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.

Adversities in childhood and young adulthood and incident cardiovascular diseases: a prospective cohort study.

Background: Much remains unknown regarding the associations of adversities in childhood and adulthood with incident cardiovascular diseases (CVD). We aimed to examine the independent and cumulative relations of adversities in childhood and adulthood with incident CVD and whether these associations can be mitigated by adopting a healthy lifestyle later in life. Methods: We included 136,073 men and women [38-72 years at baseline] free of diagnosed CVD at baseline who responded to surveys on adversities in childhood and adulthood in the United Kingdom Biobank prospective cohort. They were recruited between 2006 and 2010 and were followed-up until 28 January 2021. Adversities included physical abuse, emotional abuse, sexual abuse, emotional neglect, and physical neglect. Participants were categorised into four groups according to the exposure periods, which were no adversity, childhood adversity only, adulthood adversity only, and cumulative adversity (both childhood and adulthood). The primary outcomes included incident fatal and non-fatal CVD events. The modifiable lifestyle factors were smoking, physical activity, diet, sleeping, social or leisure activities, and friend or family visits. Findings: We identified 16,415 (10.71/1000 person-year) incident CVD during a median follow-up of 11.8 years. Compared with participants with no adversity, CVD incidence increased by 11% in those with childhood adversity only (adjusted hazard ratio [HR]: 1.11 [95% CI 1.06-1.17], p < 0.001), 4% in those with adulthood adversity only (1.04 [1.00-1.09], p = 0.05), and 21% in those with cumulative adversity (1.21 [1.16-1.26], p < 0.001). Analysis of interactions showed that adulthood adversity amplified the childhood adversity-CVD association (p for interaction = 0.03). Compared with the participants with one or fewer ideal lifestyle factors, those with more than four ideal factors had a 25%-36% lower risk of CVD across the three adversity groups. Interpretation: Our findings suggested that childhood adversities were associated with an increased risk of CVD which can be magnified by adulthood adversities and substantially mitigated by adopting a healthy lifestyle later in life. Funding: The National Natural Science Foundation of China and Guangzhou Foundation for Basic and Applied Basic Research.

Machine learning-based prediction of COVID-19 mortality using immunological and metabolic biomarkers.

COVID-19 mortality prediction Background COVID-19 has become a major global public health problem, despite prevention and efforts. The daily number of COVID-19 cases rapidly increases, and the time and financial costs associated with testing procedure are burdensome. Method To overcome this, we aim to identify immunological and metabolic biomarkers to predict COVID-19 mortality using a machine learning model. We included inpatients from Hong Kong's public hospitals between January 1, and September 30, 2020, who were diagnosed with COVID-19 using RT-PCR. We developed three machine learning models to predict the mortality of COVID-19 patients based on data in their electronic medical records. We performed statistical analysis to compare the trained machine learning models which are Deep Neural Networks (DNN), Random Forest Classifier (RF) and Support Vector Machine (SVM) using data from a cohort of 5,059 patients (median age = 46 years; 49.3% male) who had tested positive for COVID-19 based on electronic health records and data from 532,427 patients as controls. Result We identified top 20 immunological and metabolic biomarkers that can accurately predict the risk of mortality from COVID-19 with ROC-AUC of 0.98 (95% CI 0.96-0.98). Of the three models used, our result demonstrate that the random forest (RF) model achieved the most accurate prediction of mortality among COVID-19 patients with age, glomerular filtration, albumin, urea, procalcitonin, c-reactive protein, oxygen, bicarbonate, carbon dioxide, ferritin, glucose, erythrocytes, creatinine, lymphocytes, PH of blood and leukocytes among the most important biomarkers identified. A cohort from Kwong Wah Hospital (131 patients) was used for model validation with ROC-AUC of 0.90 (95% CI 0.84-0.92). Conclusion We recommend physicians closely monitor hematological, coagulation, cardiac, hepatic, renal and inflammatory factors for potential progression to severe conditions among COVID-19 patients. To the best of our knowledge, no previous research has identified important immunological and metabolic biomarkers to the extent demonstrated in our study. Supplementary Information: The online version contains supplementary material available at 10.1186/s44247-022-00001-0.

Identification of shared genetic architecture between non-alcoholic fatty liver disease and type 2 diabetes: A genome-wide analysis.

Background: The incidence of complications of non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes (T2D) has been increasing. Method: In order to identify the shared genetic architecture of the two disease phenotypes of NAFLD and T2D, a European population-based GWAS summary and a cross-trait meta-analysis was used to identify significant shared genes for NAFLD and T2D. The enrichment of shared genes was then determined through the use of functional enrichment analysis to investigate the relationship between genes and phenotypes. Additionally, differential gene expression analysis was performed, significant differentially expressed genes in NAFLD and T2D were identified, genes that overlapped between those that were differentially expressed and cross-trait results were reported, and enrichment analysis was performed on the core genes that had been obtained in this way. Finally, the application of a bidirectional Mendelian randomization (MR) approach determined the causal link between NAFLD and T2D. Result: A total of 115 genes were discovered to be shared between NAFLD and T2D in the GWAS analysis. The enrichment analysis of these genes showed that some were involved in the processes such as the decomposition and metabolism of lipids, phospholipids, and glycerophospholipids. Additionally, through the use of differential gene expression analysis, 15 core genes were confirmed to be linked to both T2D and NAFLD. They were correlated with carcinoma cells and inflammation. Furthermore, the bidirectional MR identified a positive causal relationship between NAFLD and T2D. Conclusion: Our study determined the genetic structure shared between NAFLD and T2D, offering a new reference for the genetic pathogenesis and mechanism of NAFLD and T2D comorbidities.

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.

Stroke mortality prediction based on ensemble learning and the combination of structured and textual data.

For severe cerebrovascular diseases such as stroke, the prediction of short-term mortality of patients has tremendous medical significance. In this study, we combined machine learning models Random Forest classifier (RF), Adaptive Boosting (AdaBoost), Extremely Randomised Trees (ExtraTree) classifier, XGBoost classifier, TabNet, and DistilBERT to construct a multi-level prediction model that used bioassay data and radiology text reports from haemorrhagic and ischaemic stroke patients to predict six-month mortality. The performances of the prediction models were measured using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), precision, recall, and F1-score. The prediction models were built with the use of data from 19,616 haemorrhagic stroke patients and 50,178 ischaemic stroke patients. Novel six-month mortality prediction models for these patients were developed, which enhanced the performance of the prediction models by combining laboratory test data, structured data, and textual radiology report data. The achieved performances were as follows: AUROC = 0.89, AUPRC = 0.70, precision = 0.52, recall = 0.78, and F1 score = 0.63 for haemorrhagic patients, and AUROC = 0.88, AUPRC = 0.54, precision = 0.34, recall = 0.80, and F1 score = 0.48 for ischaemic patients. Such models could be used for mortality risk assessment and early identification of high-risk stroke patients. This could contribute to more efficient utilisation of healthcare resources for stroke survivors.

Sex Hormone-Binding Globulin and Risk of Coronary Heart Disease in Men and Women.

BACKGROUND: The role of sex hormone-binding globulin (SHBG) levels in clinical risk stratification and intervention for coronary heart disease (CHD) remains uncertain. We aimed to examine whether circulating levels of SHBG are predictive of CHD risk in men and women. METHODS: We investigated the association between SHBG and the risk of incident CHD in 128 322 men and 135 103 women free of CHD at baseline in the prospective United Kingdom Biobank (UKB) cohort. The unconfounded associations were estimated using Mendelian randomization (MR) analysis. We further conducted a meta-analysis to integrate currently available prospective evidence. CHD events included nonfatal and fatal myocardial infarction and coronary revascularization. RESULTS: In the UKB, during a median of 11.7 follow-up years, 10 405 men and 4512 women developed CHD. Serum levels of SHBG were monotonically associated with a decreased risk of CHD in both men (adjusted hazard ratio [HR] per log nmol/L increase in SHBG: 0.88 [0.83-0.94]) and women (HR: 0.89 [0.83-0.96]). MR-based analyses suggested causality and a dose-response relationship of SHBG with CHD risk. A cumulative meta-analysis including 216 417 men and 138 282 women from 11 studies showed that higher levels of SHBG were prospectively associated with decreased CHD risk in men comparing the highest with the lowest quartile: pooled relative risk (RR) 0.81 (0.74-0.89) and women (pooled RR: 0.86 [0.78-0.94]). CONCLUSIONS: Higher circulating SHBG levels were directly and independently predictive of lower CHD risk in both men and women. The utility of SHBG for CHD risk stratification and prediction warrants further study.

Genetic evidence for causal relationships between age at natural menopause and the risk of ageing-associated adverse health outcomes.

BACKGROUND: A later age at natural menopause (ANM) has been linked to several ageing-associated traits including an increased risk of breast and endometrial cancer and a decreased risk of lung cancer, osteoporosis and Alzheimer disease. However, ANM is also related to several proxies for overall health that may confound these associations. METHODS: We investigated the causal association of ANM with these clinical outcomes using Mendelian randomization (MR). Participants and outcomes analysed were restricted to post-menopausal females. We conducted a one-sample MR analysis in both the Women's Health Initiative and UK Biobank. We further analysed and integrated several additional data sets of post-menopausal women using a two-sample MR design. We used ≤55 genetic variants previously discovered to be associated with ANM as our instrumental variable. RESULTS: A 5-year increase in ANM was causally associated with a decreased risk of osteoporosis [odds ratio (OR) = 0.80, 95% CI (0.70-0.92)] and fractures (OR = 0.76, 95% CI, 0.62-0.94) as well as an increased risk of lung cancer (OR = 1.35, 95% CI, 1.06-1.71). Other associations including atherosclerosis-related outcomes were null. CONCLUSIONS: Our study confirms that the decline in bone density with menopause causally translates into fractures and osteoporosis. Additionally, this is the first causal epidemiological analysis to our knowledge to find an increased risk of lung cancer with increasing ANM. This finding is consistent with molecular and epidemiological studies suggesting oestrogen-dependent growth of lung tumours.

Prenatal exposure to famine and the development of diabetes later in life: an age-period-cohort analysis of the China health and nutrition survey (CHNS) from 1997 to 2015.

PURPOSE: Prenatal exposure to famine has been linked to increased diabetes risk in adulthood. However, one fundamental issue to be addressed is that the reported famine-diabetes relation may be confounded by the age differences between the exposed and non-exposed groups. We aimed to determine the association between prenatal exposure to the Chinese famine of 1959-1962 and risk of diabetes by applying age well-controlled strategies. METHODS: Among 20,535 individuals born in 1955-1966 who participated in the China Health and Nutrition Survey from 1997 to 2015, we constructed age-matched exposed vs. non-exposed groups to investigate the role of prenatal exposure to the Chinese famine of 1959-1962 in relation to diabetes. We also built a hierarchical age-period-cohort (HAPC) model to specifically examine the relation of famine to diabetes risk independent of age. RESULTS: Compared to the age-balanced men in the non-exposed group, the exposed men born in 1961 had a 154% increased risk of diabetes [odds ratio (OR) 2.54 (95% CI 1.07-6.03), P = 0.04). In the HAPC analysis, the predicted probabilities of diabetes peaked in the 1961-birth cohort of men [3.4% (95% CI 2.4%-5.0%)], as compared to the average probability of diabetes (reference) of 1.8% for men overall. Neither analytical strategy revealed any strong relation between famine exposure and diabetes risk in women. CONCLUSION: Among the pre-defined Chinese famine period of 1959-1962, early-life exposure to famine was associated with increased diabetes risk in men but not in women, and these relations were independent of age.

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.

Added Sugar, Sugar-Sweetened Beverages, and Artificially Sweetened Beverages and Risk of Cardiovascular Disease: Findings from the Women's Health Initiative and a Network Meta-Analysis of Prospective Studies.

Much remains unknown about the role of added sugar in relation to cardiovascular disease (CVD) and the relative contributions of sugar-sweetened beverages (SSB) or artificially sweetened beverages (ASB) to CVD risk. Among the 109,034 women who participated in Women's Health Initiative, we assessed average intakes of added sugar, SSB and ASB, and conducted Cox regression to estimate the hazard ratios (HRs) and their 95% confidence intervals for CVD risk. The consistency of findings was compared to a network meta-analysis of all available cohorts. During an average of 17.4 years of follow-up, 11,597 cases of total CVD (nonfatal myocardial infarction, coronary heart disease (CHD) death, stroke, coronary revascularization, and/or incident heart failure) were confirmed. Added sugar as % energy intake daily (%EAS) at ≥15.0% was positively associated with total CVD (HR = 1.08 [1.01, 1.15]) and CHD (HR = 1.20 [1.09, 1.32]). There was also a higher risk of total CVD associated with ≥1 serving of SSB intake per day (HR = 1.29 [1.17, 1.42]), CHD (1.35 [1.16, 1.57]), and total stroke (1.30 [1.10, 1.53]). Similarly, ASB intake was associated with an increased risk of CVD (1.14 [1.03, 1.26]) and stroke (1.24 [1.04, 1.48]). According to the network meta-analysis, there was a large amount of heterogeneity across studies, showing no consistent pattern implicating added sugar, ASB, or SSB in CVD outcomes. A diet containing %EAS ≥15.0% and consuming ≥1 serving of SSB or ASB may be associated with a higher CVD incidence. The relative contribution of added sugar, SSB, and ASB to CVD risk warrants further investigation.

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.

A Mendelian randomization-based exploration of red blood cell distribution width and mean corpuscular volume with risk of hemorrhagic strokes.

Red blood cell distribution width (RCDW) and mean corpuscular volume (MCV) are associated with different risk factors for hemorrhagic stroke. However, whether RCDW and MCV are causally related to hemorrhagic stroke remains poorly understood. Therefore, we explored the causality between RCDW/MCV and nontraumatic hemorrhagic strokes using Mendelian randomization (MR) methods. We extracted exposure and outcome summary statistics from the UK Biobank and FinnGen. We evaluated the causality of RCDW/MCV on four outcomes (subarachnoid hemorrhage [SAH], intracerebral hemorrhage [ICH], nontraumatic intracranial hemorrhage [nITH], and a combination of SAH, cerebral aneurysm, and aneurysm operations) using univariable MR (UMR) and multivariable MR (MVMR). We further performed colocalization and mediation analyses. UMR and MVMR revealed that higher genetically predicted MCV is protective of ICH (UMR: odds ratio [OR] = 0.89 [0.8-0.99], p = 0.036; MVMR: OR = 0.87 [0.78-0.98], p = 0.021) and nITH (UMR: OR = 0.89 [0.82-0.97], p = 0.005; MVMR: OR = 0.88 [0.8-0.96], p = 0.004). There were no strong causal associations between RCDW/MCV and any other outcome. Colocalization analysis revealed a shared causal variant between MCV and ICH; it was not reported to be associated with ICH. Proportion mediated via diastolic blood pressure was 3.1% (0.1%,14.3%) in ICH and 3.4% (0.2%,15.8%) in nITH. The study constitutes the first MR analysis on whether genetically elevated RCDW and MCV affect the risk of hemorrhagic strokes. UMR, MVMR, and mediation analysis revealed that MCV is a protective factor for ICH and nITH, which may inform new insights into the treatments for hemorrhagic strokes.

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.

Evaluating the Causal Effects of Gestational Diabetes Mellitus, Heart Disease, and High Body Mass Index on Maternal Alzheimer's Disease and Dementia: Multivariable Mendelian Randomization.

Introduction: Gestational diabetes mellitus (GDM), heart disease (HD) and high body mass index (BMI) are strongly related to Alzheimer's disease (AD) dementia in pregnant women. Therefore, we aimed to determine the total effects of GDM, heart disease, and high BMI on maternal AD dementia. Methods: We used data from the genome-wide association studies of European populations including more than 30,000 participants. We performed two-sample Mendelian randomization (MR) and multivariable MR (MVMR) to systematically estimate the direct effects of GDM, HD, and high BMI on maternal AD and dementia. Multiple sensitivity analyses involving classical MR approaches and expanded MR-pleiotropy residual sum and outlier analysis. Results: In two-sample MR analysis, the inverse-variance weighted method in our study demonstrated no significant causality between GDM and maternal dementia (ß = -0.006 ± 0.0026, p = 0.82). This method also revealed no significant causality between high BMI and maternal dementia (ß = 0.0024 ± 0.0043, p = 0.57), and it was supported by the MR-Egger regression results, which showed no causal effect of high BMI on maternal Alzheimer's disease and dementia (ß = 0.0027 ± 0.0096, p = 0.78). The IVW method showed no significant causal relationship between maternal HD and maternal Alzheimer's disease and dementia (ß = -0.05 ± 0.0042, p = 0.117) and MR-Egger regression analysis gave a similar result (ß = -0.12 ± 0.0060, p = 0.079). In MVMR analysis, we found no significant causal relationship between GDM, high BMI, or HD and maternal Alzheimer's disease and dementia (p = 0.94, 0.82, and 0.13, respectively). Thus, the MVMR estimates were consistent with our results from the two-sample MR analysis. We confirmed that these results showed no horizontal pleiotropy and enhanced the robustness of our results through multiple sensitivity analyses. Conclusion: In two-sample MR analysis, we found no significant causal relationship between GDM, HD, high BMI and maternal AD and dementia. These results differed from previous observational studies showing HD is a significant predictor of dementia. MVMR analysis supported no significant causal relationship between GDM, HD, high BMI and maternal AD and dementia. Sensitivity analysis broadly increased the robustness of two-sample MR and MVMR analysis results.

Machine learning algorithms identify demographics, dietary features, and blood biomarkers associated with stroke records.

OBJECTIVE: We conducted a comprehensive evaluation of features associated with stroke records. METHODS: We screened the dietary nutrients, blood biomarkers, and clinical information from the National Health and Nutrition Examination Survey (NHANES) 2015-16 database to assess a self-reported history of all strokes (136 strokes, n = 4381). We computed feature importance, built machine learning (ML) models, developed a nomogram, and validated the nomogram on NHANES 2007-08, 2017-18, and the baseline UK Biobank. We calculated the odds ratios with/without adjusting sampling weights (OR/ORw). RESULTS: The clinical features have the best predictive power compared to dietary nutrients and blood biomarkers, with 22.8% increased average area under the receiver operating characteristic curves (AUROC) in ML models. We further modeled with ten most important clinical features without compromising the predictive performance. The key features positively associated with stroke include age, cigarette smoking, tobacco smoking, Caucasian or African American race, hypertension, diabetes mellitus, asthma history; the negatively associated feature is the family income. The nomogram based on these key features achieved good performances (AUROC between 0.753 and 0.822) on the test set, the NHANES 2007-08, 2017-18, and the UK Biobank. Key features from the nomogram model include age (OR = 1.05, ORw = 1.06), Caucasian/African American (OR = 2.68, ORw = 2.67), diabetes mellitus (OR = 2.30, ORw = 1.99), asthma (OR = 2.10, ORw = 2.41), hypertension (OR = 1.86, ORw = 2.10), and income (OR = 0.83, ORw = 0.81). CONCLUSIONS: We identified clinical key features and built predictive models for assessing stroke records with high performance. A nomogram consisting of questionnaire-based variables would help identify stroke survivors and evaluate the potential risk of stroke.

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.

Identifying Predictors of COVID-19 Mortality Using Machine Learning.

(1) Background: Coronavirus disease 2019 (COVID-19) is a dominant, rapidly spreading respiratory disease. However, the factors influencing COVID-19 mortality still have not been confirmed. The pathogenesis of COVID-19 is unknown, and relevant mortality predictors are lacking. This study aimed to investigate COVID-19 mortality in patients with pre-existing health conditions and to examine the association between COVID-19 mortality and other morbidities. (2) Methods: De-identified data from 113,882, including 14,877 COVID-19 patients, were collected from the UK Biobank. Different types of data, such as disease history and lifestyle factors, from the COVID-19 patients, were input into the following three machine learning models: Deep Neural Networks (DNN), Random Forest Classifier (RF), eXtreme Gradient Boosting classifier (XGB) and Support Vector Machine (SVM). The Area under the Curve (AUC) was used to measure the experiment result as a performance metric. (3) Results: Data from 14,876 COVID-19 patients were input into the machine learning model for risk-level mortality prediction, with the predicted risk level ranging from 0 to 1. Of the three models used in the experiment, the RF model achieved the best result, with an AUC value of 0.86 (95% CI 0.84-0.88). (4) Conclusions: A risk-level prediction model for COVID-19 mortality was developed. Age, lifestyle, illness, income, and family disease history were identified as important predictors of COVID-19 mortality. The identified factors were related to COVID-19 mortality.