Summary Of Data Research Articles

The protective role of vitamin d in nasopharyngeal carcinoma: insights from Mendelian randomization and meta-analysis

BackgroundIn recent years, the anti-tumor effects of vitamin D have garnered increasing attention. However, previous epidemiological studies on the relationship between vitamin D and nasopharyngeal carcinoma (NPC) have yielded inconsistent results. This study aims to further explore whether vitamin D helps reduce the risk of NPC through Mendelian randomization (MR) and meta-analysis.MethodsBased on the core assumption of MR study, instrumental variables (IVs) for vitamin D, serving as genetic proxies, were obtained from summary data of large genome-wide association study (GWAS). Inverse-variance weighted (IVW) was utilized as the primary MR analytical method to explore the causal relationship between vitamin D and NPC. Sensitivity analyses included heterogeneity testing and horizontal pleiotropy testing. To further validate the robustness of the result, meta-analysis was employed to obtain pooled effects from databases of different sources.ResultsIn the discovery cohort, the IVW result suggest that vitamin D is a potential protective factor against NPC (odds ratio (OR) = 0.35, 95% confidence interval (CI): 0.13–0.89, P = 0.028). The finding was further corroborated by two independent replication cohorts [OR = 0.32, 95% CI: 0.13–0.80, P = 0.018 (ukb-d-30890_irnt); OR = 0.34, 95% CI: 0.13–0.90, P = 0.029(ebi-a-GCST90025967)]. Subsequent meta-analysis indicated that vitamin D markedly reduces the risk of NPC (OR = 0.34, 95% CI: 0.19–0.58, P < 0.001). Multiple sensitivity analyses, including heterogeneity analysis and horizontal pleiotropy tests, did not reveal any significant findings (all P > 0.05).ConclusionThis study provides robust evidence that vitamin D significantly reduces the risk of NPC. Through MR and meta-analysis, we have demonstrated a protective role of vitamin D in NPC development. These findings suggest that maintaining adequate vitamin D levels may be a potential strategy for reducing NPC. Further research is warranted to confirm these results and explore the underlying mechanisms involved.

Trends in Medicare Reimbursement and Service Volume of Vitreoretinal Procedures: 2000 to 2021

Purpose: To characterize trends in service volume and inflation-adjusted Medicare reimbursement of vitreoretinal procedures over the past 2 decades. Methods: Medicare Part B National Summary Data Files were accessed to identify the number of allowed services for vitreoretinal procedures. The Medicare Physician Fee Schedule was accessed to identify average annual national reimbursement rates. The Spearman correlation coefficient was used to evaluate time trends for each procedure. All analyses were conducted using Prism 9.5.1 software with 2-sided significance testing and statistical significance set at P ≤ .05. Results: From 2000 to 2021, the 2-tailed Spearman correlation showed that 20 of 38 procedures had statistically significant decreases in service volume over time. Intravitreal injections increased more than 1000-fold, from 2922 in 2000 to 3 444 500 injections in 2021 (ρ = 0.997; P &lt; .001). Panretinal photocoagulation treatments declined from 104 865 to 48 533 procedures (ρ = −0.966; P = .003). Scleral buckling declined from 6502 to 587 procedures (ρ = −0.999; P &lt; .001). Pars plana vitrectomy–associated procedures increased from 71 039 to 95 429 (ρ = 0.691; P &lt; .001). From 2000 to 2021, the 2-tailed Spearman correlation showed that 29 of 38 procedures had statistically significant decreases in reimbursement over time. No procedure had a significant increase in payment. Conclusions: Vitreoretinal practice patterns have changed dramatically over the past 2 decades, with significant declines in inflation-adjusted Medicare reimbursement for most procedures. Awareness of service volume and reimbursement trends is vital to assessing economic viability and patient coverage under the current Medicare payment policies.

Exploring the mediating role of immune cells in the pathogenesis of IgA nephropathy through the inflammatory axis of gut microbiota from a genomic perspective.

IgA nephropathy (IgAN) is a chronic glomerular disease characterized by the deposition of IgA antibodies in the kidney's mesangium. Its pathogenesis involves genetic, immune, and environmental factors, particularly within the mucosal immune system and gut microbiota. Immune cells play a central role in mediating these processes, which this study investigates using Mendelian Randomization (MR) to explore causal relationships among gut microbiota, inflammatory markers, blood cells, and immune cells in IgAN pathogenesis. We conducted a two-sample MR analysis using Genome-Wide Association Study (GWAS) summary data to assess the causal effects of gut microbiota, inflammatory markers, and blood cell traits on IgAN. Data sources included the FinnGen dataset for IgAN and relevant GWAS datasets for immune traits, blood cells, and inflammatory markers. Inverse variance weighting (IVW) was the primary MR method, supported by sensitivity analyses. We particularly examined the mediation effect of immune cells on these exposures' influence on IgAN. Significant associations were found between several factors and IgAN. Gut microbiota traits, such as Firmicutes E and Sporomusales, increased IgAN risk, while Citrobacter A and Herbinix reduced it. Inflammatory markers, including Interleukin-10 and Fibroblast Growth Factor 23, promoted IgAN onset. Blood cell traits like red blood cell perturbation response increased risk, while monocyte perturbation response was protective. Immune traits played a key mediating role, with Transitional %B cells reducing IgAN risk and CD28- CD25 + + CD8br %T cells increasing it. This study highlights the pivotal mediating role of immune cells in the interactions between gut microbiota, inflammatory markers, and IgAN risk. These findings identify potential biomarkers and therapeutic targets, providing new insights into the immune mechanisms underlying IgAN and opportunities for intervention.

Racial Disparities in Utilization and Outcomes of Cervical Disc Arthroplasty

Study Design: Retrospective study. Objective: We examined racial disparities in (1) cervical disc arthroplasty (CDA) versus anterior cervical discectomy and fusion (ACDF) utilization and (2) CDA in-hospital outcomes. Summary of Background Data: ACDF and CDA are established treatments for cervical disc disease. While CDA may offer certain advantages over ACDF, its utilization patterns have not been comprehensively explored. Methods: This study of 2012 to 2019 discharges from the National Inpatient Sample included White, Black, and Hispanic patients aged 18 years and older who underwent elective ACDF or CDA. Patient demographics, comorbidities, cervical spine diagnoses, and hospital characteristics were extracted. Survey-weighted logistic regression modeled the adjusted association between race and CDA (vs. ACDF) utilization; an interaction between race and year examined temporal changes in disparities. For CDA outcomes, multivariable logistic regression was used for binary outcomes (nonhome discharge, combined complications, and dysphagia) and linear regression for length of stay. Results: The cohort included 712,355 weighted procedures (97.6% ACDF; 84.2% White, 9.7% Black, 6.1% Hispanic). CDA utilization increased from 1.0% of the procedures in 2012 to 3.8% in 2019. Black and Hispanic patients had significantly lower odds than White patients of receiving CDA versus ACDF (OR=0.77, 95% CI: 0.66–0.89, P=0.001; OR=0.80, 95% CI: 0.69–0.93, P=0.003) respectively. There was no statistically significant interaction between race and discharge year (P=0.50). For in-hospital CDA-specific outcomes, Black (vs. White) patients were more likely to experience dysphagia (OR=2.70, 95% CI: 1.53–4.78, P=0.001) and combined complications (OR=3.10, 95% CI: 1.91–5.05, P &lt;0.001). There were no significant differences in any CDA outcome for Hispanic versus White patients. Conclusions: This study revealed decreased utilization of CDA versus ACDF in minority patients, a pattern that persisted over time despite overall increasing CDA utilization. In addition, a higher burden of dysphagia and combined complications following CDA in Black patients warrants further examination. Level of Evidence: III

Regulatory T cells-related gene in primary sclerosing cholangitis: evidence from Mendelian randomization and transcriptome data.

The present study utilized large-scale genome-wide association studies (GWAS) summary data (731 immune cell subtypes and three primary sclerosing cholangitis (PSC) GWAS datasets), meta-analysis, and two PSC transcriptome data to elucidate the pivotal role of Tregs proportion imbalance in the occurrence of PSC. Then, we employed weighted gene co-expression network analysis (WGCNA), differential analysis, and 107 combinations of 12 machine-learning algorithms to construct and validate an artificial intelligence-derived diagnostic model (Tregs classifier) according to the average area under curve (AUC) (0.959) in two cohorts. Quantitative real-time polymerase chain reaction (qRT-PCR) verified that compared to control, Akap10, Basp1, Dennd3, Plxnc1, and Tmco3 were significantly up-regulated in the PSC mice model yet the expression level of Klf13, and Scap was significantly lower. Furthermore, immune cell infiltration and functional enrichment analysis revealed significant associations of the hub Tregs-related gene with M2 macrophage, neutrophils, megakaryocyte-erythroid progenitor (MEP), natural killer T cell (NKT), and enrichment scores of the autophagic cell death, complement and coagulation cascades, metabolic disturbance, Fc gamma R-mediated phagocytosis, mitochondrial dysfunction, potentially mediating PSC onset. XGBoost algorithm and SHapley Additive exPlanations (SHAP) identified AKAP10 and KLF13 as optimal genes, which may be an important target for PSC.

Changes in resting-state functional connectivity of large-scale brain networks in bulimia nervosa: evidence from causal analysis.

Bulimia nervosa (BN) has been observationally linked to the functional connectivity (FC) of large-scale brain networks, but the biological mechanisms remain unclear. This study used two-sample Mendelian randomization (MR) with genetic variations as instrumental variables (IVs) to explore potential causal relationships between FC and BN. Summary data from genome-wide association studies (GWAS) involving 2,564 individuals were analyzed to identify genetically predicted BN. Functional magnetic resonance imaging parameters and materials were sourced from the UK Biobank. The variables underwent independent component analysis processing by the database to generate the final GWAS dataset. Various methods, including MR Pleiotropy RESidual Sum and Outlier, MR Egger, and weighted median, were employed to detect heterogeneity and pleiotropy, with inverse variance weighting serving as the principal estimation method (P < 0.05). The FC imaging-derived phenotypes revealed that BN exerted a causal influence on the FC between large-scale networks, including the visual network, default mode network (DMN), frontoparietal network, somatosensory network (SSN), and ventral attention network. Additionally, BN had a causal impact on the within-network FC of both the DMN and SSN. The study provides evidence that BN leads to further changes in FC patterns within and between large-scale brain networks.

GWAS Meta-analysis of Kidney Function Traits in Japanese Populations.

Genetic epidemiological evidence for the kidney function traits in East Asian populations, including Japanese, remain still relatively unclarified. Especially, the number of genome-wide association studies (GWASs) for kidney traits reported still remains limited, and the sample size of each independent study is relatively small. Given the genetic variability between ancestries/ethnicities, implementation of GWAS with sufficiently large sample sizes in specific population of Japanese is considered meaningful. We conducted the GWAS meta-analyses of kidney traits by leveraging the GWAS summary data of the representative large genome cohort studies with about 200,000 Japanese participants (n = 202,406 for estimated glomerular filtration rate [eGFR] and n = 200,845 for serum creatinine [SCr]). In the present GWAS meta-analysis, we identified 110 loci with 169 variants significantly associated with eGFR (on chromosomes 1-13 and 15-22; P < 5 × 10-8), whereas we also identified 112 loci with 176 variants significantly associated with SCr (on chromosomes 1-22; P < 5 × 10-8), of which one locus (more than 1 Mb distant from known loci) with one variant (CD36 rs146148222 on chromosome 7) for SCr was considered as the truly novel finding. The present GWAS meta-analysis of the largest genome cohort studies in Japanese subjects provided some original genomic loci associated with kidney function, which may contribute to the possible development of personalized prevention of kidney diseases based on genomic information in the near future.

Am I attached? A patient-partnered approach to creating infographics about attachment to primary care in Ontario, Canada

BackgroundHaving a primary care provider is associated with better care experiences and lower care costs. In 2021, INSPIRE-PHC released Primary Care Data Reports - publicly available summaries of administrative billing data about how populations in each of Ontario’s 60 health teams use primary care services. Given the characterization of Canadian primary care systems as ‘in crisis’, publicly available data about primary care at the regional level presented a significant opportunity for knowledge mobilization. An understandable resource could ground the public conversation about primary care access in data. Recognizing the role that lived experience plays in ensuring the public understands research findings, a partnership between patient advisors, Ontario Health Team representatives, researchers, and trainees was established to co-produce public-facing infographics based on primary care data.MethodsEvidence-based guidelines for public health infographic creation and elements of transformative action research guided a six-meeting process to engage up to 14 patient advisors, three Ontario Health Team staff and two primary care trainees. Patient advisors were affiliated with a provincial patient-oriented primary health care research group or a Hamilton-based Ontario Health Team. Ninety-minute meetings were conducted virtually, and notes were shared with attendees to ensure they accurately reflected the conversation. Two consultations with Ontario Health Team-affiliated primary care providers provided direction and ensured project outputs aligned with local priorities.ResultsProject partners shared feedback on draft infographics, audience identification, priority elements from Primary Care Data Reports to include in the infographics, and aesthetic features (e.g., headings, colour scheme, charts). Project partners felt the most important metrics to convey to the public were those that simultaneously reinforced the benefits of primary care on individual health outcomes and health system costs.ConclusionsPatient engagement in research is becoming widespread, but co-developing knowledge products with patient and health system partners is less common. Our approach to engaging patients prevented both oversimplification and unnecessary complexity in a public-facing visual about attachment to primary care.

Trends in Medicare Utilization and Reimbursement of Tracheostomy From 2000 to 2022.

To analyze the utilization and reimbursement for tracheostomy. Retrospective Cross-Sectional Study. Centers for Medicare & Medicaid Services (CMS) Medicare Provider Utilization and Payment Data (2013 and 2021) and Part B Medicare Fee-For-Service National Summary Data (2000-2022). Utilization, payment, and specialty breakdown were analyzed for planned tracheostomy (Current Procedural Terminology [CPT] codes 31600, 31601, 31610) and emergency tracheostomy (CPT codes 31603, 31605). From 2000 to 2022, there was a 48.9% decrease (40,754-20,812) in number of planned tracheostomies and a 75.3% decrease (3277-811) in number of emergency tracheostomies, leading to an overall decrease of 51%. Similarly, there was a 59.3% inflation-adjusted decrease ($13.4-$5.5 million) in total reimbursement for planned tracheostomies and an 82.1% inflation-adjusted decrease ($1.1 million-$205 thousand) in total reimbursement for emergency tracheostomies. There was a 20.3% inflation-adjusted decrease ($329-$262) in reimbursement per planned tracheostomy and a 27.7% inflation-adjusted decrease ($349-$252) in reimbursement per emergency tracheostomy. In our sample of 280 high-volume tracheostomy providers in 2021 (28.2% otolaryngology, 28.2% general surgery, 14.6% thoracic surgery, 14.3% pulmonary disease, 6.4% critical care), the average provider performed 15.8 tracheostomies and was reimbursed $5362. Despite significant declines in tracheostomy utilization and reimbursement, understanding trends for these lifesaving procedures are critical for otolaryngologists and other providers in delivering high-quality care, and can be used by surgeons, hospital systems, and policymakers to guide future health care legislation.

Evaluation of Bayesian Linear Regression models for gene set prioritization in complex diseases.

Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the model's performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Comparative analyses with MAGMA (Multi-marker Analysis of GenoMic Annotation) demonstrate BLR's superior performance, especially in highly overlapped gene sets. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR model's ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits.

Associations of human blood metabolome with optic neurodegenerative diseases: a bi-directionally systematic mendelian randomization study

BackgroundMetabolic disruptions were observed in patients with optic neurodegenerative diseases (OND). However, evidence for the causal association between metabolites and OND is limited.MethodsTwo-sample Mendelian randomization (MR). Summary data for 128 blood metabolites was selected from three genome-wide association study (GWASs) involving 147,827 participants of European descent. GWASs Data for glaucoma (20906 cases and 391275 controls) and age-related macular degeneration (AMD, 9721 cases and 381339 controls) came from FinnGen consortium. A bi-directional MR was conducted to assess causality, and a Mediation MR was further applied to explore the indirect effect, a phenome-wide MR analysis was then performed to identify possible side-effects of the therapies.ResultsAll the results underwent correction for multiple testing and rigorous sensitivity analyses. We identified N-acetyl glycine, serine, uridine were linked to an elevated risk of glaucoma. 1-arachidonic-glycerol-phosphate-ethanolamine, 4-acetamido butanoate, o-methylascorbate, saturated fatty acids, monounsaturated fatty acids, VLDL cholesterol, serum total cholesterol, X-11,529 were linked to reduced risk of glaucoma. There were 4 metabolites linked to a reduced risk of AMD, including tryptophan betaine, 4-androsten-3beta-17beta-diol disulfate, apolipoprotein B, VLDL cholesterol. We discovered IOP, AS, T2D as glaucoma risk factors, while BMI, AS, GCIPL as AMD factors. And 6 metabolites showed associations with risk factors in the same direction as their associations with glaucoma/AMD. Phenome-wide MR indicated that selected metabolites had protective/adverse effects on other diseases.ConclusionsBy integrating genomics and metabolomics, this study supports new insights into the intricate mechanisms, and helps prevent and screen glaucoma and AMD.

Benchmark dose modeling for epidemiological dose-response assessment using case-control studies.

Following a previous article that focused on integrating epidemiological data from prospective cohort studies into toxicological risk assessment, this paper shifts the focus to case-control studies. Specifically, it utilizes the odds ratio (OR) as the main epidemiological measure, aligning it with the benchmark dose (BMD) methodology as the standard dose-response modeling approach to determine chemical toxicity values for regulatory risk assessment. A standardized BMD analysis framework has been established for toxicological data, including input data requirements, dose-response models, definitions of benchmark response, and consideration of model uncertainty. This framework has been enhanced by recent methods capable of handling both cohort and case-control studies using summary data that have been adjusted for confounders. The present study aims to investigate and compare the "effective count" based BMD modeling approach, merged with an algorithm used for converting odds ratio to relative risk in cohort studies with partial data information (i.e., the Wang algorithm), with the adjusted OR-based BMD analysis approach. The goal is to develop an adequate BMD modeling framework that can be generalized for analyzing published case-control study data. As in the previous study, these methods were applied to a database examining the association between bladder and lung cancer and inorganic arsenic exposure. The results indicate that estimated BMDs and BMDLs are relatively consistent across both methods. However, modeling adjusted OR values as continuous data for BMD estimation aligns better with established practices in toxicological BMD analysis, making it a more generalizable approach.

Association of depression with longitudinal changes in brain structure across the lifespan: A mendelian randomization study.

Understanding the impact of depression on brain aging benefits the prognosis of this disease and of the risk that other age-related brain disorders will develop in the same population. The aim of the present study was to explore the genetic effect of depression on longitudinal changes in brain structure throughout the lifespan using a Mendelian randomization approach. Summary data from a genome-wide association study of 195,321 to 377,277 participants in the FinnGen consortium were used to predict depression, anxiety disorders, mood disorders, and antidepressant use genetically. Data from 15,640 participants in the ENIGMA consortium were included to predict changes in 15 brain structures throughout the lifespan. The causal relationship between these depressive traits and the brain structure parameters was assessed by two-sample Mendelian randomization (including inverse-variance weighted). Sensitivity analyses were conducted for quality control. Depression slowed the decrease of cortical gray matter volume significantly throughout the lifespan (p = 0.001). Depression, anxiety, and mood disorders nominally decreased the rates of change of volume in the cerebellum gray matter, lateral ventricles, and cortical gray matter throughout the lifespan (p = 0.048, p = 0.021, p = 0.038, respectively). Antidepressants did not affect these rates of change significantly (p > 0.05). Sensitivity analyses confirmed the reliability of this study. Depression and its main symptoms have a slight effect on longitudinal changes in a few brain structures throughout the lifespan at the genetic level. These findings do not support the notion that depression affects macro-aging in the brain crucially.

Genetically Predicted Sleep Traits and Sensorineural Hearing Loss: A Mendelian Randomization Study.

Observational studies suggest a potential association between sleep characteristics, sensorineural hearing loss (SNHL), and sudden SNHL (SSNHL), but causal evidence is scarce. We sought to clarify this issue using two-sample Mendelian randomization analysis. The inverse-variance weighted (IVW) method was performed as primary analysis to assess bidirectional causal associations between sleep traits (chronotype, sleep duration, insomnia, daytime sleepiness, and snoring) and SNHL/SSNHL using publicly available Genome-Wide Association Studies summary data from two large consortia (UK Biobank and FinnGen). Sensitivity analyses, including Mendelian randomization (MR)-Egger, Mendelian randomization pleiotropy residual sum and outlier, weight median, Cochran's Q test, leave-one-out analysis, and potential pleiotropy analysis, were conducted to ensure robustness. IVW analysis found suggestive associations of morning chronotype (odds ratio [OR] = 1.08, 95% confidence interval [CI] = 1.01-1.16, p = 0.031) and daytime sleepiness (OR = 1.88, 95% CI = 1.24-2.87, p = 0.003) with SNHL onset. Additionally, morning chronotype was nominally associated with SSNHL onset using IVW method (OR = 1.37, 95% CI = 1.10-1.71, p = 0.006). However, there was no evidence for the causal effect of SNHL and SSNHL on different sleep traits (all p > 0.05). Sensitivity analysis showed that the results were stable. Within the MR limitations, morning chronotype and daytime sleepiness were underlying causal contributors to the burden of SNHL, indicating that optimal sleep might facilitate the prevention and development of SNHL. 3 Laryngoscope, 134:4723-4729, 2024.

Can early gut microbiota screening reduce the incidence of cognitive impairment? A Mendelian randomization study.

Background: Gastrointestinal symptoms are now detected early in the clinical course of many dementia patients, and studies of the microbiome-gut-brain axis have confirmed bidirectional interactions between the gut and the brain. However, the causal relationship between gut microbiota and cognitive impairment has not been fully established. Therefore, this study conducted a bidirectional Mendelian randomization study to elucidate the potential causal relationship of gut microbiota to cognitive impairment. Objective: Using Mendelian randomization to identify gut flora with a genetic causal effect on the development of cognitive impairment. Methods: This study utilized publicly available genome-wide association study summary data to perform MR analysis, with gut microbiota as the exposure and various cognitive function indicators as well as scores for Alzheimer's disease as outcomes. This study selected single nucleotide polymorphisms as instrumental variables based on p-values, F-statistics, and r2. Bidirectional Mendelian randomization was conducted using methods such as inverse variance weighted, MR-Egger, simple mode, and weighted mode to assess the causal relationship. Concurrently, this study carried out Cochran's Q test, MR-Egger intercept test, and leave-one-out analysis to identify potential heterogeneity and horizontal pleiotropy. Results: This study identified a total of 31 gut microbes that have a causal relationship with cognitive impairment, which include 1 phylum, 4 classes, 3 orders, 2 families, and 21 genera. Conclusions: This study unveiled specific gut microbiota associated with cognitive impairment, offering new insights and approaches for the prevention and treatment of cognitive impairment through gut microbiota such as Bifidobacterium and Ruminococcus gnavus group.

Associations between T-cell traits and narcolepsy type 1: new insights from a Mendelian randomization study

BackgroundNarcolepsy type 1 (NT1) is primarily caused by a malfunctioning immune system in which T-cells damage the hypothalamus. To elucidate the causal relationships between biomarkers in T-cells and NT1, we employed Mendelian randomization (MR) analysis.MethodsWe conducted a two-sample MR analysis utilizing genetically predicted T-cell traits to examine their effects on NT1. Genome-wide association study summary data were extracted from studies by Valeria (3,757 participants) for 211 T-cell traits, Ollila (6,073 cases and 84,856 controls) for NT1. The MR analysis was executed at two threshold levels. Inverse variance weighted, Wald ratio, weighted median, and MR-Egger regression methods were used for the MR analysis. Odds ratios (ORs) were calculated, and heterogeneity tests, as well as pleiotropy tests, were conducted.ResultsAfter Bonferroni correction at the significant level (p &amp;lt; 1.18 × 10−4), a higher ratio of naive CD4− CD8− T-cells was identified as a risk factor for NT1 (OR = 10.50; 95% CI: 6.98, 15.90, p = 3.89 ×10−29). Conversely, CD4 on HLA DR+ CD4+ T cells (mean fluorescence intensity, MFI) exhibited a negative correlation with NT1. At nominally significant levels (p &amp;lt; 0.05) for both threshold levels, HVEM (herpesvirus entry mediator) on naive CD8+ T cells (MFI) was suggested as a protective factor for NT1. Additionally, a higher ratio of CD25++ CD45RA− CD4 not regulatory T cells, CD127 on CD45RA− CD4 not regulatory T cells (MFI), CD127 on CD28+ CD4+ T cells (MFI), CD3 on HLA DR+ T cells (MFI), and CD3 on HLA DR+ CD4+ T cells (MFI) were suggested as risk factors for NT1.ConclusionThis study confirmed the causal effects of CD4+ and CD8+ T-cells on NT1 and found several novel T-cell-related characteristics.

Cost-effectiveness analysis of emergency department-based hepatitis C screening and linkage-to-care program

BackgroundIn the United States (US), hepatitis C virus (HCV) screening is not covered by payers in settings outside of primary care. A non-traditional, emergency department (ED)-based HCV screening program can be cost-effective and identify infection in vulnerable populations with a high HCV risk. This study examined the long-term cost-effectiveness of routine HCV screening and linkage-to-care for high-risk patients in the ED from the payer’s perspective.MethodsThe University of Illinois Hospital and Health Sciences System (UIH) implemented Project HEAL (HIV & HCV Screening, Education, Awareness, Linkage-to-Care). Under this initiative, patients who presented to the ED received opt-out HCV screening if they were at high risk for HCV infection (birth cohort between 1945 and 1964, persons who inject drugs, and HIV infection) with subsequent linkage-to-care if infected. Using the summary data from Project HEAL, a hybrid decision-analytic Markov model was developed based on the HCV screening procedure in the ED and the natural history of HCV. A 30-year time horizon and 1-year cycle length were used. All patients who received the ED-based HCV screening were referred for treatment with direct-acting antiviral (DAA) regardless of their fibrosis stage.ResultsWhen unscreened/untreated patients received DAA treatment at F1, F2, F3, and compensated cirrhosis stages, the incremental cost-effectiveness ratio (ICER) ranged from $6,084 to $77,063 per quality-adjusted life year (QALY) gained. When unscreened/untreated patients received DAA treatment at the decompensated cirrhosis stage, no HCV screening was dominated.ConclusionED-based HCV screening and linkage-to-care was cost-effective at the willingness-to-pay (WTP) threshold of $100,000/QALY in all scenarios. A reduction in infected persons in the community may provide additional benefits not evaluated in this study.

Causal Associations Between the Gut Microbiota and Hypertension-Related Traits Through Mendelian Randomization: A Cross-Sectional Cohort Study.

Previous studies have suggested a link between the gut microbiome and hypertension-related traits like blood pressure. However, these reports are often limited by weak causal evidence. This study investigates the potential causal association between gut microbiota and hypertension-related traits using Mendelian randomization with summary data from genome-wide association studies. The inverse-variance weighted method revealed that the Clostridium innocuum group (Odds ratio [OR]: 1.0047, 95% confidence interval [CI]: 1.0004-1.0090, p=0.0336), Eubacterium fissicatena group (OR: 1.0047, 95% CI: 1.0005-1.0088, p=0.0266), Lachnospiraceae FCS020 group (OR: 1.0063, 95% CI: 1.0004-1.0122, p=0.0361), and Olsenella (OR: 1.0044, 95% CI: 1.0001-1.0088, p=0.0430) were associated with an increased risk of hypertension. Conversely, Flavonifractor (OR: 0.9901, 95% CI: 0.9821-0.9982, p=0.0166), Parabacteroides (OR: 0.9874, 95% CI: 0.9776-0.9972, p=0.0121), and Senegalimassilia (OR: 0.9907, 95% CI: 0.9842-0.9974, p=0.0063) were associated with a decreased risk of hypertension. External validation with the Guangdong Gut Microbiome Project confirmed a negative correlation between Parabacteroides and hypertension, potentially through metabolic pathways. These findings provide further evidence supporting the hypothesis that microbes and their metabolites play a role in blood pressure regulation.

Narrowing socioeconomic inequalities in coronary heart disease: insight from Mendelian randomization

Abstract Background Socioeconomic inequalities in coronary heart disease (CHD) represent a pressing global public health concern. However, which dimensions of socioeconomic status (SES) affect CHD and the potential modifiable mediators remain elusive. Methods We performed two-sample Mendelian randomization (MR) analyses based on summary data from large-scale genome-wide association studies in populations of European ancestry. Specifically, by applying bidirectional univariable MR (UVMR) and multivariable MR (MVMR), we investigated the causal relationships of education, occupation, income, Townsend deprivation index (TDI) with CHD and the independent effects of these SES indicators. We further employed a two-step MR to comprehensively profile the mediating effects of 23 behavioral, 3 psychological and 5 biological factors in the relationships. Results An independent causal association was observed between genetically predicated education and CHD, with a 30% lower risk of CHD (OR: 0.70; 95% CI: 0.53-0.93) for each 4.2-year of schooling. Each one-s.d. higher household income was causally, but not independently, associated with a 39% lower risk of CHD (OR: 0.61; 95% CI: 0.49-0.77). No significant causal association was suggested for occupation and TDI with CHD, and no reverse causal effect was observed for CHD on the SES indicators. Of 31 candidate mediators, nine factors mediated the effect of education on CHD significantly, with an individual mediation proportion of 33.5% for smoking, 29.2% for sedentary behaviour, 14.4% for blood lipids, 13.4% for BMI, 11.7% for systolic blood pressure, 9.5% for diastolic blood pressure, 7.8% for well-being spectrum, 6.4% for depression and 2.8% for blood glucose. Conclusions This study highlights that education was the primary SES indicator affecting CHD with considerable mediation by a number of modifiable risk factors. These findings inform policies to mitigate disparities of CHD risk attributable to socioeconomic inequalities. Key messages • Education can be prioritized over other common SES indicators as an essential strategy to reduce the burden of CHD. • Interventions for narrowing socioeconomic inequalities in CHD could focus on more easily modifiable mediating factors.

Statistical methods leveraging the hierarchical structure of adverse events for signal detection in clinical trials: a scoping review of the methodological literature

BackgroundIn randomised controlled trials with efficacy-related primary outcomes, adverse events are collected to monitor potential intervention harms. The analysis of adverse event data is challenging, due to the complex nature of the data and the large number of unprespecified outcomes. This is compounded by a lack of guidance on best analysis approaches, resulting in widespread inadequate practices and the use of overly simplistic methods; leading to sub-optimal exploitation of these rich datasets. To address the complexities of adverse events analysis, statistical methods are proposed that leverage existing structures within the data, for instance by considering groupings of adverse events based on biological or clinical relationships.MethodsWe conducted a methodological scoping review of the literature to identify all existing methods using structures within the data to detect signals for adverse reactions in a trial. Embase, MEDLINE, Scopus and Web of Science databases were systematically searched. We reviewed the analysis approaches of each method, extracted methodological characteristics and constructed a narrative summary of the findings.ResultsWe identified 18 different methods from 14 sources. These were categorised as either Bayesian approaches (n=11), which flagged events based on posterior estimates of treatment effects, or error controlling procedures (n=7), which flagged events based on adjusted p-values while controlling for some type of error rate. We identified 5 defining methodological characteristics: the type of outcomes considered (e.g. binary outcomes), the nature of the data (e.g. summary data), the timing of the analysis (e.g. final analysis), the restrictions on the events considered (e.g. rare events) and the grouping systems used.ConclusionsWe found a large number of analysis methods that use the group structures of adverse events. Continuous methodological developments in this area highlight the growing awareness that better practices are needed. The use of more adequate analysis methods could help trialists obtain a better picture of the safety-risk profile of an intervention. The results of this review can be used by statisticians to better understand the current methodological landscape and identify suitable methods for data analysis - although further research is needed to determine which methods are best suited and create adequate recommendations.