APOE ɛ4 Allele Testing and Risk of Alzheimer Disease

What Are the Reliable Plasma Biomarkers for Mild Cognitive Impairment? A Clinical 4D Proteomics Study and Validation

Objective

At present, Alzheimer's disease (AD) lacks effective treatment means, and early diagnosis and intervention are the keys to treatment. Therefore, for mild cognitive impairment (MCI) and AD patients, blood sample analysis using the 4D nonstandard (label-free) proteomic in-depth quantitative analysis, looking for specific protein marker expression differences, is important. These marker levels change as AD progresses, and the analysis of these biomarkers changes with this method, which has the potential to show the degree of disease progression and can be used for the diagnosis and preventive treatment of MCI and AD.

Materials and Methods

Patients were recruited according to the inclusion and exclusion criteria and divided into three groups according to scale scores. Elderly patients diagnosed with AD were selected as the AD group (n = 9). Patients diagnosed with MCI were classified into the MCI group (n = 10). Cognitively healthy elderly patients were included in the normal cognition control group (n = 10). Patients' blood samples were used for 4D label-free proteomic in-depth quantitative analysis to identify potential blood biomarkers. The sample size of each group was expanded (n = 30), and the selected biomarkers were verified by enzyme-linked immunosorbent assay (ELISA) to verify the accuracy of the proteomic prediction.

Results

Six specific blood markers, namely, APOE, MMP9, UBR5, PLA2G7, STAT5B, and S100A8, were detected by 4D label-free proteomic quantitative analysis. These markers showed a statistically significant upregulation trend in the MCI and AD groups compared with the normal cognition control group (P < 0.05). ELISA results showed that the levels of these six proteins in the MCI group were significantly higher than those in the normal cognition control group, and the levels of these six proteins in the AD group were significantly higher than those in the MCI group (P < 0.05).

Conclusion

The plasma levels of APOE, MMP9, UBR5, PLA2G7, STAT5B, and S100A8 in cognitively healthy elderly patients and patients with MCI and AD were significantly different and, more importantly, showed a trend of increasing expression. These results indicate that these six human plasma markers have important diagnostic and therapeutic potential in the identification of cognitive impairment and have value for in-depth research and clinical application.

Retinal Alterations Predict Early Prodromal Signs of Neurodegenerative Disease

Neurodegenerative diseases are an increasingly common group of diseases that occur late in life with a significant impact on personal, family, and economic life. Among these, Alzheimer's disease (AD) and Parkinson's disease (PD) are the major disorders that lead to mild to severe cognitive and physical impairment and dementia. Interestingly, those diseases may show onset of prodromal symptoms early after middle age. Commonly, the evaluation of these neurodegenerative diseases is based on the detection of biomarkers, where functional and structural magnetic resonance imaging (MRI) have shown a central role in revealing early or prodromal phases, although it can be expensive, time-consuming, and not always available. The aforementioned diseases have a common impact on the visual system due to the pathophysiological mechanisms shared between the eye and the brain. In Parkinson's disease, α-synuclein deposition in the retinal cells, as well as in dopaminergic neurons of the substantia nigra, alters the visual cortex and retinal function, resulting in modifications to the visual field. Similarly, the visual cortex is modified by the neurofibrillary tangles and neuritic amyloid β plaques typically seen in the Alzheimer's disease brain, and this may reflect the accumulation of these biomarkers in the retina during the early stages of the disease, as seen in postmortem retinas of AD patients. In this light, the ophthalmic evaluation of retinal neurodegeneration could become a cost-effective method for the early diagnosis of those diseases, overcoming the limitations of functional and structural imaging of the deep brain. This analysis is commonly used in ophthalmic practice, and interest in it has risen in recent years. This review will discuss the relationship between Alzheimer's disease and Parkinson's disease with retinal degeneration, highlighting how retinal analysis may represent a noninvasive and straightforward method for the early diagnosis of these neurodegenerative diseases.

The influence of 17q21.31 and APOE genetic ancestry on neurodegenerative disease risk

Advances in genomic research over the last two decades have greatly enhanced our knowledge concerning the genetic landscape and pathophysiological processes involved in multiple neurodegenerative diseases. However, current insights arise almost exclusively from studies on individuals of European ancestry. Despite this, studies have revealed that genetic variation differentially impacts risk for, and clinical presentation of neurodegenerative disease in non-European populations, conveying the importance of ancestry in predicting disease risk and understanding the biological mechanisms contributing to neurodegeneration. We review the genetic influence of two important disease-associated loci, 17q21.31 (the "MAPT locus") and APOE, to neurodegenerative disease risk in non-European populations, touching on global population differences and evolutionary genetics by ancestry that may underlie some of these differences. We conclude there is a need to increase representation of non-European ancestry individuals in genome-wide association studies (GWAS) and biomarker analyses in order to help resolve existing disparities in understanding risk for, diagnosis of, and treatment for neurodegenerative diseases in diverse populations.

Multimodal deep learning for Alzheimer's disease dementia assessment

Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer's disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accomplishes multiple diagnostic steps in successive fashion to identify persons with normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD). We demonstrate a range of models capable of accepting flexible combinations of routinely collected clinical information, including demographics, medical history, neuropsychological testing, neuroimaging, and functional assessments. We then show that these frameworks compare favorably with the diagnostic accuracy of practicing neurologists and neuroradiologists. Lastly, we apply interpretability methods in computer vision to show that disease-specific patterns detected by our models track distinct patterns of degenerative changes throughout the brain and correspond closely with the presence of neuropathological lesions on autopsy. Our work demonstrates methodologies for validating computational predictions with established standards of medical diagnosis.

Association between dementia parental family history and mid-life modifiable risk factors for dementia: a cross-sectional study using propensity score matching within the Lifelines cohort

OBJECTIVE

Individuals with a parental family history (PFH) of dementia have an increased risk to develop dementia, regardless of genetic risks. The aim of this study is to investigate the association between a PFH of dementia and currently known modifiable risk factors for dementia among middle-aged individuals using propensity score matching (PSM).

DESIGN

A cross-sectional study.

SETTING AND PARTICIPANTS

A subsample of Lifelines (35-65 years), a prospective population-based cohort study in the Netherlands was used.

OUTCOME MEASURES

Fourteen modifiable risk factors for dementia and the overall Lifestyle for Brain Health (LIBRA) score, indicating someone's potential for dementia risk reduction (DRR).

RESULTS

The study population included 89 869 participants of which 10 940 (12.2%) had a PFH of dementia (mean (SD) age=52.95 (7.2)) and 36 389 (40.5%) without a PFH of dementia (mean (SD) age=43.19 (5.5)). Of 42 540 participants (47.3%), PFH of dementia was imputed. After PSM, potential confounding variables were balanced between individuals with and without PFH of dementia. Individuals with a PFH of dementia had more often hypertension (OR=1.19; 95% CI 1.14 to 1.24), high cholesterol (OR=1.24; 95% CI 1.18 to 1.30), diabetes (OR=1.26; 95% CI 1.11 to 1.42), cardiovascular diseases (OR=1.49; 95% CI 1.18 to 1.88), depression (OR=1.23; 95% CI 1.08 to 1.41), obesity (OR=1.14; 95% CI 1.08 to 1.20) and overweight (OR=1.10; 95% CI 1.05 to 1.17), and were more often current smokers (OR=1.20; 95% CI 1.14 to 1.27) and ex-smokers (OR=1.21; 95% CI 1.16 to 1.27). However, they were less often low/moderate alcohol consumers (OR=0.87; 95% CI 0.83 to 0.91), excessive alcohol consumers (OR=0.93; 95% CI 0.89 to 0.98), socially inactive (OR=0.84; 95% CI 0.78 to 0.90) and physically inactive (OR=0.93; 95% CI 0.91 to 0.97). Having a PFH of dementia resulted in a higher LIBRA score (RC=0.15; 95% CI 0.11 to 0.19).

CONCLUSION

We found that having a PFH of dementia was associated with several modifiable risk factors. This suggests that middle-aged individuals with a PFH of dementia are a group at risk and could benefit from DRR. Further research should explore their knowledge, beliefs and attitudes towards DRR, and whether they are willing to assess their risk and change their lifestyle to reduce dementia risk.

The role of the immune system in Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disorder where the accumulation of amyloid plaques and the formation of tau tangles are the prominent pathological hallmarks. Increasing preclinical and clinical studies have revealed that different components of the immune system may act as important contributors to AD etiology and pathogenesis. The recognition of misfolded Aβ and tau by immune cells can trigger a series of complex immune responses in AD, and then lead to neuroinflammation and neurodegeneration. In parallel, genome-wide association studies have also identified several immune related loci associated with increased - risk of AD by interfering with the function of immune cells. Other immune related factors, such as impaired immunometabolism, defective meningeal lymphatic vessels and autoimmunity might also be involved in the pathogenesis of AD. Here, we review the data showing the alterations of immune cells in the AD trajectory and seek to demonstrate the crosstalk between the immune cell dysfunction and AD pathology. We then discuss the most relevant research findings in regards to the influences of gene susceptibility of immune cells for AD. We also consider impaired meningeal lymphatics, immunometabolism and autoimmune mechanisms in AD. In addition, immune related biomarkers and immunotherapies for AD are also mentioned in order to offer novel insights for future research.

Race-Related Association between APOE Genotype and Alzheimer's Disease: A Systematic Review and Meta-Analysis

BACKGROUND

The global race-dependent association of Alzheimer's disease (AD) and apolipoprotein E (APOE) genotype is not well understood. Transethnic analysis of APOE could clarify the role of genetics in AD risk across populations.

OBJECTIVE

This study aims to determine how race and APOE genotype affect the risks for AD.

METHODS

We performed a systematic search of PubMed, Embase, Web of Science, and the Cochrane Library since 1993 to Aug 25, 2020. A total of 10,395 reports were identified, and 133 were eligible for analysis with data on 77,402 participants. Studies contained AD clinical diagnostic and APOE genotype data. Homogeneous data sets were pooled in case-control analyses. Odds ratios and 95% confidence intervals for developing AD were calculated for populations of different races and APOE genotypes.

RESULTS

The proportion of APOE genotypes and alleles differed between populations of different races. Results showed that APOEɛ4 was a risk factor for AD, whereas APOEɛ2 protected against it. The effects of APOEɛ4 and ɛ2 on AD risk were distinct in various races, they were substantially attenuated among Black people. Sub-group analysis found a higher frequency of APOEɛ4/ɛ4 and lower frequency of APOEɛ3/ɛ3 among early-onset AD than late-onset AD in a combined group and different races.

CONCLUSION

Our meta-analysis suggests that the association of APOE genotypes and AD differ between races. These results enhance our understanding of APOE-related risk for AD across race backgrounds and provide new insights into precision medicine for AD.

Right entorhinal cortical thickness is associated with Mini-Mental State Examination scores from multi-country datasets using MRI

PURPOSE

To discover common biomarkers correlating with the Mini-Mental State Examination (MMSE) scores from multi-country MRI datasets.

METHODS

The first dataset comprised 112 subjects (49 men, 63 women; range, 46-94 years) at the National Hospital Organization Kyushu Medical Center. A second dataset comprised 300 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (177 men, 123 women; range, 57-91 years). Three-dimensional T1-weighted MR images were collected from both datasets. In total, 14 deep gray matter volumes and 70 cortical thicknesses were obtained from MR images using FreeSurfer software. Total hippocampal volume and the ratio of hippocampus to cerebral volume were also calculated. Correlations between each variable and MMSE scores were assessed using Pearson's correlation coefficient. Parameters with moderate correlation coefficients (r > 0.3) from each dataset were determined as independent variables and evaluated using general linear model (GLM) analyses.

RESULTS

In Pearson's correlation coefficient, total and bilateral hippocampal volumes, right amygdala volume, and right entorhinal cortex (ERC) thickness showed moderate correlation coefficients (r > 0.3) with MMSE scores from the first dataset. The ADNI dataset showed moderate correlations with MMSE scores in more variables, including bilateral ERC thickness and hippocampal volume. GLM analysis revealed that right ERC thickness correlated significantly with MMSE score in both datasets. Cortical thicknesses of the left parahippocampal gyrus, left inferior parietal lobe, and right fusiform gyrus also significantly correlated with MMSE score in the ADNI dataset (p < 0.05).

CONCLUSION

A positive correlation between right ERC thickness and MMSE score was identified from multi-country datasets.