Mendelian randomization

Gut microbiota as a residual risk factor causally influencing cardiac structure and function: Mendelian randomization analysis and biological annotation

Background

The gut microbiota (GM) is widely acknowledged to have a significant impact on cardiovascular health and may act as a residual risk factor affecting cardiac structure and function. However, the causal relationship between GM and cardiac structure and function remains unclear.

Objective

This study aims to employ a two-sample Mendelian randomization (MR) approach to investigate the causal association between GM and cardiac structure and function.

Methods

Data on 119 GM genera were sourced from a genome-wide association study (GWAS) meta-analysis (13,266 European participants) conducted by the MiBioGen consortium, while data on 16 parameters of cardiac structure and function were obtained from the UK Biobank's GWAS of cardiac magnetic resonance imaging (up to 41,135 European participants). Inverse variance weighted (IVW), MR-Egger, and weighted median (WM) methods were utilized for causal association assessments, with sensitivity analyses conducted to reinforce the findings. Finally, biological annotation was performed on the GWAS data of GM and cardiac phenotypes with causal associations to explore potential mechanisms.

Results

The MR analysis, predominantly based on the IVW model, revealed 93 causal associations between the genetically predicted abundance of 44 GM genera and 16 cardiac structure and function parameters. These associations maintained consistent directions in MR-Egger and WM models, with no evidence of pleiotropy detected. Biological annotations suggest that GM may influence cardiac structure and function through pathways involved in myocardial cell development, cardiac contractility, and apoptosis.

Conclusion

The MR analysis supports a causal association between certain abundances of genetically predicted GM and cardiac structure and function, suggesting that GM could be a residual risk factor impacting cardiac phenotypes.

Menopausal age does not affect ventricular structure and function: a Mendelian randomization study

OBJECTIVES

Observational studies have suggested an association between age at natural menopause (ANM) and ventricular structure and function. Nevertheless, the causal relationship remains unclear. This study aimed to evaluate the causal effects of ANM on ventricular structure and function by Mendelian randomization (MR) analysis.

METHODS

Genome-wide association summary statistics for ANM and 16 ventricular structures and functions were obtained. The inverse variance weighted (IVW) method was the primary MR approach for assessing causal associations. In addition, three additional MR methods (MR-Egger, weighted median and weighted mode) were performed to complement the IVW method. Furthermore, various sensitivity tests were conducted to evaluate the reliability of the MR results.

RESULTS

The IVW method identified no causal association between ANM and all 16 ventricular structures or functions (p > 0.05). Three additional MR methods yielded parallel results to the IVW approach (p > 0.05). Various sensitivity tests revealed stability of the MR results, indicating no heterogeneity or horizontal pleiotropy.

CONCLUSION

The present MR study indicated that ANM would not causally affect ventricular structure or function. Therefore, the correlation between ANM and ventricular characteristics in previous observational studies might be attributed to shared upstream cardiovascular risk factors or unidentified genetic mutations that simultaneously affect both ANM and ventricular structure and function.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

The associations between dysregulation of human blood metabolites and lung cancer risk: evidence from genetic data

BACKGROUND

Metabolic dysregulation is recognized as a significant hallmark of cancer progression. Although numerous studies have linked specific metabolic pathways to cancer incidence, the causal relationship between blood metabolites and lung cancer risk remains unclear.

METHODS

Genomic data from 29,266 lung cancer patients and 56,450 control individuals from the Transdisciplinary Research in Cancer of the Lung and the International Lung Cancer Consortium (TRICL-ILCCO) were utilized, and findings were replicated using additional data from the FinnGen consortium. The analysis focused on the associations between 486 blood metabolites and the susceptibility to overall lung cancer and its three major clinical subtypes. Various Mendelian randomization methods, including inverse-variance weighting, weighted median estimation, and MR-Egger regression, were employed to ensure the robustness of our findings.

RESULTS

A total of 19 blood metabolites were identified with significant associations with lung cancer risk. Specifically, oleate (OR per SD = 2.56, 95% CI: 1.51 to 4.36), 1-arachidonoylglyceropholine (OR = 1.79, 95% CI: 1.22 to 2.65), and arachidonate (OR = 1.67, 95% CI: 1.16 to 2.40) were associated with a higher risk of lung cancer. Conversely, 1-linoleoylglycerophosphoethanolamine (OR = 0.57, 95% CI: 0.40 to 0.82), ADpSGEGDFXAEGGGVR, a fibrinogen cleavage peptide (OR = 0.60, 95% CI: 0.47 to 0.77), and isovalerylcarnitine (OR = 0.62, 95% CI: 0.49 to 0.78) were associated with a lower risk of lung cancer. Notably, isoleucine (OR = 9.64, 95% CI: 2.55 to 36.38) was associated with a significantly higher risk of lung squamous cell cancer, while acetyl phosphate (OR = 0.11, 95% CI: 0.01 to 0.89) was associated with a significantly lower risk of small cell lung cancer.

CONCLUSION

This study reveals the complex relationships between specific blood metabolites and lung cancer risk, highlighting their potential as biomarkers for lung cancer prevention, screening, and treatment. The findings not only deepen our understanding of the metabolic mechanisms of lung cancer but also provide new insights for future treatment strategies.

MRBEE: A bias-corrected multivariable Mendelian randomization method

Mendelian randomization (MR) is an instrumental variable approach used to infer causal relationships between exposures and outcomes, which is becoming increasingly popular because of its ability to handle summary statistics from genome-wide association studies. However, existing MR approaches often suffer the bias from weak instrumental variables, horizontal pleiotropy and sample overlap. We introduce MRBEE (MR using bias-corrected estimating equation), a multivariable MR method capable of simultaneously removing weak instrument and sample overlap bias and identifying horizontal pleiotropy. Our extensive simulations and real data analyses reveal that MRBEE provides nearly unbiased estimates of causal effects, well-controlled type I error rates and higher power than comparably robust methods and is computationally efficient. Our real data analyses result in consistent causal effect estimates and offer valuable guidance for conducting multivariable MR studies, elucidating the roles of pleiotropy, and identifying total 42 horizontal pleiotropic loci missed previously that are associated with myopia, schizophrenia, and coronary artery disease.

Using Genetics to Investigate Relationships between Phenotypes: Application to Endometrial Cancer

Genome-wide association studies (GWAS) have accelerated the exploration of genotype-phenotype associations, facilitating the discovery of replicable genetic markers associated with specific traits or complex diseases. This narrative review explores the statistical methodologies developed using GWAS data to investigate relationships between various phenotypes, focusing on endometrial cancer, the most prevalent gynecological malignancy in developed nations. Advancements in analytical techniques such as genetic correlation, colocalization, cross-trait locus identification, and causal inference analyses have enabled deeper exploration of associations between different phenotypes, enhancing statistical power to uncover novel genetic risk regions. These analyses have unveiled shared genetic associations between endometrial cancer and many phenotypes, enabling identification of novel endometrial cancer risk loci and furthering our understanding of risk factors and biological processes underlying this disease. The current status of research in endometrial cancer is robust; however, this review demonstrates that further opportunities exist in statistical genetics that hold promise for advancing the understanding of endometrial cancer and other complex diseases.

Gaining new insights into the etiology of ulcerative colitis through a cross-tissue transcriptome-wide association study

Background

Genome-wide association studies (GWASs) have identified 38 loci associated with ulcerative colitis (UC) susceptibility, but the risk genes and their biological mechanisms remained to be comprehensively elucidated.

Methods

Multi-marker analysis of genomic annotation (MAGMA) software was used to annotate genes on GWAS summary statistics of UC from FinnGen database. Genetic analysis was performed to identify risk genes. Cross-tissue transcriptome-wide association study (TWAS) using the unified test for molecular signatures (UTMOST) was performed to compare GWAS summary statistics with gene expression matrix (from Genotype-Tissue Expression Project) for data integration. Subsequently, we used FUSION software to select key genes from the individual tissues. Additionally, conditional and joint analysis was conducted to improve our understanding on UC. Fine-mapping of causal gene sets (FOCUS) software was employed to accurately locate risk genes. The results of the four genetic analyses (MAGMA, UTMOST, FUSION and FOCUS) were combined to obtain a set of UC risk genes. Finally, Mendelian randomization (MR) analysis and Bayesian colocalization analysis were conducted to determine the causal relationship between the risk genes and UC. To test the robustness of our findings, the same approaches were taken to verify the GWAS data of UC on IEU.

Results

Multiple correction tests screened PIM3 as a risk gene for UC. The results of Bayesian colocalization analysis showed that the posterior probability of hypothesis 4 was 0.997 and 0.954 in the validation dataset. MR was conducted using the inverse variance weighting method and two single nucleotide polymorphisms (SNPs, rs28645887 and rs62231924) were included in the analysis (p < 0.001, 95%CI: 1.45-1.89). In the validation dataset, MR result was p < 0.001, 95%CI: 1.19-1.72, indicating a clear causal relationship between PIM3 and UC.

Conclusion

Our study validated PIM3 as a key risk gene for UC and its expression level may be related to the risk of UC, providing a novel reference for further improving the current understanding on the genetic structure of UC.

Causal relationship between type 2 diabetes and glioblastoma: bidirectional Mendelian randomization analysis

As the prevalence of Type 2 Diabetes Mellitus (T2DM) and Glioblastoma (GBM) rises globally, the relationship between T2DM and GBM remains controversial. This study aims to investigate whether genetically predicted T2DM is causally associated with GBM. We performed bidirectional Mendelian randomization (MR) analysis using data from genome-wide studies on T2DM (N = 62,892) and GBM (N = 218,792) in European populations. The results of the inverse-variance weighted (IVW) approach served as the primary outcomes. We applied Cochran's Q test and MR-Egger regression for heterogeneity assessment. Leave-one-out analysis was used to evaluate whether any single SNP significantly influenced the observed effect. Our findings reveal a significant causal association between T2DM and an increased risk of GBM (OR [95% CI] 1.70 [1.09, 2.65], P = 0.019). Conversely, the reverse association between T2DM and GBM was insignificant (OR [95% CI] 1.00 [0.99, 1.01], P = 0.408) (P > 0.40). Furthermore, the results from Cochran's Q-test and funnel plots in the MR-Egger method indicated no evidence of pleiotropy between the SNPs and GBM. Additionally, we mapped causal SNPs to genes and identified 10 genes, including MACF1, C1orf185, PTGFRN, NOTCH2, ABCB10, GCKR, THADA, RBMS1, SPHKAP, and PPARG, located on chromosomes 1, 2, and 3. These genes are involved in key biological processes such as the BMP signaling pathway and various metabolic pathways relevant to both conditions. This study provides robust evidence of a significant causal relationship between T2DM and an increased risk of GBM. The identified SNP-mapped genes highlight potential biological mechanisms underlying this association.

Casual effects of telomere length on sarcoidosis: a bidirectional Mendelian randomization analysis

Background

Telomere length, crucial for genomic stability, have been implicated in various inflamm-aging diseases, but their role in sarcoidosis remains unexplored.

Objective

This study aims to explore the casual effects between TL and sarcoidosis via a bidirectional Mendelian Randomization (MR) study.

Methods

We examined single nucleotide polymorphisms (SNPs) associated with TL and sarcoidosis, utilizing available open-access genome-wide association study (GWAS) databases from the UK Biobank and FinnGen. We employed five MR techniques, including Inverse Variance Weighted (IVW), MR Egger, weighted median (WM), Robust adjusted profile score (RAPS), and Maximum likelihood, to assess causal relationships and explore pleiotropy.

Results

Summary data extracted from GWAS datasets of TL (n = 472,174) and (n = 217,758) of European ancestry. Employing 130 SNPs with genome-wide significance as instrumental factors for TL, we detect a significant negative correlation between TL and sarcoidosis (OR: 0.682, 95% confidence interval: 0.524-0.888, p : 0.0045). Similarly, utilizing 6 SNPs with genome-wide significance as instrumental factors for sarcoidosis, we fail to identify a noteworthy association between sarcoidosis and TL (OR: 0.992, 95% confidence interval: 0.979-1.005, p : 0.2424).

Conclusion

Our results suggest that longer telomeres may reduce the risk of sarcoidosis, highlighting TL as a potential biomarker for diagnosis and long-term monitoring. Understanding the critical role of telomere shortening enables more effective focus on diagnosing, treating, and curing sarcoidosis linked to telomeres. Clinical investigations into treatments that enhance TL are warranted.

Insights into inflammation and implications for the pathogenesis and long-term outcomes of endometrial cancer: genome-wide surveys and a clinical cohort study

BACKGROUND

Despite evidence showing a connection between inflammation and endometrial cancer (EC) risk, the surveys on genetic correlation and cohort studies investigating the impact on long-term outcomes have yet to be refined. We aimed to address the impact of inflammation factors on the pathogenesis, progression and consequences of EC.

METHODS

For the genetic correlation analyses, a two-sample of Mendelian randomization (MR) study was applied to investigate inflammation-related single-nucleotide polymorphisms involved with endometrial cancer from GWAS databases. The observational retrospective study included consecutive patients diagnosed with EC (stage I to IV) with surgeries between January 2010 and October 2020 at the Cancer Hospital of Shantou University Medical College.

RESULTS

The 2-sample MR surveys indicated no causal relationship between inflammatory cytokines and endometrial cancer. 780 cases (median age, 55.0 years ) diagnosed with EC were included in the cohort and followed up for an average of 6.8 years. Increased inflammatory parameters at baseline were associated with a higher FIGO stage and invasive EC risk (odds ratios [OR] 1.01 to 4.20). Multivariate-cox regression suggested that multiple inflammatory indicators were significantly associated with overall survival (OS) and progression-free survival (PFS) (P < 0.05). Nomogram models based on inflammatory risk and clinical factors were developed for OS and PFS with C-index of 0.811 and 0.789, respectively. LASSO regression for the validation supported the predictive efficacy of inflammatory and clinical factors on the long-term outcomes of EC.

CONCLUSIONS

Despite the fact that the genetic surveys did not show a detrimental impact of inflammatory cytokines on the endometrial cancer risk, our cohort study suggested that inflammatory level was associated with the progression and long-term outcomes of EC. This evidence may contribute to new strategies targeted at decreasing inflammation levels during EC therapy.

Causal links between serum micronutrients and epilepsy: a Mendelian randomization analysis

Background

Micronutrient levels play a critical role in epilepsy. This study investigates the impact of micronutrient levels on epilepsy via Mendelian randomization (MR).

Methods

A two-sample MR framework evaluated the genetic association between 15 serum micronutrients and epilepsy phenotypes. The analysis included calcium, iron, zinc, selenium, copper, magnesium, potassium, folate, vitamins B6, B12, C, D, E, retinol, and carotene against all epilepsy, generalized epilepsy, childhood absence epilepsy (CAE), juvenile absence epilepsy (JAE), juvenile myoclonic epilepsy (JME), generalized tonic-clonic seizures alone and with spike-wave electroencephalography (GTCS), and various focal epilepsy phenotypes [with hippocampal sclerosis (HS), lesions other than HS, lesion-negative]. The random-effects inverse-variance weighted (IVW) model was the primary method used, supported by heterogeneity and pleiotropy assessments. Multivariable Mendelian randomization analyses (MVMR) were used to identify micronutrients that are significantly causally associated with different epilepsy subtypes and to confirm the most potential causal risk factors for these subtypes.

Results

Zinc conferred an increased risk of focal epilepsy with HS (OR = 1.01; p = 0.045). Carotene was similarly linked to higher risks of lesion-negative cases (OR = 1.129; p = 0.037). Conversely, vitamin B6 was associated with reduced risks of focal epilepsy with HS (OR = 0.949; p = 0.020), and vitamin D was linked to decreased risks of both CAE (OR = 0.976, 95% CI: 0.959-0.993, p = 0.006) and JAE (OR = 0.986, 95% CI: 0.973-0.999, p = 0.032). These associations were robust, showing minimal heterogeneity and no evidence of pleiotropy across various sensitivity analyses. After adjustment using MVMR, significant causal relationships between vitamin D and both CAE and JAE remained. Furthermore, the causal relationship between zinc and vitamin B6 on focal epilepsy with HS became non-significant, while carotene shifted from a risk factor to a protective factor for focal epilepsy lesion-negative after adjusting for vitamin D.

Conclusion

MR estimates provide robust evidence for the causal effects of vitamin D on reducing the risk of CAE, and JAE, which might provide alternative treatment strategies.

Gut microbiota linked to hydrocephalus through inflammatory factors: a Mendelian randomization study

Background

The gut microbiota (GM) has been implicated in neurological disorders, but the relationship with hydrocephalus, especially the underlying mechanistic pathways, is unclear. Using Mendelian randomization (MR), we aim to discover the mediating role of inflammatory factors in the relationship between GM and hydrocephalus.

Methods

After removing confounders, univariable and multivariable MR analyses were performed using summary statistics to assess the causal relationships between GM, inflammatory factors (IL-17A and IL-27), and types of hydrocephalus. Meta-analyses were used to reconcile the differences in MR results between different hydrocephalus sources. Finally, mediator MR analyses were applied to determine the mediating effect of inflammatory factors. Various sensitivity analysis methods were employed to ensure the reliability and stability of the results.

Results

After correction for P-values, Firmicutes (phylum) (OR, 0.34; 95%CI, 0.17-0.69; P = 2.71E-03, P FDR = 2.44E-02) significantly reduced the risk of obstructive hydrocephalus. The remaining 18 different taxa of GM had potential causal relationships for different types of hydrocephalus. In addition, Firmicutes (phylum) decreased the risk of obstructive hydrocephalus by increasing levels of IL-17A (mediating effect = 21.01%), while Eubacterium ruminantium group (genus) increased the risk of normal-pressure hydrocephalus by decreasing levels of IL-27 (mediating effect = 7.48%).

Conclusion

We reveal the connection between GM, inflammatory factors (IL-17A and IL-27), and hydrocephalus, which lays the foundation for unraveling the mechanism between GM and hydrocephalus.

Causal inference in the field of arrhythmia: An introduction to mendelian randomization

Mendelian randomization (MR) uses genetic variants associated with an exposure (eg, high blood pressure) as instrumental variables to test causal effects on an outcome (eg, atrial fibrillation [AF]). By leveraging the random assortment of genetic variants during gamete formation, MR reduces biases like confounding and reverse causation. We screened 391 papers, examining 277 that applied MR to investigate arrhythmia and, in others, cardiovascular traits, lifestyle, behavioral traits, and body composition. Our analysis focused on MR studies of arrhythmia and cardiovascular traits. Key findings highlight high systolic blood pressure, low resting heart rate, elevated cardiac troponin I levels, coronary artery disease, and heart failure as risk factors for AF, whereas AF itself increases heart failure risk. As genetic data become more accessible, MR's relevance grows. Sensitivity analyses and integrating MR with other methodologies in a triangulation framework enhance the robustness of causal inferences by navigating different biases.

Causal effects of denture wearing on epigenetic age acceleration and the mediating pathways: a mendelian randomization study

BACKGROUND

The epigenetic-age acceleration (EAA) represents the difference between chronological age and epigenetic age, reflecting accelerated biological aging. Observational studies suggested that oral disorders may impact DNA methylation patterns and aging, but their causal relationship remains largely unexplored. This study aimed to investigate potential causal associations between dental traits and EAA, as well as to identify possible mediators.

METHODS

Using summary statistics of genome-wide association studies of predominantly European ancestry, we conducted univariable and multivariable Mendelian randomization (MR) to estimate the overall and independent effects of ten dental traits (dentures, bleeding gums, painful gums, loose teeth, toothache, ulcers, periodontitis, number of teeth, and two measures of caries) on four EAA subtypes (GrimAge acceleration [GrimAA], PhenoAge acceleration [PhenoAA], HannumAge acceleration [HannumAA] and intrinsic EAA [IEAA]), and used two-step Mendelian randomization to evaluate twelve potential mediators of the associations. Comprehensive sensitivity analyses were used to verity the robustness, heterogeneity, and pleiotropy.

RESULTS

Univariable inverse variance weighted MR analyses revealed a causal effect of dentures on greater GrimAA (β: 2.47, 95% CI: 0.93-4.01, p = 0.002), PhenoAA (β: 3.00, 95% CI: 1.15-4.85, p = 0.001), and HannumAA (β: 1.96, 95% CI: 0.58-3.33, p = 0.005). In multivariable MR, the associations remained significant after adjusting for periodontitis, caries, number of teeth and bleeding gums. Three out of 12 aging risk factors were identified as mediators of the association between dentures and EAA, including body mass index, body fat percentage, and waist circumference. No evidence for reverse causality and pleiotropy were detected (p > 0.05).

CONCLUSIONS

Our findings supported the causal effects of genetic liability for denture wearing on epigenetic aging, with partial mediation by obesity. More attention should be paid to the obesity-monitoring and management for slowing EAA among denture wearers.

Causal effects of gut microbiota on risk of interstitial cystitis: a two-sample Mendelian randomization study

Background

The correlation between gut microbiota and interstitial cystitis has garnered significant attention in previous studies. Nevertheless, the causal relationship between them remains to be clarified.

Methods

Genetic variation serves as a tool in Mendelian randomization analyses, facilitating the inference of causal relationships between exposure factors and disease outcomes. In this study, summary statistics derived from a comprehensive genome-wide association study conducted by the MiBioGen consortium were utilized as exposure factors, while interstitial cystitis data sourced from the GWAS Catalog served as the disease outcome. Then, a two-sample Mendelian randomization analysis was performed by applying inverse variance-weighted, MR-Egger, Weighted Median, Simple Mode, and Weighted Mode. In addition, heterogeneity and horizontal pleiotropy were excluded by sensitivity analysis.

Results

IVW results confirmed that genus Haemophilus (OR = 2.20, 95% CI: 1.16-4.15, p = 0.015), genus Butyricimonas (OR = 2.26, 95% CI: 1.15-4.45, p = 0.018), genus Bacteroides (OR = 4.27, 95% CI: 1.36-13.4, p = 0.013) and Coprococcus1 (OR = 3.39, 95% CI: 1.28-8.99, p = 0.014) had a risk effect on interstitial cystitis. Sensitivity analysis did not find outlier SNPs.

Conclusion

Our analysis has identified a causal relationship between specific genera and interstitial cystitis. However, further validation through randomized controlled trials is essential to substantiate these findings.

Trauma, adversity, and biological aging: behavioral mechanisms relevant to treatment and theory

Although stress and adversity are largely universal experiences, people exposed to greater hardship are at increased risk for negative health consequences. Recent studies identify accelerated biological aging as a mechanism that could explain how trauma and adversity gives rise to poor health, and advances in this area of study coincide with technological innovations in the measurement of biological aging, particularly epigenetic profiles consistent with accelerated aging derived from DNA methylation. In this review, we provide an overview of the current literature examining how adversity might accelerate biological aging, with a specific focus on social and health behaviors. The most extensive evidence in this area suggests that health-compromising behaviors, particularly smoking, may partially explain the association between adversity and accelerated aging. Although there is relatively less published support for the role of social behaviors, emerging evidence points to the importance of social connection as a mechanism for future study. Our review highlights the need to determine the extent to which the associations from adversity to accelerated aging are consistent with causal processes. As we consider these questions, the review emphasizes methodological approaches from the causal inference literature that can help deepen our understanding of how stress and trauma might result in poor health. The use of these methodologies will help provide evidence as to which behavioral interventions might slow aging and improve health, particularly among populations that more often experience adversity and trauma.

Exploring the causal relationship between glutamine metabolism and leukemia risk: a Mendelian randomization and LC-MS/MS analysis

Objective

This investigation sought to delineate the causal nexus between plasma glutamine concentrations and leukemia susceptibility utilizing bidirectional Mendelian Randomization (MR) analysis and to elucidate the metabolic ramifications of asparaginase therapy on glutamine dynamics in leukemia patients.

Methods

A bidirectional two-sample MR framework was implemented, leveraging genetic variants as instrumental variables from extensive genome-wide association studies (GWAS) tailored to populations of European descent. Glutamine quantification was executed through a rigorously validated Liquid Chromatography-Mass Spectrometry/Mass Spectrometry (LC-MS/MS) protocol. Comparative analyses of glutamine levels were conducted across leukemia patients versus healthy controls, pre- and post-asparaginase administration. Statistical evaluations employed inverse variance weighted (IVW) models, MR-Egger regression, and sensitivity tests addressing pleiotropy and heterogeneity.

Results

The MR findings underscored a significant inverse association between glutamine levels and leukemia risk (IVW p = 0.03558833), positing lower glutamine levels as a contributory factor to heightened leukemia susceptibility. Conversely, the analysis disclosed no substantive causal impact of leukemia on glutamine modulation (IVW p = 0.9694758). Notably, post-asparaginase treatment, a marked decrement in plasma glutamine concentrations was observed in patients (p = 0.0068), underlining the profound metabolic influence of the therapeutic regimen.

Conclusion

This study corroborates the hypothesized inverse relationship between plasma glutamine levels and leukemia risk, enhancing our understanding of glutamine's role in leukemia pathophysiology. The pronounced reduction in glutamine levels following asparaginase intervention highlights the critical need for meticulous metabolic monitoring to refine therapeutic efficacy and optimize patient management in clinical oncology. These insights pave the way for more tailored and efficacious treatment modalities in the realm of personalized medicine.

The causal relationship between gut microbiota and diabetic neuropathy: a bi-directional two-sample Mendelian randomization study

Background

Many studies suggest a strong correlation between gut microbiota (GM) and diabetic neuropathy (DN). However, the precise causal relationship between GM and DN has yet to be fully elucidated. Hence, a bi-directional Mendelian randomization (MR) analysis was used to examine the association between GM and DN.

Methods

Widely known genome-wide association study (GWAS) of GM was collected from the MiBio Gen project. Summary-level datasets for DN were taken from the FinnGen project. Inverse variance weighted approach was used for evaluating the causal relationship between GM and DN. Subsequently, pleiotropy and heterogeneity tests were performed to verify the reliability of the data. Furthermore, a bidirectional two-sample MR analysis was done to investigate the directionality of the causal relationships. Gene Ontology analysis was conducted to identify the associations that could indicate biological functions.

Results

We identified potential causal associations between GM and DN (p< 0.05 in all three MR methods). Among them, we found increased levels of Christensenellaceae R-7 (Odds ratio, OR= 1.52; 95% confidence interval, CI = 1.03-2.23; p = 0.03), Ruminococcaceae UCG013 (OR =1.35; 95% CI = 1.00-1.85; p = 0.04), and Eggerthella groups (OR = 1.27; 95% CI = 1.05-1.55; p = 0.01), which may be associated with a higher risk of DN, while increased levels of Peptococcaceae (OR = 0.69; 95% CI = 0.54-0.90; p< 0.01) and Eubacterium coprostanoligenes groups (OR = 0.68; 95% CI = 0.49-0.93; p = 0.01) could be associated with a lower risk. Gene Ontology pathway analysis revealed enrichment of genes regulated by the associated single-nucleotide polymorphisms (SNPs) in the apical plasma membrane, glycosyltransferase activity, hexosyltransferase activity and membrane raft. Reverse MR analyses indicated that DN was associated with five microbial taxa in all three MR methods.

Conclusion

The results of our study validate the possible causative relationship between GM and DN. This discovery gives new perspectives into the mechanism on how GM influences DN, and establishes a theoretical foundation for future investigations into targeted preventive measures.

Causal relationship between Alzheimer's disease and unstable angina: a bidirectional Mendelian randomization analysis

Background

Research from observational studies has demonstrated a link between Alzheimer's disease (AD) and a higher risk of cardiovascular disease (CVD). Uncertainty surrounds the exact genetic cause of AD and coronary heart disease, particularly unstable angina (UA). Mendelian randomization (MR) analysis was used to examine the causal genetic link between AD and UA to evaluate the impact of AD on UA.

Methods

The purpose of the bidirectional MR analysis was to investigate the link between exposure and illness causation. Genetic instrumental variables for AD were obtained from European populations using genome-wide association studies (GWAS). The primary causal conclusions were obtained using the inverse variance weighted approach (IVW), and other sensitivity analysis techniques were employed. Sensitivity analyses were carried out to evaluate heterogeneity and horizontal pleiotropy to guarantee accurate MR results.

Results

An elevated risk of UA was linked to genetically predicted AD (IVW: OR=3.439, 95% CI: 1.565-7.555, P=0.002). A substantial genetic relationship between UA and the risk of AD was not supported by any evidence in the reverse study (IVW: OR=0.998, 95% CI: 0.995-1.001, P=0.190). Various MR techniques produced consistent results. Sensitivity analysis revealed no discernible heterogeneity or horizontal pleiotropy.

Conclusions

One risk factor for UA that we found in our bidirectional Mendelian randomization trial was AD. This highlights the necessity of researching the underlying molecular mechanisms linked to AD and UA as well as the possibility of creating individualized treatment plans based on genetic data.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

Intestinal microbiota and tuberculosis: Insights from Mendelian randomization

Respiratory tuberculosis (RTB), a global health concern affecting millions of people, has been observationally linked to the gut microbiota, but the depth and nature of this association remain elusive. Despite these findings, the underlying causal relationship is still uncertain. Consequently, we used the Mendelian randomization (MR) method to further investigate this potential causal connection. We sourced data on the gut microbiota from a comprehensive genome-wide association study (GWAS) conducted by the MiBioGen Consortium (7686 cases, and 115,893 controls). For RTB, we procured 2 distinct datasets, labeled the Fingen R9 TBC RESP and Fingen R9 AB1 RESP, from the Finnish Genetic Consortium. To decipher the potential relationship between the gut microbiota and RTB, we employed MR on both datasets. Our primary mode of analysis was the inverse variance weighting (IVW) method. To ensure robustness and mitigate potential confounders, we meticulously evaluated the heterogeneity and potential pleiotropy of the outcomes. In the TBC RESP (RTB1) dataset related to the gut microbiota, the IVW methodology revealed 7 microbial taxa that were significantly associated with RTB. In a parallel vein, the AB1 RESP (RTB2) dataset highlighted 4 microbial taxa with notable links. Notably, Lachnospiraceae UCG010 was consistently identified across both datasets. This correlation was especially evident in the data segments designated Fingen R9 TBC RESP (OR = 1.799, 95% CI = 1.243-2.604) and Finngen R9 AB1 RESP (OR = 2.131, 95% CI = 1.088-4.172). Our study identified a causal relationship between particular gut microbiota and RTB at the level of prediction based on genetics. This discovery sheds new light on the mechanisms of RTB development, which are mediated by the gut microbiota.

Identifying novel potential drug targets for endometriosis via plasma proteome screening

Background

Endometriosis (EM) is a chronic painful condition that predominantly affects women of reproductive age. Currently, surgery or medication can only provide limited symptom relief. This study used a comprehensive genetic analytical approach to explore potential drug targets for EM in the plasma proteome.

Methods

In this study, 2,923 plasma proteins were selected as exposure and EM as outcome for two-sample Mendelian randomization (MR) analyses. The plasma proteomic data were derived from the UK Biobank Pharmaceutical Proteomics Project (UKB-PPP), while the EM dataset from the FinnGen consortium R10 release data. Several sensitivity analyses were performed, including summary-data-based MR (SMR) analyses, heterogeneity in dependent instruments (HEIDI) test, reverse MR analyses, steiger detection test, and bayesian co-localization analyses. Furthermore, proteome-wide association study (PWAS) and single-cell transcriptomic analyses were also conducted to validate the findings.

Results

Six significant (p < 3.06 × 10-5) plasma protein-EM pairs were identified by MR analyses. These included EPHB4 (OR = 1.40, 95% CI: 1.20 - 1.63), FSHB (OR = 3.91, 95% CI: 3.13 - 4.87), RSPO3 (OR = 1.60, 95% CI: 1.38 - 1.86), SEZ6L2 (OR = 1.44, 95% CI: 1.23 - 1.68) and WASHC3 (OR = 2.00, 95% CI: 1.54 - 2.59) were identified as risk factors, whereas KDR (OR = 0.80, 95% CI: 0.75 - 0.90) was found to be a protective factor. All six plasma proteins passed the SMR test (P < 8.33 × 10-3), but only four plasma proteins passed the HEIDI heterogeneity test (PHEIDI > 0.05), namely FSHB, RSPO3, SEZ6L2 and EPHB4. These four proteins showed strong evidence of co-localization (PPH4 > 0.7). In particular, RSPO3 and EPHB4 were replicated in the validated PWAS. Single-cell analyses revealed high expression of SEZ6L2 and EPHB4 in stromal and epithelial cells within EM lesions, while RSPO3 exhibited elevated expression in stromal cells and fibroblasts.

Conclusion

Our study identified FSHB, RSPO3, SEZ6L2, and EPHB4 as potential drug targets for EM and highlighted the critical role of stromal and epithelial cells in disease development. These findings provide new insights into the diagnosis and treatment of EM.

Potential association of rheumatic diseases with bone mineral density and fractures: a bi-directional mendelian randomization study

BACKGROUND

Previous studies have implicated rheumatoid arthritis as an independent risk factor for bone density loss. However, whether there is a causal relationship between rheumatic diseases and bone mineral density (BMD) and fractures is still controversial. We employed a bidirectional Mendelian analysis to explore the causal relationship between rheumatic diseases and BMD or fractures.

METHODS

The rheumatic diseases instrumental variables (IVs) were obtained from a large Genome-wide association study (GWAS) meta-analysis dataset of European descent. Analyses were performed for the three rheumatic diseases: ankylosing spondylitis (AS) (n = 22,647 cases, 99,962 single nucleotide polymorphisms [SNPs]), rheumatoid arthritis (RA) (n = 58,284 cases, 13,108,512 SNPs), and systemic lupus erythematosus (SLE) (n = 14,267 cases, 7,071,163 SNPs). Two-sample Mendelian randomization (MR) analyses were carried out by using R language TwoSampleMR version 0.5.7. The inverse-variance weighted (IVW), MR-Egger, and weighted median methods were used to analyze the causal relationship between rheumatic diseases and BMD or fracture.

RESULTS

The MR results revealed that there was absence of evidence for causal effect of AS on BMD or fracture. However, there is a positive causal relationship of RA with fracture of femur (95% CI = 1.0001 to 1.077, p = 0.046), and RA and fracture of forearm (95% CI = 1.015 to 1.064, p = 0.001). SLE had positive causal links for fracture of forearm (95% CI = 1.004 to 1.051, p = 0.020). Additionally, increasing in heel bone mineral density (Heel-BMD) and total bone mineral density (Total-BMD) can lead to a reduced risk of AS without heterogeneity or pleiotropic effects. The results were stable and reliable. There was absence of evidence for causal effect of fracture on RA (95% CI = 0.929 to 1.106, p = 0.759), and fracture on SLE (95% CI = 0.793 to 1.589, p = 0.516).

CONCLUSIONS

RA and SLE are risk factors for fractures. On the other hand, BMD increasing can reduce risk of AS. Our results indicate that rheumatic diseases may lead to an increased risk of fractures, while increased BMD may lead to a reduced risk of rheumatic diseases. These findings provide insight into the risk of BMD and AS, identifying a potential predictor of AS risk as a reduction in BMD.

Exploring Causal Relationships Between Gut Microbiota and Alzheimer's Disease: A Bidirectional Mendelian Randomization Study

Background

Many observational studies have investigated the link between the gut microbiota and Alzheimer's disease (AD), but the causality remains uncertain.

Objective

This study aimed to evaluate the causal impact of gut microbiota on AD.

Methods

A two-sample Mendelian randomization (MR) study was conducted employing summary data. Summary statistics for AD were from the latest genome-wide association study (cases and proxy cases: 85,934; controls: 401,577). Summary data for gut microbiota were acquired from MiBioGen consortium. Causal effect estimations primarily relied on the inverse variance weighting method along with the sensitivity analyses for testing for pleiotropy and heterogeneity. Additionally, reverse MR analyses were performed to examine potential reverse causality.

Results

Seven gut microbiota were identified as associated with AD risk. Order Selenomonadales (odds ratio [OR] 1.13, 95% confidence interval [CI] 1.03-1.24, p = 0.01), Family Pasteurellaceae (OR 1.07, 95% CI 1.01-1.13, p = 0.01), and Genus Methanobrevibacter (OR 1.07, 95% CI 1.00-1.13, p = 0.04) were correlated with an elevated likelihood of AD, while Class Mollicutes (OR 0.87, 95% CI 0.79-0.95, p = 0.00), Genus Ruminiclostridium9 (OR 0.87, 95% CI 0.78-0.97, p = 0.01), Genus Clostridiuminnocuumgroup (OR 0.94, 95% CI 0.89-0.99, p = 0.03), and Genus Eggerthella (OR 0.94, 95% CI 0.89-1.00, p = 0.04) exerted beneficial impact in mitigating AD. No statistically significant reverse causality was found between AD and each of these seven specific gut microbiota species.

Conclusions

This study unveiled a causal link between certain gut microbiota and AD, offering new insights for advancing clinical treatments.

Unravelling the role of gut microbiota in acute pancreatitis: integrating Mendelian randomization with a nested case-control study

Background

Gut microbiota may influence the development of acute pancreatitis (AP), a serious gastrointestinal disease with high morbidity and mortality. This study aimed to identify a causal link by investigating the relationship between gut microbiota and AP.

Methods

Mendelian randomization (MR) and a nested case-control study were used to explore associations between gut microbiota composition and AP. 16S rRNA sequencing, random forest modelling (RF), support vector machine (SVM), and Kaplan-Meier survival analysis was applied to identify significant gut microbiota and their correlation with hospitalization duration in AP patients.

Results

Bidirectional MR results confirmed a causal link between specific gut microbiota and AP (15 and 8 microbial taxa identified via forward and reverse MR, respectively). The 16S rRNA sequencing analysis demonstrated a pronounced difference in gut microbiota composition between cases and controls. Notably, after a comprehensive evaluation of the results of RF and SVM, Bacteroides plebeius (B. plebeius) was found to play a significant role in influencing the hospital status. Using a receiver operating characteristic (ROC) curve, the predictive power (0.757) of B. plebeius. Kaplan-Meier survival analysis offered further insight that patients with an elevated abundance of B. plebeius experienced prolonged hospital stays.

Conclusion

Combining MR with nested case-control studies provided a detailed characterization of interactions between gut microbiota and AP. B. plebeius was identified as a significant contributor, suggesting its role as both a precursor and consequence of AP dynamics. The findings highlight the multifactorial nature of AP and its complex relationship with the gut microbiota. This study lays the groundwork for future therapeutic interventions targeting microbial dynamics in AP treatment.

Mendelian randomization study of lipid metabolism characteristics and migraine risk

BACKGROUND

The association between serum lipids and migraine is controversial. However, randomized controlled trials have suggested that statins may be efficacious for the prevention of migraine. In this study, we aim to investigate the relationship between lipids metabolism and migraine risk.

METHODS

Single-nucleotide polymorphisms (SNPs), relating to the serum lipid traits and the effect of lipid-lowering drugs that target APOB, CETP, HMGCR, NPC1L1, and PCSK9, were extracted from genome-wide association studies (GWAS) summary data. The GWAS summary data were obtained from the Global Lipids Genetic Consortium (GLGC), the UK Biobank, and the FinnGen study, respectively. Mendelian randomization (MR) analysis was performed to evaluate the association between serum lipid traits and lipid-lowering drugs with migraine risk.

RESULTS

Regarding serum lipids, it was found that SNPs related to high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), total cholesterol (TC), or triglycerides (TG) levels were not associated with migraine, migraine with aura (MA) or migraine without aura (MO). In addition, genotypes of HMGCR related to higher LDL-C levels were associated with increased risk of migraine (OR = 1.46, p = 0.035) and MA (OR = 2.03, p = 0.008); However, genotypes of PCSK9 related to higher LDL-C levels were associated with decreased risk of migraine (OR = 0.75, p = 0.001) and MA (OR = 0.69, p = 0.004); And genotypes of APOB related to higher LDL-C levels were associated with decreased risk of MO (OR = 0.62, p = 0.000).

CONCLUSIONS

There is a relationship between lipid metabolism characteristics and migraine risk.

SIGNIFICANCE

Based on the genome-wide association summary data, single-nucleotide polymorphisms (SNPs) related to high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), total cholesterol (TC), or triglycerides (TG) level were not associated with risk of migraine, migraine with aura (MA) or migraine without aura (MO). However, genotypes of HMGCR related to higher LDL-C levels have shown an increased risk on migraine and MA. And genotypes of APOB or PCSK9 related to higher LDL-C levels have shown a decreased risk on MO, or migraine and MA, respectively. These results suggested that there may be a relationship between lipid metabolism characteristics and the risk for migraine development.

Towards a Novel Frontier in the Use of Epigenetic Clocks in Epidemiology

Health problems associated with aging are a major public health concern for the future. Aging is a complex process with wide intervariability among individuals. Therefore, there is a need for innovative public health strategies that target factors associated with aging and the development of tools to assess the effectiveness of these strategies accurately. Novel approaches to measure biological age, such as epigenetic clocks, have become relevant. These clocks use non-sequential variable information from the genome and employ mathematical algorithms to estimate biological age based on DNA methylation levels. Therefore, in the present study, we comprehensively review the current status of the epigenetic clocks and their associations across the human phenome. We emphasize the potential utility of these tools in an epidemiological context, particularly in evaluating the impact of public health interventions focused on promoting healthy aging. Our review describes associations between epigenetic clocks and multiple traits across the life and health span. Additionally, we highlighted the evolution of studies beyond mere associations to establish causal mechanisms between epigenetic age and disease. We explored the application of epigenetic clocks to measure the efficacy of interventions focusing on rejuvenation.

Causal relationship and shared genes between air pollutants and amyotrophic lateral sclerosis: A large-scale genetic analysis

OBJECTIVE

Air pollutants have been reported to have a potential relationship with amyotrophic lateral sclerosis (ALS). The causality and underlying mechanism remained unknown despite several existing observational studies. We aimed to investigate the potential causality between air pollutants (PM2.5, NOX, and NO2) and the risk of ALS and elucidate the underlying mechanisms associated with this relationship.

METHODS

The data utilized in our study were obtained from publicly available genome-wide association study data sets, in which single nucleotide polymorphisms (SNPs) were employed as the instrumental variantswith three principles. Two-sample Mendelian randomization and transcriptome-wide association (TWAS) analyses were conducted to evaluate the effects of air pollutants on ALS and identify genes associated with both pollutants and ALS, followed by regulatory network prediction.

RESULTS

We observed that exposure to a high level of PM2.5 (OR: 2.40 [95% CI: 1.26-4.57], p = 7.46E-3) and NOx (OR: 2.35 [95% CI: 1.32-4.17], p = 3.65E-3) genetically increased the incidence of ALS in MR analysis, while the effects of NO2 showed a similar trend but without sufficient significance. In the TWAS analysis, TMEM175 and USP35 turned out to be the genes shared between PM2.5 and ALS in the same direction.

CONCLUSION

Higher exposure to PM2.5 and NOX might causally increase the risk of ALS. Avoiding exposure to air pollutants and air cleaning might be necessary for ALS prevention.

Mendelian randomization analyses reveal causal relationships between brain functional networks and risk of psychiatric disorders

Dysfunction of brain resting-state functional networks has been widely reported in psychiatric disorders. However, the causal relationships between brain resting-state functional networks and psychiatric disorders remain largely unclear. Here we perform bidirectional two-sample Mendelian randomization (MR) analyses to investigate the causalities between 191 resting-state functional magnetic resonance imaging (rsfMRI) phenotypes (n = 34,691 individuals) and 12 psychiatric disorders (n = 14,307 to 698,672 individuals). Forward MR identified 8 rsfMRI phenotypes causally associated with the risk of psychiatric disorders. For example, the increase in the connectivity of motor, subcortical-cerebellum and limbic network was associated with lower risk of autism spectrum disorder. In adddition, increased connectivity in the default mode and central executive network was associated with lower risk of post-traumatic stress disorder and depression. Reverse MR analysis revealed significant associations between 4 psychiatric disorders and 6 rsfMRI phenotypes. For instance, the risk of attention-deficit/hyperactivity disorder increases the connectivity of the attention, salience, motor and subcortical-cerebellum network. The risk of schizophrenia mainly increases the connectivity of the default mode and central executive network and decreases the connectivity of the attention network. In summary, our findings reveal causal relationships between brain functional networks and psychiatric disorders, providing important interventional and therapeutic targets for psychiatric disorders at the brain functional network level.

Use of Mendelian randomization to assess the causal status of modifiable exposures for rheumatic diseases

The explosion in Mendelian randomization (MR) publications is hard to ignore and shows no signs of slowing. Clinician readers, who may not be familiar with jargon-ridden methods, are expected to discern the good from the many low-quality studies that make overconfident claims of causality or stretch the plausibility of what MR can investigate. We aim to equip readers with foundational concepts, contextualized using examples in rheumatology, to appraise the many MR papers that are or will appear in their journals. We highlight the importance of assessing whether exposures are under plausibly specific genetic influence, whether the hypothesized causal pathways make biological sense, and whether results stand up to replication and use of control outcomes. Quality of research can vary substantially using MR as with any design, and all methods have inherent limitations. MR studies have provided and can still contribute valuable insights in the context of evidence triangulation.

The impact of educational attainment on mental health: A Causal Assessment from the UKB and FinnGen Cohorts

Observational studies report inverse associations between educational attainment and depression/anxiety risks, but confounding hinders causal inference. This study aimed to assess potential causal relationships using Mendelian randomization (MR). Two-sample MR analysis was conducted using genetic instruments for education, smoking, body mass index, and physical activity from published genome-wide association studies. Depression and anxiety data came from the UK Biobank ([UKB] 117,782 individuals) and FinnGen (215,644 individuals) cohorts. Inverse variance weighted regression determined associations between exposures and mental health outcomes. Increased educational attainment was causally associated with reduced risks of depression (odds ratio [OR] = 0.99 per year, 95% confidence interval [CI]: 0.990-0.996, P < .001) and anxiety (OR = 0.99, CI: 0.98-0.991, P < .001) in both cohorts. Smoking initiation conferred higher risks of depression (UKB OR = 1.05, CI: 1.03-1.06, P < .001; FinnGen OR = 1.20, CI: 1.10-1.32, P < .001) and anxiety (FinnGen only, OR = 1.10, CI: 1.01-1.21, P < .05). Likewise, maternal smoking history associated with greater depression (UKB OR = 1.15, CI: 1.10-1.35, P = .027) and anxiety susceptibility (FinnGen OR = 3.02, CI: 1.67-5.46, P = .011). Higher body mass index elevated depression risk in both cohorts. Physical activity showed no clear associations. This MR study provides evidence that education may causally reduce mental health disorder risk. Smoking, obesity, and low activity appear detrimentally linked to depression and anxiety. Improving access to education could offer effective strategies for lowering population psychiatric burden.

The genetic architecture of biological age in nine human organ systems

Investigating the genetic underpinnings of human aging is essential for unraveling the etiology of and developing actionable therapies for chronic diseases. Here, we characterize the genetic architecture of the biological age gap (BAG; the difference between machine learning-predicted age and chronological age) across nine human organ systems in 377,028 participants of European ancestry from the UK Biobank. The BAGs were computed using cross-validated support vector machines, incorporating imaging, physical traits and physiological measures. We identify 393 genomic loci-BAG pairs (P < 5 × 10-8) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary and renal systems. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system (organ specificity) while exerting pleiotropic links with other organ systems (interorgan cross-talk). We find that genetic correlation between the nine BAGs mirrors their phenotypic correlation. Further, a multiorgan causal network established from two-sample Mendelian randomization and latent causal variance models revealed potential causality between chronic diseases (for example, Alzheimer's disease and diabetes), modifiable lifestyle factors (for example, sleep duration and body weight) and multiple BAGs. Our results illustrate the potential for improving human organ health via a multiorgan network, including lifestyle interventions and drug repurposing strategies.

Association of obstructive sleep apnea syndrome with polycystic ovary syndrome through bidirectional Mendelian randomization

Background

Observational studies have established a link between polycystic ovary syndrome (PCOS) and obstructive sleep apnea syndrome (OSAS), with obesity being a significant confounding factor that complicates the understanding of causality. This study seeks to clarify the causal relationship by utilizing bidirectional two-sample Mendelian randomization (MR) analysis.

Methods

A bidirectional MR strategy was implemented to investigate the potential causal relationship between PCOS and OSAS. Instrumental variables (IVs) for PCOS were sourced from a dataset comprising 3,609 cases and 229,788 controls. For OSAS, statistical data were obtained from a genome-wide association study (GWAS) involving 38,998 subjects, alongside a control group of 336,659 individuals. Our MR analysis utilized several methods, including inverse variance weighted (IVW), weighted mode, weighted median, simple mode, and MR-Egger, primarily focusing on the IVW technique. Sensitivity tests were conducted to ensure the robustness of our findings.

Results

Utilizing the IVW method, we identified a notable causal association from OSAS to PCOS, with an odds ratio (OR) of 1.463 and a 95% confidence interval (CI) of 1.086-1.971 (p = 0.012). In the opposite direction, PCOS also appeared to significantly affect OSAS development, indicated by an OR of 1.041 and a 95% CI of 1.012-1.072 (p = 0.006). The MR-Egger intercept test showed no evidence of directional pleiotropy, affirming the credibility of our causal findings (p > 0.05).

Conclusion

This study suggests a bidirectional causal relationship between PCOS and an increased risk of OSAS. These insights could guide future screening and prevention strategies for both conditions.

Deciphering the lipid-cancer nexus: comprehensive Mendelian randomization analysis of the associations between lipid profiles and digestive system cancer susceptibility

BACKGROUND

Digestive system cancers represent a significant global health challenge and are attributed to a combination of demographic and lifestyle changes. Lipidomics has emerged as a pivotal area in cancer research, suggesting that alterations in lipid metabolism are closely linked to cancer development. However, the causal relationship between specific lipid profiles and digestive system cancer risk remains unclear.

METHODS

Using a two-sample Mendelian randomization (MR) approach, we elucidated the causal relationships between lipidomic profiles and the risk of five types of digestive system cancer: stomach, liver, esophageal, pancreatic, and colorectal cancers. The aim of this study was to investigate the effect impact of developing lipid profiles on the risk of digestive system cancers utilizing data from public databases such as the GWAS Catalog and the UK Biobank. The inverse‒variance weighted (IVW) method and other strict MR methods were used to evaluate the potential causal links. In addition, we performed sensitivity analyses and reverse MR analyses to ensure the robustness of the results.

RESULTS

Significant causal relationships were identified between certain lipidomic traits and the risk of developing digestive system cancers. Elevated sphingomyelin (d40:1) levels were associated with a reduced risk of developing gastric cancer (odds ratio (OR) = 0.68, P < 0.001), while elevated levels of phosphatidylcholine (16:1_20:4) increased the risk of developing esophageal cancer (OR = 1.31, P = 0.02). Conversely, phosphatidylcholine (18:2_0:0) had a protective effect against colorectal cancer (OR = 0.86, P = 0.036). The bidirectional analysis did not suggest reverse causality between cancer risk and lipid levels. Strict MR methods demonstrated the robustness of the above causal relationships.

CONCLUSION

Our findings underscore the significant causal relationships between specific lipidomic traits and the risk of developing various digestive system cancers, highlighting the potential of lipid profiles in informing cancer prevention and treatment strategies. These results reinforce the value of MR in unraveling complex lipid-cancer interactions, offering new avenues for research and clinical application.

Association between frailty and gestational diabetes mellitus: a bidirectional and multivariable Mendelian randomization study

Background

The causality between frailty and gestational diabetes mellitus (GDM) has not yet been fully explored. A potential bidirectional causality was also needed to be confirmed.

Methods

A bidirectional two-sample Mendelian randomization (MR) was conducted, with frailty-related data was collected from UK Biobank and TwinGen and GDM-related data was collected from the FinnGen consortium. We performed univariable and multivariable-adjusted MR with adjustments for body mass index (BMI). Several methodologies of MR were conducted to confirm the robustness of results.

Results

Frailty was significantly associated with elevated risks of GDM (OR, 3.563; 95% CI, 1.737 to 7.309; P< 0.001) and GDM was also significantly associated with elevated risks of frailty ( β , 0.087; 95% CI, 0.040 to 0.133; P< 0.001). There is no evidence demonstrating the existence of horizontal pleiotropy and heterogeneity. This association was robust after adjustments for BMI. The sensitivity analyses with Weighted median, Maximum likelihood, Penalised weighted median, MR Egger and MR PRESSO methods indicated consistent results.

Conclusion

Our study provides evidence of the bidirectional causal association between frailty and GDM from genetic perspectives, signaling that the identification and assessment of frailty should become a standard strategy during the early stages and care of gestational diabetes.

Limited Clinical Impact of Genetic Associations between Celiac Disease and Type 2 Inflammatory Diseases: Insights from Mendelian Randomization

Background: Celiac disease, a gluten-triggered autoimmune disorder, is known for its systemic inflammatory effects. Its genetic associations with type 2 inflammatory diseases like asthma, allergic rhinitis, and atopic dermatitis remain unclear, prompting this study to explore their potential genetic interplay. Methods: Utilizing two-sample Mendelian randomization (TSMR), we examined the genetic associations using 15 genetic instruments from GWAS datasets. Our analysis focused on celiac disease and its relation to asthma, allergic rhinitis, atopic dermatitis, and IgE-mediated food allergies. A power analysis was conducted to determine the study's detection capabilities, and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using various MR methods. Results: Our Mendelian randomization analysis identified statistically significant genetic associations between celiac disease and several type 2 inflammatory diseases, although these were practically insignificant. Specifically, celiac disease was associated with a slight increase in the risk of atopic dermatitis (OR = 1.037) and a minor protective effect against asthma (OR = 0.97). The link with allergic rhinitis was statistically detectable (OR = 1.002) but practically negligible. Despite robust statistical confirmation through various sensitivity analyses, all observed effects remained within the range of practical equivalence (ROPE). Conclusions: Our study identifies potential genetic associations between celiac disease and certain type 2 inflammatory diseases. However, these associations, predominantly within the ROPE range, suggest only limited clinical implications. These findings highlight the need for cautious interpretation and indicate that further exploration for clinical applications may not be warranted at this stage.

The circadian syndrome is a better predictor for psoriasis than the metabolic syndrome via an explainable machine learning method - the NHANES survey during 2005-2006 and 2009-2014

Objective

To explore the association between circadian syndrome (CircS) and Metabolic Syndrome (MetS) with psoriasis. Compare the performance of MetS and CircS in predicting psoriasis.

Methods

An observational study used data from the NHANES surveys conducted in 2005-2006 and 2009-2014. We constructed three multiple logistic regression models to investigate the relationship between MetS, CircS, and their components with psoriasis. The performance of MetS and CircS in predicting psoriasis was compared using five machine-learning algorithms, and the best-performing model was explained via SHAP. Then, bidirectional Mendelian randomization analyses with the inverse variance weighted (IVW) as the primary method were employed to determine the causal effects of each component.

Result

A total of 9,531 participants were eligible for the study. Both the MetS (OR = 1.53, 95%CI: 1.07-2.17, P = 0.02) and CircS (OR = 1.40, 95%CI: 1.02-1.91, P = 0.039) positively correlated with psoriasis. Each CircS algorithmic model performs better than MetS, with Categorical Features+Gradient Boosting for CircS (the area under the precision-recall curve = 0.969) having the best prediction effect on psoriasis. Among the components of CircS, elevated blood pressure, depression symptoms, elevated waist circumference (WC), and short sleep contributed more to predicting psoriasis. Under the IVW methods, there were significant causal relationships between WC (OR = 1.52, 95%CI: 1.34-1.73, P = 1.35e-10), hypertension (OR = 1.68, 95%CI: 1.19-2.37, P = 0.003), depression symptoms (OR = 1.39, 95%CI: 1.17-1.65, P = 1.51e-4), and short sleep (OR = 2.03, 95%CI: 1.21-3.39, p = 0.007) with psoriasis risk.

Conclusion

CircS demonstrated superior predictive ability for prevalent psoriasis compared to MetS, with elevated blood pressure, depression symptoms, and elevated WC contributing more to the prediction.

The impact of telomere length on the risk of idiopathic normal pressure hydrocephalus: a bidirectional Mendelian randomization study

Idiopathic normal pressure hydrocephalus (iNPH) affects mainly aged populations. The gradual shortening of telomere length (TL) is one of the hallmarks of aging. Whereas the genetic contribution of TL to the iNPH is incompletely understood. We aimed to investigate the causal relationship between TL and iNPH through the Mendelian randomization (MR) analysis. We respectively obtained 186 qualified single nucleotide polymorphisms (SNPs) of TL and 20 eligible SNPs of iNPH for MR analysis. The result of MR analysis showed that genetically predicted longer TL was significantly associated with a reduced odd of iNPH (odds ratio [OR] = 0.634 95% Confidence interval [CI] 0.447-0.899, p = 0.011). The causal association remained consistent in multivariable MR (OR = 0.530 95% CI 0.327-0.860, p = 0.010). However, there was no evidence that the iNPH was causally associated with the TL (OR = 1.000 95% CI 0.996-1.004, p = 0.955). Our study reveals a potential genetic contribution of TL to the etiology of iNPH, that is a genetically predicted increased TL might be associated with a reduced risk of iNPH.

Do iron homeostasis biomarkers mediate the associations of liability to type 2 diabetes and glycemic traits in liver steatosis and cirrhosis: a two-step Mendelian randomization study

BACKGROUND

Previous studies, including Mendelian randomization (MR), have demonstrated type 2 diabetes (T2D) and glycemic traits are associated with increased risk of metabolic dysfunction-associated steatotic liver disease (MASLD). However, few studies have explored the underlying pathway, such as the role of iron homeostasis.

METHODS

We used a two-step MR approach to investigate the associations of genetic liability to T2D, glycemic traits, iron biomarkers, and liver diseases. We analyzed summary statistics from various genome-wide association studies of T2D (n = 933,970), glycemic traits (n ≤ 209,605), iron biomarkers (n ≤ 246,139), MASLD (n ≤ 972,707), and related biomarkers (alanine aminotransferase (ALT) and proton density fat fraction (PDFF)). Our primary analysis was based on inverse-variance weighting, followed by several sensitivity analyses. We also conducted mediation analyses and explored the role of liver iron in post hoc analysis.

RESULTS

Genetic liability to T2D and elevated fasting insulin (FI) likely increased risk of liver steatosis (ORliability to T2D: 1.14 per doubling in the prevalence, 95% CI: 1.10, 1.19; ORFI: 3.31 per log pmol/l, 95% CI: 1.92, 5.72) and related biomarkers. Liability to T2D also likely increased the risk of developing liver cirrhosis. Genetically elevated ferritin, serum iron, and liver iron were associated with higher risk of liver steatosis (ORferritin: 1.25 per SD, 95% CI 1.07, 1.46; ORliver iron: 1.15 per SD, 95% CI: 1.05, 1.26) and liver cirrhosis (ORserum iron: 1.31, 95% CI: 1.06, 1.63; ORliver iron: 1.34, 95% CI: 1.07, 1.68). Ferritin partially mediated the association between FI and liver steatosis (proportion mediated: 7%, 95% CI: 2-12%).

CONCLUSIONS

Our study provides credible evidence on the causal role of T2D and elevated insulin in liver steatosis and cirrhosis risk and indicates ferritin may play a mediating role in this association.

COVID-19 susceptibility, hospitalization and severity and the risk of brain cortical structure: a Mendelian randomization study

BACKGROUND

Observational studies have reported structural changes in the brains of patients with coronavirus disease 2019 (COVID-19); it remains unclear whether these associations are causal.

AIM

We evaluated the causal effects of COVID-19 susceptibility, hospitalization and severity on cortical structures.

DESIGN

Mendelian randomization (MR) study.

METHODS

Data on the different COVID-19 phenotypes were obtained from the latest large-scale genome-wide association study (R7) of the COVID-19 Host Genetics Initiative. Brain structure data, including cortical thickness (TH) and surface area (SA), were obtained from the ENIGMA Consortium. Additionally, we employed the round 5 dataset released in January 2021 as the validation cohort. The inverse-variance weighted (IVW) method was used as the primary analysis in MR. Sensitivity analyses were conducted to evaluate heterogeneity and pleiotropy. We performed enrichment analysis on the MR analyses that passed the sensitivity analysis filtering.

RESULTS

After IVW and sensitivity analyses, we observed causal associations between COVID-19 susceptibility and rostral middle frontal SAw (P = 0.0308, β = -39.1236), cuneus THw (P = 0.0170, β = -0.0121), medial orbitofrontal THw (P = 0.0002, β = 0.0225), postcentral THw (P = 0.0217, β = -0.0106), temporal pole THw (P = 0.0077, β = 0.0359), medial orbitofrontal SAnw (P = 0.0106, β = -24.0397), medial orbitofrontal THnw (P = 0.0007, β = 0.0232), paracentral SAnw (P = 0.0483, β = -20.1442), rostral middle frontal SAnw (P = 0.0368, β = -81.9719) and temporal pole THnw (P = 0.0429, β = 0.0353). COVID-19 hospitalization had causal effects on medial orbitofrontal THw (P = 0.0053, β = 0.0063), postcentral THw (P = 0.0143, β = -0.0042), entorhinal THnw (P = 0.0142, β = 0.0142), medial orbitofrontal THnw (P = 0.0147, β = 0.0065) and paracentral SAnw (P = 0.0119, β = -7.9970). COVID-19 severity had causal effects on rostral middle frontal SAw (P = 0.0122, β = -11.8296), medial orbitofrontal THw (P = 0.0155, β = 0.0038), superior parietal THw (P = 0.0291, β = -0.0021), lingual SAnw (P = 0.0202, β = -11.5270), medial orbitofrontal THnw (P = 0.0290, β = 0.0039), paracentral SAnw (P = 0.0180, β = -5.7744) and pars triangularis SAnw (P = 0.0151, β = -5.4520).

CONCLUSION

Our MR results demonstrate a causal relationship between different COVID-19 phenotypes and cortical structures.

The Impact of Blood and Urine Biomarkers on Age-Related Macular Degeneration: Insights from Mendelian Randomization and Cross-sectional Study from NHANES

INTRODUCTION

Age-related macular degeneration (AMD) is a leading cause of blindness, affecting millions worldwide. Its complex pathogenesis involves a variety of risk factors, including lipid metabolism and inflammation. This study aims to elucidate the causal relationships between biomarkers related to these processes and AMD, leveraging Mendelian randomization (MR) and cross-sectional analysis from the National Health and Nutrition Examination Survey (NHANES).

METHOD

We conducted a two-phase study, initially using MR to explore the causality between 35 biomarkers and various AMD subtypes, followed by observational analysis with NHANES data to validate these findings.

RESULTS

MR analysis identified a protective role of TG and a risk factor role of HDL-C and CRP in AMD development. NHANES data corroborated these findings, highlighting a nuanced relationship between these biomarkers and AMD. Notably, lipid metabolism-related biomarkers showed stronger associations with early AMD, whereas CRP's significance was pronounced in late AMD.

CONCLUSION

This comprehensive analysis, combining MR with NHANES data, reinforces the importance of lipid metabolism and inflammation in AMD's etiology. Future research should further investigate these biomarkers' mechanisms and their potential as therapeutic targets for AMD prevention and treatment.

Prediction of causal genes at GWAS loci with pleiotropic gene regulatory effects using sets of correlated instrumental variables

Multivariate Mendelian randomization (MVMR) is a statistical technique that uses sets of genetic instruments to estimate the direct causal effects of multiple exposures on an outcome of interest. At genomic loci with pleiotropic gene regulatory effects, that is, loci where the same genetic variants are associated to multiple nearby genes, MVMR can potentially be used to predict candidate causal genes. However, consensus in the field dictates that the genetic instruments in MVMR must be independent (not in linkage disequilibrium), which is usually not possible when considering a group of candidate genes from the same locus. Here we used causal inference theory to show that MVMR with correlated instruments satisfies the instrumental set condition. This is a classical result by Brito and Pearl (2002) for structural equation models that guarantees the identifiability of individual causal effects in situations where multiple exposures collectively, but not individually, separate a set of instrumental variables from an outcome variable. Extensive simulations confirmed the validity and usefulness of these theoretical results. Importantly, the causal effect estimates remained unbiased and their variance small even when instruments are highly correlated, while bias introduced by horizontal pleiotropy or LD matrix sampling error was comparable to standard MR. We applied MVMR with correlated instrumental variable sets at genome-wide significant loci for coronary artery disease (CAD) risk using expression Quantitative Trait Loci (eQTL) data from seven vascular and metabolic tissues in the STARNET study. Our method predicts causal genes at twelve loci, each associated with multiple colocated genes in multiple tissues. We confirm causal roles for PHACTR1 and ADAMTS7 in arterial tissues, among others. However, the extensive degree of regulatory pleiotropy across tissues and the limited number of causal variants in each locus still require that MVMR is run on a tissue-by-tissue basis, and testing all gene-tissue pairs with cis-eQTL associations at a given locus in a single model to predict causal gene-tissue combinations remains infeasible. Our results show that within tissues, MVMR with dependent, as opposed to independent, sets of instrumental variables significantly expands the scope for predicting causal genes in disease risk loci with pleiotropic regulatory effects. However, considering risk loci with regulatory pleiotropy that also spans across tissues remains an unsolved problem.

The causal relationship and potential mediators between plasma lipids and atopic dermatitis: a bidirectional two-sample, two-step mendelian randomization

BACKGROUND

Observational studies have indicated that the plasma lipid profiles of patients with atopic dermatitis show significant differences compared to healthy individuals. However, the causal relationship between these differences remains unclear due to the inherent limitations of observational studies. Our objective was to explore the causal effects between 179 plasma lipid species and atopic dermatitis, and to investigate whether circulating inflammatory proteins serve as mediators in this causal pathway.

METHODS

We utilized public genome-wide association studies data to perform a bidirectional two-sample, two-step mendelian randomization study. The inverse variance-weighted method was adopted as the primary analysis technique. MR-Egger and the weighted median were used as supplementary analysis methods. MR-PRESSO, Cochran's Q test, and MR-Egger intercept test were applied for sensitivity analyses to ensure the robustness of our findings.

RESULTS

The Mendelian randomization analysis revealed that levels of Phosphatidylcholine (PC) (18:1_20:4) (OR: 0.950, 95% CI: 0.929-0.972, p = 6.65 × 10- 6), Phosphatidylethanolamine (O-18:1_20:4) (OR: 0.938, 95% CI: 0.906-0.971, p = 2.79 × 10- 4), Triacylglycerol (TAG) (56:6) (OR: 0.937, 95% CI: 0.906-0.969, p = 1.48 × 10- 4) and TAG (56:8) (OR: 0.918, 95% CI: 0.876-0.961, p = 2.72 × 10- 4) were inversely correlated with the risk of atopic dermatitis. Conversely, PC (18:1_20:2) (OR: 1.053, 95% CI: 1.028-1.079, p = 2.11 × 10- 5) and PC (O-18:1_20:3) (OR: 1.086, 95% CI: 1.039-1.135, p = 2.47 × 10- 4) were positively correlated with the risk of atopic dermatitis. The results of the reverse directional Mendelian randomization analysis indicated that atopic dermatitis exerted no significant causal influence on 179 plasma lipid species. The level of circulating IL-18R1 was identified as a mediator for the increased risk of atopic dermatitis associated with higher levels of PC (18:1_20:2), accounting for a mediation proportion of 9.07%.

CONCLUSION

Our research suggests that plasma lipids can affect circulating inflammatory proteins and may serve as one of the pathogenic factors for atopic dermatitis. Targeting plasma lipid levels as a treatment for atopic dermatitis presents a potentially novel approach.

Exploring the Bidirectional Causal Pathways Between Smoking Behaviors and Headache: A Mendelian Randomization Study

INTRODUCTION

Although observational data suggest a relationship between headache and smoking, there remain questions about causality. Smoking may increase headache risk, individuals may smoke to alleviate headaches, or smoking and headache may share common risk factors. Mendelian randomization (MR) is a method that uses genetic variants as instruments for making causal inferences about an exposure and an outcome.

AIMS AND METHODS

First, we conducted logistic regression of observational data in UK Biobank assessing the association between smoking behaviors (smoking status, cigarettes per day amongst daily smokers, and lifetime smoking score) on the risk of self-reported headache (in the last month and for more than 3 months). Second, we used genetic instruments for smoking behaviors and headache (identified in independent genome-wide association studies [GWAS]) to perform bidirectional MR analysis.

RESULTS

Observationally, there is a weak association between smoking behavior and experiencing headache, with increased cigarettes per day associated with increased headache risk. In the MR analysis, genetic liability to smoking initiation and lifetime smoking increased odds of headache in the last month but not odds of headaches lasting more than 3 months. In the opposite direction, there was weak evidence for higher genetic liability to headaches decreasing the chance of quitting.

CONCLUSIONS

There was weak evidence for a partially bidirectional causal relationship between smoking behaviors and headache in the last month. Given this relationship is distinct from smoking heaviness, it suggests headache and smoking may share common risk factors such as personality traits.

IMPLICATIONS

Using MR, this study addresses the uncertainty regarding the observed relationship between headache and smoking. There was evidence for weak causal effects of smoking initiation and lifetime smoking (but not smoking heaviness) on likelihood of experiencing headache in the last month, but not over a prolonged period of more than 3 months. Those with higher genetic liability to headaches were also less likely to successfully stop smoking. This partially bidirectional causal relationship distinct from smoking heaviness suggests that observed associations are unlikely due to biological effects of tobacco smoke exposure and may be explained by shared personality traits.

Exploring causal correlations of inflammatory biomarkers in idiopathic normal-pressure hydrocephalus: insights from bidirectional Mendelian randomization analysis

Background and objective

Neuroinflammatory processes have been identified as playing a crucial role in the pathophysiology of various neurodegenerative diseases, including idiopathic normal-pressure hydrocephalus (iNPH). iNPH, defined as a common disease of cognitive impairment in older adults, poses major challenges for therapeutic interventions owing to the stringent methodological requirements of relevant studies, clinical heterogeneity, unclear etiology, and uncertain diagnostic criteria. This study aims to assess the relationship between circulating inflammatory biomarkers and iNPH risk using bidirectional two-sample Mendelian randomization (MR) combined with meta-analysis.

Methods

In our bidirectional MR study, genetic data from a genome-wide association study (GWAS) involving 1,456 iNPH cases and 409,726 controls of European ancestry were employed. Single-nucleotide polymorphisms (SNPs) associated with exposures served as instrumental variables for estimating the causal relationships between iNPH and 132 types of circulating inflammatory biomarkers from corresponding GWAS data. Causal associations were primarily examined using the inverse variance-weighted method, supplemented by MR-Egger, weighted median, simple mode, and weighted mode analyses. In the results, heterogeneity was assessed using the Cochran Q test. Horizontal pleiotropy was evaluated through the MR-Egger intercept test and the MR pleiotropy residual sum and outliers test. Sensitivity analysis was conducted through leave-one-out analysis. Reverse MR analyses were performed to mitigate bias from reverse causality. Meta-analyses of identical inflammatory biomarkers from both data sources strengthened the findings.

Results

Results indicated a genetically predicted association between Interleukin-16 (IL-16) [OR: 1.228, 95% CI: 1.049-1.439, p = 0.011], TNF-related apoptosis ligand (TRAIL) [OR: 1.111, 95% CI: 1.019-1.210, p = 0.017] and Urokinase-type plasminogen activator (uPA) [OR: 1.303, 95% CI: 1.025-1.658, p = 0.031] and the risk of iNPH. Additionally, changes in human Glial cell line-derived neurotrophic factor (hGDNF) [OR: 1.044, 95% CI: 1.006-1.084, p = 0.023], Matrix metalloproteinase-1 (MMP-1) [OR: 1.058, 95% CI: 1.020, 1.098, p = 0.003] and Interleukin-12p70 (IL-12p70) [OR: 0.897, 95% CI: 0.946-0.997, p = 0.037] levels were identified as possible consequences of iNPH.

Conclusion

Our MR study of inflammatory biomarkers and iNPH, indicated that IL-16, TRAIL, and uPA contribute to iNPH pathogenesis. Furthermore, iNPH may influence the expression of hGDNF, MMP-1, and IL-12p70. Therefore, targeting specific inflammatory biomarkers could be promising strategy for future iNPH treatment and prevention.

Serum vitamin D, blood pressure and hypertension risk in the HUNT study using observational and Mendelian randomization approaches

Limited studies have triangulated the relationship between serum 25-hydroxyvitamin D [25(OH)D] levels and systolic blood pressure (SBP), diastolic blood pressure (DBP) or hypertension risk utilizing both observational and Mendelian randomization (MR) approaches. We employed data from the Norwegian Trøndelag Health Study (HUNT) to conduct cross-sectional (n = 5854) and prospective (n = 3592) analyses, as well as one-sample MR (n = 86,324). We also used largest publicly available data for two-sample MR. Our cross-sectional analyses showed a 25 nmol/L increase in 25(OH)D was associated with a 1.73 mmHg decrease in SBP (95% CI - 2.46 to - 1.01), a 0.91 mmHg decrease in DBP (95% CI - 1.35 to - 0.47) and 19% lower prevalence of hypertension (OR 0.81, 95% CI 0.74 to 0.90) after adjusting for important confounders. However, these associations disappeared in prospective analyses. One-sample and two-sample MR results further suggested no causal relationship between serum vitamin D levels and blood pressure or hypertension risk in the general population.

Major Psychiatric Disorders, Substance Use Behaviors, and Longevity

Importance

Observational studies suggest that major psychiatric disorders and substance use behaviors reduce longevity, making it difficult to disentangle their relationships with aging-related outcomes.

Objective

To evaluate the associations between the genetic liabilities for major psychiatric disorders, substance use behaviors (smoking and alcohol consumption), and longevity.

Design, Settings, and Participants

This 2-sample mendelian randomization (MR) study assessed associations between psychiatric disorders, substance use behaviors, and longevity using single-variable and multivariable models. Multiomics analyses were performed elucidating transcriptomic underpinnings of the MR associations and identifying potential proteomic therapeutic targets. This study sourced summary-level genome-wide association study (GWAS) data, gene expression, and proteomic data from cohorts of European ancestry. Analyses were performed from May 2022 to November 2023.

Exposures

Genetic susceptibility for major depression (n = 500 199), bipolar disorder (n = 413 466), schizophrenia (n = 127 906), problematic alcohol use (n = 435 563), weekly alcohol consumption (n = 666 978), and lifetime smoking index (n = 462 690).

Main Outcomes and Measures

The main outcome encompassed aspects of health span, lifespan, and exceptional longevity. Additional outcomes were epigenetic age acceleration (EAA) clocks.

Results

Findings from multivariable MR models simultaneously assessing psychiatric disorders and substance use behaviorsm suggest a negative association between smoking and longevity in cohorts of European ancestry (n = 709 709; 431 503 [60.8%] female; β, -0.33; 95% CI, -0.38 to -0.28; P = 4.59 × 10-34) and with increased EAA (n = 34 449; 18 017 [52.3%] female; eg, PhenoAge: β, 1.76; 95% CI, 0.72 to 2.79; P = 8.83 × 10-4). Transcriptomic imputation and colocalization identified 249 genes associated with smoking, including 36 novel genes not captured by the original smoking GWAS. Enriched pathways included chromatin remodeling and telomere assembly and maintenance. The transcriptome-wide signature of smoking was inversely associated with longevity, and estimates of individual smoking-associated genes, eg, XRCC3 and PRMT6, aligned with the smoking-longevity MR analyses, suggesting underlying transcriptomic mediators. Cis-instrument MR prioritized brain proteins associated with smoking behavior, including LY6H (β, 0.02; 95% CI, 0.01 to 0.03; P = 2.37 × 10-6) and RIT2 (β, 0.02; 95% CI, 0.01 to 0.03; P = 1.05 × 10-5), which had favorable adverse-effect profiles across 367 traits evaluated in phenome-wide MR.

Conclusions

The findings suggest that the genetic liability of smoking, but not of psychiatric disorders, is associated with longevity. Transcriptomic associations offer insights into smoking-related pathways, and identified proteomic targets may inform therapeutic development for smoking cessation strategies.

Integrated mendelian randomization analyses highlight AFF3 as a novel eQTL-mediated susceptibility gene in renal cancer and its potential mechanisms

BACKGROUNDS

A growing number of expression quantitative trait loci (eQTLs) have been found to be linked with tumorigenesis. In this article, we employed integrated Mendelian randomization (MR) analyses to identify novel susceptibility genes in renal cancer (RC) and reveal their potential mechanisms.

METHODS

Two-sample MR analyses were performed to infer causal relationships between eQTLs, metabolites, and RC risks through the "TwoSampleMR" R package. Sensitivity analyses, such as heterogeneity, pleiotropy, and leave-one-out analysis, were used to assess the stability of our outcomes. Summary-data-based MR (SMR) analyses were used to verify the causal relationships among cis-eQTLs and RC risks via the SMR 1.3.1 software.

RESULTS

Our results provided the first evidence for AFF3 eQTL elevating RC risks, suggesting its oncogenic roles (IVW method; odds ratio (OR) = 1.0005; 95% confidence interval (CI) = 1.0001-1.0010; P = 0.0285; heterogeneity = 0.9588; pleiotropy = 0.8397). Further SMR analysis validated the causal relationships among AFF3 cis-eQTLs and RC risks (P < 0.05). Moreover, the TCGA-KIRC, the ICGC-RC, and the GSE159115 datasets verified that the AFF3 gene was more highly expressed in RC tumors than normal control via scRNA-sequencing and bulk RNA-sequencing (P < 0.05). Gene set enrichment analysis (GSEA) analysis identified six potential biological pathways of AFF3 involved in RC. As for the potential mechanism of AFF3 in RC, we concluded in this article that AFF3 eQTL could negatively modulate the levels of the X-11,315 metabolite (IVW method; OR = 0.9127; 95% CI = 0.8530-0.9765; P = 0.0081; heterogeneity = 0.4150; pleiotropy = 0.8852), exhibiting preventive effects against RC risks (IVW method; OR = 0.9987; 95% CI = 0.9975-0.9999; P = 0.0380; heterogeneity = 0.5362; pleiotropy = 0.9808).

CONCLUSIONS

We concluded that AFF3 could serve as a novel eQTL-mediated susceptibility gene in RC and reveal its potential mechanism of elevating RC risks via negatively regulating the X-11,315 metabolite levels.