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Abstract 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

BackgroundFormer research has emphasized a correlation between lung cancer (LC) and sepsis, but the causative link remains unclear.MethodThis 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.ResultsLDSC 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, Pfdr = 2.49 × 10−4), squamous cell lung carcinoma (OR = 1.098, 95% CI 1.021–1.181, p = 0.012, Pfdr = 0.012), and lung adenocarcinoma (OR = 1.098, 95% CI 1.024–1.178, p = 0.009, Pfdr = 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 Pfdr > 0.05).ConclusionThe study suggests a causative link between LC and increased sepsis risk, underscoring the need for integrated sepsis management in LC patients.

BackgroundPrevious studies have highlighted the association between schizophrenia (SCZ) and chronic obstructive pulmonary disease (COPD), yet the causal relationship remains unestablished.MethodsUnder the genome-wide significance threshold (P 0.05 & PFDR > 0.05).ConclusionsThis 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.

BackgroundPrevious studies have demonstrated that autoimmune diseases are closely associated with bronchiectasis (BE). However, the causal effects between autoimmune diseases and BE remain elusive.MethodsAll 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.ResultsThe 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.ConclusionLDSC 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.

Background and aimsPrevious research has underscored the association between oily fish intake and type 2 diabetes (T2DM), yet the causality remains elusive.MethodsA 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.ResultsThe 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.ConclusionTo encapsulate, there’s an inverse association between oily fish intake and T2DM risk, suggesting potential benefits of oily fish intake in T2DM prevention.

BackgroundPrevious studies have indicated that antioxidant diets may have a positive impact on vitiligo by interfering with oxidative stress mechanisms. However, there has been a lack of research utilizing the Mendelian randomization (MR) method to analyze the relationship between antioxidant diet intake and vitiligo.MethodsIn this study, we employed both univariate Mendelian randomization (UVMR) and multivariate Mendelian randomization (MVMR) approaches. The specific antioxidant dietary supplements (such as coffee intake, green tea intake, herbal tea intake, standard tea intake, and average weekly red wine intake) as well as diet-derived circulating antioxidants, including Vit. C (ascorbate), Vit. E (α-tocopherol), Vit. E (γ-tocopherol), Carotene, Vit. A (retinol), Zinc, and Selenium (N = 2,603–428,860) were significantly associated with independent single-nucleotide polymorphisms (SNPs). We obtained pooled statistics on vitiligo from a meta-analysis of three genome-wide association studies (GWASs) of European ancestry, including 4,680 cases and 39,586 controls. Inverse variance weighted (IVW) was employed as the primary analytical method, and sensitivity analysis was conducted to assess the robustness of the main findings.ResultsGenetically, coffee intake [odds ratio (OR) = 0.17, 95% confidence interval (CI) 0.07–0.37, p = 1.57 × 10–5], average weekly red wine intake (OR = 0.28, 95% CI 0.08–1.00, p = 0.049), and standard tea intake (OR = 0.99, 95% CI 0.98–0.99, p = 5.66 × 10–7) were identified as protective factors against vitiligo. However, no causal effect between the intake of other antioxidant diets and vitiligo was found. Moreover, no instances of pleiotropy or heterogeneity were observed in this study.ConclusionOur study indicates that coffee, standard tea, and red wine consumption can potentially reduce the risk of vitiligo. However, there is insufficient evidence to support that other antioxidant diets have a significant effect on vitiligo.

BackgroundPrevious studies have emphasized the association between the intake of artificial sweeteners (AS) and type 2 diabetes mellitus (T2DM), but the causative relationship remains ambiguous.MethodsThis 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.ResultsLDSC 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 (β = 0.013, 95% CI: 0.004-0.022, P = 0.004, PFDR = 0.012), cereal (β = 0.007, 95% CI: 0.002-0.012, P = 0.004, PFDR = 0.012), and tea (β = 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).ConclusionThe 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.

PurposesIncreasing evidence suggests that intestinal microbiota correlates with the pathological processes of many lung diseases. This study aimed to investigate the causality of gut microbiota and lung diseases.MethodsGenetic information on intestinal flora and lung diseases [asthma, chronic bronchitis, chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD), lower respiratory tract infection (LRTI), pulmonary arterial hypertension (PAH)] and lung function was obtained from UK Biobank, FinnGen, and additional studies. A Mendelian randomization (MR) analysis was conducted to explore the causal association between gut microbiota and lung diseases.ResultsThe genetic liability to lung diseases may be associated with the abundance of certain microbiota taxa. Specifically, the genus Prevotella (p = 0.041) was related to a higher risk of asthma; the family Defluviitaleaceae (p = 0.002) and its child taxon were identified as a risk factor for chronic bronchitis; the abundance of the genus Prevotella (p = 0.020) was related to a higher risk of ILD; the family Coriobacteriaceae (p = 0.011) was identified to have a positive effect on the risk of LRTI; the genus Lactobacillus (p = 0.0297) has been identified to be associated with an increased risk of PAH, whereas the genus Holdemanella (p = 0.0154) presented a causal decrease in COPD risk; the order Selenomonadales was identified to have a positive effect on the risk of FEV1(p = 0.011). The reverse TSMR analysis also provided genetic evidence of reverse causality from lung diseases to the gut microbiota.ConclusionThis data-driven MR analysis revealed that gut microbiota was causally associated with lung diseases, providing genetic evidence for further mechanistic and clinical studies to understand the crosstalk between gut microbiota and lung diseases.

BackgroundPrevious 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.MethodsUnivariate 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.ResultsThe 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.ConclusionThe 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.

Renewable energy such as wind power, hydro-power and photovoltaic are connected to the power system as distributed power sources will increase the instability of power system operation. To ensure the stable operation of the power system after renewable energy and load access, it is necessary to properly partition the power grid. A grid partition method considering renewable energy access and load fluctuation is proposed. First, cluster analysis was carried out on the operation scenarios of renewable energy and load by using the improved K-means algorithm, and several operation scenarios of power system were obtained. The three operation scenarios with the highest probability were selected as the three normal operation states of power system. Then, the power system is partitioned, and the comprehensive flexibility evaluation indexes are proposed from the perspectives of regional supply and demand balance and inter-regional transmission capacity. The flexibility evaluation indexes from these two perspectives are calculated under three operating states, and the two flexibility evaluation indexes are weighted to evaluate the comprehensive flexibility of the power system after the partition. The comprehensive flexibility index of the power system is different under different partition strategies. The partition strategy with the best evaluation of the comprehensive flexibility index is chosen as the final partition strategy. Finally, according to the historical data of renewable energy and load in a certain region, the IEEE 39-bus system with renewable energy access and load fluctuation is partitioned under different partitioning strategies, and the comprehensive flexibility of the partitioned power system is evaluated, and the final partitioning strategy is determined according to the comprehensive flexibility index of the power system.

Numerical simulation of fluid flow and transport processes in the subsurface must account for the presence of wells. The pressure at a gridblock that contains a well is different from the average pressure in that block and different from the flowing bottom hole pressure for the well [17]. Various finite difference well models have been developed to account for the difference. This paper presents a systematical derivation of well models for other numerical methods such as standard finite element, control volume finite element, and mixed finite element methods. Numerical results for a simple well example illustrating local grid refinement effects are given to validate these well models. The well models have particular applications to groundwater hydrology and petroleum reservoirs. [ABSTRACT FROM AUTHOR]