Our staff editors continue to share exciting, interesting, and thought-provoking reading material in the recommended articles series.
This week, we would like to share several latest articles are related to Alzheimer's disease and Parkinson’s disease.
Title: Evaluation of common and rare variants of Alzheimer's disease-causal genes in Parkinson's disease
Authors: Qian Zeng, Hongxu Pan, Yuwen Zhao, Yige Wang, Qian Xu, Jieqiong Tan, Xinxiang Yan, Jinchen Li, Beisha Tang, Jifeng Guo
Type: Research Article
●Two different NGS technologies were applied in early-onset or familial PD and sporadic late-onset PD separately.
●Rs75733498 in the PSEN2 gene was significantly associated with PD.
●A suggestive contribution of damaging missense variants in APP to early-onset or familial PD was detected.
Parkinson's disease (PD) and Alzheimer's disease (AD) are the most common neurodegenerative diseases in the elderly. Recently, some variants of AD-causal genes (APP, PSEN1, PSEN2) have been reported in PD. In this study, we investigated the association between coding variants of AD‐causal genes and PD in a large Chinese population cohort.
We performed whole-exome sequencing (WES) on 1,917 patients with early-onset or familial PD and 1,652 controls, and whole-genome sequencing (WGS) on 1,962 sporadic late-onset PD and 1,279 controls. Genetic and phenotypic data were analyzed with regression analyses and the optimized sequence kernel association test. Further validation study was performed by Fisher's exact test.
We found that rs75733498 in the PSEN2 gene was significantly associated with early-onset or familial PD; however, no significant relationship was discovered between rs75733498 and sporadic late-onset PD. The result of the validation study still revealed a significant association between rs75733498 and PD. We observed a suggestive association with APP gene in early-onset or familial PD when considering damaging missense variants alone (p = 0.018) or combined with loss‐of‐function variants (p = 0.029). Further phenotypic analysis did not demonstrate any significant associations.
Our results support a possible genetic contribution of AD-causal genes to PD. These findings warrant further genetic and functional confirmation, and more powerful association studies will better decipher the mechanisms of PD.
Access this article: https://doi.org/10.1016/j.parkreldis.2022.02.016
Title: Cell models for Alzheimer’s and Parkinson’s disease: At the interface of biology and drug discovery
Authors: Sandra Cetin, Damijan Knez, Stanislav Gobec, Janko Kos, Anja Pišlar
Type: Review Article
●Treatments that effectively slow the progression of neurodegeneration are lacking.
●Cell-based models of neurodegeneration are pivotal in drug discovery projects.
●Phenotypic screening is useful approach to screen for novel therapeutic molecules.
●Functional assays of neuronal death can be used to screen therapeutic compounds.
Neurodegenerative diseases are severely debilitating conditions characterized primarily by progressive neuronal loss and impairment of the nervous system. Alzheimer’s and Parkinson’s diseases are the most common neurodegenerative disorders, and their impact is increasing as average life expectancy increases worldwide. Although the underlying mechanisms of both progressive diseases have been extensively studied, we still lack a comprehensive understanding of the molecular basis of both diseases. Current therapeutic options do not slow the progression of the diseases and only provide symptom relief. Cell models that resemble the characteristics of the disease in question are important in drug discovery projects because they provide information about the therapeutic benefits of drugs under development. Here, we review current in vitro cell models used to study the molecular basis of Alzheimer’s and Parkinson’s disease focusing on their potential for discovering of disease-modifying therapeutics to combat neurodegenerative diseases. We discuss phenotypic screening as an important approach for identifying novel therapeutic molecules. Advances in the development of cell-based assays for drug discovery are discussed, ranging from simple monoculture cell models to high-throughput three-dimensional cell models. Finally, we critically present the limitations of cell models and the caveats encountered in drug discovery to find effective treatment for neurodegenerative diseases.
Access this article: https://doi.org/10.1016/j.biopha.2022.112924
Title: Classification of healthy, Alzheimer and Parkinson populations with a multi-branch neural network
Authors: José Francisco Pedrero-Sánchez, Juan-Manuel Belda-Lois, Pilar Serra-Añó, Marta Inglés, Juan López-Pascual
Type: Research Article
●Use of raw signals from IMU provides more information for classifying participants.
●Android-based functional test with artificial intelligence identifies pathologies.
●New methodology to identify the Alzheimer and Parkinson disease.
Signal processing, for delimitation of the target events and parametrization, is usually required when instrumented assessment is conducted to determine an individual’s functional status. However, these procedures may rule out relevant information obtained by sensors. To prevent this, the use of models based on neural networks that automatically extract relevant features from the raw signal may improve the characterization of the functional status. Thus, the aim of the study was to determine the classification accuracy of a multi-head convolutional layered neural network (CNN) using a simple functional mobility test in people with different conditions. The raw data from an inertial sensor embedded in a smartphone worn by 90 volunteers (i.e. 30 volunteers with Alzheimer’s disease, 30 with Parkinson’s disease and 30 healthy elderly people) was obtained. The CNN classification accuracy was compared to that of the two parametric classifiers, namely, linear discriminant analysis and multilayer perceptron, a neural network-based classifier.
As a result, the validation process revealed that the CNN classifier correctly assigned 100% of the participants to each group. The best accuracy in pathology classification for the two parametric classifiers ranged from 55% to 88%.
Therefore, the CNN model provided enhanced classification accuracy as compared to the parametric approaches, even better than the neural network-based classifier. Non parametrization may increase relevant information, thus enhancing pathology impact characterization.
Access this article: https://doi.org/10.1016/j.bspc.2022.103617
Title: Possible role of endocannabinoids in olfactory and taste dysfunctions in Alzheimer’s and Parkinson’s patients and volumetric changes in the brain
Authors: Emine Petekkaya, Berna Kuş, Serdar Doğan, Hanifi Bayaroğulları, Turay Mutlu, İsmet Murat Melek, Abdullah Arpacı
Type: Research Article
●Alzheimer’s and Parkinson’s patients show volumetric changes in brain.
●Alzheimer’s patients showed more cortical volume changes than Parkinson’s.
●Volumetric changes were observed both in primary and secondary smell/taste areas.
●Endocannabinoids were higher in Alzheimer’s and Parkinson’s patients than controls.
●Endocannabinoids may have a possible role in olfactory and taste dysfunctions.
The purpose of this study is to determine the volumes of primary brain regions associated with smell and taste in Alzheimer’s and Parkinson’s patients and healthy controls using MR imaging and examine volumetric changes in comparison to smell/taste questionnaire and test results and endocannabinoid (EC) levels. The study included 15 AD patients with mild cognitive dysfunction scored as 18 ≤ MMSE ≤ 23, 15 PD patients with scores of 18 < MoCA < 26 and 18 ≤ MMSE ≤ 23, and 15 healthy controls. A taste and smell questionnaire was given to the participants, and their taste and smell statuses were examined using the Sniffin’ Sticks smell identification test and Burghart Taste Strips. EC levels were analyzed in the blood serum samples of the participants using the ELISA method. The volumes of the left olfactory bulb (p = 0.001), left amygdala (p = 0.004), left hippocampus (p = 0.008), and bilateral insula (left p = 0.000, right p = 0.000) were significantly smaller in the Alzheimer’s patients than the healthy controls. The volumes of the left olfactory bulb (p = 0.001) and left hippocampus (p = 0.009) were significantly smaller in the Parkinson’s patients than the healthy controls. A significant correlation was determined between volume reduction in the left Rolandic operculum cortical region and taste dysfunction. EC levels were significantly higher in both AD (p = 0.000) and PD (p = 0.006) in comparison to the controls. Our results showed that volumetric changes occur in the brain regions associated with smell and taste in Alzheimer’s and Parkinson’s patients. It was observed that ECs played a role in these volumetric changes and the olfactory and taste dysfunctions of the patients.
Access this article: https://doi.org/10.1016/j.jocn.2022.03.047
Title: A systematic review on Data Mining Application in Parkinson's disease
Authors: Adesh Kumar Srivastava, Klinsega Jeberson, Wilson Jeberson
Type: Review Article
Data mining techniques have taken a significant role in the diagnosis and prognosis of many health diseases. Still, very little work has been initialized in neurological medical informatics or neurodegenerative disease. Parkinson's Disease (PD) is the second significant neurodegenerative disease (after Alzheimer's), which causes severe complications for patients. PD is a nervous disorder that affects millions of people worldwide. Most of the cases go undetected due to a lack of standard detection methods. This paper attempts to review literature related to PD diagnosis, its stages, and its management using data mining techniques (DMT). The review has been done by exploring the Scopus indexed literature using the query containing the keywords data-mining and Parkinson's disease. This study's focus is to observe how DMT, its applications have developed in PD during the past 16 years. This paper reviews data mining techniques, their applications, and development, through a review of the literature and articles' classification, from 2004 to 2020. We have used keyword indices and article abstracts to identify 273 articles concerning DMT applications from 159 academic journals from Scopus online database. Another objective of this paper is to provide directions to researchers in data mining applications in Parkinson's disease.
Access this article: https://doi.org/10.1016/j.neuri.2022.100064
Title: Dietary Inflammatory Index score and prodromal Parkinson's disease incidence: The HELIAD study
Authors: Vassilis Balomenos, Lamprini Bounou, Socratis Charisis, Maria Stamelou, Eva Ntanasi, Kyriaki Georgiadi, Ioannis Mourtzinos, Katerina Tzima, Costas A. Anastasiou, Georgia Xiromerisiou, Maria Maraki, Mary Yannakoulia, Mary H. Kosmidis, Nikolaos Scarmeas
Type: Research Article
The aim of the present study was to investigate the association of the inflammatory potential of diet with prodromal Parkinson's disease (pPD) probability and incidence among community-dwelling older individuals without clinical features of parkinsonism at baseline.
The sample consisted of 1,030 participants 65 years old or older, drawn from a population-based cohort study of older adults in Greece (Hellenic Longitudinal Investigation of Aging and Diet - HELIAD). We calculated pPD probability, according to International Parkinson and Movement Disorder Society research criteria. Dietary Inflammatory Index (DII) was used to measure the dietary inflammatory potential, with higher index score reflecting a more pro-inflammatory diet. Associations of baseline DII with pPD probability cross-sectionally, and with possible/probable pPD incidence (pPD probability ≥30%) during the follow-up period, were examined via general linear models and generalized estimating equations, respectively.
Cross-sectionally, one unit increase of DII score [DII (min, max) = -5.83, 6.01] was associated with 4.9% increased pPD probability [β=0.049, 95%CI (0.025-0.090), p<0.001]. Prospectively, 62 participants developed pPD during 3.1±0.9 (mean±SD) years of follow-up. One unit increase in DII was associated with 20.3% increased risk for developing pPD [RR=1.203, 95%CI (1.070–1.351), p=0.002]. Participants in the highest tertile of DII score were 2.6 times more likely to develop pPD [β=2.594, 95%CI (1.332–5.050), p=0.005], compared to those in the lowest tertile.
More pro-inflammatory diet was related with higher pPD probability and pPD incidence (pPD probability ≥30%) in a community-dwelling older adult population. Further studies are needed to confirm these findings.
Access this article: https://doi.org/10.1016/j.jnutbio.2022.108994