Two-step Analysis Research Articles

Dissecting the shared genetic architecture between nonalcoholic fatty liver disease and type 2 diabetes.

Observational studies have reported a bidirectional correlation between nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes (T2D), but the shared genetic basis between the two conditions remains unclear. Using genome-wide association study (GWAS) summary data from European-ancestry populations, we examined the cross-trait genetic correlation and identified genomic overlaps and shared risk loci. We employed a latent causal variable model and Mendelian randomization (MR) analysis to infer causal relationships. Colocalization analysis and conditional/conjunctional false discovery rate (condFDR/conjFDR) were used to identify genomic overlaps and shared risk loci. Two-step MR analysis was utilized to identify potential mediators. We observed a strong positive genomic correlation between NAFLD and T2D (rg = 0.652, P = 5.67 × 10-6) and identified tissue-specific transcriptomic correlations in the pancreas, liver, skeletal muscle, subcutaneous adipose, and blood. Genetic enrichment was observed in NAFLD conditional on associations with T2D and vice versa, indicating significant polygenic overlaps. We found robust evidence for the causal effect of NAFLD on T2D, particularly insulin-related T2D, rather than vice versa. Colocalization analysis identified shared genomic regions between NAFLD and T2D, including GCKR, FTO, MAU2-TM6SF2, and PNPLA3-SAMM50. High-density lipoprotein cholesterol and insulin were partly mediated the association between NAFLD and T2D. These findings unveil a close genetic link between NAFLD and T2D, shedding light on the biological mechanisms connecting NAFLD progression to T2D.

INFLUENCE OF ANOMALOUS VALUES ON THE ACCURACY OF FORECASTING LOSSES IN DISTRIBUTION NETWORKS

The relevance of the study is determined by modern trends in managing the operation modes of distribution electrical networks using Smart Grid technologies, as well as the need to reduce the costs incurred by distribution system operators for purchasing electricity. Accurate load forecasting results at network nodes for different forecasting horizons are crucial for this purpose. Sudden changes in network topology can increase the errors in loss forecasts as a single time series, negatively impacting network management efficiency and increasing the costs of electricity procurement to cover losses. The study proposes using forecasting methods based on artificial neural networks for the calculation and prediction of electricity losses, along with a comparison of these methods. The calculations were performed using data from one of Ukraine's distribution system operators, and the test electrical network was adapted based on the CIGRE scheme for modeling electricity losses. Since the nodal load data contained gaps and anomalies, a two-step data analysis algorithm was employed using the DBSCAN clustering method for detection and correction. As a result of loss calculations based on cleaned data, the error was reduced threefold compared to calculations based on load factors. Applying data analysis methods and forecasting methods based on artificial neural networks significantly improves the accuracy of loss calculations and minimizes errors.

Causality between gut microbiota, immune cells, and breast cancer: Mendelian randomization analysis.

The association between gut microbiota (GM) and breast cancer (BC) has been studied. Nevertheless, the causal relationship between them and the potential mediating factors have not been clearly defined. Therefore, in this study, Mendelian randomization analysis (MR) was employed to explore the causal relationship between 473 GM and BC, as well as the mediating effect of potential immune cells. In this investigation, we availed ourselves of the publicly accessible summary statistics from the genome-wide association study to undertake two-sample and reverse Mendelian randomization analyses on GM and BC, with the intention of clarifying the causal association between GM and BC. Subsequently, through the application of the two-step Mendelian randomization analysis, it was revealed that the relationship between GM and BC was mediated by immune cells. The stability of the research outcomes was verified via sensitivity analysis. Mendelian randomization analysis elucidated the protective impacts of 8 genera on BC (such as Phylum Actinobacteriota, Species Bacteroides A plebeius A, Species Bifidobacterium adolescentis, Species CAG-841 sp002479075, Family Fibrobacteraceae, Order Fibrobacterales, Class Fibrobacteria, and Species Phascolarctobacterium sp003150755). Additionally, there are 23 immune cell traits related to BC. Our research findings showed that the species Megamonas funiformis was associated with an increased risk of BC, and 11.20% of this effect was mediated by CD38 on IgD+ CD24-. Likewise, HLA DR on CD33br HLA DR+ CD14- mediated the causal relationship between Species Prevotellamassilia and BC, having a mediating ratio of 7.89%. This study clarifies a potential causal relationship between GM, immune cells, and BC and provides genetic evidence for this causal connection. It offers research directions for the subsequent prevention and treatment of BC through the interaction between GM and immune cells, and provides a reference for future mechanistic and clinical studies in this field.

Genetically predicted serum metabolites mediate the association between inflammatory proteins and polycystic ovary syndrome: a Mendelian randomization study.

To investigate the association between polycystic ovary syndrome (PCOS) and inflammatory proteins, and to identify and quantify the role of serum metabolites as potential mediators. Utilizing summary-level data from a genome-wide association study (GWAS), we conducted a two-sample Mendelian Randomization (MR) analysis, a genetic approach that uses genetic variants as instrumental variables to assess the causal relationships between risk factors and outcomes. This analysis involved genetically predicted PCOS (1,639 cases and 218,970 controls) and inflammatory proteins (14,824 participants of primarily European descent). Additionally, a two-step MR analysis was performed to quantify the proportion of the effect of serum metabolites-mediated inflammatory proteins on PCOS. The Inverse Variance Weighted (IVW) method, a statistical technique used within MR to combine data from multiple genetic variants, was used to estimate the causal effects. The IVW method revealed that the inflammatory proteins IFN-γ (p-value = 0.037, OR = 1.396, 95% CI = 1.020-1.910) and CCL7 (p-value = 0.033, OR = 1.294, 95% CI = 1.021-1.641) were associated with an increased risk of PCOS, while IL-6 (p-value = 0.015, OR = 0.678, 95% CI = 0.495-0.929) and MMP-10 (p-value = 0.025, OR = 0.753, 95% CI = 0.587-0.967) were associated with a decreased risk. No significant evidence suggested an effect of genetically predicted PCOS on inflammatory proteins. The serum metabolite X-11444 was found to mediate 5.44% (95% CI: 10.8-0.0383%) of the effect of MMP-10 on PCOS. This study not only introduces novel causal associations between inflammatory proteins and PCOS but also highlights the mediating role of serum metabolites in these associations. By applying MR, we were able to minimize confounding and reverse causality, offering robust insights into the biological mechanisms underlying PCOS. These findings advance the understanding of PCOS pathogenesis, particularly in relation to inflammatory pathways and serum metabolite interactions, and suggest potential therapeutic targets that could inform future clinical interventions aimed at mitigating inflammation-related PCOS risks.

Analysis of vibration responses in a large airport ground transportation centre caused by maglev and subway trains

This study aims to assess the vibration response of the ground transportation centre (GTC) at an airport integrated transportation hub during the starting and braking processes of underground maglev and subway trains. In this work, we considered the actual motion states of trains entering and exiting the GTC and calculated the traction characteristics of maglev and subway trains. On this basis, the vertical coupling vibration models for a medium- and low-speed maglev train-guideway and a subway train-floating slab track considering variable-speed motion, as well as the vehicle longitudinal motion models, were established, and the vertical and longitudinal fastener forces of the tracks were obtained accordingly. Finally, based on the two-step analysis method, the vertical and longitudinal fastener forces were simultaneously applied to the track–GTC–soil finite element model to evaluate the structural vibrations induced by maglev and subway trains. The results show that the amplitude of the vertical fastener force is an order of magnitude greater than that of the longitudinal fastener force, and the amplitude of the vertical fastener force caused by subway trains is greater than that caused by maglev trains. The starting process of both maglev trains and subway trains results in a greater vertical vibration response of the GTC than the braking process, with the impact of subway trains being greater than that of maglev trains. Within the same area, the vertical vibration level observed at structural column observation points is lower than that at slab observation points. When maglev trains and subway trains start simultaneously, the vibration level response at some points exceeds the specified limit of 75dB.

Exploring the global impact of obesity and diet on dementia burden: the role of national policies and sex differences.

Obesity is a significant modifiable risk factor for dementia. This study aims to quantify the global impact of obesity on dementia burden and examine how national strategies for managing overweight/obesity and dietary factors influence dementia prevalence and mortality, with a focus on sex-specific differences. We used data from the Global Burden of Disease (GBD) and World Health Organization (WHO) to evaluate the association between obesity age-standardized prevalence rate (ASPR) and dementia age-standardized mortality rate (ASMR) and ASPR across 161 countries. A two-step multivariate analysis adjusted for socioeconomic and lifestyle factors was performed. Temporal trends in dementia were analyzed based on the presence of national obesity management strategies and varying dietary scores. A 1% increase in national obesity prevalence was associated with a 0.36% increase in dementia mortality (OR: 1.0036; 95% CI: 1.0028-1.0045) in males and 0.12% in females (OR: 1.0012; 95% CI: 1.0007-1.0018). A 1% increase in national obesity ASPR was associated with an increase in ASPR of dementia by 0.26% for males (OR: 1.0026, 95% CI: 1.0024-1.0028) and 0.05% for females (OR: 1.0005, 95% CI: 1.0004-1.0006). Males exhibited a higher susceptibility to obesity-related dementia. Countries with national obesity management strategies showed a significantly greater reduction in dementia mortality, particularly among females (P = 0.025). Higher dietary scores were associated with a more significant decrease in dementia prevalence across both sexes. Rising obesity prevalence is linked to increased dementia burden globally, with males being more vulnerable to this relationship. National management of overweight/obesity and healthier dietary habits may help mitigate the dementia burden, emphasizing the need for integrated public health interventions.

The role of cerebrospinal fluid metabolites in mediating the impact of lipids on Late-Onset Alzheimer’s Disease: a two-step mendelian randomization analysis

BackgroundAlthough research has indicated correlations between lipids, cerebrospinal fluid (CSF) metabolites, and Late-Onset Alzheimer’s Disease (LOAD), the specific causal relationships among these elements, as well as the roles and mechanisms of the cerebrospinal fluid metabolites, remain unclear.MethodsStatistical datasets derived from Genome-Wide Association Studies (GWAS) were utilized to assess the bidirectional causal relationships between lipids and LOAD. Subsequently, genetic variants associated with CSF metabolites and established lipids underwent a two-step Mendelian randomization (MR) analysis to explore potential mediators and analyze mediation effects. Sensitivity analyses were employed to assess the robustness of the detection systems.ResultsGenetically predicted cholesterol (IVW OR = 0.989; 95% CI 0.982–0.996) was found to reduce the risk of LOAD, whereas Phosphatidylcholine (PC) (18:1_0:0) (IVW OR = 1.015; 95% CI 1.005–1.025) posed a risk factor. The potential mediator, CSF metabolite N-acetylneuraminate (NeuAC), was identified with a mediation proportion of 21.02% (3.25%, 45.50%). No pleiotropy or heterogeneity was detected across MR analyses.ConclusionsThe findings underscore the pivotal role of CSF metabolomics in elucidating the lipid-mediated pathogenesis of LOAD, highlighting potential diagnostic and preventative biomarkers.

Causal relationship between diabetes mellitus and lung cancer: a two-sample Mendelian randomization and mediation analysis.

Diabetes mellitus (DM) is the common comorbidity with lung cancer (LC), and metabolic disorders have been identified as significant contributors to the pathogenesis of both DM and LC. The causality between diabetes mellitus and lung cancer is still controversial. Hence, the causal effects of DM on the risk of LC was systemically investigated, and the mediating role of blood metabolites in this relationship was further explored. This study utilized a comprehensive Mendelian randomization (MR) analysis to investigate the association between diabetes mellitus and lung cancer. The inverse variance weighted method was employed as the principle approach. MR Egger and weighted median were complementary calculations for MR assessment. A two-step MR analysis was performed to evaluate the mediating effects of blood metabolites as potential intermediate factors. Simultaneously, sensitivity analyses were performed to confirm the lack of horizontal pleiotropy and heterogeneity. The two-sample MR analysis illustrated the overall effect of type 1 diabetes mellitus (T1DM) on lung squamous cell carcinoma (LUSC) (OR: 1.040, 95% CI: 1.010-1.072, p = 0.009). No causal connection was found between T2DM and the subtypes of lung cancer. Two-step MR identified two candidate mediators partially mediating the total effect of T1DM on LUSC, including glutamine conjugate of C6H10O2 levels (17.22%) and 2-hydroxyoctanoate levels (5.85%). Our findings supported a potentially causal effect of T1DM against LUSC, and shed light on the importance of metabolites as risk factors in understanding this relationship.

Immune traits in combination with inflammatory proteins revealing the pathogenesis of autoimmune liver diseases: A Mendelian randomization study

BackgroundPrior observational research has shown relationships between immune cells, inflammatory proteins, and autoimmune liver diseases (AILD), but their causal associations remain controversial. Therefore, we aimed to clarify the causal association between them. MethodsWe carried out a comprehensive Mendelian randomization (MR) analysis to clarify causal associations between 731 immune traits, 91 circulating inflammatory proteins, and AILD, including primary biliary cholangitis (PBC), primary sclerosing cholangitis (PSC), and autoimmune hepatitis (AIH). A two-step MR analysis was used to explore the mediating role of circulating inflammatory proteins. Additionally, we performed sensitivity analyses to evaluate the robustness of the results. ResultsCD27 on IgD+CD24+B cell, CD27 on IgD−CD38dimB cell, CD27 on unswitched memory B cell, CD27 on switched memory B cell, and CD27 on CD24+CD27+B cell were risk factors for PBC. However, we detected protective effects of CD25 on IgD−CD27−B cell against PBC and CD28 on resting CD4+Treg cell against PSC. Circulating CD40, Interleukin-33, and Delta and Notch-like epidermal growth factor-related receptor were protective factors for PBC. Furthermore, CD40 mediated the association between immune traits and PBC, with the mediated proportions ranging from 18.3 % to 35.4 %. Tumor necrosis factor superfamily member 12 was identified as a risk factor for PSC, and monocyte chemotactic protein 3 was identified as a protective factor for PSC. Additionally, PBC and PSC had effects on eleven immune traits, which are suggested to be the consequences of them. We found no causal association between immune traits, circulating inflammatory proteins, and AIH. Sensitivity analyses demonstrated our results were robust. ConclusionsOur results demonstrate the causal roles of immune traits and inflammatory proteins in PBC and PSC, which reveals their pathogenesis. It is necessary to investigate the specific mechanism by which immune cells and inflammatory proteins affecting the occurrence of AILD.

Relationship between immune cell traits, circulating inflammatory cytokines, and the risk of incisional hernia after gastric surgery.

The systemic and local inflammatory response in patients after surgical operation is closely related to the quality of the wound healing. Low-quality wound healing and defects in the suture technique lead to the occurrence of incisional hernia (IH). However, the causal relationship between human circulating inflammatory cytokines, immune cell traits, and the risk of IH remains unclear. We used summary data from genome-wide association studies to assess the causal relationship between 91 types of circulating inflammatory factors, 731 types of circulating immune cell traits, and the risk of IH. The outcome dataset was obtained from FinnGen, including 6,336 patients with IH and 232,612 controls. We performed Mendelian Randomization (MR) analysis to identify their causal relationship and immune cell phenotypes upstream of inflammatory factors. Inverse variance weighting is considered to be the main analysis method. Among the identified cytokines, TNF-related activation-induced cytokine levels were associated with a lower risk of IH (OR: 0.89; 95% CI: 0.82-0.96; P = 0.003). In contrast, interleukin-5 levels were associated with an increased risk of IH (OR: 1.18; 95% CI: 1.06-1.31; P = 0.003). Additionally, a significant causal relationship was found between four immune cell traits and the risk of IH (P < 0.01). Through two-step MR analysis, we determined that interleukin-5 levels mediate the causal relationship between the relative count of CD25hi % CD4 + in Treg cells and the higher risk of IH. This study found a causal relationship between specific inflammatory cytokines, immune cell traits, and risk of IH. These results can help surgeons predict the risk of IH and take preventive measures.

Exploring the genetic associations and causal relationships between antibody responses, immune cells, and various types of breast cancer.

There may be potential associations between various pathogens, antibody immune responses, and breast cancer (BC), but the specific mechanisms and causal relationships remain unclear. First, multiple Mendelian randomization (MR) methods were used for univariable MR analysis to explore potential causal relationships between 34 antibody immune responses (related to 12 pathogens), 46 antibody immune responses (related to 13 pathogens), antibody responses post-COVID-19 vaccination, 731 immune cell types, and various BC subtypes (including overall BC, ER-positive, ER-negative, Luminal A, Luminal B, Luminal B HER2-negative, HER2-positive, and triple-negative BC). The primary results were then subjected to reverse MR analysis, heterogeneity testing using Cochran's Q, and horizontal pleiotropy testing. Robust findings were further used to design mediation pathways involving antibody immune responses, immune cells, and BC. After adjusting the effect estimates using multivariable MR (MVMR), a two-step mediation analysis was conducted to explore mediation pathways and mediation proportions. Finally, linkage disequilibrium score regression (LDSC) was applied to analyze the genetic correlation between phenotypes along mediation pathways, and cross-phenotype association analysis (CPASSOC) was performed to identify pleiotropic SNPs among three phenotypes along these pathways. Bayesian colocalization tests were conducted on pleiotropic SNPs using the multiple-trait-coloc (moloc). We identified potential causal relationships between 15 antibody immune responses to 8 pathogens (Hepatitis B virus, Herpes Simplex Virus 2, Human Herpesvirus 6, Polyomavirus 2, BK polyomavirus, Cytomegalovirus, Helicobacter pylori, Chlamydia trachomatis), 250 immune cell phenotypes, and various BC subtypes. MVMR-adjusted mediation analysis revealed four potential mediation pathways. LDSC results showed no significant genetic correlation between phenotypes pairwise. CPASSOC analysis identified two potential mediation pathways with common pleiotropic SNPs (rs12121677, rs281378, rs2894250). However, none of these SNPs passed the Bayesian colocalization test by moloc. These results excluded horizontal pleiotropy, stabilizing MR analysis results. This study utilized MR methods to analyze potential causal relationships between various antibody immune responses, immune cell types, and BC subtypes, identifying four potential regulatory mediation pathways. The findings of this study offer potential targets and research directions for virus-related and immunotherapy-related studies, providing a certain level of theoretical support. However, limitations such as GWAS sample size constraints and unclear specific pathophysiological mechanisms need further improvement and validation in future studies.

Genetically predicted blood metabolites mediate the association between circulating immune cells and severe COVID-19: A Mendelian randomization study.

Investigating the causal relationship between circulating immune cells, blood metabolites, and severe COVID-19 and revealing the role of blood metabolite-mediated circulating immune cells in disease onset and progression. Genetic variation data of 731 circulating immune cells, 1400 blood metabolites, and severe COVID-19 from genome-wide association study open-access database (https://gwas.mrcieu.ac.uk) were used as instrumental variables for bidirectional and two-step Mendelian randomization analysis. The study identified 11 circulating immune cells with unidirectional causality to severe COVID-19. Two-step Mendelian randomization analysis showed 10 blood metabolites were causally associated with severe COVID-19, and blood Myristate and Citrulline to phosphate ratio mediated the association of circulating effector memory double negative % DN and CD8dim natural killer T cell % T cells, respectively, with severe COVID-19 (Myristate mediated effect ratio was 10.20%, P = .011; Citrulline to phosphate ratio mediated effect ratio was -9.21%, P = .017). This study provides genetic evidence assessing the causal relationship between circulating immune cells, blood metabolites, and severe COVID-19, elucidates the role of blood metabolite-mediated circulating immune cells in severe COVID-19 development, and offers new insights into severe COVID-19 etiology and related preventive and targeted therapeutic strategies.

Computational Identification of Milk Trait Regulation Through Transcription Factor Cooperation in Murciano-Granadina Goats.

The Murciano-Granadina goat (MUG) is a renowned dairy breed, known for its adaptability and resilience, as well as for its exceptional milk traits characterized by high protein and fat content, along with low somatic cell counts. These traits are governed by complex biological processes, crucial in shaping phenotypic diversity. Thus, it is imperative to explore the factors regulating milk production and lactation for this breed. In this study, we investigated the genetic architecture of seven milk traits in MUGs, employing a two-step computational analysis to examine genotype-phenotype associations. Initially, a random forest algorithm identified the relative importance of each single-nucleotide polymorphism (SNP) in determining the traits of interest. The second step applied an information theory-based approach to exploring the complex genetic architecture of quantitative milk traits, focusing on epistatic interactions that may have been overlooked in the first step. These approaches allowed us to identify an almost distinct set of candidate genes for each trait. In contrast, by analyzing the promoter regions of these genes, we revealed common regulatory networks among the milk traits under study. These findings are crucial for understanding the molecular mechanisms underlying gene regulation, and they highlight the pivotal role of transcription factors (TFs) and their preferential interactions in the development of these traits. Notably, TFs such as DBP, HAND1E47, HOXA4, PPARA, and THAP1 were consistently identified for all traits, highlighting their important roles in immunity within the mammary gland and milk production during lactation.

Classifiability Analysis of Spectroscopic Profiling Datasets in Food Safety-related Discriminative Tasks

Discriminative tasks, i.e., the identification of different food materials, brands, and origins, have become an essential part of food safety control. In recent years, spectroscopic profiling combined with machine learning is becoming popular for food-related discriminative tasks, but finding an appropriate classification model can be challenging. Compared to the current “trial-and-error” practice, this paper proposes a dedicated two-step classifiability analysis framework to address this issue. The first step collects more than 90 diversified metrics to measure the dataset separability from different perspectives. The second step synthesizes these metrics into a quantitative score using meta-learner and decomposition-based strategies. Finally, two Raman spectroscopic profiling case studies were conducted to validate the method, demonstrating higher scores for the easily separable liquor dataset (around 1.0) compared to the more challenging table salt dataset (<0.5). This score can guide researchers to determine the required model complexity and assess the adequacy of the current physio-chemical profiling instrument. We expected the classifiability analysis framework proposed in this research to be generalized to a wide range of machine learning applications within the realm of food, where data-driven classification or discriminative tasks are involved.

Brain structures as potential mediators of the causal effect of COVID 19 on migraine risk

Migraine is a common neurological disorder observed after coronavirus disease 2019 (COVID 19) infection. However, the intricate relationship between COVID 19 and migraine, particularly the potential mediating role of brain imaging-derived phenotypes (BIPs), remains unclear. This study used linkage disequilibrium score regression (LDSC), a bidirectional two-sample Mendelian randomization (MR) approach, and two-step MR analysis to investigate potential causal links. The robustness of the MR findings was corroborated through generalized summary-data-based Mendelian randomization (GSMR) and MR-Steiger methods. The results of the LDSC analysis revealed that the genetic correlation coefficient between COVID 19 traits and migraine was 0.0277 for infection (P = 0.0051), 0.1690 for hospitalization (P = 0.0016), and 0.1147 for severity (P = 0.0330). The genetic correlation coefficients between COVID 19 infection, hospitalization, severity and migraine and migraine with aura (MA) were 0.2654 (P = 0.0012), 0.2065 (P = 0.0043), and 0.1537 (P = 0.0230), respectively. Two-sample MR analysis revealed a significant causal association of COVID 19 infection (odds ratio [OR] 1.2502, P = 0.0083; OR 1.4956, P = 0.0084), hospitalization (OR 1.0689, P = 0.0138; OR 1.0919, P = 0.0208), and severity (OR 1.0644, P = 0.0072; OR 1.0844, P = 0.0098) with increased risk of migraine and migraine with aura (MA). Cortical thickness (CT), total surface area (TSA), and fractional anisotropy (FA) were identified as BIP intermediaries in the risk trajectory from COVID 19 to migraine. The TSA exhibited a more pronounced mediating effect than did CT. This study revealed that genetically predicted COVID 19 is associated with an increased risk of migraine and MA, and BIPs act as potential mediators of these causal relationships, offering insights into the neurobiological underpinnings of migraine in the context of COVID 19.

Abstract 4123509: The Intersection of Race and Ethnicity, Social Determinants of Health, and Hypertension Prevalence: A Community-Level Analysis

Background: Despite being one of the most diverse cities in the United States, Chicago is also notably segregated. The distinct separation among its 77 communities provides an opportunity to examine racial and ethnic segregation and the impact of social determinants of health (SDOH) on health outcomes. Research Question: How do race/ethnicity and SDOH influence hypertension (HTN) prevalence? Aim: To examine the relationships between race/ethnicity, SDOH, and HTN prevalence at the community level. Methods: This cross-sectional study used data from the Chicago Health Atlas collected between 2021 and 2022. The sample included data from all 77 Chicago communities and 2,738,956 of its residents. Two-step latent profile analyses were performed to classify each of the community according to the predominant race/ethnicity of its residents (Asian, Black, Hispanic, or White) and SDOH level (low, moderate, high), which was defined as how favorable the physical and social conditions are within the community and determined using 14 indicators (Table 1). Kruskal-Wallis tests were used to compare the prevalence of HTN among the communities by predominant race/ethnicity and SDOH level. Results: Among the 77 communities, 28 were predominantly Black, 27 were predominantly White, 21 were predominantly Hispanic, and 1 was predominantly Asian. HTN prevalence was highest in predominantly Black communities (39.9%), then the Asian community (35.8%), Hispanic communities (28.8%), and White communities (25.2%), p &lt;.001. Based on the 14 SDOH indicators, 26 of the communities were classified as low SDOH (unfavorable conditions), 25 as moderate SDOH (moderately favorable), and 26 as high SDOH (favorable). HTN prevalence was highest in low SDOH communities (36.3%), followed by moderate SDOH communities (31.4%), and lowest in high SDOH communities (27.2%), p =.003. Conclusions: The findings highlight significant disparities in HTN prevalence across Chicago's communities, with higher rates observed in predominantly Black and low SDOH areas. These results underscore the need for targeted public health interventions to address the intersection of racial/ethnic segregation and SDOH.

Patient-related characteristics or local tradition: what predicts the admission to a locked ward or the use of coercive measures in psychiatric inpatient treatment?

Prior research shows that locked doors and coercive measures are not only applied due to safety concerns, but also due to the specific local tradition of an institution. We examined the association of the use of coercive measures and the admission to a locked ward with person-related characteristics compared to the admission to a specific clinic. In this 15-year, naturalistic observational study, we examined 230,684 admissions to 14 German psychiatric inpatient clinics from Jan 1, 1998, to Dec 31, 2012. To analyze the degree to which admission to a locked ward and coercive measures (received vs. not received) were connected with person- and clinic-specific factors, two-step logistic regression analyses were applied. 27% of the variance of the admission to a locked ward were explained by person-related characteristics (Nagelkerke r2 = 0.269). By adding the clinic the person was admitted to, the explained variance increased by 15% (Nagelkerke r2 = 0.418). 36% of the variance of the use of coercive measures were explained by person-related characteristics (Nagelkerke r2 = 0.364). By adding the clinic the person was admitted to, the explained variance increased by 4% (Nagelkerke r2 = 0.400). The local tradition of a psychiatric clinic seems to play a more prominent role for the decision to admit a person to a locked ward than for the decision to use coercive measures. Clinicians should be made aware of the connection of local traditions with clinical pathways in acute psychiatry to avoid unnecessary admissions to locked wards.

A two-step, two-sample Mendelian randomization analysis investigating the interplay between gut microbiota, immune cells, and melanoma skin cancer.

This study aims to rigorously explore the potential causal relationships among gut microbiota (GM), immune cells, and melanoma skin cancer among participants from Europe, where this disease exhibits significant prevalence and profound societal impact. Using the genome-wide association analysis database, a double-sample Mendelian randomization (MR) analysis was drawn upon to investigate GM, immune cells, and melanoma skin cancer. The inverse variance weighted approach was applied to estimate the causal connections among these variables. A two-step MR analysis was employed to quantitatively gauge the impact of immune cells mediated GM on melanoma skin cancer. To address potential sources of bias, such as pleiotropy and heterogeneity, multiple analytical techniques were integrated. The MR analysis pinpointed 6 GM taxa related to either an augmented or declined risk of late-stage melanoma skin cancer. In the same vein, 32 immune cell phenotypes were noticed as correlates with modified risk of melanoma skin cancer. Our study also implies that the probable association between GM and melanoma could be facilitated by 5 immune cell phenotypes. The findings of our study underline certain GM taxa and immune cells as potential influencers on the onset and development of melanoma skin cancer. Importantly, our results spotlight 5 immune cell phenotypes as potential agents mediating this association.

Association between gut microbiota, plasma metabolites, and ovarian cancer: A Mendelian randomization study.

Numerous studies have demonstrated a correlation between alterations in gut microbiota (GM) and levels of body metabolites in ovarian cancer (OC). However, the specific causal relationships underlying these associations remain unclear. This study utilized summary statistics of GM from the MiBioGen consortium, along with an unprecedented dataset comprising 1091 blood metabolites and 309 metabolite ratios from the UK Biobank, in conjunction with OC data from the FinnGen Consortium R9 release. We conducted bidirectional Mendelian randomization (MR) analyses to investigate the causal relationships between GM and OC. Additionally, a two-step MR approach was employed to identify potential mediating metabolites. Our analysis revealed significant associations between 6 specific microbiota taxa and OC. Furthermore, we identified several plasma metabolites that act as mediators of the association between GM and OC. In the two-step MR analysis, we observed a negative correlation between 4-methoxyphenol sulfate and pregnenetriol disulfate levels with OC. The genus Lachnospiraceae UCG008 potentially increases the risk of OC by decreasing 4-methoxyphenol sulfate levels, while the genus Howardella may elevate the risk of OC by reducing pregnenetriol disulfate levels, with mediation proportions of 22.35% and 4.23%, respectively. Additionally, levels of dilinoleoyl-GPE (18:2/18:2) and N-acetylkynurenine (2) were positively correlated with OC. The inhibitory effect of the genus Ruminococcus 1 on OC may be mediated through 1,2-dilinoleoyl-GPE (18:2/18:2) and N-acetylkynurenine (2), with mediation proportions of 10.15% and 11.32%, respectively. Our findings highlight the complex relationship among GM, plasma metabolites, and OC. The identified associations and mediation effects offer valuable insights into potential therapeutic approaches targeting GM for the management of OC.

Determinants of receiving government support by small and medium-sized enterprises in Russia

Government support measures help companies overcome various restrictions impeding their growth and develop ment, which is of special relevance for small and medium-sized enterprises (SMEs). These measures, therefore, must reach the target companies and ensure efficient allocation of resources. The article reveals key characteristics of SMEs in Russia that increase the probability of them receiving government support. The research methodology is based on public interest theory, public choice theory and the concept of economic freedom. The research method is a two-step regression analysis, in which the binary choice model is first evaluated, and then the LASSO model is used to assess the impact of the textual content of the companies’ websites. The dataset of the study is represented by data on 2,176 Russian metallurgical enterprises for 2020–2023, of which 57% were receiving government support. The results of the first step of evaluation showed that the organization’s age and size meas ured by revenue were the key factors associated with the probability of receiving government support. At the second step, we found that the mention of certain goods or materials on the websites, the enterprise’s location and orientation towards foreign stakeholders were significantly related to the likelihood of getting state support. The study offers tools for analyzing the determi nants of government support for SMEs and highlights the importance of this analysis for more efficient allocation of government resources.