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Schlesinger S, Lang A, Christodoulou N, Linnerz P, Pafili K, Kuss O, Herder C, Neuenschwander M, Barbaresko J, Roden M. Risk phenotypes of diabetes and association with COVID-19 severity and death: an update of a living systematic review and meta-analysis. Diabetologia 2023; 66:1395-1412. [PMID: 37204441 PMCID: PMC10198038 DOI: 10.1007/s00125-023-05928-1] [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: 04/27/2022] [Accepted: 03/16/2023] [Indexed: 05/20/2023]
Abstract
AIMS/HYPOTHESIS To provide a systematic overview of the current body of evidence on high-risk phenotypes of diabetes associated with COVID-19 severity and death. METHODS This is the first update of our recently published living systematic review and meta-analysis. Observational studies investigating phenotypes in individuals with diabetes and confirmed SARS-CoV-2 infection with regard to COVID-19-related death and severity were included. The literature search was conducted from inception up to 14 February 2022 in PubMed, Epistemonikos, Web of Science and the COVID-19 Research Database and updated using PubMed alert to 1 December 2022. A random-effects meta-analysis was used to calculate summary relative risks (SRRs) with 95% CIs. The risk of bias was evaluated using the Quality in Prognosis Studies (QUIPS) tool and the certainty of evidence using the GRADE approach. RESULTS A total of 169 articles (147 new studies) based on approximately 900,000 individuals were included. We conducted 177 meta-analyses (83 on COVID-19-related death and 94 on COVID-19 severity). Certainty of evidence was strengthened for associations between male sex, older age, blood glucose level at admission, chronic insulin use, chronic metformin use (inversely) and pre-existing comorbidities (CVD, chronic kidney disease, chronic obstructive pulmonary disease) and COVID-19-related death. New evidence with moderate to high certainty emerged for the association between obesity (SRR [95% CI] 1.18 [1.04, 1.34], n=21 studies), HbA1c (53-75 mmol/mol [7-9%]: 1.18 [1.06, 1.32], n=8), chronic glucagon-like peptide-1 receptor agonist use (0.83 [0.71, 0.97], n=9), pre-existing heart failure (1.33 [1.21, 1.47], n=14), pre-existing liver disease (1.40 [1.17, 1.67], n=6), the Charlson index (per 1 unit increase: 1.33 [1.13, 1.57], n=2), high levels of C-reactive protein (per 5 mg/l increase: 1.07 [1.02, 1.12], n=10), aspartate aminotransferase level (per 5 U/l increase: 1.28 [1.06, 1.54], n=5), eGFR (per 10 ml/min per 1.73 m2 increase: 0.80 [0.71, 0.90], n=6), lactate dehydrogenase level (per 10 U/l increase: 1.03 [1.01, 1.04], n=7) and lymphocyte count (per 1×109/l increase: 0.59 [0.40, 0.86], n=6) and COVID-19-related death. Similar associations were observed between risk phenotypes of diabetes and severity of COVID-19, with some new evidence on existing COVID-19 vaccination status (0.32 [0.26, 0.38], n=3), pre-existing hypertension (1.23 [1.14, 1.33], n=49), neuropathy and cancer, and high IL-6 levels. A limitation of this study is that the included studies are observational in nature and residual or unmeasured confounding cannot be ruled out. CONCLUSIONS/INTERPRETATION Individuals with a more severe course of diabetes and pre-existing comorbidities had a poorer prognosis of COVID-19 than individuals with a milder course of the disease. REGISTRATION PROSPERO registration no. CRD42020193692. PREVIOUS VERSION This is a living systematic review and meta-analysis. The previous version can be found at https://link.springer.com/article/10.1007/s00125-021-05458-8 FUNDING: The German Diabetes Center (DDZ) is funded by the German Federal Ministry of Health and the Ministry of Culture and Science of the State North Rhine-Westphalia. This study was supported in part by a grant from the German Federal Ministry of Education and Research to the German Center for Diabetes Research (DZD).
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Affiliation(s)
- Sabrina Schlesinger
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany.
| | - Alexander Lang
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Nikoletta Christodoulou
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Philipp Linnerz
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kalliopi Pafili
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Oliver Kuss
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
- Centre for Health and Society, Faculty of Medicine, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Christian Herder
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Manuela Neuenschwander
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
| | - Janett Barbaresko
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Michael Roden
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Uchihara M, Bouchi R, Kodani N, Saito S, Miyazato Y, Umamoto K, Sugimoto H, Kobayashi M, Hikida S, Akiyama Y, Ihana‐Sugiyama N, Osugi M, Tanabe A, Ueki K, Takasaki J, Hojo M, Kajio H. Impact of newly diagnosed diabetes on COVID‐19 severity and hyperglycemia. J Diabetes Investig 2022; 13:1086-1093. [PMID: 35075818 PMCID: PMC9153833 DOI: 10.1111/jdi.13754] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/11/2022] [Accepted: 01/19/2022] [Indexed: 01/08/2023] Open
Abstract
Aims/Introduction Materials and Methods Results Conclusions
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Affiliation(s)
- Masaki Uchihara
- Department of Diabetes, Endocrinology and Metabolism National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
| | - Ryotaro Bouchi
- Department of Diabetes, Endocrinology and Metabolism National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
- Diabetes and Metabolism Information Center Research Institute National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
| | - Noriko Kodani
- Department of Diabetes, Endocrinology and Metabolism National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
| | - Sho Saito
- Disease Control and Prevention Center National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
| | - Yusuke Miyazato
- Disease Control and Prevention Center National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
| | - Kotaro Umamoto
- Department of Diabetes, Endocrinology and Metabolism National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
| | - Hirofumi Sugimoto
- Department of Diabetes, Endocrinology and Metabolism National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
| | - Michi Kobayashi
- Department of Diabetes, Endocrinology and Metabolism National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
| | - Sayaka Hikida
- Disease Control and Prevention Center National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
| | - Yutaro Akiyama
- Disease Control and Prevention Center National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
| | - Noriko Ihana‐Sugiyama
- Department of Diabetes, Endocrinology and Metabolism National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
- Diabetes and Metabolism Information Center Research Institute National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
| | - Mitsuru Osugi
- Department of Diabetes, Endocrinology and Metabolism National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
- Diabetes and Metabolism Information Center Research Institute National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
| | - Akiyo Tanabe
- Department of Diabetes, Endocrinology and Metabolism National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
| | - Kojiro Ueki
- Department of Diabetes, Endocrinology and Metabolism National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
- Department of Molecular Diabetic Medicine Diabetes Research Center Research Institute National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
| | - Jin Takasaki
- Disease Control and Prevention Center National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
- Department of Respiratory Medicine National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
| | - Masayuki Hojo
- Department of Respiratory Medicine National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
| | - Hiroshi Kajio
- Department of Diabetes, Endocrinology and Metabolism National Center for Global Health and Medicine Toyama 1‐21‐1, Shinjuku‐ku Tokyo 162‐8655 Japan
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Wang W, Chai Z, Cooper ME, Zimmet PZ, Guo H, Ding J, Yang F, Chen X, Lin X, Zhang K, Zhong Q, Li Z, Zhang P, Wu Z, Guan X, Zhang L, He K. High Fasting Blood Glucose Level With Unknown Prior History of Diabetes Is Associated With High Risk of Severe Adverse COVID-19 Outcome. Front Endocrinol (Lausanne) 2021; 12:791476. [PMID: 34956098 PMCID: PMC8692378 DOI: 10.3389/fendo.2021.791476] [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] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 11/15/2021] [Indexed: 01/08/2023] Open
Abstract
Background We aimed to understand how glycaemic levels among COVID-19 patients impact their disease progression and clinical complications. Methods We enrolled 2,366 COVID-19 patients from Huoshenshan hospital in Wuhan. We stratified the COVID-19 patients into four subgroups by current fasting blood glucose (FBG) levels and their awareness of prior diabetic status, including patients with FBG<6.1mmol/L with no history of diabetes (group 1), patients with FBG<6.1mmol/L with a history of diabetes diagnosed (group 2), patients with FBG≥6.1mmol/L with no history of diabetes (group 3) and patients with FBG≥6.1mmol/L with a history of diabetes diagnosed (group 4). A multivariate cause-specific Cox proportional hazard model was used to assess the associations between FBG levels or prior diabetic status and clinical adversities in COVID-19 patients. Results COVID-19 patients with higher FBG and unknown diabetes in the past (group 3) are more likely to progress to the severe or critical stage than patients in other groups (severe: 38.46% vs 23.46%-30.70%; critical 7.69% vs 0.61%-3.96%). These patients also have the highest abnormal level of inflammatory parameters, complications, and clinical adversities among all four groups (all p<0.05). On day 21 of hospitalisation, group 3 had a significantly higher risk of ICU admission [14.1% (9.6%-18.6%)] than group 4 [7.0% (3.7%-10.3%)], group 2 [4.0% (0.2%-7.8%)] and group 1 [2.1% (1.4%-2.8%)], (P<0.001). Compared with group 1 who had low FBG, group 3 demonstrated 5 times higher risk of ICU admission events during hospitalisation (HR=5.38, 3.46-8.35, P<0.001), while group 4, where the patients had high FBG and prior diabetes diagnosed, also showed a significantly higher risk (HR=1.99, 1.12-3.52, P=0.019), but to a much lesser extent than in group 3. Conclusion Our study shows that COVID-19 patients with current high FBG levels but unaware of pre-existing diabetes, or possibly new onset diabetes as a result of COVID-19 infection, have a higher risk of more severe adverse outcomes than those aware of prior diagnosis of diabetes and those with low current FBG levels.
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Affiliation(s)
- Wenjun Wang
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Translational Medical Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Medical Artificial Intelligence Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Medical Big Data Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Zhonglin Chai
- Department of Diabetes, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Mark E. Cooper
- Department of Diabetes, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Paul Z. Zimmet
- Department of Diabetes, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - Hua Guo
- Department of Pulmonary and Critical Care Medicine, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Junyu Ding
- Department of Pulmonary and Critical Care Medicine, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Feifei Yang
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Translational Medical Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Medical Artificial Intelligence Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Medical Big Data Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Xu Chen
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Translational Medical Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Medical Artificial Intelligence Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Medical Big Data Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Xixiang Lin
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Translational Medical Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Medical Artificial Intelligence Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Medical Big Data Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Kai Zhang
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Qin Zhong
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Translational Medical Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Medical Artificial Intelligence Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Medical Big Data Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Zongren Li
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Translational Medical Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Medical Artificial Intelligence Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Medical Big Data Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Peifang Zhang
- BioMind Technology, Zhongguancun Medical Engineering Center, Beijing, China
| | - Zhenzhou Wu
- BioMind Technology, Zhongguancun Medical Engineering Center, Beijing, China
| | - Xizhou Guan
- Department of Pulmonary and Critical Care Medicine, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
| | - Lei Zhang
- Department of Diabetes, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, China
- Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Kunlun He
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Translational Medical Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Medical Artificial Intelligence Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Medical Big Data Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
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