Changes in lipid, liver, and renal test profiles among patients with severe COVID-19 during and after hospital admission at Saint Peter Specialized Hospital, Addis Ababa, Ethiopia

Changes in lipid, liver, and renal test profiles among patients with severe COVID-19 during and after hospital admission at Saint Peter Specialized Hospital, Addis Ababa, Ethiopia | 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 Short Report Changes in lipid, liver, and renal test profiles among patients with severe COVID-19 during and after hospital admission at Saint Peter Specialized Hospital, Addis Ababa, Ethiopia Gedamnesh Wolde, Belete Woldesemayat, Endalkchew Biranu, Wossene Habtu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4598405/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 Objective: The progression of COVID-19 affects multiple organs, abnormal lipid, liver, and renal function tests have beenreported. Hence, this study aimed to determine differences in organ function and lipid profile among patients with severe COVID-19 during and after hospital admission. Methods: A follow-up study was conducted among COVID-19-admitted patients at St. Peter Specialized Hospital from January 1, 2021, to April 30, 2021. A total of 162 patients were included in the study. Five millilitersof venous blood was collected during admission and on the verge of discharge. Lipid, renal and liver function tests were performedusing aCobas 311 analyser. The data were entered and analysed with SPSS version 25. Results: The mean differences in total cholesterol, HDL, and LDL at admission and discharge were 20.13 (95% CI; 13.41-26.84; P<0.001), 7.53 (95% CI; 5.24-9.81; P <0.001), and 0.10 (95% CI; 0.06-0.14; P<0.001), respectively. Albumin concentrationincreased significantly at discharge, while the ALT concentration decreasedsignificantly at discharge (P<0.05). Conclusion: Dyslipidemia and low levels of Albumin were recorded during the progression of COVID-19 (at admission). This indicated severe COVID-19 disease leads to lipid alteration and Additional studies need to better define the disease's association with liver and renal function tests. COVID-19 Lipid profile Liver function test Renal function test Figures Figure 1 1. Introduction Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a newly identified coronavirus, severe acute respiratory syndrome 2 (SARS-CoV-2). Millions of people across hundreds of countries have been impacted by this pandemic. Many cases rapidly progress to acute respiratory disease, multiorgan failure, and septic shock, with a markedly increased mortality rate [ 1 , 2 ]. As an initial target for antiviral therapy, lipid metabolic pathways and the structural components of membranes might be targeted to specifically hinder the life cycle of the virus. Furthermore, lipoproteins play a significant role in SARS-CoV-2 infection. In particular, HDL may make it simpler for SARS-CoV-2 to enter host cells through the SR-B1 receptor [ 3 ]. The lipid metabolism of SARS-CoV-2 is considered to be regulated. However, the modifications and effects of high-density lipoprotein cholesterol (HDL-C) in patients with COVID-19 have rarely been observed. In patients with COVID-19, total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels decrease significantly [ 4 ]. Hepatic involvement in COVID-19 could be related to the direct cytopathic effect of the virus, an uncontrolled immune reaction, sepsis, or drug-induced liver injury. Given the increased expression of angiotensin-converting enzyme-2 (ACE2) receptors in cholangiocytes, the liver is a potential target for SARS-CoV-2 infection treatment. Moreover, COVID-19 may cause worsening of underlying chronic liver disease, leading to hepatic decomposition and acute-on-chronic liver failure, with increased mortality [ 5 ]. Complications reported to be associated with COVID-19 include myocardial injury, heart failure, acute kidney injury and electrolyte disturbances. In addition to the observation that older patients, males and those with pre-existing comorbidities such as cardiovascular disease, diabetes, chronic kidney disease, and chronic liver disease are at the highest risk for severe illness or death, COVID-19 complications have been shown to correlate with disease severity. Progression to multiorgan damage/failure after monitoring in patients with severe COVID-19 is indicated by abnormal hepatic, cardiac, renal, and liver function and LDH results [ 6 , 7 ]. Prospective and descriptive studies conducted in Northwest Mexico indicated that patients with COVID-19 had significant changes in their lipid profiles. Low levels of total cholesterol (TCHOL), LDL, and HDL have been reported, while triglyceride (TG) levels have been reported to be high. These abnormalities were the same among noncritical and critical COVID-19 patients [ 8 ]. A prospective cohort study performed by Cheng et al. involving 701 patients with COVID-19 admitted to a tertiary teaching hospital in Wuhan in 2020 revealed that 14.4% of patients had elevated serum creatinine and 13.1% had elevated blood urea nitrogen [ 9 ]. Renal manifestations such as a slight increase in creatinine, modest proteinuria, and hematuria have been observed in COVID-19 patients, possibly as a result of kidney tropism and multiorgan failure [ 10 ]. A meta-analysis performed by Yanyan W et al. revealed that the pooled incidence of any abnormal liver biochemical indicators at admission and during hospitalization was 27.2% and 36%, respectively. Moreover, abnormal liver biochemical test results are common and closely related to the severity and prognosis of COVID-19 [ 11 ]. Hence, this study aimed to determine the changes in common laboratory test parameters (lipid profiles and liver and kidney function test results) at admission and during discharge among COVID-19 patients admitted to St. Peter Hospital, Addis Ababa, Ethiopia, to ultimately complement clinical data for risk stratification and improve the overall clinical management of COVID-19 patients. 2. Methods 2.1. Study area and design A prospective cohort study was conducted from January 1, 2021, to April 30, 2021, at St. Peter Specialized Hospital in Addis Ababa, Ethiopia. St. Peter Specialized Hospital was established in 1953. It is located in Gulele Subcity and is managed by the Federal Democratic Republic of Ethiopia, Ministry of Health (FMoH). 2.2. The eligibility criteria All newly admitted COVID-19 patients in the hospital were included. Individuals with a known history of chronic kidney disease, chronic liver disease or dyslipidemia were excluded. 2.3. Sample size determination and sampling method The sample size was determined by two means with an equal sample size comparison formula by using a previous study of lipid profile differences (specifically HDL difference), which was 6.6 mg/dl [ 12 ]. An 80% estimation power and 99% confidence level were considered in the sample size calculation. Finally, the minimum sample size was 147, and we recruited all 162 participants admitted to the hospital during the time frame using a convenient sampling method. 2.4. Sample collection and laboratory analysis Approximately 5 ml of venous blood was collected aseptically from each study participant by trained laboratory technologists in the morning after 8:00 hours of overnight fast. The blood sample was dispensed into jelly-coated serum separator test tubes or plain tubes labelled with a unique ID number. The collected blood sample was left for 30 minutes to facilitate clotting at room temperature. Then, the clotted blood samples were centrifuged for 5 minutes at 300 g (RCF) to separate the serum from the formed elements. Lipid profiles, liver function tests, and renal function tests were analysed by a Cobas c311 (Roche Diagnostics, Indianapolis, USA) fully automated clinical chemistry analyser following the manufacturer’s instructions within the same day of sample collection. Lipid panels, including TC, HDL-C, LDL-C, and TG, were analysed by cholesterol oxidase, phenol 4-amino antipyrine peroxidase, direct enzymatic methods, direct determination and glycerin phosphate oxidase peroxidase, respectively. Liver function tests, such as those for ALB, alanine transferase (ALT) and aspartate transferase (AST), were performed with the method of enzymatic activity based on the IFCC recommendation. Bilirubin direct (BILD) and bilirubin total (BILT) were measured via the colorimetric diazo method. Similarly, renal function tests, creatinine and urea were performed with the enzymatic test principle. 2.5. Data quality control Nurses in charge of the patients were well-oriented on how to prepare patients and collect specimens. The quality of laboratory analysis was maintained by following standard operating procedures of the preanalytical, analytical, and postanalytical stages. The hospital laboratory was in the process of accreditation, and it used to exercise a good laboratory quality management system. Internal quality control was performed by using normal and pathological quality control materials. 2.6. Data analysis Data entry and analysis were performed with SPSS version 25 statistical software. Descriptive statistics were employed to explain sociodemographic and clinical characteristics, distribution and values of lipid profiles, liver function tests, and renal function tests. A paired test analysis was performed to determine the difference between admission and discharge values of the tests by using a nonparametric Wilcoxon test. The Bland‒Altman test was also employed to determine the mean relative differences in the non-statistically significant different parameters between the admission and discharge periods. Measure analysis of variance (ANOVA) was performed to determine the mean difference between different experimental scenarios. Post hock analysis was employed to locate the place of significance between more than two categories. The level of significance was set at p < 0.05. 3. Results 3.1. Sociodemographic characteristics of the study participants A total of 162 study participants were recruited for this study. Data from six participants, who were deceased (n=4) and referred to other hospitals for further management (n=2) were excluded. Overall, 156 participants were included. During the final analysis, 96 (61.5%) of these participants were male, and the mean age of the participants was ± standard deviation (55.46 ± 14.32). A majority (80.4%) of the participants were above the age of 41, 70.5% (110) of the participants were married, and only 18.6% (29) of the participants were from rural areas ( Supplementary Table 1 ). 3.2. Lipid, liver and renal function tests among participants at admission and during the follow-up period In this study, there were differences in laboratory findings between the admission and discharge times. The nonparametric Wilcoxon test indicated that total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), albumin, and alanine transaminase (ALT) levels were significantly different between the admission and discharge periods (P< 0.05). Moreover, total cholesterol, HDL, LDL and albumin increased significantly at discharge (P<0.05). ALT was significantly greater at admission than at discharge (P<0.05). On the other hand, renal parameters (creatinine and urea) were not significantly different between the admission and discharge periods ( Table 1 ). Table 1: Paired analysis of lipid profiles, liver enzymes, and renal test profiles at admission and discharge (n=156) Parameters Admission Discharge Nonparametric Wilcoxon test (p value) Median Standard deviation Median Standard deviation Total cholesterol (mg/dl) 125.05 43.86 150.60 48.91 <0.001* HDL (mg/dl) 29.55 12.90 34.10 15.11 <0.001* LDL (mg/dl) 61.70 32.18 80.85 35.95 <0.001* TG (mg/dl) 137.05 79.17 151.05 80.18 0.742 Albumin (g/l) 26.30 6.29 27.85 6.45 0.003* AST(U/L) 31.00 34.51 30.40 28.07 0.339 ALT(U/L) 26.80 34.94 18.40 39.17 0.001* Direct Bilirubin (mg/dl) 0.15 0.21 0.15 0.20 0.465 Total bilirubin (mg/dl) 0.27 0.28 0.28 2.16 0.456 Creatinine (mg/dl) 0.72 0.63 0.74 0.67 0.864 Urea (mg/dl) 32.90 32.58 32.35 24.94 0.999 Abbreviations: HDL- high-density lipoprotein, LDL; low-density lipoprotein, AST; aspartate aminotransferase, ALT; alanine aminotransferase TG; triglyceride, g/dl; gram per deciliter, mg/dl; milligram per deciliter, U/L; international unit per liter * P values indicate the differences between admission and discharge values (median discharge-median admission). P < 0.05 was considered to indicate statistical significance. According to Table 2, the majority of lipid profiles at admission and at discharge were significantly different. However, for some parameters (TG, AST, total bilirubin, urea, and creatinine), there were no significant differences in the cohort. Based on Bland‒Altman analysis illustrating the mean difference between the admission and discharge periods, the triglyceride measurements indicated that 9 (5.8%) measurements (sample values) had a pooled 95% confidence interval (i.e., mean difference ±1.96 SD). Similarly, in the AST measurements, 8 samples (5.1%) met the pooled 95% limit. Differences in the other test parameters (total bilirubin, creatinine, and urea) measured in the Bland‒Altman plot were less than the pooled 5% difference (Figure 1C, 1D, 1E) . In addition, only 5 (five) participants had TG percentages above the 95% confidence level (32.24%), and 1 (one) participant had TG percentages below the 95% confidence level (-28.31%). However, the majority (150, 96.2%) of the test values were between -28.31% and 32.24%. The relative mean differences in triglyceride ( Figure 1A) and AST ( Figure 1B) levels at discharge were greater than those at admission. On the other hand, the relative mean differences in creatinine and urea were more consistent, and the majority of values were within the 95% confidence limits (Figure 1D and 1E ). Table 2: Measurement analysis of variance (one-way ANOVA) of laboratory tests at admission with age group and Gender Dependent variable age groups and Gender category Mean 95% CI for Mean F test p value lower upper Total cholesterol 60years (n=63) 124.6571 113.7170 135.5973 HDL 60years (n=63) 30.3365 27.3704 33.3026 LDL 60years (n=63) 60.7048 53.0290 68.3805 Triglyceride 60years (n=63) 147.9127 128.9418 166.8836 AST 60years (n=63) 43.7016 35.2720 52.1312 ALT 60years (n=63) 25.0854 18.9448 31.2260 Albumin 60years (n=63) 25.3000 23.8661 26.7339 Direct bilirubin 60years (n=63) 0.1918 0.1599 0.2237 Bilirubin total 60years (n=63) 0.3441 0.2906 0.3975 Creatinine 60years (n=63) 0.8859 0.7387 1.0330 Urea 60years (n=63) 48.6810 39.3816 57.9803 Total cholesterol Male (n=96) 133.7708 124.8125 142.7292 0.022 0.882 Female (n=60) 134.8467 123.5752 146.1182 HDL Male (n=96) 29.8448 27.4629 32.2267 1.056 0.306 Female (n=60) 32.0267 28.2673 35.7861 LDL Male (n=96) 65.7708 59.1767 72.3650 0.010 0.920 Female (n=60) 65.2350 57.0030 73.4670 Triglyceride Male (n=96) 167.4531 150.6982 184.2081 2.141 0.145 Female (n=60) 148.4600 129.7798 167.1402 AST Male (n=96) 45.6029 37.5123 53.6936 0.843 0.360 Female (n=60) 39.9067 31.1741 48.6392 ALT Male (n=96) 38.5654 29.2725 47.8583 7.920 0.006* Female (n=60) 20.8163 15.3591 26.2735 Albumin Male (n=96) 25.7646 24.5619 26.9673 1.772 0.185 Female (n=60) 27.1400 25.3844 28.8956 Direct bilirubin Male (n=96) 0.2323 0.1665 0.2981 0.218 0.641 Female (n=60) 0.2097 0.1493 0.2701 Total bilirubin Male (n=96) 0.3464 0.2905 0.4024 0.092 0.762 Female (n=60) 0.3323 0.2562 0.4084 Creatinine Male (n=96) 0.9171 0.7692 1.0650 2.891 0.091 Female (n=60) 0.7430 0.6421 0.8439 Urea Male (n=96) 45.0430 38.0941 51.9919 2.250 0.136 Female (n=60) 37.0333 29.4831 44.5836 * indicates that the significance level was less than 0.05. 3.3. Factors Affecting Admission Laboratory Test Results In this study, variance analysis between different categories of age and sex was performed to determine which category of age group or sex was significantly different. According to Table 2, none of the tests, except for ALT, were significantly different between male and female participants. ALT levels at admission were significantly different between males and females, and the mean ALT level was significantly greater in males than in females (F=7.920; p=0.006). On the other hand, one-way ANOVA of the different age groups indicated that the ALT, albumin, bilirubin, direct, and total levels were significantly different between the categories (p<0.05) ( Table 2 ). Based on these findings, multiple variable analyses (post hoc tests) were performed to differentiate specific categories. The results showed that AST values at admission were significantly greater in the <30 years age group than in the 30-40 and 41-60 years age groups (p<0.05). Similarly, the mean admission value of ALT in the 60 years) (p<0.05). On the other hand, the mean admission value of urea in the 60 years age group (mean difference = -17.31; 95% CI; -33.04 to -1.58; p=0.031) ( Table 3 ). Table 3: Multiple comparisons ( post hoc analysis) of age groups stratified by AST, ALT, albumin , direct and total bilirubin and urea Dependent variable Category comparison of groups (age in years) Mean difference 95% confidence level of the difference p value Lower bound Upper bound AST <30 (n=8) Vs 30-40 (n=22) 34.39205 3.8656 64.9185 0.027 <30 (n=8) Vs 41-60 (n=63) 28.76480 1.0133 56.5163 0.042 ALT <30 (n=8) Vs 30-40 (n=22) 37.45568 6.2562 68.6552 0.019 <30 (n=8) Vs 41-60 (n=63) 38.57433 10.2110 66.9377 0.008 60 (n=63) 46.15210 17.7888 74.5154 0.002 Albumin 30-40 (n=22) Vs 41-60 y(n=63) 3.87706 .8576 6.8965 0.012 30-40 n=23)Vs >60(n=63) 4.48182 1.4623 7.5013 0.004 Bilirubin direct <30 (n=8)Vs 30-40 (n=22) 0.44814 0.2196 .6767 <0.001 30-40 (n=22)Vs 41-60 (n=63) 0.38208 0.1743 0.5898 60 (n=63) 0.41819 0.2104 0.6259 <0.001 Bilirubin total 60 (n=63) 0.30831 0.1043 0.5123 0.003 Urea 30-40(n=23) Vs >60 (n=63) -17.30823 -33.0413 -1.5752 0.031 4. Discussion This follow-up study aimed to analyse the effect of COVID-19 on common laboratory test results, including lipid profiles, liver function tests, and renal function tests. In this study, the total cholesterol, HDL-C and LDL-C levels were significantly lower at admission than at discharge. These low values of HDL-C, LDL-C and total cholesterol were consistent with previous findings reported from France and China [13, 14]. Many reports of low lipid levels during the admission period, especially in critical/severe patients, have been published [15-19]. In general, the development of hypolipidemia has been reported to be associated with disease severity [20]. Concurrent with these reports, our patients had lower lipid levels during the admission period. There are many reasons for a low lipid profile, including genetic alterations, liver damage, or inflammatory conditions secondary to bacterial or viral infections. In the case of COVID-19, high levels of the proinflammatory cytokines TNF-α and IL-6, which are produced during cytokine storms, have been suggested to minimize the transport of cholesterol and increase the consumption of primary lipids, HDL, TC, TG and LDL-C in addition to inhibiting lipid metabolism by hepatocytes [15, 16, 19, 21]. In contrast to the cholesterol level, there was no significant difference in the TG level between the admission and discharge periods, which is consistent with the findings of H. However, our study participants’ TG levels were similar between admission and discharge, which contradicted the report that elevated TG was reported in very severe cases and can be an indicator of non-survivors of COVID-19 patients [23, 24]. Moreover, it can be a result of the overproduction of free fatty acids and elevated TG synthesis due to the increase in inflammatory cytokines caused by SARS-CoV-2 infection [1, 25]. These differences might be attributed to differences in the study design. This finding requires further investigation with different study populations. This study also addressed the effect of COVID-19 infection on liver function tests (AST, ALT, albumin, direct, and total bilirubin). During admission, our patients’ ALT and AST levels were greater than those at discharge, but the difference was not statistically significant (P>0.05). However, multiple studies have reported that during the progression of COVID-19, liver enzymes are elevated due to inflammatory cytokine-induced tissue damage secondary to SARS-CoV-2 infection [26, 27, 28, 29]. Moreover, liver enzyme elevation is related to tissue damage secondary to SARS-CoV-2 infection, specifically high levels of inflammatory cytokine production, but AST is not directly related to liver tissue damage; rather, it could be proportional to increases in ALT [30]. The serum ALB concentration at admission was significantly lower than that at discharge (P<0.05). A lower serum ALB can indicate malnutrition, underlying disease, or infection. However, it was reported to be an indicator of the prognosis of patients with severe COVID-19 [29, 31]. On the other hand, the abnormal results of liver function tests might not provide a clear picture of the effect of SARS-CoV-2 infection on the liver. Hence, some evidence indicates that abnormal results could be a result of pre-existing liver disease [32]. Abnormalities in liver function test results differ according to several factors, including age and sex. Our study also revealed that the mean admission ALT level in the 60 years) (P<0.05). However, Xu et al reported that the severity of COVID-19 and the ALT level significantly increased in patients older than 60 years [33]. This difference might be due to differences in sample size and type of study population, and further investigation is needed to determine the specific cause of this difference. This study has several limitations, including that statistical analysis was not performed for deceased participants during the follow-up period due to the small sample size, and we were performing limited laboratory tests to determine the effect of COVID-19 on laboratory parameters. Moreover, the study could not assess the effect of cultural substance (medicine) intake on the laboratory test results. 5. Conclusion In this study, the total cholesterol, HDL, LDL, and albumin levels were significantly lower at admission than at discharge. On the other hand, ALT was significantly greater during the admission period than at discharge. This study also revealed that the ALT level was significantly greater in males and individuals aged less than 30 years. Triglycerides were significantly affected by hypertension, total bilirubin was significantly affected by comorbidities, and urea was also significantly affected by age greater than 60 years and male gender. We investigated the associations of multiple organ function tests and lipid profiles among patients with COVID-19 to support further research and to assist clinicians in making informed decisions for their patients. Declarations Ethics approval and consent to participate Ethical approval was obtained from the research and ethical review committee of the Department of Medical Laboratory Sciences of Addis Ababa University (protocol number DRERC/583/21/MLS). Before starting the data collection, permission was obtained from the Ministry of Health and St. Peter Specialized Hospital. Moreover, after the purpose and relevance of the study were explained, written informed consent was obtained from each study participant. The confidentiality of the information (results) was maintained between the study participant and the investigators. Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article. Funding This research received no specific grant from any funding institution. Authors' contributions GW conceived the idea, supervised the laboratory analysis and wrote the manuscript; BW analysed the data and wrote the manuscript; EB and WH offered technical support and revised the manuscript; and SK and AE revised the final version of the manuscript and approved the publication. All the authors have read and approved the final version of the manuscript. 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Abnormal Liver Function Tests in Patients With COVID-19: Relevance and Potential Pathogenesis. Hepatology . 2020;72(5):1864–72 Vancsa S, Hegyi PJ, Zadori N, Szako L, Vörhendi N, Ocskay K, Földi M, Dembrovszky F, Dömötör ZR, Janosi K, Rakonczay Jr Z. Pre-existing liver diseases and on-admission liver-related laboratory tests in COVID-19: a prognostic accuracy meta-analysis with systematic review. Frontiers in medicine . 2020; 13(7):572115. Zhang J, Wang X, Jia X, Li J, Hu K, Chen G, Wei J, Gong Z, Zhou C, Yu H, Yu M. Risk factors for disease severity, unimprovement, and mortality in COVID-19 patients in Wuhan, China. Clinical microbiology and infection . 2020; 26(6):767-72. Additional Declarations No competing interests reported. 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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-4598405","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":316818380,"identity":"bae49292-38bf-473d-8bde-6437ffdde92b","order_by":0,"name":"Gedamnesh Wolde","email":"","orcid":"","institution":"1.\tDepartment of Medical Laboratory, St. Peter’s Specialized Hospital, Addis Ababa","correspondingAuthor":false,"prefix":"","firstName":"Gedamnesh","middleName":"","lastName":"Wolde","suffix":""},{"id":316818381,"identity":"c4049d6a-edc7-43ac-bfe8-42484493bb85","order_by":1,"name":"Belete Woldesemayat","email":"data:image/png;base64,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","orcid":"","institution":"2.\tNational HIV Reference Laboratory, Ethiopian Public Health Institute, Addis Ababa","correspondingAuthor":true,"prefix":"","firstName":"Belete","middleName":"","lastName":"Woldesemayat","suffix":""},{"id":316818382,"identity":"bf2a801c-a753-4e66-a2a2-a57377f5c55b","order_by":2,"name":"Endalkchew Biranu","email":"","orcid":"","institution":"1.\tDepartment of Medical Laboratory, St. Peter’s Specialized Hospital, Addis Ababa","correspondingAuthor":false,"prefix":"","firstName":"Endalkchew","middleName":"","lastName":"Biranu","suffix":""},{"id":316818383,"identity":"e77a9281-0cad-4e34-8dda-d6574a35e4ef","order_by":3,"name":"Wossene Habtu","email":"","orcid":"","institution":"3.\tNational Clinical Chemistry Laboratory, Ethiopian Public Health Institute, Addis Ababa","correspondingAuthor":false,"prefix":"","firstName":"Wossene","middleName":"","lastName":"Habtu","suffix":""},{"id":316818384,"identity":"3dc26eab-ad25-410a-b051-a1db24a39d20","order_by":4,"name":"Abebe Edao","email":"","orcid":"","institution":"4.\tDepartment of Medical Laboratory Science, College of Health Science, Addis Ababa University, Addis Ababa","correspondingAuthor":false,"prefix":"","firstName":"Abebe","middleName":"","lastName":"Edao","suffix":""},{"id":316818385,"identity":"8030aa2e-0f88-4e19-bd0d-19bd5361b111","order_by":5,"name":"Samuel Kinde","email":"","orcid":"","institution":"4.\tDepartment of Medical Laboratory Science, College of Health Science, Addis Ababa University, Addis Ababa","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Kinde","suffix":""}],"badges":[],"createdAt":"2024-06-18 08:14:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4598405/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4598405/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59120288,"identity":"8c5a1ed2-065b-49ef-a170-ecb352fb7932","added_by":"auto","created_at":"2024-06-26 14:46:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":123599,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBland‒Altman plot of the relative mean differences in laboratory profile values between admission and discharge\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA) Triglyceride relative mean difference in admission and discharge\u003c/p\u003e\n\u003cp\u003eB) AST relative mean difference between admission and discharge\u003c/p\u003e\n\u003cp\u003eC) Total Bilirubin relative mean difference between admission and discharge\u003c/p\u003e\n\u003cp\u003eD) Creatinine relative mean difference between admission and discharge\u003c/p\u003e\n\u003cp\u003eE) Urea relative mean difference between admission and discharge\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4598405/v1/3b06025444ed43511b9743eb.png"},{"id":59121333,"identity":"51ecc2ec-f660-4c36-a137-9c043bc702fd","added_by":"auto","created_at":"2024-06-26 14:54:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1092731,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4598405/v1/12f0ec61-ab80-4201-ad86-518991ee6059.pdf"},{"id":59120289,"identity":"6fe57d8f-b7a6-438e-a872-f585333221d1","added_by":"auto","created_at":"2024-06-26 14:46:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32156,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable.docx","url":"https://assets-eu.researchsquare.com/files/rs-4598405/v1/6ba94eea172e65c1a3dc7726.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Changes in lipid, liver, and renal test profiles among patients with severe COVID-19 during and after hospital admission at Saint Peter Specialized Hospital, Addis Ababa, Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCoronavirus disease 2019 (COVID-19) is an infectious disease caused by a newly identified coronavirus, severe acute respiratory syndrome 2 (SARS-CoV-2). Millions of people across hundreds of countries have been impacted by this pandemic. Many cases rapidly progress to acute respiratory disease, multiorgan failure, and septic shock, with a markedly increased mortality rate [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs an initial target for antiviral therapy, lipid metabolic pathways and the structural components of membranes might be targeted to specifically hinder the life cycle of the virus. Furthermore, lipoproteins play a significant role in SARS-CoV-2 infection. In particular, HDL may make it simpler for SARS-CoV-2 to enter host cells through the SR-B1 receptor [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The lipid metabolism of SARS-CoV-2 is considered to be regulated. However, the modifications and effects of high-density lipoprotein cholesterol (HDL-C) in patients with COVID-19 have rarely been observed. In patients with COVID-19, total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels decrease significantly [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHepatic involvement in COVID-19 could be related to the direct cytopathic effect of the virus, an uncontrolled immune reaction, sepsis, or drug-induced liver injury. Given the increased expression of angiotensin-converting enzyme-2 (ACE2) receptors in cholangiocytes, the liver is a potential target for SARS-CoV-2 infection treatment. Moreover, COVID-19 may cause worsening of underlying chronic liver disease, leading to hepatic decomposition and acute-on-chronic liver failure, with increased mortality [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Complications reported to be associated with COVID-19 include myocardial injury, heart failure, acute kidney injury and electrolyte disturbances. In addition to the observation that older patients, males and those with pre-existing comorbidities such as cardiovascular disease, diabetes, chronic kidney disease, and chronic liver disease are at the highest risk for severe illness or death, COVID-19 complications have been shown to correlate with disease severity. Progression to multiorgan damage/failure after monitoring in patients with severe COVID-19 is indicated by abnormal hepatic, cardiac, renal, and liver function and LDH results [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProspective and descriptive studies conducted in Northwest Mexico indicated that patients with COVID-19 had significant changes in their lipid profiles. Low levels of total cholesterol (TCHOL), LDL, and HDL have been reported, while triglyceride (TG) levels have been reported to be high. These abnormalities were the same among noncritical and critical COVID-19 patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A prospective cohort study performed by Cheng et al. involving 701 patients with COVID-19 admitted to a tertiary teaching hospital in Wuhan in 2020 revealed that 14.4% of patients had elevated serum creatinine and 13.1% had elevated blood urea nitrogen [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Renal manifestations such as a slight increase in creatinine, modest proteinuria, and hematuria have been observed in COVID-19 patients, possibly as a result of kidney tropism and multiorgan failure [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA meta-analysis performed by Yanyan W et al. revealed that the pooled incidence of any abnormal liver biochemical indicators at admission and during hospitalization was 27.2% and 36%, respectively. Moreover, abnormal liver biochemical test results are common and closely related to the severity and prognosis of COVID-19 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Hence, this study aimed to determine the changes in common laboratory test parameters (lipid profiles and liver and kidney function test results) at admission and during discharge among COVID-19 patients admitted to St. Peter Hospital, Addis Ababa, Ethiopia, to ultimately complement clinical data for risk stratification and improve the overall clinical management of COVID-19 patients.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area and design\u003c/h2\u003e \u003cp\u003eA prospective cohort study was conducted from January 1, 2021, to April 30, 2021, at St. Peter Specialized Hospital in Addis Ababa, Ethiopia. St. Peter Specialized Hospital was established in 1953. It is located in Gulele Subcity and is managed by the Federal Democratic Republic of Ethiopia, Ministry of Health (FMoH).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. The eligibility criteria\u003c/h2\u003e \u003cp\u003eAll newly admitted COVID-19 patients in the hospital were included. Individuals with a known history of chronic kidney disease, chronic liver disease or dyslipidemia were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Sample size determination and sampling method\u003c/h2\u003e \u003cp\u003eThe sample size was determined by two means with an equal sample size comparison formula by using a previous study of lipid profile differences (specifically HDL difference), which was 6.6 mg/dl [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. An 80% estimation power and 99% confidence level were considered in the sample size calculation. Finally, the minimum sample size was 147, and we recruited all 162 participants admitted to the hospital during the time frame using a convenient sampling method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Sample collection and laboratory analysis\u003c/h2\u003e \u003cp\u003eApproximately 5 ml of venous blood was collected aseptically from each study participant by trained laboratory technologists in the morning after 8:00 hours of overnight fast. The blood sample was dispensed into jelly-coated serum separator test tubes or plain tubes labelled with a unique ID number. The collected blood sample was left for 30 minutes to facilitate clotting at room temperature. Then, the clotted blood samples were centrifuged for 5 minutes at 300 g (RCF) to separate the serum from the formed elements. Lipid profiles, liver function tests, and renal function tests were analysed by a Cobas c311 (Roche Diagnostics, Indianapolis, USA) fully automated clinical chemistry analyser following the manufacturer\u0026rsquo;s instructions within the same day of sample collection.\u003c/p\u003e \u003cp\u003eLipid panels, including TC, HDL-C, LDL-C, and TG, were analysed by cholesterol oxidase, phenol 4-amino antipyrine peroxidase, direct enzymatic methods, direct determination and glycerin phosphate oxidase peroxidase, respectively. Liver function tests, such as those for ALB, alanine transferase (ALT) and aspartate transferase (AST), were performed with the method of enzymatic activity based on the IFCC recommendation. Bilirubin direct (BILD) and bilirubin total (BILT) were measured via the colorimetric diazo method. Similarly, renal function tests, creatinine and urea were performed with the enzymatic test principle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Data quality control\u003c/h2\u003e \u003cp\u003eNurses in charge of the patients were well-oriented on how to prepare patients and collect specimens. The quality of laboratory analysis was maintained by following standard operating procedures of the preanalytical, analytical, and postanalytical stages. The hospital laboratory was in the process of accreditation, and it used to exercise a good laboratory quality management system. Internal quality control was performed by using normal and pathological quality control materials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Data analysis\u003c/h2\u003e \u003cp\u003eData entry and analysis were performed with SPSS version 25 statistical software. Descriptive statistics were employed to explain sociodemographic and clinical characteristics, distribution and values of lipid profiles, liver function tests, and renal function tests. A paired test analysis was performed to determine the difference between admission and discharge values of the tests by using a nonparametric Wilcoxon test. The Bland‒Altman test was also employed to determine the mean relative differences in the non-statistically significant different parameters between the admission and discharge periods. Measure analysis of variance (ANOVA) was performed to determine the mean difference between different experimental scenarios. Post hock analysis was employed to locate the place of significance between more than two categories. The level of significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSociodemographic characteristics of the\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003estudy participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 162 study participants were recruited for this study. Data from six participants, who were deceased (n=4) and referred to other hospitals for further management (n=2) were excluded. Overall, 156 participants were included. During the final analysis, 96 (61.5%) of these participants were male, and the mean age of the participants was \u0026plusmn; standard deviation (55.46 \u0026plusmn; 14.32). A majority (80.4%) of the participants were above the age of 41, 70.5% (110) of the participants were married, and only 18.6% (29) of the participants were from rural areas (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eLipid, liver and renal function tests among participants at admission and during the follow-up period\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, there were differences in laboratory findings between the admission and discharge times. The nonparametric Wilcoxon test indicated that\u0026nbsp;total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), albumin, and alanine transaminase (ALT) levels were significantly different between the admission and discharge periods (P\u0026lt; 0.05). Moreover, total cholesterol, HDL, LDL and albumin increased significantly at discharge (P\u0026lt;0.05). ALT was significantly greater at admission than at discharge (P\u0026lt;0.05). On the other hand, renal parameters (creatinine and urea) were not significantly different between the admission and discharge periods (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Paired analysis of lipid profiles, liver enzymes, and renal test profiles\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eat admission and discharge\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(n=156)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.510204081632654%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDischarge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNonparametric Wilcoxon test (p value)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.568627450980394%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.49019607843137%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Standard deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.49019607843137%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.45098039215686%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003eTotal cholesterol \u0026nbsp;(mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e125.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e43.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e150.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e48.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003eHDL (mg/dl)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e29.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e12.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e34.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e15.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003eLDL (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e61.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e32.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e80.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e35.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003eTG (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e137.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e79.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e151.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e80.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003eAlbumin (g/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e26.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e6.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e27.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e6.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e0.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003eAST(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e31.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e34.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e30.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e28.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003eALT(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e34.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e39.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003eDirect Bilirubin (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003eTotal bilirubin (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003eCreatinine (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003eUrea (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e32.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e32.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e32.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e24.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cu\u003eAbbreviations:\u003c/u\u003e\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;HDL- high-density lipoprotein, LDL; low-density lipoprotein, AST; aspartate aminotransferase, ALT; alanine aminotransferase TG; triglyceride, g/dl; gram per deciliter, mg/dl; milligram per deciliter, U/L; international unit per liter\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e* P values indicate the differences between admission and discharge values (median discharge-median admission). P \u0026lt; 0.05 was considered to indicate statistical significance.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAccording to Table 2, the majority of lipid profiles at admission and at discharge were significantly different. However, for some parameters (TG, AST, total bilirubin, urea, and creatinine), there were no significant differences in the cohort. Based on Bland‒Altman analysis illustrating the mean difference between the admission and discharge periods, the triglyceride measurements indicated that 9 (5.8%) measurements (sample values) had a pooled 95% confidence interval (i.e., mean difference \u0026plusmn;1.96 SD). Similarly, in the AST measurements, 8 samples (5.1%) met the pooled 95% limit. Differences in the other test parameters (total bilirubin, creatinine, and urea) measured in the Bland‒Altman plot were less than the pooled 5% difference \u003cstrong\u003e(Figure 1C, 1D, 1E)\u003c/strong\u003e. In addition, only 5 (five) participants had TG percentages above the 95% confidence level (32.24%), and 1 (one) participant had TG percentages below the 95% confidence level (-28.31%). However, the majority (150, 96.2%) of the test values were between -28.31% and 32.24%. The relative mean differences in triglyceride (\u003cstrong\u003eFigure 1A)\u0026nbsp;\u003c/strong\u003eand AST (\u003cstrong\u003eFigure 1B)\u0026nbsp;\u003c/strong\u003elevels at discharge were greater than those at admission. On the other hand, the relative mean differences in creatinine and urea were more consistent, and the majority of values were within the 95% confidence \u003cstrong\u003elimits (Figure 1D and 1E\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Measurement analysis of variance (one-way ANOVA) of laboratory tests at admission with age group and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eage groups and Gender category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.291079812206572%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI for Mean \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003elower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eupper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eTotal cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 30 years (n=8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e155.0625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e115.9204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e194.2046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 years (n=22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e144.1318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e127.3701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e160.8936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e41-60 years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e137.5873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e126.3422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e148.8324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;60years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e124.6571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e113.7170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e135.5973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 30 years(n=8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e28.8250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e14.9295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e42.7205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 years (n=22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e31.5045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e26.6542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e36.3549\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e41-60 years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e30.9810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e27.3769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e34.5850\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;60years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e30.3365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e27.3704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e33.3026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 30 years (n=8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e72.7500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e43.3835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e102.1165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 years (n=22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e73.0864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e58.8860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e87.2867\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e41-60 years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e66.8857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e58.4526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e75.3188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;60years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e60.7048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e53.0290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e68.3805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eTriglyceride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 30 years (n=8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e163.5500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e97.0764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e230.0236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e1.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 years (n=22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e148.8818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e117.3398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e180.4239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e41-60 years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e175.8857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e154.6410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e197.1304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;60years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e147.9127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e128.9418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e166.8836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 30 years(n=8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e70.8375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e18.3459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e123.3291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e1.714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 years (n=22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e36.4455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e24.7953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e48.0956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e41-60 years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e42.0727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e31.8911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e52.2543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;60years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e43.7016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e35.2720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e52.1312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 30 years(n=8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e71.2375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e2.1264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e140.3486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e3.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.017*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 years (n=22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e33.7818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e14.4497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e53.1140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e41-60 years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e32.6632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e22.6628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e42.6636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;60years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e25.0854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e18.9448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e31.2260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 30 years(n=8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e27.5875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e20.8972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e34.2778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e3.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.029*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 years (n=22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e29.7818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e27.3414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e32.2222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e41-60 years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e25.9048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e24.2444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e27.5651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;60years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e25.3000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e23.8661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e26.7339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eDirect bilirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 30 years(n=8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e0.6100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e-0.1630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e1.3830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e5.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 years (n=22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.1619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.1095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.2142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e41-60 years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.2279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.1589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.2969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;60years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.1918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.1599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.2237\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eBilirubin total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 30 years (n=8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e0.6524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e0.1098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e1.1950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e3.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 years (n=22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.3172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.2132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.4213\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e41-60 years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.3067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.2381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.3754\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;60years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.3441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.2906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.3975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 30 years (n=8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e0.7925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e0.3684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e1.2166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 years (n=22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.7382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.6055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.8709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e41-60 years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.8608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.6703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e1.0513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;60years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.8859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.7387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e1.0330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eUrea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 30 years (n=8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e26.3288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e11.7580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e40.8995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e2.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 years (n=22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e31.3727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e24.1018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e38.6437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e41-60 years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e40.9270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e32.7810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e49.0730\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;60years (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e48.6810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e39.3816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e57.9803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTotal cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003eMale (n=96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e133.7708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e124.8125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e142.7292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003eFemale (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e134.8467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e123.5752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e146.1182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003eMale (n=96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e29.8448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e27.4629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e32.2267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003eFemale (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e32.0267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e28.2673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e35.7861\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003eMale (n=96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e65.7708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e59.1767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e72.3650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003eFemale (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e65.2350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e57.0030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e73.4670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTriglyceride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003eMale (n=96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e167.4531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e150.6982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e184.2081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003eFemale (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e148.4600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e129.7798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e167.1402\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003eMale (n=96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e45.6029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e37.5123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e53.6936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003eFemale (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e39.9067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e31.1741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e48.6392\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003eMale (n=96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e38.5654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e29.2725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e47.8583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e7.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003eFemale (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e20.8163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e15.3591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e26.2735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003eMale (n=96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e25.7646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e24.5619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e26.9673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003eFemale (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e27.1400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e25.3844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e28.8956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDirect bilirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003eMale (n=96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e0.2323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e0.1665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e0.2981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003eFemale (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.2097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.1493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.2701\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eTotal bilirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003eMale (n=96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e0.3464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e0.2905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e0.4024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003eFemale (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.3323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.2562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.4084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003eMale (n=96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e0.9171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e0.7692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e1.0650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003eFemale (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.7430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.6421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e0.8439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.302034428794991%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eUrea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.474178403755868%\" valign=\"top\"\u003e\n \u003cp\u003eMale (n=96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e45.0430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e38.0941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.145539906103286%\" valign=\"top\"\u003e\n \u003cp\u003e51.9919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.546165884194053%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e2.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.241001564945227%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.3134328358209%\" valign=\"top\"\u003e\n \u003cp\u003eFemale (n=60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e37.0333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e29.4831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.895522388059703%\" valign=\"top\"\u003e\n \u003cp\u003e44.5836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e* indicates that the significance level was less than 0.05.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFactors\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAffecting Admission Laboratory Test Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, variance analysis between different categories of age and sex was performed to determine which category of age group or sex was significantly different. According to Table 2, none of the tests, except for ALT, were significantly different between male and female participants. ALT levels at admission were significantly different between males and females, and the mean ALT level was significantly greater in males than in females (F=7.920; p=0.006). On the other hand, one-way ANOVA of the different age groups indicated that the ALT, albumin, bilirubin, direct, and total levels were significantly different between the categories (p\u0026lt;0.05) (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eBased on these findings, multiple variable analyses (post hoc tests) were performed to differentiate specific categories. The results showed that AST values at admission were significantly greater in the \u0026lt;30 years age group than in the 30-40 and 41-60 years age groups (p\u0026lt;0.05). Similarly, the mean admission value of ALT in the \u0026lt;30 years age group was significantly greater than that in all other age groups (30-40, 41-60, and \u0026gt;60 years) (p\u0026lt;0.05). On the other hand, the mean admission value of urea in the \u0026lt;30 years age group was lower than that in the \u0026gt;60 years age group (mean difference = -17.31; 95% CI; -33.04 to -1.58; p=0.031) (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Multiple comparisons (\u003c/strong\u003e\u003cstrong\u003epost\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;hoc analysis) of age\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003egroups stratified by\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;AST, ALT,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ealbumin\u003c/strong\u003e\u003cstrong\u003e, direct and total bilirubin and urea\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.084507042253522%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent variable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.35211267605634%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory comparison of groups (age in years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean difference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.80281690140845%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% confidence level of the difference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.737089201877934%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower bound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper bound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.084507042253522%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.35211267605634%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;30 (n=8) Vs 30-40 (n=22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e34.39205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e3.8656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e64.9185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.737089201877934%\" valign=\"top\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.508196721311474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;30 (n=8) Vs 41-60 (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.48633879781421%\" valign=\"top\"\u003e\n \u003cp\u003e28.76480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e1.0133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e56.5163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.66120218579235%\" valign=\"top\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.084507042253522%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.35211267605634%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;30 (n=8) Vs 30-40 (n=22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e37.45568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e6.2562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e68.6552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.737089201877934%\" valign=\"top\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.508196721311474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;30 (n=8) Vs 41-60 (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.48633879781421%\" valign=\"top\"\u003e\n \u003cp\u003e38.57433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e10.2110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e66.9377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.66120218579235%\" valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.508196721311474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;30 (n=8) Vs \u0026gt;60 (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.48633879781421%\" valign=\"top\"\u003e\n \u003cp\u003e46.15210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e17.7888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e74.5154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.66120218579235%\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.084507042253522%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.35211267605634%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 (n=22) Vs 41-60 y(n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e3.87706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e.8576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e6.8965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.737089201877934%\" valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.508196721311474%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 n=23)Vs \u0026gt;60(n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.48633879781421%\" valign=\"top\"\u003e\n \u003cp\u003e4.48182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e1.4623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e7.5013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.66120218579235%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.084507042253522%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eBilirubin direct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.35211267605634%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;30 (n=8)Vs 30-40 (n=22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e0.44814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e0.2196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e.6767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.737089201877934%\" 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=\"29.508196721311474%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 (n=22)Vs 41-60 (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.48633879781421%\" valign=\"top\"\u003e\n \u003cp\u003e0.38208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e0.1743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e0.5898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.66120218579235%\" 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=\"29.508196721311474%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 (n=23)Vs \u0026gt;60 (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.48633879781421%\" valign=\"top\"\u003e\n \u003cp\u003e0.41819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e0.2104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e0.6259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.66120218579235%\" 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=\"14.084507042253522%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eBilirubin total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.35211267605634%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;30 (n=8)s Vs 30-40 (n=23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e0.33515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e0.1108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e0.5595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.737089201877934%\" valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.508196721311474%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 (n=23)Vs 41-60 (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.48633879781421%\" valign=\"top\"\u003e\n \u003cp\u003e0.34566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e0.1417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e0.5497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.66120218579235%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.508196721311474%\" valign=\"top\"\u003e\n \u003cp\u003e30-40 (n=23)Vs \u0026gt;60 (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.48633879781421%\" valign=\"top\"\u003e\n \u003cp\u003e0.30831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e0.1043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e0.5123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.66120218579235%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.084507042253522%\" valign=\"top\"\u003e\n \u003cp\u003eUrea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.35211267605634%\" valign=\"top\"\u003e\n \u003cp\u003e30-40(n=23) Vs \u0026gt;60 (n=63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.023474178403756%\" valign=\"top\"\u003e\n \u003cp\u003e-17.30823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e-33.0413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e-1.5752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.737089201877934%\" valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4.\tDiscussion","content":"\u003cp\u003eThis follow-up study aimed to analyse the effect of COVID-19 on common laboratory test results, including lipid profiles, liver function tests, and renal function tests.\u0026nbsp;In this study, the total\u0026nbsp;cholesterol, HDL-C and LDL-C\u0026nbsp;levels\u0026nbsp;were significantly lower at admission\u0026nbsp;than at\u0026nbsp;discharge.\u0026nbsp;These\u0026nbsp;low\u0026nbsp;values\u0026nbsp;of HDL-C, LDL-C and total cholesterol\u0026nbsp;were\u0026nbsp;consistent with previous findings reported from France and China [13, 14]. Many reports\u0026nbsp;of low lipid levels during\u0026nbsp;the admission period,\u0026nbsp;especially\u0026nbsp;in critical/severe patients, have been published\u0026nbsp;[15-19]. In general, the development of hypolipidemia has been reported to be associated with disease severity [20].\u003c/p\u003e\n\u003cp\u003eConcurrent with these reports, our patients had lower lipid levels during the admission period.\u0026nbsp;There are many\u0026nbsp;reasons\u0026nbsp;for a low lipid profile,\u0026nbsp;including genetic alterations, liver damage, or inflammatory conditions secondary to bacterial or viral infections. In the case of COVID-19, high\u0026nbsp;levels\u0026nbsp;of\u0026nbsp;the proinflammatory\u0026nbsp;cytokines TNF-α and IL-6,\u0026nbsp;which\u0026nbsp;are produced\u0026nbsp;during cytokine\u0026nbsp;storms, have been\u0026nbsp;suggested to minimize the transport of cholesterol and increase the consumption of primary lipids, HDL, TC, TG and LDL-C\u0026nbsp;in addition to\u0026nbsp;inhibiting lipid metabolism by hepatocytes [15, 16, 19, 21].\u003c/p\u003e\n\u003cp\u003eIn contrast to the\u0026nbsp;cholesterol\u0026nbsp;level, there was no significant difference in\u0026nbsp;the\u0026nbsp;TG level between the admission and discharge\u0026nbsp;periods, which is\u0026nbsp;consistent\u0026nbsp;with the\u0026nbsp;findings of H.\u0026nbsp;However, our study participants’ TG levels were similar\u0026nbsp;between\u0026nbsp;admission and discharge, which contradicted the report that elevated TG was reported in very severe cases and can be an indicator of\u0026nbsp;non-survivors\u0026nbsp;of COVID-19 patients [23, 24]. Moreover, it can be a result of the overproduction of free fatty acids and\u0026nbsp;elevated TG\u0026nbsp;synthesis due to the\u0026nbsp;increase in\u0026nbsp;inflammatory cytokines\u0026nbsp;caused\u0026nbsp;by SARS-CoV-2\u0026nbsp;infection\u0026nbsp;[1, 25]. These differences might be attributed to differences in the study design. This\u0026nbsp;finding requires\u0026nbsp;further investigation with different study populations.\u003c/p\u003e\n\u003cp\u003eThis study also addressed the effect of COVID-19 infection on liver function tests (AST, ALT, albumin, direct, and total bilirubin). During admission, our patients’ ALT and AST\u0026nbsp;levels\u0026nbsp;were\u0026nbsp;greater\u0026nbsp;than\u0026nbsp;those at discharge, but the difference was\u0026nbsp;not statistically significant (P\u0026gt;0.05). However, multiple studies\u0026nbsp;have\u0026nbsp;reported that during the progression of COVID-19, liver enzymes\u0026nbsp;are\u0026nbsp;elevated due to inflammatory cytokine-induced tissue damage secondary to SARS-CoV-2 infection [26, 27, 28, 29].\u003c/p\u003e\n\u003cp\u003eMoreover, liver enzyme elevation is related to tissue damage secondary to SARS-CoV-2 infection, specifically high levels of inflammatory cytokine production, but AST\u0026nbsp;is\u0026nbsp;not directly related to liver tissue damage;\u0026nbsp;rather,\u0026nbsp;it could be proportional to\u0026nbsp;increases in ALT\u0026nbsp;[30]. The\u0026nbsp;serum ALB concentration at\u0026nbsp;admission was significantly lower than\u0026nbsp;that at\u0026nbsp;discharge (P\u0026lt;0.05).\u0026nbsp;A lower\u0026nbsp;serum\u0026nbsp;ALB\u0026nbsp;can\u0026nbsp;indicate\u0026nbsp;malnutrition, underlying disease, or\u0026nbsp;infection. However, it was reported to be an indicator of the prognosis of patients with severe COVID-19 [29, 31].\u003c/p\u003e\n\u003cp\u003eOn the other hand, the abnormal results of liver function tests might not\u0026nbsp;provide\u0026nbsp;a clear picture of the effect of SARS-CoV-2 infection on the liver. Hence, some evidence\u0026nbsp;indicates\u0026nbsp;that abnormal\u0026nbsp;results\u0026nbsp;could be a result of pre-existing liver disease [32].\u0026nbsp;Abnormalities in liver\u0026nbsp;function test\u0026nbsp;results differ according to several factors,\u0026nbsp;including age and\u0026nbsp;sex. Our\u0026nbsp;study also\u0026nbsp;revealed\u0026nbsp;that the mean admission ALT\u0026nbsp;level\u0026nbsp;in the \u0026lt;30-year-old group\u0026nbsp;was significantly\u0026nbsp;greater\u0026nbsp;than\u0026nbsp;that in the\u0026nbsp;other age groups (30-40, 41-60 and \u0026gt;60 years) (P\u0026lt;0.05). However, Xu et al\u0026nbsp;reported that\u0026nbsp;the severity of COVID-19 and\u0026nbsp;the\u0026nbsp;ALT\u0026nbsp;level\u0026nbsp;significantly increased\u0026nbsp;in patients older\u0026nbsp;than 60 years [33]. This difference might be due to\u0026nbsp;differences in\u0026nbsp;sample size and type of study population,\u0026nbsp;and further investigation\u0026nbsp;is needed to determine\u0026nbsp;the specific cause of this difference.\u003c/p\u003e\n\u003cp\u003eThis study has\u0026nbsp;several\u0026nbsp;limitations,\u0026nbsp;including\u0026nbsp;that\u0026nbsp;statistical analysis was not performed for deceased participants during the follow-up period due to the small sample size, and we were performing limited laboratory tests to determine the effect of COVID-19 on laboratory parameters. Moreover, the study could not assess the effect of cultural substance (medicine) intake on the laboratory test results.\u003c/p\u003e"},{"header":"5.\tConclusion","content":"\u003cp\u003eIn this study, the total cholesterol, HDL, LDL, and albumin\u0026nbsp;levels\u0026nbsp;were significantly lower\u0026nbsp;at\u0026nbsp;admission\u0026nbsp;than at\u0026nbsp;discharge. On the other hand, ALT was significantly\u0026nbsp;greater\u0026nbsp;during the admission period\u0026nbsp;than at\u0026nbsp;discharge. This study also\u0026nbsp;revealed\u0026nbsp;that\u0026nbsp;the\u0026nbsp;ALT\u0026nbsp;level\u0026nbsp;was significantly\u0026nbsp;greater\u0026nbsp;in males and\u0026nbsp;individuals aged\u0026nbsp;less than 30 years.\u0026nbsp;Triglycerides were\u0026nbsp;significantly affected by hypertension, total bilirubin was significantly affected by\u0026nbsp;comorbidities, and\u0026nbsp;urea was also significantly affected by age greater than 60\u0026nbsp;years\u0026nbsp;and male\u0026nbsp;gender. We\u0026nbsp;investigated the associations\u0026nbsp;of multiple organ function tests and lipid profiles among patients with COVID-19 to\u0026nbsp;support\u0026nbsp;further research and\u0026nbsp;to\u0026nbsp;assist clinicians in making informed decisions for their patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the research and ethical review committee of the Department of Medical Laboratory Sciences of Addis Ababa University (protocol number DRERC/583/21/MLS). Before starting the data collection, permission was obtained from the Ministry of Health and St. Peter Specialized Hospital. Moreover, after the purpose and relevance of the study were explained, written informed consent was obtained from each study participant. The confidentiality of the information (results) was maintained between the study participant and the investigators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author\u0026nbsp;upon\u0026nbsp;reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding\u0026nbsp;institution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGW conceived the idea, supervised the laboratory analysis and wrote the manuscript; BW analysed the data and wrote the manuscript;\u0026nbsp;EB and WH offered technical support and revised the manuscript; and SK and AE revised the final version of the manuscript and approved the publication. All the authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all members of the Medical Laboratory Department at St. Peter Specialized Hospital for their support in laboratory investigations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSun JT, Chen Z, Nie P, Ge H, Shen L, Yang F, et al. Lipid Profile Features and Their Associations With Disease Severity and Mortality in Patients With COVID-19. \u003cem\u003eFront Cardiovasc Med.\u003c/em\u003e 2020;7(1):1\u0026ndash;12.\u003c/li\u003e\n \u003cli\u003eKhan MMA, Khan MN, Mustagir MG, Rana J, Islam MS, Kabir MI. Effects of underlying morbidities on the occurrence of deaths in COVID-19 patients: A systematic review and meta-analysis. \u003cem\u003eJ Glob Health\u003c/em\u003e. 2020;10(2):020503.\u003c/li\u003e\n \u003cli\u003eKočar E, Režen T, Rozman D. Cholesterol, lipoproteins, and COVID-19: Basic concepts and clinical applications. \u003cem\u003eBiochimica et Biophysica Acta (BBA)-Molecular and Cell Biology of Lipids\u003c/em\u003e. 2021; 1866(2):158849.\u003c/li\u003e\n \u003cli\u003eWang G, Zhang Q, Zhao X, Dong H, Wu C, Wu F, et al. Low high-density lipoprotein level is correlated with the severity of COVID-19 patients: An observational study. \u003cem\u003eLipids Health Dis\u003c/em\u003e. 2020;19(1):1\u0026ndash;7.\u003c/li\u003e\n \u003cli\u003eJothimani D, Venugopal R, Abedin MF, Kaliamoorthy I, Rela M. COVID-19 and the liver. \u003cem\u003eJ Hepatol.\u003c/em\u003e 2020;73(5):1231\u0026ndash;40.\u003c/li\u003e\n \u003cli\u003eLi Y, Zhang Y, Lu R, Dai M, Shen M, Zhang J, Cui Y, Liu B, Lin F, Chen L, Han D. Lipid metabolism changes in patients with severe COVID-19. \u003cem\u003eClinicaChimica Acta\u003c/em\u003e. 2021; 517:66-73.\u003c/li\u003e\n \u003cli\u003eZaim S, Chong JH, Sankaranarayanan V, Harky A. 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Hypolipidemia is associated with the severity of COVID-19. \u003cem\u003eJ Clin Lipidology\u003c/em\u003e. 2020;14 (https://doi.org/10.1016/j.jacl.2020.04.008):297\u0026ndash;304.\u003c/li\u003e\n \u003cli\u003eFan J. Letter to the Editor: Low-density lipoprotein is a potential predictor of poor prognosis in patients with coronavirus disease 2019. \u003cem\u003eMetab Clin Exp\u003c/em\u003e. 2020;107 (https://doi.org/10.1016/j.metabol.2020.154243).\u003c/li\u003e\n \u003cli\u003eMalik J, Ishaq U, Laique T, Ashraf A, Malik A, Rathore MA, et al. Effect of COVID-19 on Lipid Profile and its Correlation with Acute Phase Reactants. \u003cem\u003emedRxiv\u003c/em\u003e. 2021. (https://doi.org/10.1101/2021.04.13.21255142):2021.04.13.21255142.\u003c/li\u003e\n \u003cli\u003eHu X, Chen D, Wu L, He G, Ye W. Declined serum high-density lipoprotein cholesterol is associated with the severity of COVID-19 infection. \u003cem\u003eClin Chim Acta\u003c/em\u003e. 2020;510:105\u0026ndash;10.\u003c/li\u003e\n \u003cli\u003eQin C, Minghan H, Ziwen Z, Yukun L. Alteration of lipid profile and value of lipids in the prediction of the length of hospital stay in COVID-19 pneumonia patients. \u003cem\u003eFood Sci Nutr\u003c/em\u003e. 2020;8(11):6144\u0026ndash;52.\u003c/li\u003e\n \u003cli\u003eZhu Z, Fan L, Lou K. A Preliminary Study on Blood Lipid Pro le in Patients with COVID-19. \u003cem\u003eResearch square\u003c/em\u003e.2020. https://doi.org/10.21203/rs.3.rs-57301/v1.\u003c/li\u003e\n \u003cli\u003eLiu W, Tao ZW, Wang L, Yuan ML, Liu K, Zhou L, et al. Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease. \u003cem\u003eChin Med J (Engl).\u003c/em\u003e 2020;133(9):1032\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eJin H, He J, Dong C, Cao L, Qi X, Huang T, et al. Altered Lipid Profile is a Risk Factor for the Progression Andrecurrenceof COVID-19: From Two Retrospective Cohorts. \u003cem\u003eRes Sq\u003c/em\u003e. 2020. (https://doi.org/10.21203/rs.3.rs-60159/v1).\u003c/li\u003e\n \u003cli\u003eLopes GPR, Munhoz MAG, Antonangelo L. Evaluation of the relationship between hematocrit and lipid profile in adults. \u003cem\u003eJ Bras Patol e Med Lab\u003c/em\u003e. 2018;54(3):146\u0026ndash;52.\u003c/li\u003e\n \u003cli\u003eHu X, Chen D, Wu L, He G, Ye W. Declined serum high-density lipoprotein cholesterol is associated with the severity of COVID-19 infection. \u003cem\u003eClinicaChimica Acta\u003c/em\u003e. 2020; 510:105-10.\u003c/li\u003e\n \u003cli\u003eChangaripour S, Sajadi E, Eskandariroozbahani N. A Case-Control Study on Blood Lipid Profile in Patients with COVID-19. \u003cem\u003eRes Sq\u003c/em\u003e. 2021. (https://doi.org/10.21203/rs.3.rs-423471/v2).\u003c/li\u003e\n \u003cli\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. \u003cem\u003eJournal of Clinical Lipidology\u003c/em\u003e. 2020; 14(3):297-304.\u003c/li\u003e\n \u003cli\u003eMohammedsaeed W, Alahamadey Z, Khan M. Alteration of lipid profile in COVID-19 Saudi patients at Al-Madinah Al-Munawarah. \u003cem\u003eInfection\u003c/em\u003e. 2020; 14:15.\u003c/li\u003e\n \u003cli\u003eParohan M, Yaghoubi S, Seraji A. Liver injury is associated with severe coronavirus disease 2019 (COVID-19) infection: A systematic review and meta-analysis of retrospective studies. \u003cem\u003eHepatol Res\u003c/em\u003e. 2020;50(8):924\u0026ndash;35.\u003c/li\u003e\n \u003cli\u003eBloom PP, Meyerowitz EA, Reinus Z, Daidone M, Gustafson J, Kim AY, et al. Liver Biochemistries in Hospitalized Patients With COVID-19. \u003cem\u003eHepatology\u003c/em\u003e. 2021;73(3):890\u0026ndash;900.\u003c/li\u003e\n \u003cli\u003eDe la Rica R, Borges M, Aranda M, Del Castillo A, Socias A, Payeras A, Rialp G, Socias L, Masmiquel L, Gonzalez-Freire M. Low albumin levels are associated with poorer outcomes in a case series of COVID-19 patients in Spain: a retrospective cohort study. \u003cem\u003eMicroorganisms\u003c/em\u003e. 2020; 8(8):1106.\u003c/li\u003e\n \u003cli\u003eAziz M, Fatima R, Lee-Smith W, Assaly R. The association of low serum albumin level with severe COVID-19: a systematic review and meta-analysis. \u003cem\u003eCritical Care\u003c/em\u003e. 2020; 24(1):1-4.\u003c/li\u003e\n \u003cli\u003eBertolini A, van de Peppel IP, Bodewes FAJA, Moshage H, Fantin A, Farinati F, et al. Abnormal Liver Function Tests in Patients With COVID-19: Relevance and Potential Pathogenesis. \u003cem\u003eHepatology\u003c/em\u003e. 2020;72(5):1864\u0026ndash;72\u003c/li\u003e\n \u003cli\u003eVancsa S, Hegyi PJ, Zadori N, Szako L, V\u0026ouml;rhendi N, Ocskay K, F\u0026ouml;ldi M, Dembrovszky F, D\u0026ouml;m\u0026ouml;t\u0026ouml;r ZR, Janosi K, Rakonczay Jr Z. Pre-existing liver diseases and on-admission liver-related laboratory tests in COVID-19: a prognostic accuracy meta-analysis with systematic review. \u003cem\u003eFrontiers in medicine\u003c/em\u003e. 2020; 13(7):572115.\u003c/li\u003e\n \u003cli\u003eZhang J, Wang X, Jia X, Li J, Hu K, Chen G, Wei J, Gong Z, Zhou C, Yu H, Yu M. Risk factors for disease severity, unimprovement, and mortality in COVID-19 patients in Wuhan, China. \u003cem\u003eClinical microbiology and infection\u003c/em\u003e. 2020; 26(6):767-72.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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, Lipid profile, Liver function test, Renal function test","lastPublishedDoi":"10.21203/rs.3.rs-4598405/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4598405/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThe progression of COVID-19 affects multiple organs, abnormal lipid, liver, and renal function tests have beenreported. Hence, this study aimed to determine differences in organ function and lipid profile among patients with severe COVID-19 during and after hospital admission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA follow-up study was conducted among COVID-19-admitted patients at St. Peter Specialized Hospital from January 1, 2021, to April 30, 2021. A total of 162 patients were included in the study. Five millilitersof venous blood was collected during admission and on the verge of discharge. Lipid, renal and liver function tests were performedusing aCobas 311 analyser. The data were entered and analysed with SPSS version 25.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe mean differences in total cholesterol, HDL, and LDL at admission and discharge were 20.13 (95% CI; 13.41-26.84; P\u0026lt;0.001), 7.53 (95% CI; 5.24-9.81; P \u0026lt;0.001), and 0.10 (95% CI; 0.06-0.14; P\u0026lt;0.001), respectively. Albumin concentrationincreased significantly at discharge, while the ALT concentration decreasedsignificantly at discharge (P\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eDyslipidemia and low levels of Albumin were recorded during the progression of COVID-19 (at admission). This indicated severe COVID-19 disease leads to lipid alteration and Additional studies need to better define the disease's association with liver and renal function tests.\u003c/p\u003e","manuscriptTitle":"Changes in lipid, liver, and renal test profiles among patients with severe COVID-19 during and after hospital admission at Saint Peter Specialized Hospital, Addis Ababa, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-26 14:46:16","doi":"10.21203/rs.3.rs-4598405/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":"0e5ae7c3-d5f7-4abd-bff1-fe770fe08ee1","owner":[],"postedDate":"June 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-26T14:46:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-26 14:46:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4598405","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4598405","identity":"rs-4598405","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}