[Clinical analysis of 18 children with aggressive mature B-cell lymphoma after liver transplantation]

Objective: To summarize the clinical characteristics, prognostic factors and treatment outcomes of childhood aggressive mature B-cell lymphoma after liver transplantation. Methods: This retrospective study included 18 children with newly diagnosed aggressive mature B-cell lymphoma after liver transplantation and treated from June 2018 to June 2022 in the Department of Hematology and Oncology of Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine. Clinical characteristics, treatment and outcomes of patients at last evaluation were analyzed. Overall survival (OS) and event free survival (EFS) rates were calculated by Kaplan-Meier method and Log-Rank analysis was performed to find factors of poor prognosis. Results: Among all 18 patients, there were 6 males and 12 females, and the age of onset was 40 (35, 54) months. The interval from transplant to tumor diagnosis was 21 (17, 35) months and 5 patients had early onset disease (<1 year since transplant). Seventeen patients had abdominal lesions. Diarrhea, vomiting and abdominal masses were the main clinical manifestations. All patients were Epstein-Barr virus (EBV) related posttransplant lymphoproliferative disorders (PTLD). One patient received individualized therapy due to critical sick at diagnosis, and the remaining 17 patients received CP (cyclophosphamide, methylprednisolone plus rituximab) and/or modified EPOCH (prednisone, etoposide, doxorubicin, vincristine, cyclophosphamide plus rituximab) regimens. Of all 18 patients, 15 cases got complete response, 2 cases got partial response, 1 patient died of severe infection. The 2-year OS and EFS rates of 18 patients were (94±5)% and (83±8)%, respectively. None of age, gender or early onset disease had effect on OS and EFS rates in univariate analysis (all P>0.05). Conclusions: The symptoms of PTLD were atypical. Close surveillance of EBV-DNA for patients after liver transplantation was crucial to early stage PTLD diagnosis. CP or modified EPOCH regimen was efficient for pediatric patients with aggressive mature B cell lymphoma after liver transplantation.

Metabolites, Healthy Lifestyle, and Polygenic Risk Score Associated with Upper Gastrointestinal Cancer: Findings from the UK Biobank Study

Previous metabolomics studies have highlighted the predictive value of metabolites on upper gastrointestinal (UGI) cancer, while most of them ignored the potential effects of lifestyle and genetic risk on plasma metabolites. This study aimed to evaluate the role of lifestyle and genetic risk in the metabolic mechanism of UGI cancer. Differential metabolites of UGI cancer were identified using partial least-squares discriminant analysis and the Wilcoxon test. Then, we calculated the healthy lifestyle index (HLI) score and polygenic risk score (PRS) and divided them into three groups, respectively. A total of 15 metabolites were identified as UGI-cancer-related differential metabolites. The metabolite model (AUC = 0.699) exhibited superior discrimination ability compared to those of the HLI model (AUC = 0.615) and the PRS model (AUC = 0.593). Moreover, subgroup analysis revealed that the metabolite model showed higher discrimination ability for individuals with unhealthy lifestyles compared to that with healthy individuals (AUC = 0.783 vs 0.684). Furthermore, in the genetic risk subgroup analysis, individuals with a genetic predisposition to UGI cancer exhibited the best discriminative performance in the metabolite model (AUC = 0.770). These findings demonstrated the clinical significance of metabolic biomarkers in UGI cancer discrimination, especially in individuals with unhealthy lifestyles and a high genetic risk.

The effects of Mediterranean diet on cardiovascular risk factors, glycemic control and weight loss in patients with type 2 diabetes: a meta-analysis

To explore the impact of the Mediterranean diet on cardiovascular risk factors, glycemic control and weight loss in patients with type 2 diabetes(T2D) by a meta-analysis of randomized controlled trials (RCTs). We systematically searched PubMed, Cochrance Library, EMBASE and four Chinese databases to identify RCTs that compared the Mediterranean diet with control diets in patients with T2D up to December 2021. The Risk of Bias of the included studies was assessed using the version 2 of the Cochrane risk-of-bias tools for randomized trials (ROB 2). Seven RCTs with 1371 patients met the eligibility criteria and entered into the meta-analysis. Compared to control diets, the beneficial effects of Mediterranean diet were not statistically significant in high-density lipoprotein (MD = 2.33; 95% CI: -0.27 to 4.92), low-density lipoprotein (MD = -2.34; 95% CI -5.67 to 0.99) and total cholesterol (MD = 2.60; 95% CI: -0.95 to 6.15). But Mediterranean diet led to reduce the level of diastolic blood pressure (MD = -1.20; 95% CI: -2.21 to -0.19) and systolic blood pressure (MD = -4.17; 95% CI: -7.12 to -1.22). Meanwhile, Mediterranean diet showed beneficial effects in glycemic control (HbA1[%]: MD = -0.39, 95% CI: -0.58 to -0.20; fasting plasma glucose: MD = -15.12, 95% CI: -24.69 to -5.55) and weight loss (BMI: MD = -0.71, 95% CI: -1.30 to -0.78; WC: MD = -1.69; 95% CI: -3.35 to -0.02) compared to the control diets. The meta-analysis presented evidence supporting the beneficial effects of the Mediterranean diet on blood pressure, glycemic control, and weight loss. However, the impact of the Mediterranean diet on the lipid profile was not found to be significant, warranting further verification. This Meta-analysis was registered on the INPLASY website (Registration number: INPLASY 202160096).

Personalised follow-up and management schema for patients with screen-detected pulmonary nodules: A dynamic modelling study

BACKGROUND

Selecting the time target for follow-up testing in lung cancer screening is challenging. We aim to devise dynamic, personalized lung cancer screening schema for patients with pulmonary nodules detected through low-dose computed tomography.

METHODS

We developed and validated dynamic models using data of pulmonary nodule patients (aged 55-74 years) from the National Lung Screening Trial. We predicted patient-specific risk profiles at baseline (R0) and updated the risk evaluation results in repeated screening rounds (R1 and R2). We used risk cutoffs to optimize time-dependent sensitivity at an early decision point (3 months) and time-dependent specificity at a late decision point (1 year).

RESULTS

In validation, area under receiver operating characteristic curve for predicting 12-month lung cancer onset was 0.867 (95 % confidence interval: 0.827-0.894) and 0.807 (0.765-0.948) at R0 and R1-R2, respectively. The personalized schema, compared with National Comprehensive Cancer Network (NCCN) guideline and Lung-RADS, yielded lower rates of delayed diagnosis (1.7% vs. 1.7% vs. 6.9 %) and over-testing (4.9% vs. 5.6% vs. 5.6 %) at R0, and lower rates of delayed diagnosis (0.0% vs. 18.2% vs. 18.2 %) and over-testing (2.6% vs. 8.3% vs. 7.3 %) at R2. Earlier test recommendation among cancer patients was more frequent using the personalized schema (vs. NCCN: 29.8% vs. 20.9 %, p = 0.0065; vs. Lung-RADS: 33.2% vs. 22.8 %, p = 0.0025), especially for women, patients aged ≥65 years, and part-solid or non-solid nodules.

CONCLUSIONS

The personalized schema is easy-to-implement and more accurate compared with rule-based protocols. The results highlight value of personalized approaches in realizing efficient nodule management.

MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods

Major histocompatibility complex Class II (MHCII) proteins initiate and regulate immune responses by presentation of antigenic peptides to CD4+ T-cells and self-restriction. The interactions between MHCII and peptides determine the specificity of the immune response and are crucial in immunotherapy and cancer vaccine design. With the ever-increasing amount of MHCII-peptide binding data available, many computational approaches have been developed for MHCII-peptide interaction prediction over the last decade. There is thus an urgent need to provide an up-to-date overview and assessment of these newly developed computational methods. To benchmark the prediction performance of these methods, we constructed an independent dataset containing binding and non-binding peptides to 20 human MHCII protein allotypes from the Immune Epitope Database, covering DP, DR and DQ alleles. After collecting 11 known predictors up to January 2022, we evaluated those available through a webserver or standalone packages on this independent dataset. The benchmarking results show that MixMHC2pred and NetMHCIIpan-4.1 achieve the best performance among all predictors. In general, newly developed methods perform better than older ones due to the rapid expansion of data on which they are trained and the development of deep learning algorithms. Our manuscript not only draws a full picture of the state-of-art of MHCII-peptide binding prediction, but also guides researchers in the choice among the different predictors. More importantly, it will inspire biomedical researchers in both academia and industry for the future developments in this field.

Greenness mitigate cause-specific mortality associated with air pollutants in ischemic and hemorrhagic stroke patients: An ecological health cohort study

BACKGROUND

Air pollution is one of the most serious environmental risks to mortality of stroke. However, there exists a noteworthy knowledge gap concerning the different stroke subtypes, causes of death, the susceptibility of stroke patient, and the role of greenness in this context.

METHODS

We analyzed data from an ecological health cohort, which included 334,261 patients aged ≥40 years with stroke (comprising 288,490 ischemic stroke and 45,771 hemorrhagic stroke) during the period 2013-2019. We used Cox proportional hazards models with time-varying exposure to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) to assess the associations of annual average fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) with both all-cause and cause-specific mortality. Additionally, we conducted analyses to examine the effect modification by greenness and identify potential susceptibility factors through subgroup analyses.

RESULT

In multivariable-adjusted models, long-term exposure to PM2.5 and NO2 was associated with increased risk of all-cause mortality (HR: 1.038, 95% CI: 1.029-1.047 for PM2.5; HR: 1.055, 95% CI: 1.026-1.085 for NO2, per 10 μg/m3, for ischemic stroke patients; similar for hemorrhagic stroke patients). Gradually increasing effect sizes were shown for CVD mortality and stroke mortality. The HRs of mortality were slightly weaker with high versus low vegetation exposure. Cumulative exposures increased the HRs of pollutant-related mortality, and greater greenness decreased this risk. Two subtypes of stroke patients exhibited diverse patterns of benefit.

CONCLUSION

Increasing residential greenness attenuates the increased risk of mortality with different patterns due to chronic air pollutants for ischemic and hemorrhagic stroke, offering valuable insights for precise tertiary stroke prevention strategies.

Ideal cardiovascular health and risk of death in a large Swedish cohort

BACKGROUND

Ideal cardiovascular health (CVH) can be assessed by 7 metrics: smoking, body mass index, physical activity, diet, hypertension, dyslipidemia and diabetes, proposed by the American Heart Association. We examined the association of ideal CVH metrics with risk of all-cause, CVD and non-CVD death in a large cohort.

METHODS

A total of 29,557 participants in the Swedish National March Cohort were included in this study. We ascertained 3,799 deaths during a median follow-up of 19 years. Cox regression models were used to estimate hazard ratios with 95% confidence intervals (95% CIs) of the association between CVH metrics with risk of death. Laplace regression was used to estimate 25th, 50th and 75th percentiles of age at death.

RESULTS

Compared with those having 6-7 ideal CVH metrics, participants with 0-2 ideal metrics had 107% (95% CI = 46-192%) excess risk of all-cause, 224% (95% CI = 72-509%) excess risk of CVD and 108% (31-231%) excess risk of non-CVD death. The median age at death among those with 6-7 vs. 0-2 ideal metrics was extended by 4.2 years for all-causes, 5.8 years for CVD and 2.9 years for non-CVD, respectively. The observed associations were stronger among females than males.

CONCLUSIONS

The strong inverse association between number of ideal CVH metrics and risk of death supports the application of the proposed seven metrics for individual risk assessment and general health promotion.

Conditional transcriptome-wide association study for fine-mapping candidate causal genes

Transcriptome-wide association studies (TWASs) aim to integrate genome-wide association studies with expression-mapping studies to identify genes with genetically predicted expression (GReX) associated with a complex trait. In the present report, we develop a method, GIFT (gene-based integrative fine-mapping through conditional TWAS), that performs conditional TWAS analysis by explicitly controlling for GReX of all other genes residing in a local region to fine-map putatively causal genes. GIFT is frequentist in nature, explicitly models both expression correlation and cis-single nucleotide polymorphism linkage disequilibrium across multiple genes and uses a likelihood framework to account for expression prediction uncertainty. As a result, GIFT produces calibrated P values and is effective for fine-mapping. We apply GIFT to analyze six traits in the UK Biobank, where GIFT narrows down the set size of putatively causal genes by 32.16-91.32% compared with existing TWAS fine-mapping approaches. The genes identified by GIFT highlight the importance of vessel regulation in determining blood pressures and lipid metabolism for regulating lipid levels.

MRSL: a causal network pruning algorithm based on GWAS summary data

Causal discovery is a powerful tool to disclose underlying structures by analyzing purely observational data. Genetic variants can provide useful complementary information for structure learning. Recently, Mendelian randomization (MR) studies have provided abundant marginal causal relationships of traits. Here, we propose a causal network pruning algorithm MRSL (MR-based structure learning algorithm) based on these marginal causal relationships. MRSL combines the graph theory with multivariable MR to learn the conditional causal structure using only genome-wide association analyses (GWAS) summary statistics. Specifically, MRSL utilizes topological sorting to improve the precision of structure learning. It proposes MR-separation instead of d-separation and three candidates of sufficient separating set for MR-separation. The results of simulations revealed that MRSL had up to 2-fold higher F1 score and 100 times faster computing time than other eight competitive methods. Furthermore, we applied MRSL to 26 biomarkers and 44 International Classification of Diseases 10 (ICD10)-defined diseases using GWAS summary data from UK Biobank. The results cover most of the expected causal links that have biological interpretations and several new links supported by clinical case reports or previous observational literatures.

Predicting lung cancer survival prognosis based on the conditional survival bayesian network

Lung cancer is a leading cause of cancer deaths and imposes an enormous economic burden on patients. It is important to develop an accurate risk assessment model to determine the appropriate treatment for patients after an initial lung cancer diagnosis. The Cox proportional hazards model is mainly employed in survival analysis. However, real-world medical data are usually incomplete, posing a great challenge to the application of this model. Commonly used imputation methods cannot achieve sufficient accuracy when data are missing, so we investigated novel methods for the development of clinical prediction models. In this article, we present a novel model for survival prediction in missing scenarios. We collected data from 5,240 patients diagnosed with lung cancer at the Weihai Municipal Hospital, China. Then, we applied a joint model that combined a BN and a Cox model to predict mortality risk in individual patients with lung cancer. The established prognostic model achieved good predictive performance in discrimination and calibration. We showed that combining the BN with the Cox proportional hazards model is highly beneficial and provides a more efficient tool for risk prediction.

Development and Validation of a Bayesian Network-Based Model for Predicting Coronary Heart Disease Risk From Electronic Health Records

BACKGROUND

Traditional risk evaluation models have been applied to guide public health and clinical practice in various studies. However, the application of existing methods to data sets with missing and censored data, as is often the case in electronic health records, requires additional considerations. We aimed to develop and validate a predictive model that exhibits high performance with data sets that contain missing and censored data.

METHODS AND RESULTS

This is a retrospective cohort study of coronary heart disease at Weihai Municipal Hospital on unique patients aged 18 to 96 years between 2013 and 2021. A total of 169 692 participants formed our study population, of which 10 895 participants were diagnosed with coronary heart disease. Models were built for the risk of coronary heart disease based on demographic, laboratory, and medical history variables. All complete samples were assigned to the training set (n=110 325), whereas the remaining samples were assigned to the validation set (n=59 367). The area under the receiver operating characteristic curve value was 0.800 (95% CI, 0.794-0.805), and the C statistic was 0.796 (95% CI, 0.791-0.801) in the derivation cohort, and the corresponding values were 0.837 (95% CI, 0.821-0.853) and 0.838 (95% CI, 0.822-0.854) in the validation cohort. The calibration curve demonstrated its good calibration ability, and decision curve analysis showed its clinical usefulness.

CONCLUSIONS

Our proposed risk prediction model has demonstrated significant effectiveness in handling the complexities of electronic health record data, which often involve extensive missing data and censoring. This approach may offer potential assistance in the use of electronic health records to enhance patient outcomes.

Association between type 1 diabetes and systemic lupus erythematosus: a Mendelian randomization study

OBJECTIVES

Observational studies have shown that there is a bidirectional relationship between type 1 diabetes (T1D) and systemic lupus erythematosus (SLE); the causality of this association remains elusive and may be affected by confusion and reverse causality. There is also a lack of large-scale randomized controlled trials to verify. Therefore, this Mendelian randomization (MR) study aimed to investigate the causal association between T1D and SLE.

METHODS

We aggregated data using publicly available genome-wide association studies (GWAS), all from European populations. Select independent (R2 < 0.001) and closely related to exposure (P < 5 × 10-8) as instrumental variables (IVs). The inverse-variance weighted (IVW) method was used as the primary method. We also used MR-Egger, the weighted median method, MR-Robust, MR-Lasso, and other methods leveraged as supplements.

RESULTS

T1D had a positive causal association with SLE (IVW, odds ratio [OR] = 1.358, 95% confidence interval [CI], 1.205 - 1.530; P < 0.001). The causal association was verified in an independent validation set (IVW, OR = 1.137, 95% CI, 1.033 - 1.251; P = 0.001). SLE had a positive causal association with T1D (IVW, OR = 1.108, 95% CI, 1.074 - 1.144; P < 0.001). The causal association was verified in an independent validation set (IVW, OR = 1.085, 95% CI, 1.046 - 1.127; P < 0.001). These results have also been verified by sensitivity analysis.

CONCLUSION

The MR analysis results indicated a causal association between T1D and SLE. Therefore, further research is needed to clarify the potential biological mechanism between T1D and SLE. Key Points • Observational studies have shown that there is a bidirectional relationship between T1D and SLE. • We evaluated causal effects between T1D and SLE by Mendelian randomization analyses. • The MR analysis results indicated a causal association between T1D and SLE.

Protein-centric omics integration analysis identifies candidate plasma proteins for multiple autoimmune diseases

It remains challenging to translate the findings from genome-wide association studies (GWAS) of autoimmune diseases (AIDs) into interventional targets, presumably due to the lack of knowledge on how the GWAS risk variants contribute to AIDs. In addition, current immunomodulatory drugs for AIDs are broad in action rather than disease-specific. We performed a comprehensive protein-centric omics integration analysis to identify AIDs-associated plasma proteins through integrating protein quantitative trait loci datasets of plasma protein (1348 proteins and 7213 individuals) and totally ten large-scale GWAS summary statistics of AIDs under a cutting-edge systematic analytic framework. Specifically, we initially screened out the protein-AID associations using proteome-wide association study (PWAS), followed by enrichment analysis to reveal the underlying biological processes and pathways. Then, we performed both Mendelian randomization (MR) and colocalization analyses to further identify protein-AID pairs with putatively causal relationships. We finally prioritized the potential drug targets for AIDs. A total of 174 protein-AID associations were identified by PWAS. AIDs-associated plasma proteins were significantly enriched in immune-related biological process and pathways, such as inflammatory response (P = 3.96 × 10-10). MR analysis further identified 97 protein-AID pairs with potential causal relationships, among which 21 pairs were highly supported by colocalization analysis (PP.H4 > 0.75), 10 of 21 were the newly discovered pairs and not reported in previous GWAS analyses. Further explorations showed that four proteins (TLR3, FCGR2A, IL23R, TCN1) have corresponding drugs, and 17 proteins have druggability. These findings will help us to further understand the biological mechanism of AIDs and highlight the potential of these proteins to develop as therapeutic targets for AIDs.

Opportunistic Screening With Low-Dose Computed Tomography and Lung Cancer Mortality in China

Importance

Despite the recommendations of lung cancer screening guidelines and the evidence supporting the effectiveness of population-based lung screening, a common barrier to effective lung cancer screening is that the participation rates of low-dose computed tomography (LDCT) screening among individuals with the highest risk are not large. There are limited data from clinical practice regarding whether opportunistic LDCT screening is associated with reduced lung-cancer mortality.

Objective

To evaluate whether opportunistic LDCT screening is associated with improved prognosis among adults with lung cancer in mainland China.

Design, Setting, and Participants

This cohort study included patients diagnosed with lung cancer at Weihai Municipal Hospital Healthcare Group, Weihai City, China, from 2016 to 2021. Data were analyzed from January 2022 to February 2023.

Exposures

Data collected included demographic indicators, tumor characteristics, comorbidities, blood indexes, and treatment information. Patients were classified into screened and nonscreened groups on the basis of whether or not their lung cancer diagnosis occurred through opportunistic screening.

Main Outcomes and Measures

Follow-up outcome indicators included lung cancer-specific mortality and all-cause mortality. Propensity score matching (PSM) was adopted to account for potential imbalanced factors between groups. The associations between LDCT screening and outcomes were analyzed using Cox regression models based on the matched data. Propensity score regression adjustment and inverse probability treatment weighting were used for sensitivity analysis.

Results

A total of 5234 patients (mean [SD] baseline age, 61.8 [9.8] years; 2518 [48.1%] female) with complete opportunistic screening information were included in the analytical sample, with 2251 patients (42.91%) receiving their lung cancer diagnosis through opportunistic screening. After 1:1 PSM, 2788 patients (1394 in each group) were finally included. The baseline characteristics of the matched patients were balanced between groups. Opportunistic screening with LDCT was associated with a 49% lower risk of lung cancer death (HR, 0.51; 95% CI, 0.42-0.62) and 46% lower risk of all-cause death (HR, 0.54; 95% CI, 0.45-0.64).

Conclusions and Relevance

In this cohort study of patients with lung cancer, opportunistic lung cancer screening with LDCT was associated with lower lung cancer mortality and all-cause mortality. These findings suggest that opportunistic screening is an important supplement to population screening to improve prognosis of adults with lung cancer.

Postoperative prognostic nomogram for adult grade II/III astrocytoma in the Chinese Han population

Background

Prognostic models of glioma have been the focus of many studies. However, most of them are based on Western populations. Additionally, because of the complexity of healthcare data in China, it is important to select a suitable model based on existing clinical data. This study aimed to develop and independently validate a nomogram for predicting the overall survival (OS) with newly diagnosed grade II/III astrocytoma after surgery.

Methods

Data of 472 patients with astrocytoma (grades II-III) were collected from Qilu Hospital as training cohort while data of 250 participants from Linyi People's Hospital were collected as validation cohort. Cox proportional hazards model was used to construct the nomogram and individually predicted 1-, 3-, and 5-year survival probabilities. Calibration ability, and discrimination ability were analyzed in both training and validation cohort.

Results

Overall survival was negatively associated with histopathology, age, subtotal resection, multiple tumors, lower KPS and midline tumors. Internal validation and external validation showed good discrimination (The C-index for 1-, 3-, and 5-year survival were 0.791, 0.748, 0.733 in internal validation and 0.754, 0.735, 0.730 in external validation, respectively). The calibration curves showed good agreement between the predicted and actual 1-, 3-, and 5-year OS rates.

Conclusion

This is the first nomogram study that integrates common clinicopathological factors to provide an individual probabilistic prognosis prediction for Chinese Han patients with astrocytoma (grades II-III). This model can serve as an easy-to-use tool to advise patients and establish optimized surveillance approaches after surgery.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-023-00223-0.

Causal associations between female reproductive behaviors and psychiatric disorders: a lifecourse Mendelian randomization study

BACKGROUND

The timings of reproductive life events have been examined to be associated with various psychiatric disorders. However, studies have not considered the causal pathways from reproductive behaviors to different psychiatric disorders. This study aimed to investigate the nature of the relationships between five reproductive behaviors and twelve psychiatric disorders.

METHODS

Firstly, we calculated genetic correlations between reproductive factors and psychiatric disorders. Then two-sample Mendelian randomization (MR) was conducted to estimate the causal associations among five reproductive behaviors, and these reproductive behaviors on twelve psychiatric disorders, using genome-wide association study (GWAS) summary data from genetic consortia. Multivariable MR was then applied to evaluate the direct effect of reproductive behaviors on these psychiatric disorders whilst accounting for other reproductive factors at different life periods.

RESULTS

Univariable MR analyses provide evidence that age at menarche, age at first sexual intercourse and age at first birth have effects on one (depression), seven (anxiety disorder, ADHD, bipolar disorder, bipolar disorder II, depression, PTSD and schizophrenia) and three psychiatric disorders (ADHD, depression and PTSD) (based on p<7.14×10-4), respectively. However, after performing multivariable MR, only age at first sexual intercourse has direct effects on five psychiatric disorders (Depression, Attention deficit or hyperactivity disorder, Bipolar disorder, Posttraumatic stress disorder and schizophrenia) when accounting for other reproductive behaviors with significant effects in univariable analyses.

CONCLUSION

Our findings suggest that reproductive behaviors predominantly exert their detrimental effects on psychiatric disorders and age at first sexual intercourse has direct effects on psychiatric disorders.

Biocompatibility of Antifogging SiO-doped Diamond-Like Carbon Laparoscope Coatings

Laparoscopes can suffer from fogging and contamination difficulties, resulting in a reduced field of view during surgery. A series of diamond-like carbon films, doped with SiO, were produced by pulsed laser deposition for evaluation as biocompatible, antifogging coatings. DLC films doped with SiO demonstrated hydrophilic properties with water contact angles under 40°. Samples subjected to plasma cleaning had improved contact angle results, with values under 5°. Doping the DLC films with SiO led to an average 40% decrease in modulus and 60% decrease in hardness. Hardness of the doped films, 12.0 - 13.2 GPa, was greater than that of the uncoated fused silica substrate, 9.2 GPa. The biocompatibility was assessed through CellTiter-Glo assays, with the films demonstrating statistically similar levels of cell viability when compared to the control media. The absence of ATP released by blood platelets in contact with the DLC coatings suggests in vivo hemocompatibility. The SiO doped films displayed improved transparency levels in comparison to undoped films, achieving up to an average of 80% transmission over the visible spectrum and an attenuation coefficient of 1.1 × 10⁴ cm^-1 at the 450 nm wavelength. The SiO doped DLC films show promise as a method of fog prevention for laparoscopes.

Effect of neighbourhood greenness on the association between air pollution and risk of stroke first onset: A case-crossover study in shandong province, China

BACKGROUND

Higher neighbourhood greenness is associated with beneficial health outcomes, and short-term exposure to air pollution is associated with an elevated risk of stroke onset. However, little is known about their interactions.

METHODS

Daily data on stroke first onset were collected from 20 counties in Shangdong Province, China, from 2013 to 2019. The enhanced vegetation index (EVI) and concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and sulfur dioxide (SO2) were calculated for each individual at the village or community level based on their home address to measure their neighbourhood exposure to greenness and air pollution. EVI was categorised as low or high, and a time-stratified case-crossover design was used to estimate the percent excess risk (ER%) of stroke associated with short-term exposure to air pollution. We further stratified greenness on the basis of EVI values into quartiles and introduced interaction terms between air pollutant concentrations and the median EVI values of the quartiles to assess the effect of greenness on the associations between short-term exposure and stroke.

RESULTS

Individuals living in the high-greenness areas had weaker associations between total stroke risk and exposure to NO2 (low greenness: ER% = 1.765% [95% CI 1.205%-2.328%]; high greenness: ER% = 0.368% [95% CI -0.252% to 0.991%]; P = 0.001), O3 (low greenness: 0.476% [95% CI 0.246%-0.706%]; high greenness: ER% = 0.085% [95% CI -0.156% to 0.327%]; P = 0.011), and SO2 (low greenness: 0.632% [95% CI 0.138%-1.129%]; high greenness: ER% = -0.177% [95% CI -0.782% to 0.431%]; P = 0.035).

CONCLUSION

Residence in areas with higher greenness was related to weaker associations between air pollution and stroke risk, suggesting that effectively planning green spaces can improve public health.

Personalized prediction for multiple chronic diseases by developing the multi-task Cox learning model

Personalized prediction of chronic diseases is crucial for reducing the disease burden. However, previous studies on chronic diseases have not adequately considered the relationship between chronic diseases. To explore the patient-wise risk of multiple chronic diseases, we developed a multitask learning Cox (MTL-Cox) model for personalized prediction of nine typical chronic diseases on the UK Biobank dataset. MTL-Cox employs a multitask learning framework to train semiparametric multivariable Cox models. To comprehensively estimate the performance of the MTL-Cox model, we measured it via five commonly used survival analysis metrics: concordance index, area under the curve (AUC), specificity, sensitivity, and Youden index. In addition, we verified the validity of the MTL-Cox model framework in the Weihai physical examination dataset, from Shandong province, China. The MTL-Cox model achieved a statistically significant (p<0.05) improvement in results compared with competing methods in the evaluation metrics of the concordance index, AUC, sensitivity, and Youden index using the paired-sample Wilcoxon signed-rank test. In particular, the MTL-Cox model improved prediction accuracy by up to 12% compared to other models. We also applied the MTL-Cox model to rank the absolute risk of nine chronic diseases in patients on the UK Biobank dataset. This was the first known study to use the multitask learning-based Cox model to predict the personalized risk of the nine chronic diseases. The study can contribute to early screening, personalized risk ranking, and diagnosing of chronic diseases.

The long-term mediation role of cytokines on the causal pathway from maternal gestational age to offspring eye diseases: Lifecourse-Network Mendelian randomization

BACKGROUND

Gestational duration has a significant impact on eye diseases. A large number of evidences suggest that cytokines are associated with gestational duration and eye diseases. However, the causal relationships among cytokines, maternal gestational impairment and offspring eye diseases remain unclear.

METHODS

We performed lifecourse-network Mendelian randomization (MR) to explore the causal relationships between maternal gestational duration (from the Early Growth Genetics (EGG) Consortium and the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) study, N = 84,689), neonatal/adult cytokines (from the NHGRI-EBI Catalog, N = 764/4,618), and adult eye diseases (from FinnGen consotium, N = 309,154) using summary-level data from large genome-wide association studies. Multiplicative random effects inverse variance weighted (IVW) and multivariable-IVW methods were the main analysis methods, and the other 15 pleiotropy-robust methods, weak IV-robust methods, and outliers-robust methods were used as auxiliary methods.

RESULTS

Maternal gestational age (early preterm birth, preterm birth, gestational duration, and post-term birth) had a causal relationship with 42 eye diseases. Four neonatal cytokines, Tumor Necrosis Factor-α(TNF-α), IL10, GROA, and CTACK, as well as four adult cytokines, CTACK, IL10, IL12p70 and IL6 are mediators in the causal relationships between early preterm birth and preterm birth in eight eye diseases. However, after adjusting for these mediators, a null direct causal effect of early preterm birth and preterm birth on eight eye diseases was found. In addition, there was no mediator in the causal relationship between gestational duration and post-term birth to eye diseases.

CONCLUSION

The effects of maternal gestational duration on offspring eye diseases through cytokines are long-term and life-course effects.

[Discussion from metabolism to fatty liver disease: rethinking the origin of disease and the endpoint of new drugs or metabolic dysfunction-associated steatotic liver disease]

The diagnosis of nonalcoholic fatty liver disease (NAFLD) is an exclusionary diagnosis, and the name does not reflect the disease's nature or mechanism. Internationally, MAFLD (metabolic dysfunction-associated fatty liver disease) and MASLD (metabolic dysfunction-associated steatotic liver disease) have recently been proposed, which are translated as metabolic dysfunction-associated fatty liver disease in Chinese. The new name reflects the initiating factors of the disease, its occurrence and development process, and the clinical significance of associated outcomes. Through interventions against metabolic disorders, liver disease (hepatic manifestations of systemic disease) reflecting MASLD (MAFLD) has the potential to improve clinical outcomes.

MPI-VGAE: protein-metabolite enzymatic reaction link learning by variational graph autoencoders

Enzymatic reactions are crucial to explore the mechanistic function of metabolites and proteins in cellular processes and to understand the etiology of diseases. The increasing number of interconnected metabolic reactions allows the development of in silico deep learning-based methods to discover new enzymatic reaction links between metabolites and proteins to further expand the landscape of existing metabolite-protein interactome. Computational approaches to predict the enzymatic reaction link by metabolite-protein interaction (MPI) prediction are still very limited. In this study, we developed a Variational Graph Autoencoders (VGAE)-based framework to predict MPI in genome-scale heterogeneous enzymatic reaction networks across ten organisms. By incorporating molecular features of metabolites and proteins as well as neighboring information in the MPI networks, our MPI-VGAE predictor achieved the best predictive performance compared to other machine learning methods. Moreover, when applying the MPI-VGAE framework to reconstruct hundreds of metabolic pathways, functional enzymatic reaction networks and a metabolite-metabolite interaction network, our method showed the most robust performance among all scenarios. To the best of our knowledge, this is the first MPI predictor by VGAE for enzymatic reaction link prediction. Furthermore, we implemented the MPI-VGAE framework to reconstruct the disease-specific MPI network based on the disrupted metabolites and proteins in Alzheimer's disease and colorectal cancer, respectively. A substantial number of novel enzymatic reaction links were identified. We further validated and explored the interactions of these enzymatic reactions using molecular docking. These results highlight the potential of the MPI-VGAE framework for the discovery of novel disease-related enzymatic reactions and facilitate the study of the disrupted metabolisms in diseases.

Association between dietary habits and the risk of migraine: a Mendelian randomization study

Objective

The important contribution of dietary triggers to migraine pathogenesis has been recognized. However, the potential causal roles of many dietary habits on the risk of migraine in the whole population are still under debate. The objective of this study was to determine the potential causal association between dietary habits and the risk of migraine (and its subtypes) development, as well as the possible mediator roles of migraine risk factors.

Methods

Based on summary statistics from large-scale genome-wide association studies, we conducted two-sample Mendelian randomization (MR) and bidirectional MR to investigate the potential causal associations between 83 dietary habits and migraine and its subtypes, and network MR was performed to explore the possible mediator roles of 8 migraine risk factors.

Results

After correcting for multiple testing, we found evidence for associations of genetically predicted coffee, cheese, oily fish, alcohol (red wine), raw vegetables, muesli, and wholemeal/wholegrain bread intake with decreased risk of migraine, those odds ratios ranged from 0.78 (95% CI: 0.63-0.95) for overall cheese intake to 0.61 (95% CI: 0.47-0.80) for drinks usually with meals among current drinkers (yes + it varies vs. no); while white bread, cornflakes/frosties, and poultry intake were positively associated with the risk of migraine. Additionally, genetic liability to white bread, wholemeal/wholegrain bread, muesli, alcohol (red wine), cheese, and oily fish intake were associated with a higher risk of insomnia and (or) major depression disorder (MDD), each of them may act as a mediator in the pathway from several dietary habits to migraine. Finally, we found evidence of a negative association between genetically predicted migraine and drinking types, and positive association between migraine and cups of tea per day.

Significance

Our study provides evidence about association between dietary habits and the risk of migraine and demonstrates that some associations are partly mediated through one or both insomnia and MDD. These results provide new insights for further nutritional interventions for migraine prevention.

Highlighting the importance of healthy sleep patterns in the risk of adult asthma under the combined effects of genetic susceptibility: a large-scale prospective cohort study of 455 405 participants

BACKGROUND

Individuals with asthma usually have comorbid sleep disturbances; however, whether sleep quality affects asthma risk is still unclear. We aimed to determine whether poor sleep patterns could increase the risk of asthma and whether healthy sleep patterns could mitigate the adverse effect of genetic susceptibility.

METHODS

A large-scale prospective study was performed in the UK Biobank cohort involving 455 405 participants aged 38-73 years. Polygenic risk scores (PRSs) and comprehensive sleep scores, including five sleep traits, were constructed. A multivariable Cox proportional hazards regression model was used to investigate the independent and combined effects of sleep pattern and genetic susceptibility (PRS) on asthma incidence. Subgroup analysis across sex and sensitivity analysis, including a 5-year lag, different covariate adjustments and repeat measurements were performed.

RESULTS

A total of 17 836 individuals were diagnosed with asthma during over 10 years of follow-up. Compared with the low-risk group, the HRs and 95% CIs for the highest PRS group and the poor sleep pattern group were 1.47 (95% CI: 1.41 to 1.52) and 1.55 (95% CI: 1.45 to 1.65), respectively. A combination of poor sleep and high genetic susceptibility led to a twofold higher risk compared with the low-risk combination (HR (95% CI): 2.22 (1.97 to 2.49), p<0.001). Further analysis showed that a healthy sleep pattern was associated with a lower risk of asthma in the low, intermediate and high genetic susceptibility groups (HR (95% CI): 0.56 (0.50 to 0.64), 0.59 (0.53 to 0.67) and 0.63 (0.57 to 0.70), respectively). Population-attributable risk analysis indicated that 19% of asthma cases could be prevented when these sleep traits were improved.

CONCLUSIONS

Individuals with poor sleep patterns and higher genetic susceptibility have an additive higher asthma risk. A healthy sleep pattern reflected a lower risk of asthma in adult populations and could be beneficial to asthma prevention regardless of genetic conditions. Early detection and management of sleep disorders could be beneficial to reduce asthma incidence.

Therapeutic drug combinations against COVID-19 obtained by employing a collaborative filtering method

The outbreak of coronavirus disease 2019 (COVID-19) has severely harmed human society and health. Because there is currently no specific drug for the treatment and prevention of COVID-19, we used a collaborative filtering algorithm to predict which traditional Chinese medicines (TCMs) would be effective in combination for the prevention and treatment of COVID-19. First, we performed drug screening based on the receptor structure prediction method, molecular docking using q-vina to measure the binding ability of TCMs, TCM formulas, and neo-coronavirus proteins, and then performed synergistic filtering based on Laplace matrix calculations to predict potentially effective TCM formulas. Combining the results of molecular docking and synergistic filtering, the new recommended formulas were analyzed by reviewing data platforms or tools such as PubMed, Herbnet, the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the Guide to the Dispensing of Medicines for Clinical Evidence, and the Dictionary of Chinese Medicine Formulas, as well as medical experts' treatment consensus in terms of herbal efficacy, modern pharmacological studies, and clinical identification and typing of COVID-19 pneumonia, to determine the recommended solutions. We found that the therapeutic effect of a combination of six TCM formulas on the COVID-19 virus is the result of the overall effect of the formula rather than that of specific components of the formula. Based on this, we recommend a formula similar to that of Jinhua Qinggan Granules for the treatment of COVID-19 pneumonia. This study may provide new ideas and new methods for future clinical research.

Classification

Biological Science.

Role of the Gut-Brain Axis in the Shared Genetic Etiology Between Gastrointestinal Tract Diseases and Psychiatric Disorders: A Genome-Wide Pleiotropic Analysis

IMPORTANCE

Comorbidities and genetic correlations between gastrointestinal tract diseases and psychiatric disorders have been widely reported, with the gut-brain axis (GBA) hypothesized as a potential biological basis. However, the degree to which the shared genetic determinants are involved in these associations underlying the GBA is unclear.

OBJECTIVE

To investigate the shared genetic etiology between gastrointestinal tract diseases and psychiatric disorders and to identify shared genomic loci, genes, and pathways.

DESIGN, SETTING, AND PARTICIPANTS

This genome-wide pleiotropic association study using genome-wide association summary statistics from publicly available data sources was performed with various statistical genetic approaches to sequentially investigate the pleiotropic associations from genome-wide single-nucleotide variation (SNV; formerly single-nucleotide polymorphism [SNP]), and gene levels and biological pathways to disentangle the underlying shared genetic etiology between 4 gastrointestinal tract diseases (inflammatory bowel disease, irritable bowel syndrome, peptic ulcer disease, and gastroesophageal reflux disease) and 6 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, attention-deficit/hyperactivity disorder, posttraumatic stress disorder, and anorexia nervosa). Data were collected from March 10, 2021, to August 25, 2021, and analysis was performed from January 8 through May 30, 2022.

MAIN OUTCOMES AND MEASURES

The primary outcomes consisted of a list of genetic loci, genes, and pathways shared between gastrointestinal tract diseases and psychiatric disorders.

RESULTS

Extensive genetic correlations and genetic overlaps were found among 22 of 24 trait pairs. Pleiotropic analysis under a composite null hypothesis identified 2910 significant potential pleiotropic SNVs in 19 trait pairs, with 83 pleiotropic loci and 24 colocalized loci detected. Gene-based analysis found 158 unique candidate pleiotropic genes, which were highly enriched in certain GBA-related phenotypes and tissues, whereas pathway enrichment analysis further highlighted biological pathways primarily involving cell adhesion, synaptic structure and function, and immune cell differentiation. Several identified pleiotropic loci also shared causal variants with gut microbiomes. Mendelian randomization analysis further illustrated vertical pleiotropy across 8 pairwise traits. Notably, many pleiotropic loci were identified for multiple pairwise traits, such as 1q32.1 (INAVA), 19q13.33 (FUT2), 11q23.2 (NCAM1), and 1p32.3 (LRP8).

CONCLUSIONS AND RELEVANCE

These findings suggest that the pleiotropic genetic determinants between gastrointestinal tract diseases and psychiatric disorders are extensively distributed across the genome. These findings not only support the shared genetic basis underlying the GBA but also have important implications for intervention and treatment targets of these diseases simultaneously.

Development and Validation of Esophageal Squamous Cell Carcinoma Risk Prediction Models Based on an Endoscopic Screening Program

IMPORTANCE

Assessment tools are lacking for screening of esophageal squamous cell cancer (ESCC) in China, especially for the follow-up stage. Risk prediction to optimize the screening procedure is urgently needed.

OBJECTIVE

To develop and validate ESCC prediction models for identifying people at high risk for follow-up decision-making.

DESIGN, SETTING, AND PARTICIPANTS

This open, prospective multicenter diagnostic study has been performed since September 1, 2006, in Shandong Province, China. This study used baseline and follow-up data until December 31, 2021. The data were analyzed between April 6 and May 31, 2022. Eligibility criteria consisted of rural residents aged 40 to 69 years who had no contraindications for endoscopy. Among 161 212 eligible participants, those diagnosed with cancer or who had cancer at baseline, did not complete the questionnaire, were younger than 40 years or older than 69 years, or were detected with severe dysplasia or worse lesions were eliminated from the analysis.

EXPOSURES

Risk factors obtained by questionnaire and endoscopy.

MAIN OUTCOMES AND MEASURES

Pathological diagnosis of ESCC and confirmation by cancer registry data.

RESULTS

In this diagnostic study of 104 129 participants (56.39% women; mean [SD] age, 54.31 [7.64] years), 59 481 (mean [SD] age, 53.83 [7.64] years; 58.55% women) formed the derivation set while 44 648 (mean [SD] age, 54.95 [7.60] years; 53.51% women) formed the validation set. A total of 252 new cases of ESCC were diagnosed during 424 903.50 person-years of follow-up in the derivation cohort and 61 new cases from 177 094.10 person-years follow-up in the validation cohort. Model A included the covariates age, sex, and number of lesions; model B included age, sex, smoking status, alcohol use status, body mass index, annual household income, history of gastrointestinal tract diseases, consumption of pickled food, number of lesions, distinct lesions, and mild or moderate dysplasia. The Harrell C statistic of model A was 0.80 (95% CI, 0.77-0.83) in the derivation set and 0.90 (95% CI, 0.87-0.93) in the validation set; the Harrell C statistic of model B was 0.83 (95% CI, 0.81-0.86) and 0.91 (95% CI, 0.88-0.95), respectively. The models also had good calibration performance and clinical usefulness.

CONCLUSIONS AND RELEVANCE

The findings of this diagnostic study suggest that the models developed are suitable for selecting high-risk populations for follow-up decision-making and optimizing the cancer screening process.

Associations of ambient temperature with mortality for ischemic and hemorrhagic stroke and the modification effects of greenness in Shandong Province, China

BACKGROUND

Evidence is scant on the relative and attributable contributions of ambient temperature on stroke subtypes mortality. Few studies have examined modification effects of multiple greenness indicators on such contributions, especially in China. We quantified the associations between ambient temperature and overall, ischemic, and hemorrhagic stroke mortality; further examined whether the associations were modified by greenness.

METHODS

We conducted a multicenter time-series analysis from January 1, 2013 to December 31, 2019. we adopted a distributed lag non-linear model to evaluate county-specific temperature-stroke mortality associations. We then applied a random-effects meta-analysis to pool county-specific effects. Attributable mortality was calculated for cold and heat, defined as temperatures below and above the minimum mortality temperature (MMT). Finally, We conducted a multivariate meta-regression to determine associations between greenness and stroke mortality risks for cold and heat, using normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) as quantitative indicators of greenness exposure.

RESULTS

In the study period, 138,749 deaths from total stroke were reported: 86,873 ischemic and 51,876 hemorrhagic stroke. We observed significant W-shaped relationships between temperature and stroke mortality, with substantial differences among counties and regions. With MMT as the temperature threshold, 17.16 % (95 % empirical CI, 13.38 %-19.75 %) of overall, 20.05 % (95 % eCI, 16.46 %-22.70 %) of ischemic, and 12.55 % (95 % eCI, 5.59 %-16.24 %) of hemorrhagic stroke mortality were attributable to non-optimum temperature (combining cold and heat), more mortality was caused by cold (14.94 %; 95 % eCI, 11.57 %-17.34 %) than by heat (2.22 %; 95 % eCI, 1.54 %-2.72 %). Higher levels of NDVI, SAVI and EVI were related to mitigated effects of non-optimum temperatures-especially heat.

CONCLUSIONS

Exposure to non-optimum temperatures aggravated stroke mortality risks; increasing greenness could alleviate that risks. This evidence has important implications for local communities in developing adaptive strategies to minimize the health consequences of adverse temperatures.

Identification of a linear epitope in the capsid protein of goose astrovirus with monoclonal antibody

Goose astrovirus (GoAstV) is a novel avastrovirus that typically causes gosling gout and results in 2 to 20% mortality. GoAstV capsid protein is the sole structural protein, which is responsible for viral attachment, assembly, maturation as well as eliciting host antibodies. However, the epitopes within capsid protein have not been well studied. In this study, a monoclonal antibody, named 1D7, was generated against GoAstV capsid protein by hybridoma technology. Western blot results showed that this MAb could react with recombinant capsid protein expressed in E. coli. Also, it recognized the precursor of capsid protein, VP90 and VP70, in GoAstV-infected cells. Besides, excellent specificity of MAb 1D7 was further demonstrated in indirect immunofluorescence assay and immunohistochemical analysis. Epitope mapping results revealed that MAb 1D7 recognized the epitope 33QKVY 36 within Cap protein. Sequence alignment indicated that 33QKVY 36 is a conserved epitope among the isolates of goose astrovirus type 2 (GoAstV-2), suggesting the potential for its use in GoAstV-2 specific diagnostic assay. These findings may provide some insight into a function of the GoAstV capsid protein and further contribute to the development of diagnostic methods for GoAstV infection.

Causal relationship between type 1 diabetes and hypothyroidism: A Mendelian randomization study

OBJECTIVES

Although an association between type 1 diabetes (T1D) and hypothyroidism has been found in multiple observational studies, whether T1D plays a causal role in the development of hypothyroidism remains uncertain. Therefore, this Mendelian randomization (MR) study aimed to investigate the causal association between T1D and hypothyroidism.

METHODS

Independent single-nucleotide polymorphisms associated with T1D with genome-wide significance were selected as instrumental variables from a large genome-wide association study (GWAS) of T1D. Hypothyroidism GWAS summary statistics were obtained from the Thyroidomics Consortium. The inverse-variance weighted (IVW) method was used as the primary analysis for estimating the effect of the exposure on the outcome. We also used MR-Egger, the weighted median method, MR-Robust, and other methods to confirm the results.

RESULTS

T1D had a positive causal association with hypothyroidism [IVW, odds ratio (OR) = 1.083, 95% confidence interval (CI), 1.046-1.122; p &lt; .001]. MR-Egger regression indicated that directional pleiotropy did not bias the result (intercept = 0.006; p = .295). The causal association was verified in an independent validation set (IVW, OR = 1.099, 95% CI, 1.018-1.186; p = .017). The results were robust according to various MR methods, and the results of the reverse MR analysis did not support reverse causation (p &gt; .05).

CONCLUSIONS

The MR analysis results indicated a causal association between T1D and hypothyroidism. Therefore, it is recommended that patients with T1D undergo thyroid function tests regularly to minimize the risk of undiagnosed hypothyroidism among young patients with T1D.

Multi-trajectories of systolic and diastolic hypertension and coronary heart disease in middle-aged and older adults

Objective

This study aimed to investigate multi-trajectories of systolic and diastolic hypertension and assess their association with the risk of coronary heart disease (CHD) in middle-aged and older Chinese adults.

Methods

The study cohort comprised 4,102 individuals aged 40-75 years with records of at least four systolic blood pressure (SBP) and diastolic blood pressure (DBP). A group-based multi-trajectory model was adopted to identify multi-trajectories of systolic and diastolic hypertension, followed by a logistic model to assess the independent associations between these trajectories and CHD risk. The multinomial logistic model was used to evaluate the impact of baseline covariates on trajectory groups.

Results

Six distinct trajectories for systolic and diastolic hypertension were identified which represent distinct stages of hypertension and were characterized as low-stable, low-increasing, medium-decreasing, medium-increasing-decreasing, isolated systolic hypertension phase, and high-decreasing. Compared with the low-stable group, the adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were 2.23 (1.34-3.70) for the medium-increasing-decreasing group and 1.87 (1.12-3.11) for the high-decreasing group after adjustment for baseline covariates. Compared with the low-increasing group, the ORs and 95% CIs were 1.88 (1.06-3.31) for the medium-increasing-decreasing group. Age, gender, drinking, body mass index (BMI), triglyceride (TG), and fasting plasma glucose (FPG) were independent predictors for trajectory groups 4 and 6.

Conclusion

Novel, clinically defined multi-trajectories of systolic and diastolic hypertension were identified. Middle-aged and older adults with medium-increasing-decreasing or high-decreasing blood pressure trajectories are potentially critical periods for the development of CHD. Preventing adverse changes in hypertension status and reducing the high risk of CHD is necessary for people in distinct trajectory groups.

Building a knowledge base for colorectal cancer patient care using formal concept analysis

Background

Colorectal cancer (CRC) is a heterogeneous disease with different responses to targeted therapies due to various factors, and the treatment effect differs significantly between individuals. Personalize medical treatment (PMT) is a method that takes individual patient characteristics into consideration, making it the most effective way to deal with this issue. Patient similarity and clustering analysis is an important aspect of PMT. This paper describes how to build a knowledge base using formal concept analysis (FCA), which clusters patients based on their similarity and preserves the relations between clusters in hierarchical structural form.

Methods

Prognostic factors (attributes) of 2442 CRC patients, including patient age, cancer cell differentiation, lymphatic invasion and metastasis stages were used to build a formal context in FCA. A concept was defined as a set of patients with their shared attributes. The formal context was formed based on the similarity scores between each concept identified from the dataset, which can be used as a knowledge base.

Results

A hierarchical knowledge base was constructed along with the clinical records of the diagnosed CRC patients. For each new patient, a similarity score to each existing concept in the knowledge base can be retrieved with different similarity calculations. The ranked similarity scores that are associated with the concepts can offer references for treatment plans.

Conclusions

Patients that share the same concept indicates the potential similar effect from same clinical procedures or treatments. In conjunction with a clinician’s ability to undergo flexible analyses and apply appropriate judgement, the knowledge base allows faster and more effective decisions to be made for patient treatment and care.

Quantifying substantial carcinogenesis of genetic and environmental factors from measurement error in the number of stem cell divisions

Background

The relative contributions of genetic and environmental factors versus unavoidable stochastic risk factors to the variation in cancer risk among tissues have become a widely-discussed topic. Some claim that the stochastic effects of DNA replication are mainly responsible, others believe that cancer risk is heavily affected by environmental and hereditary factors. Some of these studies made evidence from the correlation analysis between the lifetime number of stem cell divisions within each tissue and tissue-specific lifetime cancer risk. However, they did not consider the measurement error in the estimated number of stem cell divisions, which is caused by the exposure to different levels of genetic and environmental factors. This will obscure the authentic contribution of environmental or inherited factors.

Methods

In this study, we proposed two distinct modeling strategies, which integrate the measurement error model with the prevailing model of carcinogenesis to quantitatively evaluate the contribution of hereditary and environmental factors to cancer development. Then, we applied the proposed strategies to cancer data from 423 registries in 68 different countries (global-wide), 125 registries across China (national-wide of China), and 139 counties in Shandong province (Shandong provincial, China), respectively.

Results

The results suggest that the contribution of genetic and environmental factors is at least 92% to the variation in cancer risk among 17 tissues. Moreover, mutations occurring in progenitor cells and differentiated cells are less likely to be accumulated enough for cancer to occur, and the carcinogenesis is more likely to originate from stem cells. Except for medulloblastoma, the contribution of genetic and environmental factors to the risk of other 16 organ-specific cancers are all more than 60%.

Conclusions

This work provides additional evidence that genetic and environmental factors play leading roles in cancer development. Therefore, the identification of modifiable environmental and hereditary risk factors for each cancer is highly recommended, and primary prevention in early life-course should be the major focus of cancer prevention.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10219-w.

Subclones of bone marrow CD34+ cells in acute myeloid leukemia at diagnosis confer responses of patients to induction chemotherapy

BACKGROUND

Acute myeloid leukemia (AML) is a hematopoietic malignancy with a prognosis that varies with genetic heterogeneity of hematopoietic stem/progenitor cells (HSPCs). Induction chemotherapy with cytarabine and anthracycline has been the standard care for newly diagnosed AML, but about 30% of patients have no response to this regimen. The resistance mechanisms require deeper understanding.

METHODS

In our study, using single-cell RNA sequencing, we analyzed the heterogeneity of bone marrow CD34⁺ cells from newly diagnosed patients with AML who were then divided into sensitive and resistant groups according to their responses to induction chemotherapy with cytarabine and anthracycline. We verified our findings by TCGA database, GEO datasets, and multiparameter flow cytometry.

RESULTS

We established a landscape for AML CD34⁺ cells and identified HSPC types based on the lineage signature genes. Interestingly, we found a cell population with CRIP1^high LGALS1^high S100As^high showing features of granulocyte-monocyte progenitors was associated with poor prognosis of AML. And two cell populations marked by CD34⁺ CD52⁺ or CD34⁺ CD74⁺ DAP12⁺ were related to good response to induction therapy, showing characteristics of hematopoietic stem cells.

CONCLUSION

Our study indicates the subclones of CD34⁺ cells confers for outcomes of AML and provides biomarkers to predict the response of patients with AML to induction chemotherapy.

Effect of lactose-free milk powder on lactose intolerance symptoms and nutritional status of pet dogs

The effects of standard cow, goat and lactose-free cow milk powder on lactose intolerance symptoms and the nutritional status of dogs was studied. Forty adult Springer Spaniels with lactose intolerance were randomly allocated into four groups with ten dogs each and fed one of the milk powders or water for 21 d. The milk powders were reconstituted daily by diluting 10 g milk powder to 60 g water and 2 g milk powder/kg body weight was provided ad libitum and refusals were monitored daily. Lactose intolerance was assessed by faecal composition and pH, water and lactose contents. Biochemical markers for nutritional status were analysed. Feeding intolerance and lactose in faecal samples were observed in dogs fed the standard cow and goat milk powder, but not for the lactose-free milk powder or control groups. The high- and low-density lipoprotein cholesterol increased in all milk powder groups, while there were no other differences in nutritional status measurement. The results suggested that feeding lactose-free milk powder to dogs with lactose intolerance reduced the occurrence of symptoms, thereby enhancing the health and wellbeing of dogs.

Exposure to various ambient air pollutants increases the risk of venous thromboembolism: A cohort study in UK Biobank

Epidemiological evidence for the association between air pollutants exposure and venous thromboembolism (VTE) remains controversial. In this study, a total of 389,659 participants from the UK Biobank who were free of VTE in 2010 were included, and the annual mean concentrations of air pollutants near where participants lived were collected. During a median follow-up period of 8.25 years, 4986 VTEs were determined from the hospital admission records. The Cox proportional hazard model was used to examine the association between air pollutants and VTE. We firstly investigated the associations between air pollutants concentration and VTE and found only NO2 and NO increased VTE risk (P < 0.05). We further calculated the product of air pollutant concentrations and outdoor time to measure personal daily cumulative exposure and found that the hazard rates (HRs) of VTE for a 50-μg/m³∗day increase in daily cumulative exposure to PM10, PM2.5, PM2.5-10, NO, and NO2 were 1.08 (1.05-1.12), 1.16 (1.09-1.24), 1.23 (1.11-1.37), 1.04 (1.01-1.06), and 1.05 (1.03-1.08), respectively. To measure joint exposure to various air pollutants and its effect on VTE, we created a weighted air pollutants exposure score (APES) and found a dose-response relationship between APES and VTE risk (P < 0.001 for trend). Compared with participants in the lowest quintile of APES, the HRs of VTE were 1.19 (1.08-1.30) for those within the highest quintile groups. Furthermore, we also found the effect of air pollutants on VTE was statistically significant only in individuals with low-middle VTE genetic risk score (GRS) (P < 0.05), but not in the high VTE GRS groups (P > 0.05). Our findings suggest that exposure to various air pollutants including PM2.5, PM2.5-10, PM10, NO, and NO2, either individually or jointly, were associated with an increased risk of VTE in a dose-response pattern. Our study highlights the importance of a comprehensive assessment of various air pollutants in VTE prevention.

Identification of novel proteins associated with movement-related adverse antipsychotic effects by integrating GWAS data and human brain proteomes

Genome-wide association studies (GWAS) have identified some variants for movement-related adverse antipsychotic effects (MAAE), while how these variants confer MAAE remains unclear. We used the probabilistic Mendelian randomization (PMR) method to identify candidate proteins for MAAE by integrating MAAE GWASs and protein quantitative trait loci (pQTL) data. An independent pQTL data from the Banner project and brain-derived eQTL data were used to perform confirmatory PMR. A total of 56 proteins were identified as candidate targets for MAAE after false discovery rates (FDR) correction, such as GRIN2B, ADRA1A, and PED4B. 12 genes were replicated in the confirmatory PMR, and 18 genes had consistent evidence at the transcript level. Furthermore, we investigated the associations between candidate proteins and the motor symptoms of Parkinson's disease (PD). There were 24, 38, and 10 candidate proteins that were significantly associated with PD, PD motor subtypes, and PD motor progression, respectively. Enrichment analysis identified 34 GO terms and 17 pathways that may be involved in MAAE, such as glutamatergic synapse, glutamate receptor complex, and GABAergic synapse. Our study identified multiple candidate genes and pathways that were associated with MAAE, providing new insights into the biological mechanism of MAAE and targets for further mechanistic and therapeutic studies.

212 FATIGUE EXPERIENCED BY ADULTS WITH LONG-COVID WAS NOT ASSOCIATED WITH AN EMG-DERIVED MUSCLE FATIGUE INDEX

Background

One of the most prominent challenges of the COVID-19 pandemic is the emergence of the post-COVID-19 syndrome or ‘long-COVID’ following the acute phase of the viral infection. With a plethora of debilitating symptoms across many physiological systems, the pathophysiology of long-COVID remains elusive and subject of intense research efforts. Adults with long COVID are often affected by prolonged fatigue, which could be linked to neuromuscular function impairment. We hypothesised that there might be a correlation between subjective fatigue and an Electromyograph(EMG)-derived muscle fatigue index in adults with long-COVID.

Methods

As part of a neuro-cardiovascular instability assessment protocol, participants were asked to squeeze the thigh muscles as hard as possible for ten seconds during both supine resting and after active and passive standing (head-up tilt) tests. Surface EMG was used to capture on each leg the physiological activity of the vastus lateralis and rectus femoris during the squeezes, from which the muscle fatigue index was computed based on both the mean and median frequencies of each 10-second maximum prompted muscle contraction. Using two-sided Spearman correlation coefficients, we examined the associations between the muscle fatigue indices and self-reported fatigue measured by the Chalder Fatigue Scale (CFQ).

Results

108 participants were included, 77 female and 31 male, with a mean age of 46 (range 25-78) years, 22% of whom were hospitalised in the acute phase of SARS-CoV-2 infection. Strong intercorrelations were found between the muscle fatigue indices derived from the mean and median frequencies of thigh muscle contractions. However, no significant correlations were detected between the muscle fatigue indices and CFQ scores.

Conclusion

Self-reported fatigue as measured by the CFQ was not associated with an EMG-derived muscle fatigue index in this long-COVID cohort.

[The epidemiology and screening of hepatitis D]

Hepatitis D virus (HDV) is a defective RNA virus with 8 genotypes, which requires hepatitis B virus (HBV) for its replication both in co-infection (HDV infection occurs when people become infected with both hepatitis B and D simultaneously) and super-infection (HDV infection after infected with hepatitis B). Due to persistency of anti-HDV in the superinfected patient, the most epidemiology data of HDV infection suggests HBV/HDV super-infection. Because of inadequate awareness and effective HBV vaccination programme, HDV infection screen was not specific recommended, except for risk population recommended by AASLD. However, the disease burden of HDV infection was not fully understood and might be underestimated. This review summarized the anti-HDV and HDV genotype epidemiology and the screening of HDV infection.

Effects of antipsychotics on triglyceride trajectories and its implications in CVD: A longitudinal cohort study

Background

Although the association between short-term antipsychotics exposure and triglycerides (TG) levels has been confirmed, the effects of long-term antipsychotics exposure on TG trajectories and its implications in cardiovascular disease (CVD) remains largely unknown.

Methods

A total of 39,988 participants with at least 3 TG measurements between January 2014 and February 2021 were included in this longitudinal study, with a median follow-up was 4.48 years. A latent class growth mixed model (LCGMM) was used to identify TG trajectories. Based on the LCGMM parameters, we calculated the area under the curve (AUC) and estimated the effect of antipsychotics on AUC and TG trajectory slopes. The primary outcome was CVD events. We also investigated and compared the association between antipsychotics and CVD in subgroups stratified by TG trajectory and TG levels.

Findings

A total of 11,543 CVD events were documented and the incidence density was 64.64 per 1000 person-years. We identified two TG trajectories labeled as inverse-U shape (30.77%, n=12306) and low-decreasing (69.23%, n=27682). The antipsychotic exposure increased total AUC by 13% and increased the slopes of TG trajectories before age 48 years. In the inverse-U and low-decreasing group, the adjusted hazard ratios (HRs) and 95% confidence intervals (CI) for antipsychotics associated with CVD were 1.40 (1.21-1.62) and 1.29 (1.14-1.45), respectively, and the difference between the two trajectory groups become larger with the increase of the antipsychotic exposure. The association of antipsychotics with CVD (HR=1.72, 95%CI: 1.36-2.19) in inverse-U trajectory and high TG group was stronger than that in other subgroups.

Interpretation

Long-term antipsychotic exposure increased the TG burden and TG increase rate early in life. The strength of the association between antipsychotics and CVD risk in the inverse-U group was stronger than that in the low-decreasing group.

Funding

The National Key Research and Development Program of China, Shandong Province Major Science and Technology Innovation Project, and National Natural Science Foundation of China.

A Prognostic Model of Non-Small Cell Lung Cancer With a Radiomics Nomogram in an Eastern Chinese Population

Background

The aim of this study was to build and validate a radiomics nomogram by integrating the radiomics features extracted from the CT images and known clinical variables (TNM staging, etc.) to individually predict the overall survival (OS) of patients with non-small cell lung cancer (NSCLC).

Methods

A total of 1,480 patients with clinical data and pretreatment CT images during January 2013 and May 2018 were enrolled in this study. We randomly assigned the patients into training (N = 1036) and validation cohorts (N = 444). We extracted 1,288 quantitative features from the CT images of each patient. The Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model was applied in feature selection and radiomics signature building. The radiomics nomogram used for the prognosis prediction was built by combining the radiomics signature and clinical variables that were derived from clinical data. Calibration ability and discrimination ability were analyzed in both training and validation cohorts.

Results

Eleven radiomics features were selected by LASSO Cox regression derived from CT images, and the radiomics signature was built in the training cohort. The radiomics signature was significantly associated with NSCLC patients’ OS (HR = 3.913, p < 0.01). The radiomics nomogram combining the radiomics signature with six clinical variables (age, sex, chronic obstructive pulmonary disease, T stage, N stage, and M stage) had a better prognostic performance than the clinical nomogram both in the training cohort (C-index, 0.861, 95% CI: 0.843–0.879 vs. C-index, 0.851, 95% CI: 0.832–0.870; p < 0.001) and in the validation cohort (C-index, 0.868, 95% CI: 0.841–0.896 vs. C-index, 0.854, 95% CI: 0.824–0.884; p = 0.002). The calibration curves demonstrated optimal alignment between the prediction and actual observation.

Conclusion

The established radiomics nomogram could act as a noninvasive prediction tool for individualized survival prognosis estimation in patients with NSCLC. The radiomics signature derived from CT images may help clinicians in decision-making and hold promise to be adopted in the patient care setting as well as the clinical trial setting.

Relationships of sleep traits with prostate cancer risk: A prospective study of 213,999 UK Biobank participants

BACKGROUND

The effect of sleep on the occurrence of prostate cancer (PCa) remains unclear. This study explored the influence of sleep traits on the incidence of PCa using a UK Biobank cohort study.

METHODS

In this prospective cohort study, 213,999 individuals free of PCa at recruitment from UK Biobank were included. Missing data were imputed using multiple imputation by chained equations. Cox proportional hazards models were used to calculate the adjusted hazard ratios and 95% confidence intervals for PCa (6747 incident cases) across seven sleep traits (sleep duration, chronotype, insomnia, snoring, nap, difficulty to get up in the morning, and daytime sleepiness). In addition, we newly created a healthy sleep quality score according to sleep traits to assess the impact of the overall status of night and daytime sleep on PCa development. E values were used to assess unmeasured confounding.

RESULTS

We identified 6747 incident cases, of which 344 died from PCa. Participants who usually suffered from insomnia had a higher risk of PCa (hazard ratio [HR]: 1.11; 95% confidence interval [CI]: 1.04-1.19, E value: 1.46). Finding it fairly easy to get up in the morning was also positively associated with PCa (HR: 1.09; 95% CI: 1.04-1.15, E value: 1.40). Usually having a nap was associated with a lower risk of PCa (HR: 0.91; 95% CI: 0.83-0.99, E value: 1.42).

CONCLUSIONS

Fairly easy to get up in the morning and usually experiencing insomnia were associated with an increased incidence of PCa. Moreover, usually having a nap was associated with a lower risk of PCa. Therefore, sleep behaviors are modifiable risk factors that may have a potential impact on PCa risk.

Signaling repurposable drug combinations against COVID-19 by developing the heterogeneous deep herb-graph method

BACKGROUND

Coronavirus disease 2019 (COVID-19) has spurred a boom in uncovering repurposable existing drugs. Drug repurposing is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication.

MOTIVATION

Current works of drug repurposing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are mostly limited to only focusing on chemical medicines, analysis of single drug targeting single SARS-CoV-2 protein, one-size-fits-all strategy using the same treatment (same drug) for different infected stages of SARS-CoV-2. To dilute these issues, we initially set the research focusing on herbal medicines. We then proposed a heterogeneous graph embedding method to signaled candidate repurposing herbs for each SARS-CoV-2 protein, and employed the variational graph convolutional network approach to recommend the precision herb combinations as the potential candidate treatments against the specific infected stage.

METHOD

We initially employed the virtual screening method to construct the 'Herb-Compound' and 'Compound-Protein' docking graph based on 480 herbal medicines, 12,735 associated chemical compounds and 24 SARS-CoV-2 proteins. Sequentially, the 'Herb-Compound-Protein' heterogeneous network was constructed by means of the metapath-based embedding approach. We then proposed the heterogeneous-information-network-based graph embedding method to generate the candidate ranking lists of herbs that target structural, nonstructural and accessory SARS-CoV-2 proteins, individually. To obtain precision synthetic effective treatments forvarious COVID-19 infected stages, we employed the variational graph convolutional network method to generate candidate herb combinations as the recommended therapeutic therapies.

RESULTS

There were 24 ranking lists, each containing top-10 herbs, targeting 24 SARS-CoV-2 proteins correspondingly, and 20 herb combinations were generated as the candidate-specific treatment to target the four infected stages. The code and supplementary materials are freely available at https://github.com/fanyang-AI/TCM-COVID19.

The dual role of glioma exosomal microRNAs: glioma eliminates tumor suppressor miR-1298-5p via exosomes to promote immunosuppressive effects of MDSCs

Clear evidence shows that tumors could secrete microRNAs (miRNAs) via exosomes to modulate the tumor microenvironment (TME). However, the mechanisms sorting specific miRNAs into exosomes are still unclear. In order to study the biological function and characterization of exosomal miRNAs, we performed whole-transcriptome sequencing in 59 patients' whole-course cerebrospinal fluid (CSF) small extracellular vesicles (sEV) and matched glioma tissue samples. The results demonstrate that miRNAs could be divided into exosome-enriched miRNAs (ExomiRNAs) and intracellular-retained miRNAs (CLmiRNAs), and exosome-enriched miRNAs generally play a dual role. Among them, miR-1298-5p was enriched in CSF exosomes and suppressed glioma progression in vitro and vivo experiments. Interestingly, exosomal miR-1298-5p could promote the immunosuppressive effects of myeloid-derived suppressor cells (MDSCs) to facilitate glioma. Therefore, we found miR-1298-5p had different effects on glioma cells and MDSCs. Mechanically, downstream signaling pathway analyses showed that miR-1298-5p plays distinct roles in glioma cells and MDSCs via targeting SETD7 and MSH2, respectively. Moreover, reverse verification was performed on the intracellular-retained miRNA miR-9-5p. Thus, we confirmed that tumor-suppressive miRNAs in glioma cells could be eliminated through exosomes and target tumor-associated immune cells to induce tumor-promoting phenotypes. Glioma could get double benefit from it. These findings uncover the mechanisms that glioma selectively sorts miRNAs into exosomes and modulates tumor immunity.

Causal mediation analysis with multiple causally non-ordered and ordered mediators based on summarized genetic data

Causal mediation analysis investigates the mechanism linking exposure and outcome. Dealing with the impact of unobserved confounders among exposure, mediator and outcome is an issue of great concern. Moreover, when multiple mediators exist, this causal pathway intertwines with other causal pathways, rendering it difficult to estimate the path-specific effects. In this study, we propose a method (PSE-MR) to identify and estimate path-specific effects of an exposure (e.g. education) on an outcome (e.g. osteoarthritis risk) through multiple causally ordered and non-ordered mediators (e.g. body mass index and pack-years of smoking) using summarized genetic data, when the sequential ignorability assumption is violated. Specifically, PSE-MR requires a specific rank condition in which the number of instrumental variables is larger than the number of mediators. Furthermore, we illustrate the utility of PSE-MR by providing guidance for practitioners and exploring the mediation effects of body mass index and pack-years of smoking in the causal pathways from education to osteoarthritis risk. Additionally, the results of simulation reveal that the causal estimates of path-specific effects are almost unbiased with good coverage and Type I error properties. Also, we summarize the least number of instrumental variables for the specific number of mediators to achieve 80% power.

The TARGET Nurses' health cohort study protocol: Towards a revolution in getting nurses' health ticked

AIM

To evaluate the health status of nurses in China and explore the impact of work-related stress, work environment and lifestyle factors on their health outcomes.

DESIGN

The Chinese Nurses' Health Study is a multicentred, prospective cohort study.

METHODS

We plan to recruit approximately 80,000 registered nurses aged between 18 and 65 years. Eligible nurses will be introduced to complete a series of web-based questionnaires after obtaining their informed consent. Follow-up questionnaires will be completed at 2-year interval to continuously track subsequent exposures. Health-related indicators will be obtained through self-reporting by nurses and the provincial and national registry platforms such as National Central Cancer Registry. The funding was approved in July 2020 and Research Ethics Committee approval was granted in February 2021.

DISCUSSION

The study is the first multicentred prospective cohort study that aims to assess the impact of work-related stress, work environment and lifestyle factors on the health of Chinese nurses. The results of the Chinese Nurses' Health Cohort Study will potentially draw a picture of the current situation of general health and well-being among nurses in China and their health risks. This will be critical in recommending locally tailored strategic preventive measures and policies to reduce health and well-being threats for nurses and potentially general public, thereby promoting the quality of healthcare in China and globally.

IMPACT

This study will help to understand the health status and working environment characteristics of Chinese nurses, and provide valuable epidemiological evidence for improving working environment and promoting well-being. The results of this study are potentially of great significance for formulating targeted nursing strategies to promote the nurses' health, nursing quality and patient safety in China and even around the world.

CLINICAL TRIAL REGISTRATION NUMBER AND NAME OF TRIAL REGISTER

ChiCTR.org (ID:ChiCTR2100043202), The Nurses' Health Cohort Study of Shandong.

Impact of nonrandom selection mechanisms on the causal effect estimation for two-sample Mendelian randomization methods

Nonrandom selection in one-sample Mendelian Randomization (MR) results in biased estimates and inflated type I error rates only when the selection effects are sufficiently large. In two-sample MR, the different selection mechanisms in two samples may more seriously affect the causal effect estimation. Firstly, we propose sufficient conditions for causal effect invariance under different selection mechanisms using two-sample MR methods. In the simulation study, we consider 49 possible selection mechanisms in two-sample MR, which depend on genetic variants (G), exposures (X), outcomes (Y) and their combination. We further compare eight pleiotropy-robust methods under different selection mechanisms. Results of simulation reveal that nonrandom selection in sample II has a larger influence on biases and type I error rates than those in sample I. Furthermore, selections depending on X+Y, G+Y, or G+X+Y in sample II lead to larger biases than other selection mechanisms. Notably, when selection depends on Y, bias of causal estimation for non-zero causal effect is larger than that for null causal effect. Especially, the mode based estimate has the largest standard errors among the eight methods. In the absence of pleiotropy, selections depending on Y or G in sample II show nearly unbiased causal effect estimations when the casual effect is null. In the scenarios of balanced pleiotropy, all eight MR methods, especially MR-Egger, demonstrate large biases because the nonrandom selections result in the violation of the Instrument Strength Independent of Direct Effect (InSIDE) assumption. When directional pleiotropy exists, nonrandom selections have a severe impact on the eight MR methods. Application demonstrates that the nonrandom selection in sample II (coronary heart disease patients) can magnify the causal effect estimation of obesity on HbA1c levels. In conclusion, nonrandom selection in two-sample MR exacerbates the bias of causal effect estimation for pleiotropy-robust MR methods.

It is well known that nonrandom selection in one-sample Mendelian Randomization (MR) can result in biased estimates and inflated type I error rates. Actually, two-sample MR analyses are more prone to be affected by nonrandom selection than one-sample MR analyses, because two samples for genome-wide association studies (GWAS) may be selected each under different mechanisms from the source population. Summary-level genetic association statistics in two-sample MR may be derived from different study designs such as case-control, case-only and cohort studies, which further inevitably affect the causal effect estimation of exposure on outcome. In this study, we firstly propose a theorem for causal effect invariance under different selection mechanisms. In the simulation, we design 49 combinations of nonrandom selection mechanisms in sample I and sample II, which are widespread in practical applications. The simulation results reveal that the selection mechanisms in sample II have a larger influence on biases and type I error rates than those in sample I. As an illustrative example, we find the nonrandom selection in sample II (coronary heart disease patients) can magnify the causal effect estimation of obesity on the HbA1c levels.