Investigating the Genetic Association of 40 Biochemical Indicators with Parkinson's Disease.

The mechanisms of Parkinson's disease (PD) are not fully understood, which hinders the development of effective therapies. Research indicates that lower levels of biochemical indicators like bilirubin, vitamin D, and cholesterol may elevate the risk of PD. However, clinical studies on abnormal levels of biochemical indicators in PD patients' circulation are inconsistent, leading to ongoing debate about their association with PD. Here, we investigate the genetic correlation between 40 biochemical indicators and PD using a bidirectional two-sample Mendelian randomization (MR) approach to uncover potential causal relationships. Data from genome-wide association studies (GWAS) were utilized, with genetic variations from specific lineages serving as instrumental variables (IVs). The methodology followed the STROBE-MR checklist and adhered to the three principal assumptions of MR. Statistical analyses employed methods including inverse variance weighting (IVW), MR-Egger, weighted median, and weighted mode. Biochemical indicators including albumin, C-reactive protein (CRP), and sex hormone-binding globulin (SHBG) showed significant associations with PD risk. Elevated levels of albumin (OR = 1.246, 95% CI 1.006-1.542, P = 0.043) and SHBG (OR = 1.239, 95% CI 1.065-1.439, P = 0.005) were linked to higher PD risk. Conversely, increased CRP levels (OR = 0.663, 95% CI 0.517-0.851; P = 0.001) could potentially lower PD risk. The robustness of the results was confirmed through various MR analysis techniques, including assessments of directional pleiotropy and heterogeneity using MR-Egger intercept and MR-PRESSO methods. This study systematically reveals, for the first time at the genetic level, the relationship between 40 biochemical indicators and PD risk. Our research verifies the role of inflammation in PD and provides new genetic evidence, further advancing the understanding of PD pathogenesis. The study shows a positive correlation between albumin and SHBG with PD risk and a negative correlation between CRP and PD risk. This study identifies for the first time that SHBG may be involved in the onset of PD and potentially worsen disease progression.

Delineating Region-Specific contributions and connectivity patterns for semantic association and categorization through ROI and Granger causality analysis.

The neural mechanisms supporting semantic association and categorization are examined in this study. Semantic association involves linking concepts through shared themes, events, or scenes, while semantic categorization organizes meanings hierarchically based on defining features. Twenty-three adults participated in an fMRI study performing categorization and association judgment tasks. Results showed stronger activation in the inferior frontal gyrus during association and marginally weaker activation in the posterior middle temporal gyrus (pMTG) during categorization. Granger causality analysis revealed bottom-up connectivity from the visual cortex to the hippocampus during semantic association, whereas semantic categorization exhibited strong reciprocal connections between the pMTG and frontal semantic control regions, together with information flow from the visual association area and hippocampus to the pars triangularis. We propose that demands on semantic retrieval, precision of semantic representation, perceptual experiences and world knowledge result in observable differences between these two semantic relations.

Circulating immune cells and apolipoprotein A mediation: a Mendelian randomization study on hypertensive disorder of pregnancy.

Background: Studies using observational epidemiology have indicated that inflammation and immunological dysregulation are important contributors to placental and renal failure, which ultimately results in maternal hypertension. The potential causal relationships between the immunophenotypes and hypertensive disorder of pregnancy (HDP) are yet unclear. Methods: We conducted two-sample Mendelian randomization (MR) analyses to thoroughly examine the relationship between immunophenotypes and HDP. The GWAS data on immunological traits was taken from public catalog for 731 immunophenotypes and the summarized GWAS data in 4 types of HDP were retrieved from FinnGen database. The link between immune cell traits and HDP was examined through our study methodology, taking into account both direct relationships and mediation effects of apolipoprotein A (apoA). The inverse variance weighted (IVW) method served as the main analysis, while sensitivity analysis was carried out as a supplement. Results: We identified 14 highly correlative immunophenotypes and 104 suggestive possible factors after investigating genetically predicted immunophenotype biomarkers. According to the IVW analysis, there was a strong correlation between HDP and HLA DR on DC and plasmacytoid DC. Reverse MR analysis showed that there was no statistically significant effect of HDP on immune cells in our investigation. Mediation analysis confirmed that apoA mediates the interaction between HLA DR on DC and HDP. Conclusion: Our results highlight the complex interplay of immunophenotypes, apoA, and HDP. Moreover, the pathophysiological link between HLA DR on DC and HDP was mediated by the level of apoA.

An information-theoretic framework for conditional causality analysis of brain networks.

Identifying directed network models for multivariate time series is a ubiquitous problem in data science. Granger causality measure (GCM) and conditional GCM (cGCM) are widely used methods for identifying directed connections between time series. Both GCM and cGCM have frequency-domain formulations to characterize the dependence of time series in the spectral domain. However, the original methods were developed using a heuristic approach without rigorous theoretical explanations. To overcome the limitation, the minimum-entropy (ME) estimation approach was introduced in our previous work (Ning & Rathi, 2018) to generalize GCM and cGCM with more rigorous frequency-domain formulations. In this work, this information-theoretic framework is further generalized with three formulations for conditional causality analysis using techniques in control theory, such as state-space representations and spectral factorizations. The three conditional causal measures are developed based on different ME estimation procedures that are motivated by equivalent formulations of the classical minimum mean squared error estimation method. The relationship between the three formulations of conditional causality measures is analyzed theoretically. Their performance is evaluated using simulations and real neuroimaging data to analyze brain networks. The results show that the proposed methods provide more accurate network structures than the original approach.

This paper introduces a theoretical framework for causal inference in brain networks using time series measurements based on the principle of minimum-entropy regression. Three types of conditional causality measures are derived based on varying formulations of minimum-entropy regressions. The standard time-domain conditional Granger causality measure is formulated as a special case but with a different expression of the frequency-domain measure. The methods were evaluated using simulations and real resting-state functional MRI data of human brains and compared with standard Granger causality measures and directed transfer functions. Two new formulations of minimum-entropy-based causality measures showed better performance than other methods. The algorithms developed from this work may provide new insights to understand information flow in brain networks.

An optimized instrument variable selection approach to improve causality estimation in association studies.

Mendelian randomization (MR) is an emerging tool for inferring causality in genetic epidemiology. MR studies suffer bias from weak genetic instrument variables (IVs) and horizontal pleiotropy. We introduce a robust integrative framework strictly adhering with STROBE-MR guidelines to improve causality inference through MR studies. We implemented novel t-statistics-based criteria to improve the reliability of selected IVs followed by various MR methods. Further, we include sensitivity analyses to remove horizontal-pleiotropy bias. For functional validation, we perform enrichment analysis of identified causal SNPs. We demonstrate effectiveness of our proposed approach on 5 different MR datasets selected from diverse populations. Our pipeline outperforms its counterpart MR analyses using default parameters on these datasets. Notably, we found a significant association between total cholesterol and coronary artery disease (P = 1.16 × 10-71) in a single-sample dataset using our pipeline. Contrarily, this same association was deemed ambiguous while using default parameters. Moreover, in a two-sample dataset, we uncover 13 new causal SNPs with enhanced statistical significance (P = 1.06 × 10-11) for liver-iron-content and liver-cell-carcinoma. Likewise, these SNPs remained undetected using the default parameters (P = 7.58 × 10-4). Furthermore, our analysis confirmed previously known pathways, such as hyperlipidemia in heart diseases and gene ME1 in liver cancer. In conclusion, we propose a robust and powerful framework to infer causality across diverse populations and easily adaptable to different diseases.

Characteristics of Immune Cells and Causal Relationship with Chondromalacia: A Two-Sample, Bidirectional Mendelian Randomization Study.

Chondromalacia, characterized by the softening of cartilage, is a prevalent condition affecting joint health with complex etiology. The immune system's role in its pathogenesis has been implicated but remains to be fully elucidated. To address a critical knowledge gap, we conducted a two-sample Mendelian randomization analysis of 731 immune cell phenotypes, assessing parameters like fluorescence, cell count, and morphology. After sensitivity and pleiotropy checks, and applying a false discovery rate correction, our study linked 17 phenotypes to chondromalacia(P < 0.05). Among them, 7 immune cell phenotypes were found to have a protective effect against chondromalacia (IVW: P < 0.05, OR < 1), while 10 were considered risk factors (IVW : P< 0.05, OR > 1). Despite the constraints of sample size and possible genetic differences among populations, our research has identified a notable genetic correlation between specific immune cell indicators and chondromalacia. This breakthrough sheds light on the pathophysiological mechanisms of the condition. The identification of protective and risk-associated immune cell phenotypes provides a foundation for further exploration of immunological mechanisms in chondromalacia and may pave the way for targeted interventions. Future research is warranted to validate these findings and explore their clinical implications.

Low CD3 level is a risk factor for amyotrophic lateral sclerosis: a Mendelian randomization study.

Amyotrophic lateral sclerosis (ALS) is a progressive and fatal disease characterized by neuronal degeneration of the spinal cord and brain and believed to be related to the immune system. In this study, our aim is to use Mendelian randomization (MR) to search for immune markers related to ALS. A total of 731 immune cell traits were included in this study. MR analysis was used to identify the causality between 731 immune cell traits (with 3,757 Europeans) and ALS (with 138,086 Europeans). Colocalization analysis was used to verify the found causality, protein-protein interaction prediction was used to look for the interacting proteins that are known to be involved in ALS. We found low expression levels of CD3 on central memory CD8+ T cell is risk factor for ALS (OR = 0.90, 95% CI: 0.86-0.95, P = 0.0000303). CD3 can interact with three ALS-related proteins: VCP, HLA-DRA and HLA-DRB5, which are associated with adaptive immune response. Our study reported for the first time that low-level CD3 is a risk factor for ALS and the possible mechanism, which could provide a potential strategy for ALS diagnosis and therapy.

Genetic causality between modifiable risk factors and the risk of rheumatoid arthritis: Evidence from Mendelian randomization.

OBJECTIVES: Emerging research has investigated the potential impact of several modifiable risk factors on the risks of rheumatoid arthritis (RA), but the findings did not yield consistent results. This study aimed to comprehensively explore the genetic causality between modifiable risk factors and the susceptibility of RA risk using the Mendelian randomization (MR) approach. METHODS: Genetic instruments for modifiable risk factors were selected from several genome-wide association studies at the genome-wide significance level (p < 5 × 10-8), respectively. Summary-level data for RA were sourced from a comprehensive meta-analysis. The causal estimates linking modifiable risk factors to RA risk were assessed using MR analysis with inverse variance weighting (IVW), MR-Egger, weighted, and weighted median methods. RESULTS: After Bonferroni correction for multiple tests, we found the presence of causality between educational attainment and RA, where there were protective effects of educational attainment (college completion) (odds ratio [OR] = 0.50, 95% CI = 0.36, 0.69, p = 2.87E-05) and educational attainment (years of education) (OR = 0.93, 95% CI = 0.90, 0.96, p = 4.18E-06) on the lower RA risks. Nevertheless, smoking initiation was observed to be associated with increased RA risks (OR = 1.27, 95% CI = 1.09, 1.47, p = .002). Moreover, there was no indication of horizontal pleiotropy of genetic variants during causal inference between modifiable risk factors and RA. CONCLUSIONS: Our study reveals the genetic causal impacts of educational attainment and smoking on RA risks, suggesting that the early monitoring and recognition of modifiable risk factors would be beneficial for the preventive counseling/treatment strategies for RA.

Cheers to anxiety: Granger causality insights on alcohol consumption patterns across 13 South American countries.

BACKGROUND: The relationship between alcohol consumption and mental health is complex; drinking may exacerbate anxiety, and in turn, anxiety can lead to excessive drinking. This study explores the relationship between alcohol consumption patterns including wine, beer, and spirits, and anxiety prevalence in selected 13 South American nations. METHODS: This study utilises secondary data spanning 29 years from 1991 to 2019 obtained from the Our World in Data database. It investigates the causal link between the prevalence of anxiety and alcohol consumption in the selected countries using the Granger causality test. RESULTS: Anxiety was found to have a unidirectional effect on wine and beer consumption in Chile, Suriname, Uruguay, and Trinidad and Tobago. Additionally, drinking alcohol consumption appears to impact anxiety levels in Brazil. Argentina demonstrates a bidirectional relationship between anxiety and all three types of alcohol consumption, with similar patterns observed in Brazil (wine and beer), Chile (spirits), and Paraguay (spirits). CONCLUSION: No significant causal relationships for alcohol consumption patterns were found in other nations. The identified Granger causal links follow four distinct directions in this study. These findings provide valuable insights for policymakers, governments, and international investors for informed decision-making regarding regulation and policy tools.

Abnormal inter-hemispheric effective connectivity from left to right auditory regions during Mismatch Negativity (MMN) tasks in psychosis.

Anomalous Mismatch Negativity (MMN) in psychosis could be a consequence of disturbed neural oscillatory activity at sensory/perceptual stages of stimulus processing. This study investigated effective connectivity within and between the auditory regions during auditory odd-ball deviance tasks. The analyses were performed on two magnetoencephalography (MEG) datasets: one on duration MMN in a cohort with various diagnoses within the psychosis spectrum and neurotypical controls, and one on duration and pitch MMN in first-episode psychosis patients and matched neurotypical controls. We applied spectral Granger causality to MEG source-reconstructed signals to compute effective connectivity within and between the left and right auditory regions. Both experiments showed that duration-deviance detection was associated with early increases of effective connectivity in the beta band followed by increases in the alpha and theta bands, with the connectivity strength linked to the laterality of the MMN amplitude. Compared to controls, people with psychosis had overall smaller effective connectivity, particularly from left to right auditory regions, in the pathway where bilateral information converges toward lateralized processing, often rightward. Blunted MMN in psychosis might reflect a deficit in inter-hemispheric communication between auditory regions, highlighting a "dysconnection" already at preattentive stages of stimulus processing as a model system of widespread pathophysiology.