 61), or without psychiatric disorders or cognitive impairment (healthy control participants; n = 24). We estimated measures related to neurite density, orientation dispersion, and free water (isotropic volume fraction) using a biophysically plausible model (neurite orientation dispersion and density imaging).

Results

Results showed that increasing age was correlated with an increase in isotropic volume fraction and a decrease in orientation dispersion index, which is consistent with neuropathology dendritic loss. In addition, this relationship between age and increased isotropic volume fraction was more disrupted in the MCI group than in the remitted MDD or healthy control groups. However, the association between age and orientation dispersion index was similar for all 3 groups.

Conclusions

The findings suggest that the neurite orientation dispersion and density imaging measures could be used to identify biological risk factors for Alzheimer's disease, signifying both conventional neurodegeneration observed with MCI and dendritic loss seen in MDD.

Biomarkers of aging

Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.

Mental health care for older adults: recent advances and new directions in clinical practice and research

The world's population is aging, bringing about an ever-greater burden of mental disorders in older adults. Given multimorbidities, the mental health care of these people and their family caregivers is labor-intensive. At the same time, ageism is a big problem for older people, with and without mental disorders. Positive elements of aging, such as resilience, wisdom and prosocial behaviors, need to be highlighted and promoted, both to combat stigma and to help protect and improve mental health in older adults. The positive psychiatry of aging is not an oxymoron, but a scientific construct strongly informed by research evidence. We champion a broader concept of geriatric psychiatry - one that encompasses health as well as illness. In the present paper, we address these issues in the context of four disorders that are the greatest source of years lived with disability: neurocognitive disorders, major depression, schizophrenia, and substance use disorders. We emphasize the need for implementation of multidisciplinary team care, with comprehensive assessment, clinical management, intensive outreach, and coordination of mental, physical and social health services. We also underscore the need for further research into moderators and mediators of treatment response variability. Because optimal care of older adults with mental disorders is both patient-focused and family-centered, we call for further research into enhancing the well-being of family caregivers. To optimize both the safety and efficacy of pharmacotherapy, further attention to metabolic, cardiovascular and neurological tolerability is much needed, together with further development and testing of medications that reduce the risk for suicide. At the same time, we also address positive aging and normal cognitive aging, both as an antidote to ageism and as a catalyst for change in the way we think about aging per se and late-life mental disorders more specifically. It is in this context that we provide directions for future clinical care and research.

TREM2 and CD163 Ameliorate Microglia-Mediated Inflammatory Environment in the Aging Brain

Aging decreases cognitive functions, especially learning and memory. Neuroinflammation is mediated by microglia and occurs in age-related neurodegenerative diseases. The expression profiles in a dataset of cognitively normal controls (GSE11882) were obtained from the Gene Expression Omnibus (GEO) database. Microarray data were used to explore the expression of age-related genes in the human hippocampus. A total of 120 differentially expressed genes (DEGs) were identified and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein-protein interaction (PPI) network was constructed. A total of 18 key genes were identified by the plugin cytoHubba in Cytoscape software. Two genes with a positive impact on cognition during aging were teased out: triggering receptor expressed on myeloid cells 2 (TREM2) and a scavenger receptor (CD163). Finally, the results of reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting (WB) verified that the mRNA expression of these two genes was significantly upregulated in aged mice. Moreover, the levels of the inflammatory factors IL-1β and IL-6 were significantly increased. TREM2 and CD163 may be upregulated to alleviate the inflammatory environment resulting from microglial activation in the aging brain, thereby delaying cognitive decline.

Longitudinal Analysis of Brain-Predicted Age in Amnestic and Non-amnestic Sporadic Early-Onset Alzheimer's Disease

Objective: Predicted age difference (PAD) is a score computed by subtracting chronological age from "brain" age, which is estimated using neuroimaging data. The goal of this study was to evaluate the PAD as a marker of phenotypic heterogeneity and severity among early-onset Alzheimer's disease (EOAD) patients. Methods: We first used 3D T1-weighted (3D-T1) magnetic resonance images (MRI) of 3,227 healthy subjects aged between 18 and 85 years to train, optimize, and evaluate the brain age model. A total of 123 participants who met the criteria for early-onset (<65 years) sporadic form of probable Alzheimer's disease (AD) and presented with two distinctive clinical presentations [an amnestic form (n = 74) and a non-amnestic form (n = 49)] were included at baseline and followed-up for a maximum period of 4 years. All the participants underwent a work-up at baseline and every year during the follow-up period, which included clinical examination, neuropsychological testing and genotyping, and structural MRI. In addition, cerebrospinal fluid biomarker assay was recorded at baseline. PAD score was calculated by applying brain age model to 3D-T1 images of the EOAD patients and healthy controls, who were matched based on age and sex. At baseline, between-group differences for neuropsychological and PAD scores were assessed using linear models. Regarding longitudinal analysis of neuropsychological and PAD scores, differences between amnestic and non-amnestic participants were analyzed using linear mixed-effects modeling. Results: PAD score was significantly higher for non-amnestic patients (2.35 ± 0.91) when compared to amnestic patients (2.09 ± 0.74) and controls (0.00 ± 1). Moreover, PAD score was linearly correlated with the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating Sum of Boxes (CDR-SB), for both amnestic and non-amnestic sporadic forms. Longitudinal analyses showed that the gradual development of the disease in patients was accompanied by a significant increase in PAD score over time, for both amnestic and non-amnestic patients. Conclusion: PAD score was able to separate amnestic and non-amnestic sporadic forms. Regardless of the clinical presentation, as PAD score was a way of quantifying an early brain age acceleration, it was an appropriate method to detect the development of AD and follow the evolution of the disease as a marker of severity as MMSE and CDR-SB.