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The association between thyroid function and dyslipidemia has been well documented in observational studies. However, observational studies are prone to confounding, making it difficult to conduct causal inference. We performed a 2-sample bidirectional Mendelian randomization (MR) using summary statistics from large-scale genome-wide association studies of thyroid stimulating hormone (TSH), free T4 (FT4), and blood lipids. We chose the inverse variance–weighted (IVW) method for the main analysis, and consolidated results through various sensitivity analyses involving 6 different MR methods under different model specifications. We further conducted genetic correlation analysis and colocalization analysis to deeply reflect the causality. The IVW method showed per 1 SD increase in normal TSH was significantly associated with a 0.048 SD increase in total cholesterol (TC; P < 0.001) and a 0.032 SD increase in low-density lipoprotein cholesterol (LDL; P = 0.021). A 1 SD increase in normal FT4 was significantly associated with a 0.056 SD decrease in TC (P = 0.014) and a 0.072 SD decrease in LDL (P = 0.009). Neither TSH nor FT4 showed causal associations with high-density lipoprotein cholesterol and triglycerides. No significant causal effect of blood lipids on normal TSH or FT4 can be detected. All results were largely consistent when using several alternative MR methods, and were reconfirmed by both genetic correlation analysis and colocalization analysis. Our study suggested that, even within reference range, higher TSH or lower FT4 are causally associated with increased TC and LDL, whereas no reverse causal association can be found.
Yanjun Wang; Ping Guo; Lu Liu; Yanan Zhang; Ping Zeng; Zhongshang Yuan. Mendelian Randomization Highlights the Causal Role of Normal Thyroid Function on Blood Lipid Profiles. Endocrinology 2021, 162, 1 .
AMA StyleYanjun Wang, Ping Guo, Lu Liu, Yanan Zhang, Ping Zeng, Zhongshang Yuan. Mendelian Randomization Highlights the Causal Role of Normal Thyroid Function on Blood Lipid Profiles. Endocrinology. 2021; 162 (5):1.
Chicago/Turabian StyleYanjun Wang; Ping Guo; Lu Liu; Yanan Zhang; Ping Zeng; Zhongshang Yuan. 2021. "Mendelian Randomization Highlights the Causal Role of Normal Thyroid Function on Blood Lipid Profiles." Endocrinology 162, no. 5: 1.
Summary A transcriptome-wide association study (TWAS) integrates data from genome-wide association studies and gene expression mapping studies for investigating the gene regulatory mechanisms underlying diseases. Existing TWAS methods are primarily univariate in nature, focusing on analyzing one outcome trait at a time. However, many complex traits are correlated with each other and share a common genetic basis. Consequently, analyzing multiple traits jointly through multivariate analysis can potentially improve the power of TWASs. Here, we develop a method, moPMR-Egger (multiple outcome probabilistic Mendelian randomization with Egger assumption), for analyzing multiple outcome traits in TWAS applications. moPMR-Egger examines one gene at a time, relies on its cis-SNPs that are in potential linkage disequilibrium with each other to serve as instrumental variables, and tests its causal effects on multiple traits jointly. A key feature of moPMR-Egger is its ability to test and control for potential horizontal pleiotropic effects from instruments, thus maximizing power while minimizing false associations for TWASs. In simulations, moPMR-Egger provides calibrated type I error control for both causal effects testing and horizontal pleiotropic effects testing and is more powerful than existing univariate TWAS approaches in detecting causal associations. We apply moPMR-Egger to analyze 11 traits from 5 trait categories in the UK Biobank. In the analysis, moPMR-Egger identified 13.15% more gene associations than univariate approaches across trait categories and revealed distinct regulatory mechanisms underlying systolic and diastolic blood pressures.
Lu Liu; Ping Zeng; Fuzhong Xue; Zhongshang Yuan; Xiang Zhou. Multi-trait transcriptome-wide association studies with probabilistic Mendelian randomization. The American Journal of Human Genetics 2021, 108, 240 -256.
AMA StyleLu Liu, Ping Zeng, Fuzhong Xue, Zhongshang Yuan, Xiang Zhou. Multi-trait transcriptome-wide association studies with probabilistic Mendelian randomization. The American Journal of Human Genetics. 2021; 108 (2):240-256.
Chicago/Turabian StyleLu Liu; Ping Zeng; Fuzhong Xue; Zhongshang Yuan; Xiang Zhou. 2021. "Multi-trait transcriptome-wide association studies with probabilistic Mendelian randomization." The American Journal of Human Genetics 108, no. 2: 240-256.
Idiopathic pulmonary fibrosis (IPF) is a type of scarring lung disease characterized by a chronic, progressive, and irreversible decline in lung function. The genetic basis of IPF remains elusive. A transcriptome-wide association study (TWAS) of IPF was performed by FUSION using gene expression weights of three tissues combined with a large-scale genome-wide association study (GWAS) dataset, totally involving 2,668 IPF cases and 8,591 controls. Significant genes identified by TWAS were then subjected to gene ontology (GO) and pathway enrichment analysis. The overlapped GO terms and pathways between enrichment analysis of TWAS significant genes and differentially expressed genes (DEGs) from the genome-wide mRNA expression profiling of IPF were also identified. For TWAS significant genes, protein–protein interaction (PPI) network and clustering modules analyses were further conducted using STRING and Cytoscape. Overall, TWAS identified a group of candidate genes for IPF under the Bonferroni corrected P value threshold (0.05/14929 = 3.35 × 10–6), such as DSP (PTWAS = 1.35 × 10–29 for lung tissue), MUC5B (PTWAS = 1.09 × 10–28 for lung tissue), and TOLLIP (PTWAS = 1.41 × 10–15 for whole blood). Pathway enrichment analysis identified multiple candidate pathways, such as herpes simplex infection (P value = 7.93 × 10–5) and antigen processing and presentation (P value = 6.55 × 10–5). 38 common GO terms and 8 KEGG pathways shared by enrichment analysis of TWAS significant genes and DEGs were identified. In the PPI network, 14 genes (DYNLL1, DYNC1LI1, DYNLL2, HLA-DRB5, HLA-DPB1, HLA-DQB2, HLA-DQA2, HLA-DQB1, HLA-DRB1, POLR2L, CENPP, CENPK, NUP133, and NUP107) were simultaneously detected by hub gene and module analysis. In conclusion, through integrative analysis of TWAS and mRNA expression profiles, we identified multiple novel candidate genes, GO terms and pathways for IPF, which contributes to the understanding of the genetic mechanism of IPF.
Weiming Gong; Ping Guo; Lu Liu; Qingbo Guan; Zhongshang Yuan. Integrative Analysis of Transcriptome-Wide Association Study and mRNA Expression Profiles Identifies Candidate Genes Associated With Idiopathic Pulmonary Fibrosis. Frontiers in Genetics 2020, 11, 1 .
AMA StyleWeiming Gong, Ping Guo, Lu Liu, Qingbo Guan, Zhongshang Yuan. Integrative Analysis of Transcriptome-Wide Association Study and mRNA Expression Profiles Identifies Candidate Genes Associated With Idiopathic Pulmonary Fibrosis. Frontiers in Genetics. 2020; 11 ():1.
Chicago/Turabian StyleWeiming Gong; Ping Guo; Lu Liu; Qingbo Guan; Zhongshang Yuan. 2020. "Integrative Analysis of Transcriptome-Wide Association Study and mRNA Expression Profiles Identifies Candidate Genes Associated With Idiopathic Pulmonary Fibrosis." Frontiers in Genetics 11, no. : 1.
Complex diseases are believed to be the consequence of intracellular network(s) involving a range of factors. An improved understanding of a disease-predisposing biological network could lead to better identification of genes and pathways that confer disease risk and therefore inform drug development. The group difference in biological networks, as is often characterized by graphs of nodes and edges, is attributable to effects of these nodes and edges. Here we introduced pointwise mutual information (PMI) as a measure of the connection between a pair of nodes with either a linear relationship or nonlinear dependence. We then proposed a PMI-based network regression (PMINR) model to differentiate patterns of network changes (in node or edge) linking a disease outcome. Through simulation studies with various sample sizes and inter-node correlation structures, we showed that PMINR can accurately identify these changes with higher power than current methods and be robust to the network topology. Finally, we illustrated, with publicly available data on lung cancer and gene methylation data on aging and Alzheimer’s disease, an evaluation of the practical performance of PMINR. We concluded that PMI is able to capture the generic inter-node correlation pattern in biological networks, and PMINR is a powerful and efficient approach for biological network analysis.
Weiqiang Lin; Jiadong Ji; Yuchen Zhu; Mingzhuo Li; Jinghua Zhao; Fuzhong Xue; Zhongshang Yuan. PMINR: Pointwise Mutual Information-Based Network Regression – With Application to Studies of Lung Cancer and Alzheimer’s Disease. Frontiers in Genetics 2020, 11, 556259 .
AMA StyleWeiqiang Lin, Jiadong Ji, Yuchen Zhu, Mingzhuo Li, Jinghua Zhao, Fuzhong Xue, Zhongshang Yuan. PMINR: Pointwise Mutual Information-Based Network Regression – With Application to Studies of Lung Cancer and Alzheimer’s Disease. Frontiers in Genetics. 2020; 11 ():556259.
Chicago/Turabian StyleWeiqiang Lin; Jiadong Ji; Yuchen Zhu; Mingzhuo Li; Jinghua Zhao; Fuzhong Xue; Zhongshang Yuan. 2020. "PMINR: Pointwise Mutual Information-Based Network Regression – With Application to Studies of Lung Cancer and Alzheimer’s Disease." Frontiers in Genetics 11, no. : 556259.
Background Genome-wide association studies (GWAS) have successfully identified genetic susceptible variants for complex diseases. However, the underlying mechanism of such association remains largely unknown. Most disease-associated genetic variants have been shown to reside in noncoding regions, leading to the hypothesis that regulation of gene expression may be the primary biological mechanism. Current methods to characterize gene expression mediating the effect of genetic variant on diseases, often analyzed one gene at a time and ignored the network structure. The impact of genetic variant can propagate to other genes along the links in the network, then to the final disease. There could be multiple pathways from the genetic variant to the final disease, with each having the chain structure since the first node is one specific SNP (Single Nucleotide Polymorphism) variant and the end is disease outcome. One key but inadequately addressed question is how to measure the between-node connection strength and rank the effects of such chain-type pathways, which can provide statistical evidence to give the priority of some pathways for potential drug development in a cost-effective manner. Results We first introduce the maximal correlation coefficient (MCC) to represent the between-node connection, and then integrate MCC with K shortest paths algorithm to rank and identify the potential pathways from genetic variant to disease. The pathway importance score (PIS) was further provided to quantify the importance of each pathway. We termed this method as “MCC-SP”. Various simulations are conducted to illustrate MCC is a better measurement of the between-node connection strength than other quantities including Pearson correlation, Spearman correlation, distance correlation, mutual information, and maximal information coefficient. Finally, we applied MCC-SP to analyze one real dataset from the Religious Orders Study and the Memory and Aging Project, and successfully detected 2 typical pathways from APOE genotype to Alzheimer’s disease (AD) through gene expression enriched in Alzheimer’s disease pathway. Conclusions MCC-SP has powerful and robust performance in identifying the pathway(s) from the genetic variant to the disease. The source code of MCC-SP is freely available at GitHub (https://github.com/zhuyuchen95/ADnet).
Yuchen Zhu; Jiadong Ji; Weiqiang Lin; Mingzhuo Li; Lu Liu; Huanhuan Zhu; Fuzhong Xue; Xiujun Li; Xiang Zhou; Zhongshang Yuan. MCC-SP: a powerful integration method for identification of causal pathways from genetic variants to complex disease. BMC Genetics 2020, 21, 1 -12.
AMA StyleYuchen Zhu, Jiadong Ji, Weiqiang Lin, Mingzhuo Li, Lu Liu, Huanhuan Zhu, Fuzhong Xue, Xiujun Li, Xiang Zhou, Zhongshang Yuan. MCC-SP: a powerful integration method for identification of causal pathways from genetic variants to complex disease. BMC Genetics. 2020; 21 (1):1-12.
Chicago/Turabian StyleYuchen Zhu; Jiadong Ji; Weiqiang Lin; Mingzhuo Li; Lu Liu; Huanhuan Zhu; Fuzhong Xue; Xiujun Li; Xiang Zhou; Zhongshang Yuan. 2020. "MCC-SP: a powerful integration method for identification of causal pathways from genetic variants to complex disease." BMC Genetics 21, no. 1: 1-12.
Understanding the different genetic landscape between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) is important for understanding the underlying molecular mechanism, which may facilitate the development of effective and precise treatments. Although previous studies have identified a number of differentially expressed genes (DEGs) responsible for lung cancer, it is unknown which of these genes are causal. The present study integrated DNA methylation, RNA sequencing, clinical characteristics and survival outcomes of patients with LUAD and LUSC from The Cancer Genome Atlas. DEGs were first identified using edgeR by comparing tumor and normal tissue, and differentially methylated probes (DMPs) were assessed using ChAMP. Candidate genes for further time‑to‑event instrumental variable analysis were selected as the intersecting genes between DEGs and the genes including DMP CpG sites within the transcription start site (TSS1500), with DMPs in TSS1500 region being the instrumental variables. Extensive sensitivity analyses were conducted to assess the robustness of the results. The present study identified 906 DEGs for LUAD, among which 538 also had DMPs in the TSS1500 region. In addition, 1,543 DEGs were identified for LUSC, among which 1,053 also had DMPs in the TSS1500 region. Time‑to‑event instrumental variable analysis detected eight potential causal genes for LUAD survival, including aryl hydrocarbon receptor nuclear translocator like 2, semaphorin 3G, serum deprivation-response protein, chloride intracellular channel protein 5, LIM zinc finger domain containing 2, epithelial membrane protein 2, carbonic anhydrase 7 and LOC116437. The results also identified that phosphatidylinositol‑3,4,5-trisphosphate-dependent Rac exchange factor 2 may be a potential causal gene for LUSC. Therefore, the results of the present study suggested that there was molecular heterogeneity between these two lung cancer subtypes. Such analysis framework can be extended to other cancer genomics research.
Lu Liu; Ping Zeng; Sheng Yang; Zhongshang Yuan. Leveraging methylation to identify the potential causal genes associated with survival in lung adenocarcinoma and lung squamous cell carcinoma. Oncology Letters 2020, 1 .
AMA StyleLu Liu, Ping Zeng, Sheng Yang, Zhongshang Yuan. Leveraging methylation to identify the potential causal genes associated with survival in lung adenocarcinoma and lung squamous cell carcinoma. Oncology Letters. 2020; ():1.
Chicago/Turabian StyleLu Liu; Ping Zeng; Sheng Yang; Zhongshang Yuan. 2020. "Leveraging methylation to identify the potential causal genes associated with survival in lung adenocarcinoma and lung squamous cell carcinoma." Oncology Letters , no. : 1.
Background: There have been controversial debates on the relationship between socioeconomic status and the distribution of HIV in Cameroon. We aim to illustrate the vulnerability of socioeconomic disparities and the risk of getting HIV for public health interventions. Methods: Descriptive statistics was conducted to quantify the socioeconomic gradients of HIV. A Multilevel logistic regression model was used to study the relationship between socioeconomic factors and HIV. The effect of the factors was presented as odds ratios (OR), with 95% confidence intervals (CIs). P-value less than 0.05 was considered to be statistically significant. We further mapped HIV prevalence in ArcGIS to visualize the regional distribution of HIV.Results: HIV was significantly associated with age (p and distributed in the East, South, and Yaoundé regions. Age, sex, region, education level, and ethnicity were significantly associated with the odds of having HIV from the multilevel regression model. Conclusion: Our finding recommends for novel intervention programs that will target the various socioeconomic factors associated with the odds of having HIV for proper public health management of the disease in Cameroon.
Zhongshang Yuan; Weiqiang Lin; Marlvin Anemey Tewara; Liu Yunxia; Helen Binang Barong; Che Pantalius Nji; Nguedia Meguimfouet Linda; Omam Francine Ariane; Tchio Tchoumba Michele Audre; Prisca N. Mbah-Fongkimeh. Socioeconomic disparities and the distribution of HIV in Cameroon: a multilevel logistic regression analysis. 2020, 1 .
AMA StyleZhongshang Yuan, Weiqiang Lin, Marlvin Anemey Tewara, Liu Yunxia, Helen Binang Barong, Che Pantalius Nji, Nguedia Meguimfouet Linda, Omam Francine Ariane, Tchio Tchoumba Michele Audre, Prisca N. Mbah-Fongkimeh. Socioeconomic disparities and the distribution of HIV in Cameroon: a multilevel logistic regression analysis. . 2020; ():1.
Chicago/Turabian StyleZhongshang Yuan; Weiqiang Lin; Marlvin Anemey Tewara; Liu Yunxia; Helen Binang Barong; Che Pantalius Nji; Nguedia Meguimfouet Linda; Omam Francine Ariane; Tchio Tchoumba Michele Audre; Prisca N. Mbah-Fongkimeh. 2020. "Socioeconomic disparities and the distribution of HIV in Cameroon: a multilevel logistic regression analysis." , no. : 1.
In the competing risks frame, the cause-specific hazard model (CSHM) can be used to test the effects of some covariates on one particular cause of failure. Sometimes, however, the observed covariates cannot explain the large proportion of variation in the time-to-event data coming from different areas such as in a multi-center clinical trial or a multi-center cohort study. In this study, a multi-center competing risks model (MCCRM) is proposed to deal with multi-center survival data, then this model is compared with the CSHM by simulation. A center parameter is set in the MCCRM to solve the spatial heterogeneity problem caused by the latent factors, hence eliminating the need to develop different models for each area. Additionally, the effects of the exposure factors in the MCCRM are kept consistent for each individual, regardless of the area they inhabit. Therefore, the coefficient of the MCCRM model can be easily explained using the scenario of each model for each area. Moreover, the calculating approach of the absolute risk is given. Based on a simulation study, we show that the estimate of coefficients of the MCCRM is unbiased and precise, and the area under the curve (AUC) is larger than that of the CSHM when the heterogeneity cannot be ignored. Furthermore, the disparity of the AUC increases progressively as the standard deviation of the center parameter (SDCP) rises. In order to test the calibration, the expected number (E) of strokes is calculated and then compared with the corresponding observed number (O). The result is promising, so the SDCP can be used to select the most appropriate model. When the SDCP is less than 0.1, the performance of the MCCRM and CSHM is analogous, but when the SDCP is equal to or greater than 0.1, the performance of the MCCRM is significantly superior to the CSHM. This suggests that the MCCRM should be selected as the appropriate model.
Jintao Wang; Zhongshang Yuan; Yi Liu; Fuzhong Xue. A Multi-Center Competing Risks Model and Its Absolute Risk Calculation Approach. International Journal of Environmental Research and Public Health 2019, 16, 3435 .
AMA StyleJintao Wang, Zhongshang Yuan, Yi Liu, Fuzhong Xue. A Multi-Center Competing Risks Model and Its Absolute Risk Calculation Approach. International Journal of Environmental Research and Public Health. 2019; 16 (18):3435.
Chicago/Turabian StyleJintao Wang; Zhongshang Yuan; Yi Liu; Fuzhong Xue. 2019. "A Multi-Center Competing Risks Model and Its Absolute Risk Calculation Approach." International Journal of Environmental Research and Public Health 16, no. 18: 3435.
Integrating association results from both genome-wide association studies (GWASs) and expression quantitative trait locus (eQTL) mapping studies has the potential to shed light on the molecular mechanisms underlying disease etiology. Several statistical methods have been recently developed to integrate GWASs with eQTL studies in the form of transcriptome-wide association studies (TWASs). These existing methods can all be viewed as a form of two sample Mendelian randomization (MR) analysis, which has been widely applied in various GWASs for inferring the causal relationship among complex traits. Unfortunately, most existing TWAS and MR methods make an unrealistic modeling assumption and assume that instrumental variables do not exhibit horizontal pleiotropic effects. However, horizontal pleiotropic effects have been recently discovered to be wide spread across complex traits, and, as we will show here, are also wide spread across gene expression traits. Therefore, not allowing for horizontal pleiotropic effects can be overly restrictive, and, as we will be show here, can lead to a substantial inflation of test statistics and subsequently false discoveries in TWAS applications. Here, we present a probabilistic MR method, which we refer to as PMR-Egger, for testing and controlling for horizontal pleiotropic effects in TWAS applications. PMR-Egger relies on an MR likelihood framework that unifies many existing TWAS and MR methods, accommodates multiple correlated instruments, tests the causal effect of gene on trait in the presence of horizontal pleiotropy, and, with a newly developed parameter expansion version of the expectation maximization algorithm, is scalable to hundreds of thousands of individuals. With extensive simulations, we show that PMR-Egger provides calibrated type I error control for causal effect testing in the presence of horizontal pleiotropic effects, is reasonably robust for various types of horizontal pleiotropic effect mis-specifications, is more powerful than existing MR approaches, and, as a by-product, can directly test for horizontal pleiotropy. We illustrate the benefits of PMR-Egger in applications to 39 diseases and complex traits obtained from three GWASs including the UK Biobank. In these applications, we show how PMR-Egger can lead to new biological discoveries through integrative analysis.
Zhongshang Yuan; Huanhuan Zhu; Ping Zeng; Sheng Yang; Shiquan Sun; Can Yang; Jin Liu; Xiang Zhou. Testing and controlling for horizontal pleiotropy with the probabilistic Mendelian randomization in transcriptome-wide association studies. 2019, 691014 .
AMA StyleZhongshang Yuan, Huanhuan Zhu, Ping Zeng, Sheng Yang, Shiquan Sun, Can Yang, Jin Liu, Xiang Zhou. Testing and controlling for horizontal pleiotropy with the probabilistic Mendelian randomization in transcriptome-wide association studies. . 2019; ():691014.
Chicago/Turabian StyleZhongshang Yuan; Huanhuan Zhu; Ping Zeng; Sheng Yang; Shiquan Sun; Can Yang; Jin Liu; Xiang Zhou. 2019. "Testing and controlling for horizontal pleiotropy with the probabilistic Mendelian randomization in transcriptome-wide association studies." , no. : 691014.
Previous studies have reported that the potassium voltage-gated channel subfamily Q member 1 (KCNQ1) gene is associated with diabetes in both European and Asian population. This study aims to find a predictable single nucleotide polymorphism (SNP) to predict the risk of metabolic syndrome (MetS) through investigating the association of SNP in KCNQ1 gene with MetS in Han Chinese women of northern urban area. Six SNPs were selected and genotyped in 1381 unrelated women aged 21 and above, who have had physical check-up in Shandong Provincial Qianfoshan Hospital. Cox proportional model was conducted to access the association between SNPs and MetS. Sixty one women developed MetS between 2010 and 2015 during the 3055 person-year of follow-up. The cumulative incidence density was 19.964/1000 person-year. The SNP rs163182 was associated with MetS both in the additive genetic model (RR = 1.658, 95% CI: 1.144-2.402) and in the recessive genetic model (RR = 2.461, 95% CI: 1.347-4.496). It remained significant after adjustment. This relationship was also observed in MetS components (BMI and SBP). A novel association between rs163182 and MetS was found in this study, which can predict the occurrence of MetS among northern urban Han Chinese women. More investigations are needed to be done to assess the possible pathway in which KCNQ1 gene affects MetS.
Yafei Liu; Chunxia Wang; Yafei Chen; Zhongshang Yuan; Tao Yu; Wenchao Zhang; Fang Tang; Jianhua Gu; Qinqin Xu; Xiaotong Chi; Lijie Ding; Fuzhong Xue; Chengqi Zhang. A variant in KCNQ1 gene predicts metabolic syndrome among northern urban Han Chinese women. BMC Medical Genetics 2018, 19, 153 .
AMA StyleYafei Liu, Chunxia Wang, Yafei Chen, Zhongshang Yuan, Tao Yu, Wenchao Zhang, Fang Tang, Jianhua Gu, Qinqin Xu, Xiaotong Chi, Lijie Ding, Fuzhong Xue, Chengqi Zhang. A variant in KCNQ1 gene predicts metabolic syndrome among northern urban Han Chinese women. BMC Medical Genetics. 2018; 19 (1):153.
Chicago/Turabian StyleYafei Liu; Chunxia Wang; Yafei Chen; Zhongshang Yuan; Tao Yu; Wenchao Zhang; Fang Tang; Jianhua Gu; Qinqin Xu; Xiaotong Chi; Lijie Ding; Fuzhong Xue; Chengqi Zhang. 2018. "A variant in KCNQ1 gene predicts metabolic syndrome among northern urban Han Chinese women." BMC Medical Genetics 19, no. 1: 153.
Although there is growing evidence linking chronic obstructive pulmonary disease (COPD) hospital admissions to the exposure to ambient air pollution, the effect can vary depending on the local geography, pollution type, and pollution level. The number of large-scale multicity studies remains limited in China. This study aims to assess the short-term effects of ambient air pollution (PM2.5, PM10, SO2, NO2) on chronic obstructive pulmonary disease hospital admissions from 2015 to 2016, with a total of 216,159 records collected from 207 hospitals in 17 cities all over the Shandong province, east China. Generalized additive models and penalized splines were applied to study the data whilst controlling for confounding meteorological factors and long-term trends. The air pollution was analyzed with 0–6 day lag effects and the percentage change of hospital admissions was assessed for a 10-μg/m3 increase in the air pollution levels. We also examined the percentage changes for different age groups and gender, respectively. The results showed that air pollution was significantly associated with adverse health outcomes and stronger effects were observed for females. The air pollution health effects were also impacted by geographical factors such that the air pollution had weaker health effects in coastal cities.
Yi Liu; Jingjie Sun; Yannong Gou; Xiubin Sun; Xiujun Li; Zhongshang Yuan; Lizhi Kong; Fuzhong Xue. A Multicity Analysis of the Short-Term Effects of Air Pollution on the Chronic Obstructive Pulmonary Disease Hospital Admissions in Shandong, China. International Journal of Environmental Research and Public Health 2018, 15, 774 .
AMA StyleYi Liu, Jingjie Sun, Yannong Gou, Xiubin Sun, Xiujun Li, Zhongshang Yuan, Lizhi Kong, Fuzhong Xue. A Multicity Analysis of the Short-Term Effects of Air Pollution on the Chronic Obstructive Pulmonary Disease Hospital Admissions in Shandong, China. International Journal of Environmental Research and Public Health. 2018; 15 (4):774.
Chicago/Turabian StyleYi Liu; Jingjie Sun; Yannong Gou; Xiubin Sun; Xiujun Li; Zhongshang Yuan; Lizhi Kong; Fuzhong Xue. 2018. "A Multicity Analysis of the Short-Term Effects of Air Pollution on the Chronic Obstructive Pulmonary Disease Hospital Admissions in Shandong, China." International Journal of Environmental Research and Public Health 15, no. 4: 774.
Thyroid nodules are highly prevalent, but a robust, feasible method for malignancy differentiation has not yet been well documented. This study aimed to establish a practical model for thyroid nodule discrimination. Records for 2984 patients who underwent thyroidectomy were analyzed. Clinical, laboratory, and US variables were assessed retrospectively. Multivariate logistic regression analysis was performed and a mathematical model was established for malignancy prediction. The results showed that the malignant group was younger and had smaller nodules than the benign group (43.5 ± 11.6 vs. 48.5 ± 11.5 y, p < 0.001; 1.96 ± 1.16 vs. 2.75 ± 1.70 cm, p < 0.001, respectively). The serum thyrotropin (TSH) level (median = 1.63 mIU/L, IQR (0.89–2.66) vs. 1.19 (0.59–2.10), p < 0.001) was higher in the malignant group than in the benign group. Patients with malignancies tested positive for anti-thyroglobulin antibody (TGAb) and anti-thyroid peroxidase antibody (TPOAb) more frequently than those with benign nodules (TGAb, 30.3% vs. 15.0%, p < 0.001; TPOAb, 25.6% vs. 18.0%, p = 0.028). The prevalence of ultrasound (US) features (irregular shape, ill-defined margin, solid structure, hypoechogenicity, microcalcifications, macrocalcifications and central intranodular flow) was significantly higher in the malignant group. Multivariate logistic regression analysis confirmed that age (OR = 0.963, 95% CI = 0.934–0.993, p = 0.017), TGAb (OR = 4.435, 95% CI = 1.902–10.345, p = 0.001), hypoechogenicity (OR = 2.830, 95% CI = 1.113–7.195, p = 0.029), microcalcifications (OR = 4.624, 95% CI = 2.008–10.646, p < 0.001), and central intranodular flow (OR = 2.155, 95% CI = 1.011–4.594, p < 0.05) were independent predictors of thyroid malignancy. A predictive model including four variables (age, TGAb, hypoechogenicity and microcalcification) showed an optimal discriminatory accuracy (area under the curve, AUC) of 0.808 (95% CI = 0.761–0.855). The best cut-off value for prediction was 0.52, achieving sensitivity and specificity of 84.6% and 76.3%, respectively. A predictive model of malignancy that combines clinical, laboratory and sonographic characteristics would aid clinicians in avoiding unnecessary procedures and making better clinical decisions.
Jia Liu; Dongmei Zheng; Qiang Li; XuLei Tang; Zuojie Luo; Zhongshang Yuan; Ling Gao; Jiajun Zhao. A predictive model of thyroid malignancy using clinical, biochemical and sonographic parameters for patients in a multi-center setting. BMC Endocrine Disorders 2018, 18, 1 -7.
AMA StyleJia Liu, Dongmei Zheng, Qiang Li, XuLei Tang, Zuojie Luo, Zhongshang Yuan, Ling Gao, Jiajun Zhao. A predictive model of thyroid malignancy using clinical, biochemical and sonographic parameters for patients in a multi-center setting. BMC Endocrine Disorders. 2018; 18 (1):1-7.
Chicago/Turabian StyleJia Liu; Dongmei Zheng; Qiang Li; XuLei Tang; Zuojie Luo; Zhongshang Yuan; Ling Gao; Jiajun Zhao. 2018. "A predictive model of thyroid malignancy using clinical, biochemical and sonographic parameters for patients in a multi-center setting." BMC Endocrine Disorders 18, no. 1: 1-7.
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. 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. 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. 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.
Yuanyuan Yu; Hongkai Li; Xiaoru Sun; Ping Su; Tingting Wang; Yi Liu; Zhongshang Yuan; Yanxun Liu; Fuzhong Xue. The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams. BMC Medical Research Methodology 2017, 17, 177 .
AMA StyleYuanyuan Yu, Hongkai Li, Xiaoru Sun, Ping Su, Tingting Wang, Yi Liu, Zhongshang Yuan, Yanxun Liu, Fuzhong Xue. The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams. BMC Medical Research Methodology. 2017; 17 (1):177.
Chicago/Turabian StyleYuanyuan Yu; Hongkai Li; Xiaoru Sun; Ping Su; Tingting Wang; Yi Liu; Zhongshang Yuan; Yanxun Liu; Fuzhong Xue. 2017. "The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams." BMC Medical Research Methodology 17, no. 1: 177.
Background: Hyperuricemia (HUA) contributes to gout and many other diseases. Many hyperuricemia-related risk factors have been discovered, which provided the possibility for building the hyperuricemia prediction model. In this study we aimed to explore the incidence of hyperuricemia and develop hyperuricemia prediction models based on the routine biomarkers for both males and females in urban Han Chinese adults. Methods: A cohort of 58,542 members of the urban population (34,980 males and 23,562 females) aged 20–80 years old, free of hyperuricemia at baseline examination, was followed up for a median 2.5 years. The Cox proportional hazards regression model was used to develop gender-specific prediction models. Harrell’s C-statistics was used to evaluate the discrimination ability of the models, and the 10-fold cross-validation was used to validate the models. Results: In 7139 subjects (5585 males and 1554 females), hyperuricemia occurred during a median of 2.5 years of follow-up, leading to a total incidence density of 49.63/1000 person years (64.62/1000 person years for males and 27.12/1000 person years for females). The predictors of hyperuricemia were age, body mass index (BMI) systolic blood pressure, serum uric acid for males, and BMI, systolic blood pressure, serum uric acid, triglycerides for females. The models’ C statistics were 0.783 (95% confidence interval (CI), 0.779–0.786) for males and 0.784 (95% CI, 0.778–0.789) for females. After 10-fold cross-validation, the C statistics were still steady, with 0.782 for males and 0.783 for females. Conclusions: In this study, gender-specific prediction models for hyperuricemia for urban Han Chinese adults were developed and performed well.
Jin Cao; Chunxia Wang; Guang Zhang; Xiang Ji; Yanxun Liu; Xiubin Sun; Zhongshang Yuan; Zheng Jiang; Fuzhong Xue. Incidence and Simple Prediction Model of Hyperuricemia for Urban Han Chinese Adults: A Prospective Cohort Study. International Journal of Environmental Research and Public Health 2017, 14, 67 .
AMA StyleJin Cao, Chunxia Wang, Guang Zhang, Xiang Ji, Yanxun Liu, Xiubin Sun, Zhongshang Yuan, Zheng Jiang, Fuzhong Xue. Incidence and Simple Prediction Model of Hyperuricemia for Urban Han Chinese Adults: A Prospective Cohort Study. International Journal of Environmental Research and Public Health. 2017; 14 (1):67.
Chicago/Turabian StyleJin Cao; Chunxia Wang; Guang Zhang; Xiang Ji; Yanxun Liu; Xiubin Sun; Zhongshang Yuan; Zheng Jiang; Fuzhong Xue. 2017. "Incidence and Simple Prediction Model of Hyperuricemia for Urban Han Chinese Adults: A Prospective Cohort Study." International Journal of Environmental Research and Public Health 14, no. 1: 67.
The prevalence of cardiovascular disease has been increasing worldwide. As a common pathogenic risk factor, dyslipidemia played a great role in the incidence and progress of these diseases. We investigated to achieve accurate and up-to-date information on the prevalence of dyslipidemia and its associations with other lipid-related diseases in rural North China. Using a complex, multistage, probability sampling design, we conducted a large-scale cross-sectional study of 8528 rural participants aged over 18 years in Shandong Province. Prevalence and characteristics of dyslipidemia were demonstrated. The odds ratios between dyslipidemia types and lipid-related diseases were further analyzed by logistic regression. Among the overall population, 45.8 % suffered from dyslipidemia. The prevalence of lipid abnormality (including high and very high levels) was 18.6, 12.7, 9.8 and 12.7 % for total cholesterol (TC), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) cholesterol and triglycerides (TG), respectively. Among all participants with dyslipidemia, 23.9 % were aware, only 11.5 % were treated, 10.0 % were controlled. For subjects with dyslipidemia, the risk for non-alcoholic fatty liver disease (NAFLD) was highest with a 3.3-fold over that of non-dyslipidmia (OR = 3.30, P < 0.001); followed by hyperuricemia and diabetes mellitus (DM), while with 2-fold increase (OR = 1.99, P < 0.001; OR = 1.92, P < 0.001); with only 1.5-fold risk for atherosclerosis (AS) (OR = 1.47, P < 0.001). The presence of high cholesterol was mainly associated with AS, while abnormal TG was correlated with NAFLD and DM. Dyslipidemia has become a serious public health issue in rural North China. The rapid increase of high TC and incremental risk of high TG may contribute to the epidemic of AS, NAFLD and DM. It is imperative to develop individualized prevention and treatment guidelines according to dyslipidemia phenotypes.
Nannan Gao; Yong Yu; Bingchang Zhang; Zhongshang Yuan; Haiqing Zhang; Yongfeng Song; Meng Zhao; Jiadong Ji; Lu Liu; Chao Xu; Jiajun Zhao. Dyslipidemia in rural areas of North China: prevalence, characteristics, and predictive value. Lipids in Health and Disease 2016, 15, 154 .
AMA StyleNannan Gao, Yong Yu, Bingchang Zhang, Zhongshang Yuan, Haiqing Zhang, Yongfeng Song, Meng Zhao, Jiadong Ji, Lu Liu, Chao Xu, Jiajun Zhao. Dyslipidemia in rural areas of North China: prevalence, characteristics, and predictive value. Lipids in Health and Disease. 2016; 15 (1):154.
Chicago/Turabian StyleNannan Gao; Yong Yu; Bingchang Zhang; Zhongshang Yuan; Haiqing Zhang; Yongfeng Song; Meng Zhao; Jiadong Ji; Lu Liu; Chao Xu; Jiajun Zhao. 2016. "Dyslipidemia in rural areas of North China: prevalence, characteristics, and predictive value." Lipids in Health and Disease 15, no. 1: 154.
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. 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 (β) 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 ( β ⌢ $$ \overset{\frown }{\beta } $$ ) of causal effect. The bias ( β ⌢ ‐ β $$ \overset{\frown }{\beta}\hbox{-} \beta $$ ) and standard error ( S E β ⌢ $$ SE\left(\overset{\frown }{\beta}\right) $$ ) were used to evaluate their performances. 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 β 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. 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.
Hongkai Li; Zhongshang Yuan; Ping Su; Tingting Wang; Yuanyuan Yu; Xiaoru Sun; Fuzhong Xue. A simulation study on matched case-control designs in the perspective of causal diagrams. BMC Medical Research Methodology 2016, 16, 102 .
AMA StyleHongkai Li, Zhongshang Yuan, Ping Su, Tingting Wang, Yuanyuan Yu, Xiaoru Sun, Fuzhong Xue. A simulation study on matched case-control designs in the perspective of causal diagrams. BMC Medical Research Methodology. 2016; 16 (1):102.
Chicago/Turabian StyleHongkai Li; Zhongshang Yuan; Ping Su; Tingting Wang; Yuanyuan Yu; Xiaoru Sun; Fuzhong Xue. 2016. "A simulation study on matched case-control designs in the perspective of causal diagrams." BMC Medical Research Methodology 16, no. 1: 102.
We propose a novel Markov Blanket-based repeated-fishing strategy (MBRFS) in attempt to increase the power of existing Markov Blanket method (DASSO-MB) and maintain its advantages in omic data analysis. Both simulation and real data analysis were conducted to assess its performances by comparing with other methods including χ(2) test with Bonferroni and B-H adjustment, least absolute shrinkage and selection operator (LASSO) and DASSO-MB. A serious of simulation studies showed that the true discovery rate (TDR) of proposed MBRFS was always close to zero under null hypothesis (odds ratio = 1 for each SNPs) with excellent stability in all three scenarios of independent phenotype-related SNPs without linkage disequilibrium (LD) around them, correlated phenotype-related SNPs without LD around them, and phenotype-related SNPs with strong LD around them. As expected, under different odds ratio and minor allel frequency (MAFs), MBRFS always had the best performances in capturing the true phenotype-related biomarkers with higher matthews correlation coefficience (MCC) for all three scenarios above. More importantly, since proposed MBRFS using the repeated fishing strategy, it still captures more phenotype-related SNPs with minor effects when non-significant phenotype-related SNPs emerged under χ(2) test after Bonferroni multiple correction. The various real omics data analysis, including GWAS data, DNA methylation data, gene expression data and metabolites data, indicated that the proposed MBRFS always detected relatively reasonable biomarkers. Our proposed MBRFS can exactly capture the true phenotype-related biomarkers with the reduction of false negative rate when the phenotype-related biomarkers are independent or correlated, as well as the circumstance that phenotype-related biomarkers are associated with non-phenotype-related ones.
Hongkai Li; Zhongshang Yuan; Jiadong Ji; Jing Xu; Tao Zhang; Xiaoshuai Zhang; Fuzhong Xue. A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data. BMC Genetics 2016, 17, 51 .
AMA StyleHongkai Li, Zhongshang Yuan, Jiadong Ji, Jing Xu, Tao Zhang, Xiaoshuai Zhang, Fuzhong Xue. A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data. BMC Genetics. 2016; 17 (1):51.
Chicago/Turabian StyleHongkai Li; Zhongshang Yuan; Jiadong Ji; Jing Xu; Tao Zhang; Xiaoshuai Zhang; Fuzhong Xue. 2016. "A novel Markov Blanket-based repeated-fishing strategy for capturing phenotype-related biomarkers in big omics data." BMC Genetics 17, no. 1: 51.
Complex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. A key but inadequately addressed issue is how to test possible differences of the networks between two groups. Group-level comparison of network properties may shed light on underlying disease mechanisms and benefit the design of drug targets for complex diseases. We therefore proposed a powerful score-based statistic to detect group difference in weighted networks, which simultaneously capture the vertex changes and edge changes. Simulation studies indicated that the proposed network difference measure (NetDifM) was stable and outperformed other methods existed, under various sample sizes and network topology structure. One application to real data about GWAS of leprosy successfully identified the specific gene interaction network contributing to leprosy. For additional gene expression data of ovarian cancer, two candidate subnetworks, PI3K-AKT and Notch signaling pathways, were considered and identified respectively. The proposed method, accounting for the vertex changes and edge changes simultaneously, is valid and powerful to capture the group difference of biological networks.
Jiadong Ji; Zhongshang Yuan; Xiaoshuai Zhang; Fuzhong Xue. A powerful score-based statistical test for group difference in weighted biological networks. BMC Bioinformatics 2016, 17, 1 -10.
AMA StyleJiadong Ji, Zhongshang Yuan, Xiaoshuai Zhang, Fuzhong Xue. A powerful score-based statistical test for group difference in weighted biological networks. BMC Bioinformatics. 2016; 17 (1):1-10.
Chicago/Turabian StyleJiadong Ji; Zhongshang Yuan; Xiaoshuai Zhang; Fuzhong Xue. 2016. "A powerful score-based statistical test for group difference in weighted biological networks." BMC Bioinformatics 17, no. 1: 1-10.
The genetic variants identified by Genome-wide association study (GWAS) can only account for a small proportion of the total heritability for complex disease. The existence of gene-gene joint effects which contains the main effects and their co-association is one of the possible explanations for the "missing heritability" problems. Gene-gene co-association refers to the extent to which the joint effects of two genes differ from the main effects, not only due to the traditional interaction under nearly independent condition but the correlation between genes. Generally, genes tend to work collaboratively within specific pathway or network contributing to the disease and the specific disease-associated locus will often be highly correlated (e.g. single nucleotide polymorphisms (SNPs) in linkage disequilibrium). Therefore, we proposed a novel score-based statistic (SBS) as a gene-based method for detecting gene-gene co-association. Various simulations illustrate that, under different sample sizes, marginal effects of causal SNPs and co-association levels, the proposed SBS has the better performance than other existed methods including single SNP-based and principle component analysis (PCA)-based logistic regression model, the statistics based on canonical correlations (CCU), kernel canonical correlation analysis (KCCU), partial least squares path modeling (PLSPM) and delta-square (δ (2)) statistic. The real data analysis of rheumatoid arthritis (RA) further confirmed its advantages in practice. SBS is a powerful and efficient gene-based method for detecting gene-gene co-association.
Jing Xu; Zhongshang Yuan; Jiadong Ji; Xiaoshuai Zhang; Hongkai Li; Xuesen Wu; Fuzhong Xue; Yanxun Liu. A powerful score-based test statistic for detecting gene-gene co-association. BMC Genetics 2016, 17, 31 .
AMA StyleJing Xu, Zhongshang Yuan, Jiadong Ji, Xiaoshuai Zhang, Hongkai Li, Xuesen Wu, Fuzhong Xue, Yanxun Liu. A powerful score-based test statistic for detecting gene-gene co-association. BMC Genetics. 2016; 17 (1):31.
Chicago/Turabian StyleJing Xu; Zhongshang Yuan; Jiadong Ji; Xiaoshuai Zhang; Hongkai Li; Xuesen Wu; Fuzhong Xue; Yanxun Liu. 2016. "A powerful score-based test statistic for detecting gene-gene co-association." BMC Genetics 17, no. 1: 31.
Elevated levels of fibrinogen may contribute to a prothrombotic state. Cross-sectional studies suggest fibrinogen possibly linked with MetS/its components, while results of cohort studies remain controversial. Thus, this study was designed to identify the association of plasma fibrinogen with metabolic syndrome (MetS) and further to clarify the role of fibrinogen in the development of MetS. A large-scale prospective cohort study was conducted in routine health check-up population. 6209 participants free of MetS at baseline were included in the original cohort, with annually routine health check-up for incident MetS from 2005 to 2011. Then, 4 pre-MetS sub-cohorts, with overweight, hypertension, hyperglycemia and dyslipidemia at baseline respectively, were also created from the original cohort. Various strategies of Cox model analysis were performed for attempting to confirm the role of fibrinogen in the development of MetS. Total MetS incidence density was 75.58 per 1000 person-years. Cox regression analysis by adjusting for potential confounders as well as four MetS components showed a significant effect of fibrinogen on MetS just in female, with risk ratio (RR) (95 % CI) of 1.48 (1.02, 2.13) for Q4 vs. Q1. Further analysis in the 4 pre-MetS female sub-cohorts revealed this significant effect only in overweight sub-cohort, with RR (95 % CI) of 1.97 (1.20, 3.23), but no significant interaction of overweight with fibrinogen on MetS was revealed in original female cohort. Then, stratification analysis among the 4 sub-groups of fibrinogen quartiles showed that effects of overweight on MetS were different among the 4 sub-groups of fibrinogen quartiles, with RR of 2.98 for Q1, 4.40 for Q2, 3.93 for Q3, and 4.82 for Q4 respectively. Fibrinogen was associated with MetS just in overweight sub-cohort of female individuals, and fibrinogen might be a potential modifier in the pathway from overweight to MetS.
Lijie Ding; Chengqi Zhang; Guang Zhang; Tao Zhang; Min Zhao; Xiaokang Ji; Zhongshang Yuan; Ruihong Liu; Fang Tang; Fuzhong Xue. A new insight into the role of plasma fibrinogen in the development of metabolic syndrome from a prospective cohort study in urban Han Chinese population. Diabetology & Metabolic Syndrome 2015, 7, 110 .
AMA StyleLijie Ding, Chengqi Zhang, Guang Zhang, Tao Zhang, Min Zhao, Xiaokang Ji, Zhongshang Yuan, Ruihong Liu, Fang Tang, Fuzhong Xue. A new insight into the role of plasma fibrinogen in the development of metabolic syndrome from a prospective cohort study in urban Han Chinese population. Diabetology & Metabolic Syndrome. 2015; 7 (1):110.
Chicago/Turabian StyleLijie Ding; Chengqi Zhang; Guang Zhang; Tao Zhang; Min Zhao; Xiaokang Ji; Zhongshang Yuan; Ruihong Liu; Fang Tang; Fuzhong Xue. 2015. "A new insight into the role of plasma fibrinogen in the development of metabolic syndrome from a prospective cohort study in urban Han Chinese population." Diabetology & Metabolic Syndrome 7, no. 1: 110.