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de Fátima Cobre A, Alves AC, Gotine ARM, Domingues KZA, Lazo REL, Ferreira LM, Tonin FS, Pontarolo R. Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. Intern Emerg Med 2024:10.1007/s11739-024-03547-1. [PMID: 38416303 DOI: 10.1007/s11739-024-03547-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/16/2024] [Indexed: 02/29/2024]
Abstract
This study aims to apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection.Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonia (but with negative COVID-19 RT-PCR), and healthy volunteers (control) from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC-MS, LC-MS, and NMR. Machine learning models (PCA and PLS-DA) were developed to predict the diagnosis and prognosis of COVID-19 and identify biomarkers associated with these outcomes.A total of 1410 patient samples were analyzed. The PLS-DA model presented a diagnostic and prognostic accuracy of around 95% of all analyzed data. A total of 23 biomarkers (e.g., spermidine, taurine, L-aspartic, L-glutamic, L-phenylalanine and xanthine, ornithine, and ribothimidine) have been identified as being associated with the diagnosis and prognosis of COVID-19. Additionally, we also identified for the first time five new biomarkers (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate) that are also associated with the severity and diagnosis of COVID-19. These five new biomarkers were elevated in severe COVID-19 patients compared to patients with mild disease or healthy volunteers.The PLS-DA model was able to predict the diagnosis and prognosis of COVID-19 around 95%. Additionally, our investigation pinpointed five novel potential biomarkers linked to the diagnosis and prognosis of COVID-19: N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. These biomarkers exhibited heightened levels in severe COVID-19 patients compared to those with mild COVID-19 or healthy volunteers.
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Affiliation(s)
| | - Alexessander Couto Alves
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | | | | | | | - Luana Mota Ferreira
- Department of Pharmacy, Universidade Federal do Paraná, Campus III, Av. Pref. Lothário Meissner, 632, Jardim Botânico, Curitiba, PR, 80210-170, Brazil
| | - Fernanda Stumpf Tonin
- H&TRC - Health & Technology Research Centre, ESTeSL, Escola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa, Lisbon, Portugal
| | - Roberto Pontarolo
- Department of Pharmacy, Universidade Federal do Paraná, Campus III, Av. Pref. Lothário Meissner, 632, Jardim Botânico, Curitiba, PR, 80210-170, Brazil.
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El-Derany MO, Hanna DMF, Youshia J, Elmowafy E, Farag MA, Azab SS. Metabolomics-directed nanotechnology in viral diseases management: COVID-19 a case study. Pharmacol Rep 2023; 75:1045-1065. [PMID: 37587394 PMCID: PMC10539420 DOI: 10.1007/s43440-023-00517-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 07/28/2023] [Accepted: 07/28/2023] [Indexed: 08/18/2023]
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently regarded as the twenty-first century's plague accounting for coronavirus disease 2019 (COVID-19). Besides its reported symptoms affecting the respiratory tract, it was found to alter several metabolic pathways inside the body. Nanoparticles proved to combat viral infections including COVID-19 to demonstrate great success in developing vaccines based on mRNA technology. However, various types of nanoparticles can affect the host metabolome. Considering the increasing proportion of nano-based vaccines, this review compiles and analyses how COVID-19 and nanoparticles affect lipids, amino acids, and carbohydrates metabolism. A search was conducted on PubMed, ScienceDirect, Web of Science for available information on the interrelationship between metabolomics and immunity in the context of SARS-CoV-2 infection and the effect of nanoparticles on metabolite levels. It was clear that SARS-CoV-2 disrupted several pathways to ensure a sufficient supply of its building blocks to facilitate its replication. Such information can help in developing treatment strategies against viral infections and COVID-19 based on interventions that overcome these metabolic changes. Furthermore, it showed that even drug-free nanoparticles can exert an influence on biological systems as evidenced by metabolomics.
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Affiliation(s)
- Marwa O El-Derany
- Department of Biochemistry, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt
| | - Diana M F Hanna
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Ain Shams University, 11566, Cairo, Egypt
| | - John Youshia
- Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt
| | - Enas Elmowafy
- Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt
| | - Mohamed A Farag
- Pharmacognosy Department, College of Pharmacy, Cairo University, Kasr El-Aini St., P.B. 11562, Cairo, Egypt
| | - Samar S Azab
- Department of Pharmacology and Toxicology, Faculty of Pharmacy, Ain Shams University, 11566, Cairo, Egypt.
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J-shaped associations and joint effects of fasting glucose with inflammation and cytokines on COVID-19 mortality. Int J Infect Dis 2022; 122:285-294. [PMID: 35661688 PMCID: PMC9155189 DOI: 10.1016/j.ijid.2022.05.060] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 05/24/2022] [Accepted: 05/27/2022] [Indexed: 01/25/2023] Open
Abstract
OBJECTIVES The aim of this study was to investigate the dose-response relationship of admission fasting glucose (FBG) with corona virus disease 2019 (COVID-19) mortality and to further evaluate potential interactions of hyperglycemia with inflammation and hypercoagulation on COVID-19 outcomes. METHODS This retrospective study included 2555 consecutively hospitalized patients with COVID-19, until death or discharge, in Wuhan Union hospital between January 1 and April 9, 2020. The poor early outcomes included admission to intensive care unit, intubation, and deaths occurring within 28 days. We used splines nested in Cox regression to visualize dose-response associations and generalized additive models to fit three-dimensional (3D) trend plots for joint effects of FBG with markers of inflammation and coagulation. RESULTS J-shaped associations existed between hospitalized mortality or poor early outcomes and FBG with a nadir at 5 mmol/L, which were more evident in women. 3D plots demonstrated significant joint effect trends, and patients with hyperglycemia and high neutrophil-lymphocyte ratio, C-reactive protein, lactate dehydrogenase, procalcitonin, d-dimer, and interleukin-6 had 7.4-25.3-fold risks; the proportions of joint associations attributed to additive interactions reached 30% to 54%. CONCLUSIONS FBG was associated with hospitalized mortality and poor early outcomes in a J-shaped manner, and a combination of hyperglycemia, inflammation, hypercoagulation, and cytokines conferred a dramatically higher risk.
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Abstract
Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, is a global pandemic impacting 254 million people in 190 countries. Comorbidities, particularly cardiovascular disease, diabetes, and hypertension, increase the risk of infection and poor outcomes. SARS-CoV-2 enters host cells through the angiotensin-converting enzyme-2 receptor, generating inflammation and cytokine storm, often resulting in multiorgan failure. The mechanisms and effects of COVID-19 on patients with high-risk diabetes are not yet completely understood. In this review, we discuss the variety of coronaviruses, structure of SARS-CoV-2, mutations in SARS-CoV-2 spike proteins, receptors associated with viral host entry, and disease progression. Furthermore, we focus on possible mechanisms of SARS-CoV-2 in diabetes, leading to inflammation and heart failure. Finally, we discuss existing therapeutic approaches, unanswered questions, and future directions.
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Affiliation(s)
- Chandrakala Aluganti Narasimhulu
- Division of Metabolic and Cardiovascular Sciences, Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, Orlando, Florida, United States
| | - Dinender K Singla
- Division of Metabolic and Cardiovascular Sciences, Burnett School of Biomedical Sciences, College of Medicine, University of Central Florida, Orlando, Florida, United States
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Carrasco-Sánchez FJ, López-Carmona MD, Martínez-Marcos FJ, Pérez-Belmonte LM, Hidalgo-Jiménez A, Buonaiuto V, Suárez Fernández C, Freire Castro SJ, Luordo D, Pesqueira Fontan PM, Blázquez Encinar JC, Magallanes Gamboa JO, de la Peña Fernández A, Torres Peña JD, Fernández Solà J, Napal Lecumberri JJ, Amorós Martínez F, Guisado Espartero ME, Jorge Ripper C, Gómez Méndez R, Vicente López N, Román Bernal B, Rojano Rivero MG, Ramos Rincón JM, Gómez Huelgas R. Admission hyperglycaemia as a predictor of mortality in patients hospitalized with COVID-19 regardless of diabetes status: data from the Spanish SEMI-COVID-19 Registry. Ann Med 2021; 53:103-116. [PMID: 33063540 PMCID: PMC7651248 DOI: 10.1080/07853890.2020.1836566] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 10/08/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Hyperglycaemia has emerged as an important risk factor for death in coronavirus disease 2019 (COVID-19). The aim of this study was to evaluate the association between blood glucose (BG) levels and in-hospital mortality in non-critically patients hospitalized with COVID-19. METHODS This is a retrospective multi-centre study involving patients hospitalized in Spain. Patients were categorized into three groups according to admission BG levels: <140 mg/dL, 140-180 mg/dL and >180 mg/dL. The primary endpoint was all-cause in-hospital mortality. RESULTS Of the 11,312 patients, only 2128 (18.9%) had diabetes and 2289 (20.4%) died during hospitalization. The in-hospital mortality rates were 15.7% (<140 mg/dL), 33.7% (140-180 mg) and 41.1% (>180 mg/dL), p<.001. The cumulative probability of mortality was significantly higher in patients with hyperglycaemia compared to patients with normoglycaemia (log rank, p<.001), independently of pre-existing diabetes. Hyperglycaemia (after adjusting for age, diabetes, hypertension and other confounding factors) was an independent risk factor of mortality (BG >180 mg/dL: HR 1.50; 95% confidence interval (CI): 1.31-1.73) (BG 140-180 mg/dL; HR 1.48; 95%CI: 1.29-1.70). Hyperglycaemia was also associated with requirement for mechanical ventilation, intensive care unit (ICU) admission and mortality. CONCLUSIONS Admission hyperglycaemia is a strong predictor of all-cause mortality in non-critically hospitalized COVID-19 patients regardless of prior history of diabetes. KEY MESSAGE Admission hyperglycaemia is a stronger and independent risk factor for mortality in COVID-19. Screening for hyperglycaemia, in patients without diabetes, and early treatment of hyperglycaemia should be mandatory in the management of patients hospitalized with COVID-19. Admission hyperglycaemia should not be overlooked in all patients regardless prior history of diabetes.
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Affiliation(s)
| | | | | | | | | | - Verónica Buonaiuto
- Internal Medicine Department, Málaga Regional University Hospital, Málaga, Spain
| | | | | | - Davide Luordo
- Internal Medicine Department, Infanta Cristina University Hospital, Parla, Spain
| | | | | | | | | | - José David Torres Peña
- Lipis and Atherosclerosis Unit, Department of Interna Medicine, Maimonides Biomedical Research Institute of Córdoba (IMIBIC), Reina Sofia University Hospital, University of Córdoba, Spain
- CIBER Fisiopatologia de la Obesidad y Nutrición (CIBEROBN), Insituto de Salud Carlos III, Córdoba, Spain
| | | | | | | | | | - Carlos Jorge Ripper
- Internal Medicine Department, Insular de Gran Canaria Hospital, Las Palmas de Gran Canaria, Spain
| | - Raquel Gómez Méndez
- Internal Medicine Department, Lucus Augusti University Hospital, Lugo, Spain
| | | | - Berta Román Bernal
- Internal Medicine Department, Doctor José Molina Orosa Hospital, Arrecife, Spain
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Metabolomics Signatures of SARS-CoV-2 Infection. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1376:45-59. [PMID: 34735713 DOI: 10.1007/5584_2021_674] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
For a very long time, viral infections have been considered as one of the most important causes of death and disability around the world. Through the viral infection, viruses as small pathogens enter the host cells and use hosts' biosynthesis machinery to replicate and collect infectious lineages. Moreover, they can modify hosts' metabolic pathways in order to their own purposes. Nowadays (in 2019-2020), the most famous type of viral infection which was caused by a novel type of coronavirus is called COVID-19 disease. It has claimed the lives of many people around the world and is a very serious threat to health. Since investigations of the effects of viruses on host metabolism using metabolomics tools may have given focuses on novel appropriate treatments, in the current review the authors highlighted the virus-host metabolic interactions and metabolomics perspective in COVID-19.
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Ouedraogo E, Allard L, Bihan H, Goupil de Bouillé J, Giroux-Leprieur B, Sutton A, Baudry C, Josse C, Didier M, Deutsch D, Rezgani I, Bouchaud O, Cosson E. The association of metabolic syndrome and COVID-19 deterioration. Nutr Metab Cardiovasc Dis 2021; 31:3236-3242. [PMID: 34629251 PMCID: PMC8386103 DOI: 10.1016/j.numecd.2021.08.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 08/08/2021] [Accepted: 08/16/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND AND AIMS To evaluate the prevalence and prognostic value of metabolic syndrome (MetS) in patients admitted for coronavirus disease 2019 (COVID-19). METHODS AND RESULTS In this monocentric cohort retrospective study, we consecutively included all adult patients admitted to COVID-19 units between April 9 and May 29, 2020 and between February 1 and March 26, 2021. MetS was defined when at least three of the following components were met: android obesity, high HbA1c, hypertension, hypertriglyceridemia, and low HDL cholesterol. COVID-19 deterioration was defined as the need for nasal oxygen flow ≥6 L/min within 28 days after admission. We included 155 patients (55.5% men, mean age 61.7 years old, mean body mass index 29.8 kg/m2). Fifty-six patients (36.1%) had COVID-19 deterioration. MetS was present in 126 patients (81.3%) and was associated with COVID-19 deterioration (no-MetS vs MetS: 13.7% and 41.2%, respectively, p < 0.01). Logistic regression taking into account MetS, age, gender, ethnicity, period of inclusion, and Charlson Index showed that COVID-19 deterioration was 5.3 times more likely in MetS patients (95% confidence interval 1.3-20.2) than no-MetS patients. CONCLUSIONS Over 81.3% of patients hospitalized in COVID-19 units had MetS. This syndrome appears to be an independent risk factor of COVID-19 deterioration.
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Affiliation(s)
- Elise Ouedraogo
- Department of Infectious Disease, AP-HP, Avicenne Hospital, Bobigny, France
| | - Lucie Allard
- Department of Endocrinology-Diabetology-Nutrition, AP-HP, Avicenne Hospital, Bobigny, France
| | - Hélène Bihan
- Department of Endocrinology-Diabetology-Nutrition, AP-HP, Avicenne Hospital, Bobigny, France; LEPS (Laboratoire Educations et Pratiques de Santé) EA 3412-Université Sorbonne Paris Nord, Bobigny, France
| | | | | | - Angela Sutton
- Department of Biology Laboratory, AP-HP, Avicenne Hospital, Bobigny, France; Laboratory for Vascular Translational Science (LVTS), Inserm U1148 Groupe Biothérapies et Glycoconjugués, Université Paris 13 - Sorbonne Paris Nord, France
| | - Camille Baudry
- Department of Endocrinology-Diabetology-Nutrition, AP-HP, Avicenne Hospital, Bobigny, France
| | | | - Morgane Didier
- Department of Respiratory Disease, AP-HP, Avicenne Hospital, Bobigny, France
| | - David Deutsch
- Department of Gastroenterology and Digestive Oncology, AP-HP, Avicenne Hospital, Bobigny, France
| | - Imen Rezgani
- Department of Endocrinology-Diabetology-Nutrition, AP-HP, Avicenne Hospital, Bobigny, France
| | - Olivier Bouchaud
- Department of Infectious Disease, AP-HP, Avicenne Hospital, Bobigny, France
| | - Emmanuel Cosson
- Department of Endocrinology-Diabetology-Nutrition, AP-HP, Avicenne Hospital, Bobigny, France; Equipe de Recherche en Epidémiologie Nutritionnelle (EREN), UMR U1153 Inserm / U1125 Inrae / Cnam / Université Paris 13 - Sorbonne Paris Nord, Centre de Recherche en Epidémiologie et Statistiques - Université de Paris (CRESS), SMBH PARIS 13, Bobigny, France.
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Banoei MM, Dinparastisaleh R, Zadeh AV, Mirsaeidi M. Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2021; 25:328. [PMID: 34496940 PMCID: PMC8424411 DOI: 10.1186/s13054-021-03749-5] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 08/27/2021] [Indexed: 01/08/2023]
Abstract
Background The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes. Methods Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients (N = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die. Results SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power (Q2 = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors. Conclusions An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.
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Affiliation(s)
- Mohammad M Banoei
- Department of Critical Care Medicine, University of Calgary, Alberta, Canada.,Department of Biological Science, University of Calgary, Alberta, Canada
| | - Roshan Dinparastisaleh
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Ali Vaeli Zadeh
- Division of Pulmonary and Critical Care, Miami VA Medical Center, Miami, FL, USA
| | - Mehdi Mirsaeidi
- Division of Pulmonary and Critical Care, Department of Medicine, University of Miami, Miami, FL, USA.
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Peng X, Liu Q, Chen Z, Wen G, Li Q, Chen Y, Xiong J, Meng X, Ding Y, Shi Y, Tang S. Clinical course and management of 73 hospitalized moderate patients with COVID-19 outside Wuhan. PLoS One 2021; 16:e0249655. [PMID: 33983981 PMCID: PMC8118515 DOI: 10.1371/journal.pone.0249655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 03/23/2021] [Indexed: 01/08/2023] Open
Abstract
Moderate cases account for the majority in hospitalized patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and can also progress to severe/critical condition. Here, we investigated the clinical course and management of hospitalized moderate SARS-CoV-2 patients. The medical records and follow-up data were analyzed from the SARS-CoV-2 patients outside Wuhan. A total of 73 moderate patients (38 men, 35 women) were included, with median age of 47.0 (38.5–57.5) years. Among them, only one patient (1.4%) died using active treatment to improve symptoms. The median duration of the four main symptoms cough, fever, chest tightness, and fatigue were 11.0, 8.0, 11.0, and 7.0 days, respectively; the median duration of the positive nucleic acid test (NAT) results for SARS-CoV-2 was 16.5 days; the median hospitalization time was 25.0 days in 72 moderate survivors. The duration of cough and fever was positively correlated with the duration of the positive NAT results. On admission, 50% had lymphopenia; less than 30% had abnormal blood biochemistry findings involving hyperglycemia, liver function and myocardial enzymes. At discharge, the laboratory indexes were substantially improved. Two weeks after discharge, 5.6% survivors experienced a recurrence of the positive NAT results. Moderate SARS-CoV-2 patients have a good prognosis by the active treatment. A small proportion of the recovered moderate patients still may be virus carriers and require an additional round of viral detection.
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Affiliation(s)
- Xiaojuan Peng
- Department of Endocrinology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, Hunan, P. R. China
| | - Qi Liu
- Department of Infectious Diseases, The First People’s Hospital of Xiaochang County, Hubei, P. R. China
| | - Zhaolin Chen
- Department of Gastroenterology, The First Affiliated Hospital, Jinan University, Guangzhou, Guangdong, P. R. China
| | - Guiyan Wen
- Department of Gastroenterology, The First Affiliated Hospital, Jinan University, Guangzhou, Guangdong, P. R. China
| | - Qing Li
- Department of Interventional vascular surgery, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, Hunan, P. R. China
| | - Yanfang Chen
- Department of Gastroenterology, The First Affiliated Hospital, Jinan University, Guangzhou, Guangdong, P. R. China
| | - Jie Xiong
- Department of Infectious Diseases, The First People’s Hospital of Xiaochang County, Hubei, P. R. China
| | - Xinzhou Meng
- Department of Cardiology, The First People’s Hospital of Xiaochang County, Hubei, P. R. China
| | - Yuanjin Ding
- Department of Hepatobiliary surgery, The First People’s Hospital of Xiaochang County, Hubei, P. R. China
| | - Ying Shi
- Department of Gastroenterology, The First Affiliated Hospital, Jinan University, Guangzhou, Guangdong, P. R. China
| | - Shaohui Tang
- Department of Gastroenterology, The First Affiliated Hospital, Jinan University, Guangzhou, Guangdong, P. R. China
- * E-mail:
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Efficient management strategy of COVID-19 patients based on cluster analysis and clinical decision tree classification. Sci Rep 2021; 11:9626. [PMID: 33953307 PMCID: PMC8100107 DOI: 10.1038/s41598-021-89187-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 04/16/2021] [Indexed: 01/08/2023] Open
Abstract
Early classification and risk assessment for COVID-19 patients are critical for improving their terminal prognosis, and preventing the patients deteriorate into severe or critical situation. We performed a retrospective study on 222 COVID-19 patients in Wuhan treated between January 23rd and February 28th, 2020. A decision tree algorithm has been established including multiple factor logistic for cluster analyses that were performed to assess the predictive value of presumptive clinical diagnosis and features including characteristic signs and symptoms of COVID-19 patients. Therapeutic efficacy was evaluated by adopting Kaplan–Meier survival curve analysis and cox risk regression. The 222 patients were then clustered into two groups: cluster I (common type) and cluster II (high-risk type). High-risk cases can be judged from their clinical characteristics, including: age > 50 years, chest CT images with multiple ground glass or wetting shadows, etc. Based on the classification analysis and risk factor analysis, a decision tree algorithm and management flow chart were established, which can help well recognize individuals who needs hospitalization and improve the clinical prognosis of the COVID-19 patients. Our risk factor analysis and management process suggestions are useful for improving the overall clinical prognosis and optimize the utilization of public health resources during treatment of COVID-19 patients.
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Mahrooz A, Muscogiuri G, Buzzetti R, Maddaloni E. The complex combination of COVID-19 and diabetes: pleiotropic changes in glucose metabolism. Endocrine 2021; 72:317-325. [PMID: 33886062 PMCID: PMC8060688 DOI: 10.1007/s12020-021-02729-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 04/09/2021] [Indexed: 01/08/2023]
Abstract
PURPOSE Angiotensin converting enzyme 2 (ACE2) is the door for SARS-CoV-2, expressed in critical metabolic tissues. So, it is rational that the new virus causes pleiotropic alterations in glucose metabolism, resulting in the complication of pre-existing diabetes's pathophysiology or creating new disease mechanisms. However, it seems that less attention has been paid to this issue. This review aimed to highlight the importance of long-term consequences and pleiotropic alterations in glucose metabolism following COVID-19 and emphasize the need for basic and clinical research in metabolism and endocrinology. RESULTS SARS-CoV-2 shifts cellular metabolism from oxidative phosphorylation to glycolysis, which leads to a decrease in ATP generation. Together with metabolic imbalance, the impaired immune system elevates the susceptibility of patients with diabetes to this deadly virus. SARS-CoV-2-induced metabolic alterations in immune cells can result in hyper inflammation and a cytokine storm. Metabolic dysfunction may affect therapies against SARS-CoV-2 infection. The effective control of metabolic complications could prove useful therapeutic targets for combating COVID-19. It is also necessary to understand the long-term consequences that will affect patients with diabetes who survived COVID-19. CONCLUSIONS Since the pathophysiology of COVID-19 is still mostly unknown, identifying the metabolic mechanisms contributing to its progression is essential to provide specific ways to prevent and improve this dangerous virus's detrimental effects. The findings show that the new virus may induce new-onset diabetes with uncertain metabolic and clinical features, supporting a potential role of COVID-19 in the development of diabetes.
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Affiliation(s)
- Abdolkarim Mahrooz
- Molecular and Cell Biology Research Center, Mazandaran University of Medical Sciences, Sari, Iran.
- Diabetes Research Center, Imam Khomeini Hospital, Mazandaran University of Medical Sciences, Sari, Iran.
| | - Giovanna Muscogiuri
- Sezione di Endocrinologia, Dipartimento di Medicina Clinica e Chirurgia, Università Federico II Napoli, Napoli, Italy
| | - Raffaella Buzzetti
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
| | - Ernesto Maddaloni
- Department of Experimental Medicine, Sapienza University of Rome, Rome, Italy
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Pang Z, Zhou G, Chong J, Xia J. Comprehensive Meta-Analysis of COVID-19 Global Metabolomics Datasets. Metabolites 2021; 11:44. [PMID: 33435351 PMCID: PMC7827862 DOI: 10.3390/metabo11010044] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 12/30/2020] [Accepted: 01/04/2021] [Indexed: 12/15/2022] Open
Abstract
The novel coronavirus SARS-CoV-2 has spread across the world since 2019, causing a global pandemic. The pathogenesis of the viral infection and the associated clinical presentations depend primarily on host factors such as age and immunity, rather than the viral load or its genetic variations. A growing number of omics studies have been conducted to characterize the host immune and metabolic responses underlying the disease progression. Meta-analyses of these datasets have great potential to identify robust molecular signatures to inform clinical care and to facilitate therapeutics development. In this study, we performed a comprehensive meta-analysis of publicly available global metabolomics datasets obtained from three countries (United States, China and Brazil). To overcome high heterogeneity inherent in these datasets, we have (a) implemented a computational pipeline to perform consistent raw spectra processing; (b) conducted meta-analyses at pathway levels instead of individual feature levels; and (c) performed visual data mining on consistent patterns of change between disease severities for individual studies. Our analyses have yielded several key metabolic signatures characterizing disease progression and clinical outcomes. Their biological interpretations were discussed within the context of the current literature. To the best of our knowledge, this is the first comprehensive meta-analysis of global metabolomics datasets of COVID-19.
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Affiliation(s)
- Zhiqiang Pang
- Institute of Parasitology, McGill University, 21111 Lakeshore Road, Ste Anne de Bellevue, QC H9X 3V9, Canada; (Z.P.); (G.Z.); (J.C.)
| | - Guangyan Zhou
- Institute of Parasitology, McGill University, 21111 Lakeshore Road, Ste Anne de Bellevue, QC H9X 3V9, Canada; (Z.P.); (G.Z.); (J.C.)
| | - Jasmine Chong
- Institute of Parasitology, McGill University, 21111 Lakeshore Road, Ste Anne de Bellevue, QC H9X 3V9, Canada; (Z.P.); (G.Z.); (J.C.)
| | - Jianguo Xia
- Institute of Parasitology, McGill University, 21111 Lakeshore Road, Ste Anne de Bellevue, QC H9X 3V9, Canada; (Z.P.); (G.Z.); (J.C.)
- Department of Animal Science, McGill University, 21111 Lakeshore Road, Ste Anne de Bellevue, QC H9X 3V9, Canada
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13
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Clinical features of critically ill patients infected with SARS-CoV-2 outside Wuhan with and without diabetes. Int J Diabetes Dev Ctries 2020; 40:482-490. [PMID: 33169053 PMCID: PMC7642858 DOI: 10.1007/s13410-020-00888-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 10/21/2020] [Indexed: 01/08/2023] Open
Abstract
Aim Some patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection rapidly develop to critical condition. Here, we investigated the clinical features of critically ill SARS-CoV-2 patients with and without diabetes and identified risk factors for death of these patients. Methods The medical records including epidemiological, demographic, clinical, and laboratory data from 49 critically ill SARS-CoV-2 patients were collected and analyzed in Huanggang City and Xiaogan City, Hubei Province, outside Wuhan. Results Sixty-seven percent (33) of patients survived and 33% (16) of patients died in 49 critically ill patients (32 men, 17 women), with a median age of 63 years (IQR 53-73). Univariate analyses indicated that the deceased patients were more often associated with two or more comorbidities, one or more gastrointestinal symptoms, high neutrophil percentage, low lymphocytes and lymphocyte percentage, high C-reactive protein, high procalcitonin, high fasting blood glucose (FBG), and high lactate dehydrogenase (LDH) compared with the survivors; moreover, the patients with T2DM had the higher neutrophil percentage, the lower lymphocyte percentage, and the higher levels of FBG and LDH compared with the patients without T2DM. Multivariable logistic regression analyses indicated that gastrointestinal symptoms (≥ 1 symptoms), decreased lymphocytes (< 1.1 × 109/L), and increased FBG (≥ 7.0 mmol/L) were the independent risk factors for death of critically ill patients. Conclusions Critically ill COVID patients with T2DM had more severe damages of the lymphocytes, islet cells, and heart function, and gastrointestinal symptoms, lymphopenia, and increased FBG may be early predictors for poor prognosis. Supplementary Information The online version contains supplementary material available at 10.1007/s13410-020-00888-3.
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14
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Estudos experimentais sobre COVID-19: panorama da produção científica mundial. ACTA PAUL ENFERM 2020. [DOI: 10.37689/acta-ape/2020ao01215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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15
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Zhu B, Jin S, Wu L, Hu C, Wang Z, Bu L, Sun H, Wang X, Qu S, Chen D. J-shaped association between fasting blood glucose levels and COVID-19 severity in patients without diabetes. Diabetes Res Clin Pract 2020; 168:108381. [PMID: 32853687 PMCID: PMC7445136 DOI: 10.1016/j.diabres.2020.108381] [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: 06/25/2020] [Revised: 07/31/2020] [Accepted: 08/18/2020] [Indexed: 01/04/2023]
Abstract
AIMS Coronavirus disease 2019 (COVID-19) has become a recognized worldwide pandemic. Researchers now know that mortality from COVID-19 can be reduced through early prevention measures. This retrospective, multi-centered study of 293 COVID-19 patients without diabetes explores the association between fasting blood glucose (FBG) levels and the risk of COVID-19 disease progression, with the goal of providing clinical evidence for glycemic targets in patients. METHODS The multivariate stepwise binary logistic regression analysis was used to test the dose-response effects of FBG levels on the risk of severe and critical condition in COVID-19 patients. RESULTS FBG levels were plotted in quintiles with set at <4.74, 4.74-5.21, 5.21-5.78, 5.78-7.05, and ≧7.05 mmol/L. The constituent ratio of severe or critical cases in each FBG quintile was 20.7%, 1.7%, 13.8%, 27.1%, and 67.2%, respectively (P < 0.0001). When the second quintile was used as the reference, the adjusted odds ratios (AORs) (95%CI) for the risk of severe/critical condition in COVID-19 was 25.33 (2.77, 231.64), 1.00 (Reference), 3.13 (0.33, 29.67), 10.59 (1.23, 91.24), 38.93 (4.36, 347.48) per FBG quintile respectively (P < 0.001). CONCLUSIONS We provide evidence of J-shaped associations between FBG and risk of severe and critical condition in non-diabetes patients with COVID-19, with nadir at 4.74-5.78 mmol/L.
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Affiliation(s)
- Bing Zhu
- Department of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Shengwei Jin
- Department of Anaesthesia and Critical Care, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Zhejiang, China
| | - Lianpeng Wu
- Departments of Infectious Disease, The Ding Li Clinical College of Wenzhou Medical University and Sixth People's Hospital of Wenzhou, Wenzhou, Zhejiang, China
| | - Chenchan Hu
- Departments of Infectious Disease, The Ding Li Clinical College of Wenzhou Medical University and Sixth People's Hospital of Wenzhou, Wenzhou, Zhejiang, China
| | - Zhen Wang
- Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Le Bu
- Department of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Hang Sun
- Department of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xingchun Wang
- Department of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Shen Qu
- Department of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
| | - Dong Chen
- Departments of Infectious Disease, The Ding Li Clinical College of Wenzhou Medical University and Sixth People's Hospital of Wenzhou, Wenzhou, Zhejiang, China.
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Bonny V, Maillard A, Mousseaux C, Plaçais L, Richier Q. [COVID-19: Pathogenesis of a multi-faceted disease]. Rev Med Interne 2020; 41:375-389. [PMID: 32507520 PMCID: PMC7250743 DOI: 10.1016/j.revmed.2020.05.003] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 05/13/2020] [Accepted: 05/16/2020] [Indexed: 02/07/2023]
Abstract
SARS-CoV-2 infection, named COVID-19, can lead to a dysregulated immune response and abnormal coagulation responsible for a viral sepsis. In this review, we specify physiopathological mechanisms of each phase of COVID-19 - viral, immune and pro-thrombotic - notably because they involve different treatment. Finally, we specify the physiopathological mechanisms of organ injury.
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Affiliation(s)
- V Bonny
- Interne en DES de pneumologie, Sorbonne-université, France
| | - A Maillard
- Interne en DES de maladies infectieuses, MSc, Université de Paris, France
| | - C Mousseaux
- DES de néphrologie, MSc, Sorbonne-université, France
| | - L Plaçais
- Interne en DES de médecine interne, MSc, Sorbonne-université, France
| | - Q Richier
- Interne en DES de médecine interne Paris, MSc, Université de Paris, France.
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