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Zhao Y, Ansarullah, Kumar P, Mahoney JM, He H, Baker C, George J, Li S. Causal network perturbation analysis identifies known and novel type-2 diabetes driver genes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.22.595431. [PMID: 38826370 PMCID: PMC11142180 DOI: 10.1101/2024.05.22.595431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The molecular pathogenesis of diabetes is multifactorial, involving genetic predisposition and environmental factors that are not yet fully understood. However, pancreatic β-cell failure remains among the primary reasons underlying the progression of type-2 diabetes (T2D) making targeting β-cell dysfunction an attractive pathway for diabetes treatment. To identify genetic contributors to β-cell dysfunction, we investigated single-cell gene expression changes in β-cells from healthy (C57BL/6J) and diabetic (NZO/HlLtJ) mice fed with normal or high-fat, high-sugar diet (HFHS). Our study presents an innovative integration of the causal network perturbation assessment (ssNPA) framework with meta-cell transcriptome analysis to explore the genetic underpinnings of type-2 diabetes (T2D). By generating a reference causal network and in silico perturbation, we identified novel genes implicated in T2D and validated our candidates using the Knockout Mouse Phenotyping (KOMP) Project database.
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Affiliation(s)
- Yue Zhao
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Ansarullah
- Center for Biometric Analysis, The Jackson Laboratory, Bar Harbor, ME, USA
| | - Parveen Kumar
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | | | - Hao He
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Candice Baker
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Joshy George
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Sheng Li
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Department of Genetics and Genome Sciences, University of Connecticut School of Medicine, Farmington, CT, USA
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Wang Y, Zhang J, Li H, Kong W, Zheng J, Li Y, Wei Q, Li Q, Yang L, Xu Y, Li L, Wang H, Sun H, Xia W, Liu G, Zhong X, Qiu K, Wang H, Liu H, Song X, Xiong S, Liu Y, Cui Z, Chen L, Zeng T. Prognostic Value of Leucocyte to High-Density Lipoprotein-Cholesterol Ratios in COVID-19 Patients and the Diabetes Subgroup. Front Endocrinol (Lausanne) 2021; 12:727419. [PMID: 34589058 PMCID: PMC8473871 DOI: 10.3389/fendo.2021.727419] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 08/30/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Blood parameters, such as neutrophil-to-lymphocyte ratio, have been identified as reliable inflammatory markers with diagnostic and predictive value for the coronavirus disease 2019 (COVID-19). However, novel hematological parameters derived from high-density lipoprotein-cholesterol (HDL-C) have rarely been studied as indicators for the risk of poor outcomes in patients with severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infection. Here, we aimed to assess the prognostic value of these novel biomarkers in COVID-19 patients and the diabetes subgroup. METHODS We conducted a multicenter retrospective cohort study involving all hospitalized patients with COVID-19 from January to March 2020 in five hospitals in Wuhan, China. Demographics, clinical and laboratory findings, and outcomes were recorded. Neutrophil to HDL-C ratio (NHR), monocyte to HDL-C ratio (MHR), lymphocyte to HDL-C ratio (LHR), and platelet to HDL-C ratio (PHR) were investigated and compared in both the overall population and the subgroup with diabetes. The associations between blood parameters at admission with primary composite end-point events (including mechanical ventilation, admission to the intensive care unit, or death) were analyzed using Cox proportional hazards regression models. Receiver operating characteristic curves were used to compare the utility of different blood parameters. RESULTS Of 440 patients with COVID-19, 67 (15.2%) were critically ill. On admission, HDL-C concentration was decreased while NHR was high in patients with critical compared with non-critical COVID-19, and were independently associated with poor outcome as continuous variables in the overall population (HR: 0.213, 95% CI 0.090-0.507; HR: 1.066, 95% CI 1.030-1.103, respectively) after adjusting for confounding factors. Additionally, when HDL-C and NHR were examined as categorical variables, the HRs and 95% CIs for tertile 3 vs. tertile 1 were 0.280 (0.128-0.612) and 4.458 (1.817-10.938), respectively. Similar results were observed in the diabetes subgroup. ROC curves showed that the NHR had good performance in predicting worse outcomes. The cutoff point of the NHR was 5.50. However, the data in our present study could not confirm the possible predictive effect of LHR, MHR, and PHR on COVID-19 severity. CONCLUSION Lower HDL-C concentrations and higher NHR at admission were observed in patients with critical COVID-19 than in those with noncritical COVID-19, and were significantly associated with a poor prognosis in COVID-19 patients as well as in the diabetes subgroup.
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Affiliation(s)
- Yuxiu Wang
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Jiaoyue Zhang
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Huiqing Li
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Wen Kong
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Juan Zheng
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Yan Li
- Department of Endocrinology, Wuhan Wuchang Hospital, Wuchang Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Qi Wei
- Department of Endocrinology, Red Cross Hospital of Wuhan City, Wuhan, China
| | - Qin Li
- Department of Endocrinology, General Hospital of the Yangtze River Shipping, Wuhan, China
| | - Li Yang
- Department of Endocrinology, Hankou Hospital of Wuhan City, Wuhan, China
| | - Ying Xu
- Department of Endocrinology, The Fifth Hospital of Wuhan, Wuhan, China
| | - Li Li
- Department of Endocrinology, Wuhan Wuchang Hospital, Wuchang Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Hanyu Wang
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Hui Sun
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Wenfang Xia
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Geng Liu
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Xueyu Zhong
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Kangli Qiu
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Han Wang
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Hua Liu
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Xiaoli Song
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Si Xiong
- Department of Endocrinology, The Fifth Hospital of Wuhan, Wuhan, China
| | - Yumei Liu
- Department of Endocrinology, Hankou Hospital of Wuhan City, Wuhan, China
| | - Zhenhai Cui
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Lulu Chen
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
- *Correspondence: Tianshu Zeng, ; Lulu Chen,
| | - Tianshu Zeng
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
- *Correspondence: Tianshu Zeng, ; Lulu Chen,
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Cai Y, Shi S, Yang F, Yi B, Chen X, Li J, Wen Z. Fasting blood glucose level is a predictor of mortality in patients with COVID-19 independent of diabetes history. Diabetes Res Clin Pract 2020; 169:108437. [PMID: 32920103 PMCID: PMC7482587 DOI: 10.1016/j.diabres.2020.108437] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 08/31/2020] [Accepted: 09/08/2020] [Indexed: 12/31/2022]
Abstract
AIM No study elucidated the role of fasting blood glucose (FBG) level in the prognosis
of coronavirus disease 2019 (COVID-19). METHODS This cohort study was conducted in a single center at Renmin Hospital of Wuhan University, Wuhan, China. Clinical laboratory, and treatment data of inpatients with laboratory-confirmed COVID-19 were collected and analyzed. Outcomes of patients with and without pre-existing diabetes were compared. The associations of diabetes history and/or FBG levels with mortality were analyzed. Multivariate cox regression analysis on the risk factors associated with mortality in patients with COVID-19 was performed. RESULTS A total of 941 hospitalized patients with COVID-19 were enrolled in the study. There was a positive relationship between pre-existing diabetes and the mortality of patients who developed COVID-19 (21 of 123 [17.1%] vs 76 of 818 [9.3%]; P = 0.012). FBG ≥7.0 mmol/L was an independent risk factor for the mortality of COVID-19 regardless of the presence or not of a history of diabetes (hazard ratio, 2.20 [95% CI, 1.21-4.03]; P = 0.010). CONCLUSIONS We firstly showed FBG ≥7.0 mmol/L predicted worse outcome in hospitalized patients with COVID-19 independent of diabetes history. Our findings indicated screening FBG level is an effective method to evaluate the prognosis of patients with COVID-19.
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Affiliation(s)
- Yuli Cai
- Department of Endocrinology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Shaobo Shi
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China.
| | - Fan Yang
- Department of Infectious Diseases, Renmin Hospital of Wuhan University, Wuhan, China
| | - Bo Yi
- Department of Endocrinology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiaolin Chen
- Department of Endocrinology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Junfeng Li
- Department of Endocrinology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhongyuan Wen
- Department of Endocrinology, Renmin Hospital of Wuhan University, Wuhan, China.
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