Causal relationships between lung cancer and sepsis: a genetic correlation and multivariate mendelian randomization analysis.

Background: Former research has emphasized a correlation between lung cancer (LC) and sepsis, but the causative link remains unclear. Method: This study used univariate Mendelian Randomization (MR) to explore the causal relationship between LC, its subtypes, and sepsis. Linkage Disequilibrium Score (LDSC) regression was used to calculate genetic correlations. Multivariate MR was applied to investigate the role of seven confounding factors. The primary method utilized was inverse-variance-weighted (IVW), supplemented by sensitivity analyses to assess directionality, heterogeneity, and result robustness. Results: LDSC analysis revealed a significant genetic correlation between LC and sepsis (genetic correlation = 0.325, p = 0.014). Following false discovery rate (FDR) correction, strong evidence suggested that genetically predicted LC (OR = 1.172, 95% CI 1.083-1.269, p = 8.29 × 10-5, P fdr = 2.49 × 10-4), squamous cell lung carcinoma (OR = 1.098, 95% CI 1.021-1.181, p = 0.012, P fdr = 0.012), and lung adenocarcinoma (OR = 1.098, 95% CI 1.024-1.178, p = 0.009, P fdr = 0.012) are linked to an increased incidence of sepsis. Suggestive evidence was also found for small cell lung carcinoma (Wald ratio: OR = 1.156, 95% CI 1.047-1.277, p = 0.004) in relation to sepsis. The multivariate MR suggested that the partial impact of all LC subtypes on sepsis might be mediated through body mass index. Reverse analysis did not find a causal relationship (p > 0.05 and P fdr > 0.05). Conclusion: The study suggests a causative link between LC and increased sepsis risk, underscoring the need for integrated sepsis management in LC patients.

Synergistic Steric Effect of Precursor And Antisolvent Enables Strongly Confined Perovskite Films with Efficient and Spectral Stable Blue Emission.

Reducing the crystal size of perovskites to the strong quantum confinement regime is an effective way to realize blue luminescence for light-emitting applications. However, challenges remain in directly constraining the crystal growth during film preparation to achieve three-dimensional quantum confinement, and the widely used long-chain ligands may bring difficulties for charge transport and unfavorably affect the device performance. Herein, we report a novel strategy for fabricating strongly confined blue-emitting perovskite nanocrystalline films via synergistic steric effect modulation by precursors and antisolvents. We synthesize cesium pentafluoropropanoate (CsPFPA) as a new type of precursor agent, where the steric effect of the PFPA group can help constrain the growth of perovskite crystals and passivate the defects. Furthermore, different types of antisolvents with varied molecular sizes and steric hindrance are used to regulate the size of perovskite crystals and improve film quality. Consequently, highly emissive blue perovskite films are realized with the emission wavelength effectively tuned in the blue region by varying the concentration of CsPFPA as well as the type of antisolvents. Based on the strongly confined perovskite films, blue light-emitting diodes (LEDs) are constructed, showing good spectral tunability and stability in the electroluminescence. This work demonstrates a novel pathway for developing bright perovskite blue emitters for LEDs, which may potentially advance their future applications in display and lighting.

Body mass index, smoking behavior, and depression mediated the effects of schizophrenia on chronic obstructive pulmonary disease: trans-ethnic Mendelian-randomization analysis.

Background: Previous studies have highlighted the association between schizophrenia (SCZ) and chronic obstructive pulmonary disease (COPD), yet the causal relationship remains unestablished. Methods: Under the genome-wide significance threshold (P<5×10-8), data from individuals of European (EUR) and East Asian (EAS) ancestries with SCZ were selected for analysis. Univariable Mendelian randomization (MR) explored the causal relationship between SCZ and COPD. Linkage disequilibrium score (LDSC) regression was used to calculate genetic correlation, while multivariable and mediation MR further investigated the roles of six confounding factors and their mediating effects. The primary method utilized was inverse-variance weighted (IVW), complemented by a series of sensitivity analyses and false discovery rate (FDR) correction. Results: LDSC analysis revealed a significant genetic correlation between SCZ and COPD within EUR ancestry (rg = 0.141, P = 6.16×10-7), with no such correlation found in EAS ancestry. IVW indicated a significant causal relationship between SCZ and COPD in EUR ancestry (OR = 1.042, 95% CI 1.013-1.071, P = 0.003, PFDR = 0.015). Additionally, replication datasets provide evidence of consistent causal associations(P < 0.05 & PFDR < 0.05). Multivariable and mediation MR analyses identified body mass index (BMI)(Mediation effect: 50.57%, P = 0.02), age of smoking initiation (Mediation effect: 27.42%, P = 0.02), and major depressive disorder (MDD) (Mediation effect: 60.45%, P = 6.98×10-5) as partial mediators of this causal relationship. No causal associations were observed in EAS (OR = 0.971, 95% CI 0.875-1.073, P = 0.571, PFDR = 0.761) ancestry. No causal associations were found in the reverse analysis across the four ancestries (P > 0.05 & PFDR > 0.05). Conclusions: This study confirmed a causal relationship between SCZ and the risk of COPD in EUR ancestry, with BMI, smoking, and MDD serving as key mediators. Future research on a larger scale is necessary to validate the generalizability of these findings across other ancestries.

The impact of bone mineral density on the risk of falling: evidence from genetic correlation and Mendelian randomization analysis.

BACKGROUND: Falls are the most common consequence of low bone mineral density (BMD). However, due to limitations inherent in observational studies, the causal relationship between the two remains unestablished. METHODS: This study utilized Mendelian Randomization (MR) analysis to explore the causal relationship between BMD and the risk of falling, incorporating linkage disequilibrium score (LDSC) regression for genetic correlation assessment. The primary method was inverse-variance weighted (IVW), supplemented with sensitivity analyses and the causal analysis using summary effect estimates (CAUSE) to address heterogeneity and pleiotropy biases. RESULTS: LDSC analysis indicated significant genetic correlations between BMD at various sites and falling risk (rg range: -0.82 to 0.76, all P < 0.05). IVW analysis, with False Discovery Rate (FDR) correction, showed a protective causal effect of total body BMD (OR = 0.85, 95% CI 0.82-0.88, P = 7.63 × 10-17, PFDR = 1.91 × 10-16), femoral neck BMD (OR = 0.81, 95% CI 0.75-0.88, P = 3.33 × 10-7, PFDR = 5.55 × 10-7), lumbar spine BMD (OR = 0.85, 95% CI 0.79-0.91, P = 9.56 × 10-7, PFDR = 1.20 × 10-6), and heel BMD (OR = 0.82, 95% CI 0.79-0.81, P = 1.69 × 10-39, PFDR = 8.45 × 10-39) on falling risk. No causal relationship was found for forearm BMD (OR = 1.02, 95% CI 0.94-1.11, P = 0.64, PFDR = 0.64). Replication datasets and CAUSE analysis provided causal evidence consistent with the main findings. CONCLUSION: The study established a causal relationship between BMD at four different sites and the risk of falling, highlighting potential areas for targeted prevention strategies.

The impact of cannabis use on erectile dysfunction and sex hormones: a Mendelian randomization analysis.

Previous study has highlighted an association between cannabis use (CU) and an increased risk of erectile dysfunction (ED), potentially due to indirect effects on sex hormonal balance. However, the evidence remains controversial, and the causal relationship is unclear. This study utilized genome-wide association study (GWAS) data to investigate the causal relationships between cannabis use disorder (CUD), lifetime cannabis use (LCU), and ED, as well as levels of sex hormones including estradiol (E2), bioavailable testosterone (BT), follicle-stimulating hormone (FSH), and luteinizing hormone (LH) through Mendelian randomization (MR) analysis. The primary method of analysis was the inverse variance weighted (IVW) method. Data from the FinnGen and UK Biobank were used for replication and meta-analysis. The results indicated no causal relationship between genetically predicted CUD (OR = 0.97, 95% CI 0.87-1.10, P = 0.66) and LCU (OR = 1.13, 95% CI 0.84-1.50, P = 0.42) with the risk of ED. The meta-analysis provided consistent evidence (P > 0.05). No causal relationships were found between CUD and LCU with E2(CUD: ß = 0.00, 95% CI 0.00-0.01, P = 0.37; LCU: ß = 0.00, 95% CI -0.02-0.01, P = 0.62), BT (CUD: ß = 0.00, 95% CI -0.03-0.02, P = 0.90; LCU: ß = 0.02, 95% CI -0.04-0.09, P = 0.46), FSH (CUD: ß = 0.01, 95% CI -0.18-0.20, P = 0.92; LCU: ß = 0.01, 95% CI -0.44-0.47, P = 0.95), and LH (CUD: ß = 0.01, 95% CI -0.18-0.21, P = 0.90; LCU: ß = 0.13, 95% CI -0.22-0.49, P = 0.46). Sensitivity analyses detected no evidence of horizontal pleiotropy or heterogeneity, ensuring the robustness of the results. In conclusion, this MR analysis did not provide evidence supporting a causal relationship between CU and ED or sex hormone levels.

Multidimensional plasma lipid composition and its causal association with type 2 diabetes mellitus: A Mendelian randomization study.

BACKGROUND AND AIM: Recent research extends our knowledge of plasma lipid species, building on established links between serum lipid levels and Type 2 Diabetes Mellitus (T2DM) risk. Identifying the causal roles of these lipid species is key to improving T2DM risk assessment. METHODS AND RESULTS: This study employs Mendelian randomization (MR) to investigate the causal relationship between 179 lipid species across 13 lipid categories and T2DM. Summary-level data were sourced from genome-wide association studies. The primary analytical methods included the inverse variance weighted (IVW) approach and the Wald ratio, complemented by a series of sensitivity analyses to ensure the robustness of results. The IVW analysis reveals a significant causal association between elevated levels of ceramide (d40:2) (OR = 1.071, 95% CI 1.034-1.109, P = 1.36 × 10-4), sphingomyelin (d38:1) (OR = 1.052, 95% CI 1.028-1.077, P = 1.80 × 10-5), and triacylglycerol (56:8) (OR = 1.174, 95% CI 1.108-1.243, P = 4.65 × 10-8), and an increased risk of T2DM. Conversely, Wald ratio analysis indicates that higher levels of phosphatidylcholine (O-16:1_16:0) (OR = 0.928, 95% CI 0.892-0.966, P = 2.37 × 10-4), phosphatidylcholine (O-16:1_20:4) (OR = 0.932, 95% CI 0.897-0.967, P = 2.37 × 10-4), and phosphatidylcholine (O-18:2_20:4) (OR = 0.872, 95% CI 0.812-0.935, P = 1.24 × 10-4) are significantly associated with a reduced risk of T2DM. Furthermore, suggestive causal evidence for 22 additional lipid species was identified. CONCLUSIONS: This MR study establishes a causal relationship between specific lipid classes in modulating the risk of T2DM. It offers new insights for risk assessment and potential therapeutic targets in T2DM.

Genetic variations in anti-diabetic drug targets and COPD risk: evidence from mendelian randomization.

BACKGROUND: Previous research has emphasized the potential benefits of anti-diabetic medications in inhibiting the exacerbation of Chronic Obstructive Pulmonary Disease (COPD), yet the role of anti-diabetic drugs on COPD risk remains uncertain. METHODS: This study employed a Mendelian randomization (MR) approach to evaluate the causal association of genetic variations related to six classes of anti-diabetic drug targets with COPD. The primary outcome for COPD was obtained from the Global Biobank Meta-analysis Initiative (GBMI) consortium, encompassing a meta-analysis of 12 cohorts with 81,568 cases and 1,310,798 controls. Summary-level data for HbA1c was derived from the UK Biobank, involving 344,182 individuals. Positive control analysis was conducted for Type 2 Diabetes Mellitus (T2DM) to validate the choice of instrumental variables. The study applied Summary-data-based MR (SMR) and two-sample MR for effect estimation and further adopted colocalization analysis to verify evidence of genetic variations. RESULTS: SMR analysis revealed that elevated KCNJ11 gene expression levels in blood correlated with reduced COPD risk (OR = 0.87, 95% CI = 0.79-0.95; p = 0.002), whereas an increase in DPP4 expression corresponded with an increased COPD incidence (OR = 1.18, 95% CI = 1.03-1.35; p = 0.022). Additionally, the primary method within MR analysis demonstrated a positive correlation between PPARG-mediated HbA1c and both FEV1 (OR = 1.07, 95% CI = 1.02-1.13; P = 0.013) and FEV1/FVC (OR = 1.08, 95% CI = 1.01-1.14; P = 0.007), and a negative association between SLC5A2-mediated HbA1c and FEV1/FVC (OR = 0.86, 95% CI = 0.74-1.00; P = 0.045). No colocalization evidence with outcome phenotypes was detected (all PP.H4 < 0.7). CONCLUSION: This study provides suggestive evidence for anti-diabetic medications' role in improving COPD and lung function. Further updated MR analyses are warranted in the future, following the acquisition of more extensive and comprehensive data, to validate our results.

Autoimmune diseases and their genetic link to bronchiectasis: insights from a genetic correlation and Mendelian randomization study.

Background: Previous studies have demonstrated that autoimmune diseases are closely associated with bronchiectasis (BE). However, the causal effects between autoimmune diseases and BE remain elusive. Methods: All summary-level data were obtained from large-scale Genome-Wide Association Studies (GWAS). The univariate Mendelian randomization (UVMR) was utilized to investigate the genetic causal correlation (rg) of 12 autoimmune diseases and bronchiectasis, The Multivariable Mendelian Randomization (MVMR) method was used to explore the effects of the confounding factors. Further investigation was conducted to identify potential intermediate factors using mediation analysis. Finally, the linkage disequilibrium score regression (LDSC) method was used to identify genetic correlations among complex traits. A series of sensitivity analyses was performed to validate the robustness of the results. Results: The LDSC analysis revealed significant genetic correlations between BE and Crohn's disease (CD) (rg = 0.220, P = 0.037), rheumatoid arthritis (RA) (rg = 0.210, P = 0.021), and ulcerative colitis (UC) (rg = 0.247, P = 0.023). However, no genetic correlation was found with other autoimmune diseases (P > 0.05). The results of the primary IVW analysis suggested that for every SD increase in RA, there was a 10.3% increase in the incidence of BE (odds ratio [OR] = 1.103, 95% confidence interval [CI] 1.055-1.154, P = 1.75×10-5, FDR = 5.25×10-5). Furthermore, for every standard deviation (SD) increase in celiac disease (CeD), the incidence of BE reduced by 5.1% (OR = 0.949, 95% CI 0.902-0.999, P = 0.044, FDR = 0.044). We also observed suggestive evidence corresponding to a 3% increase in BE incidence with T1DM (OR = 1.033, 95% CI 1.001-1.066, P = 0.042, FDR = 0.063). Furthermore, MVMR analysis showed that RA was an independent risk factor for BE, whereas mediator MR analysis did not identify any mediating factors. The sensitivity analyses corroborated the robustness of these findings. Conclusion: LDSC analysis revealed significant genetic correlations between several autoimmune diseases and BE, and further MVMR analysis showed that RA is an independent risk factor for BE.

The protective role of oily fish intake against type 2 diabetes: insights from a genetic correlation and Mendelian randomization study.

Background and aims: Previous research has underscored the association between oily fish intake and type 2 diabetes (T2DM), yet the causality remains elusive. Methods: A bidirectional univariable Mendelian Randomization (MR) analysis was employed to evaluate the causal effects of oily fish and non-oily fish intake on T2DM. Replication analysis and meta-analysis were conducted to ensure robust results. Multivariable MR analysis was utilized to assess confounders, and further mediation MR analysis discerned mediating effects. Linkage Disequilibrium Score (LDSC) analysis was undertaken to compute genetic correlations. Inverse variance weighted (IVW) was the primary method, complemented by a series of sensitivity analyses. Results: The LDSC analysis unveiled a significant genetic correlation between oily fish intake and T2DM (Genetic correlation: -0.102, p = 4.43 × 10-4). For each standard deviation (SD) increase in genetically predicted oily fish intake, the risk of T2DM was reduced by 38.6% (OR = 0.614, 95% CI 0.504 ~ 0.748, p = 1.24 × 10-6, False Discovery Rate (FDR) = 3.72 × 10-6). The meta-analysis across three data sources highlighted a persistent causal association (OR = 0.728, 95% CI 0.593 ~ 0.895, p = 0.003). No other causal effects were identified (all p > 0.5, FDR > 0.5). The main outcomes remained consistent in most sensitivity analyses. Both MVMR and mediation MR analyses emphasized the mediating roles of triglycerides (TG), body mass index (BMI), and 25-hydroxyvitamin D (25OHD) levels. Conclusion: To encapsulate, there's an inverse association between oily fish intake and T2DM risk, suggesting potential benefits of oily fish intake in T2DM prevention.

Associations between artificial sweetener intake from cereals, coffee, and tea and the risk of type 2 diabetes mellitus: A genetic correlation, mediation, and mendelian randomization analysis.

BACKGROUND: Previous studies have emphasized the association between the intake of artificial sweeteners (AS) and type 2 diabetes mellitus (T2DM), but the causative relationship remains ambiguous. METHODS: This study employed univariate Mendelian randomization (MR) analysis to assess the causal link between AS intake from various sources and T2DM. Linkage disequilibrium score (LDSC) regression was used to evaluate the correlation between phenotypes. Multivariate and mediation MR were applied to investigate confounding factors and mediating effects. Data on AS intake from different sources (N = 64,949) were sourced from the UK Biobank, while T2DM data were derived from the DIAbetes Genetics Replication And Meta-analysis.The primary method adopted was inverse variance weighted (IVW), complemented by three validation techniques. Additionally, a series of sensitivity analyses were performed to evaluate pleiotropy and heterogeneity. RESULTS: LDSC analysis unveiled a significant genetic correlation between AS intake from different sources and T2DM (rg range: -0.006 to 0.15, all P < 0.05). After correction by the false discovery rate (FDR), the primary IVW method indicated that AS intake in coffee was a risk factor for T2DM (OR = 1.265, 95% CI: 1.035-1.545, P = 0.021, PFDR = 0.042). Further multivariable and mediation MR analyses pinpointed high density lipoprotein-cholesterol (HDL-C) as mediating a portion of this causal relationship. In reverse MR analysis, significant evidence suggested a positive correlation between T2DM and AS intake in coffee (ß = 0.013, 95% CI: 0.004-0.022, P = 0.004, PFDR = 0.012), cereal (ß = 0.007, 95% CI: 0.002-0.012, P = 0.004, PFDR = 0.012), and tea (ß = 0.009, 95% CI: 0.001-0.017, P = 0.036, PFDR = 0.049). No other causal associations were identified (P > 0.05, PFDR > 0.05). CONCLUSION: The MR analysis has established a causal relationship between AS intake in coffee and T2DM. The mediation by HDL-C emphasizes potential metabolic pathways underpinning these relationships.

Genetic Insights into the causal relationship between cannabis use and diabetic phenotypes: A genetic correlation and Mendelian randomization study.

BACKGROUND: Previous studies have highlighted the association between cannabis use and diabetes and its complications; however, the causality remains ambiguous. METHODS: Univariate Mendelian randomization (MR), multivariate MR, mediation MR, and linkage disequilibrium score (LDSC) analysis to assess the causal relationship between cannabis use and 12 diabetic phenotypes. Summary statistics for lifetime cannabis use (N = 184,765) and cannabis use disorder (CUD) (N = 374,287) from genome-wide association studies. The primary method used was inverse-variance-weighted (IVW). A range of sensitivity analyses ensured the robustness of the results. RESULTS: LDSC analysis revealed a significant genetic correlation between CUD and T2DM, as well as between lifetime cannabis use and four diabetic phenotypes (P < 0.05). After correction by false discovery rate (FDR), the primary IVW analysis indicates that the genetically predicted CUD is positively associated with the risk of diabetic hypoglycemia (OR = 1.11, 95% CI 1.04-1.20, P = 0.003, PFDR = 0.04) and proliferative diabetic retinopathy (PDR) (OR = 1.12, 95% CI 1.04-1.19, P = 4.89×10-4, PFDR =0.01). Additionally, suggestive evidence links CUD with increased risks of diabetic nephropathy, type 1 diabetes mellitus (T1DM), diabetic retinopathy, and T1DM associated with diabetic ketoacidosis (P < 0.05 & PFDR > 0.05). No causal relationship was detected between lifetime cannabis use and diabetic phenotypes (P > 0.05 & PFDR > 0.05). Multivariable and mediation MR analyses revealed that glycated hemoglobin A1c partially mediates the causal effect of CUD on PDR and diabetic hypoglycemia. CONCLUSION: This MR study suggests that CUD may have a causal role in several diabetic disease phenotypes.

Serum uric acid and risk of diabetic neuropathy: a genetic correlation and mendelian randomization study.

Background: Previous observational studies have indicated an association between serum uric acid (SUA) and diabetic neuropathy (DN), but confounding factors and reverse causality have left the causality of this relationship uncertain. Methods: Univariate Mendelian randomization (MR), multivariate MR and linkage disequilibrium score (LDSC) regression analysis were utilized to assess the causal link between SUA and DN. Summary-level data for SUA were drawn from the CKDGen consortium, comprising 288,648 individuals, while DN data were obtained from the FinnGen consortium, with 2,843 cases and 271,817 controls. Causal effects were estimated primarily using inverse variance weighted (IVW) analysis, supplemented by four validation methods, with additional sensitivity analyses to evaluate pleiotropy, heterogeneity, and result robustness. Results: The LDSC analysis revealed a significant genetic correlation between SUA and DN (genetic correlation = 0.293, P = 2.60 × 10-5). The primary methodology IVW indicated that each increase of 1 mg/dL in SUA would increase DN risk by 17% (OR = 1.17, 95% CI 1.02-1.34, P = 0.02), while no causal relationship was found in reverse analysis (OR = 1.00, 95% CI 0.98~1.01, P = 0.97). Multivariate MR further identified that the partial effect of SUA on DN may be mediated by physical activity, low density lipoprotein cholesterol (LDL-C), insulin resistance (IR), and alcohol use. Conclusion: The study establishes a causal link between elevated SUA levels and an increased risk of DN, with no evidence for a reverse association. This underscores the need for a comprehensive strategy in DN management, integrating urate-lowering interventions with modulations of the aforementioned mediators.

Fabrication of Highly Luminescent Quasi Two-Dimensional CsPbBr3 Perovskite Films in High Humidity Air for Light-Emitting Diodes.

Perovskite light-emitting diodes (LEDs) have attracted extensive attention in recent years due to their outstanding performance and promise in lighting and display applications. However, the fabrication of perovskite LEDs usually requires a low-humidity atmosphere, which is unfavorable for industrial production. Herein, we report an effective strategy to fabricate highly luminescent quasi two-dimensional CsPbBr3 perovskite films in an ambient atmosphere with a humidity up to 60%. We reveal that the hole transport layer (HTL) plays a significant role in the morphology and optical properties of the perovskite films. Using hydrophobic self-assembled monolayer materials as HTLs can remarkably improve the quality of the perovskite films processed in high humidity air. The resultant perovskite LEDs show reduced leakage current and significantly enhanced performance. Furthermore, surface treatment is conducted to prevent water invasion and promote radiative recombination in perovskite films and LEDs. Eventually, the perovskite LEDs exhibit bright green emission with an external quantum efficiency of 4.87%. The present work provides a feasible pathway to overcome the humidity limitation for obtaining bright perovskite films and LEDs, which would contribute to further reducing the fabrication cost of perovskite LEDs and promoting their applications.

Control of n-Phase Distribution in Quasi Two-Dimensional Perovskite for Efficient Blue Light-Emitting Diodes.

Pure-bromide quasi-2D perovskite (PBQ-2DP) promises high-performance light-emitting diodes (LEDs), while a challenge remains on control over its n-phase distribution for bright true-blue emission. Present work addresses the challenge through exploring the passivation molecule of amino acid with reinforced binding energy, which generates narrow n-phase distribution preferentially at n = 3 with true blue emission at 478 nm. Consequently, a peak external quantum efficiency of 5.52% and a record brightness of 512 cd m-2 are achieved on the PBQ-2DP-based true blue PeLED, these both values located among the top in the records of similar devices. We further reveal that the electron-phonon coupling results in the red-shifted emission in the PBQ-2DP film, suggesting that the view of n-phase distribution dominated true-blue emission in PBQ-2DP needs to be revisited, pointing out a guideline of electron-phonon coupling suppression to relieve the strait of realizing true blue or even deep blue emission in the PBQ-2DP film.