The predictive values of dynamic blood lipid profile for mortality in COVID-19 patients

The predictive values of dynamic blood lipid profile for mortality in COVID-19 patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The predictive values of dynamic blood lipid profile for mortality in COVID-19 patients Jiayi Deng, Yihao Yuan, Ting Zhang, Fanglin Li, Min Xu, Guobao Wu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4386935/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Lipid metabolism is particularly affected in response to acute infectious diseases caused by viruses, bacteria, or parasites. The association between levels of lipid profiles and mortality in COVID-19 patients has become the subject of increasing interest. Objective To evaluate the predictive capacity of dyslipidemia for COVID-19 mortality based on dynamic data. Methods we conducted a retrospective, observational study, involving 135 COVID-19 patients admitted between January 1 and March 26, 2020. Results We found that non-survivals with COVID-19 displayed persistent dyslipidemia, including lower levels of total cholesterol (TC), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) with higher levels of triglycerides during the early stages of hospitalization. Notably, both the absolute values or the changes of TC, HDL, and LDL were closely related to mortality, and the AUCs of these three indicators at all time points and their changes were greater than 0.7. Notably, the values of AUCs of TC, HDL, and LDL at week 3 were 0.891, 0.895, and 0.879, while the AUCs for change of TC, HDL, and LDL were 0.975, 0.950 and 0.925 at week 3 − 1 . Spearman correlation analyses showed that TC, HDL and LDL were significantly associated with CRP, D-dimer, BUN, CK and BNP at all four time points. Conclusion Blood lipid levels in the third week and changes from week 1 to week 3 are critical for predicting the mortality of COVID-19. Healthcare providers should pay close attention to the dynamic changes in lipid levels of COVID-19 patients. Health sciences/Biomarkers Health sciences/Risk factors COVID-19 SARS-CoV-2 Dyslipidemia Lipid profile Mortality Figures Figure 1 Figure 2 Figure 3 Introduction The COVID-19 pandemic continues to spread uncontrollably around the world, resulting in a considerable number of fatalities ( 1 ). Global reports as of 14 Jan 2024, showed over 774 million confirmed cases and over 7.0 million deaths attributed to COVID-19 ( 2 ). Despite the development and deployment of numerous efficient vaccines, COVID-19 continues to pose a substantial risk to the well-being of the general public ( 3 ). The severity of the illness varies among individuals, with some experiencing mild flu-like symptoms and recovering quickly, while others tragically deteriorated and succumb to the disease ( 4 ). It is crucial to identify and anticipate the risk factors associated with COVID-19 patient mortality for healthcare providers, to intervene timely and effectively, thereby reducing the chances of death. The human body undergoes significant changes in response to acute infectious diseases caused by viruses, bacteria, or parasites. Among these changes, lipid metabolism is particularly affected ( 5 ). In the cases of COVID-19, it has been observed that lipid metabolism disorders are common in severe or critical cases, with low levels of total cholesterol (TC), high-density lipoprotein (HDL-C), and low-density lipoprotein (LDL-C) being associated with disease progression. In contrast, high levels of triglycerides (TG) are considered a predictor of COVID-19 severity ( 6 – 9 ). The association between levels of lipid profiles and mortality in COVID-19 patients has become the subject of increasing interest. Dyslipidemia has been demonstrated to be significantly related to higher mortality and severity of COVID-19, according to a recent meta-analysis ( 10 ). Moreover, several studies have shown that lipid profile changes can predict COVID-19 mortality ( 6 , 7 , 11 – 13 ). For example, in the early stage of the pandemic, TC levels measured 2–3 days after admission were found to be linked to mortality in severe COVID-19 cases ( 6 ). Additionally, lower levels of LDL-C and TC throughout the course of the disease were associated with non-survivors, and both were negatively correlated with inflammatory markers. ( 12 ). Other studies have also shown that changes in specific lipid components, such as HDL-C, can be particularly indicative of COVID-19 mortality risk. For example, in one study of 424 COVID-19 patients, non-survivors showed a dramatic decrease in HDL-C levels ( 7 ). Consistently, another study manifested that higher levels of HDL-C, TC, and LDL-C were found to offer protection to mortality ( 13 ). However, many of these studies only have cross-sectional data for a single time point, making it difficult to evaluate the dynamic changes in lipid profiles or continuously monitor lipid-related test indicators and their associations with COVID-19 mortality. Therefore, this paper explores the predictive value of changes in lipid profiles for COVID-19 mortality based on longitudinal data. By analyzing the dynamic changes in lipid profiles over time, we can gain a better understanding of how lipid metabolism disorders contribute to COVID-19 severity and mortality, and identify potential targets for therapeutic interventions. Ultimately, this research may lead to more effective treatments for COVID-19 patients and improve their chances of survival. Methods Design and Participants We carried out an observational and retrospective study at Zhongfa Hospital, Wuhan, China between January 1 and March 26, 2020. The study enrolled patients diagnosed with COVID-19 who were admitted successively during that period. None of the patients had other familial dyslipidemia syndromes. Of the 135 patients who underwent at least one lipid test, 76 survived (survival group) while 59 died (non-survival group). Reverse-transcriptase–polymerase-chain-reaction (RT-PCR) was used to establish the presence of COVID-19. The primary outcome of the study was defined as in-hospital death. The study was approved by the Ethics Committee of the Second Xiangya Hospital, Central South University (No. 2020001).And all methods were performed in accordance with the relevant guidelines and regulations. Because the study was retrospective and illness was a newly developing infectious disease, the thics Committee of the Second Xiangya Hospital, Central South University waived the requirement for written informed consent. Measurement of laboratory parameters Blood routine and coagulation function were measured by XN-20 (Sysmex, Germany) and CS5100 (Sysmex, Germany), respectively. Levels of TC, TG, HDL-C and LDL-C as well as liver and renal function, were determined using immunoturbidimetry by an automatic system COBAS 8000 (Roche, Barcelona, Switzerland). Erythrocyte sedimentation rate (ESR) and C reactive protein (CRP) were detected using Monitor 100 (Jumu Dedical Devices Co., Ltd, Shanghai, China) and Immage 800 (Beckman coulter Inc., United States), respectively. Data Collection We extracted clinical records and laboratory parameters of the COVID-19 patients through the electronic medical system. The baseline characteristics, symptoms, comorbidities, treatment, outcomes (survival or death) and laboratory findings at admission were also collected and evaluated, including Blood routine [(white blood cell (WBC), hemoglobin (HGB), platelet (PLT)], infection markers (ESR, CRP), coagulation function [prothrombin time (PT), activated partial thromboplastin time (APTT), D-dimer)], liver function [(ALT, AST, Tbil, Alb)], renal function [serum creatinine (SCr)], cardiac function [creatine kinase (CK), creatine kinase isoenzyme (CK-MB)] and the situation of chest CT results. Blood lipid (TC, TG, HDL-C, LDL-C) examination results were mainly collected from the first four weeks after admission. Two experienced clinicians reviewed and verified all data independently. Meanwhile, the data of dynamic lipids were used to calculate the following parameters: (1) Lipid-W x-1 = lipid W x – lipid W 1 (2) Lipid-standard deviation (SD) as the SD of the levels of lipid parameters (≥ 2 time points) (3) Lipid- coefficient of variation (CV) as the CV of the lipids were calculated using SD/Mean *100% Statistical Analysis All analyses and drawing were conducted using SPSS version 26.0 and R studio. Either the χ2 test or Fisher’s exact test were used to analyze the categorical variables, representing as numbers (percentages, %). For continuous variables, medians with interquartile range (IQR) were provided and compared using the Mann-Whitney U test for the non-normal distribution. To evaluate the sensitivity and specificity of TC, HDL-C, LDL-C, and TG levels each week, their changes compared to the first week, and the coefficient of variation of these four indicators in predicting death, receiver operating characteristic (ROC) curves were constructed. The area under the curve (AUC) was computed, where higher values indicate stronger discriminatory power. Spearman correlation analyses were conducted to demonstrate the associations between lipid profiles and other laboratory parameters. Statistical significance was set at p < 0.05 (two-tailed) for all analyses. Results Demographics and baseline characteristics This study enrolled a total of 135 COVID-19 patients, and table 1 presents the demographics and baseline characteristics of the study population. During hospitalization, 59 patients died, while 76 survived throughout the study period. Common symptoms included fever, cough, shortness of breath, anorexia, fatigue, and chill. The non-survival group tend to be older [70 (63, 78) vs. 65 (48.25, 70) y, p < 0.001] and had high proportions of cough (78% vs. 60.5%, p = 0.020) and shortness of breath (37.3% vs. 19%, p = 0.020) compared to the survival group. Compared with the survivors, non-survivors exhibited a higher likelihood of having received a variety of therapies, such as high-flow oxygen (47.5% vs. 19.7%, p = 0.001), non-invasive ventilator (72.9% vs. 6.6%, p < 0.001), invasive ventilator (84.7% vs. 1.3%, p < 0.001), glucocorticoid (74.6% vs. 38.2%, p < 0.001), and immunoglobulin (35.6% vs. 17.1%, p = 0.014). Laboratory findings There were significant discrepancies in the laboratory findings upon admission between survivors and non-survivors (Table 2). Non-survivals exhibited substantially higher levels of WBC and neutrophils, ALT, AST, TBil, sCr, CK, CK-MB, CRP, and D-dimer, along with a significantly prolonged PT. Conversely, they had lower lymphocyte and platelet count, and Alb levels, compared to the surviving patients. Blood lipid levels during the first four weeks of admission in the COVID-19 patients and the predictive accuracy of these lipid parameters for mortality We compared the blood lipid levels of COVID-19 patients who survived and those who did not during the first four weeks after admission (Table 3). We monitored the lipid levels for four weeks to provide dynamic information on the blood lipid levels of both surviving and non-surviving patients and to confirm the potential markers for predicting mortality. The levels of HDL and LDL were significantly lower in non-survival at all the time points, and TC was significantly lower in non-survival except for the fourth week. The TG concentration of non-survivors was only significantly higher during the first week than in survivors. ROC curves were conducted to evaluate the predictive roles of lipid parameters at different time points for forecasting mortality among COVID-19 patients (Fig. 1 ). During the first week, the AUCs of TC, HDL, LDL, and TG were 0.766, 0.770, 0.769, and 0.647. The AUCs of TC, HDL, and LDL were equally more than 0.7 at week 2 and week 3, and the value of AUC increased with time. Then, correlation analyses were conducted to demonstrate the associations between lipid parameters and other laboratory parameters at each time points. The results showed that TC, HDL and LDL were significantly associated with CRP, D-dimer, BUN, CK and BNP at all four time points, and Lys, PCT, PT, APTT, INR, Alb and TNT-I for three of these time points (Fig. 2 & Supplementary table 1 ). Changes of blood lipid in the first four weeks after admission in COVID-19 patients and the predictive accuracy of the changes in lipid parameters for mortality Furthermore, we conducted an analysis of blood lipid levels changes in COVID-19 patients during the first four weeks. These findings may shed light on critical points of disease progression. Interestingly, there were significantly greater changes in non-survivals in TC, HDL, and LDL during the second and third weeks from the first week, compared to survivals ( p < 0.05, Table 4 ). To assess the predictive value of lipid changes at various time points, we generated ROC curves for TC, HDL, LDL, and TG throughout the study period (Fig. 3 ). During the second week, the corresponding AUCs for changes in TC, HDL, LDL, and TG were 0.730, 0.718, and 0.769. Notably, during the third week, the AUCs were 0.975, 0.950, and 0.925, respectively. Additionally, we also assessed the predictive value of standard deviation (SD) and coefficient of variation (CV) of lipids in mortality and the results showed that the AUCs were significantly lower than those of changes in TC, HDL, LDL, and TG (Supplementary Fig. 1). Discussion This retrospective study examined the association between blood lipid status and mortality of COVID-19 patients. While previous studies have established the frequency of dyslipidemia among individuals with COVID-19, our focus was on the predictive value of dynamic changes in lipid levels for mortality. Our results indicate that non-surviving COVID-19 patients exhibit persistent dyslipidemia, characterized by reduced levels of HDL-C, LDL-C and TC, along with increased levels of TG. By comparing the changes in lipid parameters between surviving and non-surviving patients during the early stages of hospitalization, we observed that both the levels and fluctuations in TC, HDL, and LDL were strongly associated with mortality, with AUCs values exceeding 0.7. Notable, in the third week of hospitalization, the AUCs for TC, HDL, and LDL were 0.891, 0.895, and 0.879, respectively, while the AUCs for changes in these parameters were even higher, at 0.975, 0.950, and 0.925, respectively. Our findings shed light on the dynamic evolution of lipidology in COVID-19 patients and offer insights into predicting outcomes and understanding the mechanism underlying dyslipidemia. There have been several observational studies that have shown a significant correlation between blood lipid parameters and mortality. The available evidence suggests that non-survivals of COVID-19 patients exhibited decreased LDL, HDL and TC than survivors, with or without elevated TG levels( 12 , 14 ). Additionally, HDL has been identified as a predictor for one-year mortality after COVID-19 ( 15 ). Consequently, it appears that the lipid profile may play a crucial role in determining the prognosis of patients with COVID-19, both in the long-term and the short-term. Therefore, there is a significant need to investigate the evolution of the lipid parameters in COVID-19 patients. One of the pertinent questions to address is the underlying causes of dyslipidemia in patients with COVID-19. There were several possible explanations for the changes in lipid profile during COVID-19 infection. Firstly, SARS-CoV-2 induced acute inflammation may lead to dyslipidemia. Studies have shown that inflammatory mediators, such as interleukin-6 (IL-6), interleukin-1 beta (IL-1β), and tumor necrosis-alpha (TNF-α), can dose-dependently reduce the production and/or release of apolipoproteins in hepatic cell lines ( 16 ). This suggests that a more pronounced dyslipidemia may result from a stronger inflammatory response. Our study found that non-surviving COVID-19 patients exhibited a robust inflammatory response, as indicated by higher levels of WBC, neutrophil count, and CRP, which is consistent with previous studies ( 7 , 13 , 15 , 17 – 19 ). In sepsis patients, TC and HDL decreased during the acute phase and slowly increased within 28 days ( 20 ). Similarly, our repeated measurement data demonstrated that lipid levels gradually recovered during the first four weeks in surviving COVID-19 patients, while hypolipidemia persisted in the non-survivors. Lipid metabolism and inflammatory response interact and regulate each other. Our study found that CRP were significantly correlated with TC, HDL and LDL at all four time points. Secondly, COVID-19 may impair liver function and affect lipid metabolism. The liver is essential for lipid synthesis and metabolism, and virus infections may damage hepatocytes, thereby altering the synthesis of TG and cholesterol. Consistent with previous studies ( 6 , 7 , 13 , 18 ), our data revealed that non-surviving COVID-19 patients exhibited significant abnormal liver function, as evidenced by increased levels of ALT, AST, and TBil. Impaired liver function also led to abnormal coagulation function, with significantly prolonged PT and APTT in non-survivals. Pro-inflammatory cytokines IL-6, IL-1b and TNF-a and regulate lipid metabolism though altering liver synthesis and decomposition functions and reducing cholesterol transport ( 21 ). Thirdly, medication side effects may generate dyslipidemia. For instance, tocilizumab has a significant correlation to high TG in COVID-19 patients ( 22 ). In the present study, the non-survival group had a higher proportion of received the treatment of immunoglobulin, with a higher level of TG. Fourthly, the massive replication of the virus may consume lipids, leading to dyslipidemia ( 23 ). Fifthly, increased free radicals in host cells infected by the virus may rapidly degrade lipids leading the decreased levels of LDL, HDL, and total cholesterol ( 24 ). Finally, vascular permeability may be easily altered by virus infection in critical patients with COVID-19 so that exudates could be formed in tissues, such as alveolar spaces, accumulated by a series of leaked cholesterol particles ( 25 ). There is indeed a link between lipid abnormalities and poor outcomes in COVID-19 patients. Our research has shown that dyslipidemia can predict a worst prognosis, which in line with previous reports that it is an independent risk factor of for a bad outcome. Dyslipidemia may also exacerbate both the disease severity and mortality of patients with COVID-19 ( 10 ). The role of cholesterol in immunity is increasingly recognized in multiple observational studies. It has been suggested that low cholesterol levels could be regarded as a marker for a worse prognosis in sepsis patient ( 26 ). COVID-19 patients with lower LDL levels are more likely to experience immunological and inflammatory dysfunction, renal dysfunction, hepatic dysfunction, and cardiac dysfunction ( 9 ). Decreased LDL level indicates poor prognosis of severe and critical COVID-19 patients ( 9 ). In the present study, LDL levels of non-survivals were significantly decreased from W1 to W3 than those of survivals, though survivals had higher levels of LDL than non-survivals at W1, W2 and W3. Conversely, a recent meta-analysis of retrospective cohort reported raised LDL is a risk factor for severe COVID-19 infection ( 27 ). Both high and low levels of LDL were markedly related with bad outcome of COVID-19 ( 28 ). Thus, the role of LDL in COVID-19 is complex and remained controversial. TC, HDL and LDL were significantly correlated with organ dysfunction (eg. BUN, CK, BNP) in this study. Furthermore, the endothelium may suffer from an elevation of TG and a drop of HDL in COVID-19 patients ( 13 ), which can contribute to endothelial dysfunction and coagulopathy, both of which are severe and potentially fatal risk factors for COVID-19 ( 29 ). Our data showed that TC, HDL and LDL were significantly associated with D-dimer. It also reported that D-dimer levels greater than 1ug /ml can help identify COVID-19 patients with poor prognosis ( 30 ). Our results have confirmed that the D-dimer levels of non-surviving patients were significantly higher than 11ug /ml and significantly associated with levels of TC, HDL and LDL at all four time points. Dynamic monitoring of D-dimer is more useful than cross-sectional studies because it can help track the evolution of the disease and facilitate the study of its pathogenesis ( 19 ) , ( 31 ). In contrast to traditional markers of severity, such as CRP, PCT, and cytokines, which have short half-lives and unpredictable peaks that are only briefly associated with disease severity, HDL has a greater predictive value because of its immediate decline and long-term stability ( 32 ). Therefore, our study of dynamic lipid profile, which are relatively stable parameters, is significance for predicting poor prognosis. This is a supplementary finding to a previous study that suggested a correlation between HDL and a higher risk of developing severe events in COVID-19 patients ( 33 ). Limitations: There are several limitations to this study. Firstly, it is a retrospective analysis conducted in a single center. Not all patients in the study had blood lipid measurements at each time point, and as the time point increased, there were fewer instances of blood lipid results available. Meanwhile, we lack the information regarding the lipid levels and lipid-regulating medication use of the study population prior to disease onset. Secondly, we lack information regarding the lipid levels and lipid-lowering medication use of the study population prior to disease onset. Therefore, considering the intricate nature of dyslipidemia in COVID-19 patients, it is imperative to conduct extensive prospective studies with a substantial sample size to gain a thorough comprehension of the condition. Conclusion In conclusion, our findings indicate that TC, HDL, and LDL are significantly predictive of short-term mortality in COVID-19 patients. Therefore, clinicians should be particularly vigilant when hyperlipemia persists, especially during the third week of disease progression. Declarations Data availability statement The datasets presented in this article are not readily available. Please contact with the corresponding authors. Requests to access the datasets should be directed to xlz, [email protected] . Ethics statement The study was approved by the institutional ethics board of the Second Xiangya Hospital of Central South University (No.2020001). The ethics committee waived the requirement of written informed consent for participation. Written informed consent from the participants’ legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements. Written informed consent was not obtained from the individual(s), nor the minor(s)’ legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article. Author contributions XLZ, JYD, YHY and YZ were involved in study design, interpreting data, statistical analysis, and writing of the manuscript. FLL, TZ, MX, CW and GW were involved in collecting data. All authors contributed to the article and approved the submitted version. Funding This work was supported by the Scientific Research Launch Project for new employees of the Second Xiangya Hospital of Central South University. Conflict of interest All authors declare that there was no conflict of interest. References Wang H, Paulson KR, Pease SA, Watson S, Comfort H, Zheng P, Aravkin AY, Bisignano C, Barber RM, Alam T, Fuller JE, May EA, Jones DP, Frisch ME, Abbafati C, Adolph C, Allorant A, Amlag JO, Bang-Jensen B, Bertolacci GJ, Bloom SS, Carter A, Castro E, Chakrabarti S, Chattopadhyay J, Cogen RM, Collins JK, Cooperrider K, Dai X, Dangel WJ, Daoud F, Dapper C, Deen A, Duncan BB, Erickson M, Ewald SB, Fedosseeva T, Ferrari AJ, Frostad JJ, Fullman N, Gallagher J, Gamkrelidze A, Guo G, He J, Helak M, Henry NJ, Hulland EN, Huntley BM, Kereselidze M, Lazzar-Atwood A, LeGrand KE, Lindstrom A, Linebarger E, Lotufo PA, Lozano R, Magistro B, Malta DC, Mansson J, Herrera AMM, Marinho F, Mirkuzie AH, Misganaw AT, Monasta L, Naik P, Nomura S, O'Brien EG, O'Halloran JK, Olana LT, Ostroff SM, Penberthy L, Reiner Jr RC, Reinke G, Ribeiro ALP, Santomauro DF, Schmidt MI, Shaw DH, Sheena BS, Sholokhov A, Skhvitaridze N, Sorensen RJD, Spurlock EE, Syailendrawati R, Topor-Madry R, Troeger CE, Walcott R, Walker A, Wiysonge CS, Worku NA, Zigler B, Pigott DM, Naghavi M, Mokdad AH, Lim SS, Hay SI, Gakidou E, Murray CJL, Collabor C-EM. Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020-21 [Article]. Lancet. 2022;399(10334):1513–1536. doi: 10.1016/s0140-6736(21)02796-3 . Cited in: Pubmed; PMID WOS:000795611300024. WHO. Weekly epidemiological update on COVID-19–14 Jan 2024. 2024. Available from: https://data.who.int/dashboards/covid19/cases?n=c . Vnucak M, Granak K, Beliancinova M, Jesenak M, Machalekova KK, Benko J, Samos M, Dedinska I. Acute kidney rejection after anti-SARS-CoV-2 virus-vectored vaccine-case report. NPJ Vaccines. 2022;7(1):30. The authors declare no competing interests. Epub 20220302. doi: 10.1038/s41541-022-00445-5 . Cited in: Pubmed; PMID 35236844. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, Xia Ja, Yu T, Zhang X, Zhang L. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study [Article]. Lancet. 2020;395(10223):507–513. doi: 10.1016/s0140-6736(20)30211-7 . Cited in: Pubmed; PMID WOS:000514576900033. Filippas-Ntekouan S, Liberopoulos E, Elisaf M. Lipid testing in infectious diseases: possible role in diagnosis and prognosis. Infection. 2017;45(5):575–588. Epub 20170508. doi: 10.1007/s15010-017-1022-3 . Cited in: Pubmed; PMID 28484991. Yue J, Xu H, Zhou Y, Liu W, Han X, Mao Q, Li S, Tam LS, Ma J, Liu W. Dyslipidemia Is Related to Mortality in Critical Patients With Coronavirus Disease 2019: A Retrospective Study. Front Endocrinol (Lausanne). 2021;12:611526. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Epub 20210623. doi: 10.3389/fendo.2021.611526 . Cited in: Pubmed; PMID 34248834. Li Y, Zhang Y, Lu R, Dai M, Shen M, Zhang J, Cui Y, Liu B, Lin F, Chen L, Han D, Fan Y, Zeng Y, Li W, Li S, Chen X, Li H, Pan P. Lipid metabolism changes in patients with severe COVID-19. Clin Chim Acta. 2021;517:66–73. eng. Epub 20210224. doi: 10.1016/j.cca.2021.02.011 . Cited in: Pubmed; PMID 33639119. Masana L, Correig E, Ibarretxe D, Anoro E, Arroyo JA, Jerico C, Guerrero C, Miret M, Naf S, Pardo A, Perea V, Perez-Bernalte R, Plana N, Ramirez-Montesinos R, Royuela M, Soler C, Urquizu-Padilla M, Zamora A, Pedro-Botet J, group S-Xr. Low HDL and high triglycerides predict COVID-19 severity. Sci Rep. 2021;11(1):7217. L. Masana: Has received personal fees for lectures and/or advisory work from Amgen, Sanofi, Mylan, Servier, MSD, Novartis, and Daiichi-Sankyo.N. Plana: Personal fees for lectures from Amgen, Mylan, and Sanofi. J. Pedro-Botet: Personal fees for lectures and/or advisory work from Amgen, Astra-Zeneca, Esteve, Ferrer, MSD, Mylan, Sanofi, Servier, and Rovi. C. Soler: Personal fees for lectures from Almirall, MSD, VISO farmaceutica, Mylan, and Esteve. M. Miret: Personal fees for lectures and/or advisory work from Sanofi, Novartis, Novonordisk, Astra-Zeneca, Esteve, Lilly, MSD, Ferrer, and Janssen, C. Jerico: Personal fees for lectures and/or advisory work from Sanofi, Amgen, MSD, Ferrer, Astra-Zeneca, Boehringer-Ingelheim, Lilly, Esteve, Novo-Nordisk, and Rovi. D. Ibarretxe: Personal fees for lectures from Sanofi, Rubio.E. Anoro, E. Correig, A. Pardo, C. Guerrero, S. Naf, JA. Arroyo, M. Urquizu-Padilla, V. Perea, R. Perez-Berbalte, R. Ramirez-Montesinos, M. Royuela, and A. Zamora: Declare no conflicts of interest. Epub 20210330. doi: 10.1038/s41598-021-86747-5 . Cited in: Pubmed; PMID 33785815. Zhao M, Luo Z, He H, Shen B, Liang J, Zhang J, Ye J, Xu Y, Wang Z, Ye D, Wang M, Wan J. Decreased Low-Density Lipoprotein Cholesterol Level Indicates Poor Prognosis of Severe and Critical COVID-19 Patients: A Retrospective, Single-Center Study. Front Med (Lausanne). 2021;8:585851. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Epub 20210526. doi: 10.3389/fmed.2021.585851 . Cited in: Pubmed; PMID 34124081. Atmosudigdo IS, Lim MA, Radi B, Henrina J, Yonas E, Vania R, Pranata R. Dyslipidemia Increases the Risk of Severe COVID-19: A Systematic Review, Meta-analysis, and Meta-regression. Clin Med Insights Endocrinol Diabetes. 2021;14:1179551421990675. Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Epub 20210324. doi: 10.1177/1179551421990675 . Cited in: Pubmed; PMID 35173508. Dai W, Lund H, Chen Y, Zhang J, Osinski K, Jones SZ, Kreuziger LB, Lopez JA, Benjamin IJ, Silverstein RL, Zheng Z. Hypertriglyceridemia during hospitalization independently associates with mortality in patients with COVID-19. J Clin Lipidol. 2021 Sep-Oct;15(5):724–731. Epub 2021/09/03. doi:10.1016/j.jacl.2021.08.002. Cited in: Pubmed; PMID 34470719. Aparisi A, Iglesias-Echeverria C, Ybarra-Falcon C, Cusacovich I, Uribarri A, Garcia-Gomez M, Ladron R, Fuertes R, Candela J, Tobar J, Hinojosa W, Duenas C, Gonzalez R, Nogales L, Calvo D, Carrasco-Moraleja M, San Roman JA, Amat-Santos IJ, Andaluz-Ojeda D. Low-density lipoprotein cholesterol levels are associated with poor clinical outcomes in COVID-19. Nutr Metab Cardiovasc Dis. 2021;31(9):2619–2627. Declaration of competing interest None. Epub 20210706. doi: 10.1016/j.numecd.2021.06.016 . Cited in: Pubmed; PMID 34353699. Sampedro-Nunez M, Aguirre-Moreno N, Garcia-Fraile Fraile L, Jimenez-Blanco S, Knott-Torcal C, Sanz-Martin P, Fernandez-Jimenez G, Marazuela M. Finding answers in lipid profile in COVID-19 patients. Endocrine. 2021;74(3):443–454. The authors declare no competing interests. Epub 20211019. doi: 10.1007/s12020-021-02881-0 . Cited in: Pubmed; PMID 34668172. Zhang JJ, Cao YY, Tan G, Dong X, Wang BC, Lin J, Yan YQ, Liu GH, Akdis M, Akdis CA, Gao YD. Clinical, radiological, and laboratory characteristics and risk factors for severity and mortality of 289 hospitalized COVID-19 patients. Allergy. 2021;76(2):533–550. CA reports grants from Allergopharma, Idorsia, Swiss National Science Foundation, Christine Kuhne-Center for Allergy Research and Education, European Commission's Horizon's 2020 Framework Programme, Cure, Novartis Research Institutes, AstraZeneca, Scibase, and advisory role in Sanofi/Regeneron. All other authors declare no conflict of interest outside the submitted work. Epub 20200824. doi: 10.1111/all.14496 . Cited in: Pubmed; PMID 32662525. Baycan OF, Bolen F, Atalay B, Agirbasli M. Prognostic significance of HDL-C on long-term mortality in patients with COVID-19 pneumonia in the Turkish population: A potential mechanism for population differences. Bosn J Basic Med Sci. 2022;22(6):1016–1024. Conflicts of interest: Authors declare no conflicts of interest. Epub 20221023. doi: 10.17305/bjbms.2022.7545 . Cited in: Pubmed; PMID 35997994. Ettinger WH, Varma VK, Sorci-Thomas M, Parks JS, Sigmon RC, Smith TK, Verdery RB. Cytokines decrease apolipoprotein accumulation in medium from Hep G2 cells. Arterioscler Thromb. 1994;14(1):8–13. doi: 10.1161/01.atv.14.1.8 . Cited in: Pubmed; PMID 8274481. Lavillegrand JR, Garnier M, Spaeth A, Mario N, Hariri G, Pilon A, Berti E, Fieux F, Thietart S, Urbina T, Turpin M, Darrivere L, Fartoukh M, Verdonk F, Dumas G, Tedgui A, Guidet B, Maury E, Chantran Y, Voiriot G, Ait-Oufella H. Elevated plasma IL-6 and CRP levels are associated with adverse clinical outcomes and death in critically ill SARS-CoV-2 patients: inflammatory response of SARS-CoV-2 patients. Ann Intensive Care. 2021;11(1):9. eng. The authors declare that they have no competing interests. Epub 20210113. doi: 10.1186/s13613-020-00798-x . Cited in: Pubmed; PMID 33439360. Chen X, Gao W, Li J, You D, Yu Z, Zhang M, Shao F, Wei Y, Zhang R, Lange T, Wang Q, Chen F, Lu X, Zhao Y. A predictive paradigm for COVID-19 prognosis based on the longitudinal measure of biomarkers. Brief Bioinform. 2021;22(6). eng. doi: 10.1093/bib/bbab206 . Cited in: Pubmed; PMID 34081102. Ye W, Chen G, Li X, Lan X, Ji C, Hou M, Zhang D, Zeng G, Wang Y, Xu C, Lu W, Cui R, Cai Y, Huang H, Yang L. Dynamic changes of D-dimer and neutrophil-lymphocyte count ratio as prognostic biomarkers in COVID-19. Respir Res. 2020;21(1):169. eng. The authors declared no competing interests. Epub 20200703. doi: 10.1186/s12931-020-01428-7 . Cited in: Pubmed; PMID 32620118. van Leeuwen HJ, Heezius EC, Dallinga GM, van Strijp JA, Verhoef J, van Kessel KP. Lipoprotein metabolism in patients with severe sepsis. Crit Care Med. 2003;31(5):1359–66. doi: 10.1097/01.CCM.0000059724.08290.51 . Cited in: Pubmed; PMID 12771603. Wei X, Zeng W, Su J, Wan H, Yu X, Cao X, Tan W, Wang H. Hypolipidemia is associated with the severity of COVID-19. J Clin Lipidol. 2020 May-Jun;14(3):297–304. Epub 20200430. doi: 10.1016/j.jacl.2020.04.008 . Cited in: Pubmed; PMID 32430154. Morrison AR, Johnson JM, Ramesh M, Bradley P, Jennings J, Smith ZR. Acute hypertriglyceridemia in patients with COVID-19 receiving tocilizumab. J Med Virol. 2020;92(10):1791–1792. The authors declare that there are no conflict of interests. Epub 20200603. doi: 10.1002/jmv.25907 . Cited in: Pubmed; PMID 32314799. Yan B, Chu H, Yang D, Sze KH, Lai PM, Yuan S, Shuai H, Wang Y, Kao RY, Chan JF, Yuen KY. Characterization of the Lipidomic Profile of Human Coronavirus-Infected Cells: Implications for Lipid Metabolism Remodeling upon Coronavirus Replication. Viruses. 2019;11(1). J.F.-W.C. has received travel grants from Pfizer Corporation Hong Kong and Astellas Pharma Hong Kong Corporation Limited, and was an invited speaker for Gilead Sciences Hong Kong Limited and Luminex Corporation. The other authors declared no conflict of interest. The funding sources had no role in study design, data collection, analysis or interpretation or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. Epub 20190116. doi: 10.3390/v11010073 . Cited in: Pubmed; PMID 30654597. Zidar DA, Juchnowski S, Ferrari B, Clagett B, Pilch-Cooper HA, Rose S, Rodriguez B, McComsey GA, Sieg SF, Mehta NN, Lederman MM, Funderburg NT. Oxidized LDL Levels Are Increased in HIV Infection and May Drive Monocyte Activation. J Acquir Immune Defic Syndr. 2015;69(2):154–60. Epub 2015/02/04. doi: 10.1097/QAI.0000000000000566 . Cited in: Pubmed; PMID 25647528. Tian S, Hu W, Niu L, Liu H, Xu H, Xiao SY. Pulmonary Pathology of Early-Phase 2019 Novel Coronavirus (COVID-19) Pneumonia in Two Patients With Lung Cancer. J Thorac Oncol. 2020;15(5):700–704. Epub 2020/03/03. doi: 10.1016/j.jtho.2020.02.010 . Cited in: Pubmed; PMID 32114094. Qasim A, Kousa O, Andukuri VG. Hypolipidemia contributing to the severity of sepsis triggered by influenza a virus: A case report. J Clin Transl Res. 2020;6(6):198–202. Epub 2021/02/11. Cited in: Pubmed; PMID 33564724. Hariyanto TI, Kurniawan A. Dyslipidemia is associated with severe coronavirus disease 2019 (COVID-19) infection. Diabetes Metab Syndr. 2020 Sep-Oct;14(5):1463–1465. Epub 2020/08/11. doi: 10.1016/j.dsx.2020.07.054 . Cited in: Pubmed; PMID 32771919. Gong J, Chen Y, Jie Y, Tan M, Jiang Z, Yuan L, Cao J, Li G, Chong Y, Qu J, Shi Y, Hu B. U-Shaped Relationship of Low-Density Lipoprotein Cholesterol With Risk of Severe COVID-19 From a Multicenter Pooled Analysis. Front Cardiovasc Med. 2021;8:604736. Epub 2021/09/11. doi: 10.3389/fcvm.2021.604736 . Cited in: Pubmed; PMID 34504873. Helms J, Tacquard C, Severac F, Leonard-Lorant I, Ohana M, Delabranche X, Merdji H, Clere-Jehl R, Schenck M, Fagot Gandet F, Fafi-Kremer S, Castelain V, Schneider F, Grunebaum L, Angles-Cano E, Sattler L, Mertes PM, Meziani F, Group CT. High risk of thrombosis in patients with severe SARS-CoV-2 infection: a multicenter prospective cohort study. Intensive Care Med. 2020;46(6):1089–1098. The authors have no conflicts of interest to declare. Epub 20200504. doi: 10.1007/s00134-020-06062-x . Cited in: Pubmed; PMID 32367170. Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, Guan L, Wei Y, Li H, Wu X, Xu J, Tu S, Zhang Y, Chen H, Cao B. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054–1062. Epub 20200311. doi: 10.1016/S0140-6736(20)30566-3 . Cited in: Pubmed; PMID 32171076. Lu G, Wang J. Dynamic changes in routine blood parameters of a severe COVID-19 case. Clin Chim Acta. 2020;508:98–102. eng. Epub 20200513. doi: 10.1016/j.cca.2020.04.034 . Cited in: Pubmed; PMID 32405079. Vavrova L, Rychlikova J, Mrackova M, Novakova O, Zak A, Novak F. Increased inflammatory markers with altered antioxidant status persist after clinical recovery from severe sepsis: a correlation with low HDL cholesterol and albumin. Clin Exp Med. 2016;16(4):557–569. Epub 20150928. doi: 10.1007/s10238-015-0390-1 . Cited in: Pubmed; PMID 26416404. Wang G, Zhang Q, Zhao X, Dong H, Wu C, Wu F, Yu B, Lv J, Zhang S, Wu G, Wu S, Wang X, Wu Y, Zhong Y. Low high-density lipoprotein level is correlated with the severity of COVID-19 patients: an observational study. Lipids Health Dis. 2020;19(1):204. The authors declare that they have no conflict of interest. Epub 20200907. doi: 10.1186/s12944-020-01382-9 . Cited in: Pubmed; PMID 32892746. Tables Table 1 Baseline characteristics of survival and non-survival COVID-19 patients Survival (n=76) Non-survival (n=59) P value Gender (male/female) 42 38 0.255 Smoking (n, %) 14 11 0.883 Age, y, M (IQR) 65 (48.25,70) 70 (63, 78) < 0.001 Symptoms Fever (n, %) 61 49 0.528 Fatigue (n, %) 27 26 0.275 Cough (n, %) 46 46 0.020 Shortness of breath (n, %) 15 22 0.020 Pharyngalgia (n, %) 6 4 0.827 Vomiting (n, %) 5 7 0.270 Diarrhea (n, %) 18 15 0.772 Abdominal pain (n, %) 5 4 0.687 Nausea (n, %) 10 8 0.915 Anorexia (n, %) 33 21 0.399 Myalgia (n, %) 16 10 0.580 Chill (n, %) 14 19 0.056 Dizziness (n, %) 3 4 0.466 Headache (n, %) 13 7 0.418 Hemoptysis (n, %) 1 2 0.578 Comorbidities Hypertension (n, %) 30 24 0.887 Cerebrovascular disease (n, %) 2 6 0.140 Diabetes (n, %) 19 15 0.955 COPD (n, %) 1 2 0.581 Tumor (n, %) 5 5 0.677 Therapy High flow oxygen therapy (n, %) 15 28 0.001 Non-invasive ventilator (n, %) 5 43 < 0.001 Invasive ventilator (n, %) 1 50 < 0.001 Glucocorticoid (n, %) 29 44 < 0.001 Immunoglobulin 13 21 0.014 Table 2 Laboratory findings of survival and non-survival COVID-19 patients Survival (n=76) Non-survival (n=59) P value WBC, ×10 9 /L, M (IQR) 6.07 (4.62,8.44) 11.27 (6.97,15.52) < 0.001* Lymphocyte count, ×10 9 /L, M (IQR) 1.29 (0.86,1.56) 0.53 (0.38,0.7) < 0.001* Neutrophil count, ×10 9 /L, M (IQR) 3.86 (3,6.04) 10.22 (5.82,13.37) < 0.001* HGB, g/L, M (IQR) 125 (112,133) 129 (105,140) 0.370 PLT, ×10 9 /L, M (IQR) 242 (176,358) 159 (105,224) < 0.001* ESR, mm/h, M (IQR) 35 (5,59.5) 33 (12,63) 0.562 CRP, mg/L, M (IQR) 16.9 (2.475,58.9) 99.5 (61.5,186.3) < 0.001* ALT, U/L, M (IQR) 24 (16.25,38) 32 (20,47) 0.032* AST, U/L, M (IQR) 23 (18,32.75) 44 (28,65) < 0.001* Tbil, μmol/L, M (IQR) 9.4 (6.85,12.675) 13.4 (9.3,20.3) < 0.001* Alb, g/L, M (IQR) 35.6 (31.325,39.45) 29.8 (26.4,33.2) < 0.001* SCr, mmol/L, M (IQR) 72 (60,84.75) 95 (72,160) < 0.001* CK, U/L, M (IQR) 42.5 (23,124.75) 145.5 (66.25,475.25) < 0.001* CK-MB, M (IQR) 0.4 (0.2,0.65) 2.1 (0.3,7.5) < 0.001* Prothrombin time, s, M (IQR) 13.95 (13.4,14.4) 15.3 (14.5,18.6) < 0.001* APTT, s, M (IQR) 39 (36.025,42.6) 39.8 (34.4,46.2) 0.425 D-dimer, ug/mL, M (IQR) 0.64 (0.2925,1.86) 5.78 (1.77,21) < 0.001* Chest CT positive rate (n, %) 43 33 0.940 * means a significant difference, WBC: White Blood Cell Count, ESR: erythrocyte sedimentation rate, CRP: C-reactive protein, ALT: alanine aminotranspherase AST: aspartate aminotransferase, TBil: total bilirubin, SCr: serum creatinine, CK: Creatine kinase, CK-MB: Creatine kinase isoenzyme, APTT: activated partial thromboplastin time. Table 3 Blood lipid levels in the first four weeks after admission of survival and non-survival COVID-19 patients Survival (N = 76) Non-survival (N=59) P value TC-W1, mmol/L, M (IQR) 3.87(3.36, 4.37) 2.83(2.38, 3.56) < 0.001* HDL-W1, mmol/L, M (IQR) 0.92(0.79, 1.13) 0.69(0.50, 0.90) < 0.001* LDL-W1, mmol/L, M (IQR) 2.45(2.06, 3.05) 1.53(1.13, 2.19) < 0.001* TG-W1, mmol/L, M (IQR) 1.29(0.90, 1.82) 1.52(1.21, 2.31) 0.011 TC-W2, mmol/L, M (IQR) 3.92(3.40, 4.84) 2.62(2.03, 3.44) < 0.001* HDL-W2, mmol/L, M (IQR) 1.05(0.90, 1.26) 0.72(0.58, 0.95) < 0.001* LDL-W2, mmol/L, M (IQR) 2.42(2.14, 3.14) 1.35(0.96, 2.07) < 0.001* TG-W2, mmol/L, M (IQR) 1.55(1.08, 1.84) 1.44(0.84, 2.20) 0.925 TC-W3, mmol/L, M (IQR) 4.79(3.91, 5.27) 3.23(2.07, 3.60) < 0.001* HDL-W3, mmol/L, M (IQR) 1.03(0.90, 1.42) 0.54(0.36, 0.85) < 0.001* LDL-W3, mmol/L, M (IQR) 2.99(2.29, 3.43) 1.92(0.81, 2.28) < 0.001* TG-W3, mmol/L, M (IQR) 1.29(0.90, 1.88) 1.51(1.05, 3.42) 0.362 TC-W4, mmol/L, M (IQR) 4.7(3.24, 5.07) 3.17(1.97, 3.80) 0.054 HDL-W4, mmol/L, M (IQR) 1.00(0.90, 1.12) 0.58(0.47, 0.82) 0.004 LDL-W4, mmol/L, M (IQR) 2.79(2.04, 3.43) 1.91(0.67, 2.25) 0.040 TG-W4, mmol/L, M (IQR) 1.44(1.17, 2.22) 2.04(1.37, 2.81) 0.397 TC: Total cholesterol, HDL: High-density lipoprotein, LDL: Low-density lipoprotein, TG: Triglyceride, W: week. Table 4 Changes of blood lipid in the first four weeks after admission of survival and non-survival COVID-19 patients Survival (N = 76) Non-survival (N=59) P value TC-W 2-1 , mmol/L, M (IQR) 0.178 (-0.280, 0.840) -0.600 (-1.123, 0.174) 0.038 HDL-W 2-1 , mmol/L, M (IQR) 0.075 (-0.078, 0.421) -0.090 (-0.311, 0.124) 0.048 LDL-W 2-1 , mmol/L, M (IQR) 0.145 (-0.580, 0.543) -0.653 (-0.841, 0.098) 0.048 TG-W 2-1 , mmol/L, M (IQR) -0.185 (-0.328, 0.461) -0.240 (-0.540, 0.182) 0.325 TC-W 3-1 , mmol/L, M (IQR) 1.100 (0.568, 1.450) -1.205 (-1.751, -0.304) 0.003 HDL-W 3-1 , mmol/L, M (IQR) 0.130 (-0.024, 0.550) -0.268 (-0.382, -0.146) 0.006 LDL-W 3-1 , mmol/L, M (IQR) 0.660 (0.493, 0.900) -0.803 (-1.163, -0.318) 0.011 TG-W 3-1 , mmol/L, M (IQR) 0.470 (-0.170, 0.874) 0.293 (-0.060, 2.051) 0.724 TC: Total cholesterol, HDL: High-density lipoprotein, LDL: Low-density lipoprotein, TG: Triglyceride, W: week. In this table, lipids-W 4-1 and lipids-W3-2 were not shown for the small number of sample size. Additional Declarations No competing interests reported. Supplementary Files supplementaryfigureandtable.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4386935","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":304837205,"identity":"0c926200-d140-41b8-9a11-9a8d81655c5b","order_by":0,"name":"Jiayi Deng","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jiayi","middleName":"","lastName":"Deng","suffix":""},{"id":304837207,"identity":"d5381ad7-f7c5-4d45-9ff9-195f7b4a4b21","order_by":1,"name":"Yihao Yuan","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yihao","middleName":"","lastName":"Yuan","suffix":""},{"id":304837209,"identity":"d128889b-0e47-4287-aa1a-ca984d54c184","order_by":2,"name":"Ting Zhang","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Zhang","suffix":""},{"id":304837210,"identity":"2a26aa3d-418f-4d5f-9f72-f97558e867c1","order_by":3,"name":"Fanglin Li","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Fanglin","middleName":"","lastName":"Li","suffix":""},{"id":304837211,"identity":"e7ddf44c-bd46-4b44-b6e3-bf5ec3144d8d","order_by":4,"name":"Min Xu","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Xu","suffix":""},{"id":304837212,"identity":"2958633a-16fe-4fc5-90be-ed37496fe002","order_by":5,"name":"Guobao Wu","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Guobao","middleName":"","lastName":"Wu","suffix":""},{"id":304837213,"identity":"e26f5921-950e-4498-ade7-f799e1c5bf9b","order_by":6,"name":"Chenfang Wu","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Chenfang","middleName":"","lastName":"Wu","suffix":""},{"id":304837214,"identity":"64055363-0439-4fba-b5c8-547d563e884d","order_by":7,"name":"Yanjun Zhong","email":"","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yanjun","middleName":"","lastName":"Zhong","suffix":""},{"id":304837218,"identity":"b75cbc6c-c758-445d-bb08-24371f26cffa","order_by":8,"name":"Xiaoli Zhong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYDACZiB+YMDAYADifICIGRDWkgDVwjiDKC0gkABVxsxDjBaD48wPHyQUMNibs/cefm1TU5fYwN68TYKh5g5OLZLNbMYGQIcxW/acS7POOXY4sYHnWJkEw7FnOLXwMzOYSQC1sBncyDEzzm04kNggkWMmwdhwGKcWNmb2byAtPGAtlg1Ah8m/wa+Fn5kHbIsEUIvxY8YGZqAtPPi1SDbzFAP9ImFgcOaMGWPPscPGbTxpxRYJx3BrMTh/fOODD39s7A2O9xh/+FFTJ9vPfnjjjQ81uLVAgQTYXxASRCQQ0gAFzB+IVDgKRsEoGAUjDAAAm4VK/JEUVBoAAAAASUVORK5CYII=","orcid":"","institution":"Second Xiangya Hospital of Central South University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Zhong","suffix":""}],"badges":[],"createdAt":"2024-05-08 06:26:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4386935/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4386935/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57291852,"identity":"79bb533e-1b53-4f10-8ed4-f0f908a0868a","added_by":"auto","created_at":"2024-05-28 18:01:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":277313,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves were conducted to evaluate the predictive roles of lipid parameters at different time points for forecasting mortality among COVID-19 patients. A-D: ROC curves for TC, HDL, LDL and TG in W1; E-G: ROC curves for TC, HDL, and LDL in W2; H-J: ROC curves for TC, HDL, and LDL in W3; TG in W2 and W3 were not shown for the low AUCs. TC: Cholesterol; HDL: high-density lipoprotein; LDL: low-density lipoprotein; TG: triglycerides; W: week; AUC: area under the curve.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4386935/v1/bb7c79814c2f84bbd00a4d1e.png"},{"id":57292642,"identity":"71ebd28b-e194-45a3-aa42-f4ad1dc05498","added_by":"auto","created_at":"2024-05-28 18:09:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2609063,"visible":true,"origin":"","legend":"\u003cp\u003eThe associations between lipid profiles and other laboratory parameters using Spearman correlation analyses at W1 (A), W2 (B), W3 (C) and W4 (D). The size of the ball indicates the correlation coefficient. TC: Cholesterol, HDL: high-density lipoprotein, LDL: low-density lipoprotein, TG: triglyceride, WBC: White Blood Cell Count, HGB: Hemoglobin, PLT: Platelet, Lys: Lymphocyte count, MO%: Neutrophil ratio, EO%: Eosinophil ratio, CRP: C-reactive protein, PCT: Procalcitonin, ESR: Erythrocyte sedimentation, PT: Prothrombin time, APTT: Partial prothrombin time, INR: International standardized ratio, Fib: Fibrogen, ESR: erythrocyte sedimentation rate, ALT: alanine aminotranspherase AST: aspartate aminotransferase, TBil: total bilirubin, Alb: albumin, Cr: serum creatinine, CK: Creatine kinase, CK-MB: Creatine kinase isoenzyme, LDH: Lactate dehydrogenase, TNT-I: Troponin, BNP: Brain natriuretic peptide.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4386935/v1/e46f1c9e115188110e23cd90.png"},{"id":57291851,"identity":"29d535b9-9b30-40a3-80ac-ca59bd804ae4","added_by":"auto","created_at":"2024-05-28 18:01:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":142472,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves were conducted to evaluate the predictive roles of changes in lipid parameters for forecasting mortality among COVID-19 patients. A-C: ROC curves for TC, HDL, and LDL in W\u003csub\u003e2-1\u003c/sub\u003e; D-F: ROC curves for TC, HDL, and LDL in W\u003csub\u003e3-1\u003c/sub\u003e; triglycerides in W\u003csub\u003e2-1\u003c/sub\u003e and W\u003csub\u003e3-1\u003c/sub\u003e were not shown for the low AUCs. TC: Cholesterol; HDL: high-density lipoprotein; LDL: low-density lipoprotein; W: week; AUC: area under the curve.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4386935/v1/be20f5c9cf0a00df95128057.png"},{"id":59493052,"identity":"9405ba7c-ecbc-4146-b580-81968325ca6d","added_by":"auto","created_at":"2024-07-02 12:34:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3484878,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4386935/v1/5bd9efe4-5b99-4932-a5d9-8d2269cd1d22.pdf"},{"id":57291855,"identity":"4dd1e3b6-0054-4be9-940d-6909f992ea49","added_by":"auto","created_at":"2024-05-28 18:01:02","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":379768,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfigureandtable.docx","url":"https://assets-eu.researchsquare.com/files/rs-4386935/v1/3359c7e6bea0454269784856.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The predictive values of dynamic blood lipid profile for mortality in COVID-19 patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe COVID-19 pandemic continues to spread uncontrollably around the world, resulting in a considerable number of fatalities (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Global reports as of 14 Jan 2024, showed over 774\u0026nbsp;million confirmed cases and over 7.0\u0026nbsp;million deaths attributed to COVID-19 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Despite the development and deployment of numerous efficient vaccines, COVID-19 continues to pose a substantial risk to the well-being of the general public (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The severity of the illness varies among individuals, with some experiencing mild flu-like symptoms and recovering quickly, while others tragically deteriorated and succumb to the disease (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). It is crucial to identify and anticipate the risk factors associated with COVID-19 patient mortality for healthcare providers, to intervene timely and effectively, thereby reducing the chances of death.\u003c/p\u003e \u003cp\u003eThe human body undergoes significant changes in response to acute infectious diseases caused by viruses, bacteria, or parasites. Among these changes, lipid metabolism is particularly affected (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In the cases of COVID-19, it has been observed that lipid metabolism disorders are common in severe or critical cases, with low levels of total cholesterol (TC), high-density lipoprotein (HDL-C), and low-density lipoprotein (LDL-C) being associated with disease progression. In contrast, high levels of triglycerides (TG) are considered a predictor of COVID-19 severity (\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe association between levels of lipid profiles and mortality in COVID-19 patients has become the subject of increasing interest. Dyslipidemia has been demonstrated to be significantly related to higher mortality and severity of COVID-19, according to a recent meta-analysis (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Moreover, several studies have shown that lipid profile changes can predict COVID-19 mortality (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). For example, in the early stage of the pandemic, TC levels measured 2\u0026ndash;3 days after admission were found to be linked to mortality in severe COVID-19 cases (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Additionally, lower levels of LDL-C and TC throughout the course of the disease were associated with non-survivors, and both were negatively correlated with inflammatory markers. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Other studies have also shown that changes in specific lipid components, such as HDL-C, can be particularly indicative of COVID-19 mortality risk. For example, in one study of 424 COVID-19 patients, non-survivors showed a dramatic decrease in HDL-C levels (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Consistently, another study manifested that higher levels of HDL-C, TC, and LDL-C were found to offer protection to mortality (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, many of these studies only have cross-sectional data for a single time point, making it difficult to evaluate the dynamic changes in lipid profiles or continuously monitor lipid-related test indicators and their associations with COVID-19 mortality. Therefore, this paper explores the predictive value of changes in lipid profiles for COVID-19 mortality based on longitudinal data. By analyzing the dynamic changes in lipid profiles over time, we can gain a better understanding of how lipid metabolism disorders contribute to COVID-19 severity and mortality, and identify potential targets for therapeutic interventions. Ultimately, this research may lead to more effective treatments for COVID-19 patients and improve their chances of survival.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign and Participants\u003c/h2\u003e \u003cp\u003eWe carried out an observational and retrospective study at Zhongfa Hospital, Wuhan, China between January 1 and March 26, 2020. The study enrolled patients diagnosed with COVID-19 who were admitted successively during that period. None of the patients had other familial dyslipidemia syndromes. Of the 135 patients who underwent at least one lipid test, 76 survived (survival group) while 59 died (non-survival group). Reverse-transcriptase\u0026ndash;polymerase-chain-reaction (RT-PCR) was used to establish the presence of COVID-19. The primary outcome of the study was defined as in-hospital death. The study was approved by the Ethics Committee of the Second Xiangya Hospital, Central South University (No. 2020001).And all methods were performed in accordance with the relevant guidelines and regulations. Because the study was retrospective and illness was a newly developing infectious disease, the thics Committee of the Second Xiangya Hospital, Central South University waived the requirement for written informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of laboratory parameters\u003c/h2\u003e \u003cp\u003eBlood routine and coagulation function were measured by XN-20 (Sysmex, Germany) and CS5100 (Sysmex, Germany), respectively. Levels of TC, TG, HDL-C and LDL-C as well as liver and renal function, were determined using immunoturbidimetry by an automatic system COBAS 8000 (Roche, Barcelona, Switzerland). Erythrocyte sedimentation rate (ESR) and C reactive protein (CRP) were detected using Monitor 100 (Jumu Dedical Devices Co., Ltd, Shanghai, China) and Immage 800 (Beckman coulter Inc., United States), respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eWe extracted clinical records and laboratory parameters of the COVID-19 patients through the electronic medical system. The baseline characteristics, symptoms, comorbidities, treatment, outcomes (survival or death) and laboratory findings at admission were also collected and evaluated, including Blood routine [(white blood cell (WBC), hemoglobin (HGB), platelet (PLT)], infection markers (ESR, CRP), coagulation function [prothrombin time (PT), activated partial thromboplastin time (APTT), D-dimer)], liver function [(ALT, AST, Tbil, Alb)], renal function [serum creatinine (SCr)], cardiac function [creatine kinase (CK), creatine kinase isoenzyme (CK-MB)] and the situation of chest CT results. Blood lipid (TC, TG, HDL-C, LDL-C) examination results were mainly collected from the first four weeks after admission. Two experienced clinicians reviewed and verified all data independently. Meanwhile, the data of dynamic lipids were used to calculate the following parameters:\u003c/p\u003e \u003cp\u003e(1) Lipid-W\u003csub\u003ex-1\u003c/sub\u003e = lipid W\u003csub\u003ex\u003c/sub\u003e \u0026ndash; lipid W\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(2) Lipid-standard deviation (SD) as the SD of the levels of lipid parameters (\u0026ge;\u0026thinsp;2 time points)\u003c/p\u003e \u003cp\u003e(3) Lipid- coefficient of variation (CV) as the CV of the lipids were calculated using SD/Mean *100%\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll analyses and drawing were conducted using SPSS version 26.0 and R studio. Either the χ2 test or Fisher\u0026rsquo;s exact test were used to analyze the categorical variables, representing as numbers (percentages, %). For continuous variables, medians with interquartile range (IQR) were provided and compared using the Mann-Whitney U test for the non-normal distribution. To evaluate the sensitivity and specificity of TC, HDL-C, LDL-C, and TG levels each week, their changes compared to the first week, and the coefficient of variation of these four indicators in predicting death, receiver operating characteristic (ROC) curves were constructed. The area under the curve (AUC) was computed, where higher values indicate stronger discriminatory power. Spearman correlation analyses were conducted to demonstrate the associations between lipid profiles and other laboratory parameters. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed) for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eDemographics and baseline characteristics\u003c/h2\u003e\n \u003cp\u003eThis study enrolled a total of 135 COVID-19 patients, and table 1 presents the demographics and baseline characteristics of the study population. During hospitalization, 59 patients died, while 76 survived throughout the study period. Common symptoms included fever, cough, shortness of breath, anorexia, fatigue, and chill. The non-survival group tend to be older [70 (63, 78) vs. 65 (48.25, 70) y, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001] and had high proportions of cough (78% vs. 60.5%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020) and shortness of breath (37.3% vs. 19%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020) compared to the survival group. Compared with the survivors, non-survivors exhibited a higher likelihood of having received a variety of therapies, such as high-flow oxygen (47.5% vs. 19.7%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), non-invasive ventilator (72.9% vs. 6.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), invasive ventilator (84.7% vs. 1.3%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), glucocorticoid (74.6% vs. 38.2%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and immunoglobulin (35.6% vs. 17.1%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eLaboratory findings\u003c/h2\u003e\n \u003cp\u003eThere were significant discrepancies in the laboratory findings upon admission between survivors and non-survivors (Table\u0026nbsp;2). Non-survivals exhibited substantially higher levels of WBC and neutrophils, ALT, AST, TBil, sCr, CK, CK-MB, CRP, and D-dimer, along with a significantly prolonged PT. Conversely, they had lower lymphocyte and platelet count, and Alb levels, compared to the surviving patients.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBlood lipid levels during the first four weeks of admission in the COVID-19 patients and the predictive accuracy of these lipid parameters for mortality\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWe compared the blood lipid levels of COVID-19 patients who survived and those who did not during the first four weeks after admission (Table 3). We monitored the lipid levels for four weeks to provide dynamic information on the blood lipid levels of both surviving and non-surviving patients and to confirm the potential markers for predicting mortality. The levels of HDL and LDL were significantly lower in non-survival at all the time points, and TC was significantly lower in non-survival except for the fourth week. The TG concentration of non-survivors was only significantly higher during the first week than in survivors. ROC curves were conducted to evaluate the predictive roles of lipid parameters at different time points for forecasting mortality among COVID-19 patients (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). During the first week, the AUCs of TC, HDL, LDL, and TG were 0.766, 0.770, 0.769, and 0.647. The AUCs of TC, HDL, and LDL were equally more than 0.7 at week 2 and week 3, and the value of AUC increased with time. Then, correlation analyses were conducted to demonstrate the associations between lipid parameters and other laboratory parameters at each time points. The results showed that TC, HDL and LDL were significantly associated with CRP, D-dimer, BUN, CK and BNP at all four time points, and Lys, PCT, PT, APTT, INR, Alb and TNT-I for three of these time points (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; Supplementary table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eChanges of blood lipid in the first four weeks after admission in COVID-19 patients and the predictive accuracy of the changes in lipid parameters for mortality\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFurthermore, we conducted an analysis of blood lipid levels changes in COVID-19 patients during the first four weeks. These findings may shed light on critical points of disease progression. Interestingly, there were significantly greater changes in non-survivals in TC, HDL, and LDL during the second and third weeks from the first week, compared to survivals (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). To assess the predictive value of lipid changes at various time points, we generated ROC curves for TC, HDL, LDL, and TG throughout the study period (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). During the second week, the corresponding AUCs for changes in TC, HDL, LDL, and TG were 0.730, 0.718, and 0.769. Notably, during the third week, the AUCs were 0.975, 0.950, and 0.925, respectively. Additionally, we also assessed the predictive value of standard deviation (SD) and coefficient of variation (CV) of lipids in mortality and the results showed that the AUCs were significantly lower than those of changes in TC, HDL, LDL, and TG (Supplementary Fig. 1).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective study examined the association between blood lipid status and mortality of COVID-19 patients. While previous studies have established the frequency of dyslipidemia among individuals with COVID-19, our focus was on the predictive value of dynamic changes in lipid levels for mortality. Our results indicate that non-surviving COVID-19 patients exhibit persistent dyslipidemia, characterized by reduced levels of HDL-C, LDL-C and TC, along with increased levels of TG. By comparing the changes in lipid parameters between surviving and non-surviving patients during the early stages of hospitalization, we observed that both the levels and fluctuations in TC, HDL, and LDL were strongly associated with mortality, with AUCs values exceeding 0.7. Notable, in the third week of hospitalization, the AUCs for TC, HDL, and LDL were 0.891, 0.895, and 0.879, respectively, while the AUCs for changes in these parameters were even higher, at 0.975, 0.950, and 0.925, respectively. Our findings shed light on the dynamic evolution of lipidology in COVID-19 patients and offer insights into predicting outcomes and understanding the mechanism underlying dyslipidemia.\u003c/p\u003e \u003cp\u003eThere have been several observational studies that have shown a significant correlation between blood lipid parameters and mortality. The available evidence suggests that non-survivals of COVID-19 patients exhibited decreased LDL, HDL and TC than survivors, with or without elevated TG levels(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Additionally, HDL has been identified as a predictor for one-year mortality after COVID-19 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Consequently, it appears that the lipid profile may play a crucial role in determining the prognosis of patients with COVID-19, both in the long-term and the short-term. Therefore, there is a significant need to investigate the evolution of the lipid parameters in COVID-19 patients. One of the pertinent questions to address is the underlying causes of dyslipidemia in patients with COVID-19.\u003c/p\u003e \u003cp\u003eThere were several possible explanations for the changes in lipid profile during COVID-19 infection. Firstly, SARS-CoV-2 induced acute inflammation may lead to dyslipidemia. Studies have shown that inflammatory mediators, such as interleukin-6 (IL-6), interleukin-1 beta (IL-1β), and tumor necrosis-alpha (TNF-α), can dose-dependently reduce the production and/or release of apolipoproteins in hepatic cell lines (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This suggests that a more pronounced dyslipidemia may result from a stronger inflammatory response. Our study found that non-surviving COVID-19 patients exhibited a robust inflammatory response, as indicated by higher levels of WBC, neutrophil count, and CRP, which is consistent with previous studies (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In sepsis patients, TC and HDL decreased during the acute phase and slowly increased within 28 days (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Similarly, our repeated measurement data demonstrated that lipid levels gradually recovered during the first four weeks in surviving COVID-19 patients, while hypolipidemia persisted in the non-survivors. Lipid metabolism and inflammatory response interact and regulate each other. Our study found that CRP were significantly correlated with TC, HDL and LDL at all four time points. Secondly, COVID-19 may impair liver function and affect lipid metabolism. The liver is essential for lipid synthesis and metabolism, and virus infections may damage hepatocytes, thereby altering the synthesis of TG and cholesterol. Consistent with previous studies (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), our data revealed that non-surviving COVID-19 patients exhibited significant abnormal liver function, as evidenced by increased levels of ALT, AST, and TBil. Impaired liver function also led to abnormal coagulation function, with significantly prolonged PT and APTT in non-survivals. Pro-inflammatory cytokines IL-6, IL-1b and TNF-a and regulate lipid metabolism though altering liver synthesis and decomposition functions and reducing cholesterol transport (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Thirdly, medication side effects may generate dyslipidemia. For instance, tocilizumab has a significant correlation to high TG in COVID-19 patients (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In the present study, the non-survival group had a higher proportion of received the treatment of immunoglobulin, with a higher level of TG. Fourthly, the massive replication of the virus may consume lipids, leading to dyslipidemia (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Fifthly, increased free radicals in host cells infected by the virus may rapidly degrade lipids leading the decreased levels of LDL, HDL, and total cholesterol (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Finally, vascular permeability may be easily altered by virus infection in critical patients with COVID-19 so that exudates could be formed in tissues, such as alveolar spaces, accumulated by a series of leaked cholesterol particles (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is indeed a link between lipid abnormalities and poor outcomes in COVID-19 patients. Our research has shown that dyslipidemia can predict a worst prognosis, which in line with previous reports that it is an independent risk factor of for a bad outcome. Dyslipidemia may also exacerbate both the disease severity and mortality of patients with COVID-19 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The role of cholesterol in immunity is increasingly recognized in multiple observational studies. It has been suggested that low cholesterol levels could be regarded as a marker for a worse prognosis in sepsis patient (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). COVID-19 patients with lower LDL levels are more likely to experience immunological and inflammatory dysfunction, renal dysfunction, hepatic dysfunction, and cardiac dysfunction (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Decreased LDL level indicates poor prognosis of severe and critical COVID-19 patients (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In the present study, LDL levels of non-survivals were significantly decreased from W1 to W3 than those of survivals, though survivals had higher levels of LDL than non-survivals at W1, W2 and W3. Conversely, a recent meta-analysis of retrospective cohort reported raised LDL is a risk factor for severe COVID-19 infection (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Both high and low levels of LDL were markedly related with bad outcome of COVID-19 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Thus, the role of LDL in COVID-19 is complex and remained controversial. TC, HDL and LDL were significantly correlated with organ dysfunction (eg. BUN, CK, BNP) in this study. Furthermore, the endothelium may suffer from an elevation of TG and a drop of HDL in COVID-19 patients (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), which can contribute to endothelial dysfunction and coagulopathy, both of which are severe and potentially fatal risk factors for COVID-19 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Our data showed that TC, HDL and LDL were significantly associated with D-dimer.\u003c/p\u003e \u003cp\u003eIt also reported that D-dimer levels greater than 1ug /ml can help identify COVID-19 patients with poor prognosis (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Our results have confirmed that the D-dimer levels of non-surviving patients were significantly higher than 11ug /ml and significantly associated with levels of TC, HDL and LDL at all four time points. Dynamic monitoring of D-dimer is more useful than cross-sectional studies because it can help track the evolution of the disease and facilitate the study of its pathogenesis (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003csup\u003e,\u003c/sup\u003e(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast to traditional markers of severity, such as CRP, PCT, and cytokines, which have short half-lives and unpredictable peaks that are only briefly associated with disease severity, HDL has a greater predictive value because of its immediate decline and long-term stability (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Therefore, our study of dynamic lipid profile, which are relatively stable parameters, is significance for predicting poor prognosis. This is a supplementary finding to a previous study that suggested a correlation between HDL and a higher risk of developing severe events in COVID-19 patients (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLimitations:\u003c/p\u003e \u003cp\u003eThere are several limitations to this study. Firstly, it is a retrospective analysis conducted in a single center. Not all patients in the study had blood lipid measurements at each time point, and as the time point increased, there were fewer instances of blood lipid results available. Meanwhile, we lack the information regarding the lipid levels and lipid-regulating medication use of the study population prior to disease onset. Secondly, we lack information regarding the lipid levels and lipid-lowering medication use of the study population prior to disease onset. Therefore, considering the intricate nature of dyslipidemia in COVID-19 patients, it is imperative to conduct extensive prospective studies with a substantial sample size to gain a thorough comprehension of the condition.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our findings indicate that TC, HDL, and LDL are significantly predictive of short-term mortality in COVID-19 patients. Therefore, clinicians should be particularly vigilant when hyperlipemia persists, especially during the third week of disease progression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eThe datasets presented in this article are not readily available. Please contact with the corresponding authors. Requests to access the datasets should be directed to xlz, [email protected].\u003c/p\u003e\n\u003cp\u003eEthics statement\u003c/p\u003e\n\u003cp\u003eThe study was approved by the institutional ethics board of the Second Xiangya Hospital of Central South University (No.2020001). The ethics committee waived the requirement of written informed consent for participation.\u0026nbsp;Written informed consent from the participants\u0026rsquo;\u0026nbsp;legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements. Written informed consent was not obtained from the individual(s), nor the minor(s)\u0026rsquo;\u0026nbsp;legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eXLZ, JYD, YHY and YZ were involved in study design, interpreting data, statistical analysis, and writing of the manuscript. FLL, TZ, MX, CW and \u0026nbsp;GW were involved in collecting data. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Scientific Research Launch Project for new employees of the Second Xiangya Hospital of Central South University.\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eAll authors declare that there was no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang H, Paulson KR, Pease SA, Watson S, Comfort H, Zheng P, Aravkin AY, Bisignano C, Barber RM, Alam T, Fuller JE, May EA, Jones DP, Frisch ME, Abbafati C, Adolph C, Allorant A, Amlag JO, Bang-Jensen B, Bertolacci GJ, Bloom SS, Carter A, Castro E, Chakrabarti S, Chattopadhyay J, Cogen RM, Collins JK, Cooperrider K, Dai X, Dangel WJ, Daoud F, Dapper C, Deen A, Duncan BB, Erickson M, Ewald SB, Fedosseeva T, Ferrari AJ, Frostad JJ, Fullman N, Gallagher J, Gamkrelidze A, Guo G, He J, Helak M, Henry NJ, Hulland EN, Huntley BM, Kereselidze M, Lazzar-Atwood A, LeGrand KE, Lindstrom A, Linebarger E, Lotufo PA, Lozano R, Magistro B, Malta DC, Mansson J, Herrera AMM, Marinho F, Mirkuzie AH, Misganaw AT, Monasta L, Naik P, Nomura S, O'Brien EG, O'Halloran JK, Olana LT, Ostroff SM, Penberthy L, Reiner Jr RC, Reinke G, Ribeiro ALP, Santomauro DF, Schmidt MI, Shaw DH, Sheena BS, Sholokhov A, Skhvitaridze N, Sorensen RJD, Spurlock EE, Syailendrawati R, Topor-Madry R, Troeger CE, Walcott R, Walker A, Wiysonge CS, Worku NA, Zigler B, Pigott DM, Naghavi M, Mokdad AH, Lim SS, Hay SI, Gakidou E, Murray CJL, Collabor C-EM. Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020-21 [Article]. Lancet. 2022;399(10334):1513\u0026ndash;1536. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0140-6736(21)02796-3\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(21)02796-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID WOS:000795611300024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO. Weekly epidemiological update on COVID-19\u0026ndash;14 Jan 2024. 2024. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.who.int/dashboards/covid19/cases?n=c\u003c/span\u003e\u003cspan address=\"https://data.who.int/dashboards/covid19/cases?n=c\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVnucak M, Granak K, Beliancinova M, Jesenak M, Machalekova KK, Benko J, Samos M, Dedinska I. Acute kidney rejection after anti-SARS-CoV-2 virus-vectored vaccine-case report. NPJ Vaccines. 2022;7(1):30. The authors declare no competing interests. Epub 20220302. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41541-022-00445-5\u003c/span\u003e\u003cspan address=\"10.1038/s41541-022-00445-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 35236844.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, Xia Ja, Yu T, Zhang X, Zhang L. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study [Article]. Lancet. 2020;395(10223):507\u0026ndash;513. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0140-6736(20)30211-7\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(20)30211-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID WOS:000514576900033.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFilippas-Ntekouan S, Liberopoulos E, Elisaf M. Lipid testing in infectious diseases: possible role in diagnosis and prognosis. Infection. 2017;45(5):575\u0026ndash;588. Epub 20170508. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s15010-017-1022-3\u003c/span\u003e\u003cspan address=\"10.1007/s15010-017-1022-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 28484991.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYue J, Xu H, Zhou Y, Liu W, Han X, Mao Q, Li S, Tam LS, Ma J, Liu W. Dyslipidemia Is Related to Mortality in Critical Patients With Coronavirus Disease 2019: A Retrospective Study. Front Endocrinol (Lausanne). 2021;12:611526. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Epub 20210623. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fendo.2021.611526\u003c/span\u003e\u003cspan address=\"10.3389/fendo.2021.611526\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 34248834.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Zhang Y, Lu R, Dai M, Shen M, Zhang J, Cui Y, Liu B, Lin F, Chen L, Han D, Fan Y, Zeng Y, Li W, Li S, Chen X, Li H, Pan P. Lipid metabolism changes in patients with severe COVID-19. Clin Chim Acta. 2021;517:66\u0026ndash;73. eng. Epub 20210224. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cca.2021.02.011\u003c/span\u003e\u003cspan address=\"10.1016/j.cca.2021.02.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 33639119.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMasana L, Correig E, Ibarretxe D, Anoro E, Arroyo JA, Jerico C, Guerrero C, Miret M, Naf S, Pardo A, Perea V, Perez-Bernalte R, Plana N, Ramirez-Montesinos R, Royuela M, Soler C, Urquizu-Padilla M, Zamora A, Pedro-Botet J, group S-Xr. Low HDL and high triglycerides predict COVID-19 severity. Sci Rep. 2021;11(1):7217. L. Masana: Has received personal fees for lectures and/or advisory work from Amgen, Sanofi, Mylan, Servier, MSD, Novartis, and Daiichi-Sankyo.N. Plana: Personal fees for lectures from Amgen, Mylan, and Sanofi. J. Pedro-Botet: Personal fees for lectures and/or advisory work from Amgen, Astra-Zeneca, Esteve, Ferrer, MSD, Mylan, Sanofi, Servier, and Rovi. C. Soler: Personal fees for lectures from Almirall, MSD, VISO farmaceutica, Mylan, and Esteve. M. Miret: Personal fees for lectures and/or advisory work from Sanofi, Novartis, Novonordisk, Astra-Zeneca, Esteve, Lilly, MSD, Ferrer, and Janssen, C. Jerico: Personal fees for lectures and/or advisory work from Sanofi, Amgen, MSD, Ferrer, Astra-Zeneca, Boehringer-Ingelheim, Lilly, Esteve, Novo-Nordisk, and Rovi. D. Ibarretxe: Personal fees for lectures from Sanofi, Rubio.E. Anoro, E. Correig, A. Pardo, C. Guerrero, S. Naf, JA. Arroyo, M. Urquizu-Padilla, V. Perea, R. Perez-Berbalte, R. Ramirez-Montesinos, M. Royuela, and A. Zamora: Declare no conflicts of interest. Epub 20210330. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-021-86747-5\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-86747-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 33785815.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao M, Luo Z, He H, Shen B, Liang J, Zhang J, Ye J, Xu Y, Wang Z, Ye D, Wang M, Wan J. Decreased Low-Density Lipoprotein Cholesterol Level Indicates Poor Prognosis of Severe and Critical COVID-19 Patients: A Retrospective, Single-Center Study. Front Med (Lausanne). 2021;8:585851. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Epub 20210526. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmed.2021.585851\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2021.585851\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 34124081.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtmosudigdo IS, Lim MA, Radi B, Henrina J, Yonas E, Vania R, Pranata R. Dyslipidemia Increases the Risk of Severe COVID-19: A Systematic Review, Meta-analysis, and Meta-regression. Clin Med Insights Endocrinol Diabetes. 2021;14:1179551421990675. Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Epub 20210324. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1179551421990675\u003c/span\u003e\u003cspan address=\"10.1177/1179551421990675\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 35173508.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai W, Lund H, Chen Y, Zhang J, Osinski K, Jones SZ, Kreuziger LB, Lopez JA, Benjamin IJ, Silverstein RL, Zheng Z. Hypertriglyceridemia during hospitalization independently associates with mortality in patients with COVID-19. J Clin Lipidol. 2021 Sep-Oct;15(5):724\u0026ndash;731. Epub 2021/09/03. doi:10.1016/j.jacl.2021.08.002. Cited in: Pubmed; PMID 34470719.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAparisi A, Iglesias-Echeverria C, Ybarra-Falcon C, Cusacovich I, Uribarri A, Garcia-Gomez M, Ladron R, Fuertes R, Candela J, Tobar J, Hinojosa W, Duenas C, Gonzalez R, Nogales L, Calvo D, Carrasco-Moraleja M, San Roman JA, Amat-Santos IJ, Andaluz-Ojeda D. Low-density lipoprotein cholesterol levels are associated with poor clinical outcomes in COVID-19. Nutr Metab Cardiovasc Dis. 2021;31(9):2619\u0026ndash;2627. Declaration of competing interest None. Epub 20210706. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.numecd.2021.06.016\u003c/span\u003e\u003cspan address=\"10.1016/j.numecd.2021.06.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 34353699.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSampedro-Nunez M, Aguirre-Moreno N, Garcia-Fraile Fraile L, Jimenez-Blanco S, Knott-Torcal C, Sanz-Martin P, Fernandez-Jimenez G, Marazuela M. Finding answers in lipid profile in COVID-19 patients. Endocrine. 2021;74(3):443\u0026ndash;454. The authors declare no competing interests. Epub 20211019. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12020-021-02881-0\u003c/span\u003e\u003cspan address=\"10.1007/s12020-021-02881-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 34668172.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang JJ, Cao YY, Tan G, Dong X, Wang BC, Lin J, Yan YQ, Liu GH, Akdis M, Akdis CA, Gao YD. Clinical, radiological, and laboratory characteristics and risk factors for severity and mortality of 289 hospitalized COVID-19 patients. Allergy. 2021;76(2):533\u0026ndash;550. CA reports grants from Allergopharma, Idorsia, Swiss National Science Foundation, Christine Kuhne-Center for Allergy Research and Education, European Commission's Horizon's 2020 Framework Programme, Cure, Novartis Research Institutes, AstraZeneca, Scibase, and advisory role in Sanofi/Regeneron. All other authors declare no conflict of interest outside the submitted work. Epub 20200824. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/all.14496\u003c/span\u003e\u003cspan address=\"10.1111/all.14496\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 32662525.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaycan OF, Bolen F, Atalay B, Agirbasli M. Prognostic significance of HDL-C on long-term mortality in patients with COVID-19 pneumonia in the Turkish population: A potential mechanism for population differences. Bosn J Basic Med Sci. 2022;22(6):1016\u0026ndash;1024. Conflicts of interest: Authors declare no conflicts of interest. Epub 20221023. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.17305/bjbms.2022.7545\u003c/span\u003e\u003cspan address=\"10.17305/bjbms.2022.7545\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 35997994.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEttinger WH, Varma VK, Sorci-Thomas M, Parks JS, Sigmon RC, Smith TK, Verdery RB. Cytokines decrease apolipoprotein accumulation in medium from Hep G2 cells. Arterioscler Thromb. 1994;14(1):8\u0026ndash;13. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/01.atv.14.1.8\u003c/span\u003e\u003cspan address=\"10.1161/01.atv.14.1.8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 8274481.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLavillegrand JR, Garnier M, Spaeth A, Mario N, Hariri G, Pilon A, Berti E, Fieux F, Thietart S, Urbina T, Turpin M, Darrivere L, Fartoukh M, Verdonk F, Dumas G, Tedgui A, Guidet B, Maury E, Chantran Y, Voiriot G, Ait-Oufella H. Elevated plasma IL-6 and CRP levels are associated with adverse clinical outcomes and death in critically ill SARS-CoV-2 patients: inflammatory response of SARS-CoV-2 patients. Ann Intensive Care. 2021;11(1):9. eng. The authors declare that they have no competing interests. Epub 20210113. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13613-020-00798-x\u003c/span\u003e\u003cspan address=\"10.1186/s13613-020-00798-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 33439360.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen X, Gao W, Li J, You D, Yu Z, Zhang M, Shao F, Wei Y, Zhang R, Lange T, Wang Q, Chen F, Lu X, Zhao Y. A predictive paradigm for COVID-19 prognosis based on the longitudinal measure of biomarkers. Brief Bioinform. 2021;22(6). eng. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bib/bbab206\u003c/span\u003e\u003cspan address=\"10.1093/bib/bbab206\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 34081102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe W, Chen G, Li X, Lan X, Ji C, Hou M, Zhang D, Zeng G, Wang Y, Xu C, Lu W, Cui R, Cai Y, Huang H, Yang L. Dynamic changes of D-dimer and neutrophil-lymphocyte count ratio as prognostic biomarkers in COVID-19. Respir Res. 2020;21(1):169. eng. The authors declared no competing interests. Epub 20200703. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12931-020-01428-7\u003c/span\u003e\u003cspan address=\"10.1186/s12931-020-01428-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 32620118.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Leeuwen HJ, Heezius EC, Dallinga GM, van Strijp JA, Verhoef J, van Kessel KP. Lipoprotein metabolism in patients with severe sepsis. Crit Care Med. 2003;31(5):1359\u0026ndash;66. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/01.CCM.0000059724.08290.51\u003c/span\u003e\u003cspan address=\"10.1097/01.CCM.0000059724.08290.51\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 12771603.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei X, Zeng W, Su J, Wan H, Yu X, Cao X, Tan W, Wang H. Hypolipidemia is associated with the severity of COVID-19. J Clin Lipidol. 2020 May-Jun;14(3):297\u0026ndash;304. Epub 20200430. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacl.2020.04.008\u003c/span\u003e\u003cspan address=\"10.1016/j.jacl.2020.04.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 32430154.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorrison AR, Johnson JM, Ramesh M, Bradley P, Jennings J, Smith ZR. Acute hypertriglyceridemia in patients with COVID-19 receiving tocilizumab. J Med Virol. 2020;92(10):1791\u0026ndash;1792. The authors declare that there are no conflict of interests. Epub 20200603. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jmv.25907\u003c/span\u003e\u003cspan address=\"10.1002/jmv.25907\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 32314799.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan B, Chu H, Yang D, Sze KH, Lai PM, Yuan S, Shuai H, Wang Y, Kao RY, Chan JF, Yuen KY. Characterization of the Lipidomic Profile of Human Coronavirus-Infected Cells: Implications for Lipid Metabolism Remodeling upon Coronavirus Replication. Viruses. 2019;11(1). J.F.-W.C. has received travel grants from Pfizer Corporation Hong Kong and Astellas Pharma Hong Kong Corporation Limited, and was an invited speaker for Gilead Sciences Hong Kong Limited and Luminex Corporation. The other authors declared no conflict of interest. The funding sources had no role in study design, data collection, analysis or interpretation or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. Epub 20190116. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/v11010073\u003c/span\u003e\u003cspan address=\"10.3390/v11010073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 30654597.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZidar DA, Juchnowski S, Ferrari B, Clagett B, Pilch-Cooper HA, Rose S, Rodriguez B, McComsey GA, Sieg SF, Mehta NN, Lederman MM, Funderburg NT. Oxidized LDL Levels Are Increased in HIV Infection and May Drive Monocyte Activation. J Acquir Immune Defic Syndr. 2015;69(2):154\u0026ndash;60. Epub 2015/02/04. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/QAI.0000000000000566\u003c/span\u003e\u003cspan address=\"10.1097/QAI.0000000000000566\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 25647528.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian S, Hu W, Niu L, Liu H, Xu H, Xiao SY. Pulmonary Pathology of Early-Phase 2019 Novel Coronavirus (COVID-19) Pneumonia in Two Patients With Lung Cancer. J Thorac Oncol. 2020;15(5):700\u0026ndash;704. Epub 2020/03/03. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jtho.2020.02.010\u003c/span\u003e\u003cspan address=\"10.1016/j.jtho.2020.02.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 32114094.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQasim A, Kousa O, Andukuri VG. Hypolipidemia contributing to the severity of sepsis triggered by influenza a virus: A case report. J Clin Transl Res. 2020;6(6):198\u0026ndash;202. Epub 2021/02/11. Cited in: Pubmed; PMID 33564724.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHariyanto TI, Kurniawan A. Dyslipidemia is associated with severe coronavirus disease 2019 (COVID-19) infection. Diabetes Metab Syndr. 2020 Sep-Oct;14(5):1463\u0026ndash;1465. Epub 2020/08/11. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.dsx.2020.07.054\u003c/span\u003e\u003cspan address=\"10.1016/j.dsx.2020.07.054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 32771919.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong J, Chen Y, Jie Y, Tan M, Jiang Z, Yuan L, Cao J, Li G, Chong Y, Qu J, Shi Y, Hu B. U-Shaped Relationship of Low-Density Lipoprotein Cholesterol With Risk of Severe COVID-19 From a Multicenter Pooled Analysis. Front Cardiovasc Med. 2021;8:604736. Epub 2021/09/11. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcvm.2021.604736\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2021.604736\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 34504873.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelms J, Tacquard C, Severac F, Leonard-Lorant I, Ohana M, Delabranche X, Merdji H, Clere-Jehl R, Schenck M, Fagot Gandet F, Fafi-Kremer S, Castelain V, Schneider F, Grunebaum L, Angles-Cano E, Sattler L, Mertes PM, Meziani F, Group CT. High risk of thrombosis in patients with severe SARS-CoV-2 infection: a multicenter prospective cohort study. Intensive Care Med. 2020;46(6):1089\u0026ndash;1098. The authors have no conflicts of interest to declare. Epub 20200504. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00134-020-06062-x\u003c/span\u003e\u003cspan address=\"10.1007/s00134-020-06062-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 32367170.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, Guan L, Wei Y, Li H, Wu X, Xu J, Tu S, Zhang Y, Chen H, Cao B. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054\u0026ndash;1062. Epub 20200311. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(20)30566-3\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(20)30566-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 32171076.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu G, Wang J. Dynamic changes in routine blood parameters of a severe COVID-19 case. Clin Chim Acta. 2020;508:98\u0026ndash;102. eng. Epub 20200513. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cca.2020.04.034\u003c/span\u003e\u003cspan address=\"10.1016/j.cca.2020.04.034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 32405079.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVavrova L, Rychlikova J, Mrackova M, Novakova O, Zak A, Novak F. Increased inflammatory markers with altered antioxidant status persist after clinical recovery from severe sepsis: a correlation with low HDL cholesterol and albumin. Clin Exp Med. 2016;16(4):557\u0026ndash;569. Epub 20150928. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10238-015-0390-1\u003c/span\u003e\u003cspan address=\"10.1007/s10238-015-0390-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 26416404.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang G, Zhang Q, Zhao X, Dong H, Wu C, Wu F, Yu B, Lv J, Zhang S, Wu G, Wu S, Wang X, Wu Y, Zhong Y. Low high-density lipoprotein level is correlated with the severity of COVID-19 patients: an observational study. Lipids Health Dis. 2020;19(1):204. The authors declare that they have no conflict of interest. Epub 20200907. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12944-020-01382-9\u003c/span\u003e\u003cspan address=\"10.1186/s12944-020-01382-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Cited in: Pubmed; PMID 32892746.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Baseline characteristics of survival and non-survival COVID-19 patients\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"508\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003eSurvival (n=76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003eNon-survival (n=59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003eGender (male/female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003eAge, y, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e65 (48.25,70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e70 (63, 78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eSymptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Fever (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Fatigue (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Cough (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Shortness of breath (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003ePharyngalgia (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Vomiting (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003eDiarrhea (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003eAbdominal pain (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003eNausea (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003eAnorexia (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003eMyalgia (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Chill (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Dizziness (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003eHeadache (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003eHemoptysis (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eComorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Hypertension (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003eCerebrovascular disease (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003eCOPD (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003eTumor (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eTherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003eHigh flow oxygen therapy (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003eNon-invasive ventilator (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003eInvasive ventilator (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003eGlucocorticoid (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.39882121807466%\" valign=\"top\"\u003e\n \u003cp\u003eImmunoglobulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.897838899803535%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.805500982318271%\" valign=\"top\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 2 Laboratory findings of survival and non-survival COVID-19 patients\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"top\"\u003e\n \u003cp\u003eSurvival (n=76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"top\"\u003e\n \u003cp\u003eNon-survival (n=59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;WBC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.07 (4.62,8.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.27 (6.97,15.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Lymphocyte count, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.29 (0.86,1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.53 (0.38,0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003eNeutrophil count, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"bottom\"\u003e\n \u003cp\u003e3.86 (3,6.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.22 (5.82,13.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003eHGB, g/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"bottom\"\u003e\n \u003cp\u003e125 (112,133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"bottom\"\u003e\n \u003cp\u003e129 (105,140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003ePLT, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"bottom\"\u003e\n \u003cp\u003e242 (176,358)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"bottom\"\u003e\n \u003cp\u003e159 (105,224)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003eESR, mm/h, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"top\"\u003e\n \u003cp\u003e35 (5,59.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"top\"\u003e\n \u003cp\u003e33 (12,63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003eCRP, mg/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"top\"\u003e\n \u003cp\u003e16.9 (2.475,58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"top\"\u003e\n \u003cp\u003e99.5 (61.5,186.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003eALT, U/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"bottom\"\u003e\n \u003cp\u003e24 (16.25,38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"bottom\"\u003e\n \u003cp\u003e32 (20,47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e0.032*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003eAST, U/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"bottom\"\u003e\n \u003cp\u003e23 (18,32.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"bottom\"\u003e\n \u003cp\u003e44 (28,65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003eTbil, \u0026mu;mol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.4 (6.85,12.675)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.4 (9.3,20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003eAlb, g/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"bottom\"\u003e\n \u003cp\u003e35.6 (31.325,39.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"bottom\"\u003e\n \u003cp\u003e29.8 (26.4,33.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003eSCr, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"bottom\"\u003e\n \u003cp\u003e72 (60,84.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"bottom\"\u003e\n \u003cp\u003e95 (72,160)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003eCK, U/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"bottom\"\u003e\n \u003cp\u003e42.5 (23,124.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"bottom\"\u003e\n \u003cp\u003e145.5 (66.25,475.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003eCK-MB, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.4 (0.2,0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.1 (0.3,7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003eProthrombin time, s, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.95 (13.4,14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.3 (14.5,18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003eAPTT, s, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"bottom\"\u003e\n \u003cp\u003e39 (36.025,42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"bottom\"\u003e\n \u003cp\u003e39.8 (34.4,46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003eD-dimer, ug/mL, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.64 (0.2925,1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.78 (1.77,21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.48905109489051%\" valign=\"top\"\u003e\n \u003cp\u003eChest CT positive rate (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.912408759124087%\" valign=\"top\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.554744525547445%\" valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.043795620437956%\" valign=\"top\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*\u0026nbsp;means a significant difference, WBC: White Blood Cell Count, ESR: erythrocyte sedimentation rate, CRP: C-reactive protein, ALT: alanine aminotranspherase AST: aspartate aminotransferase, TBil: total bilirubin, SCr: serum creatinine, CK: Creatine kinase, CK-MB: Creatine kinase isoenzyme, APTT: activated partial thromboplastin time.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"99.27007299270073%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eTable 3 Blood lipid levels in the first four weeks after admission of\u0026nbsp;\u003c/p\u003e\n \u003cp\u003esurvival and non-survival COVID-19 patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7299270072992701%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\"\u003e\n \u003cp\u003eSurvival (N = 76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\"\u003e\n \u003cp\u003eNon-survival (N=59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" colspan=\"2\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\"\u003e\n \u003cp\u003eTC-W1, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\"\u003e\n \u003cp\u003e3.87(3.36, 4.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\"\u003e\n \u003cp\u003e2.83(2.38, 3.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\"\u003e\n \u003cp\u003eHDL-W1, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\"\u003e\n \u003cp\u003e0.92(0.79, 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\"\u003e\n \u003cp\u003e0.69(0.50, 0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\"\u003e\n \u003cp\u003eLDL-W1, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\"\u003e\n \u003cp\u003e2.45(2.06, 3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\"\u003e\n \u003cp\u003e1.53(1.13, 2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\"\u003e\n \u003cp\u003eTG-W1, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\"\u003e\n \u003cp\u003e1.29(0.90, 1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\"\u003e\n \u003cp\u003e1.52(1.21, 2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" colspan=\"2\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\"\u003e\n \u003cp\u003eTC-W2, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\"\u003e\n \u003cp\u003e3.92(3.40, 4.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\"\u003e\n \u003cp\u003e2.62(2.03, 3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\"\u003e\n \u003cp\u003eHDL-W2, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\"\u003e\n \u003cp\u003e1.05(0.90, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\"\u003e\n \u003cp\u003e0.72(0.58, 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\"\u003e\n \u003cp\u003eLDL-W2, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\"\u003e\n \u003cp\u003e2.42(2.14, 3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\"\u003e\n \u003cp\u003e1.35(0.96, 2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\"\u003e\n \u003cp\u003eTG-W2, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\"\u003e\n \u003cp\u003e1.55(1.08, 1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\"\u003e\n \u003cp\u003e1.44(0.84, 2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" colspan=\"2\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\"\u003e\n \u003cp\u003eTC-W3, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\"\u003e\n \u003cp\u003e4.79(3.91, 5.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\"\u003e\n \u003cp\u003e3.23(2.07, 3.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\"\u003e\n \u003cp\u003eHDL-W3, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\"\u003e\n \u003cp\u003e1.03(0.90, 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\"\u003e\n \u003cp\u003e0.54(0.36, 0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\"\u003e\n \u003cp\u003eLDL-W3, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\"\u003e\n \u003cp\u003e2.99(2.29, 3.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\"\u003e\n \u003cp\u003e1.92(0.81, 2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\"\u003e\n \u003cp\u003eTG-W3, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\"\u003e\n \u003cp\u003e1.29(0.90, 1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\"\u003e\n \u003cp\u003e1.51(1.05, 3.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" colspan=\"2\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\"\u003e\n \u003cp\u003eTC-W4, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\"\u003e\n \u003cp\u003e4.7(3.24, 5.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\"\u003e\n \u003cp\u003e3.17(1.97, 3.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" colspan=\"2\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\"\u003e\n \u003cp\u003eHDL-W4, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\"\u003e\n \u003cp\u003e1.00(0.90, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\"\u003e\n \u003cp\u003e0.58(0.47, 0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" colspan=\"2\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\"\u003e\n \u003cp\u003eLDL-W4, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\"\u003e\n \u003cp\u003e2.79(2.04, 3.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\"\u003e\n \u003cp\u003e1.91(0.67, 2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" colspan=\"2\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.846715328467155%\"\u003e\n \u003cp\u003eTG-W4, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.087591240875913%\"\u003e\n \u003cp\u003e1.44(1.17, 2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\"\u003e\n \u003cp\u003e2.04(1.37, 2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" colspan=\"2\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTC: Total cholesterol, HDL: High-density lipoprotein, LDL: Low-density lipoprotein, TG: Triglyceride, W: week.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"562\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"bottom\"\u003e\n \u003cp\u003eTable 4 Changes of blood lipid in the first four weeks after admission of\u0026nbsp;\u003c/p\u003e\n \u003cp\u003esurvival and non-survival COVID-19 patients\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99288256227758%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.26690391459075%\"\u003e\n \u003cp\u003eSurvival\u003c/p\u003e\n \u003cp\u003e(N = 76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.88612099644128%\"\u003e\n \u003cp\u003eNon-survival\u003c/p\u003e\n \u003cp\u003e(N=59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.142348754448399%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7117437722419929%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99288256227758%\"\u003e\n \u003cp\u003eTC-W\u003csub\u003e2-1\u003c/sub\u003e, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.26690391459075%\" valign=\"top\"\u003e\n \u003cp\u003e0.178 (-0.280, 0.840)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.88612099644128%\" valign=\"top\"\u003e\n \u003cp\u003e-0.600 (-1.123, 0.174)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.142348754448399%\" valign=\"top\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7117437722419929%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99288256227758%\"\u003e\n \u003cp\u003eHDL-W\u003csub\u003e2-1\u003c/sub\u003e, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.26690391459075%\" valign=\"top\"\u003e\n \u003cp\u003e0.075 (-0.078, 0.421)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.88612099644128%\" valign=\"top\"\u003e\n \u003cp\u003e-0.090 (-0.311, 0.124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.142348754448399%\" valign=\"top\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7117437722419929%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99288256227758%\"\u003e\n \u003cp\u003eLDL-W\u003csub\u003e2-1\u003c/sub\u003e, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.26690391459075%\" valign=\"top\"\u003e\n \u003cp\u003e0.145 (-0.580, 0.543)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.88612099644128%\" valign=\"top\"\u003e\n \u003cp\u003e-0.653 (-0.841, 0.098)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.142348754448399%\" valign=\"top\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7117437722419929%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99288256227758%\"\u003e\n \u003cp\u003eTG-W\u003csub\u003e2-1\u003c/sub\u003e, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.26690391459075%\" valign=\"top\"\u003e\n \u003cp\u003e-0.185 (-0.328, 0.461)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.88612099644128%\" valign=\"top\"\u003e\n \u003cp\u003e-0.240 (-0.540, 0.182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.142348754448399%\" valign=\"top\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7117437722419929%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99288256227758%\"\u003e\n \u003cp\u003eTC-W\u003csub\u003e3-1\u003c/sub\u003e, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.26690391459075%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.100 (0.568, 1.450)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.88612099644128%\" valign=\"bottom\"\u003e\n \u003cp\u003e-1.205 (-1.751, -0.304)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.142348754448399%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7117437722419929%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99288256227758%\"\u003e\n \u003cp\u003eHDL-W\u003csub\u003e3-1\u003c/sub\u003e, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.26690391459075%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.130 (-0.024, 0.550)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.88612099644128%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.268 (-0.382, -0.146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.142348754448399%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7117437722419929%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99288256227758%\"\u003e\n \u003cp\u003eLDL-W\u003csub\u003e3-1\u003c/sub\u003e, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.26690391459075%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.660 (0.493, 0.900)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.88612099644128%\" valign=\"bottom\"\u003e\n \u003cp\u003e-0.803 (-1.163, -0.318)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.142348754448399%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7117437722419929%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.99288256227758%\"\u003e\n \u003cp\u003eTG-W\u003csub\u003e3-1\u003c/sub\u003e, mmol/L, M (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.26690391459075%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.470 (-0.170, 0.874)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.88612099644128%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.293 (-0.060, 2.051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.142348754448399%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0.7117437722419929%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTC:\u0026nbsp;Total cholesterol, HDL: High-density lipoprotein, LDL: Low-density lipoprotein, TG: Triglyceride, W: week. In this table, lipids-W\u003csub\u003e4-1\u003c/sub\u003e and lipids-W3-2 were not shown for the small number of sample size.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, SARS-CoV-2, Dyslipidemia, Lipid profile, Mortality","lastPublishedDoi":"10.21203/rs.3.rs-4386935/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4386935/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLipid metabolism is particularly affected in response to acute infectious diseases caused by viruses, bacteria, or parasites. The association between levels of lipid profiles and mortality in COVID-19 patients has become the subject of increasing interest.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo evaluate the predictive capacity of dyslipidemia for COVID-19 mortality based on dynamic data.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ewe conducted a retrospective, observational study, involving 135 COVID-19 patients admitted between January 1 and March 26, 2020.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe found that non-survivals with COVID-19 displayed persistent dyslipidemia, including lower levels of total cholesterol (TC), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) with higher levels of triglycerides during the early stages of hospitalization. Notably, both the absolute values or the changes of TC, HDL, and LDL were closely related to mortality, and the AUCs of these three indicators at all time points and their changes were greater than 0.7. Notably, the values of AUCs of TC, HDL, and LDL at week 3 were 0.891, 0.895, and 0.879, while the AUCs for change of TC, HDL, and LDL were 0.975, 0.950 and 0.925 at week\u003csub\u003e3\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/sub\u003e. Spearman correlation analyses showed that TC, HDL and LDL were significantly associated with CRP, D-dimer, BUN, CK and BNP at all four time points.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eBlood lipid levels in the third week and changes from week 1 to week 3 are critical for predicting the mortality of COVID-19. Healthcare providers should pay close attention to the dynamic changes in lipid levels of COVID-19 patients.\u003c/p\u003e","manuscriptTitle":"The predictive values of dynamic blood lipid profile for mortality in COVID-19 patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-28 18:00:58","doi":"10.21203/rs.3.rs-4386935/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"300c860e-2be7-4c4a-9a0a-0b620efae0d1","owner":[],"postedDate":"May 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32182981,"name":"Health sciences/Biomarkers"},{"id":32182982,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-07-02T12:26:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-28 18:00:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4386935","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4386935","identity":"rs-4386935","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}