Frontiers in Endocrinology (Dec 2021)
High Fasting Blood Glucose Level With Unknown Prior History of Diabetes Is Associated With High Risk of Severe Adverse COVID-19 Outcome
- Wenjun Wang,
- Wenjun Wang,
- Wenjun Wang,
- Wenjun Wang,
- Zhonglin Chai,
- Mark E. Cooper,
- Paul Z. Zimmet,
- Hua Guo,
- Junyu Ding,
- Feifei Yang,
- Feifei Yang,
- Feifei Yang,
- Feifei Yang,
- Xu Chen,
- Xu Chen,
- Xu Chen,
- Xu Chen,
- Xixiang Lin,
- Xixiang Lin,
- Xixiang Lin,
- Xixiang Lin,
- Kai Zhang,
- Qin Zhong,
- Qin Zhong,
- Qin Zhong,
- Qin Zhong,
- Zongren Li,
- Zongren Li,
- Zongren Li,
- Zongren Li,
- Peifang Zhang,
- Zhenzhou Wu,
- Xizhou Guan,
- Lei Zhang,
- Lei Zhang,
- Lei Zhang,
- Lei Zhang,
- Kunlun He,
- Kunlun He,
- Kunlun He,
- Kunlun He
Affiliations
- 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
- Wenjun Wang
- Translational Medical Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Wenjun Wang
- Medical Artificial Intelligence Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Wenjun Wang
- 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
- Feifei Yang
- Translational Medical Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Feifei Yang
- Medical Artificial Intelligence Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Feifei Yang
- 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
- Xu Chen
- Translational Medical Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Xu Chen
- Medical Artificial Intelligence Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Xu Chen
- 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
- Xixiang Lin
- Translational Medical Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Xixiang Lin
- Medical Artificial Intelligence Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Xixiang Lin
- 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
- Qin Zhong
- Translational Medical Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Qin Zhong
- Medical Artificial Intelligence Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Qin Zhong
- 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
- Zongren Li
- Translational Medical Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Zongren Li
- Medical Artificial Intelligence Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Zongren Li
- 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
- Lei Zhang
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an, China
- Lei Zhang
- Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia
- Lei Zhang
- 0Department 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
- Kunlun He
- Translational Medical Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Kunlun He
- Medical Artificial Intelligence Research Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- Kunlun He
- Medical Big Data Center, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China
- DOI
- https://doi.org/10.3389/fendo.2021.791476
- Journal volume & issue
-
Vol. 12
Abstract
BackgroundWe aimed to understand how glycaemic levels among COVID-19 patients impact their disease progression and clinical complications.MethodsWe 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.ResultsCOVID-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.ConclusionOur 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.
Keywords