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Song W, Ye L, Tang Q, Lu X, Huang X, Xie M, Yu S, Yuan Z, Chen L. Rev-erbα attenuates refractory periapical periodontitis via M1 polarization: An in vitro and in vivo study. Int Endod J 2024; 57:451-463. [PMID: 38279698 DOI: 10.1111/iej.14024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 01/04/2024] [Accepted: 01/07/2024] [Indexed: 01/28/2024]
Abstract
AIM Rev-erbα has been reported to regulate the healing of inflammatory lesions through its effect on the immune system in a variety of inflammatory disease. Moreover, the balance of macrophages polarization plays a crucial role in immune response and inflammatory progression. However, in refractory periapical periodontitis (RAP), the role of Rev-erbα in inflammatory response and bone resorption by regulating macrophage polarization remains unclarified. The aims of the present study were to investigate the expression of Rev-erbα in experimental RAP and to explore the relationship between Rev-erbα and macrophage polarization through the application of its pharmacological agonist SR9009 into the in vivo and in vitro experiments. METHODOLOGY Enterococcus faecalis-induced RAP models were established in SD rats. Histological staining and micro-computed tomography scanning were used to evaluate osteoclastogenesis and alveolar bone resorption. The expression of Rev-erbα and macrophage polarization were detected in the periapical tissues from rats by immunofluorescence, flow cytometry, and western blots. Furthermore, immunohistochemical staining and enzyme-linked immunosorbent assay were performed to explore the relationship between Rev-erbα and inflammatory cytokines related to macrophage polarization. RESULT Compared to healthy periapical tissue, the expression of Rev-erbα was significantly down-regulated in macrophages from inflammatory periapical area, especially in Enterococcus faecalis-induced periapical lesions, with obvious type-1 macrophage (M1)-like dominance and the production of pro-inflammatory cytokines. In addition, Rev-erbα activation by SR9009 could induce type-2 macrophage (M2)-like polarization in periapical tissue and THP1 cell line, followed by increased secretion of anti-inflammatory cytokines IL-10 and TGF-β. Furthermore, intracanal application of SR9009 reduced the lesion size and promoted the repair of RAP by decreasing the number of osteoclasts and enhancing the formation of mineralized tissue in periapical inflammatory lesions. CONCLUSIONS Rev-erbα played an essential role in the pathogenesis of RAP through its effect on macrophage polarization. Targeting Rev-erbα might be a promising and prospective therapy method for the prevention and management of RAP.
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Affiliation(s)
- W Song
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan, China
| | - L Ye
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan, China
| | - Q Tang
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan, China
| | - X Lu
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan, China
| | - X Huang
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan, China
| | - M Xie
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan, China
| | - S Yu
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan, China
| | - Z Yuan
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan, China
| | - L Chen
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan, China
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Liu X, Yuan J, Liu S, Wang X, Tang M, Meng X, Li Y, Chai Y, Wang Y, Tian G, Liu X, Zhou H, Kou C, Zhang L, Yuan Z, Zhang H. The causal relationship between autoimmune thyroid disorders and telomere length: A Mendelian randomization and colocalization study. Clin Endocrinol (Oxf) 2024; 100:294-303. [PMID: 38214116 DOI: 10.1111/cen.15004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 10/20/2023] [Accepted: 12/05/2023] [Indexed: 01/13/2024]
Abstract
This study aimed to evaluate whether there is a causal relationship between autoimmune thyroid disorders (AITDs) and telomere length (TL) in the European population and whether there is reverse causality. In this study, Mendelian randomization (MR) and colocalization analysis were conducted to assess the potential causal relationship between AITDs and TL using summary statistics from large-scale genome-wide association studies, followed by analysis of the relationship between TL and thyroid stimulating hormone and free thyroxine (FT4) to help interpret the findings. The inverse variance weighted (IVW) method was used to estimate the causal estimates. The weighted median, MR-Egger and leave-one-out methods were used as sensitivity analyses. The IVW method results showed a significant causal relationship between autoimmune hyperthyroidism and TL (β = -1.93 × 10-2 ; p = 4.54 × 10-5 ). There was no causal relationship between autoimmune hypothyroidism and TL (β = -3.99 × 10-3 ; p = 0.324). The results of the reverse MR analysis showed that genetically TL had a significant causal relationship on autoimmune hyperthyroidism (IVW: odds ratio (OR) = 0.49; p = 2.83 × 10-4 ) and autoimmune hypothyroidism (IVW: OR = 0.86; p = 7.46 × 10-3 ). Both horizontal pleiotropy and heterogeneity tests indicated the validity of our bidirectional MR study. Finally, colocalization analysis suggested that there were shared causal variants between autoimmune hyperthyroidism and TL, further highlighting the robustness of the results. In conclusion, autoimmune hyperthyroidism may accelerate telomere attrition, and telomere attrition is a causal factor for AITDs.
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Affiliation(s)
- Xue Liu
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Jie Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Shuai Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Xinhui Wang
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Mulin Tang
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Xue Meng
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Yuchen Li
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Yuwei Chai
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Yuyao Wang
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Guoyu Tian
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Xueying Liu
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Huizhi Zhou
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Chunjia Kou
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Li Zhang
- Department of Vascular Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Haiqing Zhang
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Jinan, China
- Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
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Dai J, Chen K, Zhu Y, Xia L, Wang T, Yuan Z, Zeng P. Identifying risk loci for obsessive-compulsive disorder and shared genetic component with schizophrenia: A large-scale multi-trait association analysis with summary statistics. Prog Neuropsychopharmacol Biol Psychiatry 2024; 129:110906. [PMID: 38043635 DOI: 10.1016/j.pnpbp.2023.110906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 11/26/2023] [Accepted: 11/28/2023] [Indexed: 12/05/2023]
Abstract
Due to limited samples, no genetic loci have been identified for obsessive-compulsive disorder (OCD) in genome-wide association studies. Additionally, although co-morbidities between OCD and schizophrenia (SCZ) were observed, their common genetic etiology was not completely known. Here, we conducted a comprehensive investigation regarding the genetic architecture of OCD and the common genetic foundation shared by OCD and SCZ using summary statistics data (2688 cases and 7037 controls for OCD; 53,386 cases and 77,258 controls for SCZ). We discovered significant genetic correlation between OCD and SCZ (r̂g=0.296, P = 2.82 × 10-11). We then performed two multi-trait association analyses to detect OCD-associated loci and colocalization analysis to detect causal variants. Parallel gene-level analyses were also implemented. We identified 323 OCD-relevant variants located within 12 loci, with four loci shared the same causal variants between OCD and SCZ. Further, the gene-level analyses discovered 8 OCD-associated genes. Finally, multiple functional analyses at both SNP and gene levels showed that these genetic association signals had significant enrichments in the regions of left ventricle and anterior cingulate cortex, and suggested an important role of pathways involving regulation of telomere maintenance, histone phosphorylation, and GnRH secretion. Overall, this study identified new genetic loci for OCD and provided substantial evidence supporting common genetic foundation underlying OCD and SCZ. The findings advanced our understanding of genetic architecture and pathophysiology of OCD as well as shedding light on shared genetic etiology of the two disorders.
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Affiliation(s)
- Jing Dai
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Keying Chen
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Yiyang Zhu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Lei Xia
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China; Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Key Laboratory of Environment and Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Xuzhou Engineering Research Innovation Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China; Jiangsu Engineering Research Center of Biological Data Mining and Healthcare Transformation, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China.
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Liu L, Yan R, Guo P, Ji J, Gong W, Xue F, Yuan Z, Zhou X. Conditional transcriptome-wide association study for fine-mapping candidate causal genes. Nat Genet 2024; 56:348-356. [PMID: 38279040 DOI: 10.1038/s41588-023-01645-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 12/08/2023] [Indexed: 01/28/2024]
Abstract
Transcriptome-wide association studies (TWASs) aim to integrate genome-wide association studies with expression-mapping studies to identify genes with genetically predicted expression (GReX) associated with a complex trait. In the present report, we develop a method, GIFT (gene-based integrative fine-mapping through conditional TWAS), that performs conditional TWAS analysis by explicitly controlling for GReX of all other genes residing in a local region to fine-map putatively causal genes. GIFT is frequentist in nature, explicitly models both expression correlation and cis-single nucleotide polymorphism linkage disequilibrium across multiple genes and uses a likelihood framework to account for expression prediction uncertainty. As a result, GIFT produces calibrated P values and is effective for fine-mapping. We apply GIFT to analyze six traits in the UK Biobank, where GIFT narrows down the set size of putatively causal genes by 32.16-91.32% compared with existing TWAS fine-mapping approaches. The genes identified by GIFT highlight the importance of vessel regulation in determining blood pressures and lipid metabolism for regulating lipid levels.
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Affiliation(s)
- Lu Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ran Yan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ping Guo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Jiadong Ji
- Institute for Financial Studies, Shandong University, Jinan, China
| | - Weiming Gong
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China.
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA.
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Hou L, Geng Z, Yuan Z, Shi X, Wang C, Chen F, Li H, Xue F. MRSL: a causal network pruning algorithm based on GWAS summary data. Brief Bioinform 2024; 25:bbae086. [PMID: 38487847 PMCID: PMC10940843 DOI: 10.1093/bib/bbae086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 02/01/2024] [Accepted: 02/15/2024] [Indexed: 03/18/2024] Open
Abstract
Causal discovery is a powerful tool to disclose underlying structures by analyzing purely observational data. Genetic variants can provide useful complementary information for structure learning. Recently, Mendelian randomization (MR) studies have provided abundant marginal causal relationships of traits. Here, we propose a causal network pruning algorithm MRSL (MR-based structure learning algorithm) based on these marginal causal relationships. MRSL combines the graph theory with multivariable MR to learn the conditional causal structure using only genome-wide association analyses (GWAS) summary statistics. Specifically, MRSL utilizes topological sorting to improve the precision of structure learning. It proposes MR-separation instead of d-separation and three candidates of sufficient separating set for MR-separation. The results of simulations revealed that MRSL had up to 2-fold higher F1 score and 100 times faster computing time than other eight competitive methods. Furthermore, we applied MRSL to 26 biomarkers and 44 International Classification of Diseases 10 (ICD10)-defined diseases using GWAS summary data from UK Biobank. The results cover most of the expected causal links that have biological interpretations and several new links supported by clinical case reports or previous observational literatures.
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Affiliation(s)
- Lei Hou
- Beijing International Center for Mathematical Research, Peking University, Beijing, People’s Republic of China, 100871
| | - Zhi Geng
- School of Mathematics and Statistics, Beijing Technology and Business University, Beijing, People’s Republic of China, 100048
| | - Zhongshang Yuan
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China, 250000
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China, 250000
| | - Xu Shi
- Department of Biostatistics, University of Michigan, Ann Arbor, USA
| | - Chuan Wang
- Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, People's Republic of China, 250000
| | - Feng Chen
- School of Public Health, Nanjing Medical University, Nanjing, China, 211166
| | - Hongkai Li
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China, 250000
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China, 250000
| | - Fuzhong Xue
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China, 250000
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China, 250000
- Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, People's Republic of China, 250000
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Li M, Liu F, Yang Y, Lao J, Yin C, Wu Y, Yuan Z, Wei Y, Tang F. Identifying vital sign trajectories to predict 28-day mortality of critically ill elderly patients with acute respiratory distress syndrome. Respir Res 2024; 25:8. [PMID: 38178157 PMCID: PMC10765902 DOI: 10.1186/s12931-023-02643-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 12/18/2023] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND The mortality rate of acute respiratory distress syndrome (ARDS) increases with age (≥ 65 years old) in critically ill patients, and it is necessary to prevent mortality in elderly patients with ARDS in the intensive care unit (ICU). Among the potential risk factors, dynamic subphenotypes of respiratory rate (RR), heart rate (HR), and respiratory rate-oxygenation (ROX) and their associations with 28-day mortality have not been clearly explored. METHODS Based on the eICU Collaborative Research Database (eICU-CRD), this study used a group-based trajectory model to identify longitudinal subphenotypes of RR, HR, and ROX during the first 72 h of ICU stays. A logistic model was used to evaluate the associations of trajectories with 28-day mortality considering the group with the lowest rate of mortality as a reference. Restricted cubic spline was used to quantify linear and nonlinear effects of static RR-related factors during the first 72 h of ICU stays on 28-day mortality. Receiver operating characteristic (ROC) curves were used to assess the prediction models with the Delong test. RESULTS A total of 938 critically ill elderly patients with ARDS were involved with five and 5 trajectories of RR and HR, respectively. A total of 204 patients fit 4 ROX trajectories. In the subphenotypes of RR, when compared with group 4, the odds ratios (ORs) and 95% confidence intervals (CIs) of group 3 were 2.74 (1.48-5.07) (P = 0.001). Regarding the HR subphenotypes, in comparison to group 1, the ORs and 95% CIs were 2.20 (1.19-4.08) (P = 0.012) for group 2, 2.70 (1.40-5.23) (P = 0.003) for group 3, 2.16 (1.04-4.49) (P = 0.040) for group 5. Low last ROX had a higher mortality risk (P linear = 0.023, P nonlinear = 0.010). Trajectories of RR and HR improved the predictive ability for 28-day mortality (AUC increased by 2.5%, P = 0.020). CONCLUSIONS For RR and HR, longitudinal subphenotypes are risk factors for 28-day mortality and have additional predictive enrichment, whereas the last ROX during the first 72 h of ICU stays is associated with 28-day mortality. These findings indicate that maintaining the health dynamic subphenotypes of RR and HR in the ICU and elevating static ROX after initial critical care may have potentially beneficial effects on prognosis in critically ill elderly patients with ARDS.
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Affiliation(s)
- Mingzhuo Li
- Department of Critical Care Medicine, Shandong Medicine and Health Key Laboratory of Emergency Medicine, Shandong Institute of Anesthesia and Respiratory Critical Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jingshi Road 16766, Jinan, China
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong Data Open Innovative Application Laboratory, Jinan, China
| | - Fen Liu
- Department of Critical Care Medicine, Shandong Medicine and Health Key Laboratory of Emergency Medicine, Shandong Institute of Anesthesia and Respiratory Critical Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jingshi Road 16766, Jinan, China
| | - Yang Yang
- Department of Critical Care Medicine, Shandong Medicine and Health Key Laboratory of Emergency Medicine, Shandong Institute of Anesthesia and Respiratory Critical Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jingshi Road 16766, Jinan, China
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong Data Open Innovative Application Laboratory, Jinan, China
| | - Jiahui Lao
- Department of Critical Care Medicine, Shandong Medicine and Health Key Laboratory of Emergency Medicine, Shandong Institute of Anesthesia and Respiratory Critical Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jingshi Road 16766, Jinan, China
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong Data Open Innovative Application Laboratory, Jinan, China
| | - Chaonan Yin
- Department of Critical Care Medicine, Shandong Medicine and Health Key Laboratory of Emergency Medicine, Shandong Institute of Anesthesia and Respiratory Critical Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jingshi Road 16766, Jinan, China
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong Data Open Innovative Application Laboratory, Jinan, China
| | - Yafei Wu
- Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yongyue Wei
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Fang Tang
- Department of Critical Care Medicine, Shandong Medicine and Health Key Laboratory of Emergency Medicine, Shandong Institute of Anesthesia and Respiratory Critical Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jingshi Road 16766, Jinan, China.
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
- Shandong Data Open Innovative Application Laboratory, Jinan, China.
- Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
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Chen Y, Liu S, Gong W, Guo P, Xue F, Zhou X, Wang S, Yuan Z. Protein-centric omics integration analysis identifies candidate plasma proteins for multiple autoimmune diseases. Hum Genet 2023:10.1007/s00439-023-02627-0. [PMID: 38143258 DOI: 10.1007/s00439-023-02627-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 11/28/2023] [Indexed: 12/26/2023]
Abstract
It remains challenging to translate the findings from genome-wide association studies (GWAS) of autoimmune diseases (AIDs) into interventional targets, presumably due to the lack of knowledge on how the GWAS risk variants contribute to AIDs. In addition, current immunomodulatory drugs for AIDs are broad in action rather than disease-specific. We performed a comprehensive protein-centric omics integration analysis to identify AIDs-associated plasma proteins through integrating protein quantitative trait loci datasets of plasma protein (1348 proteins and 7213 individuals) and totally ten large-scale GWAS summary statistics of AIDs under a cutting-edge systematic analytic framework. Specifically, we initially screened out the protein-AID associations using proteome-wide association study (PWAS), followed by enrichment analysis to reveal the underlying biological processes and pathways. Then, we performed both Mendelian randomization (MR) and colocalization analyses to further identify protein-AID pairs with putatively causal relationships. We finally prioritized the potential drug targets for AIDs. A total of 174 protein-AID associations were identified by PWAS. AIDs-associated plasma proteins were significantly enriched in immune-related biological process and pathways, such as inflammatory response (P = 3.96 × 10-10). MR analysis further identified 97 protein-AID pairs with potential causal relationships, among which 21 pairs were highly supported by colocalization analysis (PP.H4 > 0.75), 10 of 21 were the newly discovered pairs and not reported in previous GWAS analyses. Further explorations showed that four proteins (TLR3, FCGR2A, IL23R, TCN1) have corresponding drugs, and 17 proteins have druggability. These findings will help us to further understand the biological mechanism of AIDs and highlight the potential of these proteins to develop as therapeutic targets for AIDs.
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Affiliation(s)
- Yingxuan Chen
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44, Wenhua West Road, Jinan, 250012, Shandong, China
- Institute for Medical Dataology, Shandong University, 12550, Erhuan East Road, Jinan, 250003, Shandong, China
| | - Shuai Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44, Wenhua West Road, Jinan, 250012, Shandong, China
- Institute for Medical Dataology, Shandong University, 12550, Erhuan East Road, Jinan, 250003, Shandong, China
| | - Weiming Gong
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44, Wenhua West Road, Jinan, 250012, Shandong, China
- Institute for Medical Dataology, Shandong University, 12550, Erhuan East Road, Jinan, 250003, Shandong, China
| | - Ping Guo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44, Wenhua West Road, Jinan, 250012, Shandong, China
- Institute for Medical Dataology, Shandong University, 12550, Erhuan East Road, Jinan, 250003, Shandong, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44, Wenhua West Road, Jinan, 250012, Shandong, China
- Institute for Medical Dataology, Shandong University, 12550, Erhuan East Road, Jinan, 250003, Shandong, China
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Shukang Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44, Wenhua West Road, Jinan, 250012, Shandong, China.
- Institute for Medical Dataology, Shandong University, 12550, Erhuan East Road, Jinan, 250003, Shandong, China.
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44, Wenhua West Road, Jinan, 250012, Shandong, China.
- Institute for Medical Dataology, Shandong University, 12550, Erhuan East Road, Jinan, 250003, Shandong, China.
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Xia H, Yuan Z. [Discovery and distribution of and response to arbovirus in China over the past seven decades]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2023; 35:427-436. [PMID: 38148530 DOI: 10.16250/j.32.1374.2023152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
Arbovirus is a group of virus transmitted by blood-sucking arthropod bites, which infects both arthropods and vertebrates. More than 600 arboviruses have been characterized worldwide until now, including 65 highly pathogenic viruses, which pose a high threat to public health. The risk of arbovirus transmission is increasing due to climate change, international trade and urbanization. The review summarizes the discovery and distribution of emerging and reemerging arboviruses and novel arboviruses with potential pathogenic risks, and proposes responses to the arbovirus transmission risk, so as to provide insights into the research and management of arboviruses and arthropod-borne infectious diseases in China.
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Affiliation(s)
- H Xia
- Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, Hubei 430071, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Z Yuan
- Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, Hubei 430071, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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Mi L, Yuan Z, Que M, Yang Y, Fang S, Wang X. Observation of the short-term curative effect of using SuperPATH approach to treat elderly femoral neck fractures with schizophrenia. Acta Orthop Belg 2023; 89:639-643. [PMID: 38205754 DOI: 10.52628/89.4.9750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
As China enters an aging society, the incidence of femoral neck fractures is increasing year by year. For some patients, total hip arthroplasty (THA) is the treatment of choice for displaced femoral neck fractures. Schizophrenia is a common combination of elderly patients with femoral neck fractures, and there are few reports on the treatment. This study describes the short-term efficacy of the supercapsular percutaneously assisted (SuperPATH) approach in the treatment of patients suffered with displaced femoral neck fractures combined with schizophrenia. A retrospective analysis of 20 elderly patients with displaced femoral neck fractures combined with schizophrenia who underwent THA using the SuperPATH approach. Record demographic data, postoperative reexamination of X-ray film to observe the position and the loosening condition of the prosthesis, the length of hospitalization, complications in the hospital and after discharge. The Harris score of hip joint function was used to evaluate postoperative hip joint function. The average age of the 20 patients was 73.1 years. All patients were followed up by outpatient clinic or telephone. The follow-up time was 3-12 months, with an average of 9.2 months. There was no incision infection, no tissue structure damage such as important nerves and blood vessels, and no complications such as early dislocation, loosening of the joint prosthesis, and deep vein thrombosis of lower extremities. The efficacy of the last follow-up was evaluated according to the Harris score of hip joint function: an average of 91 points (78-98 points); 13 cases were excellent, 5 cases were good, and 2 cases were fair. The SuperPATH approach has the advantages of less surgical damage, shorter recovery time, good surgical safety, preserving the normal tension of the muscles around the hip joint, and reducing the incidence rate of early postoperative dislocation of the joint prosthesis. The THA of the SuperPATH approach can treat patients with displaced femoral neck fractures combined with schizophrenia safely and effectively.
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Yuan J, Liu X, Wang X, Zhou H, Wang Y, Tian G, Liu X, Tang M, Meng X, Kou C, Yang Q, Li J, Zhang L, Yuan Z, Zhang H. Association Between Educational Attainment and Thyroid Function: Results From Mendelian Randomization and the NHANES Study. J Clin Endocrinol Metab 2023; 108:e1678-e1685. [PMID: 37285488 DOI: 10.1210/clinem/dgad344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 05/13/2023] [Accepted: 06/05/2023] [Indexed: 06/09/2023]
Abstract
CONTEXT Many observational studies have reported on the association between educational attainment (EA) and thyroid function, but the causal relationship remains unclear. OBJECTIVE We aimed to obtain causal effects of EA on thyroid function and to quantify the mediating effects of modifiable risk factors. METHODS Two-sample mendelian randomization (MR) was performed by using summary statistics from large genome-wide association studies (GWAS) to assess the effect of EA on thyroid function, including hypothyroidism, hyperthyroidism, thyrotropin (TSH), and free thyroxine (FT4). A multivariable analysis was conducted to assess the mediating role of smoking and help to explain the association between EA and thyroid function. Similar analysis was further performed using data from the National Health and Nutrition Examination Survey (NHANES) 1999 to 2002. RESULTS In MR analysis, EA was causally associated with TSH (β = .046; 95% CI, 0.015-0.077; P = 4.00 × 10-3), rather than hypothyroidism, hyperthyroidism, and FT4. Importantly, smoking could serve as a mediator in the association between EA and TSH, in which the mediating proportion was estimated to be 10.38%. After adjusting for smoking in the multivariable MR analysis, the β value of EA on TSH was attenuated to 0.030 (95% CI, 0.016-0.045; P = 9.32 × 10-3). Multivariable logistic regression model in NHANES suggested a dose-response relationship between TSH (quartile [Q]4 vs Q1: odds ratio = 1.33; 95% CI, 1.05-1.68; P for trend = .023) and EA. Smoking, systolic blood pressure, and body mass index partially mediated the association between EA and TSH, with the proportion of the mediation effects being 43.82%, 12.28%, and 6.81%, respectively. CONCLUSION There is a potentially causal association between EA and TSH, which could be mediated by several risk factors, such as smoking.
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Affiliation(s)
- Jie Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, China
| | - Xue Liu
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, 250021, China
| | - Xinhui Wang
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, 250021, China
| | - Huizhi Zhou
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, 250021, China
| | - Yuyao Wang
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, 250021, China
| | - Guoyu Tian
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, 250021, China
| | - Xueying Liu
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
| | - Mulin Tang
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
| | - Xue Meng
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
| | - Chunjia Kou
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, 250021, China
| | - Qingqing Yang
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, 250021, China
| | - Juyi Li
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, 250021, China
| | - Li Zhang
- Department of Vascular Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, China
| | - Haiqing Zhang
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, 250021, China
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Jinan, 250021, China
- Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, 250021, China
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Chen Y, Liang C, Li J, Ma L, Wang B, Yuan Z, Yang S, Nong X. Effect of artesunate on cardiovascular complications in periodontitis in a type I diabetes rat model and related mechanisms. J Endocrinol Invest 2023; 46:2031-2053. [PMID: 36892740 DOI: 10.1007/s40618-023-02052-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 02/24/2023] [Indexed: 03/10/2023]
Abstract
PURPOSE Both cardiovascular disease and periodontitis are complications of diabetes that have a great impact on human life and health. Our previous research found that artesunate can effectively improve cardiovascular disease in diabetes and has an inhibitory effect on periodontal disease. Therefore, the present study aimed to explore the potential therapeutic possibility of artesunate in the protection against cardiovascular complications in periodontitis with type I diabetes rats and to elucidate the possible underlying mechanisms. METHODS Sprague‒Dawley rats were randomly divided into the healthy, diabetic, periodontitis, diabetic with periodontitis, and artesunate treatment groups (10, 30, and 60 mg/kg, i.g.). After artesunate treatment, oral swabs were collected and used to determine changes in the oral flora. Micro-CT was performed to observe changes in alveolar bone. Blood samples were processed to measure various parameters, while cardiovascular tissues were evaluated by haematoxylin-eosin, Masson, Sirius red, and TUNEL staining to observe fibrosis and apoptosis. The protein and mRNA expression levels in the alveolar bone and cardiovascular tissues were detected using immunohistochemistry and RT‒PCR. RESULTS Diabetic rats with periodontitis and cardiovascular complications maintained heart and body weight but exhibited reduced blood glucose levels, and they were able to regulate blood lipid indicators at normal levels after artesunate treatment. The staining assays suggested that treatment with 60 mg/kg artesunate has a significant therapeutic effect on myocardial apoptotic fibrosis. The high expression of NF-κB, TLR4, VEGF, ICAM-1, p38 MAPK, TGF-β, Smad2, and MMP9 in the alveolar bone and cardiovascular tissue in the type I diabetes and type I diabetes with periodontitis rat models was reduced after treatment with artesunate in a concentration-dependent manner. Micro-CT showed that treatment with 60 mg/kg artesunate effectively alleviated alveolar bone resorption and density reduction. The sequencing results suggested that each model group of rats had vascular and oral flora dysbiosis, but artesunate treatment could correct the dysbacteriosis. CONCLUSIONS Periodontitis-related pathogenic bacteria cause dysbiosis of the oral and intravascular flora in type I diabetes and aggravate cardiovascular complications. The mechanism by which periodontitis aggravates cardiovascular complications involves the NF-κB pathway, which induces myocardial apoptosis, fibrosis, and vascular inflammation.
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Affiliation(s)
- Y Chen
- College of Stomatology, Hospital of Stomatology, Guangxi Medical University, No. 10 Shuangyong Road, Nanning, 530021, Guangxi, China
- Guangxi Key Laboratory of Oral and Maxillofacial Rehabilitation and Reconstruction, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - C Liang
- College of Stomatology, Hospital of Stomatology, Guangxi Medical University, No. 10 Shuangyong Road, Nanning, 530021, Guangxi, China
- Guangxi Key Laboratory of Oral and Maxillofacial Rehabilitation and Reconstruction, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - J Li
- Life Science Institute, Guangxi Medical University, Nanning, 530021, Guangxi, China
- Medical Science Research Center, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - L Ma
- College of Stomatology, Hospital of Stomatology, Guangxi Medical University, No. 10 Shuangyong Road, Nanning, 530021, Guangxi, China
- Guangxi Key Laboratory of Oral and Maxillofacial Rehabilitation and Reconstruction, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - B Wang
- College of Stomatology, Hospital of Stomatology, Guangxi Medical University, No. 10 Shuangyong Road, Nanning, 530021, Guangxi, China
- Guangxi Key Laboratory of Oral and Maxillofacial Rehabilitation and Reconstruction, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - Z Yuan
- College of Stomatology, Hospital of Stomatology, Guangxi Medical University, No. 10 Shuangyong Road, Nanning, 530021, Guangxi, China
- Guangxi Key Laboratory of Oral and Maxillofacial Rehabilitation and Reconstruction, Guangxi Medical University, Nanning, 530021, Guangxi, China
| | - S Yang
- School of Information and Management, Nanning, 530021, Guangxi, China
| | - X Nong
- College of Stomatology, Hospital of Stomatology, Guangxi Medical University, No. 10 Shuangyong Road, Nanning, 530021, Guangxi, China.
- Guangxi Key Laboratory of Oral and Maxillofacial Rehabilitation and Reconstruction, Guangxi Medical University, Nanning, 530021, Guangxi, China.
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Yuan Z. Handcrafted Radiomics, Deep Learning Radiomics in the Prediction of Radiation Pneumonitis for NSCLC Patients Treated with Immunotherapy Followed with Thoracic Radiotherapy. Int J Radiat Oncol Biol Phys 2023; 117:e79. [PMID: 37786181 DOI: 10.1016/j.ijrobp.2023.06.822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Our previous study has shown that NSCLC patients previously received immune checkpoint inhibitors (ICIs) underwent thoracic intensity modulated radiotherapy have a higher risk of acute radiation pneumonitis (RP). This study aimed to establish machine learning models using handcrafted radiomics (HCR), deep learning-based radiomics (DLR) and clinical characteristics to improve the prediction of symptomatic radiation pneumonitis (RP) (grade ≥ 2) status for NSCLC patients treated with immunotherapy followed with thoracic radiotherapy. MATERIALS/METHODS This study retrospectively collected data of 61 NSCLC patients meeting the requirements of study enrollment. Of these 61 patients, 35 developed symptomatic graded ≥ 2 RP. We defined 3 regions of interest (ROIs) in planning CT images including gross tumor volume (GTV), planning tumor volume (PTV), PTV-GTV. We calculated the mean dose, V5, V10, V20, and V30 within TL-GTV, and the volume of GTV, PTV and total lung. A total of 516 handcrafted radiomics features and 512 deep features were extracted from each 3 ROIs. Person Correlation Analysis and Least Absolute Shrinkage and Selection Operator (LASSO) were used to reduce the dimension of features. The HCR models, DLR models and the fusion models across different ROIs with machine learning classifiers were built and compared. RESULTS In multi-classifier modeling, models with PTV under logistic regression (LR) classifiers showed better prediction than other ROIs under different machine learning algorithms. Based on PTV with LR, HCR+ DLR model had better performance, with an area under the curve (AUC) of 0.95 (95% confidence interval (CI): 0.893-1) in the training cohort and 0.87 (95% CI: 0.698-1) in the test cohort, which was higher than that of HCR model, with an AUC of 0.86 (95% CI: 0.755-0.9) in the training cohort and 0.82 (95% CI: 0.624-1) in the test cohort, the results of fusion model with HCR, DLR and 7 clinical characteristics including T, N, clinical stage, age, smoking, radiotherapy alone/combined and V30, demonstrated the best distinguishing performance, with an AUC of 0.99 (95% CI: 0.970-1) in the training cohort and 0.91 (95% CI: 0.784-1) in the test cohort. CONCLUSION The combination of HCR, DLR and clinical characteristic underwent machine learning algorithms can improve the prediction of symptomatic RP in NSCLC patients treated with ICIs followed with thoracic radiotherapy.
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Affiliation(s)
- Z Yuan
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR, China, Wuhan, China, China
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Zhang X, Chen Y, Li Z, Shang J, Yuan Z, Deng W, Luo Y, Han N, Yin P, Yin J. [Analysis of therapeutic mechanism of Liushen Wan against colitis-associated colorectal cancer based on network pharmacology and validation in mice]. Nan Fang Yi Ke Da Xue Xue Bao 2023; 43:1051-1062. [PMID: 37488787 PMCID: PMC10366510 DOI: 10.12122/j.issn.1673-4254.2023.07.01] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
OBJECTIVE To explore the therapeutic mechanism of Liushen Wan (LSW) against colitis-associated colorectal cancer (CAC) by network pharmacology. METHODS TCMSP, BATMAN-TCM, CNKI, PubMed, Genecards, OMIM, and TTD databases were used to obtain the related targets of LSW and CAC. The common targets of LSW and CAC were obtained using Venny online website. The PPI network was constructed using Cytoscape 3.8.2 to screen the core targets of LSW in the treatment of CAC. GO and KEGG enrichment analysis were conducted using DAVID database. The therapeutic effect of LSW on CAC was evaluated in a C57BL/6J mouse model of AOM/DSS-induced CAC by observing the changes in body weight, disease activity index, colon length, and size and number of the tumor. HE staining and RT-qPCR were used to analyze the effect of LSW on inflammatory mediators. Immunohistochemistry and TUNEL staining were used to evaluate the effect of LSW on the proliferation and apoptosis of AOM/DSS-treated colon tumor cells. Immunohistochemistry and Western blotting were used to detect the effects of LSW on the expression of TLR4 proteins in CAC mice. RESULTS Network pharmacology analysis identified 69 common targets of LSW and CAC, and 33 hub targets were screened in the PPI network. KEGG pathway enrichment analysis suggested that the effect of LSW on CAC was mediated by the Toll-like receptor signaling pathway. In the mouse model of AOM/DSS-induced CAC, LSW significantly inhibited colitis-associated tumorigenesis, reduced tumor number and tumor load (P < 0.05), obviously improved histopathological changes in the colon, downregulated the mRNA levels of proinflammatory cytokines, and inhibited the proliferation (P < 0.01) and promoted apoptosis of colon tumor cells (P < 0.001). LSW also significantly decreased TLR4 protein expression in the colon tissue (P < 0.05). CONCLUSION LSW can inhibit CAC in mice possibly by regulating the expression of TLR4 to reduce intestinal inflammation, inhibit colon tumor cell proliferation and promote their apoptosis.
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Affiliation(s)
- X Zhang
- School of Traditional Chinese Medicine, Shengyang Pharmaceutical University, Benxi 117004, China
- Interventional Cancer Institute of Chinese Integrative Medicine, Shanghai Institute of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200062, China
| | - Y Chen
- School of Traditional Chinese Medicine, Shengyang Pharmaceutical University, Benxi 117004, China
- Interventional Cancer Institute of Chinese Integrative Medicine, Shanghai Institute of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200062, China
| | - Z Li
- Department of General Surgery, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200062, China
- Interventional Cancer Institute of Chinese Integrative Medicine, Shanghai Institute of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200062, China
| | - J Shang
- Department of General Surgery, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200062, China
- Interventional Cancer Institute of Chinese Integrative Medicine, Shanghai Institute of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200062, China
| | - Z Yuan
- Interventional Cancer Institute of Chinese Integrative Medicine, Shanghai Institute of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200062, China
| | - W Deng
- Interventional Cancer Institute of Chinese Integrative Medicine, Shanghai Institute of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200062, China
| | - Y Luo
- Clinical Laboratory, Shanghai Changning Maternity and Infant Health Hospital, Shanghai 200000, China
| | - N Han
- School of Traditional Chinese Medicine, Shengyang Pharmaceutical University, Benxi 117004, China
| | - P Yin
- Department of General Surgery, Putuo Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200062, China
- Interventional Cancer Institute of Chinese Integrative Medicine, Shanghai Institute of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200062, China
| | - J Yin
- School of Traditional Chinese Medicine, Shengyang Pharmaceutical University, Benxi 117004, China
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Fan X, Han J, Zhao E, Fang J, Wang D, Cheng Y, Shi Y, Wang Z, Yao Z, Lu P, Liu T, Li Q, Poulsen KL, Yuan Z, Song Y, Zhao J. The effects of obesity and metabolic abnormalities on severe COVID-19-related outcomes after vaccination: A population-based study. Cell Metab 2023; 35:585-600.e5. [PMID: 36931274 PMCID: PMC9974355 DOI: 10.1016/j.cmet.2023.02.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 11/01/2022] [Accepted: 02/24/2023] [Indexed: 03/05/2023]
Abstract
Breakthrough SARS-CoV-2 infections of vaccinated individuals are being reported globally, resulting in an increased risk of hospitalization and death among such patients. Therefore, it is crucial to identify the modifiable risk factors that may affect the protective efficacy of vaccine use against the development of severe COVID-19 and thus to initiate early medical interventions. Here, in population-based studies using the UK Biobank database and the 2021 National Health Interview Survey (NHIS), we analyzed 20,362 participants aged 50 years or older and 2,588 aged 18 years or older from both databases who tested positive for SARS-COV-2, of whom 33.1% and 67.7% received one or more doses of vaccine, respectively. In the UK Biobank, participants are followed from the vaccination date until October 18, 2021. We found that obesity and metabolic abnormalities (namely, hyperglycemia, hyperlipidemia, and hypertension) were modifiable factors for severe COVID-19 in vaccinated patients (all p < 0.05). When metabolic abnormalities were present, regardless of obesity, the risk of severe COVID-19 was higher than that of metabolically normal individuals (all p < 0.05). Moreover, pharmacological interventions targeting such abnormalities (namely, antihypertensive [adjusted hazard ratio (aHR) 0.64, 95% CI 0.48-0.86; p = 0.003], glucose-lowering [aHR 0.55, 95% CI 0.36-0.83; p = 0.004], and lipid-lowering treatments [aHR 0.50, 95% CI 0.37-0.68; p < 0.001]) were significantly associated with a reduced risk for this outcome. These results show that more proactive health management of patients with obesity and metabolic abnormalities is critical to reduce the incidence of severe COVID-19 after vaccination.
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Affiliation(s)
- Xiude Fan
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Junming Han
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Enfa Zhao
- Department of Ultrasound, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province 230022, China
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China
| | - Dawei Wang
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Yiping Cheng
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Yingzhou Shi
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Zhen Wang
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Zhenyu Yao
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Peng Lu
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Tianbao Liu
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Qihang Li
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Kyle L Poulsen
- Department of Anesthesiology, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Yongfeng Song
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China.
| | - Jiajun Zhao
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong Provincial Hospital, Shandong University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China.
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Huang X, Zhang T, Guo P, Gong W, Zhu H, Zhao M, Yuan Z. Association of antihypertensive drugs with fracture and bone mineral density: A comprehensive drug-target Mendelian randomization study. Front Endocrinol (Lausanne) 2023; 14:1164387. [PMID: 37056679 PMCID: PMC10086430 DOI: 10.3389/fendo.2023.1164387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
Background Observational studies have investigated the associations between antihypertensive drugs and fracture risk as well as bone mineral density (BMD), but yielding controversial results. Methods In this study, a comprehensive drug-target Mendelian randomization (MR) analysis was conducted to systematically examine the associations between genetic proxies for eight common antihypertensive drugs and three bone health-related traits (fracture, total body BMD [TB-BMD], and estimated heel BMD [eBMD]). The main analysis used the inverse-variance weighted (IVW) method to estimate the causal effect. Multiple MR methods were also employed to test the robustness of the results. Results The genetic proxies for angiotensin receptor blockers (ARBs) were associated with a reduced risk of fracture (odds ratio [OR] = 0.67, 95% confidence interval [CI]: 0.54 to 0.84; P = 4.42 × 10-4; P-adjusted = 0.004), higher TB-BMD (β = 0.36, 95% CI: 0.11 to 0.61; P = 0.005; P-adjusted = 0.022), and higher eBMD (β = 0.30, 95% CI: 0.21 to 0.38; P = 3.59 × 10-12; P-adjusted = 6.55 × 10-11). Meanwhile, genetic proxies for calcium channel blockers (CCBs) were associated with an increased risk of fracture (OR = 1.07, 95% CI: 1.03 to 1.12; P = 0.002; P-adjusted = 0.013). Genetic proxies for potassium sparing diuretics (PSDs) showed negative associations with TB-BMD (β = -0.61, 95% CI: -0.88 to -0.33; P = 1.55 × 10-5; P-adjusted = 1.86 × 10-4). Genetic proxies for thiazide diuretics had positive associations with eBMD (β = 0.11, 95% CI: 0.03 to 0.18; P = 0.006; P-adjusted = 0.022). No significant heterogeneity or pleiotropy was identified. The results were consistent across different MR methods. Conclusions These findings suggest that genetic proxies for ARBs and thiazide diuretics may have a protective effect on bone health, while genetic proxies for CCBs and PSDs may have a negative effect.
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Affiliation(s)
- Xin Huang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Institute for Medical Dataology, Shandong University, Jinan, Shandong, China
| | - Tianxin Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Institute for Medical Dataology, Shandong University, Jinan, Shandong, China
| | - Ping Guo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Institute for Medical Dataology, Shandong University, Jinan, Shandong, China
| | - Weiming Gong
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Institute for Medical Dataology, Shandong University, Jinan, Shandong, China
| | - Hengchao Zhu
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, United States
| | - Meng Zhao
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Institute for Medical Dataology, Shandong University, Jinan, Shandong, China
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Gong W, Guo P, Li Y, Liu L, Yan R, Liu S, Wang S, Xue F, Zhou X, Yuan Z. Role of the Gut-Brain Axis in the Shared Genetic Etiology Between Gastrointestinal Tract Diseases and Psychiatric Disorders: A Genome-Wide Pleiotropic Analysis. JAMA Psychiatry 2023; 80:360-370. [PMID: 36753304 PMCID: PMC9909581 DOI: 10.1001/jamapsychiatry.2022.4974] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
IMPORTANCE Comorbidities and genetic correlations between gastrointestinal tract diseases and psychiatric disorders have been widely reported, with the gut-brain axis (GBA) hypothesized as a potential biological basis. However, the degree to which the shared genetic determinants are involved in these associations underlying the GBA is unclear. OBJECTIVE To investigate the shared genetic etiology between gastrointestinal tract diseases and psychiatric disorders and to identify shared genomic loci, genes, and pathways. DESIGN, SETTING, AND PARTICIPANTS This genome-wide pleiotropic association study using genome-wide association summary statistics from publicly available data sources was performed with various statistical genetic approaches to sequentially investigate the pleiotropic associations from genome-wide single-nucleotide variation (SNV; formerly single-nucleotide polymorphism [SNP]), and gene levels and biological pathways to disentangle the underlying shared genetic etiology between 4 gastrointestinal tract diseases (inflammatory bowel disease, irritable bowel syndrome, peptic ulcer disease, and gastroesophageal reflux disease) and 6 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, attention-deficit/hyperactivity disorder, posttraumatic stress disorder, and anorexia nervosa). Data were collected from March 10, 2021, to August 25, 2021, and analysis was performed from January 8 through May 30, 2022. MAIN OUTCOMES AND MEASURES The primary outcomes consisted of a list of genetic loci, genes, and pathways shared between gastrointestinal tract diseases and psychiatric disorders. RESULTS Extensive genetic correlations and genetic overlaps were found among 22 of 24 trait pairs. Pleiotropic analysis under a composite null hypothesis identified 2910 significant potential pleiotropic SNVs in 19 trait pairs, with 83 pleiotropic loci and 24 colocalized loci detected. Gene-based analysis found 158 unique candidate pleiotropic genes, which were highly enriched in certain GBA-related phenotypes and tissues, whereas pathway enrichment analysis further highlighted biological pathways primarily involving cell adhesion, synaptic structure and function, and immune cell differentiation. Several identified pleiotropic loci also shared causal variants with gut microbiomes. Mendelian randomization analysis further illustrated vertical pleiotropy across 8 pairwise traits. Notably, many pleiotropic loci were identified for multiple pairwise traits, such as 1q32.1 (INAVA), 19q13.33 (FUT2), 11q23.2 (NCAM1), and 1p32.3 (LRP8). CONCLUSIONS AND RELEVANCE These findings suggest that the pleiotropic genetic determinants between gastrointestinal tract diseases and psychiatric disorders are extensively distributed across the genome. These findings not only support the shared genetic basis underlying the GBA but also have important implications for intervention and treatment targets of these diseases simultaneously.
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Affiliation(s)
- Weiming Gong
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Ping Guo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Yuanming Li
- School of Medicine, Cheeloo College of Medicine, Shandong University Jinan, China
| | - Lu Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Ran Yan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Shuai Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Shukang Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor,Center for Statistical Genetics, University of Michigan, Ann Arbor
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
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Yuan Z, Cui H, Wei B. [Current status and future prospects of robotic surgical system in radical gastrectomy for gastric cancer]. Zhonghua Wei Chang Wai Ke Za Zhi 2023; 26:33-37. [PMID: 36649997 DOI: 10.3760/cma.j.cn441530-20221123-00486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Robotic gastrectomy (RG) has always been a hot topic in the field of minimally invasive surgery for gastric cancer. More and more studies have confirmed that short- and long-term outcomes of RG are similar to those of laparoscopic gastrectomy. Robotic surgical systems have more advantages in specific regional lymph node dissection. More delicate operation can reduce intraoperative blood loss and the incidence of postoperative complications. Robotic surgical systems are also more ergonomically designed. However, there are also some problems such as high surgical cost, lack of tactile feedback and prolonged total operation time. In the future, robotic surgical system may be further developed in the direction of miniaturization, intelligence and modularity. At the same time, the robotic surgical system deeply integrated with artificial intelligence technology may realize the automation of some operation steps to some extent.
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Affiliation(s)
- Z Yuan
- Department of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - H Cui
- Department of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - B Wei
- Department of General Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
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18
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Zhang Y, Fan X, Zhao C, Yuan Z, Cheng Y, Wu Y, Han J, Yuan Z, Zhao Y, Lu K. Association between metabolic obesity phenotypes and multiple myeloma hospitalization burden: A national retrospective study. Front Oncol 2023; 13:1116307. [PMID: 36910611 PMCID: PMC9996033 DOI: 10.3389/fonc.2023.1116307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Background & purpose Obesity and metabolic disorders were associated with increased risk of MM, a disease characterized by high risk of relapsing and require frequent hospitalizations. In this study, we conducted a retrospective cohort study to explore the association of metabolic obesity phenotypes with the readmission risk of MM. Patients & methods We analyzed 34,852 patients diagnosed with MM from the Nationwide Readmissions Database (NRD), a nationally representative database from US. Hospitalization diagnosis of patients were obtained using ICD-10 diagnosis codes. According to obesity and metabolic status, the population was divided into four phenotypes: metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO). The patients with different phenotypes were observed for hospital readmission at days 30-day, 60-day, 90-day and 180-day. Multivariate cox regression model was used to estimate the relationship between obesity metabolic phenotypes and readmissions risk. Results There were 5,400 (15.5%), 7,255 (22.4%), 8,025 (27.0%) and 7,839 (35.6%) unplanned readmissions within 30-day, 60-day, 90-day and 180-day follow-up, respectively. For 90-day and 180-day follow-up, compared with patients with the MHNO phenotype, those with metabolic unhealthy phenotypes MUNO (90-day: P = 0.004; 180-day: P = < 0.001) and MUO (90-day: P = 0.049; 180-day: P = 0.004) showed higher risk of readmission, while patients with only obesity phenotypes MHO (90-day: P = 0.170; 180-day: P = 0.090) experienced no higher risk. However, similar associations were not observed for 30-day and 60-day. Further analysis in 90-day follow-up revealed that, readmission risk elevated with the increase of the combined factor numbers, with aHR of 1.068 (CI: 1.002-1.137, P = 0.043, with one metabolic risk factor), 1.109 (CI: 1.038-1.184, P = 0.002, with two metabolic risk factors) and 1.125 (95% CI: 1.04-1.216, P = 0.003, with three metabolic risk factors), respectively. Conclusion Metabolic disorders, rather than obesity, were independently associated with higher readmission risk in patients with MM, whereas the risk elevated with the increase of the number of combined metabolic factors. However, the effect of metabolic disorders on MM readmission seems to be time-dependent. For MM patient combined with metabolic disorders, more attention should be paid to advance directives to reduce readmission rate and hospitalization burden.
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Affiliation(s)
- Yue Zhang
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China.,Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.,Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong, China.,Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China.,Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
| | - Xiude Fan
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.,Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong, China.,Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China.,Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
| | - Chunhui Zhao
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China.,Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.,Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong, China.,Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China.,Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
| | - Zinuo Yuan
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China.,Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.,Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong, China.,Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China.,Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
| | - Yiping Cheng
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China.,Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.,Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong, China.,Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China.,Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
| | - Yafei Wu
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.,Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong, China.,Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China.,Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
| | - Junming Han
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.,Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong, China.,Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China.,Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Shandong University, Jinan, Shandong, China
| | - Yuanfei Zhao
- Beijing Institute of Heart, Lung and Blood, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.,Beijing Advanced Innovation Center for Big Data-based Precision Medicine, Capital Medical University, Beijing, China
| | - Keke Lu
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China.,Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China
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Zhang L, Ju T, Jin X, Ji J, Han J, Zhou X, Yuan Z. Network regression analysis for binary and ordinal categorical phenotypes in transcriptome-wide association studies. Genetics 2022; 222:iyac153. [PMID: 36227056 PMCID: PMC9713396 DOI: 10.1093/genetics/iyac153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/28/2022] [Indexed: 12/13/2022] Open
Abstract
Transcriptome-wide association studies aim to integrate genome-wide association studies and expression quantitative trait loci mapping studies for exploring the gene regulatory mechanisms underlying diseases. Existing transcriptome-wide association study methods primarily focus on 1 gene at a time. However, complex diseases are seldom resulted from the abnormality of a single gene, but from the biological network involving multiple genes. In addition, binary or ordinal categorical phenotypes are commonly encountered in biomedicine. We develop a proportional odds logistic model for network regression in transcriptome-wide association study, Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study, to detect the association between a network and binary or ordinal categorical phenotype. Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study relies on 2-stage transcriptome-wide association study framework. It first adopts the distribution-robust nonparametric Dirichlet process regression model in expression quantitative trait loci study to obtain the SNP effect estimate on each gene within the network. Then, Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study uses pointwise mutual information to represent the general relationship among the network nodes of predicted gene expression in genome-wide association study, followed by the association analysis with all nodes and edges involved in proportional odds logistic model. A key feature of Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study is its ability to simultaneously identify the disease-related network nodes or edges. With extensive realistic simulations including those under various between-node correlation patterns, we show Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study can provide calibrated type I error control and yield higher power than other existing methods. We finally apply Proportional Odds LOgistic model for NEtwork regression in Transcriptome-wide association study to analyze bipolar and major depression status and blood pressure from UK Biobank to illustrate its benefits in real data analysis.
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Affiliation(s)
- Liye Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Shandong University, Jinan, Shandong 250003, China
| | - Tao Ju
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Shandong University, Jinan, Shandong 250003, China
| | - Xiuyuan Jin
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Shandong University, Jinan, Shandong 250003, China
| | - Jiadong Ji
- Institute for Financial Studies, Shandong University, Jinan, Shandong 250100, China
| | - Jiayi Han
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Shandong University, Jinan, Shandong 250003, China
| | - Xiang Zhou
- Department of Biostatistics, The University of Michigan, Ann Arbor, MI 48109, USA
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Shandong University, Jinan, Shandong 250003, China
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20
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Li M, Zhou M, Yang Y, Liu Y, Yin C, Geng W, Wang C, Tang F, Zhao Y, Xue F, Sun X, Yuan Z. Multi-trajectories of systolic and diastolic hypertension and coronary heart disease in middle-aged and older adults. Front Public Health 2022; 10:1017727. [PMID: 36505007 PMCID: PMC9729777 DOI: 10.3389/fpubh.2022.1017727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 11/03/2022] [Indexed: 11/25/2022] Open
Abstract
Objective This study aimed to investigate multi-trajectories of systolic and diastolic hypertension and assess their association with the risk of coronary heart disease (CHD) in middle-aged and older Chinese adults. Methods The study cohort comprised 4,102 individuals aged 40-75 years with records of at least four systolic blood pressure (SBP) and diastolic blood pressure (DBP). A group-based multi-trajectory model was adopted to identify multi-trajectories of systolic and diastolic hypertension, followed by a logistic model to assess the independent associations between these trajectories and CHD risk. The multinomial logistic model was used to evaluate the impact of baseline covariates on trajectory groups. Results Six distinct trajectories for systolic and diastolic hypertension were identified which represent distinct stages of hypertension and were characterized as low-stable, low-increasing, medium-decreasing, medium-increasing-decreasing, isolated systolic hypertension phase, and high-decreasing. Compared with the low-stable group, the adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were 2.23 (1.34-3.70) for the medium-increasing-decreasing group and 1.87 (1.12-3.11) for the high-decreasing group after adjustment for baseline covariates. Compared with the low-increasing group, the ORs and 95% CIs were 1.88 (1.06-3.31) for the medium-increasing-decreasing group. Age, gender, drinking, body mass index (BMI), triglyceride (TG), and fasting plasma glucose (FPG) were independent predictors for trajectory groups 4 and 6. Conclusion Novel, clinically defined multi-trajectories of systolic and diastolic hypertension were identified. Middle-aged and older adults with medium-increasing-decreasing or high-decreasing blood pressure trajectories are potentially critical periods for the development of CHD. Preventing adverse changes in hypertension status and reducing the high risk of CHD is necessary for people in distinct trajectory groups.
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Affiliation(s)
- Mingzhuo Li
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Miao Zhou
- Office of Hospital Level Review, Shenzhen Longhua District Central Hospital, Shenzhen, China
| | - Yang Yang
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Yafei Liu
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Chaonan Yin
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Wenting Geng
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Chunxia Wang
- Health Management Center, Affiliated Hospital of Jining Medical University, Jining, China
| | - Fang Tang
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Yang Zhao
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xiubin Sun
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China,*Correspondence: Xiubin Sun
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China,Zhongshang Yuan
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Zhong F, Guan Q, Zhang H, Zhang X, Zhao M, Yuan Z, Fan X, Han J, Li Q, Wang Z, Shao S, Zhao J. Association of longitudinal changes in serum lipids with the natural history of subclinical hypothyroidism: A retrospective cohort study using data from the REACTION study. EClinicalMedicine 2022; 53:101629. [PMID: 36060516 PMCID: PMC9433604 DOI: 10.1016/j.eclinm.2022.101629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/03/2022] [Accepted: 08/05/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Subclinical hypothyroidism (SCH) often leads to alterations in lipid profile, which may negatively impact humans health. Whether lipids in turn affect the natural history of SCH is unknown. We aimed to assess the association between longitudinal changes in serum lipid levels and the natural history of SCH. METHODS This retrospective cohort study using data from the REACTION study included 581 patients with SCH who were enrolled between July 1, 2011, and December 19, 2014, with a median follow-up of three [IQR, 2·86-3·21] years. Patients with missing data or conditions that can affect thyroid function were excluded. Changes in serum lipid levels were calculated from serum lipid measurements 3 years apart and classified in two ways: 1) the first, second, and third tertiles of the difference between baseline and follow-up and 2) the percent change from baseline, namely, serum lipid decrease ≥ 25%, minor change, and serum lipid increase ≥ 25%. The natural history of SCH includes regression to euthyroidism, SCH persistence, or progression to overt hypothyroidism (OH). Odds ratios (ORs) were estimated by multivariable logistic regression. Validation was performed on data from a health management cohort study conducted from January 1, 2012, to December 31, 2016, with a median follow-up of two [IQR, 1·92-2·08] years. After using the same inclusion and exclusion criteria as the REACTION cohort study, 412 patients with SCH were eligible for the validation analysis. FINDINGS There were 132 (22·7%) men and 449 (77·3%) women in the study, with a median age of 56 [IQR,49-62] years. During follow-up, 270 (46·5%), 266 (45·8%), and 27 (4·6%) patients had regression to euthyroidism, persistent SCH, and progression to OH, respectively. Both grouping manners showed a significant association between changes in lipid levels and the natural history of SCH. A total cholesterol (TC)-level increase was independently associated with a greater risk of progression to OH (OR for ≥ 25% TC increase vs. minor change: 5·40; 95% CI 1·46-21·65), whereas TC-level declines increased the likelihood of regressing to euthyroidism (OR for ≥ 25% TC decrease vs. minor change: 3·45; 95% CI 1·09-12·43). Similarly, the likelihood of regression according to changes in triglyceride (TG) levels exhibited a consistent trend with that according to TC-level changes. A similar pattern of association was observed in the validation cohort. INTERPRETATION Changes in serum lipid levels in SCH are associated with future progression or regression risk, suggesting that the changes in serum lipid levels may affect the natural history of SCH. Clinicians should pay attention to the long-term control of serum lipids levels in populations with SCH, which may benefit thyroid function. FUNDING This work was supported by grants from the National Key Research and Development Program of China (2017YFC1309800), the National Natural Science Foundation (81430020, 82070818), and the "Outstanding University Driven by Talents" Program and Academic Promotion Program of Shandong First Medical University (2019LJ007).
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Key Words
- ALT, alanine transaminase
- Cholesterol
- Cohort study
- Cr, creatinine
- FT3, free triiodothyronine
- FT4, free thyroxine
- HbA1c, glycatedhaemoglobin
- Hypothyroidism
- Lipid
- OH, overt hypothyroidism
- SBP, systolic blood pressure
- SCH, Subclinical hypothyroidism
- Subclinical hypothyroidism
- TC, total cholesterol
- TG, triglyceride
- TPOAb, thyroperoxidase antibody
- TSH, thyroid-stimulating hormone
- Thyroid
- Triglyceride
- eGFR, estimated glomerular filtration rate
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Affiliation(s)
- Fang Zhong
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
- Shandong Clinical Medical Centre of Endocrinology and Metabolism, Jinan, Shandong, 250021, China
| | - Qingbo Guan
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
- Shandong Clinical Medical Centre of Endocrinology and Metabolism, Jinan, Shandong, 250021, China
- Shandong Institute of Endocrine and Metabolic Disease, Jinan, Shandong, 250021, China
| | - Haiqing Zhang
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
- Shandong Clinical Medical Centre of Endocrinology and Metabolism, Jinan, Shandong, 250021, China
- Shandong Institute of Endocrine and Metabolic Disease, Jinan, Shandong, 250021, China
| | - Xu Zhang
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
- Shandong Clinical Medical Centre of Endocrinology and Metabolism, Jinan, Shandong, 250021, China
- Shandong Institute of Endocrine and Metabolic Disease, Jinan, Shandong, 250021, China
| | - Meng Zhao
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
- Shandong Clinical Medical Centre of Endocrinology and Metabolism, Jinan, Shandong, 250021, China
- Shandong Institute of Endocrine and Metabolic Disease, Jinan, Shandong, 250021, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Shandong University, Jinan, 250021, China
| | - Xiude Fan
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
- Shandong Clinical Medical Centre of Endocrinology and Metabolism, Jinan, Shandong, 250021, China
- Shandong Institute of Endocrine and Metabolic Disease, Jinan, Shandong, 250021, China
| | - Junming Han
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
- Shandong Clinical Medical Centre of Endocrinology and Metabolism, Jinan, Shandong, 250021, China
- Shandong Institute of Endocrine and Metabolic Disease, Jinan, Shandong, 250021, China
| | - Qihang Li
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
- Shandong Clinical Medical Centre of Endocrinology and Metabolism, Jinan, Shandong, 250021, China
| | - Zhixiang Wang
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
- Shandong Clinical Medical Centre of Endocrinology and Metabolism, Jinan, Shandong, 250021, China
| | - Shanshan Shao
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
- Shandong Clinical Medical Centre of Endocrinology and Metabolism, Jinan, Shandong, 250021, China
- Shandong Institute of Endocrine and Metabolic Disease, Jinan, Shandong, 250021, China
| | - Jiajun Zhao
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
- Shandong Clinical Medical Centre of Endocrinology and Metabolism, Jinan, Shandong, 250021, China
- Shandong Institute of Endocrine and Metabolic Disease, Jinan, Shandong, 250021, China
- Corresponding author at: Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324, Jing 5 Rd, Jinan, Shandong, 250021, China.
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Zhang Y, Niu G, Kong S, Wei F, Wang H, Dong Y, Yu L, Guan Y, Wang H, Yu X, Yin Z, Yuan Z. Predictive Model for the Radiotherapy Induced Rib Fracture (RIRF) after Stereotactic Body Radiotherapy. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.1653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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23
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Niu G, Zhang Y, Gao M, Zhao J, Wang H, Chen J, Guo X, Yu L, Guan Y, Dong Y, Yu X, Yin Z, Yuan Z, Kong S. Dosimetric Analysis of Radiation-Induced Brachial Plexopathy after Stereotactic Body Radiotherapy: The Contouring of Brachial Plexus Matters. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.1645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Liu L, Zhai W, Wang F, Yu L, Zhou F, Xiang Y, Huang S, Zheng C, Yuan Z, He Y, Yu Z, Ji J. Using machine learning to identify gene interaction networks associated with breast cancer. BMC Cancer 2022; 22:1070. [PMID: 36253742 PMCID: PMC9575346 DOI: 10.1186/s12885-022-10170-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 10/10/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Breast cancer (BC) is one of the most prevalent cancers worldwide but its etiology remains unclear. Obesity is recognized as a risk factor for BC, and many obesity-related genes may be involved in its occurrence and development. Research assessing the complex genetic mechanisms of BC should not only consider the effect of a single gene on the disease, but also focus on the interaction between genes. This study sought to construct a gene interaction network to identify potential pathogenic BC genes. METHODS The study included 953 BC patients and 963 control individuals. Chi-square analysis was used to assess the correlation between demographic characteristics and BC. The joint density-based non-parametric differential interaction network analysis and classification (JDINAC) was used to build a BC gene interaction network using single nucleotide polymorphisms (SNP). The odds ratio (OR) and 95% confidence interval (95% CI) of hub gene SNPs were evaluated using a logistic regression model. To assess reliability, the hub genes were quantified by edgeR program using BC RNA-seq data from The Cancer Genome Atlas (TCGA) and identical edges were verified by logistic regression using UK Biobank datasets. Go and KEGG enrichment analysis were used to explore the biological functions of interactive genes. RESULTS Body mass index (BMI) and menopause are important risk factors for BC. After adjusting for potential confounding factors, the BC gene interaction network was identified using JDINAC. LEP, LEPR, XRCC6, and RETN were identified as hub genes and both hub genes and edges were verified. LEPR genetic polymorphisms (rs1137101 and rs4655555) were also significantly associated with BC. Enrichment analysis showed that the identified genes were mainly involved in energy regulation and fat-related signaling pathways. CONCLUSION We explored the interaction network of genes derived from SNP data in BC progression. Gene interaction networks provide new insight into the underlying mechanisms of BC.
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Affiliation(s)
- Liyuan Liu
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, 250033, Jinan, China.,School of Mathematics, Shandong University, Jinan, 250100, China
| | - Wenli Zhai
- Institute for Financial Studies, Shandong University, Jinan, 250100, China
| | - Fei Wang
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, 250033, Jinan, China.,Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, 250100, China
| | - Lixiang Yu
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, 250033, Jinan, China.,Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, 250100, China
| | - Fei Zhou
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, 250033, Jinan, China.,Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, 250100, China
| | - Yujuan Xiang
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, 250033, Jinan, China.,Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, 250100, China
| | - Shuya Huang
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, 250033, Jinan, China.,Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, 250100, China
| | - Chao Zheng
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, 250033, Jinan, China.,Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, 250100, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China
| | - Yong He
- Institute for Financial Studies, Shandong University, Jinan, 250100, China
| | - Zhigang Yu
- Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, 250033, Jinan, China. .,Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, 250100, China.
| | - Jiadong Ji
- Institute for Financial Studies, Shandong University, Jinan, 250100, China.
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Yuan Z, Wei Q, Wang J. Long-term changes in cerebral and ocular hemodynamics after carotid endarterectomy in symptomatic patients with unilateral carotid artery stenosis. Eur Rev Med Pharmacol Sci 2022; 26:7541-7549. [PMID: 36314325 DOI: 10.26355/eurrev_202210_30025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
OBJECTIVE The aim of the current study was to describe the alternation pattern of cerebral and ocular blood flow velocities (BFVs) in symptomatic patients with unilateral carotid stenosis after carotid endarterectomy. PATIENTS AND METHODS 20 symptomatic patients underwent carotid endarterectomy for ≥ 50% unilateral carotid stenosis. Cerebral and ocular hemodynamics were evaluated by Transcranial Doppler (TCD) and Color Doppler imaging (CDI), respectively, first preoperatively, then during the following several days after carotid endarterectomy before discharge, and finally two to sixteen months later. RESULTS Statistically significant improvements in the BFVs were recorded in the ipsilateral anterior cerebral artery (ACA), middle cerebral artery (MCV) and short posterior ciliary artery (SPCA) during the following several days after carotid endarterectomy. Preoperative retrograde flows of the ipsilateral ophthalmic artery (OA) in two patients returned to anterograde direction immediately following carotid endarterectomy. At the follow-up of two to sixteen months, the BFVs of the ipsilateral ACA, MCA and SPCA tended to decline and were no longer statistically significant from the preoperative values. CONCLUSIONS Carotid endarterectomy significantly increased the flow velocities of ipsilateral cerebral anterior circulation and OA branching artery in patients with unilateral carotid stenosis early after surgery. At the long-term follow-up, the flow velocities in the ipsilateral hemisphere had the tendency to reduce and approach the preoperative level.
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Affiliation(s)
- Z Yuan
- Department of Vascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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26
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Cheng J, Miao BF, Liu Z, Yang M, He K, Zeng YL, Niu H, Yang X, Wang ZQ, Hong XH, Fu SJ, Sun L, Liu Y, Wu YZ, Yuan Z, Ding HF. Coherent Picture on the Pure Spin Transport between Ag/Bi and Ferromagnets. Phys Rev Lett 2022; 129:097203. [PMID: 36083669 DOI: 10.1103/physrevlett.129.097203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 07/26/2022] [Indexed: 06/15/2023]
Abstract
In a joint effort of both experiments and first-principles calculations, we resolve a hotly debated controversy and provide a coherent picture on the pure spin transport between Ag/Bi and ferromagnets. We demonstrate a strong inverse Rashba-Edelstein effect (IREE) at the interface in between Ag/Bi with a ferromagnetic metal (FM) but not with a ferromagnetic insulator. This is in sharp contrast to the previously claimed IREE at Ag/Bi interface or inverse spin Hall effect dominated spin transport. A more than one order of magnitude modulation of IREE signal is realized for different Ag/Bi-FM interfaces, casting strong tunability and a new direction for searching efficient spintronics materials.
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Affiliation(s)
- J Cheng
- National Laboratory of Solid State Microstructures and Department of Physics, Nanjing University, Nanjing 210093, People's Republic of China
| | - B F Miao
- National Laboratory of Solid State Microstructures and Department of Physics, Nanjing University, Nanjing 210093, People's Republic of China
- Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, People's Republic of China
| | - Z Liu
- Center for Advanced Quantum Studies and Department of Physics, Beijing Normal University, Beijing 100875, People's Republic of China
| | - M Yang
- National Laboratory of Solid State Microstructures and Department of Physics, Nanjing University, Nanjing 210093, People's Republic of China
| | - K He
- National Laboratory of Solid State Microstructures and Department of Physics, Nanjing University, Nanjing 210093, People's Republic of China
| | - Y L Zeng
- National Laboratory of Solid State Microstructures and Department of Physics, Nanjing University, Nanjing 210093, People's Republic of China
| | - H Niu
- National Laboratory of Solid State Microstructures and Department of Physics, Nanjing University, Nanjing 210093, People's Republic of China
| | - X Yang
- National Laboratory of Solid State Microstructures and Department of Physics, Nanjing University, Nanjing 210093, People's Republic of China
| | - Z Q Wang
- National Laboratory of Solid State Microstructures and Department of Physics, Nanjing University, Nanjing 210093, People's Republic of China
| | - X H Hong
- National Laboratory of Solid State Microstructures and Department of Physics, Nanjing University, Nanjing 210093, People's Republic of China
| | - S J Fu
- National Laboratory of Solid State Microstructures and Department of Physics, Nanjing University, Nanjing 210093, People's Republic of China
| | - L Sun
- National Laboratory of Solid State Microstructures and Department of Physics, Nanjing University, Nanjing 210093, People's Republic of China
- Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, People's Republic of China
| | - Y Liu
- Center for Advanced Quantum Studies and Department of Physics, Beijing Normal University, Beijing 100875, People's Republic of China
| | - Y Z Wu
- Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, People's Republic of China
- Department of Physics, Fudan University, 220 Handan Road, Shanghai 200433, People's Republic of China
| | - Z Yuan
- Center for Advanced Quantum Studies and Department of Physics, Beijing Normal University, Beijing 100875, People's Republic of China
| | - H F Ding
- National Laboratory of Solid State Microstructures and Department of Physics, Nanjing University, Nanjing 210093, People's Republic of China
- Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, People's Republic of China
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Jin X, Zhang L, Ji J, Ju T, Zhao J, Yuan Z. Correction: Network regression analysis in transcriptome-wide association studies. BMC Genomics 2022; 23:615. [PMID: 36008752 PMCID: PMC9404561 DOI: 10.1186/s12864-022-08844-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Xiuyuan Jin
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Shandong University, Jinan, 250003, Shandong, China
| | - Liye Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Shandong University, Jinan, 250003, Shandong, China
| | - Jiadong Ji
- Institute for Financial Studies, Shandong University, Jinan, 250100, Shandong, China
| | - Tao Ju
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Shandong University, Jinan, 250003, Shandong, China
| | - Jinghua Zhao
- Department of Public Health and Primary Care, Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK.
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China. .,Institute for Medical Dataology, Shandong University, Jinan, 250003, Shandong, China.
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Jin X, Zhang L, Ji J, Ju T, Zhao J, Yuan Z. Network regression analysis in transcriptome-wide association studies. BMC Genomics 2022; 23:562. [PMID: 35933330 PMCID: PMC9356418 DOI: 10.1186/s12864-022-08809-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 08/02/2022] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Transcriptome-wide association studies (TWASs) have shown great promise in interpreting the findings from genome-wide association studies (GWASs) and exploring the disease mechanisms, by integrating GWAS and eQTL mapping studies. Almost all TWAS methods only focus on one gene at a time, with exception of only two published multiple-gene methods nevertheless failing to account for the inter-dependence as well as the network structure among multiple genes, which may lead to power loss in TWAS analysis as complex disease often owe to multiple genes that interact with each other as a biological network. We therefore developed a Network Regression method in a two-stage TWAS framework (NeRiT) to detect whether a given network is associated with the traits of interest. NeRiT adopts the flexible Bayesian Dirichlet process regression to obtain the gene expression prediction weights in the first stage, uses pointwise mutual information to represent the general between-node correlation in the second stage and can effectively take the network structure among different gene nodes into account. RESULTS Comprehensive and realistic simulations indicated NeRiT had calibrated type I error control for testing both the node effect and edge effect, and yields higher power than the existed methods, especially in testing the edge effect. The results were consistent regardless of the GWAS sample size, the gene expression prediction model in the first step of TWAS, the network structure as well as the correlation pattern among different gene nodes. Real data applications through analyzing systolic blood pressure and diastolic blood pressure from UK Biobank showed that NeRiT can simultaneously identify the trait-related nodes as well as the trait-related edges. CONCLUSIONS NeRiT is a powerful and efficient network regression method in TWAS.
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Affiliation(s)
- Xiuyuan Jin
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Shandong University, Jinan, 250003, Shandong, China
| | - Liye Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Shandong University, Jinan, 250003, Shandong, China
| | - Jiadong Ji
- Institute for Financial Studies, Shandong University, Jinan, 250100, Shandong, China
| | - Tao Ju
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Shandong University, Jinan, 250003, Shandong, China
| | - Jinghua Zhao
- Department of Public Health and Primary Care, Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK.
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China. .,Institute for Medical Dataology, Shandong University, Jinan, 250003, Shandong, China.
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29
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Cheng Y, Han J, Li Q, Shi Y, Zhong F, Wu Y, Wang Z, Yuan Z, Fan X, Zhao J. Metabolic obesity phenotypes: a friend or foe of digestive polyps?-An observational study based on National Inpatient Database. Metabolism 2022; 132:155201. [PMID: 35427603 DOI: 10.1016/j.metabol.2022.155201] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 04/06/2022] [Accepted: 04/07/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Obesity is associated with an increased risk of digestive polyps, whereas all obesity are not created equally. The role of metabolic states in occurrence risks of polyps among individuals with varying degrees of obesity remains unknown. Our study aimed to evaluate the association between metabolic obesity phenotypes and the occurrence of digestive polyps. RESEARCH DESIGN AND METHODS Data from 9,278,949 patients between 2016 and 2018 from the National Inpatient Sample (NIS) database, a nationally representative database of all discharges from US health-care hospitals, were analyzed. According to obesity phenotype, the study population was classified into four groups: metabolically healthy nonobese (MHNO), metabolically unhealthy nonobese (MUNO), metabolically healthy obese (MHO) and metabolically unhealthy obese (MUO). We calculated the incidence rates of various digestive polyps (stomach/duodenum, colon and rectum polyps) among these participants by searching the hospital records for ICD-10 diagnosis codes indicating each gastric, duodenum, colon or rectal polyps. The multiple stepwise regression analysis and further in-depth subgroup analysis were used to determine the associations between metabolic obesity phenotypes and the occurrence of digestive polyps. RESULTS In the total or female population, those with the MUNO and MUO phenotypes had significantly higher prevalence of digestive polyps compared with individuals with the MHNO or MHO phenotypes (all p < 0.05) and a significant difference was not found between MUNO and MUO phenotypes (p > 0.05). Obese subjects seem to be more likely to develop stomach and duodenum polyps or colon polyps than non-obese subjects in metabolically healthy people of males (MHO vs. MHNO, p < 0.05), whereas obesity status seems to have little effect on the occurrence of digestive polyps in metabolically healthy people of females (MHO vs. MHNO, p>0.05). After adjusting for the potential confounders, the MHO, MUNO and MUO phenotypes were all risk factors for stomach and duodenum polyps (OR = 1.46, 95% CI: 1.36-1.58, p< 0.01; OR = 1.19, 95% CI: 1.14-1.25, p< 0.01; OR = 1.44, 95% CI: 1.35-1.55, p< 0.01, respectively) or colon polyps (OR = 1.28, 95% CI: 1.21-1.35, p< 0.01; OR = 1.18, 95% CI: 1.14-1.22, p< 0.01; OR = 1.46, 95% CI: 1.38-1.54, p< 0.01, respectively) compared with the MHNO phenotype,especially in menopausal female. Interestingly, we also observed in further in-depth subgroup analysis that metabolic abnormalities may have a greater impact on the occurrence of digestive polyps than obesity (all p < 0.05). CONCLUSIONS Both metabolic abnormities and obesity were associated with a higher risk of digestive polyps. The effect of metabolism on digestive polyp occurrence may be stronger than that of obesity, highlighting the importance of abnormal metabolic status modification regardless of obesity status. Clinical intervention should not only focus on obesity, but also on metabolic abnormalities to decrease digestive polyp risk.
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Affiliation(s)
- Yiping Cheng
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, China; Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong 250021, China; Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Junming Han
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong 250021, China; Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Qihang Li
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, China; Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong 250021, China; Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Yingzhou Shi
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, China; Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong 250021, China; Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Fang Zhong
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, China; Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong 250021, China; Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Yafei Wu
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong 250021, China; Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Zhixiang Wang
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong 250021, China; Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Shandong University, Jinan, China
| | - Xiude Fan
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong 250021, China; Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China.
| | - Jiajun Zhao
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, China; Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China; Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong 250021, China; Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, Shandong 250021, China; Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, Shandong 250021, China.
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Gong Z, Yuan Z, Niu Y, Zhang X, Geng J, Wei S. CARBONATED BEVERAGES AFFECT LEVELS OF ANDROGEN RECEPTOR AND TESTOSTERONE SECRETION IN MICE. Acta Endocrinol (Buchar) 2022; 18:301-305. [PMID: 36699165 PMCID: PMC9867816 DOI: 10.4183/aeb.2022.301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Objectives This work aimed to study the influences of carbonated beverages (CBs) on the testis growth and the expression levels of androgen receptor (AR) of mice. Methods Two experimental groups of 30 mice each PEP-1 and PEP-2 drank 50% and 100% Pepsi-Cola, respectively for 15 days. Other 2 experimental groups of 30 mice each COC-1 and COC-2 drank 50% and 100% Coca-Cola, respectively for 15 days. The control group (CG) of 30 mice drank water. Bilateral testes were harvested aseptically on days 0, 5, 7, 10, 13 and 15. Real-time PCR and Western blot were implemented to detect levels of androgen receptor (AR) mRNA and protein in testis tissues. Results Testes masses of PEP-2, COC-1 and COC-2 were greater than those of PEP-1 and CG (P < 0.05). On day 15, testis longitudinal diameter (TLD) of CBs-treated mice was increased as compared to CG. TLD, testes transverse diameters (TTD) and AR proteins levels of PEP-2 and COC-2 were increased in comparison with CG (P<0.05). Serum testosterone concentrations of PEP-2 were higher than that of COC-1 and CG (P < 0.05). Levels of AR mRNAs of four CBs-treated mice were increased by 60.18%, 67.26%, 65.93% and 78.76%. Conclusions A high concentration of Coca-Cola and Pepsi-Cola could raise TLD and TDD, enhance testosterone secretion, and increase serum EGF concentrations.
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Affiliation(s)
- Z. Gong
- Northwest Minzu University, Affiliated Hospital, Lanzhou, China
| | - Z. Yuan
- Northwest Minzu University, Life Science and Engineering College, Lanzhou, China
| | - Y. Niu
- Northwest Minzu University, Life Science and Engineering College, Lanzhou, China
| | - X. Zhang
- Northwest Minzu University, Life Science and Engineering College, Lanzhou, China
| | - J. Geng
- Northwest Minzu University, Life Science and Engineering College, Lanzhou, China
| | - S. Wei
- Northwest Minzu University, Life Science and Engineering College, Lanzhou, China
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Guo P, Gong W, Li Y, Liu L, Yan R, Wang Y, Zhang Y, Yuan Z. Pinpointing novel risk loci for Lewy body dementia and the shared genetic etiology with Alzheimer's disease and Parkinson's disease: a large-scale multi-trait association analysis. BMC Med 2022; 20:214. [PMID: 35729600 PMCID: PMC9214990 DOI: 10.1186/s12916-022-02404-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 05/13/2022] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The current genome-wide association study (GWAS) of Lewy body dementia (LBD) suffers from low power due to a limited sample size. In addition, the genetic determinants underlying LBD and the shared genetic etiology with Alzheimer's disease (AD) and Parkinson's disease (PD) remain poorly understood. METHODS Using the largest GWAS summary statistics of LBD to date (2591 cases and 4027 controls), late-onset AD (86,531 cases and 676,386 controls), and PD (33,674 cases and 449,056 controls), we comprehensively investigated the genetic basis of LBD and shared genetic etiology among LBD, AD, and PD. We first conducted genetic correlation analysis using linkage disequilibrium score regression (LDSC), followed by multi-trait analysis of GWAS (MTAG) and association analysis based on SubSETs (ASSET) to identify the trait-specific SNPs. We then performed SNP-level functional annotation to identify significant genomic risk loci paired with Bayesian fine-mapping and colocalization analysis to identify potential causal variants. Parallel gene-level analysis including GCTA-fastBAT and transcriptome-wide association analysis (TWAS) was implemented to explore novel LBD-associated genes, followed by pathway enrichment analysis to understand underlying biological mechanisms. RESULTS Pairwise LDSC analysis found positive genome-wide genetic correlations between LBD and AD (rg = 0.6603, se = 0.2001; P = 0.0010), between LBD and PD (rg = 0.6352, se = 0.1880; P = 0.0007), and between AD and PD (rg = 0.2136, se = 0.0860; P = 0.0130). We identified 13 significant loci for LBD, including 5 previously reported loci (1q22, 2q14.3, 4p16.3, 4q22.1, and 19q13.32) and 8 novel biologically plausible genetic associations (5q12.1, 5q33.3, 6p21.1, 8p23.1, 8p21.1, 16p11.2, 17p12, and 17q21.31), among which APOC1 (19q13.32), SNCA (4q22.1), TMEM175 (4p16.3), CLU (8p21.1), MAPT (17q21.31), and FBXL19 (16p11.2) were also validated by gene-level analysis. Pathway enrichment analysis of 40 common genes identified by GCTA-fastBAT and TWAS implicated significant role of neurofibrillary tangle assembly (GO:1902988, adjusted P = 1.55 × 10-2). CONCLUSIONS Our findings provide novel insights into the genetic determinants of LBD and the shared genetic etiology and biological mechanisms of LBD, AD, and PD, which could benefit the understanding of the co-pathology as well as the potential treatment of these diseases simultaneously.
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Affiliation(s)
- Ping Guo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Weiming Gong
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Yuanming Li
- School of Medicine, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Lu Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Ran Yan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Yanjun Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Yanan Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China. .,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.
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Dai Y, Zhao YW, Ma L, Tang M, Qiu XP, Liu Y, Yuan Z, Zhou SM. Fourfold Anisotropic Magnetoresistance of L1_{0} FePt Due to Relaxation Time Anisotropy. Phys Rev Lett 2022; 128:247202. [PMID: 35776447 DOI: 10.1103/physrevlett.128.247202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 04/06/2022] [Accepted: 05/25/2022] [Indexed: 06/15/2023]
Abstract
Experimental measurements show that the angular dependence of the anisotropic magnetoresistance (AMR) in L1_{0} ordered FePt epitaxial films on the current orientation and magnetization direction is a superposition of the corresponding dependences of twofold and fourfold symmetries. The twofold AMR exhibits a strong dependence on the current orientation, whereas the fourfold term only depends on the magnetization direction in the crystal and is independent of the current orientation. First-principles calculations reveal that the fourfold AMR arises from the relaxation time anisotropy due to the variation of the density of states near the Fermi energy under rotation of the magnetization. This relaxation time anisotropy is a universal property in ferromagnetic metals and determines other anisotropic physical properties that are observable in experiment.
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Affiliation(s)
- Y Dai
- Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology and Pohl Institute of Solid State Physics and School of Physics Science and Engineering, Tongji University, Shanghai 200092, China
| | - Y W Zhao
- Center for Advanced Quantum Studies and Department of Physics, Beijing Normal University, Beijing 100875, China
| | - L Ma
- Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology and Pohl Institute of Solid State Physics and School of Physics Science and Engineering, Tongji University, Shanghai 200092, China
| | - M Tang
- Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology and Pohl Institute of Solid State Physics and School of Physics Science and Engineering, Tongji University, Shanghai 200092, China
| | - X P Qiu
- Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology and Pohl Institute of Solid State Physics and School of Physics Science and Engineering, Tongji University, Shanghai 200092, China
| | - Y Liu
- Center for Advanced Quantum Studies and Department of Physics, Beijing Normal University, Beijing 100875, China
| | - Z Yuan
- Center for Advanced Quantum Studies and Department of Physics, Beijing Normal University, Beijing 100875, China
| | - S M Zhou
- Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology and Pohl Institute of Solid State Physics and School of Physics Science and Engineering, Tongji University, Shanghai 200092, China
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Yu Y, Hou L, Shi X, Sun X, Liu X, Yu Y, Yuan Z, Li H, Xue F. Impact of nonrandom selection mechanisms on the causal effect estimation for two-sample Mendelian randomization methods. PLoS Genet 2022; 18:e1010107. [PMID: 35298462 PMCID: PMC8963545 DOI: 10.1371/journal.pgen.1010107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 03/29/2022] [Accepted: 02/16/2022] [Indexed: 11/18/2022] Open
Abstract
Nonrandom selection in one-sample Mendelian Randomization (MR) results in biased estimates and inflated type I error rates only when the selection effects are sufficiently large. In two-sample MR, the different selection mechanisms in two samples may more seriously affect the causal effect estimation. Firstly, we propose sufficient conditions for causal effect invariance under different selection mechanisms using two-sample MR methods. In the simulation study, we consider 49 possible selection mechanisms in two-sample MR, which depend on genetic variants (G), exposures (X), outcomes (Y) and their combination. We further compare eight pleiotropy-robust methods under different selection mechanisms. Results of simulation reveal that nonrandom selection in sample II has a larger influence on biases and type I error rates than those in sample I. Furthermore, selections depending on X+Y, G+Y, or G+X+Y in sample II lead to larger biases than other selection mechanisms. Notably, when selection depends on Y, bias of causal estimation for non-zero causal effect is larger than that for null causal effect. Especially, the mode based estimate has the largest standard errors among the eight methods. In the absence of pleiotropy, selections depending on Y or G in sample II show nearly unbiased causal effect estimations when the casual effect is null. In the scenarios of balanced pleiotropy, all eight MR methods, especially MR-Egger, demonstrate large biases because the nonrandom selections result in the violation of the Instrument Strength Independent of Direct Effect (InSIDE) assumption. When directional pleiotropy exists, nonrandom selections have a severe impact on the eight MR methods. Application demonstrates that the nonrandom selection in sample II (coronary heart disease patients) can magnify the causal effect estimation of obesity on HbA1c levels. In conclusion, nonrandom selection in two-sample MR exacerbates the bias of causal effect estimation for pleiotropy-robust MR methods. It is well known that nonrandom selection in one-sample Mendelian Randomization (MR) can result in biased estimates and inflated type I error rates. Actually, two-sample MR analyses are more prone to be affected by nonrandom selection than one-sample MR analyses, because two samples for genome-wide association studies (GWAS) may be selected each under different mechanisms from the source population. Summary-level genetic association statistics in two-sample MR may be derived from different study designs such as case-control, case-only and cohort studies, which further inevitably affect the causal effect estimation of exposure on outcome. In this study, we firstly propose a theorem for causal effect invariance under different selection mechanisms. In the simulation, we design 49 combinations of nonrandom selection mechanisms in sample I and sample II, which are widespread in practical applications. The simulation results reveal that the selection mechanisms in sample II have a larger influence on biases and type I error rates than those in sample I. As an illustrative example, we find the nonrandom selection in sample II (coronary heart disease patients) can magnify the causal effect estimation of obesity on the HbA1c levels.
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Affiliation(s)
- Yuanyuan Yu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
| | - Lei Hou
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
| | - Xu Shi
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Xiaoru Sun
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
| | - Xinhui Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
| | - Yifan Yu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
| | - Hongkai Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- * E-mail: (HL); (FX)
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, People’s Republic of China
- * E-mail: (HL); (FX)
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Yuan Z, Liu L, Guo P, Yan R, Xue F, Zhou X. Likelihood-based Mendelian randomization analysis with automated instrument selection and horizontal pleiotropic modeling. Sci Adv 2022; 8:eabl5744. [PMID: 35235357 PMCID: PMC8890724 DOI: 10.1126/sciadv.abl5744] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 01/05/2022] [Indexed: 05/03/2023]
Abstract
Mendelian randomization (MR) is a common tool for identifying causal risk factors underlying diseases. Here, we present a method, MR with automated instrument determination (MRAID), for effective MR analysis. MRAID borrows ideas from fine-mapping analysis to model an initial set of candidate single-nucleotide polymorphisms that are in potentially high linkage disequilibrium with each other and automatically selects among them the suitable instruments for causal inference. MRAID also explicitly models both uncorrelated and correlated horizontal pleiotropic effects that are widespread for complex trait analysis. MRAID achieves both tasks through a joint likelihood framework and relies on a scalable sampling-based algorithm to compute calibrated P values. Comprehensive and realistic simulations show that MRAID can provide calibrated type I error control and reduce false positives while being more powerful than existing approaches. We illustrate the benefits of MRAID for an MR screening analysis across 645 trait pairs in U.K. Biobank, identifying multiple lifestyle causal risk factors of cardiovascular disease-related traits.
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Affiliation(s)
- Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Lu Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Ping Guo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Ran Yan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
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Yang F, Zhang Q, Yuan Z, Teng S, Cui L, Xue F, Wei L. Signaling Potential Therapeutic Herbal Medicine Prescription for Treating COVID-19 by Collaborative Filtering. Front Pharmacol 2022; 12:759479. [PMID: 35002701 PMCID: PMC8741270 DOI: 10.3389/fphar.2021.759479] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 11/17/2021] [Indexed: 12/19/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has aggressed in more than 200 countries and territories since Dec 2019, and 30 million cases of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 have been reported, including 950,000 deaths. Supportive treatment remains the mainstay of therapy for COVID-19. There are no small-molecule-specific antiviral drugs available to prevent and treat COVID-19 until recently. Herbal medicine can facilitate syndrome differentiation and treatment according to the clinical manifestations of patients and has demonstrated effectiveness in epidemic prevention and control. The National Health Commission (NHC) of China has recommended "three TCM prescriptions and three medicines," as a group of six effective herbal formulas against COVID-19 in the released official file "Diagnosis and Treatment Protocol for COVID-19 Patients: Herbal Medicine for the Priority Treatment of COVID-19." This study aimed to develop a collaborative filtering approach to signaling drug combinations that are similar to the six herbal formulas as potential therapeutic treatments for treating COVID-19. The results have been evaluated by herbal medicine experts' domain knowledge.
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Affiliation(s)
- Fan Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qi Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Zhongshang Yuan
- Department of Epidemiology and Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Saisai Teng
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Lizhen Cui
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Fuzhong Xue
- Department of Epidemiology and Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.,Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Leyi Wei
- School of Software, Shandong University, Jinan, China.,Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
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Wang Z, Cheng Y, Li Y, Han J, Yuan Z, Li Q, Zhong F, Wu Y, Fan X, Bo T, Gao L. The Relationship Between Obesity and Depression Is Partly Dependent on Metabolic Health Status: A Nationwide Inpatient Sample Database Study. Front Endocrinol (Lausanne) 2022; 13:880230. [PMID: 35692399 PMCID: PMC9174461 DOI: 10.3389/fendo.2022.880230] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 04/04/2022] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE Some studies have demonstrated a bidirectional association between obesity and depression, whereas others have not. This discordance might be due to the metabolic health status. We aimed to determine whether the relationship between obesity and depression is dependent on metabolic health status. METHODS In total, 9,022,089 participants were enrolled and classified as one of four obesity phenotypes: metabolically healthy nonobesity (MHNO), metabolically unhealthy nonobesity (MUNO), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUO). We then divided the population into eight phenotypes based on obesity and the number of metabolic risk factors. Furthermore, the associations of eight phenotypes, based on obesity and specific metabolic risk factors, with depression were assessed. RESULT Among all participants, a higher risk of depression was observed for MUNO, MHO and MUO than for MHNO. The risk was highest for MUO (OR = 1.442; 95% CI = 1.432, 1.451). However, the association between MHO and depression was different for men and women (OR = 0.941, men; OR = 1.132, women). The risk of depression increased as the number of metabolic risk factors increased. Dyslipidemia was the strongest metabolic risk factor. These relationships were consistent among patients ≥ 45 years of age. CONCLUSIONS The increased risk of obesity-related depression appears to partly depend on metabolic health status. The results highlight the importance of a favorable metabolic status, and even nonobese populations should be screened for metabolic disorders.
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Affiliation(s)
- Zhixiang Wang
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, China
- Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, China
| | - Yiping Cheng
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, China
- Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, China
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yuan Li
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, China
- Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, China
| | - Junming Han
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, China
- Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qihang Li
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, China
- Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, China
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Fang Zhong
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, China
- Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, China
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yafei Wu
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, China
- Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, China
| | - Xiude Fan
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, China
- Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, China
- *Correspondence: Xiude Fan, ; Tao Bo, ; Ling Gao,
| | - Tao Bo
- Central Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- *Correspondence: Xiude Fan, ; Tao Bo, ; Ling Gao,
| | - Ling Gao
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, China
- Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, China
- Central Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- *Correspondence: Xiude Fan, ; Tao Bo, ; Ling Gao,
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Liu S, Cao R, Liu L, Lv Y, Qi X, Yuan Z, Fan X, Yu C, Guan Q. Correlation Between Gut Microbiota and Testosterone in Male Patients With Type 2 Diabetes Mellitus. Front Endocrinol (Lausanne) 2022; 13:836485. [PMID: 35399957 PMCID: PMC8990747 DOI: 10.3389/fendo.2022.836485] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 02/18/2022] [Indexed: 11/22/2022] Open
Abstract
OBJECTIVE This study aimed at investigating the association between testosterone levels and gut microbiota in male patients with type 2 diabetes mellitus (T2DM) and providing a new strategy to elucidate the pathological mechanism of testosterone deficiency in T2DM patients. METHODS In an observational study including 46 T2DM male patients, the peripheral venous blood and fecal samples of all subjects were collected. The V3-V4 regions of bacterial 16S rDNA were amplified and sequenced. Alpha and beta diversities were calculated by QIIME software. The possible association between gut microbial community and clinical indicators was assessed using the Spearman correlation coefficient. The association between the relative abundance of bacteria and testosterone levels was discovered using linear regression analysis in R language. RESULTS There was no substantial difference in alpha and beta diversity. Blautia and Lachnospirales were significantly much higher in the testosterone deficiency group. Linear regression analysis showed that the abundance of Firmicutes at the phylum level and Lachnospirales at the order level were negatively correlated with testosterone level. After correcting for C-reactive protein (CRP) and homeostatic model assessment of insulin resistance (HOMA-IR), the relative abundance of Lachnospirales still had a significant negative correlation with testosterone level. Meanwhile, at the genus level, Lachnoclostridium, Blautia, and Bergeyella had a statistically significant negative association with testosterone level, respectively. Blautia was positively associated with FPG and total cholesterol level. Streptococcus was found positively associated with insulin, connecting peptide, and index of homeostatic model assessment of insulin resistance. CONCLUSION T2DM patients with testosterone deficiency have different gut microbiota compositions compared with T2DM patients alone. Low serum testosterone patients tend to have an increased abundance of opportunistic pathogens, which may be related to the occurrence and development of testosterone deficiency.
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Affiliation(s)
- Shuang Liu
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, China
| | - Ruying Cao
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Department of Endocrinology, ChangQing People’s Hospital, Jinan, China
| | - Luna Liu
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, China
| | - Youyuan Lv
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, China
| | - Xiangyu Qi
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xiude Fan
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, China
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Chunxiao Yu
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, China
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- *Correspondence: Chunxiao Yu, ; Qingbo Guan,
| | - Qingbo Guan
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Shandong Prevention and Control Engineering Laboratory of Endocrine and Metabolic Diseases, Jinan, China
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- *Correspondence: Chunxiao Yu, ; Qingbo Guan,
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Wei T, Peng SY, Li XY, Yuan Z, Lin Q. Upper Limb Lymphedema Impacts the Risk of Peripherally Inserted Central Catheter-Related Thrombosis in Patients with Breast Cancer. Lymphology 2022; 55:178-187. [PMID: 37553006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/10/2023]
Abstract
There is little information on the risk for catheter-related thrombosis in patients with upper limb lymphedema following breast cancer treatment. We investigated the association between upper limb lymphedema and the risk of peripherally inserted central catheterrelated thrombosis (PICC-RT) occurring in the contralateral limb of patients with breast cancer. A retrospective review analyzed all patients with breast cancer who underwent PICC insertion at a cancer hospital in Hunan Province from 2015 to 2019. Upper limb lymphedema was indexed from hospital information system (HIS) before the occurrence of PICC-RT developed in the contralateral limb. Cox regression analysis was used to evaluate the association of factors with outcome. A total of 1,262 patient records were found and 50 cases of PICC-RT were identified. Forty of these occurred in patients without lymphedema (n=1,236) and 10 in patients with upper limb lymphedema (n=26). After adjustment for various co-variables, Cox regression analysis showed that upper limb lymphedema was significantly associated with increased risk of PICC-RT (hazard ratio=12.128, 95% confidence interval=5.551-26.501; P<0.001). In breast cancer patients, upper limb lymphedema may be an important predictor for PICC-RT in the contralateral limb and information should be provided to patients.
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Affiliation(s)
- T Wei
- Anesthesiology Department, Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan Province, China
| | - S-Y Peng
- The Early Clinical Trial Center, Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan Province, China
| | - X-Y Li
- Department of Nursing, Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan Province, China
| | - Z Yuan
- Vascular Access Clinic, Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan Province, China
| | - Q Lin
- Vascular Access Clinic, Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan Province, China
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Zhang Y, Zhao M, Guo P, Wang Y, Liu L, Zhao J, Gao L, Yuan Z, Xue F, Zhao J. Mendelian randomisation highlights hypothyroidism as a causal determinant of idiopathic pulmonary fibrosis. EBioMedicine 2021; 73:103669. [PMID: 34749302 PMCID: PMC8586742 DOI: 10.1016/j.ebiom.2021.103669] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 10/05/2021] [Accepted: 10/20/2021] [Indexed: 11/29/2022] Open
Abstract
Background Although the association between hypothyroidism and idiopathic pulmonary fibrosis (IPF) is found in observational studies, it remains uncertain whether hypothyroidism causally influences IPF. Methods Two-sample Mendelian randomisation (MR) was conducted with hypothyroidism genome-wide association study (GWAS) data in the UK Biobank from 289,307 individuals (18,740 cases and 270,567 controls) and the largest GWAS summary statistics of IPF from 11,259 individuals (2,668 cases and 8,591 controls). Findings were verified using an independent validation dataset, as well as through different MR methods with different model assumptions. A multivariable MR based on Bayesian model averaging was further performed to evaluate whether hypothyroidism, even given several other comorbidities of IPF, remained to be the true causal one of IPF. Findings A positive causal effect of hypothyroidism on IPF was revealed (MR inverse-variance weighted [MR-IVW], odds ratio [OR]=1.125, 95% confidence interval [CI] 1.028-1.231; P=0.011), which was further verified in an independent validation set (MR-IVW, OR=1.229, 95% CI 1.054-1.432; P=0.008). The results were consistent from a variety of MR methods. Bidirectional analyses also indicated no reverse causation. Multivariable MR analysis showed hypothyroidism had the strongest marginal evidence (marginal inclusion probability=0.397, false discovery rate=0.025) compared with other comorbidities of IPF. Interpretation Our results illustrate the significant causal effect of hypothyroidism on IPF, which holds even given several other comorbidities of IPF. These findings may have an important insight into pathogenesis and possible future therapies of IPF. Funding National Natural Science Foundation of China, the Natural Science Foundation of Shandong Province and the Young Scholars Program of Shandong University.
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Affiliation(s)
- Yanan Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China; Institute for Medical Dataology, Shandong University, Jinan, Shandong, 250003, China
| | - Meng Zhao
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China; Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, China
| | - Ping Guo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China; Institute for Medical Dataology, Shandong University, Jinan, Shandong, 250003, China
| | - Yanjun Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China; Institute for Medical Dataology, Shandong University, Jinan, Shandong, 250003, China
| | - Lu Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China; Institute for Medical Dataology, Shandong University, Jinan, Shandong, 250003, China
| | - Jinghua Zhao
- Cardiovasucular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Ling Gao
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China; Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China; Institute for Medical Dataology, Shandong University, Jinan, Shandong, 250003, China.
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China; Institute for Medical Dataology, Shandong University, Jinan, Shandong, 250003, China.
| | - Jiajun Zhao
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China; Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, China.
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Wang Y, Guo P, Zhang Y, Liu L, Yan R, Yuan Z, Song Y. Joint Analysis of Genetic Correlation, Mendelian Randomization and Colocalization Highlights the Bi-Directional Causal Association Between Hypothyroidism and Primary Biliary Cirrhosis. Front Genet 2021; 12:753352. [PMID: 34671386 PMCID: PMC8521021 DOI: 10.3389/fgene.2021.753352] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 09/20/2021] [Indexed: 11/23/2022] Open
Abstract
Background: Hypothyroidism and primary biliary cirrhosis (PBC) are often co-existed in observational epidemiological studies. However, the causal relationship between them remains unclear. Methods: Genetic correlation, Mendelian randomization (MR) and colocalization analysis were combined to assess the potential causal association between hypothyroidism and PBC by using summary statistics from large-scale genome-wide association studies. Various sensitivity analyses had been conducted to assess the robustness and the consistency of the findings. Results: The linkage disequilibrium score regression demonstrated significant evidence of shared genetic architecture between hypothyroidism and PBC, with the genetic correlation estimated to be 0.117 (p = 0.006). The OR of hypothyroidism on PBC was 1.223 (95% CI, 1.072–1.396; p = 2.76 × 10−3) in MR analysis with inverse variance weighted (IVW) method. More importantly, the results from other 7MR methods with different model assumptions, were almost identical with that of IVW, suggesting the findings were robust and convincing. On the other hand, PBC was also causally associated with hypothyroidism (OR, 1.049; 95% CI, 1.010–1.089; p = 0.012), and, again, similar results can also be obtained from other MR methods. Various sensitivity analyses regarding the outlier detection and leave-one-out analysis were also performed. Besides, colocalization analysis suggested that there existed shared causal variants between hypothyroidism and PBC, further highlighting the robustness of the results. Conclusion: Our results suggest evidence for the bi-directional causal association between hypothyroidism and PBC, which may provide insights into the etiology of hypothyroidism and PBC as well as inform prevention and intervention strategies directed toward both diseases.
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Affiliation(s)
- Yanjun Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ping Guo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yanan Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Lu Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ran Yan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yongfeng Song
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China.,Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, China.,Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
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Yu X, Yuan Z, Lu H, Gao Y, Chen H, Shao Z, Yang J, Guan F, Huang S, Zeng P. Relationship between birth weight and chronic kidney disease: evidence from systematics review and two-sample Mendelian randomization analysis. Hum Mol Genet 2021; 29:2261-2274. [PMID: 32329512 DOI: 10.1093/hmg/ddaa074] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 04/14/2020] [Accepted: 04/18/2020] [Indexed: 12/13/2022] Open
Abstract
Observational studies showed an inverse association between birth weight and chronic kidney disease (CKD) in adulthood existed. However, whether such an association is causal remains fully elusive. Moreover, none of prior studies distinguished the direct fetal effect from the indirect maternal effect. Herein, we aimed to investigate the causal relationship between birth weight and CKD and to understand the relative fetal and maternal contributions. Meta-analysis (n = ~22 million) showed that low birth weight led to ~83% (95% confidence interval [CI] 37-146%) higher risk of CKD in late life. With summary statistics from large scale GWASs (n = ~300 000 for birth weight and ~481 000 for CKD), linkage disequilibrium score regression demonstrated birth weight had a negative maternal, but not fetal, genetic correlation with CKD and several other kidney-function related phenotypes. Furthermore, with multiple instruments of birth weight, Mendelian randomization showed there existed a negative fetal casual association (OR = 1.10, 95% CI 1.01-1.16) between birth weight and CKD; a negative but non-significant maternal casual association (OR = 1.09, 95% CI 0.98-1.21) was also identified. Those associations were robust against various sensitivity analyses. However, no maternal/fetal casual effects of birth weight were significant for other kidney-function related phenotypes. Overall, our study confirmed the inverse association between birth weight and CKD observed in prior studies, and further revealed the shared maternal genetic foundation between low birth weight and CKD, and the direct fetal and indirect maternal causal effects of birth weight may commonly drive this negative relationship.
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Affiliation(s)
- Xinghao Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
| | - Haojie Lu
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Yixin Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Haimiao Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Zhonghe Shao
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Jiaji Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Fengjun Guan
- Department of Pediatrics, Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Shuiping Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Ping Zeng
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
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Li M, Yuan Z, Tang Z. The accuracy of magnetic resonance imaging to measure the depth of invasion in oral tongue cancer: a systematic review and meta-analysis. Int J Oral Maxillofac Surg 2021; 51:431-440. [PMID: 34420832 DOI: 10.1016/j.ijom.2021.07.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 05/11/2021] [Accepted: 07/16/2021] [Indexed: 11/19/2022]
Abstract
The accuracy of magnetic resonance imaging (MRI)-derived depth of invasion (DOI) compared to histopathological DOI is still controversial. A meta-analysis was performed to address this controversy and further investigate the best imaging sequence to measure DOI of tongue squamous cell carcinomas (SCC). A comprehensive literature search of five electronic databases was conducted. Stata/SE was used to establish a continuous variable model to assess the consistency between MRI-derived DOI and histopathological DOI. IBM SPSS Statistics 22.0 was used to evaluate the correlation between MRI-derived DOI and histopathological DOI. The meta-analysis showed that the weighted mean difference (WMD) of DOI measured by MRI had an acceptable overestimation compared with that measured by histopathology (WMD 1.64 mm; P < 0.001). In the subgroup analyses, there was no difference between T1-weighted imaging (T1WI) and histopathological values (WMD 0.77 mm; P = 0.273), while T2-weighted imaging (T2WI) had a major overestimation (WMD 2.09 mm; P < 0.001). The overall inter-class correlation coefficient (ICC) between MRI-derived DOI and histopathological DOI was 0.869 (95% CI 0.837-0.895), and was 0.923 (95% CI 0.894-0.944) in the T1WI subgroup and 0.790 (95% CI 0.718-0.845) in the T2WI subgroup. MRI is an accurate modality for evaluating the DOI in oral tongue SCC, and T1WI showed relatively higher validity than T2WI for DOI measurements.
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Affiliation(s)
- M Li
- Hunan Key Laboratory of Oral Health Research, Hunan 3D Printing Engineering Research Centre of Oral Care, Academician Workstation for Oral-Maxillofacial and Regenerative Medicine, Hunan Clinical Research Centre of Oral Major Diseases and Oral Health, Xiangya Stomatological Hospital and Xiangya School of Stomatology, Central South University, Changsha, Hunan, China
| | - Z Yuan
- Department of Periodontics, Changsha Stomatological Hospital, Changsha, Hunan, China
| | - Z Tang
- Hunan Key Laboratory of Oral Health Research, Hunan 3D Printing Engineering Research Centre of Oral Care, Academician Workstation for Oral-Maxillofacial and Regenerative Medicine, Hunan Clinical Research Centre of Oral Major Diseases and Oral Health, Xiangya Stomatological Hospital and Xiangya School of Stomatology, Central South University, Changsha, Hunan, China.
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Zhang DW, Gu GQ, Chen XY, Zha GC, Yuan Z, Wu Y. LINC00665 facilitates the progression of osteosarcoma via sponging miR-3619-5p. Eur Rev Med Pharmacol Sci 2021; 24:9852-9859. [PMID: 33090388 DOI: 10.26355/eurrev_202010_23195] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Long non-coding RNAs (lncRNAs) play vital roles in the pathogenesis and development of multiple cancers, including osteosarcoma (OS). The present study aims to investigate the role of LINC00665 in OS progression. PATIENTS AND METHODS The expression levels of LINC00665 and miR-3619 were assessed by RT-qPCR. The correlation between LINC00665 and miR-3619 expression was evaluated by Pearson's correlation analysis. The interaction between LINC00665 and miR-3619 was predicted by starBase, which was further confirmed by Luciferase reporter assay and RIP assay. The viability, invasion, and migration of OS cells were analyzed by CCK-8 and transwell assays. RESULTS LINC00665 expression was upregulated in OS tissues and cell lines, and the high level of LINC00665 was associated with poor prognosis in OS. Moreover, LINC00665 knockdown attenuated the viability, invasion, and migration of OS cells. In addition, miR-3619 was demonstrated to be a target of LINC00665. Overexpression of miR-3619 inhibited OS progression, while this effect was abolished by the upregulation of LINC00665. CONCLUSIONS We demonstrated that LINC 00665 accelerated OS development by targeting miR-3619. These findings might provide potential treatment strategies for patients with OS.
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Affiliation(s)
- D-W Zhang
- Department of Orthopedics, The Affiliated Shuyang Hospital of Xuzhou Medical University, Jiangsu, China.
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Wang Y, Lin H, Li Q, Guan L, Zhao M, Zhong F, Liu J, Yuan Z, Guo H, Song Y, Gao L, Zhao J. Association between different obesity phenotypes and hypothyroidism: a study based on a longitudinal health management cohort. Endocrine 2021; 72:688-698. [PMID: 33818715 PMCID: PMC8159820 DOI: 10.1007/s12020-021-02677-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 02/27/2021] [Indexed: 01/07/2023]
Abstract
PURPOSE Obese individuals have an increased risk of hypothyroidism. This study investigated the sex-specific association between obesity phenotypes and the development of hypothyroidism. METHODS The study population was derived from a health management cohort in Shandong Provincial Hospital from 2012 to 2016. In total, 9011 baseline euthyroid adults were included and classified into four groups according to obesity phenotype: metabolically healthy nonobese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy nonobese (MUNO), and metabolically unhealthy obese (MUO). The median follow-up time was 1.92 (1.00-2.17) years. Incidence density was evaluated and a generalized estimation equation method was used to investigate the associations between obesity phenotypes and the development of hypothyroidism. RESULTS The incidence densities of hypothyroidism in males with a consistent obesity phenotype were 12.19 (8.62-16.76), 15.87 (11.39-21.56), 14.52 (6.74-27.57), and 19.88 (14.06-27.34) per 1000 person-years in the MHNO, MHO, MUNO, and MUO groups, respectively. After adjusting for confounding factors, compared with the MHNO phenotype, the MHO, MUNO, and MUO phenotypes were independent risk factors for developing hypothyroidism in males. In the subgroup analysis, the MHO and MUO phenotypes were independent risk factors for developing hypothyroidism in males under 55 years, while the MUNO phenotype was an independent risk factor in males over 55 years. The MHO, MUNO, and MUO phenotypes were not independent risk factors for hypothyroidism in females. CONCLUSION Both obesity and metabolic abnormities are associated with a higher risk of hypothyroidism in males. The underlying mechanism of the sex and age differences in this association needs further investigation.
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Affiliation(s)
- Yupeng Wang
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, Shandong, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Jinan, Shandong, China
| | - Haiyan Lin
- Health Management Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Qihang Li
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, Shandong, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Jinan, Shandong, China
| | - Liying Guan
- Health Management Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Meng Zhao
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, Shandong, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Jinan, Shandong, China
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Fang Zhong
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, Shandong, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Jinan, Shandong, China
| | - Jing Liu
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, Shandong, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Jinan, Shandong, China
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Shandong University, Jinan, Shandong, China
| | - Honglin Guo
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, Shandong, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Jinan, Shandong, China
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Yongfeng Song
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, Shandong, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Jinan, Shandong, China
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Ling Gao
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, Shandong, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Jinan, Shandong, China
- Department of Scientific Center, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
| | - Jiajun Zhao
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, Shandong, China.
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Jinan, Shandong, China.
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
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Wang Y, Guo P, Liu L, Zhang Y, Zeng P, Yuan Z. Mendelian Randomization Highlights the Causal Role of Normal Thyroid Function on Blood Lipid Profiles. Endocrinology 2021; 162:6136226. [PMID: 33587120 DOI: 10.1210/endocr/bqab037] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Indexed: 12/13/2022]
Abstract
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.
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Affiliation(s)
- Yanjun Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Ping Guo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Lu Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Yanan Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Ping Zeng
- Department of Epidemiology and Biostatistics, Xuzhou Medical University, Xuzhou 221004, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
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Qian XH, Zheng M, Zheng YQ, He JY, Yao YM, Tao R, Ma L, Li DM, Yuan Z. [Analysis on prediction power of HIV infection risk assessment tool in men who have sex with men in Guizhou province]. Zhonghua Liu Xing Bing Xue Za Zhi 2021; 42:672-676. [PMID: 34814449 DOI: 10.3760/cma.j.cn112338-20200923-01180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Objective: To evaluate the prediction power of HIV infection risk assessment tool and the applicability in MSM in Guizhou province. Methods: MSM were recruited through snowball sampling method. Questionnaire surveys were conducted among the MSM using HIV infection risk assessment tool, and combined with HIV serologic test results, the risk prediction power of HIV infection risk assessment tool was evaluated. Results: A total of 3 379 MSM were recruited from January 2018 to December 2019 in Guizhou. The HIV infection rate was 3.3%(111/3 379). The mean risk scores of HIV positive and HIV negative MSM were (12.15±3.08) and (12.07±3.07), respectively. The difference in risk score was significant between MSM with different HIV status (t=8.69, P<0.001). According to the principle of decision tree, individual risk scores were divided into following three categories: ≤11.96, 11.97-14.80 and >14.80, the HIV infection rate was 0.8%, 4.3% and 8.6% respectively, suggesting that the higher the individual risk score was, the higher the HIV infection rate was (trend χ2=88.18, P<0.001). Multivariate logistic regression analysis showed that the higher the individual risk score was, the higher the risk of HIV infection was. Compared to the total score ≤11.96, the aOR values at total scores of 11.97-14.80 and >14.80 were 6.34 (95%CI: 3.38-11.88) and 14.07(95%CI: 7.44-26.61), respectively. The risk of HIV infection in Miao ethnic group was higher than that in Han ethnic group (aOR=1.83, 95%CI:1.04-3.21), and the risk of HIV infection in those with education level of primary school and below was higher than that in undergraduates or those with education level of junior college and above (aOR=2.50, 95%CI:1.06-5.88), and the risk of HIV infection was higher in those who had bisexual behaviors than in those who had homosexual behaviors (aOR=1.95, 95%CI:1.19-3.19). The risk of HIV infection was higher in those who had never received HIV testing (aOR=1.53, 95%CI:1.01-2.33). The area under the receiver operating characteristic (ROC) curve and area under ROC (AUC) for HIV infection prediction was 0.751 (95%CI:0.710-0.792, P<0.001). The maximum Youden's index was individual risk score of 12.56, and the sensitivity of the risk assessment tool was 0.838, and its specificity was 0.412. Conclusions: The results of HIV infection risk assessment tool in Guizhou indicated that in MSM the higher the individual risk score, the higher the risk of HIV infection is. The tool can be used to evaluate the risk of HIV infection in MSM, but the specificity should be improved.
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Affiliation(s)
- X H Qian
- Guizhou Provincial Center for Disease Control and Prevention, Guiyang 550004, China
| | - M Zheng
- Guizhou Provincial Center for Disease Control and Prevention, Guiyang 550004, China
| | - Y Q Zheng
- Guizhou Provincial Center for Disease Control and Prevention, Guiyang 550004, China
| | - J Y He
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai 200032, China
| | - Y M Yao
- Guizhou Provincial Center for Disease Control and Prevention, Guiyang 550004, China
| | - R Tao
- Guizhou Provincial Center for Disease Control and Prevention, Guiyang 550004, China
| | - L Ma
- Guizhou Provincial Center for Disease Control and Prevention, Guiyang 550004, China
| | - D M Li
- National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Z Yuan
- Guizhou Provincial Center for Disease Control and Prevention, Guiyang 550004, China
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Xu C, Sun J, Zhang W, Yuan Z, Wang J. The safety and efficacy of Cyberknife® for thymic malignancy. Cancer Radiother 2021; 25:119-125. [PMID: 33676829 DOI: 10.1016/j.canrad.2020.06.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 05/29/2020] [Accepted: 06/07/2020] [Indexed: 12/28/2022]
Abstract
PURPOSE To evaluate the safety and efficacy of Cyberknife® (CK) for the treatment of primary or recurring thymic tumours. MATERIALS AND METHODS We retrospectively reviewed 12 patients (16 tumour lesions) with primary or recurring thymic tumours who were treated with CK between March 2008 and October 2017. Their data was stored in prospectively collected database. Kaplan-Meier method was used to calculate survival curves. RESULTS Five patients (41.7%), who had inoperable disease or refused surgery, were treated with CK initially, and 7 patients (58.3%) were treated with CK when they had recurrence diseases. The disease sites treated with CK were primary tumour site (5), regional lymph nodes (4), tumour bed (3), chest wall (2), pleura (1), and bone (1). The median target volume was 43.8 cm3 (range, 13.1-302.5cm3) for the 16 tumour lesions. The median follow-up time was 69.3 months (range, 9.7-124.8 months). The median survival time was 48.2 months, and the 5-year and 10-year OS rates were 68.2% and 45.5%, respectively. A high response rate for the tumour lesions irradiated with CK was obtained. Only one patient (8%) experienced in-field recurrence, and the 5-year local recurrence free survival was 90.9%. A case indicated that CK may induce the abscopal effect, which provides the potential to combine CK and immunotherapy. No severe radiation related toxicities were observed, and no treatment related death occurred. CONCLUSION CK treatment resulted in good outcomes, particularly local control, with minimal side effects, in highly selected patients with primary and recurring thymic tumours. More studies with larger sample are needed.
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Affiliation(s)
- C Xu
- Department of radiation oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - J Sun
- Department of radiation oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - W Zhang
- Department of radiation oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Z Yuan
- Department of radiation oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - J Wang
- Department of radiation oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
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Liu L, Zeng P, Xue F, Yuan Z, Zhou X. Multi-trait transcriptome-wide association studies with probabilistic Mendelian randomization. Am J Hum Genet 2021; 108:240-256. [PMID: 33434493 PMCID: PMC7895847 DOI: 10.1016/j.ajhg.2020.12.006] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 12/07/2020] [Indexed: 12/12/2022] Open
Abstract
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.
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Affiliation(s)
- Lu Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Ping Zeng
- Department of Epidemiology and Biostatistics, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China.
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
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Shen X, Sha W, Yang C, Pan Q, Cohen T, Cheng S, Cai Q, Kan X, Zong P, Zeng Z, Tan S, Liang R, Bai L, Xia J, Wu S, Sun P, Wu G, Cai C, Wang X, Ai K, Liu J, Yuan Z. Continuity of TB services during the COVID-19 pandemic in China. Int J Tuberc Lung Dis 2021; 25:81-83. [PMID: 33384053 DOI: 10.5588/ijtld.20.0632] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- X Shen
- Division of TB and HIV/AIDS Prevention, Shanghai Municipal Center for Disease Control and Prevention, Shanghai
| | - W Sha
- Department of Tuberculosis, Shanghai Pulmonary Hospital, Shanghai Clinical Research Center for infectious disease, Shanghai
| | - C Yang
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, CT, USA
| | - Q Pan
- Division of TB and HIV/AIDS Prevention, Shanghai Municipal Center for Disease Control and Prevention, Shanghai
| | - T Cohen
- Department of Epidemiology of Microbial Diseases, Yale School of Public Health, Yale University, New Haven, CT, USA
| | - S Cheng
- Chinese Center for Diseases Control and Prevention, Beijing
| | - Q Cai
- Division of Tuberculosis, Zhejiang Provincial Integrated Chinese and Western Medicine Hospital, Hangzhou, Zhejiang Province
| | - X Kan
- Department of Scientific Research and Education, Anhui Chest Hospital, Hefei, Anhui Province
| | - P Zong
- Division of Tuberculosis, Jiangxi Chest Hospital, Nanchang, Jiangxi Province
| | - Z Zeng
- Division of Tuberculosis, The Fifth People´s Hospital, Gangzhou, Jiangxi Province
| | - S Tan
- Department of Tuberculosis, Guangzhou Chest Hospital. Guangzhou, Guangdong Province
| | - R Liang
- Department of Tuberculosis, Henan Provincial Chest Hospital, Zhengzhou, Henan Province
| | - L Bai
- Hunan Chest Hospital, Changsha, Hunan Province
| | - J Xia
- South Five Disease Zones, Wuhan Jinyintan Hospital, Wuhan, Hubei Province
| | - S Wu
- Hebei Province Chest Hospital, Shijiazhuang, Hebei Province
| | - P Sun
- Tuberculosis Hospital of Jilin Province, Changchun, Jilin Province
| | - G Wu
- Department of Tuberculosis, Public Health Clinical Center of Chengdu, Chengdu, Sichuan Province
| | - C Cai
- Tuberculosis Diagnosis and Treatment Quality Control Center, Guiyang Public Health Treatment Center, Zunyi Medical University, Zunyi, Guizhou Province
| | - X Wang
- The Fourth People´s Hospital of Ningxia Hui Autonomous Region, Yinchuan, Ningxia Hui Autonomous Region, China
| | - K Ai
- Department of Tuberculosis, Shanghai Pulmonary Hospital, Shanghai Clinical Research Center for infectious disease, Shanghai
| | - J Liu
- Chinese Center for Diseases Control and Prevention, Beijing
| | - Z Yuan
- Division of TB and HIV/AIDS Prevention, Shanghai Municipal Center for Disease Control and Prevention, Shanghai
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Wang Y, Li Q, Yuan Z, Ma S, Shao S, Wu Y, Wang Z, Li Q, Gao L, Zhao M, Zhao J. Statin Use and Benefits of Thyroid Function: A Retrospective Cohort Study. Front Endocrinol (Lausanne) 2021; 12:578909. [PMID: 33737906 PMCID: PMC7962670 DOI: 10.3389/fendo.2021.578909] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 01/07/2021] [Indexed: 12/15/2022] Open
Abstract
PURPOSE Previous studies have suggested that cholesterol may influence thyroid function. Since statins are widely used for their cholesterol-lowering effect, we aimed to assess the association between statin use and thyroid function, and also to explore the role of the cholesterol-lowering effect in it. METHODS We performed a retrospective cohort study derived from REACTION study. Eligible subjects receiving statin therapy were included in the statin group, and sex-, age-, total cholesterol (TC)-, and thyroid function-matched participants without lipid-lowering therapy were included in the control group. The median follow-up time was three years. Outcomes of thyroid function were evaluated at the end of follow-up. We used multivariable regression models to assess the association between statin use and outcomes of thyroid function, and also performed mediation analyses to explore the role of cholesterol in it. RESULTS A total of 5,146 participants were screened, and 201 eligible subjects in the statin group and 201 well-matched subjects in the control group were analyzed. At the end of follow-up, TC and thyroid-stimulating hormone (TSH) levels in the statin group were lower than those in the control group (both p < 0.05), and the percentage of euthyroid subjects was higher in the statin group (88.06% vs. 76.12%, p = 0.002). The incidence rate of subclinical hypothyroidism (SCH) in euthyroid subjects was lower in the statin group (6.29% vs. 14.86%, p = 0.009), and the remission rate among subjects with SCH was higher in the statin group (50.00% vs. 15.38%, p = 0.008). In multivariable regression analyses, statin use was independently associated with lower TSH levels and higher odds to be euthyroid (OR 2.335, p = 0.004) at the end of follow-up. Mediation analyses showed the association between statin use and TSH levels were mediated by TC changes during follow-up. CONCLUSION Statin use was associated with benefits of thyroid function, and TC changes serve as a mediator of the association between statin use and TSH levels. Further studies are needed to clarify the possible underlying mechanism.
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Affiliation(s)
- Yupeng Wang
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
| | - Qihang Li
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Shandong University, Jinan, China
| | - Shizhan Ma
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
- Department of Endocrinology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China
| | - Shanshan Shao
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
- Department of Endocrinology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China
| | - Yafei Wu
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
- Department of Endocrinology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China
| | - Zhixiang Wang
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
- Department of Endocrinology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China
| | - Qiu Li
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
- Department of Endocrinology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China
| | - Ling Gao
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
- Department of Scientific Center, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China
| | - Meng Zhao
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
- Department of Endocrinology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China
- *Correspondence: Meng Zhao, ; Jiajun Zhao,
| | - Jiajun Zhao
- Department of Endocrinology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Shandong Provincial Key Laboratory of Endocrinology and Lipid Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
- Shandong Clinical Medical Center of Endocrinology and Metabolism, Institute of Endocrinology and Metabolism, Shandong Academy of Clinical Medicine, Jinan, China
- Department of Endocrinology, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan, China
- *Correspondence: Meng Zhao, ; Jiajun Zhao,
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