Nutritional Interventions for the Prevention of Cognitive Decline in Patients With Mild Cognitive Impairment and Alzheimer Disease: Protocol for a Network Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: Mild cognitive impairment (MCI) is the stage between cognitive decline due to physiological aging and the severity of decline seen in neurodegenerative disorders like Alzheimer disease (AD), which is among the most prevalent neurodegenerative disorders characterized by cognitive impairment. People with MCI are at increased risk of developing AD. Although MCI and AD are incurable, nutritional interventions can potentially delay or prevent their onset. Consequently, effective interventions used to decelerate or alleviate the progress of cognitive impairment in older people are a significant focus in geriatric care. Given the synergistic effects of nutrition on health, assessing the effectiveness of nutritional supplements or dietary composition in preventing MCI or AD is essential for developing interventional strategies. OBJECTIVE: Our study aims to assess the effectiveness of various nutritional interventions, including special dietary types, dietary patterns, specific foods, nutritional intake, and nutritional supplements, in preventing cognitive decline among patients diagnosed with MCI or AD. To achieve this, we will use a comprehensive approach, including network meta-analysis, pairwise meta-analysis, and systematic review of randomized controlled trials (RCTs). METHODS: The review will follow the Population, Intervention, Comparison, Outcome (PICO) model and the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) guidelines. Two investigators will independently search PubMed electronically. Data extraction will follow the inclusion criteria, and data will be assessed for risk of bias using a revised tool. Additionally, evidence quality will be evaluated using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) framework. The outcomes of interest are assessing the cognitive outcomes in patients with MCI or AD. A systematic literature search will be conducted, identifying randomized controlled trials that investigate the impact of these nutritional interventions on cognitive function decline in individuals with MCI and AD. Network meta-analyses (random-effects model) and pairwise meta-analyses will then estimate the relative effectiveness of different nutritional interventions. RESULTS: We included 51 studies, published between 1999 and 2023 (27 studies for AD and 24 studies for MCI) and involving 8420 participants. We completed data extraction for all 51 studies by December 2023. Currently, we are actively engaged in data analysis and manuscript preparation. We plan to finalize the manuscript and publish the comprehensive results by the end of 2024. CONCLUSIONS: Our study holds significant clinical relevance given the rising prevalence of AD and the potential influence of nutritional interventions on cognitive function in individuals with MCI and AD. By investigating this relationship, our research aims to inform evidence-based decision-making in the development of prevention strategies for MCI and AD. The outcomes are expected to contribute to the establishment of reliable recommendations for MCI or AD management, providing substantial support in the field. TRIAL REGISTRATION: PROSPERO CRD42022331173; http://tinyurl.com/3snjp7a4. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/47196.

Non-steroidal anti-inflammatory drug target gene associations with major depressive disorders: a Mendelian randomisation study integrating GWAS, eQTL and mQTL Data.

Previous observational studies reported associations between non-steroidal anti-inflammatory drugs (NSAIDs) and major depressive disorder (MDD), however, these associations are often inconsistent and underlying biological mechanisms are still poorly understood. We conducted a two-sample Mendelian randomisation (MR) study to examine relationships between genetic variants and NSAID target gene expression or DNA methylation (DNAm) using publicly available expression, methylation quantitative trait loci (eQTL or mQTL) data and genetic variant-disease associations from genome-wide association studies (GWAS of MDD). We also assessed drug exposure using gene expression and DNAm levels of NSAID targets as proxies. Genetic variants were robustly adjusted for multiple comparisons related to gene expression, DNAm was used as MR instrumental variables and GWAS statistics of MDD as the outcome. A 1-standard deviation (SD) lower expression of NEU1 in blood was related to lower C-reactive protein (CRP) levels of -0.215 mg/L (95% confidence interval (CI): 0.128-0.426) and a decreased risk of MDD (odds ratio [OR] = 0.806; 95% CI: 0.735-0.885; p = 5.36 × 10-6). A concordant direction of association was also observed for NEU1 DNAm levels in blood and a risk of MDD (OR = 0.886; 95% CI: 0.836-0.939; p = 4.71 × 10-5). Further, the genetic variants associated with MDD were mediated by NEU1 expression via DNAm (ß = -0.519; 95% CI: -0.717 to -0.320256; p = 3.16 × 10-7). We did not observe causal relationships between inflammatory genetic marker estimations and MDD risk. Yet, we identified a concordant association of NEU1 messenger RNA and an adverse direction of association of higher NEU1 DNAm with MDD risk. These results warrant increased pharmacovigilance and further in vivo or in vitro studies to investigate NEU1 inhibitors or supplements for MDD.

Shared genetic mechanism between type 2 diabetes and COVID-19 using pathway-based association analysis.

Recent studies have shown that, compared with healthy individuals, patients with type 2 diabetes (T2D) suffer a higher severity and mortality of COVID-19. When infected with this retrovirus, patients with T2D are more likely to face severe complications from cytokine storms and be admitted to high-dependency or intensive care units. Some COVID-19 patients are known to suffer from various forms of acute respiratory distress syndrome and have a higher mortality risk due to extreme activation of inflammatory cascades. Using a conditional false discovery rate statistical framework, an independent genome-wide association study data on individuals presenting with T2D (N = 62,892) and COVID-19 (N = 38,984) were analysed. Genome-wide association study data from 2,343,084 participants were analysed and a significant positive genetic correlation between T2D and COVID-19 was observed (T2D: r for genetic = 0.1511, p-value = 0.01). Overall, 2 SNPs (rs505922 and rs3924604) shared in common between T2D and COVID-19 were identified. Functional analyses indicated that the overlapping loci annotated into the ABO and NUS1 genes might be implicated in several key metabolic pathways. A pathway association analysis identified two common pathways within T2D and COVID-19 pathogenesis, including chemokines and their respective receptors. The gene identified from the pathway analysis (CCR2) was also found to be highly expressed in blood tissue via the GTEx database. To conclude, this study reveals that certain chemokines and their receptors, which are directly involved in the genesis of cytokine storms, may lead to exacerbated hyperinflammation in T2D patients infected by COVID-19.

Drug repositioning for esophageal squamous cell carcinoma.

Esophageal cancer (EC) remains a significant challenge globally, having the 8th highest incidence and 6th highest mortality worldwide. Esophageal squamous cell carcinoma (ESCC) is the most common form of EC in Asia. Crucially, more than 90% of EC cases in China are ESCC. The high mortality rate of EC is likely due to the limited number of effective therapeutic options. To increase patient survival, novel therapeutic strategies for EC patients must be devised. Unfortunately, the development of novel drugs also presents its own significant challenges as most novel drugs do not make it to market due to lack of efficacy or safety concerns. A more time and cost-effective strategy is to identify existing drugs, that have already been approved for treatment of other diseases, which can be repurposed to treat EC patients, with drug repositioning. This can be achieved by comparing the gene expression profiles of disease-states with the effect on gene-expression by a given drug. In our analysis, we used previously published microarray data and identified 167 differentially expressed genes (DEGs). Using weighted key driver analysis, 39 key driver genes were then identified. These driver genes were then used in Overlap Analysis and Network Analysis in Pharmomics. By extracting drugs common to both analyses, 24 drugs are predicted to demonstrate therapeutic effect in EC patients. Several of which have already been shown to demonstrate a therapeutic effect in EC, most notably Doxorubicin, which is commonly used to treat EC patients, and Ixazomib, which was recently shown to induce apoptosis and supress growth of EC cell lines. Additionally, our analysis predicts multiple psychiatric drugs, including Venlafaxine, as repositioned drugs. This is in line with recent research which suggests that psychiatric drugs should be investigated for use in gastrointestinal cancers such as EC. Our study shows that a drug repositioning approach is a feasible strategy for identifying novel ESCC therapies and can also improve the understanding of the mechanisms underlying the drug targets.

Lung adenocarcinoma-related target gene prediction and drug repositioning.

Lung cancer is the leading cause of cancer deaths globally, and lung adenocarcinoma (LUAD) is the most common type of lung cancer. Gene dysregulation plays an essential role in the development of LUAD. Drug repositioning based on associations between drug target genes and LUAD target genes are useful to discover potential new drugs for the treatment of LUAD, while also reducing the monetary and time costs of new drug discovery and development. Here, we developed a pipeline based on machine learning to predict potential LUAD-related target genes through established graph attention networks (GATs). We then predicted potential drugs for the treatment of LUAD through gene coincidence-based and gene network distance-based methods. Using data from 535 LUAD tissue samples and 59 precancerous tissue samples from The Cancer Genome Atlas, 48,597 genes were identified and used for the prediction model building of the GAT. The GAT model achieved good predictive performance, with an area under the receiver operating characteristic curve of 0.90. 1,597 potential LUAD-related genes were identified from the GAT model. These LUAD-related genes were then used for drug repositioning. The gene overlap and network distance with the target genes were calculated for 3,070 drugs and 672 preclinical compounds approved by the US Food and Drug Administration. At which, bromoethylamine was predicted as a novel potential preclinical compound for the treatment of LUAD, and cimetidine and benzbromarone were predicted as potential therapeutic drugs for LUAD. The pipeline established in this study presents new approach for developing targeted therapies for LUAD.

Exploring Lead loci shared between schizophrenia and Cardiometabolic traits.

Individuals with schizophrenia (SCZ) have, on average, a 10- to 20-year shorter expected life span than the rest of the population, primarily due to cardiovascular disease comorbidity. Genome-wide association studies (GWAS) have previously been used to separately identify common variants in SCZ and cardiometabolic traits. However, genetic variants jointly influencing both traits remain to be fully characterised. To assess overlaps (if any) between the genetic architecture of SCZ and cardiometabolic traits, we used conditional false discovery rate (FDR) and local genetic correlation statistical framework analyses. A conjunctional FDR was used to identify shared genetic traits between SCZ and cardiometabolic risk factors. We identified 144 genetic variants which were shared between SCZ and body mass index (BMI), and 15 variants shared between SCZ and triglycerides (TG). Furthermore, we discovered four novel single nucleotide polymorphisms (SNPs) (rs3865350, rs9860913, rs13307 and rs9614186) and four proximate genes (DERL2, SNX4, LY75 and EFCAB6) which were shared by SCZ and BMI. We observed that the novel genetic variant rs13307 and the most proximate gene LY75 exerted potential effects on SCZ and BMI comorbidity. Also, we observed a mixture of concordant and opposite direction associations with shared genetic variants. We demonstrated a moderate to high genetic overlap between SCZ and cardiometabolic traits associated with a pattern of bidirectional associations. Our data suggested a complex interplay between metabolism-related gene pathways in SCZ pathophysiology.

Canary: an automated tool for the conversion of MaCH imputed dosage files to PLINK files.

BACKGROUND: Previous studies have demonstrated the value of re-analysing publicly available genetics data with recent analytical approaches. Publicly available datasets, such as the Women's Health Initiative (WHI) offered by the database of genotypes and phenotypes (dbGaP), provide a wealthy resource for researchers to perform multiple analyses, including Genome-Wide Association Studies. Often, the genetic information of individuals in these datasets are stored in imputed dosage files output by MaCH; mldose and mlinfo files. In order for researchers to perform GWAS studies with this data, they must first be converted to a file format compatible with their tool of choice e.g., PLINK. Currently, there is no published tool which easily converts the datasets provided in MACH dosage files into PLINK-ready files. RESULTS: Herein, we present Canary a singularity-based tool which converts MaCH dosage files into PLINK-compatible files with a single line of user input at the command line. Further, we provide a detailed tutorial on preparation of phenotype files. Moreover, Canary comes with preinstalled software often used during GWAS studies, to further increase the ease-of-use of HPC systems for researchers. CONCLUSIONS: Until now, conversion of imputed data in the form of MaCH mldose and mlinfo files needed to be completed manually. Canary uses singularity container technology to allow users to automatically convert these MaCH files into PLINK compatible files. Additionally, Canary provides researchers with a platform to conduct GWAS analysis more easily as it contains essential software needed for conducting GWAS studies, such as PLINK and Bioconductor. We hope that this tool will greatly increase the ease at which researchers can perform GWAS with imputed data, particularly on HPC environments.

Assessment of Bidirectional Relationships between Leisure Sedentary Behaviors and Neuropsychiatric Disorders: A Two-Sample Mendelian Randomization Study.

(1) Background: Increasing evidence shows that sedentary behaviors are associated with neuropsychiatric disorders (NPDs) and thus may be a modifiable factor to target for the prevention of NPDs. However, the direction and causality for the relationship remain unknown; sedentary behaviors could increase or decrease the risk of NPDs, and/or NPDs may increase or decrease engagement in sedentary behaviors. (2) Methods: This Mendelian randomization (MR) study with two samples included independent genetic variants related to sedentary behaviors (n = 408,815), Alzheimer's disease (AD; n = 63,926), schizophrenia (SCZ; n = 105,318), and major depressive disorder (MDD; n = 500,199), which were extracted from several of the largest non-overlapping genome-wide association studies (GWASs), as instrumental variables. The summarized MR effect sizes from each instrumental variable were combined in an IVW (inverse-variance-weighted) approach, with various approaches (e.g., MR-Egger, weighted median, MR-pleiotropy residual sum and outlier), and sensitivity analyses were performed to identify and remove outliers and assess the horizontal pleiotropy. (3) Results: The MR evidence and linkage disequilibrium score regression revealed a consistent directional association between television watching and MDD (odds ratio (OR), 1.13 for MDD per one standard deviation (SD) increase in mean television watching time; 95% CI, 1.06-1.20; p = 6.80 × 10-5) and a consistent relationship between computer use and a decrease in the risk of AD (OR, 0.52 for AD per one SD increase in mean computer use time; 95% CI, 0.32-0.84; p = 8.20 × 10-3). In the reverse direction, MR showed a causal association between a reduced risk of SCZ and an increase in driving time (ß, -0.016; 95% CI, -0.027--0.004; p = 8.30 × 10-3). (4) Conclusions: Using genetic instrumental variables identified from large-scale GWASs, we found robust evidence for a causal relationship between long computer use time and a reduced risk of AD, and for a causal relationship between long television watching time and an increased risk of MDD. In reverse analyses, we found that SCZ was causally associated with reduced driving time. These findings fit in with our observations and prior knowledge as well as emphasizing the importance of distinguishing between different domains of sedentary behaviors in epidemiologic studies of NPDs.