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.

Heterozygous Spink1 c.194+2T>C mutation promotes chronic pancreatitis after acute attack in mice.

BACKGROUND & AIMS: Mutations in genes, including serine protease inhibitor Kazal-type 1 (SPINK1), influence disease progression following sentinel acute pancreatitis event (SAPE) attacks. SPINK1 c.194+2T > C intron mutation is one of the main mutants of SPINK1，which leads to the impairment of SPINK1 function by causing skipping of exon 3. Research on the pathogenesis of SAPE attacks would contribute to the understanding of the outcomes of acute pancreatitis. Therefore, the aim of the study was to clarify the role of SPINK1 c.194+2T > C mutation in the CP progression after an AP attack. METHODS: SAPE attacks were induced in wildtype and SPINK mutant (Spink1 c.194+2T > C) mice by cerulein injection. The mice were sacrificed at 24 h, 14 d, 28 d, and 42 d post-SAPE. Data-independent acquisition (DIA) proteomic analysis was performed for the identification of differentially expressed protein in the pancreatic tissues. Functional analyses were performed using THP-1 and HPSCs. RESULTS: Following SAPE attack, the Spink1 c.194+2T > C mutant mice exhibited a more severe acute pancreatitis phenotype within 24 h. In the chronic phase, the chronic pancreatitis phenotype was more severe in the Spink1 c.194+2T > C mutant mice after SAPE. Proteomic analysis revealed elevated IL-33 level in Spink1 c.194+2T > C mutant mice. Further in vitro analyses revealed that IL-33 induced M2 polarization of macrophages and activation of pancreatic stellate cells. CONCLUSION: Spink1 c.194+2T > C mutation plays an important role in the prognosis of patients following SAPE. Heterozygous Spink1 c.194+2T > C mutation promotes the development of chronic pancreatitis after an acute attack in mice through elevated IL-33 level and the induction of M2 polarization in coordination with pancreatic stellate cell activation.

Association between dietary inflammation index and inflammatory bowel disease in adults: Results from National Health and Nutrition Examination Survey 2009-2010.

Previous study has demonstrated that the Dietary Inflammation Index (DII) played a role in the risk of inflammatory bowel disease (IBD), however, the prevalence and risk factors for IBD are distinct across locations and groups, and therefore, the findings are debatable and warrant further investigation. A total of 4363 participants were calculated in the National Health and Nutrition Examination Survey (NHANES) 2009 to 2010, of whom 1.21% self-reported a history of IBD. DII values were performed as a good predictor of dietary inflammation based on data from two 24-h dietary reviews in the NHANES database. Comparing the multifarious effects along with variations of the whole population by grouping populations according to DII quartiles, dietary inflammation levels increased progressively from DII quartile 1(Q1) to quartile 4(Q4). The association between DII and IBD was tested with multi-variable logistic regression models, subgroup analyses and weighted generalized additive models. Participants in the Q4 group showed the highest levels of C-reactive protein and reduced haemoglobin and albumin levels. Logistic regression confirmed the odds ratios (95% confidence intervals) of IBD for DII were 0.99 (0.86, 1.15), 0.97 (0.84, 1.13) and 0.80 (0.66, 0.98) in models 1, 2 and 3, respectively. The negative correlation between DII and IBD among United States adults from the NHANES database became increasingly apparent as covariates were adjusted. Subgroup analyses and smoothed curve fitting confirmed the inverse results. The study revealed that DII was correlated with the overall physical well-being of participants. However, there was no significant association between DII and IBD.

Effects of sleep-disordered breathing on serum lipid levels in children:a case control study.

BACKGROUND: Sleep-disordered breathing (SDB) during childhood is common and includes a range of breathing abnormalities that range from primary snoring (PS) to obstructive sleep apnea syndrome (OSAS).Studies have shown that not only OSAS, but also PS, which is originally considered harmless, could cause cardiovascular, cognitive, behavioral, and psychosocial problems. Many researches are focused on the relation of OSA and serum lipid levels. However, little studies are focused on PS and serum lipid levels in children.We evaluated whether serum lipid (total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C),low-density lipoprotein cholesterol (LDL-C)) concentrations were associated with specific components of SDB, including indices of oxygen reduction index, lowest oxygen saturation, mean oxygen saturation. And we explored whether serum lipid levels were associated with different degree sleep disordered (PS and OSA group) and obese. METHODS: This was a cross-sectional study. Children who were complained by their guardians with habitual snoring and(or) mouth breathing were collected in the SDB group. Normal children without sleep problem were matched in the control group. Subjects in the SDB group underwent polysomnography. The serum lipid profiles of all the children included TC, TG, HDL-C and LDL-C concentrations were measured by appropriate enzymatic assays. RESULTS: A total of 241 with Apnea/Hypopnea Index ≥ 5 (AHI) were assigned to the OSAS group and the remaining 155 with normal AHI were assigned to the PS group. The values of TC, TG, LDL-C and LDL/HDL were significantly higher in the OSAS group than in the PS group, and the values in the PS group were significantly higher than the control group. Multiple regression analysis revealed serum TG only correlated negatively with lowest oxygen saturation. Body mass index-z score has a positive effect on TG in all the 1310 children (P = 0.031) and in SDB 396 children(P = 0.012). The level of serum TG in obese group was significantly higher than that in non-obese group. CONCLUSIONS: SDB had a very obvious effect on blood lipids, whereas PS without apnea and hypoxia. Obese only affects the aggregation of TG. TRIAL REGISTRATION: ChiCTR1900026807(2019.10.23).

Identification of novel factors that affect the onset of idiopathic chronic pancreatitis: The role for microRNA-323b-5p.

BACKGROUND: The c.194+2 T>C variant of serine protease inhibitor Kazal type 1 (SPINK1) is a known genetic risk factor found in Chinese patients with idiopathic chronic pancreatitis (ICP), but the early-onset mechanisms of ICP are still unclear. METHODS: Complementary experimental approaches were used to pursue other potential pathologies in the present study. The serum level of SPINK1 of ICP patients in the Han population in China was detected and verified by an enzyme-linked immunosorbent assay. Next, differentially expressed proteins and microRNAs from plasma samples of early-onset and late-onset ICP patients were screened by proteomic analysis and microarray, respectively. RESULTS: Combined with these advanced methods, the data strongly suggest that the regulatory effects of microRNAs were involved in the early-onset mechanism of the ICP by in vitro experiments. There was no significant difference in the plasma SPINK1 expression between the early-onset ICP and the late-onset patients. However, the expression of plasma glutathione peroxidase (GPx3) in early-onset ICP patients was markedly lower than that in late-onset ICP patients, although the level of hsa-miR-323b-5p was lower in late-onset patients compared to the early-onset ICP group. In vitro experiments confirmed that hsa-miR-323b-5p could increase apoptosis in caerulein-treated pancreatic acinar cells and inhibit the expression of GPx3. CONCLUSIONS: The up-regulated hsa-miR-323b-5p might play a crucial role in the early-onset mechanisms of ICP by diminishing the antioxidant activity through the down-regulation of GPx3.

Endoplasmic Reticulum Stress-Activated Neuronal and Microglial Autophagy Contributes to Postoperative Cognitive Dysfunction in Neonatal rats.

Surgery and anesthesia in neonates may lead to cognitive impairment or abnormal behaviors. It has been shown that autophagy plays a critical role in neuropsychiatric disorders, while the role of autophagy in postoperative cognitive impairment in neonates is not known. Here, we determined this role and the involvement of endoplasmic reticulum (ER) stress in regulating brain cell autophagy after surgery. Seven-day old neonatal rats (P7) had right common carotid artery exposure under anesthesia with 3% sevoflurane for 2 h. Learning and memory were tested using Barnes maze (BM) and fear conditioning (FC) on P31-42 and P42-44, respectively. In another experiment, rat brains were harvested for biochemical studies. The ratio of microtubule-associated protein 1 light chain 3 (LC3) BII/I was increased and sequestosome 1 (P62/SQSTM1) levels were decreased in the brain 24 h after surgery and anesthesia in neonatal rats. Immunofluorescent staining of LC3B was co-localized with a neuronal or a microglial marker but was not co-localized with a marker for astrocytes in rats with surgery. These rats had a poorer performance in the BM and FC tests than control rats when they were adolescent. Pretreatment with an autophagy inhibitor, 3-methyladenine, attenuated the poor performance. Surgery and anesthesia increased the expression of 78 kDa glucose-regulated protein (BIP/GRP78), an indicator of ER stress, 6 h after surgery and anesthesia. The ER stress activator tunicamycin and inhibitor tauroursodeoxycholic acid increased the markers for autophagy in control rats and decreased the autophagy markers in rats with surgery, respectively. Our results suggest that surgery in neonatal rats induces ER stress that then activates neuronal and microglial autophagy, which contributes to learning and memory impairment later in life.

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.

Identification and Estimation of Causal Effects Using a Negative-Control Exposure in Time-Series Studies With Applications to Environmental Epidemiology.

The initial aim of environmental epidemiology is to estimate the causal effects of environmental exposures on health outcomes. However, due to lack of enough covariates in most environmental data sets, current methods without enough adjustments for confounders inevitably lead to residual confounding. We propose a negative-control exposure based on a time-series studies (NCE-TS) model to effectively eliminate unobserved confounders using an after-outcome exposure as a negative-control exposure. We show that the causal effect is identifiable and can be estimated by the NCE-TS for continuous and categorical outcomes. Simulation studies indicate unbiased estimation by the NCE-TS model. The potential of NCE-TS is illustrated by 2 challenging applications: We found that living in areas with higher levels of surrounding greenness over 6 months was associated with less risk of stroke-specific mortality, based on the Shandong Ecological Health Cohort during January 1, 2010, to December 31, 2018. In addition, we found that the widely established negative association between temperature and cancer risks was actually caused by numbers of unobserved confounders, according to the Global Open Database from 2003-2012. The proposed NCE-TS model is implemented in an R package (R Foundation for Statistical Computing, Vienna, Austria) called NCETS, freely available on GitHub.

Disability and the risk of subsequent mortality in elderly: a 12-year longitudinal population-based study.

BACKGROUND: Assessment the impact of disability on mortality among the elderly is vital to healthy ageing. The present study aimed to assess the long-term influence of disability on death in the elderly based on a longitudinal study. METHOD: This study used the Chinese Longitudinal Healthy Longevity Study (CLHLS) data from 2002 to 2014, including 13,666 participants aged 65 years and older in analyses. The Katz ADL index was used to assess disability status and levels. Cumulative mortality rates were estimated by the Kaplan-Meier method. Cox proportional hazards models were conducted to estimate associations between disability and all-cause mortality for overall participants, two age groups as well as specific chronic disease groups. All reported results were adjusted by survey weights to account for the complex survey design. RESULTS: During the 12-year follow-up, the death density was 6.01 per 100 person-years. The 3-years' cumulative mortality rate of nondisabled elderly was 11.9% (95%CI: 10.9, 12.9%). As the level of disability increased, the cumulative mortality rate was from 28.1% (95%CI: 23.0, 33.1%) to 77.6% (95%CI: 63.8, 91.4%). Compared with non-disabled elderly, the multiple-adjusted hazard ratio of death due to disability was 1.68 (95% CI: 1.48, 1.90). The hazard ratios varied from 1.44 (95%CI: 1.23, 1.67) to 4.45 (95%CI: 2.69, 7.38) after classifying the disability levels. The hazard ratios of death in the young-old group (65-79 years) were higher than the old-old group (80 years and over) in both level B (HR = 1.58, 95%CI: 1.25, 2.00 vs. HR = 1.22, 95%CI: 1.06, 1.39, P = 0.029) and level G (HR = 24.09, 95%CI: 10.83, 53.60 vs. HR = 2.56, 95%CI: 1.75, 3.74, P < 0.001). For patients with hypertension, diabetes, heart disease, cerebrovascular disease as well as dementia, disability increases their relative risk of mortality by 1.64 (95%CI: 1.40, 1.93), 2.85 (95%CI: 1.46, 5.58), 1.45 (95%CI: 1.02, 2.05), 2.13 (95%CI: 1.54, 2.93) and 3.56 (95%CI: 1.22, 10.38) times, respectively. CONCLUSIONS: Disability increases the risk of all-cause death in the elderly, especially those with chronic diseases and the young-old group. Further studies are needed to better understand how to effectively prevent disability in the older population.

Study on Maternal SNPs of MTHFR Gene and HCY Level Related to Congenital Heart Diseases.

The aim of this study is to evaluate the relationship between maternal single nucleotide polymorphisms (SNPs) of methylenetetrahydrofolate reductase (MTHFR) gene with plasma homocysteine (HCY) level and offspring congenital heart diseases (CHDs). 338 mothers with offspring CHDs as case group and 306 mothers of normal children as control group were recruited. Their pregnant histories were interviewed by questionnaire and the MTHFR rsl801133 and rsl801131 were genotyped. The case-control analysis was used to find out the relationship between maternal SNPs of MTHFR gene and offspring CHDs. And the plasma HCY concentration of the mothers of CHDs children was detected. This case-case study was intended to find out the relevance between maternal HCY level and SNPs of MTHFR gene. There were significant differences in the gender of children, occupation of mothers, family history with CHDs, history of abortion, history of adverse pregnancy, early pregnancy health, fetus during pregnancy, pesticide exposure and drug exposure in CHDs group and control group (P < 0.05). MTHFR rs1801133 was significantly associated with their offspring CHDs in mothers. The polymorphism of maternal MTHFR rs1801133 increased plasma HCY level, especially the homozygous mutation. Besides the environmental factors, our results suggested that the maternal MTHFR rs1801133 polymorphism might be a risk factor of their offspring CHDs, which may be due to the hyperhomocysteinemia by abnormal metabolism of HCY.

Identification of dynamic signatures associated with smoking-related squamous cell lung cancer and chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a risk factor for the development of lung cancer. The aim of this study was to identify early diagnosis biomarkers for lung squamous cell carcinoma (SQCC) in COPD patients and to determine the potential pathogenetic mechanisms. The GSE12472 data set was downloaded from the Gene Expression Omnibus database. Differentially co-expressed links (DLs) and differentially expressed genes (DEGs) in both COPD and normal tissues, or in both SQCC + COPD and COPD samples were used to construct a dynamic network associated with high-risk genes for the SQCC pathogenetic process. Enrichment analysis was performed based on Gene Ontology annotations and Kyoto Encyclopedia of Genes and Genomes pathway analysis. We used the gene expression data and the clinical information to identify the co-expression modules based on weighted gene co-expression network analysis (WGCNA). In total, 205 dynamic DEGs, 5034 DLs and one pathway including CDKN1A, TP53, RB1 and MYC were found to have correlations with the pathogenetic progress. The pathogenetic mechanisms shared by both SQCC and COPD are closely related to oxidative stress, the immune response and infection. WGCNA identified 11 co-expression modules, where magenta and black were correlated with the "time to distant metastasis." And the "surgery due to" was closely related to the brown and blue modules. In conclusion, a pathway that includes TP53, CDKN1A, RB1 and MYC may play a vital role in driving COPD towards SQCC. Inflammatory processes and the immune response participate in COPD-related carcinogenesis.

Exploring the causal pathway from ischemic stroke to atrial fibrillation: a network Mendelian randomization study.

BACKGROUND AND PURPOSE: Previous studies have found ischemic stroke is associated with atrial fibrillation. However, the causal association between ischemic stroke and atrial fibrillation is not clear. Furthermore, the network relationship among ischemic stroke, atrial fibrillation and its risk factors need further attention. This study aims to examine the potential causal association between ischemic stroke and atrial fibrillation and further to explore potential mediators in the causal pathway from ischemic stroke to atrial fibrillation. METHODS: Summary statistics from the ISGC (case = 10,307 and control = 19,326) were used as ischemic stroke genetic instruments, AFGen Consortium data (case = 65,446 and control = 522,744) were used for atrial fibrillation, and other consortia data were used for potential mediators (fasting insulin, white blood cell count, procalcitonin, systolic and diastolic blood pressure, body mass index, waist circumference, and height). Under the framework of network Mendelian randomization, two-sample Mendelian randomization study was performed using summary statistics from several genome-wide association studies. Inverse-variance weighted method was performed to estimate causal effect. RESULTS: Blood pressure mediates the causal pathways from ischemic stroke to atrial fibrillation. The total odds ratio of ischemic stroke on atrial fibrillation was 1.05 (95% confidence interval [CI], 1.02 to 1.07; P = 1.3 × 10-5). One-unit increase of genetically determined ischemic stroke was associated with 0.02 (DBP: 95% CI, 0.001 to 0.034, P = 0.029; SBP: 95% CI, 0.006 to 0.034, P = 0.003) upper systolic and diastolic blood pressure levels. Higher genetically determined systolic and diastolic blood pressure levels were associated with higher atrial fibrillation risk (DBP: RR, 1.18; 95% CI, 1.03 to 1.35; P = 0.012. SBP: RR, 1.18; 95% CI, 1.01 to 1.38; P = 0.04). Specially, we also found the bidirectional causality between blood pressure and ischemic stroke. CONCLUSIONS: Our study provided a strong evidence that raised blood pressure in stroke patients increases the risk of atrial fibrillation and active acute blood pressure lowering can improve the outcome in ischemic stroke patients.

A novel path-specific effect statistic for identifying the differential specific paths in systems epidemiology.

BACKGROUND: Biological pathways play an important role in the occurrence, development and recovery of complex diseases, such as cancers, which are multifactorial complex diseases that are generally caused by mutation of multiple genes or dysregulation of pathways. RESULTS: We propose a path-specific effect statistic (PSE) to detect the differential specific paths under two conditions (e.g. case VS. control groups, exposure Vs. nonexposure groups). In observational studies, the path-specific effect can be obtained by separately calculating the average causal effect of each directed edge through adjusting for the parent nodes of nodes in the specific path and multiplying them under each condition. Theoretical proofs and a series of simulations are conducted to validate the path-specific effect statistic. Applications are also performed to evaluate its practical performances. A series of simulation studies show that the Type I error rates of PSE with Permutation tests are more stable at the nominal level 0.05 and can accurately detect the differential specific paths when comparing with other methods. Specifically, the power reveals an increasing trends with the enlargement of path-specific effects and its effect differences under two conditions. Besides, the power of PSE is robust to the variation of parent or child node of the nodes on specific paths. Application to real data of Glioblastoma Multiforme (GBM), we successfully identified 14 positive specific pathways in mTOR pathway contributing to survival time of patients with GBM. All codes for automatic searching specific paths linking two continuous variables and adjusting set as well as PSE statistic can be found in supplementary materials.  CONCLUSION: The proposed PSE statistic can accurately detect the differential specific pathways contributing to complex disease and thus potentially provides new insights and ways to unlock the black box of disease mechanisms.

Rational discovery of dual-indication multi-target PDE/Kinase inhibitor for precision anti-cancer therapy using structural systems pharmacology.

Many complex diseases such as cancer are associated with multiple pathological manifestations. Moreover, the therapeutics for their treatments often lead to serious side effects. Thus, it is needed to develop multi-indication therapeutics that can simultaneously target multiple clinical indications of interest and mitigate the side effects. However, conventional one-drug-one-gene drug discovery paradigm and emerging polypharmacology approach rarely tackle the challenge of multi-indication drug design. For the first time, we propose a one-drug-multi-target-multi-indication strategy. We develop a novel structural systems pharmacology platform 3D-REMAP that uses ligand binding site comparison and protein-ligand docking to augment sparse chemical genomics data for the machine learning model of genome-scale chemical-protein interaction prediction. Experimentally validated predictions systematically show that 3D-REMAP outperforms state-of-the-art ligand-based, receptor-based, and machine learning methods alone. As a proof-of-concept, we utilize the concept of drug repurposing that is enabled by 3D-REMAP to design dual-indication anti-cancer therapy. The repurposed drug can demonstrate anti-cancer activity for cancers that do not have effective treatment as well as reduce the risk of heart failure that is associated with all types of existing anti-cancer therapies. We predict that levosimendan, a PDE inhibitor for heart failure, inhibits serine/threonine-protein kinase RIOK1 and other kinases. Subsequent experiments and systems biology analyses confirm this prediction, and suggest that levosimendan is active against multiple cancers, notably lymphoma, through the direct inhibition of RIOK1 and RNA processing pathway. We further develop machine learning models to predict cancer cell-line's and a patient's response to levosimendan. Our findings suggest that levosimendan can be a promising novel lead compound for the development of safe, effective, and precision multi-indication anti-cancer therapy. This study demonstrates the potential of structural systems pharmacology in designing polypharmacology for precision medicine. It may facilitate transforming the conventional one-drug-one-gene-one-disease drug discovery process and single-indication polypharmacology approach into a new one-drug-multi-target-multi-indication paradigm for complex diseases.

A novel method for controlling unobserved confounding using double confounders.

BACKGROUND: Controlling unobserved confounding still remains a great challenge in observational studies, and a series of strict assumptions of the existing methods usually may be violated in practice. Therefore, it is urgent to put forward a novel method. METHODS: We are interested in the causal effect of an exposure on the outcome, which is always confounded by unobserved confounding. We show that, the causal effect of an exposure on a continuous or categorical outcome is nonparametrically identified through only two independent or correlated available confounders satisfying a non-linear condition on the exposure. Asymptotic theory and variance estimators are developed for each case. We also discuss an extension for more than two binary confounders. RESULTS: The simulations show better estimation performance by our approach in contrast to the traditional regression approach adjusting for observed confounders. A real application is separately applied to assess the effects of Body Mass Index (BMI) on Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Fasting Blood Glucose (FBG), Triglyceride (TG), Total Cholesterol (TC), High Density Lipoprotein (HDL) and Low Density Lipoprotein (LDL) with individuals in Shandong Province, China. Our results suggest that SBP increased 1.60 (95% CI: 0.99-2.93) mmol/L with per 1- kg/m2 higher BMI and DBP increased 0.37 (95% CI: 0.03-0.76) mmol/L with per 1- kg/m2 higher BMI. Moreover, 1- kg/m2 increase in BMI was causally associated with a 1.61 (95% CI: 0.96-2.97) mmol/L increase in TC, a 1.66 (95% CI: 0.91-55.30) mmol/L increase in TG and a 2.01 (95% CI: 1.09-4.31) mmol/L increase in LDL. However, BMI was not causally associated with HDL with effect value - 0.20 (95% CI: - 1.71-1.44). And, the effect value of FBG per 1- kg/m2 higher BMI was 0.56 (95% CI: - 0.24-2.18). CONCLUSIONS: We propose a novel method to control unobserved confounders through double binary confounders satisfying a non-linear condition on the exposure which is easy to access.

Tumor Necrosis Factor-α Is Required for Mast Cell-Mediated Host Immunity Against Cutaneous Staphylococcus aureus Infection.

Background: Mast cells (MCs) play a key role in immune process response to invading pathogens. Methods: This study assessed the involvement of MCs in controlling Staphylococcus aureus infection in a cutaneous infection model of MC-deficient (KitW-sh/W-sh) mice. Results: KitW-sh/W-sh mice developed significantly larger skin lesions after the cutaneous S. aureus challenge, when compared to wild-type (WT) mice, while MC dysfunction reduced the inflammation response to S. aureus. The levels of tumor necrosis factor (TNF)-α in skin tissues were significantly decreased in KitW-sh/W-sh mice upon infection. Moreover, the exogenous administration of MCs or recombinant TNF-α effectively restored the immune response against S. aureus in KitW-sh/W-sh mice via the recruitment of neutrophils to the infected site. These results indicate that the effects of MC deficiency are largely attributed to the decrease in production of TNF-α in cutaneous S. aureus infection. In addition, S. aureus-induced MC activation was dependent on the c-kit receptor-activated phosphoinositide 3-kinase (PI3K)/AKT/P65-nuclear factor (NF-κB) pathway, which was confirmed by treatment with Masitinib (a c-kit receptor inhibitor), Wortmannin (a PI3K inhibitor), and pyrrolidine dithiocarbamate (a NF-κB inhibitor), respectively. Conclusions: The present study identifies the critical role of MCs in the host defense against S. aureus infection.

The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams.

BACKGROUND: Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. METHODS: Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM) were compared. The "do-calculus" was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. RESULTS: Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal strategy was to adjust for the parent nodes of outcome, which obtained the highest precision. CONCLUSIONS: All adjustment strategies through logistic regression were biased for causal effect estimation, while IPW-based-MSM could always obtain unbiased estimation when the adjusted set satisfied G-admissibility. Thus, IPW-based-MSM was recommended to adjust for confounders set.

Production of CCL20 from lung cancer cells induces the cell migration and proliferation through PI3K pathway.

Tumour inflammatory microenvironment is considered to play a role in the sensitivity of tumour cells to therapies and prognosis of patients with lung cancer. The expression of CCL20, one of the critical chemoattractants responsible for inflammation cells recruitment, has been shown overexpressed in variety of tumours. This study aimed at investigating potential mechanisms of CCL20 function and production in human non-small cell lung cancer (NSCLC). Expression of CCL20 gene and protein in lung tissues of patients with NSCLC and NSCLC cells (A549) were determined. The interleukin (IL)-1ß-induced signal pathways in A549 and the effect of CCL20-induced A549 cell migration and proliferation were determined using migration assays and cell-alive monitoring system. Mechanisms of signal pathways involved in the migration of CCL20 were also studied. We initially found that NSCLC tumour tissues markedly overexpressed CCL20 in comparison with normal lung samples. In addition, IL-1ß could directly promote CCL20 production in lung cancer cells, which was inhibited by extracellular signal-regulated kinase (ERK)1/2 inhibitor, p38 mitogen-activated protein kinase (p38 MARP) inhibitor or PI3K inhibitors. CCL20 promoted lung cancer cells migration and proliferation in an autocrine manner via activation of ERK1/2-MAPK and PI3K pathways. Our data indicated that IL-1ß could stimulate CCL20 production from lung cancer cells through the activation of MAPKs and PI3K signal pathways, and the auto-secretion of CCL20 could promote lung cancer cell migration and proliferation through the activation of ERK and PI3K signal pathways. Our results may provide a novel evidence that CCL20 could be a new therapeutic target for lung cancer.

NADPH oxidase 2-derived reactive oxygen species in the hippocampus might contribute to microglial activation in postoperative cognitive dysfunction in aged mice.

Microglial activation plays a key role in the development of postoperative cognitive dysfunction (POCD). Nox2, one of the main isoforms of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase in the central nervous system, is a predominant source of reactive oxygen species (ROS) overproduction in phagocytes including microglia. We therefore hypothesized that Nox2-induced microglial activation is involved in the development of POCD. Sixteen-month-old C57BL/6 mice were subjected to exploratory laparotomy with isoflurane anesthesia to mimic the clinical human abdominal surgery. Behavioral tests were performed at 6 and 7 d post-surgery with open field and fear conditioning tests, respectively. The levels of Nox2, 8-hydroxy-2'-deoxyguanosine (8-OH-dG, a marker of DNA oxidation), CD11b (a marker of microglial activation), interleukin-1ß (IL-1ß), and brain-derived neurotrophic factor (BDNF) were determined in the hippocampus and prefrontal cortex at 1 d and 7 d post-surgery, respectively. For the interventional study, mice were treated with a NADPH oxidase inhibitor apocynin (APO). Our results showed that exploratory laparotomy with isoflurane anesthesia impaired the contextual fear memory, increased expression of Nox2, 8-OH-dG, CD11b, and IL-1ß, and down-regulated BDNF expression in the hippocampus at 7 d post-surgery. The surgery-induced microglial activation and neuroinflammation persisted to 7 d after surgery in the hippocampus, but only at 1 d in the prefrontal cortex. Notably, administration with APO could rescue these surgery-induced cognitive impairments and associated brain pathology. Together, our data suggested that Nox2-derived ROS in hippocampal microglia, at least in part, contributes to subsequent neuroinflammation and cognitive impairments induced by surgery in aged mice.

A simulation study on matched case-control designs in the perspective of causal diagrams.

BACKGROUND: In observational studies, matched case-control designs are routinely conducted to improve study precision. How to select covariates for match or adjustment, however, is still a great challenge for estimating causal effect between the exposure E and outcome D. METHODS: From the perspective of causal diagrams, 9 scenarios of causal relationships among exposure (E), outcome (D) and their related covariates (C) were investigated. Further various simulation strategies were performed to explore whether match or adjustment should be adopted. The "do calculus" and "back-door criterion" were used to calculate the true causal effect (ß) of E on D on the log-odds ratio scale. 1:1 matching method was used to create matched case-control data, and the conditional or unconditional logistic regression was utilized to get the estimators ([Formula: see text]) of causal effect. The bias ([Formula: see text]) and standard error ([Formula: see text]) were used to evaluate their performances. RESULTS: When C is exactly a confounder for E and D, matching on it did not illustrate distinct improvement in the precision; the benefit of match was to greatly reduce the bias for ß though failed to completely remove the bias; further adjustment for C in matched case-control designs is still essential. When C is associated with E or D, but not a confounder, including an independent cause of D, a cause of E but has no direct causal effect on D, a collider of E and D, an effect of exposure E, a mediator of causal path from E to D, arbitrary match or adjustment of this kind of plausible confounders C will create unexpected bias. When C is not a confounder but an effect of D, match or adjustment is unnecessary. Specifically, when C is an instrumental variable, match or adjustment could not reduce the bias due to existence of unobserved confounders U. CONCLUSIONS: Arbitrary match or adjustment of the plausible confounder C is very dangerous before figuring out the possible causal relationships among E, D and their related covariates.