Causal inference over stochastic networks.

Claiming causal inferences in network settings necessitates careful consideration of the often complex dependency between outcomes for actors. Of particular importance are treatment spillover or outcome interference effects. We consider causal inference when the actors are connected via an underlying network structure. Our key contribution is a model for causality when the underlying network is endogenous; where the ties between actors and the actor covariates are statistically dependent. We develop a joint model for the relational and covariate generating process that avoids restrictive separability and fixed network assumptions, as these rarely hold in realistic social settings. While our framework can be used with general models, we develop the highly expressive class of Exponential-family Random Network models (ERNM) of which Markov random fields and Exponential-family Random Graph models are special cases. We present potential outcome-based inference within a Bayesian framework and propose a modification to the exchange algorithm to allow for sampling from ERNM posteriors. We present results of a simulation study demonstrating the validity of the approach. Finally, we demonstrate the value of the framework in a case study of smoking in the context of adolescent friendship networks.

The interconnectedness of energy consumption with economic growth: A granger causality analysis.

In considering today's energy challenges, the link between the usage of renewable and non-renewable energy sources and economic growth has gained substantial policy attention. This research examines the complex relationship between these three variables to understand how non-renewable energy consumption and renewable energy consumption interact and what that means for economic growth. This study uses the Granger causality approach to explore the relationships between non-renewable energy consumption, renewable energy consumption, and economic development. It draws on a comprehensive dataset from the Word Bank database, including 152 nations from 1990 to 2019. The analysis is further disaggregated by four subgroups of countries; least developed, developed, transitional economies and developing countries. The result of this study provides valuable empirical evidence of uni-directional causality running from renewable energy consumption to economic growth and non-renewable energy consumption to economic growth in transitional economies. Furthermore, policymakers should focus on both variables when making decisions because the results show that energy consumption and economic growth are interconnected. Implementing global energy efficiency standards, reducing fossil fuel usage, and adopting regulatory measures are all viable policies for limiting adverse effects on the environment while encouraging economic development.

Causal associations between gut microbiota and premature rupture of membranes: a two-sample Mendelian randomization study.

Background: Previous study has indicated a potential link between gut microbiota and maternal pregnancy outcomes. However, the causal relationship between gut microbiota and premature rupture of membranes (PROM) remains a topic of ongoing debate. Methods: A two-sample Mendelian Randomization (MR) study was used to investigate the relationship between gut microbiota and PROM. Genetic data on gut microbiota was obtained from the MiBioGen consortium's largest genome-wide association study (GWAS) (n=14,306). Genetic data on PROM (3011 cases and 104247 controls) were sourced from publicly available GWAS data from the Finnish National Biobank FinnGen consortium. Various methods including Inverse variance weighted (IVW), MR-Egger, simple mode, weighted median, and weighted mode were utilized to assess the causal relationship by calculating the odd ratio (OR) value and confidence interval (CI). Sensitivity analyses for quality control were performed using MR-Egger intercept tests, Cochran's Q tests, and leave-one-out analyses. Results: The IVW method revealed that class Mollicutes (IVW, OR=0.773, 95%CI: 0.61-0.981, pval = 0.034), genus Marvinbryantia (IVW, OR=00.736, 95%CI: 0.555-0.977, pval = 0.034), genus Ruminooccaceae UCG003 (IVW, OR=0.734, 95%CI: 0.568-0.947, pval = 0.017) and phylum Tenericutes (IVW, OR=0.773, 95%CI: 0.566-1.067, pval = 0.034) were associated with a reduced risk of PROM, while genus Collinsella (IVW, OR=1.444, 95%CI: 1.028-2.026, pval = 0.034), genus Intestinibacter (IVW, OR=1.304, 95%CI: 1.047-1.623, pval = 0.018) and genus Turicibacter (IVW, OR=1.282, 95%CI: 1.02-1.611, pval = 0.033) increased the risk of PROM. Based on the other four supplementary methods, six gut microbiota may have a potential effect on PROM. Due to the presence of pleiotropy (pval=0.045), genus Lachnoclostridium should be ruled out. No evidence of horizontal pleiotropy or heterogeneity was found in other microbiota (pval >0.05). Conclusions: In this study, we have discovered a causal relationship between the presence of specific probiotics and pathogens in the host and the risk of PROM. The identification of specific gut microbiota associated with PROM through MR studies offers a novel approach to diagnosing and treating this condition, thereby providing a new strategy for clinically preventing PROM.

Challenging unverified assumptions in causal claims: Do gas stoves increase risk of pediatric asthma?

The use of unverified models for risk estimates and policy recommendations can be highly misleading, as their predictions may not reflect real-world health impacts. For example, a recent article states that NO2 from gas stoves "likely causes ∼50,000 cases of current pediatric asthma from long-term NO2 exposure alone" annually in the United States. This explicitly causal claim, which is contrary to several methodology and review articles published in this journal, among others, reflects both (a) An unverified modeling assumption that pediatric asthma burden is approximately proportional to NO2; and (b) An unverified causal assumption that the assumed proportionality between exposure and response is causal. The article is devoid of any causal analysis showing that these assumptions are likely to be true. It does not show that reducing NO2 exposure from gas stoves would reduce pediatric asthma risk. Its key references report no significant associations - let alone causation - between NO2 and pediatric asthma. Thus, the underlying data suggests that the number of pediatric asthma cases caused by gas stoves in the United States is indistinguishable from zero. This highlights the need to rigorously validate modeling assumptions and causal claims in public health risk assessments to ensure scientifically sound foundations for policy decisions.

Effect of the immune cells and plasma metabolites on rheumatoid arthritis: a mediated mendelian randomization study.

Background: Increasing evidence indicates a close relationship between alterations in human immune cells and plasma metabolites with Rheumatoid Arthritis (RA). However, limited studies have left the causal relationships behind these links unclear. Methods: A bidirectional Mendelian Randomization (MR) study was conducted, combined with mediation analysis, using data from genome-wide association study database covering 731 immune cell phenotypes and 1,400 plasma metabolite traits to explore their causal relationships with RA and potential mediating effects. The primary method used for MR analysis was inverse-variance weighted and False Discovery Rate (FDR) correction was applied to verify the robustness of our results. Results: HLA DR on CD33- HLA DR+ (myeloid cell group) (OR, 1.422; 95% CI, 1.194-1.694; P < 0.001; PFDR = 0.012) increased the risk of developing RA. CD19 on IgD+ CD38- naive (B cell group) (OR, 0.969; 95% CI, 0.954-0.985; P < 0.001; PFDR = 0.021) reduced the risk of developing RA. RA was a risk factor for HLA DR on CD14- CD16+ monocytes (monocyte group) (OR, 1.242; 95% CI, 1.102-1.401; P < 0.001; PFDR = 0.047). RA was a protective factor for memory B cell %lymphocyte (B cell group) (OR, 0.861; 95% CI, 0.795-0.933; P < 0.001; PFDR = 0.050), CD4+ CD8dim T cell %lymphocyte (TBNK group) (OR, 0.802; 95% CI, 0.711-0.904; P < 0.001; PFDR = 0.043), CD4+ CD8dim T cell %leukocyte (TBNK group) (OR, 0.814; 95% CI, 0.726-0.913; P < 0.001; PFDR = 0.046), CD24 on IgD+ CD24+ B cells (B cell group) (OR, 0.857; 95% CI, 0.793-0.927; P < 0.001; PFDR = 0.038), and CD24 on unswitched memory B cells (B cell group) (OR, 0.867; 95% CI, 0.797-0.942; P < 0.001; PFDR = 0.050). Increasing levels of docosatrienoate (22:3n3) (OR, 0.886; 95% CI, 0.838-0.936; P < 0.001; PFDR = 0.023) significantly reduced the risk of developing RA. The mediating effect of plasma metabolites in this context was not established. Conclusion: This study provides genetic evidence for the intricate relationships between immune cells, plasma metabolites, and RA, highlighting the potential mechanisms involved. This will contribute to future directions in precision medicine and research.

Bridged Treatment Comparisons: an Illustrative Application in HIV Treatment.

Comparisons of treatments, interventions, or exposures are of central interest in epidemiology, but direct comparisons are not always possible due to practical or ethical reasons. Here, we detail a fusion approach to compare treatments across studies. The motivating example entails comparing the risk of the composite outcome of death, AIDS, or greater than a 50% CD4 cell count decline in people with HIV when assigned triple versus mono antiretroviral therapy, using data from the AIDS Clinical Trial Group (ACTG) 175 (mono versus dual therapy) and ACTG 320 (dual versus triple therapy). We review a set of identification assumptions and estimate the risk difference using an inverse probability weighting estimator that leverages the shared trial arms (dual therapy). A fusion diagnostic based on comparing the shared arms is proposed that may indicate violation of the identification assumptions. Application of the data fusion estimator and diagnostic to the ACTG trials indicates triple therapy results in a reduction in risk compared to monotherapy in individuals with baseline CD4 counts between 50 and 300 cells/mm3. Bridged treatment comparisons address questions that none of the constituent data sources could address alone, but valid fusion-based inference requires careful consideration of the underlying assumptions.

Causal Effect of Macronutrient and Micronutrient Intake on Stroke: A Two-Sample Mendelian Randomization Study.

(1) Background: Estimating the causal association between nutrient intake, as a modifiable risk factor, and stroke risk is beneficial for the prevention and management of stroke. However, observational studies are unavoidably influenced by confounding factors and reverse causation. (2) Methods: We performed a two-sample Mendelian randomization (MR) to estimate the effects of nutrient intake on stroke risk. Summary statistics for nutrients, including 4 macronutrients and 14 micronutrients, were derived from 15 genome-wide association studies (GWAS). Data on stroke and its subtypes were sourced from the MEGASTROKE consortium. (3) Results: Genetically predicted magnesium levels, as the protective factors, were significantly associated with a lower risk of cardioembolic stroke (OR: 0.011, 95% CI: 0-0.25, p-value: 0.005) in the IVW method. Additionally, vitamin C reduced the risk of cardioembolic stroke (OR: 0.759, 95% CI: 0.609-0.946, p-value: 0.014) and vitamin B9 reduced the risk of small vessel stroke (OR: 0.574, 95% CI: 0.393-0.839, p-value: 0.004) with the IVW method. However, the association of vitamin B6 with an increased risk of large-artery stroke (OR: 1.546, 95% CI: 1.009-2.37, p-value: 0.046) in the Wald ratio method should be interpreted cautiously due to the limited number of SNPs. There was also suggestive evidence that magnesium might decrease the risk of both any stroke and ischemic stroke. (4) Conclusions: Our MR analysis highlights the protective roles of magnesium, vitamin C, and vitamin B9 in stroke prevention, making them key targets for public health strategies. However, the findings related to vitamin B6 are less certain and require further validation.

The Lack of Causal Link Between Myopia and Intraocular Pressure: Insights from Cross-Sectional Analysis and Mendelian Randomization Study.

OBJECTIVE: This study aimed to explore the potential causal relationship between intraocular pressure (IOP) and myopia. METHODS: The study included 3,459 patients who underwent corneal refractive surgery at our institution between 2021 and 2023. Preoperative data on IOP, spherical equivalent (SE), axial length (AL), and corneal thickness (CCT) were collected. The association between IOP and myopia was investigated through rank correlation analysis, and causal inference was examined using Mendelian randomization (MR) methods, including MR-Egger, weighted median, mode-based estimation, simple mode, and inverse variance weighted (IVW) approaches. Utilizing summary statistics from genome-wide association studies (GWAS), IOP was considered as the exposure, with myopia as the outcome variable. IVW method was employed for the primary analysis, supplemented by sensitivity analyses. RESULTS: Cross-sectional analysis revealed a non-significant association between corrected IOP (cIOP) and myopia (r = -0.019, P = 0.12). MR analysis indicated a non-significant genetic causal relationship between cIOP and myopia under the IVW method (OR = 1.001; 95% CI [0.999-1.003], P = 0.22), a finding corroborated in replication samples (OR = 0.98; 95% CI [0.96-1.00], P = 0.099). CONCLUSION: This study did not find a direct causal link between IOP and the development of myopia. These findings challenge the traditional role attributed to IOP in the progression of myopia and highlight the complex, multifactorial process of myopia development. This provides a new perspective on understanding the intricate mechanisms behind myopia progression.

A research agenda to advance the study of implementation mechanisms.

BACKGROUND: Implementation science scholars have made significant progress identifying factors that enable or obstruct the implementation of evidence-based interventions, and testing strategies that may modify those factors. However, little research sheds light on how or why strategies work, in what contexts, and for whom. Studying implementation mechanisms-the processes responsible for change-is crucial for advancing the field of implementation science and enhancing its value in facilitating equitable policy and practice change. The Agency for Healthcare Research and Quality funded a conference series to achieve two aims: (1) develop a research agenda on implementation mechanisms, and (2) actively disseminate the research agenda to research, policy, and practice audiences. This article presents the resulting research agenda, including priorities and actions to encourage its execution. METHOD: Building on prior concept mapping work, in a semi-structured, 3-day, in-person working meeting, 23 US-based researchers used a modified nominal group process to generate priorities and actions for addressing challenges to studying implementation mechanisms. During each of the three 120-min sessions, small groups responded to the prompt: "What actions need to be taken to move this research forward?" The groups brainstormed actions, which were then shared with the full group and discussed with the support of facilitators trained in structured group processes. Facilitators grouped critical and novel ideas into themes. Attendees voted on six themes they prioritized to discuss in a fourth, 120-min session, during which small groups operationalized prioritized actions. Subsequently, all ideas were collated, combined, and revised for clarity by a subset of the authorship team. RESULTS: From this multistep process, 150 actions emerged across 10 priority areas, which together constitute the research agenda. Actions included discrete activities, projects, or products, and ways to shift how research is conducted to strengthen the study of implementation mechanisms. CONCLUSIONS: This research agenda elevates actions to guide the selection, design, and evaluation of implementation mechanisms. By delineating recommended actions to address the challenges of studying implementation mechanisms, this research agenda facilitates expanding the field of implementation science, beyond studying what works to how and why strategies work, in what contexts, for whom, and with which interventions.

Causal associations between frailty and low back pain: a bidirectional two-sample mendelian randomization study.

BACKGROUND: Previous observational studies have revealed a potentially robust bidirectional relationship between frailty and low back pain (LBP). However, the precise causal relationship remains unclear. METHODS: To examine the potential causal association between frailty and LBP, we conducted bidirectional two-sample Mendelian randomization analysis (MR) study. Genetic data on frailty index (FI) and LBP were acquired from publicly available genome-wide association studies (GWAS). Various MR methodologies were utilized, such as inverse variance weighting (IVW), weighted median, and MR-Egger, to evaluate causality. Additionally, sensitivity analyses were conducted to evaluate the robustness of the findings. RESULTS: Genetically predicted higher FI (IVW, odds ratio [OR] = 1.66, 95% CI 1.17-2.36, p = 4.92E-03) was associated with a higher risk of LBP. As for the reverse direction, genetic liability to LBP showed consistent associations with a higher FI (IVW, OR = 1.13, 95% CI 1.07-1.19, p = 2.67E-05). The outcomes from various MR techniques and sensitivity analyses indicate the robustness of our findings. CONCLUSION: Our research findings provide additional evidence bolstering the bidirectional causal relationship between frailty and LBP.

Causality between neuroticism personality clusters and female reproductive diseases in European population: a two-sample bidirectional mendelian randomization study.

BACKGROUND: The causality between neuroticism, a personality trait characterized by the tendency to experience negative emotions, and female reproductive diseases remains unclear. To provide evidence for the development of effective screening and prevention strategies, this study employed Mendelian randomization (MR) to investigate the causality between neuroticism clusters and female reproductive diseases. METHODS: Instrumental variables were obtained from large-scale genome-wide association studies of populations of European descent involving three neuroticism clusters (depressed affect, worry, sensitivity to environmental stress, and adversity [SESA]) in the Complex Trait Genetics database and six female reproductive diseases (infertility, polycystic ovary syndrome [PCOS], spontaneous abortion, recurrent spontaneous abortion, endometriosis, and uterine fibroids) in the FinnGen database. The bidirectional two-sample MR analysis was conducted using the inverse variance-weighted, weighted median, and MR-Egger methods, whereas the sensitivity analysis was conducted using the Cochran's Q-test, MR-Egger intercept, and leave-one-out analysis. RESULTS: In the forward analysis, genetically predicted depressed affect and worry components of neuroticism significantly increased the risk of infertility (depressed affect: odds ratio [OR] = 1.399, 95% confidence interval [CI]: 1.054-1.856, p = 0.020; worry: OR = 1.587, 95% CI: 1.229-2.049, p = 0.000) and endometriosis (depressed affect: OR = 1.611, 95% CI: 1.234-2.102, p = 0.000; worry: OR = 1.812, 95% CI: 1.405-2.338, p = 0.000). Genetically predicted SESA component of neuroticism increased only the risk of endometriosis (OR = 1.524, 95% CI: 1.104-2.103, p = 0.010). In the reverse analysis, genetically predicted PCOS was causally associated with an increased risk of the worry component of neuroticism (Beta = 0.009, 95% CI: 0.003-0.016, p = 0.003). CONCLUSIONS: The MR study showed that the three neuroticism personality clusters had definite causal effects on at least one specific female reproductive disease. Moreover, PCOS may increase the risk of the worry component of neuroticism. This finding suggests the need to screen for specific female reproductive diseases in populations with high neuroticism and assess the psychological status of patients with PCOS.

Genetically predicted effects of 10 sleep phenotypes on revision of knee arthroplasty: a mendelian randomization study.

BACKGROUND: Accumulating evidence has suggested that sleep disturbances and disorders are common in patients who undergo knee arthroplasty. Revision surgery represents one of the most catastrophic outcomes of knee arthroplasty. However, it remains unclear whether sleep traits are the causes or consequences of knee arthroplasty revision. This study aimed to genetically examine the relationships between sleep traits and knee arthroplasty revision. METHODS: To determine the causal relationship between sleep traits and knee arthroplasty revision, we employed two-sample Mendelian randomization (MR) using summary statistics from the largest publicly available genome-wide association studies (GWASs). The MR design uses genetic variants as instrumental variables to help separate causal relationships from non-causal associations. The main analyses included an inverse variance weighted (IVW) meta-analysis to obtain primary effect estimates. Sensitivity analyses involving the weighted median approach and MR-Egger regression were also conducted to check for potential pleiotropic biases. Numerous complementary sensitivity analyses were also performed to identify statistically significant causal correlations when there were horizontal pleiotropy and heterogeneity across variants. Finally, a reverse MR analysis was performed to evaluate the possibility of reverse causation. RESULTS: In the absence of heterogeneity and horizontal pleiotropy, the IVW method revealed that genetically-predicted short sleep duration short sleep duration (average sleep duration of 24 h is 6 h or less) was positively correlated with the risk of knee arthroplasty revision (odds ratio = 1.03, 95% confidence interval = 1.01-1.05, and P = 0.003), while the association between genetically-predicted long sleep duration and knee arthroplasty was negative. The reverse MR analysis did not yield evidence supporting reverse causality relation between knee arthroplasty revision and sleep phenotypes. CONCLUSION: This research indicated that, of the 10 sleep phenotypes we analyzed, only sleep duration was causally associated with knee arthroplasty revision. These discoveries added to the understanding of the role of sleep traits in the etiology of knee arthroplasty revision, which might further expand our insights into the prevention of knee arthroplasty revision.

Computer vision and statistical insights into cycling near miss dynamics.

Across the globe, many transport bodies are advocating for increased cycling due to its health and environmental benefits. Yet, the real and perceived dangers of urban cycling remain obstacles. While serious injuries and fatalities in cycling are infrequent, "near misses"-events where a person on a bike is forced to avoid a potential crash or is unsettled by a close vehicle-are more prevalent. To understand these occurrences, researchers have turned to naturalistic studies, attaching various sensors like video cameras to bikes or cyclists. This sensor data holds the potential to unravel the risks cyclists face. Still, the sheer amount of video data often demands manual processing, limiting the scope of such studies. In this paper, we unveil a cutting-edge computer vision framework tailored for automated near-miss video analysis and for detecting various associated risk factors. Additionally, the framework can understand the statistical significance of various risk factors, providing a comprehensive understanding of the issues faced by cyclists. We shed light on the pronounced effects of factors like glare, vehicle and pedestrian presence, examining their roles in near misses through Granger causality with varied time lags. This framework enables the automated detection of multiple factors and understanding their significant weight, thus enhancing the efficiency and scope of naturalistic cycling studies. As future work, this research opens the possibility of integrating this AI framework into edge sensors through embedded AI, enabling real-time analysis.

Causal Relationship Between Micronutrient and Sleep Disorder: A Mendelian Randomization Study.

Background: Sleep played an important part in human health, and COVID-19 led to a continuous deterioration of sleep. However, the causal relationship between micronutrient and sleep disorder was not yet fully understood. Methods: In this research, the genetic causal relationship between micronutrient and sleep disorder was analyzed utilizing a two-sample Mendelian randomization (MR). Single nucleotide polymorphisms (SNPs) were used as instrumental variables. The analyses were conducted using the MR-Egger, inverse variance weighted, weighted mode, weighted median, simple mode, Cochran's Q test and leave-one-out. Results: Our results suggested that 8 genetically predicted micronutrients participated in sleep disorders, including liver iron (L-iron) and iron in sleeping too much, spleen iron (S-iron) in sleeplessness/insomnia, trouble falling or staying asleep, sleep duration (undersleepers) and nonorganic sleeping disorders, iron metabolism disorder (IMD) and vitamin B12 deficiency anaemia (VB12DA) in narcolepsy, urine sodium (uNa) in narcolepsy, sleep apnea syndrome and sleep disorder, vitamin D (VD) in sleep duration (oversleepers), 25-Hydroxyvitamin D (25(OH)D) in trouble falling or staying asleep. Conclusion: Our study used Mendelian randomization methods at the SNP level to explore the potential causal relationship among L-iron, iron, S-iron, IMD, uNa, 25(OH)D, VD, VB12DA with certain sleep disorder subtypes. Our results uncovered a micronutrient-based strategy for alleviating sleep disorder symptoms.

A comprehensive analysis of the health effects associated with smoking in the largest population using UK Biobank genotypic and phenotypic data.

Background: Smoking is a widespread behavior, while the relationship between smoking and various diseases remains a topic of debate. Objective: We conducted analysis to further examine the identified associations and assess potential causal relationships. Methods: We utilized seven single nucleotide polymorphisms (SNPs) known to be linked to smoking extracting genotype data from the UK Biobank, a large-scale biomedical repository encompassing comprehensive health-related and genetic information of European descent. Phenome-wide association study (PheWAS) analysis was conducted to map the association of genetically predicted smoking status with 1,549 phenotypes. The associations identified in the PheWAS were then meticulously examined through two-sample Mendelian randomization (MR) analysis, utilizing data from the UK Biobank (n = 487,365) and the Sequencing Consortium of Alcohol and Nicotine Use (GSCAN) (n = 337,334). This approach allowed us to comprehensively characterize the links between smoking and disease patterns. Results: The PheWAS analysis produced 34 phenotypes that demonstrated significant associations with smoking (P = 0.05/1460). Importantly, sickle cell anemia and type 2 diabetes exhibited the most significant SNPs (both 85.71% significant SNPs). Furthermore, the MR analyses provided compelling evidence supporting causal associations between smoking and the risk of following diseases: obstructive chronic bronchitis (IVW: Beta = 0.48, 95% confidence interval (CI) 0.36-0.61, P = 1.62×10-13), cancer of the bronchus (IVW: Beta = 0.92, 95% CI 0.68-1.17, P = 2.02×10-13), peripheral vascular disease (IVW: Beta = 1.09, 95% CI 0.71-1.46, P = 1.63×10-8), emphysema (IVW: Beta = 1.63, 95% CI 0.90-2.36, P = 1.29×10-5), pneumococcal pneumonia (IVW: Beta = 0.30, 95% CI 0.11-0.49, P = 1.60×10-3), chronic airway obstruction (IVW: Beta = 0.83, 95% CI 0.30-1.36, P = 2.00×10-3) and type 2 diabetes (IVW: Beta = 0.53, 95% CI 0.16-0.90, P = 5.08×10-3). Conclusion: This study affirms causal relationships between smoking and obstructive chronic bronchitis, cancer of the bronchus, peripheral vascular disease, emphysema, pneumococcal pneumonia, chronic airway obstruction, type 2 diabetes, in the European population. These findings highlight the broad health impacts of smoking and support smoking cessation efforts.

Causality between autoimmune diseases and breast cancer: a two-sample Mendelian randomization study in a European population.

BACKGROUND: The incidence of autoimmune diseases and breast cancer is significantly higher in women compared to men. Previous observational studies have not conclusively determined the relationship between these two conditions. This study utilizes the Mendelian randomization approach to investigate the genetic association between autoimmune diseases and breast cancer. METHOD: Two-sample Mendelian randomization was conducted on a European population using the GWAS database. The inverse variance-weighted method served as the primary analytical approach. The MR-PRESSO test was applied to detect horizontal pleiotropy. To ensure result robustness, the FDR correction method was used. RESULT: The study revealed that Sjögren's syndrome lowers the overall risk of breast cancer (OR 0.96, 95% CI [0.93-0.99], p = 0.011). Idiopathic inflammatory myopathy shows a protective effect against overall breast cancer (OR 0.98, 95% CI [0.97-0.99], p = 0.035). An association was identified between rheumatoid arthritis and overall breast cancer (OR 0.98, 95% CI [0.96-1.00], p = 0.050). No causal link was found between systemic lupus erythematosus, systemic sclerosis, and overall breast cancer. The study also suggests that Sjögren's syndrome, rheumatoid arthritis, and idiopathic inflammatory myopathy might reduce the risk of developing HER + breast cancer. Specifically, Sjögren's syndrome (OR = 0.90, 95% CI [0.83-0.98], p = 0.02), rheumatoid arthritis (OR = 0.94, 95% CI [0.91-0.98], p = 0.006), and idiopathic inflammatory myopathy (OR = 0.96, 95% CI [0.93-0.99], p = 0.036). Additionally, systemic lupus erythematosus was found to lower the risk of HER- breast cancer (OR = 0.95, 95% CI [0.91-0.99], p = 0.046). The study did not establish a causal relationship between these five autoimmune diseases and ER + or ER- breast cancer. CONCLUSION: This study found that autoimmune diseases may act as protective factors against breast cancer risk.

Multi-dimensional epidemiology and informatics data on COVID-19 wave at the end of zero COVID policy in China.

Background: China exited strict Zero-COVID policy with a surge in Omicron variant infections in December 2022. Given China's pandemic policy and population immunity, employing Baidu Index (BDI) to analyze the evolving disease landscape and estimate the nationwide pneumonia hospitalizations in the post Zero COVID period, validated by hospital data, holds informative potential for future outbreaks. Methods: Retrospective observational analyses were conducted at the conclusion of the Zero-COVID policy, integrating internet search data alongside offline records. Methodologies employed were multidimensional, encompassing lagged Spearman correlation analysis, growth rate assessments, independent sample T-tests, Granger causality examinations, and Bayesian structural time series (BSTS) models for comprehensive data scrutiny. Results: Various diseases exhibited a notable upsurge in the BDI after the policy change, consistent with the broader trajectory of the COVID-19 pandemic. Robust connections emerged between COVID-19 and diverse health conditions, predominantly impacting the respiratory, circulatory, ophthalmological, and neurological domains. Notably, 34 diseases displayed a relatively high correlation (r > 0.5) with COVID-19. Among these, 12 exhibited a growth rate exceeding 50% post-policy transition, with myocarditis escalating by 1,708% and pneumonia by 1,332%. In these 34 diseases, causal relationships have been confirmed for 23 of them, while 28 garnered validation from hospital-based evidence. Notably, 19 diseases obtained concurrent validation from both Granger causality and hospital-based data. Finally, the BSTS models approximated approximately 4,332,655 inpatients diagnosed with pneumonia nationwide during the 2 months subsequent to the policy relaxation. Conclusion: This investigation elucidated substantial associations between COVID-19 and respiratory, circulatory, ophthalmological, and neurological disorders. The outcomes from comprehensive multi-dimensional cross-over studies notably augmented the robustness of our comprehension of COVID-19's disease spectrum, advocating for the prospective utility of internet-derived data. Our research highlights the potential of Internet behavior in predicting pandemic-related syndromes, emphasizing its importance for public health strategies, resource allocation, and preparedness for future outbreaks.

The strength of anticipated distractors shapes EEG alpha and theta oscillations in a Working Memory task.

Working Memory (WM) requires maintenance of task-relevant information and suppression of task-irrelevant/distracting information. Alpha and theta oscillations have been extensively investigated in relation to WM. However, studies that examine both theta and alpha bands in relation to distractors, encompassing not only power modulation but also connectivity modulation, remain scarce. Here, we depicted, at the EEG-source level, the increase in power and connectivity in theta and alpha bands induced by strong relative to weak distractors during a visual Sternberg-like WM task involving the encoding of verbal items. During retention, a strong or weak distractor was presented, predictable in time and nature. Analysis focused on the encoding and retention phases before distractor presentation. Theta and alpha power were computed in cortical regions of interest, and connectivity networks estimated via spectral Granger causality and synthetized using in/out degree indices. The following modulations were observed for strong vs. weak distractors. In theta band during encoding, the power in frontal regions increased, together with frontal-to-frontal and bottom-up occipital-to-temporal-to-frontal connectivity; even during retention, bottom-up theta connectivity increased. In alpha band during retention, but not during encoding, the power in temporal-occipital regions increased, together with top-down frontal-to-occipital and temporal-to-occipital connectivity. From our results, we postulate a proactive cooperation between theta and alpha mechanisms: the first would mediate enhancement of target representation both during encoding and retention, and the second would mediate increased inhibition of sensory areas during retention only, to suppress the processing of imminent distractor without interfering with the processing of ongoing target stimulus during encoding.

Against epigenetic responsibility: The problem of causality in 'foetal programming' science.

Emerging evidence that intrauterine exposures to environmental stressors can 'programme' epigenetic modifications in offspring, leading to long-lasting health risks, has generated debate about whether prospective mothers have a specific 'epigenetic' moral responsibility. However, to date, proposals for maternal epigenetic responsibility have failed to grapple adequately with the uncertainty of scientific evidence, and specifically, whether the causal basis for intrauterine epigenetic effects is sufficiently established to ground claims of moral responsibility. Causality is widely considered a necessary condition for the attribution of moral responsibility. In this paper, we show that much foetal programming science in humans has yet to establish a causal epigenetic connection between intrauterine exposures and subsequent offspring health impacts. This research struggles to establish that the relationship between such exposures and offspring health risks is in fact causal, neither has it been able to evince the causal significance of exposures during pregnancy to such outcomes. We argue that these two challenges to establishing causality in foetal programming research seriously undercut the idea that prospective mothers may have a moral responsibility to ensure the epigenetics of their offspring.

Investigation of Electrical Signals in the Brain of People with Autism Using Effective Connectivity Network.

Unlike other functional integration methods that examine the relationship and correlation between two channels, effective connection reports the direct effect of one channel on another and expresses their causal relationship. In this article, we investigate and classify electroencephalographic (EEG) signals based on effective connectivity. In this study, we leverage the Granger causality (GC) relationship, a method for measuring effective connectivity, to analyze EEG signals from both healthy individuals and those with autism. The EEG signals examined in this article were recorded during the presentation of abstract images. Given the nonstationary nature of EEG signals, a vector autoregression model has been employed to model the relationships between signals across different channels. GC is then used to quantify the influence of these channels on one another. Selecting regions of interest (ROI) is a critical step, as the quality of the time periods under consideration significantly impacts the outcomes of the connectivity analysis among the electrodes. By comparing these effects in the ROI and various areas, we have distinguished healthy subjects from those suffering from autism. Furthermore, through statistical analysis, we have compared the results between healthy individuals and those with autism. It has been observed that the causal relationship between these two hemispheres is significantly weaker in healthy individuals compared to those with autism.