Peripheral blood lymphocyte phenotypes in Alzheimer and Parkinson's diseases

Increased levels of regulatory T cells and IL-10-producing regulatory B cells are linked to improved clinical outcome in Parkinson's disease: a 1-year observational study

Whilst the contribution of peripheral and central inflammation to neurodegeneration in Parkinson's disease and the role of the immune response in this disorder are well known, the effects of the anti-inflammatory response on the disease have not been described in depth. This study is aimed to assess the changes in the regulatory/inflammatory immune response in recently diagnosed, untreated PD patients and a year after. Twenty-one PD patients and 19 healthy controls were included and followed-up for 1 year. The levels of immunoregulatory cells (CD4+ Tregs, Bregs, and CD8+ Tregs); classical, nonclassical, and intermediate monocytes, and proinflammatory cells (Th1, Th2, and Th17) were measured by flow cytometry. Cytokine levels were determined by ELISA. Clinical follow-up was based on the Hoehn & Yahr and UDPRS scales. Our results indicate that the regulatory response in PD patients on follow-up was characterized by increased levels of active Tregs, functional Tregs, TR1, IL-10-producing functional Bregs, and IL-10-producing classical monocytes, along with decreased counts of Bregs and plasma cells. With respect to the proinflammatory immune response, peripheral levels of Th1 IFN-γ+ cells were decreased in treated PD patients, whilst the levels of CD4+ TBET+ cells, HLA-DR+ intermediate monocytes, IL-6, and IL-4 were increased after a 1-year follow-up. Our main finding was an increased regulatory T cell response after a 1-year follow-up and its link with clinical improvement in PD patients. In conclusion, after a 1-year follow-up, PD patients exhibited increased levels of regulatory populations, which correlated with clinical improvement. However, a persistent inflammatory environment and active immune response were observed.

Predicting early Alzheimer's with blood biomarkers and clinical features

Alzheimer's disease (AD) is an incurable neurodegenerative disorder that leads to dementia. This study employs explainable machine learning models to detect dementia cases using blood gene expression, single nucleotide polymorphisms (SNPs), and clinical data from Alzheimer's Disease Neuroimaging Initiative (ADNI). Analyzing 623 ADNI participants, we found that the Support Vector Machine classifier with Mutual Information (MI) feature selection, trained on all three data modalities, achieved exceptional performance (accuracy = 0.95, AUC = 0.94). When using gene expression and SNP data separately, we achieved very good performance (AUC = 0.65, AUC = 0.63, respectively). Using SHapley Additive exPlanations (SHAP), we identified significant features, potentially serving as AD biomarkers. Notably, genetic-based biomarkers linked to axon myelination and synaptic vesicle membrane formation could aid early AD detection. In summary, this genetic-based biomarker approach, integrating machine learning and SHAP, shows promise for precise AD diagnosis, biomarker discovery, and offers novel insights for understanding and treating the disease. This approach addresses the challenges of accurate AD diagnosis, which is crucial given the complexities associated with the disease and the need for non-invasive diagnostic methods.

Nutrition, Immunity and Aging: Current Scenario and Future Perspectives in Neurodegenerative Diseases

Aging is a gradual decline of physiological function and tissue homeostasis and, in many instances, is related to increased (neuro)-degeneration, together with inflammation, becoming one of the most important risks for developing neurodegenerative diseases. Certain individual nutrients or foods in combination may counteract aging and associated neurodegenerative diseases by promoting a balance between the pro- and anti-inflammatory responses. Thus, nutrition could represent a powerful modulator of this fine balance, other than a modifiable risk factor to contrast inflammaging. This narrative review explores from a broad perspective the impact of nutrition on the hallmarks of aging and inflammation in Alzheimer's disease (AD), Parkinson's disease (PD) and Amyotrophic Lateral Sclerosis Syndrome (ALS), starting from nutrients up to single foods and complex dietary patterns.

c-Diadem: a constrained dual-input deep learning model to identify novel biomarkers in Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is an incurable, debilitating neurodegenerative disorder. Current biomarkers for AD diagnosis require expensive neuroimaging or invasive cerebrospinal fluid sampling, thus precluding early detection. Blood-based biomarker discovery in Alzheimer's can facilitate less-invasive, routine diagnostic tests to aid early intervention. Therefore, we propose "c-Diadem" (constrained dual-input Alzheimer's disease model), a novel deep learning classifier which incorporates KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway constraints on the input genotyping data to predict disease, i.e., mild cognitive impairment (MCI)/AD or cognitively normal (CN). SHAP (SHapley Additive exPlanations) was used to explain the model and identify novel, potential blood-based genetic markers of MCI/AD.

METHODS

We developed a novel constrained deep learning neural network which utilizes SNPs (single nucleotide polymorphisms) and microarray data from ADNI (Alzheimer's Disease Neuroimaging Initiative) to predict the disease status of participants, i.e., CN or with disease (MCI/AD), and identify potential blood-based biomarkers for diagnosis and intervention. The dataset contains samples from 626 participants, of which 212 are CN (average age 74.6 ± 5.4 years) and 414 patients have MCI/AD (average age 72.7 ± 7.6 years). KEGG pathway information was used to generate constraints applied to the input tensors, thus enhancing the interpretability of the model. SHAP scores were used to identify genes which could potentially serve as biomarkers for diagnosis and targets for drug development.

RESULTS

Our model's performance, with accuracy of 69% and AUC of 70% in the test dataset, is superior to previous models. The SHAP scores show that SNPs in PRKCZ, PLCB1 and ITPR2 as well as expression of HLA-DQB1, EIF1AY, HLA-DQA1, and ZFP57 have more impact on model predictions.

CONCLUSIONS

In addition to predicting MCI/AD, our model has been interrogated for potential genetic biomarkers using SHAP. From our analysis, we have identified blood-based genetic markers related to Ca2+ ion release in affected regions of the brain, as well as depression. The findings from our study provides insights into disease mechanisms, and can facilitate innovation in less-invasive, cost-effective diagnostics. To the best of our knowledge, our model is the first to use pathway constraints in a multimodal neural network to identify potential genetic markers for AD.

Senescence Markers in Peripheral Blood Mononuclear Cells in Amnestic Mild Cognitive Impairment and Alzheimer's Disease

Recent studies suggest that cellular senescence plays a role in Alzheimer’s Disease (AD) pathogenesis. We hypothesize that cellular senescence markers might be tracked in the peripheral tissues of AD patients. Senescence hallmarks, including altered metabolism, cell-cycle arrest, DNA damage response (DDR) and senescence secretory associated phenotype (SASP), were measured in peripheral blood mononuclear cells (PBMCs) of healthy controls (HC), amnestic mild cognitive impairment (aMCI) and AD patients. Senescence-associated βeta-galactosidase (SA-β-Gal) activity, G0-G1 phase cell-cycle arrest, p16 and p53 were analyzed by flow cytometry, while IL-6 and IL-8 mRNA were analyzed by qPCR, and phosphorylated H2A histone family member X (γH2AX) was analyzed by immunofluorescence. Senescent cells in the brain tissue were determined with lipofuscin staining. An increase in the number of senescent cells was observed in the frontal cortex and hippocampus of advanced AD patients. PBMCs of aMCI patients, but not in AD, showed increased SA-β-Gal compared with HCs. aMCI PBMCs also had increased IL-6 and IL8 mRNA expression and number of cells arrested at G0-G1, which were absent in AD. Instead, AD PBMCs had significantly increased p16 and p53 expression and decreased γH2Ax activity compared with HC. This study reports that several markers of cellular senescence can be measured in PBMCs of aMCI and AD patients.