Using Mendelian randomization provides genetic insights into potential targets for sepsis treatment.

Sepsis is recognized as a major contributor to the global disease burden, but there is a lack of specific and effective therapeutic agents. Utilizing Mendelian randomization (MR) methods alongside evidence of causal genetics presents a chance to discover novel targets for therapeutic intervention. MR approach was employed to investigate potential drug targets for sepsis. Pooled statistics from IEU-B-4980 comprising 11,643 cases and 474,841 controls were initially utilized, and the findings were subsequently replicated in the IEU-B-69 (10,154 cases and 454,764 controls). Causal associations were then validated through colocalization. Furthermore, a range of sensitivity analyses, including MR-Egger intercept tests and Cochran's Q tests, were conducted to evaluate the outcomes of the MR analyses. Three drug targets (PSMA4, IFNAR2, and LY9) exhibited noteworthy MR outcomes in two separate datasets. Notably, PSMA4 demonstrated not only an elevated susceptibility to sepsis (OR 1.32, 95% CI 1.20-1.45, p = 1.66E-08) but also exhibited a robust colocalization with sepsis (PPH4 = 0.74). According to the present MR analysis, PSMA4 emerges as a highly encouraging pharmaceutical target for addressing sepsis. Suppression of PSMA4 could potentially decrease the likelihood of sepsis.

Intercomparison of Same-Day Remote Sensing Data for Measuring Winter Cover Crop Biophysical Traits.

Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012-2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha-1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (-25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.

Causal relationship of genetically predicted particulate matter 2.5 level with Alzheimer's disease and the mediating effect of dehydroepiandrosterone sulphate.

BACKGROUND: The causal association between particulate matter 2.5 (PM2.5) and Alzheimer's disease (AD) remains inconclusive, and the mediators of the association have yet to be explored. AIMS: We aimed to assess the potential causal relationship between PM2.5 and AD, and to investigate the mediating role of dehydroepiandrosterone sulphate (DHEAS). SUBJECTS AND METHODS: We implemented a two-sample Mendelian randomisation (MR) study to examine the genetic predisposition to PM2.5 exposure and its association with AD. The inverse-variance weighted (IVW) method served as the primary analytical tool to estimate the odds ratio (OR) and 95% confidence interval (95% CI). RESULTS: There were 6 and 4 genetic variants associated with DHEAS and PM2.5, respectively. Based on the multivariable MR analysis, we found that after adjusting for DHEAS, each standard deviation increase in PM2.5 was associated with the risk of AD (OR: 2.96, 95% CI: 1.33, 6.58, p = 0.00769). The MR Egger intercept test did not detect horizontal pleiotropy for PM2.5 (P-pleiotropy = 0.879) and DHEAS(P-pleiotropy = 0.941). According to the results of the mediation analysis, DHEAS accounted for 18.3% of the association between PM2.5 and AD. CONCLUSION: Our findings affirm a significant causal association between PM2.5 exposure and AD, with DHEAS playing a mediating role in this relationship.

Assessing the causal link between liver function and acute pancreatitis: A Mendelian randomisation study.

A correlation has been reported to exist between exposure factors (e.g. liver function) and acute pancreatitis. However, the specific causal relationship remains unclear. This study aimed to infer the causal relationship between liver function and acute pancreatitis using the Mendelian randomisation method. We employed summary data from a genome-wide association study involving individuals of European ancestry from the UK Biobank and FinnGen. Single-nucleotide polymorphisms (SCNPs), closely associated with liver function, served as instrumental variables. We used five regression models for causality assessment: MR-Egger regression, the random-effect inverse variance weighting method (IVW), the weighted median method (WME), the weighted model, and the simple model. We assessed the heterogeneity of the SNPs using Cochran's Q test. Multi-effect analysis was performed using the intercept term of the MR-Egger method and leave-one-out detection. Odds ratios (ORs) were used to evaluate the causal relationship between liver function and acute pancreatitis risk. A total of 641 SNPs were incorporated as instrumental variables. The MR-IVW method indicated a causal effect of gamma-glutamyltransferase (GGT) on acute pancreatitis (OR = 1.180, 95%CI [confidence interval]: 1.021-1.365, P = 0.025), suggesting that GGT may influence the incidence of acute pancreatitis. Conversely, the results for alkaline phosphatase (ALP) (OR = 0.997, 95%CI: 0.992-1.002, P = 0.197) and aspartate aminotransferase (AST) (OR = 0.939, 95%CI: 0.794-1.111, P = 0.464) did not show a causal effect on acute pancreatitis. Additionally, neither the intercept term nor the zero difference in the MR-Egger regression attained statistical significance (P = 0.257), and there were no observable gene effects. This study suggests that GGT levels are a potential risk factor for acute pancreatitis and may increase the associated risk. In contrast, ALP and AST levels did not affect the risk of acute pancreatitis.

Periodontitis and osteoporosis: a two-sample Mendelian randomization analysis.

The incidences of periodontitis and osteoporosis are rising worldwide. Observational studies have shown that periodontitis is associated with increased risk of osteoporosis. We performed a Mendelian randomization (MR) study to genetically investigate the causality of periodontitis on osteoporosis. We explored the causal effect of periodontitis on osteoporosis by MR analysis. A total of 9 single nucleotide polymorphisms (SNP) were related to periodontitis. The primary approach in this MR analysis was the inverse variance-weighted (IVW) method. Simple median, weighted median, and penalized weighted median were used to analyze sensitivity. The fixed-effect IVW model and random-effect IVW model showed no significant causal effect of genetically predicted periodontitis on the risk of osteoporosis (OR=1.032; 95%CI: 0.923-1.153; P=0.574; OR=1.032; 95%CI: 0.920-1.158; P=0.588, respectively). Similar results were observed in simple mode (OR=1.031; 95%CI: 0.780-1.361, P=0.835), weighted mode (OR=1.120; 95%CI: 0.944-1.328, P=0.229), simple median (OR=1.003; 95%CI: 0.839-1.197, P=0.977), weighted median (OR=1.078; 95%CI: 0.921-1.262, P=0.346), penalized weight median (OR 1.078; 95%CI: 0.919-1.264, P=0.351), and MR-Egger method (OR=1.360; 95%CI: 0.998-1.853, P=0.092). There was no heterogeneity in the IVW and MR-Egger analyses (Q=7.454, P=0.489 and Q=3.901, P=0.791, respectively). MR-Egger regression revealed no evidence of a pleiotropic influence through genetic variants (intercept: -0.004; P=0.101). The leave-one-out sensitivity analysis indicated no driven influence of any individual SNP on the association between periodontitis and osteoporosis. The Mendelian randomization analysis did not show a significant detrimental effect of periodontitis on the risk of osteoporosis.

Causal analysis of gastroesophageal reflux disease and esophageal cancer.

Patients with gastroesophageal reflux disease (GERD) are more likely to develop esophageal cancer (EC). However, a causal relationship between the 2 has been difficult to determine. Therefore, this study aimed to evaluate the impact of GERD on EC using the Mendelian randomization (MR) method. The causal association between GERD and EC was analyzed based on 2 publicly available genetic summary datasets for the GERD cohort (129,080 cases vs 473,524 controls) and the EC cohort (740 cases vs 372,016 controls). The causal inference was mainly evaluated by the inverse variance weighted MR. The MR-Egger regression, MR Pleiotropy Residual Sum and Outlier test, and leave-one-out test were used to confirm the sensitivity of the MR results. Possible interfering factors were excluded by multivariate MR (MVMR) analysis. We used 73 single nucleotide polymorphisms as instrumental variables. GERD was associated with increasing EC risk (odds ratio [OR], 1.001; 95% confidence interval, 1.001-1.002; P < .001), which was identified using the inverse variance weighted method. The sensitivity analysis also demonstrated similar results with the causal explanation, and major bias in genetic pleiotropy was not identified (intercept, 0.001; standard error, 0.001; P = .418). The multivariate MR analysis demonstrated the effect of GERD on EC even after excluding possible mediating factors (OR, 1.003; 95% confidence interval, 1.001-1.005; P = .012). This study confirmed that GERD has a causal effect on EC. Therefore, interventional measures are recommended to prevent EC.

Bayesian two-stage modeling of longitudinal and time-to-event data with an integrated fractional Brownian motion covariance structure.

It is difficult to characterize complex variations of biological processes, often longitudinally measured using biomarkers that yield noisy data. While joint modeling with a longitudinal submodel for the biomarker measurements and a survival submodel for assessing the hazard of events can alleviate measurement error issues, the continuous longitudinal submodel often uses random intercepts and slopes to estimate both between- and within-patient heterogeneity in biomarker trajectories. To overcome longitudinal submodel challenges, we replace random slopes with scaled integrated fractional Brownian motion (IFBM). As a more generalized version of integrated Brownian motion, IFBM reasonably depicts noisily measured biological processes. From this longitudinal IFBM model, we derive novel target functions to monitor the risk of rapid disease progression as real-time predictive probabilities. Predicted biomarker values from the IFBM submodel are used as inputs in a Cox submodel to estimate event hazard. This two-stage approach to fit the submodels is performed via Bayesian posterior computation and inference. We use the proposed approach to predict dynamic lung disease progression and mortality in women with a rare disease called lymphangioleiomyomatosis who were followed in a national patient registry. We compare our approach to those using integrated Ornstein-Uhlenbeck or conventional random intercepts-and-slopes terms for the longitudinal submodel. In the comparative analysis, the IFBM model consistently demonstrated superior predictive performance.

Association between multiple sclerosis and cancer risk: A two-sample Mendelian randomization study.

Multiple Sclerosis (MS) is an immune-related disease and the relationship between MS and cancer has raised attention. Previous studies of the relationship between MS and cancer have reached conflicting conclusions. In this study, the two-sample MR method is used to investigate whether MS has a causal correlation with cancers and offer scientific evidence for cancer prevention. Single nucleotide polymorphisms (SNPs) related to MS were obtained from the genome-wide association study (GWAS) based on International Multiple Sclerosis Genetics Consortium (IMSGC) and SNPs related to 15 types of cancers were obtained from the GWASs based on UK Biobank. Inverse variance weighted (IVW) method was mainly used to assess causal effects. Sensitivity analyses were conducted with Cochran's Q-test, MR Egger intercept, leave-one-out test, and MR Steiger method. IVW analysis showed that MS was only associated with a marginal increased risk of cervical cancer (OR 1.0004, 95% CI 1.0002-1.0007, p = 0.0003). Sensitivity analyses showed that the results of MR analysis were robust and found no heterogeneity, no pleiotropy, and no reverse causation. In conclusion, this study finds no causal relationship between MS and 15 types of cancers except cervical cancer.

Effects of common lifestyle factors on obstructive sleep apnea: precautions in daily life based on causal inferences.

Background: This study aimed to evaluate the causal impact of common modifiable lifestyles on obstructive sleep apnea (OSA), which is beneficial for recommendations to prevent and manage OSA. Method: Published genome-wide association study (GWAS) summary statistics were used to perform two-sample Mendelian randomization (MR). Variants associated with each exposure of smoking, drinking, and leisure sedentary behaviors at the genetic level were used as instrumental variables (IVs). Then, inverse-variance weighting (IVW) was considered the primary result for causality. Moreover, several complimented approaches were also included to verify the observed associations. MR-PRESSO and MR-Egger intercept were applied to test the horizontal pleiotropy. To assess heterogeneity, Cochran's Q test by IVW and MR-Egger were applied. Results: Regular smoking history increased OSA risk in all applied approaches [OR (95% CI)IVW = 1.28 (1.12, 1.45), p = 1.853 × 10-4], while the causality of lifetime smoking index [OR (95% CI)IVW = 1.39 (1.00, 1.91), p = 0.048], alcohol intake frequency [outliers removed OR (95% CI)IVW = 1.26 (1.08, 1.45), p = 0.002], and coffee intake behavior [OR (95% CI)IVW = 1.66 (1.03, 2.68), p = 0.039] on OSA risk were not always consistent in other approaches. In addition, no robust causal associations were observed for the effect of sedentary leisure behaviors on OSA risk. In sensitivity analysis, we observed no sign of horizontal pleiotropy or heterogeneity. Conclusion: Ever regularly smoking has a robust causal role in increasing OSA risk, which should be discouraged as precautions from developing OSA.

Longitudinal assessment of the relationship between frailty and social relationships among Japanese older adults: a random intercept cross-lagged panel model.

OBJECTIVES: This study aimed to explore the bidirectional association between frailty and social relationships in older adults while distinguishing between interpersonal and intrapersonal effects. METHODS: A prospective cohort study of community-dwelling older adults was conducted in Japan in three waves spanning six years with follow-ups in every three years. Random intercept cross-lagged panel model was used to explore temporal associations between frailty and social relationships. RESULTS: Data for 520 participants (mean age 73.02 [SD 6.38] years, 56.7% women) were analyzed. Across individuals, frailty was associated with social relationships (ß = -0.514, p < 0.001). At the interpersonal level, frailty was cross-sectionally associated with social relationships separately at T1(ß = -0.389, p < 0.01), T2 (ß = -0.343, p < 0.001) and T3 (ß = -0.273, p < 0.05). Moreover, social relationships were associated with subsequent increases in symptoms of frailty in all measurement waves (ß = -0.332, p < 0.001; ß = -0.169, p < 0.01) and vice versa (ß = -0.149, p < 0.05; ß = -0.292, p < 0.001). CONCLUSIONS: The results suggest that frailty was associated with lower levels of social relationships. Frailty improvement programs can be combined with interventions to enhance social relationships, which will be beneficial in preventing frailty. The results emphasize the importance of combining clinical treatments of frailty with interventions to improve social relationships.

Causal effects of gut microbiome on HIV infection: a two-sample mendelian randomization analysis.

BACKGROUND: The causal association between gut microbiome and HIV infection remains to be elucidated. We conducted a two-sample mendelian randomization analysis to estimate the causality between gut microbiome and HIV infection. METHODS: Publicly released genome-wide association studies summary data were collected to perform the mendelian analysis. The GWAS summary data of gut microbiome was retrieved from the MiBioGen consortium, which contains 18 340 samples from 24 cohorts. GWAS summary data of HIV infection was collected from the R5 release of FinnGen consortium, including 357 HIV infected cases and 218 435 controls. The SNPs were selected as instrumental variables according to our selection rules. And SNPs with a F-statistics less than ten were regarded as weak instrumental variables and excluded. Mendelian randomization analysis was conducted by five methods, including inverse variance weighted (IVW), MR-Egger, weighted median, weighted mode, and simple mode. The Cochran's Q test and MR-Egger intercept test were performed to identify heterogeneity and pleiotropy. Leave-one-out analysis were used to test the sensitivity of the results. RESULTS: Fifteen gut microbiota taxa showed causal effects on HIV infection according to the MR methods. Four taxa were observed to increase the risk of HIV infection, including Ruminococcaceae (OR: 2.468[1.043, 5.842], P: 0.039), Ruminococcaceae UCG005 (OR: 2.051[1.048, 4.011], P: 0.036), Subdoligranulum (OR: 3.957[1.762, 8.887], P < 0.001) and Victivallis (OR: 1.605[1.012, 2.547], P=0.044). Erysipelotrichaceae was protective factor of HIV infection (OR: 0.278[0.106, 0.731], P < 0.001) and Methanobrevibacter was also found to be associated with reduced risk of HIV infection (OR: 0.509[0.265, 0.980], P=0.043). Horizontal pleiotropy was found for Fusicatenibacter (P<0.05) according to the MR-Egger regression intercept analysis. No heterogeneity was detected. CONCLUSION: Our results demonstrate significant causal effects of gut microbiome on HIV infection. These findings facilitate future studies to develop better strategies for HIV prophylaxis through gut microbiome regulation. Further explorations are also warranted to dissect the mechanism of how gut microbiome affects HIV susceptibility.

Major depressive disorder increased the risk of hypertension: A Mendelian randomization study.

BACKGROUND: Despite the high prevalence of comorbid hypertension in patients with major depressive disorder (MDD), the relationship between the two diseases has received little attention. Previous observational studies have descripted the association between MDD and hypertension, the causality from MDD on hypertension remained unknown. The present Mendelian randomization (MR) study aimed to assess the causal effect of MDD on hypertension. METHODS: A set of genetics instrument was used for analysis, derived from publicly available genetic meta-analysis data. A total of 44 single-nucleotide polymorphisms (SNPs) associated with MDD. The largest genome-wide association study (GWAS) for hypertension (54,358 cases and 408,652 controls) was used to assess the effect of MDD on hypertension. Inverse variance weighted method (IVW), weighted median method (WM), and MR-Egger regression were used for MR analyses. The MR-Egger_intercept test and Cochran's Q statistic were used to determine the pleiotropy and the heterogeneity, respectively. RESULTS: A total of 28 independent and effective MDD genetic instrumental variables were extracted from the hypertension GWAS summary statistics. Pleiotropy analysis suggested no significant pleiotropic variant among the 28 selected MDD genetic instrument variants in hypertension GWAS datasets. As MDD based on genetic changes increased, the risk of hypertension increased using MR-Egger (OR = 1.004436, 95%CI 0.9884666-1.020663, P = 0.5932928), WM (OR = 1.000499, 95%CI 1.0000188-1.000980, P = 0.0416871), and IVW (OR = 1.000573, 95%CI 1.0000732-1.001074, P = 0.0246392). Our results were robust, with no obvious bias based on investigating the single MDD SNP on hypertension. CONCLUSIONS: Our result suggested a causal associated between genetically increased MDD and increased hypertension risk in European population.

Causal association between psoriasis vulgaris and bullous pemphigoid: a two-sample bidirectional Mendelian randomization study.

Background: The association between psoriasis vulgaris and bullous pemphigoid (BP) remains largely unknown. Objectives: To investigate whether there is a causal effect between psoriasis vulgaris and BP. Methods: Two-sample bidirectional Mendelian randomization (MR) analyses were conducted using publicly released genome-wide association studies (GWAS) summary statistics. The GWAS summary statistics for BP were downloaded online from FinnGen Biobank Documentation of the R12 release, which includes 219 BP cases and 218,066 controls. The GWAS data for psoriasis vulgaris were extracted from Sakaue et al., which comprises 5072 cases and 478,102 controls. Single-nucleotide polymorphisms (SNPs) associated with exposure were selected as instrumental variables by performing additional quality control steps. The inverse-variance-weighted (IVW) method was used for the primary MR analyses, and the MR-Egger regression, weighted mode method, weighted median method, and simple mode were employed for sensitivity analyses. The MR-Egger intercept test and "leave-one-out" sensitivity analysis were performed to evaluate the horizontal pleiotropy and the potentially influential SNPs, respectively. Results: Genetically determined log odds of psoriasis vulgaris were associated with an increased risk of BP (IVW: odds ratio (OR) = 1.263, 95% confidence interval (CI): 1.013-1.575, P=0.038). Sensitivity analyses by the weighted mode (OR=1.255, 95%CI: 0.973-1.618, P=0.106), MR Egger (OR=1.315, 95%CI: 0.951-1.817, P=0.126), simple mode (OR=1.414, 95%CI: 0.823-2.429, P=0.234) and weighted median method (OR=1.177, 95%CI: 0.889-1.559, P=0.254) derived directionally consistent relationship between the genetically predicted log odds of psoriasis vulgaris and risks of developing BP. On the contrary, we found that genetically predicted BP had no significant effect on psoriasis vulgaris (IVW: OR=0.996, P= 0.707), indicating the unidirectionality of the relationship. MR-Egger intercept tests showed no evidence of horizontal pleiotropy. No influential SNP driving the results was detected by the leave-one-out sensitivity analysis. Conclusions: Our results suggested that psoriasis vulgaris causally increases the risk of BP, highlighting the need for potential strategies for the prevention and early diagnosis of comorbid BP in patients with psoriasis vulgaris. Further researches into this association and underlying mechanisms are warranted.

Evaluation of the Sysmex XQ-320 three-part differential haematology analyser and its flagging capabilities.

BACKGROUND: Three-part differential (3PD) haematology analysers offer a quick, easy-to-use and economical way to acquire important information about a patient's physiology. In this study, we evaluated a new 3PD analyser, the Sysmex XQ-320, investigated its comparability with its predecessor (Sysmex XP-300) and the five-part differential analyser Sysmex XN-9000, and explored its flagging potential. METHODS: Analytical performance studies were conducted for repeatability, within-laboratory precision, between-day precision, carry-over and linearity with fresh blood and QC material. Method comparison was performed in 493 samples comparing XQ-320 with XP-300, using the XN-9000 as the gold standard. RESULTS: The XQ-320 excelled manufacturer's specifications in the analytical performance studies, except for MXD in within-laboratory and between-day precisions using the QC material level 1. The XQ-320 showed correlation values greater than 0.94 with XN-9000 for the majority of the 20 reportable parameters (MXD# 0.891, MXD% 0.898 and MCHC 0.849). Improvements over the XP-300 were observed in WBC in the leucocytopenic range (bias -0.038 vs. -0.097) and PLT (bias 2.568 vs. -7.877, intercept 3.880 vs. -8.845). Concordance between XQ-320 and XP-300 was 91.9% for the WBC histogram abnormal distribution flag and 95.3% for the PLT flag. Patterns of increased neutrophils and decreased mixed cells on the XQ-320 were observed in samples that raised a flag on XN-9000. CONCLUSION: The XQ-320 showed excellent analytical performance, and very good to excellent correlation with XN-9000 with improvements over XP-300. Flagging combined with parameter patterns identified additional suspected abnormal samples, thus making the XQ-320 an excellent solution for laboratories utilising 3PD analysers.

Unraveling the mystery: a Mendelian randomized exploration of gut microbiota and different types of obesity.

Background: Numerous studies have demonstrated the influence of gut microbiota on the development of obesity. In this study, we utilized Mendelian randomization (MR) analysis to investigate the gut microbiota characteristics among different types of obese patients, aiming to elucidate the underlying mechanisms and provide novel insights for obesity treatment. Methods: Two-sample multivariable Mendelian randomization (MR) analysis was employed to assess causal relationships between gut microbiota and various obesity subtypes. Gut microbiota data were obtained from the international consortium MiBioGen, and data on obese individuals were sourced from the Finnish National Biobank FinnGen. Eligible single-nucleotide polymorphisms (SNPs) were selected as instrumental variables. Various analytical methods, including inverse variance weighted (IVW), MR-Egger regression, weighted median, MR-RAPS, and Lasso regression, were applied. Sensitivity analyses for quality control included MR-Egger intercept tests, Cochran's Q tests, and leave-one-out analyses and others. Results: Mendelian randomization studies revealed distinct gut microbiota profiles among European populations with different obesity subtypes. Following multivariable MR analysis, we found that Ruminococcaceae UCG010 [Odds Ratio (OR): 0.842, 95% confidence interval (CI): 0.766-0.926, Adjusted P value: 0.028] independently reduced the risk of obesity induced by excessive calorie intake, while Butyricimonas [OR: 4.252, 95% CI: 2.177-8.307, Adjusted P value: 0.002] independently increased the risk of medication-induced obesity. For localized adiposity, Pasteurellaceae [OR: 0.213, 95% CI: 0.115-0.395, Adjusted P value: <0.001] acted as a protective factor. In the case of extreme obesity with alveolar hypoventilation, lactobacillus [OR: 0.724, 95% CI: 0.609-0.860, Adjusted P value: 0.035] reduced the risk of its occurrence. Additionally, six gut microbiota may have potential roles in the onset of different types of obesity. Specifically, the Ruminococcus torques group may increase the risk of its occurrence. Desulfovibrio and Catenabacterium may serve as protective factors in the onset of Drug-induced obesity. Oxalobacteraceae, Actinomycetaceae, and Ruminiclostridium 9, on the other hand, could potentially increase the risk of Drug-induced obesity. No evidence of heterogeneity or horizontal pleiotropy among SNPs was found in the above studies (all P values for Q test and MR-Egger intercept > 0.05). Conclusion: Gut microbiota abundance is causally related to obesity, with distinct gut microbiota profiles observed among different obesity subtypes. Four bacterial species, including Ruminococcaceae UCG010, Butyricimonas, Pasteurellaceae and lactobacillus independently influence the development of various types of obesity. Probiotic and prebiotic supplementation may represent a novel approach in future obesity management.

Secreted salt and hydrodynamic factors combine to affect dynamic fluctuations of microplastics on mangrove leaves.

Mangrove leaves have been acknowledged as crucial sink for coastal microplastics (MPs). Whereas, the temporal dynamics of MPs intercepted by mangrove leaves have remained poorly understood. Here, we detected MPs intercepted by submerged and non-submerged mangrove leaves over time and the potential driving factors. Abundance and characteristics of MPs interception by mangrove leaves exhibited dynamic fluctuations, with the coefficient of variation (CV) of submerged mangrove leaves (CV = 0.604; 1.76 n/g to 15.45 n/g) being approximately twofold higher than non-submerged mangrove leaves (CV = 0.377; 0.74 n/g to 3.28 n/g). Partial least squares path model (PLS-PM) analysis further illustrated that MPs abundance on submerged mangrove leaves were negative correlated to hydrodynamic factors (i.e., current velocity and tidal range). Intriguingly, secreted salt as a significantly driver of MPs intercepted by mangrove leaves. Results of this work highlights that MPs intercepted by mangrove leaves is characterized by dynamic fluctuations and reveals the importance of hydrodynamic factors and secreted salt. Overall, this work identifies the pivotal buffering role played by mangrove leaves in intercepting MPs, which provides basic knowledge for better understanding of microplastic pollution status and control from mangrove plants.

Machine Learning Models for Predicting Disability and Pain Following Lumbar Disc Herniation Surgery.

Importance: Lumber disc herniation surgery can reduce pain and disability. However, a sizable minority of individuals experience minimal benefit, necessitating the development of accurate prediction models. Objective: To develop and validate prediction models for disability and pain 12 months after lumbar disc herniation surgery. Design, Setting, and Participants: A prospective, multicenter, registry-based prognostic study was conducted on a cohort of individuals undergoing lumbar disc herniation surgery from January 1, 2007, to May 31, 2021. Patients in the Norwegian Registry for Spine Surgery from all public and private hospitals in Norway performing spine surgery were included. Data analysis was performed from January to June 2023. Exposures: Microdiscectomy or open discectomy. Main Outcomes and Measures: Treatment success at 12 months, defined as improvement in Oswestry Disability Index (ODI) of 22 points or more; Numeric Rating Scale (NRS) back pain improvement of 2 or more points, and NRS leg pain improvement of 4 or more points. Machine learning models were trained for model development and internal-external cross-validation applied over geographic regions to validate the models. Model performance was assessed through discrimination (C statistic) and calibration (slope and intercept). Results: Analysis included 22 707 surgical cases (21 161 patients) (ODI model) (mean [SD] age, 47.0 [14.0] years; 12 952 [57.0%] males). Treatment nonsuccess was experienced by 33% (ODI), 27% (NRS back pain), and 31% (NRS leg pain) of the patients. In internal-external cross-validation, the selected machine learning models showed consistent discrimination and calibration across all 5 regions. The C statistic ranged from 0.81 to 0.84 (pooled random-effects meta-analysis estimate, 0.82; 95% CI, 0.81-0.84) for the ODI model. Calibration slopes (point estimates, 0.94-1.03; pooled estimate, 0.99; 95% CI, 0.93-1.06) and calibration intercepts (point estimates, -0.05 to 0.11; pooled estimate, 0.01; 95% CI, -0.07 to 0.10) were also consistent across regions. For NRS back pain, the C statistic ranged from 0.75 to 0.80 (pooled estimate, 0.77; 95% CI, 0.75-0.79); for NRS leg pain, the C statistic ranged from 0.74 to 0.77 (pooled estimate, 0.75; 95% CI, 0.74-0.76). Only minor heterogeneity was found in calibration slopes and intercepts. Conclusion: The findings of this study suggest that the models developed can inform patients and clinicians about individual prognosis and aid in surgical decision-making.

Potential Structural Biomarkers in 3D Images Validated by the First Functional Biomarker for Early Age-Related Macular Degeneration - ALSTAR2 Baseline.

Purpose: Lack of valid end points impedes developing therapeutic strategies for early age-related macular degeneration (AMD). Delayed rod-mediated dark adaptation (RMDA) is the first functional biomarker for incident early AMD. The relationship between RMDA and the status of outer retinal bands on optical coherence tomography (OCT) have not been well defined. This study aims to characterize these relationships in early and intermediate AMD. Methods: Baseline data from 476 participants was assessed including eyes with early AMD (n = 138), intermediate AMD (n = 101), and normal aging (n = 237). Participants underwent volume OCT imaging of the macula and rod intercept time (RIT) was measured. The ellipsoid zone (EZ) and interdigitation zone (IZ) on all OCT B-scans of the volumes were segmented. The area of detectable EZ and IZ, and mean thickness of IZ within the Early Treatment Diabetic Retinopathy Study (ETDRS) grid were computed and associations with RIT were assessed by Spearman's correlation coefficient and age adjusted. Results: Delayed RMDA (longer RIT) was most strongly associated with less preserved IZ area (r = -0.591; P < 0.001), followed by decreased IZ thickness (r = -0.434; P < 0.001), and EZ area (r = -0.334; P < 0.001). This correlation between RIT and IZ integrity was not apparent when considering normal eyes alone within 1.5 mm of the fovea. Conclusions: RMDA is correlated with the status of outer retinal bands in early and intermediate AMD eyes, particularly, the status of the IZ. This correlation is consistent with a previous analysis of only foveal B-scans and is biologically plausible given that retinoid availability, involving transfer at the interface attributed to the IZ, is rate-limiting for RMDA.

Causal associations between gut microbiota, metabolites and asthma: a two-sample Mendelian randomization study.

BACKGROUND: While several traditional observational studies have suggested associations between gut microbiota and asthma, these studies are limited by factors such as participant selection bias, confounders, and reverse causality. Therefore, the causal relationship between gut microbiota and asthma remains uncertain. METHODS: We performed two-sample bi-directional Mendelian randomization (MR) analysis to investigate the potential causal relationships between gut microbiota and asthma as well as its phenotypes. We also conducted MR analysis to evaluate the causal effect of gut metabolites on asthma. Genetic variants for gut microbiota were obtained from the MiBioGen consortium, GWAS summary statistics for metabolites from the TwinsUK study and KORA study, and GWAS summary statistics for asthma from the FinnGen consortium. The causal associations between gut microbiota, gut metabolites and asthma were examined using inverse variance weighted, maximum likelihood, MR-Egger, weighted median, and weighted model and further validated by MR-Egger intercept test, Cochran's Q test, and "leave-one-out" sensitivity analysis. RESULTS: We identified nine gut microbes whose genetically predicted relative abundance causally impacted asthma risk. After FDR correction, significant causal relationships were observed for two of these microbes, namely the class Bacilli (OR = 0.84, 95%CI = 0.76-0.94, p = 1.98 × 10-3) and the order Lactobacillales (OR = 0.83, 95%CI = 0.74-0.94, p = 1.92 × 10-3). Additionally, in a reverse MR analysis, we observed a causal effect of genetically predicted asthma risk on the abundance of nine gut microbes, but these associations were no longer significant after FDR correction. No significant causal effect of gut metabolites was found on asthma. CONCLUSIONS: Our study provides insights into the development mechanism of microbiota-mediated asthma, as well as into the prevention and treatment of asthma through targeting specific gut microbiota.

Psoriasis may increase the risk of idiopathic pulmonary fibrosis: a two-sample Mendelian randomization study.

BACKGROUND: Although some studies have indicated that Psoriasis could contribute to the risk of idiopathic pulmonary fibrosis (IPF), no study has reported a clear causal association between them. Our aim was to explore the potential relationship between Psoriasis and IPF using Mendelian randomization (MR) design. METHODS: To explore a causal association between Psoriasis and IPF, we used genetic instruments from the largest available genome-wide association study (GWAS) of European ancestry, including psoriasis (5314 cases, 457,619 controls) and IPF (1028 cases, 196,986 controls). Our main analyses were conducted by inverse-variance weighted (IVW) method with random-effects model, with the other complementary four analyses: weighted median method, weighted mode, multivariable MR and MR-Egger approach. RESULTS: The results of IVW methods demonstrated that genetically predicted psoriasis was significantly associated with higher odds of IPF, with an odds ratio (OR) of 1.09 (95%CI, 1.01-1.18; P = 0.02). Weighted median method, weighted mode and multivariable MR also demonstrated directionally similar results (P < 0.05), while the MR-Egger regression did not reveal the impact of psoriasis on IPF (OR = 1.09, 95%CI, 0.98-1.21; P = 0.11). In addition, both funnel plots and MR-Egger intercepts indicated no directional pleiotropic effects between psoriasis and IPF. CONCLUSIONS: Our study provided potential evidence between genetically predicted psoriasis and IPF, which suggests that understanding the mutual risk factors between psoriasis and IPF can facilitate the clinical management of both diseases.