ZCWPW1 is associated with late-onset Alzheimer's disease in Han Chinese: a replication study and meta-analyses

UNVEILING THE DECISION MAKING PROCESS IN ALZHEIMER'S DISEASE DIAGNOSIS: A CASE-BASED COUNTERFACTUAL METHODOLOGY FOR EXPLAINABLE DEEP LEARNING

BACKGROUND

The field of Alzheimer's disease (AD) diagnosis is undergoing significant transformation due to the application of deep learning (DL) models. While DL surpasses traditional machine learning in disease prediction from structural magnetic resonance imaging (sMRI), the lack of explainability limits clinical adoption. Counterfactual inference offers a way to integrate causal explanations into these models, enhancing their robustness and transparency.

NEW METHOD

This study develops a novel methodology combining U-Net and generative adversarial network (GAN) models to create comprehensive counterfactual diagnostic maps for AD. The proposed methodology uses case-based counterfactual reasoning for robust decision classification for counterfactual maps to understand how changes in specific features affect the model's predictions.

COMPARISON WITH EXISTING METHODS

The proposed methodology is compared with state-of-the-art visual explanation techniques across the ADNI dataset. The proposed methodology is also benchmarked against other gradient-based and generative models for its ability to generate comprehensive counterfactual maps.

RESULTS

The results demonstrate that the proposed methodology significantly outperforms existing methods in accuracy, sensitivity, and specificity while providing detailed counterfactual maps that visualize how slight changes in brain morphology could lead to different diagnostic outcomes. The proposed methodology achieves an accuracy of 95% and an AUC of 0.97, illustrating its superiority in capturing subtle yet crucial anatomical features.

CONCLUSIONS

By generating intuitive visual explanations, the proposed methodology improves the interpretability and robustness of AD diagnostic models, making them more reliable and accountable. The use of counterfactual inference enhances clinicians' understanding of disease progression and the impact of different interventions, fostering precision medicine in AD care.

Genetic and Epigenetic Biomarkers Linking Alzheimer's Disease and Age-Related Macular Degeneration

Alzheimer's disease (AD) represents a prominent neurodegenerative disorder (NDD), accounting for the majority of dementia cases worldwide. In addition to memory deficits, individuals with AD also experience alterations in the visual system. As the retina is an extension of the central nervous system (CNS), the loss in retinal ganglion cells manifests clinically as decreased visual acuity, narrowed visual field, and reduced contrast sensitivity. Among the extensively studied retinal disorders, age-related macular degeneration (AMD) shares numerous aging processes and risk factors with NDDs such as cognitive impairment that occurs in AD. Histopathological investigations have revealed similarities in pathological deposits found in the retina and brain of patients with AD and AMD. Cellular aging processes demonstrate similar associations with organelles and signaling pathways in retinal and brain tissues. Despite these similarities, there are distinct genetic backgrounds underlying these diseases. This review comprehensively explores the genetic similarities and differences between AMD and AD. The purpose of this review is to discuss the parallels and differences between AMD and AD in terms of pathophysiology, genetics, and epigenetics.

Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental features

BACKGROUND

Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANNs) incorporating the common factors for identifying AD has not been evaluated.

METHODS

A total of 184 probable AD patients and 3773 healthy individuals aged 65 and over were enrolled. AD-related genes (51 SNPs) and 8 environmental factors were selected as features for multilayer ANN modeling. Random Forest (RF) and Support Vector Machine with RBF kernel (SVM) were also employed for comparison. Model results were verified using traditional statistics.

RESULTS

The ANN achieved high accuracy (0.98), sensitivity (0.95), and specificity (0.96) in the intrinsic test for AD classification. Excluding age and genetic data still yielded favorable results (accuracy: 0.97, sensitivity: 0.94, specificity: 0.96). The assigned weights to ANN features highlighted the importance of mental evaluation, years of education, and specific genetic variations (CASS4 rs7274581, PICALM rs3851179, and TOMM40 rs2075650) for AD classification. Receiver operating characteristic analysis revealed AUC values of 0.99 (intrinsic test), 0.60 (TWB-GWA), and 0.72 (CG-WGS), with slightly lower AUC values (0.96, 0.80, 0.52) when excluding age in ANN. The performance of the ANN model in AD classification was comparable to RF, SVM (linear kernel), and SVM (RBF kernel).

CONCLUSION

The ANN model demonstrated good sensitivity, specificity, and accuracy in AD classification. The top-weighted SNPs for AD prediction were CASS4 rs7274581, PICALM rs3851179, and TOMM40 rs2075650. The ANN model performed similarly to RF and SVM, indicating its capability to handle the complexity of AD as a disease entity.

Exploring the Genetic Landscape of Mild Behavioral Impairment as an Early Marker of Cognitive Decline: An Updated Review Focusing on Alzheimer's Disease

The clinical features and pathophysiology of neuropsychiatric symptoms (NPSs) in dementia have been extensively studied. However, the genetic architecture and underlying neurobiological mechanisms of NPSs at preclinical stages of cognitive decline and Alzheimer's disease (AD) remain largely unknown. Mild behavioral impairment (MBI) represents an at-risk state for incident cognitive impairment and is defined by the emergence of persistent NPSs among non-demented individuals in later life. These NPSs include affective dysregulation, decreased motivation, impulse dyscontrol, abnormal perception and thought content, and social inappropriateness. Accumulating evidence has recently begun to shed more light on the genetic background of MBI, focusing on its potential association with genetic factors related to AD. The Apolipoprotein E (APOE) genotype and the MS4A locus have been associated with affective dysregulation, ZCWPW1 with social inappropriateness and psychosis, BIN1 and EPHA1 with psychosis, and NME8 with apathy. The association between MBI and polygenic risk scores (PRSs) in terms of AD dementia has been also explored. Potential implicated mechanisms include neuroinflammation, synaptic dysfunction, epigenetic modifications, oxidative stress responses, proteosomal impairment, and abnormal immune responses. In this review, we summarize and critically discuss the available evidence on the genetic background of MBI with an emphasis on AD, aiming to gain insights into the potential underlying neurobiological mechanisms, which till now remain largely unexplored. In addition, we propose future areas of research in this emerging field, with the aim to better understand the molecular pathophysiology of MBI and its genetic links with cognitive decline.

Multimodal classification of Alzheimer's disease and mild cognitive impairment using custom MKSCDDL kernel over CNN with transparent decision-making for explainable diagnosis

The study presents an innovative diagnostic framework that synergises Convolutional Neural Networks (CNNs) with a Multi-feature Kernel Supervised within-class-similar Discriminative Dictionary Learning (MKSCDDL). This integrative methodology is designed to facilitate the precise classification of individuals into categories of Alzheimer's Disease, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) statuses while also discerning the nuanced phases within the MCI spectrum. Our approach is distinguished by its robustness and interpretability, offering clinicians an exceptionally transparent tool for diagnosis and therapeutic strategy formulation. We use scandent decision trees to deal with the unpredictability and complexity of neuroimaging data. Considering that different people's brain scans are different, this enables the model to make more detailed individualised assessments and explains how the algorithm illuminates the specific neuroanatomical regions that are indicative of cognitive impairment. This explanation is beneficial for clinicians because it gives them concrete ideas for early intervention and targeted care. The empirical review of our model shows that it makes diagnoses with a level of accuracy that is unmatched, with a classification efficacy of 98.27%. This shows that the model is good at finding important parts of the brain that may be damaged by cognitive diseases.

Alzheimer's Disease: Models and Molecular Mechanisms Informing Disease and Treatments

Alzheimer's Disease (AD) is a complex neurodegenerative disease resulting in progressive loss of memory, language and motor abilities caused by cortical and hippocampal degeneration. This review captures the landscape of understanding of AD pathology, diagnostics, and current therapies. Two major mechanisms direct AD pathology: (1) accumulation of amyloid β (Aβ) plaque and (2) tau-derived neurofibrillary tangles (NFT). The most common variants in the Aβ pathway in APP, PSEN1, and PSEN2 are largely responsible for early-onset AD (EOAD), while MAPT, APOE, TREM2 and ABCA7 have a modifying effect on late-onset AD (LOAD). More recent studies implicate chaperone proteins and Aβ degrading proteins in AD. Several tests, such as cognitive function, brain imaging, and cerebral spinal fluid (CSF) and blood tests, are used for AD diagnosis. Additionally, several biomarkers seem to have a unique AD specific combination of expression and could potentially be used in improved, less invasive diagnostics. In addition to genetic perturbations, environmental influences, such as altered gut microbiome signatures, affect AD. Effective AD treatments have been challenging to develop. Currently, there are several FDA approved drugs (cholinesterase inhibitors, Aß-targeting antibodies and an NMDA antagonist) that could mitigate AD rate of decline and symptoms of distress.

Alzheimer's Disease: An Updated Overview of Its Genetics

Alzheimer's disease (AD) is the most common neurodegenerative disease in the world. It is classified as familial and sporadic. The dominant familial or autosomal presentation represents 1-5% of the total number of cases. It is categorized as early onset (EOAD; <65 years of age) and presents genetic mutations in presenilin 1 (PSEN1), presenilin 2 (PSEN2), or the Amyloid precursor protein (APP). Sporadic AD represents 95% of the cases and is categorized as late-onset (LOAD), occurring in patients older than 65 years of age. Several risk factors have been identified in sporadic AD; aging is the main one. Nonetheless, multiple genes have been associated with the different neuropathological events involved in LOAD, such as the pathological processing of Amyloid beta (Aβ) peptide and Tau protein, as well as synaptic and mitochondrial dysfunctions, neurovascular alterations, oxidative stress, and neuroinflammation, among others. Interestingly, using genome-wide association study (GWAS) technology, many polymorphisms associated with LOAD have been identified. This review aims to analyze the new genetic findings that are closely related to the pathophysiology of AD. Likewise, it analyzes the multiple mutations identified to date through GWAS that are associated with a high or low risk of developing this neurodegeneration. Understanding genetic variability will allow for the identification of early biomarkers and opportune therapeutic targets for AD.

Uncovering of potential molecular markers for cervical squamous cell carcinoma (CESC) based on analysis of methylated-differentially expressed genes

OBJECTIVE

Cervical squamous cell carcinoma (CESC) is a cancer with high mortality caused by human papillomavirus. The aim of this study was to uncover potential CESC biomarkers to help early diagnosis and treatment.

MATERIALS AND METHODS

The mRNA transcriptome data and DNA methylation data were downloaded from database for the identification of differentially expressed mRNAs (DEmRNAs) and DNA methylation analysis. Functional analysis was used to reveal the molecular functions. Then, the association between differential methylation and DEmRNA was analyzed. Protein-protein interaction (PPI) network analysis was performed on the selected differentially methylated genes (DEGs). Subsequently, we analyzed the prognosis and constructed a prognostic risk model. We also performed diagnostic analyses of risk model genes. In addition, we verified the protein expression level of identified DEGs.

RESULTS

1438 DEmRNAs, 1669 differentially methylated sites (DMSs), 46 differentially methylated CpG islands and 53 differential methylation genes (DMGs) were obtained. In PPI, the highest interaction scores were MX2 and IRF8, and their interaction score was 0.928. Interestingly, 5 DMGs were found to be associated with CESC prognosis. In addition, our results demonstrated that high risk score was associated with poor prognosis of CESC. Furthermore, it was found that ZIK1, ZNRF2, HHEX, VCAM1 could be diagnostic molecular markers for CESC.

CONCLUSION

Analysis of methylated-differentially expressed genes may contribute to the identification of early diagnosis and therapeutic targets of CESC. In addition, a prognostic model based on 5 DMGs can be used to predict the prognosis of CESC.

The relationship of early- and late-onset Alzheimer’s disease genes with COVID-19

Individuals with Alzheimer’s disease and other neurodegenerative diseases have been exposed to excess risk by the COVID-19 pandemic. COVID-19’s main manifestations include high body temperature, dry cough, and exhaustion. Nevertheless, some affected individuals may have an atypical presentation at diagnosis but suffer neurological signs and symptoms as the first disease manifestation. These findings collectively show the neurotropic nature of SARS-CoV-2 virus and its ability to involve the central nervous system. In addition, Alzheimer’s disease and COVID-19 has a number of common risk factors and comorbid conditions including age, sex, hypertension, diabetes, and the expression of APOE ε4. Until now, a plethora of studies have examined the COVID-19 disease but only a few studies has yet examined the relationship of COVID-19 and Alzheimer’s disease as risk factors of each other. This review emphasizes the recently published evidence on the role of the genes of early- or late-onset Alzheimer’s disease in the susceptibility of individuals currently suffering or recovered from COVID-19 to Alzheimer’s disease or in the susceptibility of individuals at risk of or with Alzheimer’s disease to COVID-19 or increased COVID-19 severity and mortality. Furthermore, the present review also draws attention to other uninvestigated early- and late-onset Alzheimer’s disease genes to elucidate the relationship between this multifactorial disease and COVID-19.

Untangling huge literature to disinter genetic underpinnings of Alzheimer's Disease: A systematic review and meta-analysis

Drug discovery for Alzheimer's Disease (AD) is channeled towards unravelling key disease specific drug targets/genes to predict promising therapeutic candidates. Though enormous literature on AD genetics is available, there exists dearth in data pertinent to drug targets and crucial pathological pathways intertwined in disease progression. Further, the research findings revealing genetic associations failed to demonstrate consistency across different studies. This scenario prompted us to initiate a systematic review and meta-analysis with an aim of unearthing significant genetic hallmarks of AD. Initially, a Boolean search strategy was developed to retrieve case-control studies from PubMed, Cochrane, ProQuest, Europe PMC, grey literature and HuGE navigator. Subsequently, certain inclusion and exclusion criteria were framed to shortlist the relevant studies. These studies were later critically appraised using New Castle Ottawa Scale and Q-Genie followed by data extraction. Later, meta-analysis was performed only for those Single Nucleotide Polymorphisms (SNPs) which were evaluated in at least two different ethnicities from two different reports. Among, 204,351 studies retrieved, 820 met our eligibility criteria and 117 were processed for systematic review after critical appraisal. Ultimately, meta-analysis was performed for 23 SNPs associated with 15 genes which revealed significant associations of rs3865444 (CD33), rs7561528 (BIN1) and rs1801133 (MTHFR) with AD risk.

Genome-wide SNP analysis of Siamese cobra (Naja kaouthia) reveals the molecular basis of transitions between Z and W sex chromosomes and supports the presence of an ancestral super-sex chromosome in amniotes

Elucidation of the process of sex chromosome differentiation is necessary to understand the dynamics of evolutionary mechanisms in organisms. The W sex chromosome of the Siamese cobra (Naja kaouthia) contains a large number of repeats and shares amniote sex chromosomal linkages. Diversity Arrays Technology provides an effective approach to identify sex-specific loci that are epoch-making, to understand the dynamics of molecular transitions between the Z and W sex chromosomes in a snake lineage. From a total of 543 sex-specific loci, 90 showed partial homology with sex chromosomes of several amniotes and 89 loci were homologous to transposable elements. Two loci were confirmed as W-specific nucleotides after PCR amplification. These loci might result from a sex chromosome differentiation process and involve putative sex-determination regions in the Siamese cobra. Sex-specific loci shared linkage homologies among amniote sex chromosomes, supporting an ancestral super-sex chromosome.

Association Between Episodic Memory and Genetic Risk Factors for Alzheimer's Disease in South Asians from the Longitudinal Aging Study in India-Diagnostic Assessment of Dementia (LASI-DAD)

BACKGROUND/OBJECTIVES

Genetic factors play an important role in Alzheimer's disease (AD) and cognitive aging. However, it is unclear whether risk loci identified in European ancestry (EA) populations have similar effects in other groups, such as South Asians.

DESIGN

We investigated the allelic distribution and cognitive associations of 56 known AD risk single-nucleotide polymorphisms (SNPs) identified from three EA genome-wide association studies (EA-GWASs) in a South Asian population. Single SNP and genetic risk score (GRS) associations with measures of episodic memory were assessed.

SETTING

The Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD).

PARTICIPANTS

A total of 906 LASI-DAD participants from diverse states in India.

MEASUREMENTS

Participants were genotyped using the Illumina Global Screening Array and imputed with 1000G Phase 3v5. Cognitive measures included total learning and delayed word recall.

RESULTS

Although only a few SNPs were significantly associated with memory scores (P < .05), effect estimates from the EA-GWAS and the LASI-DAD showed moderate correlation (0.35-0.88) in the expected direction. GRSs were also associated with memory scores, although percentage variation explained was small (0.1%-0.6%).

CONCLUSIONS

Discrepancies in allele frequencies and cognitive association results suggest that genetic factors found predominantly through EA-GWASs may play a limited role in South Asians. However, the extent of differences in the genetic architecture of AD and cognition in EA and South Asians remains uncertain. There is also a critical need to perform a more comprehensive assessment of the mutational spectrum of South Asia to identify novel genetic variants associated with AD and cognition in this population. J Am Geriatr Soc 68:S45-S53, 2020.

Ctcf haploinsufficiency mediates intron retention in a tissue-specific manner

CTCF is a master regulator of gene transcription and chromatin organisation with occupancy at thousands of DNA target sites genome-wide. While CTCF is essential for cell survival, CTCF haploinsufficiency is associated with tumour development and hypermethylation. Increasing evidence demonstrates CTCF as a key player in several mechanisms regulating alternative splicing (AS), however, the genome-wide impact of Ctcf dosage on AS has not been investigated.

We examined the effect of Ctcf haploinsufficiency on gene expression and AS in five tissues from Ctcf hemizygous (Ctcf^+/-) mice. Reduced Ctcf levels caused distinct tissue-specific differences in gene expression and AS in all tissues. An increase in intron retention (IR) was observed in Ctcf^+/- liver and kidney. In liver, this specifically impacted genes associated with cytoskeletal organisation, splicing and metabolism. Strikingly, most differentially retained introns were short, with a high GC content and enriched in Ctcf binding sites in their proximal upstream genomic region. This study provides new insights into the effects of CTCF haploinsufficiency on organ transcriptomes and the role of CTCF in AS regulation.

Genetic risk score modifies the effect of APOE on risk and age onset of Alzheimer's disease

Single nucleotide polymorphism (SNP)-based genetic risk score (GRS) and APOE genotype are both important in risk prediction of Alzheimer's disease (AD); however, the interaction between GRS and APOE has not been extensively investigated. Our objective was to determine whether GRS modifies the APOE effect on AD risk and age at onset (AAO). The study included 774 AD cases and 767 controls of European descent. Population standardized GRS was calculated based on 17 previously implicated AD risk-associated SNPs. Association was analyzed using logistic regression, Cox proportional hazards model and Kaplan-Meier curve. We found that GRS was significantly associated with AD risk and the association was stronger among APOE ε4 carriers. Compared to ε4 non-carriers, the Odds Ratio (OR) for AD was 8.09 (95% Confidence Interval [CI]: 4.98-13.63) for ε4 carriers with high-GRS (≥1.5). In contrast, the OR was 2.55 (95% CI: 1.46-4.49) for ε4 carriers with low-GRS (<0.6). In conclusion, these results suggest SNP-based GRS may supplement APOE for better assessment of inherited risk and age of onset of AD.

A Longitudinal Imaging Genetics Study of Neuroanatomical Asymmetry in Alzheimer's Disease

BACKGROUND

Contralateral brain structures represent a unique, within-patient reference element for disease, and asymmetries can provide a personalized measure of the accumulation of past disease processes. Neuroanatomical shape asymmetries have recently been associated with the progression of Alzheimer's disease (AD), but the biological basis of asymmetric brain changes in AD remains unknown.

METHODS

We investigated genetic influences on brain asymmetry by identifying associations between magnetic resonance imaging-derived measures of asymmetry and candidate single nucleotide polymorphisms (SNPs) that have previously been identified in genome-wide association studies for AD diagnosis and for brain subcortical volumes. For analyzing longitudinal neuroimaging data (1241 individuals, 6395 scans), we used a mixed effects model with interaction between genotype and diagnosis.

RESULTS

Significant associations between asymmetry of the amygdala, hippocampus, and putamen and SNPs in the genes BIN1, CD2AP, ZCWPW1, ABCA7, TNKS, and DLG2 were found.

CONCLUSIONS

The associations between SNPs in the genes TNKS and DLG2 and AD-related increases in shape asymmetry are of particular interest; these SNPs have previously been associated with subcortical volumes of amygdala and putamen but have not yet been associated with AD pathology. For AD candidate SNPs, we extend previous work to show that their effects on subcortical brain structures are asymmetric. This provides novel evidence about the biological underpinnings of brain asymmetry as a disease marker.

Data integration for functional annotation of regulatory single nucleotide polymorphisms associated with Alzheimer's disease susceptibility

BACKGROUND

Alzheimer's disease (AD), the most common form of dementia affects 24.3 million people worldwide. More than twenty genetic loci have been associated with AD and a significant number of genetic variants were mapped within these loci. A large proportion of genome wide significant variants lie outside the coding region. However, the plausible function of these variants is still unexplored.

OBJECTIVE

The present study aimed to unravel the regulatory role of proxy single nucleotide polymorphisms (SNPs), to determine their risk of developing AD.

METHODS

The RegulomeDB was employed to predict the regulatory role of proxy SNPs. Protein association network and functional enrichment analysis was performed using String10.5 and gene ontology, respectively.

RESULTS

A total of 451 SNPs were examined through SNAP web portal (r² ≤ 0.80) which returned 2186 proxy SNPs in linkage disequilibrium (LD) with genome wide significant SNPs for AD. Out of 2186 SNPs analyzed in RegulomeDB, 151 had the scores < 3 that indicates the high degree of their potential regulatory function. Further analysis revealed that out of these 151 SNPs, 37 were genome wide significant for AD, 17 were significantly associated with diseases other than AD, 89 were proxy SNPs (not genome wide significant) for various diseases including AD while 8 SNPs were novel proxy SNPs for AD.

CONCLUSION

These findings support the notion that the non-coding variants can be strongly associated with disease risk. Further validation through genome wide association studies will be helpful for the elucidation of their regulatory potential.

Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Unimpaired Individuals Using Multi-feature Kernel Discriminant Dictionary Learning

Accurate classification of either patients with Alzheimer's disease (AD) or patients with mild cognitive impairment (MCI), the prodromal stage of AD, from cognitively unimpaired (CU) individuals is important for clinical diagnosis and adequate intervention. The current study focused on distinguishing AD or MCI from CU based on the multi-feature kernel supervised within-Class-similar discriminative dictionary learning algorithm (MKSCDDL), which we introduced in a previous study, demonstrating that MKSCDDL had superior performance in face recognition. Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir-PET data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were all included for classification of AD vs. CU, MCI vs. CU, as well as AD vs. MCI (113 AD patients, 110 MCI patients, and 117 CU subjects). By adopting MKSCDDL, we achieved a classification accuracy of 98.18% for AD vs. CU, 78.50% for MCI vs. CU, and 74.47% for AD vs. MCI, which in each instance was superior to results obtained using several other state-of-the-art approaches (MKL, JRC, mSRC, and mSCDDL). In addition, testing time results outperformed other high quality methods. Therefore, the results suggested that the MKSCDDL procedure is a promising tool for assisting early diagnosis of diseases using neuroimaging data.

Lack of association between MTHFR A1298C variant and Alzheimer's disease: evidence from a systematic review and cumulative meta-analysis

OBJECTIVES

Previous studies have investigated the association between MTHFR A1298C (rs1801131) polymorphism and susceptibility to Alzheimer's disease (AD). Nevertheless, an ultimate conclusion remains obscure. We then executed this meta-analysis to estimate this association more precisely.

METHODS

Related studies were systematically searched on PubMed, Embase, China National Knowledge Infrastructure, Google scholar, and AlzGene databases. The association was evaluated by reviewing the odds ratios (ORs) with corresponding 95% confidence intervals (CIs). Publication bias, sensitivity analysis, and cumulative meta-analysis were performed to help draw a more definite conclusion.

RESULTS

Ten eligible studies were finally enrolled in this meta-analysis. Lack of association between MTHFR A1298C polymorphism and AD risk was observed in five genetic models (allelic: OR = 1.17, 95% CI: 0.88-1.56; homozygous: OR = 1.15, 95% CI: 0.87-1.53; heterozygous: OR = 1.19, 95% CI: 0.76-1.86; dominant: OR = 1.23, 95% CI: 0.81-1.87; recessive: OR = 1.16, 95% CI: 0.89-1.52). The result of cumulative meta-analysis sorted by publication year was also detected a dynamic tendency of no correlation between MTHFR A1298C polymorphism and AD.

CONCLUSION

This meta-analysis reveals that MTHFR A1298C polymorphism may not be associated with AD risk.