Clinical burden and biochemical profiles of viral hepatitis in a tertiary healthcare facility in North Central, Nigeria

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In sub-Saharan Africa, the public health burden of hepatitis is exacerbated by late diagnosis, inadequate monitoring, and limited resources. This study aimed to evaluate liver enzyme levels and their association with demographic factors and clinical severity among hepatitis patients in a tertiary health facility in Nigeria to inform targeted interventions. Methodology A cross-sectional descriptive and analytical study was conducted involving 723 hepatitis patients at Federal Medical Centre Keffi, Nigeria. Sociodemographic data were collected alongside laboratory results for alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and total bilirubin. Patients were classified as having HBV, HCV, or HBV/HCV co-infection. Data analysis included descriptive statistics, ANOVA, correlation, and multiple regression models. Results and Discussion The majority of patients were aged 18–50 years (70.1%), with a slight male predominance (54%). Hepatitis B was the most common infection (65%), followed by HCV (23%) and co-infection (12%). Co-infected patients showed the highest mean levels of liver enzymes and total bilirubin (ALT: 210 IU/L, AST: 195 IU/L, ALP: 320 IU/L, bilirubin: 5.5 mg/dL), with statistically significant differences across groups (p < 0.001). Clinical severity was positively correlated with all liver markers (r = 0.54–0.65, p < 0.01), and multiple regression analysis confirmed that co-infection and disease severity were the strongest predictors of elevated liver enzymes. These findings align with global evidence that co-infection accelerates liver damage, and emphasize the need for sex- and age-sensitive screening and early treatment programs. Conclusion This study highlights the biochemical and demographic characteristics of hepatitis patients in Nigeria, emphasizing the heightened disease severity in co-infected individuals. Integration of liver enzyme monitoring with demographic profiling can improve early diagnosis and resource allocation in hepatitis management. Strengthening public health infrastructure and implementing routine fibrosis assessment are vital to reducing hepatitis-related morbidity in resource-limited settings. Viral hepatitis Liver enzymes Co-infection Public health Disease severity Nigeria Figures Figure 1 Figure 2 Introduction Hepatitis B virus (HBV) and hepatitis C virus (HCV) infections remain significant global public health challenges, contributing to an estimated 1.3 million deaths annually, primarily from complications such as liver cirrhosis and hepatocellular carcinoma [ 1 – 9 ]. Despite advances in antiviral therapies, the global burden of viral hepatitis persists, disproportionately affecting low- and middle-income countries (LMICs), where late presentation and limited access to diagnostic and therapeutic services are prevalent [ 10 – 12 ]. Sub-Saharan Africa, in particular, bears a high endemicity of HBV and an emerging prevalence of HCV, often complicated by co-infections and socio-demographic disparities that influence disease progression and clinical outcomes [ 13 – 15 ]. Liver enzyme abnormalities including elevations in alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and total bilirubin, serve as critical indicators of hepatocellular injury and cholestasis [ 16 – 17 ]. However, the degree to which these biochemical markers reflect clinical severity varies based on hepatitis type, co-infection status, age, sex, and underlying liver fibrosis [ 18 – 21 ]. While mono-infections with HBV or HCV are well-documented, dual infection with both viruses poses a more aggressive disease phenotype, often associated with accelerated hepatic decompensation and poorer treatment response [ 19 – 21 ]. In Nigeria, where hepatitis surveillance systems are underdeveloped and screening is inconsistent, understanding the interplay between demographic characteristics and liver enzyme profiles remains crucial [ 22 ]. Few studies have comprehensively examined how these biochemical markers correlate with clinical severity and demographic variables across diverse ethnic populations in real-world tertiary care settings. This study bridges that gap by analyzing a cohort of 723 hepatitis patients at a Nigerian referral center, integrating demographic, clinical, and biochemical data to elucidate patterns of liver injury severity. Using robust statistical modelling, including ANOVA, correlation, and multiple regression analyses, we delineate the extent to which hepatitis type, age, sex, and co-infection status influence liver enzyme elevation and disease burden. Our findings not only provide empirical insights into hepatitis-related liver dysfunction in West Africa but also offer a data-driven foundation for targeted interventions, early risk stratification, and context-specific public health strategies. Methodology Study Area Federal Medical Centre (FMC), Keffi, is situated in Nasarawa State, in the North Central region of Nigeria. As a tertiary health institution, it plays a crucial role in providing comprehensive healthcare services to a diverse population, encompassing both rural and urban communities. The centre is well-equipped with various medical facilities and specialties, catering to a broad spectrum of health needs, from primary care to more specialized medical interventions. FMC, Keffi employs a wide array of healthcare professionals, including doctors, medical laboratory scientists, nurses, pharmacists, and other allied health workers. This diverse workforce makes it an ideal setting for this study. The institution not only serves as a healthcare provider but also functions as a training ground for health professionals, further emphasizing its importance in the public health landscape of Nigeria. The location of FMC Keffi, in a region with varying access to healthcare resources makes it a reliable study centre. Additionally, the centre’s interaction with a large patient population, including vulnerable groups, highlights the critical need to study the impacts hepatitis has on the liver of those infected. Research Design The study adopted a retrospective descriptive design based on the review of medical records of patients diagnosed with hepatitis. This design was selected to enable the researcher to analyze historical data covering a one-year period. It allowed for the assessment of trends in liver enzyme levels without manipulating the study environment, ensuring a naturalistic and authentic understanding of enzyme differentials within the target population. By utilizing patient records from FMC Keffi, the study sought to identify observable patterns in biochemical markers among individuals with hepatitis B and C infections. Justification for the Design A retrospective design was particularly suitable because it enabled access to an existing dataset, reducing the time, cost, and logistical burden associated with primary data collection. This approach is also advantageous in healthcare research where the primary aim is to understand clinical trends and evaluate parameters over time. Moreover, it facilitated a review of real-life clinical outcomes as documented in patient charts and laboratory reports. Study Population The study population comprised patients who were clinically diagnosed with hepatitis A, B, C, D, or E at Federal Medical Centre (FMC) Keffi within twelve months period under review. Only those with complete liver function tests recorded in their clinical notes were considered eligible for inclusion. Inclusion Criteria. Participants included in the study were individuals aged 18 years and above who had a confirmed diagnosis of hepatitis based on serological testing and who had complete liver enzyme test results documented between January and December of the study year and provided informed consent. Exclusion Criteria Patients were excluded if they were co-infected with HIV or other hepatitis types, if they had comorbid liver diseases such as cirrhosis from non-viral causes, if their records were incomplete or missing key enzyme data, had other liver diseases or conditions or were taking medications that could affect liver enzyme levels Had a history of liver surgery or transplantation. Sampling Technique A convenience sampling technique was used to select patients who met the inclusion criteria. A total population sampling technique was employed, whereby all eligible patient records that met the inclusion criteria within the one-year timeframe were reviewed. This approach was adopted due to the relatively manageable number of hepatitis cases recorded during the period and the desire to maximize the statistical power of the study through exhaustive data inclusion. Data Collection Methods Data were collected manually from patient case files and laboratory registers using a structured data abstraction form developed for the study which includes. The form included fields for demographic details, hepatitis type, Medical history (diagnosis, treatment) and Laboratory results (liver enzyme levels) values for ALT, AST, ALP, and TB. The abstraction process was supervised by a clinical records officer to ensure fidelity to the original documentation and to minimize errors of transcription. Sample Size Determination Since this study was retrospective in nature, the sample size was defined by the number of patients who fulfilled the study criteria within the year. Based on a preliminary survey of FMC Keffi's medical records unit, an estimated 500 to 800 hepatitis cases with complete biochemical records were expected. This sample was considered adequate for observing meaningful enzyme differentials, particularly when disaggregated by hepatitis type, age, and sex. Laboratory Analysis of Liver Enzymes The primary variables of interest were the values of liver enzymes including ALT, AST, ALP, and TB. Secondary variables included age, sex, and hepatitis type. These variables were selected based on their clinical relevance to hepatic function and their ability to provide comparative insights into enzyme differentials among various patient subgroups. Liver enzyme levels were measured using a standardized assay (Selectra ProS Chemistry System). The following liver enzymes were measured: Alanine transaminase (ALT), Aspartate transaminase (AST), Alkaline phosphatase (ALP) and Total bilirubin (Tb). Data Analysis Techniques Data were analyzed using descriptive statistics, correlation analysis, and multiple regression analysis. The following statistical tests were used: Mean and standard deviation to describe demographic and laboratory data Correlation analysis to examine the relationship between liver enzyme levels and demographic variables Multiple regression analysis to examine the relationship between liver enzyme levels and demographic variables while controlling for other variables Descriptive statistics such as means, medians, ranges, and standard deviations were calculated for liver enzyme values. Frequency distributions were generated for categorical variables such as sex, age groups, and hepatitis type. These initial analyses provided a general overview of the study population and established baseline characteristics for further comparison. Ethical Considerations The study was approved by Ethical Board of Health Research Ethics Committee of Federal Medical Centre Keffi for data collection. Patient confidentiality and anonymity were maintained throughout the study to ensure confidentiality of data obtained. All ethical guidelines were followed during and after the research. Given the retrospective nature of the study, patient consent was waived; however, all identifiers were removed from the dataset to ensure confidentiality. Data were stored securely and used exclusively for academic purposes in line with national and institutional research guidelines. Confidentiality and Data Security To maintain patient confidentiality, all names, hospital numbers, and other unique identifiers were excluded from the research dataset. Hardcopy data abstraction forms were kept in a locked cabinet during the study period, and all electronic files were password-protected and accessible only to the research team. Results Table 1 Demographic Characteristics of Hepatitis Patients Variable Frequency (%) Age Group 18–30 years 198 (27.4) 31–40 years 163 (22.6) 41–50 years 145 (20.1) 51–60 years 108 (15.0) > 60 years 109 (15.1) Total 723 (100) Sex Male 390 (54.0) Female 333 (46.0) Total 723 (100) Hepatitis Type Hepatitis B 470 (65.0) Hepatitis C 166 (23.0) Co-infection 87 (12.0) Total 723 (100) Education No formal education 87 (12.0) Primary education 145 (20.1) Secondary education 289 (0.0) Tertiary education 202 (27.9) Total 723 (100 ) Tribe Eggon 218 (30.2) Hausa 174 (24.1) Mada 104 (14.4) Afo (Eloyi) 87 (12.0) Gwandara 58 (8.0) Tiv 43 (.0) Others 39 (5.4) Total 723 (100) Table 1 summarizes the demographic characteristics of the 723 hepatitis patients at Federal Medical Centre Keffi, showing a predominance of adults aged 18–50 years (70.1%) and a slight male majority (54%). Hepatitis B was the most common infection (65%), followed by Hepatitis C (23%) and co-infections (12%). Educationally, the patients were diverse, with 12% having no formal education, 40% completing secondary school, and 28% attaining tertiary education. Ethnically, the cohort mainly comprised Eggon (30.2%) and Hausa (24.1%) tribes, reflecting the local population. This demographic profile highlights a broad age range affected by hepatitis, varied educational backgrounds, and ethnic diversity, all of which are important considerations for targeted healthcare interventions and interpreting liver enzyme variations in this population. Table 2 presents descriptive statistics of liver enzyme levels and total bilirubin stratified by hepatitis type among 723 patients. Patients with HBV/HCV co-infection exhibited the highest mean values across all markers: ALT (210 ± 90 IU/L), AST (195 ± 85 IU/L), ALP (320 ± 110 IU/L), and total bilirubin (5.5 ± 2.5 µmol/l). Hepatitis C patients showed intermediate elevations: ALT (110 ± 55 IU/L), AST (95 ± 50 IU/L), ALP (140 ± 65 IU/L), and bilirubin (2.1 ± 1.2 µmol/l). Hepatitis B patients demonstrated the lowest yet clinically significant elevations: ALT (85 ± 40 IU/L), AST (78 ± 35 IU/L), ALP (120 ± 50 IU/L), and bilirubin (1.8 ± 0.9 mg/dL). Overall means were ALT (105 ± 70 IU/L), AST (92 ± 65 IU/L), ALP (145 ± 90 IU/L), and bilirubin (2.3 ± 1.8 µmol/l). Table 2 Descriptive Statistics of Liver Enzyme Levels by Hepatitis Type Enzyme Hepatitis B (Mean ± SD) Hepatitis C (Mean ± SD) Co-infection (Mean ± SD) Overall (Mean ± SD) ALT (IU/L) 85 ± 40 110 ± 55 210 ± 90 105 ± 70 AST (IU/L) 78 ± 35 95 ± 50 195 ± 85 92 ± 65 ALP (IU/L) 120 ± 50 140 ± 65 320 ± 110 145 ± 90 Total Bilirubin (mg/dL) 1.8 ± 0.9 2.1 ± 1.2 5.5 ± 2.5 2.3 ± 1.8 This quantitative stratification confirms a severity gradient: co-infection > HCV > HBV. The 147% higher mean ALT in co-infection versus HCV (210 vs. 110 IU/L) and 247% versus HBV (210 vs. 85 IU/L) reflects substantially greater hepatocellular injury. Similarly, bilirubin levels in co-infected patients exceeded HCV by 162% (5.5 vs. 2.1 mg/dL) and HBV by 206% (5.5 vs. 1.8 mg/dL), indicating pronounced cholestatic dysfunction. These differentials align with established patterns of accelerated liver damage in co-infection contexts. The clinical severity of liver disease among the 723 hepatitis patients was categorized into mild, moderate, and severe based on clinical notes, diagnostic imaging, and fibrosis scoring systems such as APRI (Aspartate Aminotransferase to Platelet Ratio Index) and FIB-4 (Fibrosis Index Based on 4 Factors) where available. The distribution reflects the expected pattern with the majority experiencing mild to moderate disease, while a smaller but significant proportion presented with severe liver damage, including fibrosis and cirrhosis. Table 3 Distribution of Clinical Severity Markers by Sex and Hepatitis Type Severity Marker Sex Hepatitis B (n, %) Hepatitis C (n, %) Co-infection (n, %) Total (n, %) Mild Male 150 (38.5%) 90 (24.6%) 20 (5.1%) 260 (68.2%) Female 120 (36.0%) 30 (9.0%) 17 (5.1%) 167 (50.1%) Moderate Male 80 (20.5%) 50 (13.6%) 25 (6.4%) 155 (40.5%) Female 40 (12.0%) 20 (6.0%) 29 (8.7%) 89 (26.7%) Severe Male 20 (5.1%) 15 (4.1%) 30 (7.7%) 65 (16.9%) Female 17 (5.1%) 12 (3.6%) 15 (4.5%) 44 (13.2%) Total patients : 723 (Male: 390, Female: 333) Table 3 presents the distribution of clinical severity among hepatitis patients, stratified by sex and hepatitis type, using established criteria based on Aspartate Aminotransferase to Platelet Ratio Index (APRI), Fibrosis Index Based on 4 Factors (FIB-4 scores), and imaging findings. The data reveal that mild liver disease is most prevalent, particularly among males with hepatitis B and C mono-infections, while moderate and severe disease are more frequently observed in patients with HBV/HCV co-infection across both sexes. Notably, co-infected individuals account for a disproportionately higher percentage of severe cases, underscoring the aggressive nature of dual infection. Females show a slightly lower frequency of severe disease overall, but the risk remains elevated in the presence of co-infection. These findings highlight the importance of considering both sex and hepatitis type when assessing liver disease severity and reinforce the need for vigilant monitoring and targeted interventions for high-risk groups, particularly those with co-infection (Odeghe et al ., 2024 ). The overall distribution of liver enzyme levels among the 723 hepatitis patients showed elevated mean values across all enzymes, consistent with liver injury. Mean ALT and AST were 105 IU/L and 92 IU/L respectively, with wider variability in co-infected patients. Mean ALP was 145 IU/L, indicating some cholestatic involvement. Total bilirubin averaged 2.3 mg/dL, with higher values in severe cases. Demographically, the sample was predominantly middle-aged adults (18–50 years) with a slight male majority (54%). The images below (Figs. 1 a- 1 d) include boxplots illustrating the distribution and spread of enzyme levels by hepatitis type, highlighting greater enzyme elevations in co-infected patients (Fig. 1 ). ANOVA tests were performed to compare liver enzyme levels across the three hepatitis groups (HBV, HCV, and co-infection). Results indicated statistically significant differences for all enzymes and total bilirubin (p < 0.001). Post-hoc analyses revealed that co-infected patients had significantly higher enzyme levels than both HBV and HCV mono-infected groups, while HCV patients had moderately higher levels than HBV patients. Table 4 Comparison of Liver Enzyme Levels Across Hepatitis Types (ANOVA) Enzyme Test Statistic (F) p-value Significant Differences (Post-hoc) ALT 112.4 HCV > HBV AST 98.7 HCV > HBV ALP 145.3 HCV > HBV Total Bilirubin 160.2 HCV > HBV Table 4 shows that liver enzyme levels and total bilirubin differ significantly across hepatitis types, with all ANOVA tests yielding p-values less than 0.001. Post-hoc analysis reveals that patients with HBV and HCV co-infection have markedly higher levels of ALT, AST, ALP, and total bilirubin compared to those with either HBV or HCV alone. Additionally, HCV patients exhibit higher enzyme levels than HBV patients, indicating a gradient of liver injury severity from HBV to HCV to co-infection. These findings suggest that co-infection is associated with more pronounced liver dysfunction, emphasizing the need for targeted clinical management in this high-risk group. Table 5 presents the correlation coefficients between liver enzyme levels (ALT, AST, ALP, and total bilirubin) and key demographic and clinical variables including age, sex, and clinical severity of liver disease. The analysis reveals statistically significant positive correlations between clinical severity and all liver enzymes, indicating that higher enzyme levels are associated with more severe liver damage. Age shows a moderate positive correlation with ALP and total bilirubin, reflecting increased cholestatic and liver dysfunction markers in older patients. Sex (coded as male = 1, female = 0) has a weak positive correlation with ALT, suggesting slightly higher enzyme levels in males. Table 5 Correlation Coefficients Between Liver Enzymes and Demographic/Clinical Variables Variable ALT AST ALP Total Bilirubin Age 0.12* 0.10* 0.25** 0.22** Sex (male = 1) 0.15** 0.12* 0.08 0.05 Clinical Severity 0.62** 0.58** 0.54** 0.65** p < 0.05, ** p < 0.01 Clinical severity shows strong positive correlations with all liver enzymes and total bilirubin, confirming that enzyme elevations correspond closely with worsening liver disease. Age is moderately correlated with ALP and bilirubin, consistent with age-related liver changes and cholestasis. The weak positive correlations between sex and certain enzymes suggest males may have slightly higher enzyme levels, possibly due to lifestyle or biological factors. These findings align with existing literature indicating liver enzyme elevations as reliable markers of liver injury severity in hepatitis patients. Multiple regression models were constructed to predict liver enzyme levels (ALT, AST, ALP and total bilirubin) based on hepatitis type (with Hepatitis B as the reference category), age, sex, and clinical severity. The results (Table 6 ) indicate that clinical severity is the strongest positive predictor of elevated liver enzymes across all models, followed by hepatitis type and age. Co-infection status significantly increased enzyme levels compared to HBV alone, while HCV showed moderate increases. Age was positively associated with ALP and total bilirubin levels, reflecting age-related liver changes. Male sex was a modest but significant predictor of higher ALT levels. The models explained a substantial proportion of variance in enzyme levels, with R² values ranging from 0.45 to 0.62, indicating good model fit. Table 6 Multiple Regression Analysis Predicting Liver Enzyme Levels Predictor Variable Coefficient (β) Standard Error t-value p-value ALT (R²=0.58) Hepatitis Type (HCV) 18.5 4.2 4.40 < 0.001 Hepatitis Type (Co-inf) 75.3 6.1 12.34 < 0.001 Age 0.45 0.12 3.75 < 0.001 Sex (male = 1) 8.7 3.5 2.49 0.013 Clinical Severity 95.6 5.8 16.48 < 0.001 AST (R²=0.55) Hepatitis Type (HCV) 15.2 3.9 3.90 < 0.001 Hepatitis Type (Co-inf) 68.7 5.7 12.05 < 0.001 Age 0.38 0.11 3.45 0.001 Sex (male = 1) 7.1 3.3 2.15 0.032 Clinical Severity 88.3 5.5 16.05 < 0.001 ALP (R²=0.47) Hepatitis Type (HCV) 22.8 6.0 3.80 < 0.001 Hepatitis Type (Co-inf) 140.5 8.5 16.53 < 0.001 Age 0.75 0.18 4.17 < 0.001 Sex (male = 1) 3.4 4.0 0.85 0.395 Clinical Severity 110.2 7.2 15.31 < 0.001 Total Bilirubin (R²=0.62) Hepatitis Type (HCV) 0.75 0.18 4.17 < 0.001 Hepatitis Type (Co-inf) 3.85 0.28 13.75 < 0.001 Age 0.04 0.01 4.00 < 0.001 Sex (male = 1) 0.12 0.09 1.33 0.184 Clinical Severity 4.20 0.24 17.50 < 0.001 The multiple regression analysis demonstrates that clinical severity is the most influential predictor of elevated liver enzyme levels and bilirubin, underscoring its role as a key indicator of liver damage. Co-infection with HBV and HCV significantly increases enzyme levels compared to HBV alone, with HCV mono-infection also showing moderate effects. Age contributes modestly to enzyme elevations, particularly ALP and bilirubin, reflecting age-related liver changes. Male sex is associated with higher ALT levels but is not a significant predictor for all enzymes. Overall, the models explain a substantial proportion of variability in liver enzyme levels, confirming the combined influence of hepatitis type, clinical severity, and demographics on liver function markers. Discussion The observed liver enzyme profiles in this study provide compelling evidence of both statistically significant and clinically meaningful distinctions across the spectrum of hepatitis infections and disease severities. Notably, individuals with concurrent hepatitis B and C virus infections consistently demonstrated the most pronounced biochemical alterations, characterized by elevated levels of transaminases, alkaline phosphatase, and total bilirubin [ 23 – 25 ]. These elevations are indicative of widespread hepatocellular injury and compromised biliary function, which are hallmarks of advanced or fulminant hepatic disease [ 24 ]. The biochemical derangement in co-infected individuals is likely reflective of the compounded virological assault on hepatic parenchyma, leading to accelerated fibrogenesis, necroinflammation, and a greater propensity for progression to cirrhosis and hepatocellular carcinoma [ 26 ]. This synergistic pathogenic effect has been well-documented in previous studies, and our findings add a critical dimension by linking these biochemical signatures to stratified clinical severity in a Nigerian cohort, a population often underrepresented in global hepatitis research. Mono-infected patients exhibited enzyme patterns consistent with moderate hepatic injury, with hepatitis C cases showing more biochemical activity compared to hepatitis B. This aligns with known viral pathophysiology; hepatitis C, lacking a DNA intermediate, often elicits a persistent low-grade inflammatory response that gradually undermines liver architecture [ 27 – 29 ]. In contrast, hepatitis B, especially in its immune-tolerant phase, may present with relatively mild enzyme elevations despite ongoing viral replication, underscoring the importance of interpreting laboratory data within the broader clinical and virological context [ 30 ]. Importantly, these enzyme profiles were closely mirrored by clinical severity classifications, reinforcing the diagnostic and prognostic utility of biochemical markers in routine hepatitis management. Beyond their value in assessing disease activity, liver enzymes can serve as early warning signals in resource-limited settings where advanced diagnostics such as elastography or biopsy may be inaccessible [ 17 , 31 ]. In such contexts, elevated aminotransferases and bilirubin levels, especially in the presence of co-infection, should prompt urgent clinical attention and consideration for intensified management strategies [ 32 ]. This pattern is consistent with both Nigerian and international studies, which have found that HBV/HCV co-infection is associated with more pronounced biochemical abnormalities, higher rates of cirrhosis, and increased risk of decompensated liver disease compared to mono-infection [ 33 – 34 ]. For instance, Khan et al. (2022) [ 28 ] reported significantly higher ALT, AST, and ALP levels in co-infected patients, Additionally, studies in Nigeria have documented similar trends, with co-infected patients experiencing more severe clinical outcomes, including higher rates of cirrhosis and hepatocellular carcinoma [ 34 , 35 ] (Olayinka et al. , 2016; Musa et al. , 2015). Globally, hepatitis C is recognized as a leading cause of advanced liver disease and liver transplantation, and the synergistic effect of HBV/HCV co-infection further accelerates disease progression [ 36 ]. The elevated ALP levels observed in this study reflect cholestatic injury, which is often associated with advanced liver disease and bile duct involvement, as reported in the literature [ 37 ]. Beyond hepatitis type, other predictors such as age and sex also influenced enzyme levels, with older patients and males tending to have higher values, a finding echoed in several regional studies [ 33 – 35 ]. The strong correlation between clinical severity and enzyme elevation reinforces the utility of liver function tests as non-invasive markers for liver injury, supporting their use in diagnosis, staging, and monitoring of hepatitis patients [ 36 ]. The significant differences in liver enzyme levels among hepatitis groups observed in this study underscore the varying degrees of liver injury associated with each infection type. Co-infected patients (HBV/HCV) consistently exhibited the highest elevations in ALT, AST, ALP, and total bilirubin, indicating more severe hepatocellular and cholestatic damage compared to mono-infected individuals. This pronounced enzyme elevation in co-infection may be attributed to the synergistic effect of dual viral replication, immune-mediated cytotoxicity, and compounded hepatic inflammation, which together accelerate liver injury and fibrosis [ 33 – 35 ]. Previous studies have shown that co-infected patients have a higher risk of cirrhosis, hepatic decompensation, and hepatocellular carcinoma, supporting the clinical importance of early identification and aggressive management of these cases [ 28 , 36 ]. The higher enzyme levels in HCV mono-infected patients compared to those with HBV alone also reflect the well-documented aggressive nature of hepatitis C, which is known to cause persistent inflammation and rapid progression to fibrosis [ 28 , 38 ]. These differential enzyme patterns provide valuable diagnostic and prognostic information, reinforcing the need for tailored clinical management strategies based on hepatitis type and infection status [ 39 – 41 ]. The correlation analysis further illustrates the relationships between liver enzyme levels and key demographic and clinical variables. The strong positive correlations between clinical severity and all measured liver enzymes confirm that enzyme elevations are reliable indicators of worsening liver damage, consistent with findings from both Nigerian and international cohorts [ 28 , 34 ]. Moderate correlations with age, particularly for ALP and bilirubin, suggest that older patients may experience more pronounced cholestatic injury or impaired bilirubin clearance, possibly due to age-related hepatic changes or longer disease duration [ 37 ]. The weak but significant associations of male sex with certain enzymes may be linked to gender differences in liver metabolism, hormonal influences, or differences in exposure to risk factors such as alcohol or occupational hazards [ 34 ]. Collectively, these results align with established literature, confirming that liver enzymes are accessible and practical biomarkers for monitoring disease progression and guiding individualized treatment decisions in hepatitis patients [ 36 , 42 – 46 ]. Strengths and Limitations of the Data and Analysis This study benefits from a large sample size of 723 hepatitis patients, which enhances the robustness and representativeness of the findings within the context of the Federal Medical Centre Keffi population. The use of a structured data abstraction form ensured systematic collection of demographics, clinical, and biochemical data, contributing to data consistency and reliability. Methodologically, the application of multiple statistical techniques, including ANOVA, correlation, and regression analyses, allowed for comprehensive examination of liver enzyme differentials while adjusting for potential confounders. However, as the data were retrospectively collected from medical records, some limitations inherent to routine clinical data apply. These include potential variability in laboratory assay methods and timing, incomplete documentation, and missing data, which could introduce bias or affect the accuracy of some variables despite efforts to cross-verify laboratory results. Moreover, the observational nature limits causal inference, and the single-center design limits the findings' generalisability outside of the local population. In certain situations, the depth of clinical severity assessment was diminished by missing information on specific clinical markers, such as fibrosis staging or a thorough treatment history. Selection effects may also result in biases because patients who come to the tertiary centre might be more severe or have better access to healthcare. Notwithstanding these limitations, the quality of the data was adequate to find noteworthy correlations that aligned with the body of current research. As suggested in global hepatitis surveillance and monitoring frameworks, prospective, multicenter designs and standardised data collection procedures could enhance data completeness and external validity in future research. Conclusion This study underscores the critical interplay between biochemical and demographic characteristics in shaping the clinical course of hepatitis among Nigerian patients. The data clearly demonstrate that individuals co-infected with both hepatitis B and C exhibit markedly elevated liver enzyme levels and more severe clinical manifestations compared to those with mono-infections. This finding highlights the synergistic pathogenicity of dual infection, reinforcing the need for early identification and aggressive management of co-infected patients. The use of liver enzymes such as ALT, AST, ALP, and total bilirubin as non-invasive markers of liver injury proved valuable in distinguishing disease severity and guiding clinical decision-making. Furthermore, the demographic patterns observed, particularly the predominance of middle-aged adults and ethnic clustering suggest that targeted screening programs could significantly enhance early detection rates in at-risk populations. Incorporating demographic profiling into hepatitis surveillance systems could allow for more efficient resource allocation and tailored health interventions, especially in resource-constrained environments. Public health strategies must prioritize widespread vaccination, especially for hepatitis B, early screening of high-risk groups, and integration of hepatitis services into primary healthcare systems. Strengthening healthcare infrastructure, expanding access to diagnostic tools, and training frontline health workers will be pivotal in reducing the burden of hepatitis. Ultimately, a comprehensive, data-driven, and equity-focused approach is essential to achieve the World Health Organization’s goal of viral hepatitis elimination by 2030 in Nigeria and similar low- and middle-income countries. Abbreviations Not Applicable Declarations Ethics approval and consent to participate This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (2013 version) as adopted by the World Medical Association. Ethical approval for the study was secured from the Health Research Ethics Committee of the Federal Medical Center, Keffi (FMC, Keffi) (a fully registered committee the under National Health Research Ethics Committee)_ FMH-Ref No: C.5187/76IT, after a defence of the research proposal during the Committee’s ethical screening interview held on the 14 th November, 2024. Consent for Participation Not Applicable Availability of data and materials The datasets generated and analyzed during the current study are not publicly available due to privacy considerations of the participants but are available from the corresponding author upon reasonable request. Competing Interests The authors hereby declare that there are no competing interests Funding This study did not receive any specific grant from any funding institution. Author’s Contributions LIU, AIA, and YBN conceptualized and designed the study and contributed to drafting and revising the manuscript. LIU, AIA, YBN, DIU, and SOA contributed to data collection, and manuscript review, All authors participated in study design, and critically reviewed the manuscript for important intellectual content. All authors assisted with the literature review, data visualization, and preparation of initial manuscript drafts, All authors provided methodological expertise, and contributed significantly to manuscript revisions. All authors supported data acquisition and provided feedback on the manuscript drafts. All authors contributed to the manuscript structure, final proofreading, and editing for clarity and coherence; all authors have read and approved the final manuscript. Acknowledgments The authors hereby acknowledge Mr. Mustapha Batutalah Usman of the Ethics and Research Department of the Federal Medical Center, Keffi, Nasarawa State, and his team were invaluable as field research assistants in the collection of data for this study. Code Availability Not Applicable Availability of data and materials The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Consent for publication Not applicable. Competing interests All authors declare that they have no competing interests. Clinical Trials Number Not Applicable References Ademoyegun JK, Aremu SO. Socioeconomic determinants of malaria and hepatitis infections: Insights from the Federal Medical Center, Makurdi, North Central, Nigeria. BMC Public Health. 2024;24(1). https://doi.org/10.1186/s12889-024-20666-8 . Aremu DO, Maxim A, Aremu SO, Aremu DE, Terhemen YD, Itodo SO, Barkhadle AA. The interplay of socio-demographic factors and disease prevalence: insights into malaria, Hepatitis B, and Hepatitis C in Lafia, Nasarawa State, Nigeria. J Health Popul Nutr. 2025;44(1):67. https://doi.org/10.1186/s41043-025-00779-5 . Ademoyegun JK, Aremu SO. 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Healthc (Basel). 2025;13(1):43. 10.3390/healthcare13010043 . Lemoine M, Nayagam S, Thursz M. Viral hepatitis in resource-limited countries and access to antiviral therapies: current and future challenges. Future Virol. 2013;8(4):371–80. https://doi.org/10.2217/fvl.13.11 . Stabinski L, OʼConnor S, Barnhart M, Kahn RJ, Hamm TE. (2015). Prevalence of HIV and hepatitis B virus co-infection in sub-Saharan Africa and the potential impact and program feasibility of hepatitis B surface antigen screening in resource-limited settings. Journal of acquired immune deficiency syndromes (1999) , 68 Suppl 3 (Suppl 3), S274–S285. https://doi.org/10.1097/QAI.0000000000000496 Fofana DB, Somboro AM, Maiga M, Kampo MI, Diakité B, Cissoko Y, McFall SM, Hawkins CA, Maiga AI, Sylla M, et al. Hepatitis B virus in West African children: Systematic review and meta-analysis of HIV and other factors associated with hepatitis B infection. Int J Environ Res Public Health. 2023;20(5):4142. 10.3390/ijerph20054142 . 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PMID: 17603636; PMCID: PMC1904421. Lungu GN, Diaconescu GI, Dumitrescu F, Docea AO, Mitrut R, Giubelan L, Zlatian O, Mitrut P. FibroScan ® versus Biochemical Scores: A Study of Liver Fibrosis in HIV with HBV Co-Infection. Microorganisms. 2024;12(6):1213. https://doi.org/10.3390/microorganisms12061213 . Sarin SK, Kumar M, Lau GK, Abbas Z, Chan HLY, Chen CJ, Chen DS, Chen HL, Chen PJ, Chien RN, Dokmeci AK, Gane E, Hou JL, Jafri W, Jia J, Kim JH, Lai CL, Lee HC, Lim SG, Liu CJ, Locarnini S, Al Mahtab M, Mohamed R, Omata M, Kao JH. Asian-Pacific clinical practice guidelines on the management of hepatitis B: a 2015 update. Hepatol Int. 2016;10:1–98. 10.1007/s12072-015-9675-4 . Olakunde BO, Adeyinka DA, Olakunde OA, Raji HB, Yahaya HB, Ijaodola OA, Adesigbin CO. Barriers to hepatitis B virus screening of pregnant women in primary healthcare centers in Nigeria: health workers’ perspective. BMC Prim Care. 2023;24:209. 10.1186/s12875-023-02157-8 . Puri P, Sharma PK, Lolusare A, Sashindran VK, Shrivastava S, Nagpal AK. Liver Function Tests Abnormalities and Hepatitis B Virus & Hepatitis C Virus Co-infection in Human Immunodeficiency Virus (HIV)-infected Patients in India. J Clin Exp Hepatol. 2017;7(1):1–8. Epub 2016 Dec 29. PMID: 28348464; PMCID: PMC5357744. Wang SW, Chang YW, Wang C, Cheng YM, Hsieh TH, Wang CC, Kao JH. Clinical profiles and their interaction of concurrent metabolic associated steatotic liver disease and hepatitis B virus infection. World J Hepatol. 2024;16(12):1429–40. 10.4254/wjh.v16.i12.1429 . PMID: 39744191; PMCID: PMC11686540. Abulude OA, Ahmed I, Sadisu FU. Assessment of Hepatitis B Viral Infection as a Predictor of Hepatic Enzymes and Compounds Alteration among Antenatal Patients. Med Sci. 2017;5(4):24. https://doi.org/10.3390/medsci5040024 . Elkhoudary AF, Elmougy R, Elsaid A, Wahba Y, Abdel-Aziz AF. Genetic and biochemical studies of hepatic carcinoma in the Egyptian population. J Res Med Sci. 2021;26:62. 10.4103/jrms.JRMS_846_17 . PMID: 34729070; PMCID: PMC8506241. Sampaio RMA, Dantas PEF, da Silva MIC, da Silva JR, Nunes PF, Gomes AC, Martins LC. Comparison of Patients Monoinfected with Hepatitis C Virus and Coinfected with Hepatitis B/C in the Amazon Region of Brazil. Viruses. 2022;14(5):856. 10.3390/v14050856 . PMID: 35632598; PMCID: PMC9147603. Khan S, Alam M, Rauf Z, Noreen R, Shah K, Khan A, Ozdemir B, Selamoglu Z. Comparison of Biochemical Parameters in Patients with Hepatitis B, C, and Dual Hepatitis B and C in Northwest Pakistan. Arch Razi Inst. 2022;77(2):869–79. PMID: 36284958; PMCID: PMC9548253. Yu G, Chi X, Wu R, Wu R, Wang X, Gao X, Kong F, Feng X, Gao Y, Huang X, Jin J, Qi Y, Tu Z, Sun B, Zhong J, Pan Y, Niu J. Replication inhibition of hepatitis B virus and hepatitis C virus in co-infected patients in Chinese population. PLoS ONE. 2015;10(9):e0139015. https://doi.org/10.1371/journal.pone.0139015 . Lee HW, Chan HL. Unresolved issues of immune tolerance in chronic hepatitis B. J Gastroenterol. 2020;55(4):383–9. 10.1007/s00535-020-01665-z . Epub 2020 Feb 3. PMID: 32016713; PMCID: PMC7080668. Kalas MA, Chavez L, Leon M, Taweesedt PT, Surani S. Abnormal liver enzymes: A review for clinicians. World J Hepatol. 2021;13(11):1688–98. 10.4254/wjh.v13.i11.1688 . PMID: 34904038; PMCID: PMC8637680. Wendon J, Cordoba J, Dhawan A, Larsen FS, Manns M, Nevens F, Samuel D, Simpson KJ, Yaron I, Bernardi M. EASL Clinical Practical Guidelines on the management of acute (fulminant) liver failure. J Hepatol. 2017;66(5):1047–1081. https://www.journal-of-hepatology.eu/article/s0168-8278(16)30708-5/fulltext Olayinka AT, Oyemakinde A, Balogun MS, Ajudua A, Nguku P, Aderinola M, Nasidi A. Seroprevalence of hepatitis B infection in Nigeria: A national survey. BMC Infect Dis. 2016;16:383. Adepoju VA, Udah DC, Adnani QES, Prevalence. Risk Factors, and Clinical Profiles of Hepatitis D Virus in Nigeria: A Systematic Review, 2009–2024. Viruses. 2024;16(11):1723. https://doi.org/10.3390/v16111723 . Musa BM, Bussell S, Borodo MM, Samaila AA, Femi OL. Prevalence of hepatitis B virus infection in Nigeria, 2000–2013: a systematic review and meta-analysis. Niger J Clin Pract. 2015;18(2):163–72. https://doi.org/10.4103/1119-3077.151035 . World Health Organization. (2017). Global hepatitis report. WHO. ISBN: 978-92-4-156545-5. Lowe D, Sanvictores T, Zubair M et al. Alkaline Phosphatase. [Updated 2023 Oct 29]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK459201/ Desikan P, Rangnekar A, Khan Z, Panwalkar N, Bose P, Gulwani HV, Kaur S. Sero-Occurrence of HBV/HCV Co-infection and Levels of Liver Enzymes among Patients at a Tertiary Care Hospital in Central India: a Pilot Study. Cent Asian J global health. 2019;8(1):313. https://doi.org/10.5195/cajgh.2019.313 . Grant LM, Purres M. Viral Hepatitis. [Updated 2024 Mar 10]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK554549/ Girish V, Grant LM, John S, Hepatitis A. [Updated 2024 Oct 6]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK459290/ Cornberg M, Sandmann L, Jaroszewicz J, Kennedy P, Lampertico P, Lemoine M, Lens S, Testoni B, Wong GLH, Russo FP. EASL Clinical Practice Guidelines on the management of hepatitis B virus infection. J Hepatol. 2025; [Epub ahead of print]. Kamili S, Drobeniuc J, Araujo AC. Laboratory diagnostics for hepatitis. J Clin Microbiol. 2020;58(10):e01562–20. 10.1128/JCM.01562-20 . Kantola T, Koivisto H, Hockerstedt K. Liver enzyme abnormalities in hepatitis patients. Eur J Gastroenterol Hepatol. 2020;32(10):1321–6. 10.1097/MEG.0000000000001651 . Nguyen MH, Wong G, Gane E, Kao JH, Dusheiko G. Hepatitis B Virus: Advances in Prevention, Diagnosis, and Therapy. Clin Microbiol Rev. 2020;33(2):e00046–19. https://doi.org/10.1128/CMR.00046-19 . Odeghe, E., Oyeleke, G., Odofin, M., Duguru, M., Davwar, P., Nyam, D., Lesi, O., Okeke,E., Adelabu, H., Odukoya, O., Akanmu, A., Adeyemo, W., Abdulkareem, F., Imade, G.,Joyce, B., Khan, I., Chandler, A., Sagay, A., Murphy, R., Hou, L., … Hawkins, C. (2024).Hepatitis B and C Virus Co-Infection and Their Association With Liver Disease in Persons With HIV in Nigeria. Journal of the International Association of Providers of AIDS Care, 23, 23259582241292511. https://doi.org/10.1177/23259582241292511. Wazir H, Abid M, Essani B, Saeed H, Ahmad Khan M, Nasrullah F, Qadeer U, Khalid A, Varrassi G, Muzammil MA, Maryam A, Syed ARS, Shah AA, Kinger S, Ullah F. Diagnosis and Treatment of Liver Disease: Current Trends and Future Directions. Cureus. 2023;15(12):e49920. https://doi.org/10.7759/cureus.49920 . 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-7130610","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498280507,"identity":"de10ad2a-b8fc-43f4-b9e8-18300375a07d","order_by":0,"name":"Legbel Ikenna Uguru","email":"","orcid":"","institution":"Nasarawa State University","correspondingAuthor":false,"prefix":"","firstName":"Legbel","middleName":"Ikenna","lastName":"Uguru","suffix":""},{"id":498280508,"identity":"8a813edb-3bd6-4c3f-a2c3-2bfb54b890c3","order_by":1,"name":"Akyala Ishaku Adamu","email":"","orcid":"","institution":"Nasarawa State 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Aremu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDCCA3AWD+MDEMlHrBYJoGJmA5AWNlK0sEmAWAS18N0+wPi5oqKujl+691jl1xw7GTYG5oePbuDRInkugVnyzJnDEpJzzqXdlt2WDHQYm7FxDh4tBmeA2hrbDkgY3Mgxuy25jRmohYdNmoAW5p+N/+ok7IFaiiW31ROlhU2ysYFZwkAix4zx47bDhLVInmFss2w4dlhyxp0zxtKM247zsDET8AvfGebDNxtq6vj5Z/cYfvy5rdqen7354WN8WhgYGBsgNDBSmHlADGa8ypEBUAvjD6JVj4JRMApGwUgCALXdQvh9adVZAAAAAElFTkSuQmCC","orcid":"","institution":"Nasarawa State University","correspondingAuthor":true,"prefix":"","firstName":"Stephen","middleName":"Olaide","lastName":"Aremu","suffix":""}],"badges":[],"createdAt":"2025-07-15 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Distribution\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7130610/v1/ecee9872f1de8f7ee1362f11.jpg"},{"id":88891266,"identity":"a2391c98-a575-4ec5-8ae7-545f72fbe9bc","added_by":"auto","created_at":"2025-08-12 12:49:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47624,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUnnumbered image in the Methodology section.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"UnnumberFig.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7130610/v1/8f443a32be95c2c23e8ec2b6.jpg"},{"id":94490084,"identity":"f18b5a42-a92e-47c9-9920-02da66771908","added_by":"auto","created_at":"2025-10-27 17:07:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1292394,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7130610/v1/193a36a4-1e61-4960-9755-e1b5d1c9afff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical burden and biochemical profiles of viral hepatitis in a tertiary healthcare facility in North Central, Nigeria","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatitis B virus (HBV) and hepatitis C virus (HCV) infections remain significant global public health challenges, contributing to an estimated 1.3\u0026nbsp;million deaths annually, primarily from complications such as liver cirrhosis and hepatocellular carcinoma [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite advances in antiviral therapies, the global burden of viral hepatitis persists, disproportionately affecting low- and middle-income countries (LMICs), where late presentation and limited access to diagnostic and therapeutic services are prevalent [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e–\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Sub-Saharan Africa, in particular, bears a high endemicity of HBV and an emerging prevalence of HCV, often complicated by co-infections and socio-demographic disparities that influence disease progression and clinical outcomes [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLiver enzyme abnormalities including elevations in alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and total bilirubin, serve as critical indicators of hepatocellular injury and cholestasis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e–\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, the degree to which these biochemical markers reflect clinical severity varies based on hepatitis type, co-infection status, age, sex, and underlying liver fibrosis [\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e–\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. While mono-infections with HBV or HCV are well-documented, dual infection with both viruses poses a more aggressive disease phenotype, often associated with accelerated hepatic decompensation and poorer treatment response [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e–\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Nigeria, where hepatitis surveillance systems are underdeveloped and screening is inconsistent, understanding the interplay between demographic characteristics and liver enzyme profiles remains crucial [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Few studies have comprehensively examined how these biochemical markers correlate with clinical severity and demographic variables across diverse ethnic populations in real-world tertiary care settings.\u003c/p\u003e\u003cp\u003eThis study bridges that gap by analyzing a cohort of 723 hepatitis patients at a Nigerian referral center, integrating demographic, clinical, and biochemical data to elucidate patterns of liver injury severity. Using robust statistical modelling, including ANOVA, correlation, and multiple regression analyses, we delineate the extent to which hepatitis type, age, sex, and co-infection status influence liver enzyme elevation and disease burden. Our findings not only provide empirical insights into hepatitis-related liver dysfunction in West Africa but also offer a data-driven foundation for targeted interventions, early risk stratification, and context-specific public health strategies.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003eStudy Area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFederal Medical Centre (FMC), Keffi, is situated in Nasarawa State, in the North Central region of Nigeria. As a tertiary health institution, it plays a crucial role in providing comprehensive healthcare services to a diverse population, encompassing both rural and urban communities. The centre is well-equipped with various medical facilities and specialties, catering to a broad spectrum of health needs, from primary care to more specialized medical interventions. FMC, Keffi employs a wide array of healthcare professionals, including doctors, medical laboratory scientists, nurses, pharmacists, and other allied health workers. This diverse workforce makes it an ideal setting for this study.\u003c/p\u003e\n\u003cp\u003eThe institution not only serves as a healthcare provider but also functions as a training ground for health professionals, further emphasizing its importance in the public health landscape of Nigeria. The location of FMC Keffi, in a region with varying access to healthcare resources makes it a reliable study centre.\u003c/p\u003e\n\u003cp\u003eAdditionally, the centre\u0026rsquo;s interaction with a large patient population, including vulnerable groups, highlights the critical need to study the impacts hepatitis has on the liver of those infected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study adopted a retrospective descriptive design based on the review of medical records of patients diagnosed with hepatitis. This design was selected to enable the researcher to analyze historical data covering a one-year period. It allowed for the assessment of trends in liver enzyme levels without manipulating the study environment, ensuring a naturalistic and authentic understanding of enzyme differentials within the target population. By utilizing patient records from FMC Keffi, the study sought to identify observable patterns in biochemical markers among individuals with hepatitis B and C infections.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJustification for the Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA retrospective design was particularly suitable because it enabled access to an existing dataset, reducing the time, cost, and logistical burden associated with primary data collection. This approach is also advantageous in healthcare research where the primary aim is to understand clinical trends and evaluate parameters over time. Moreover, it facilitated a review of real-life clinical outcomes as documented in patient charts and laboratory reports.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study population comprised patients who were clinically diagnosed with hepatitis A, B, C, D, or E at Federal Medical Centre (FMC) Keffi within twelve months period under review. Only those with complete liver function tests recorded in their clinical notes were considered eligible for inclusion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion Criteria.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants included in the study were individuals aged 18 years and above who had a confirmed diagnosis of hepatitis based on serological testing and who had complete liver enzyme test results documented between January and December of the study year and provided informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were excluded if they were co-infected with HIV or other hepatitis types, if they had comorbid liver diseases such as cirrhosis from non-viral causes, if their records were incomplete or missing key enzyme data, had other liver diseases or conditions or were taking medications that could affect liver enzyme levels Had a history of liver surgery or transplantation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling Technique\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA convenience sampling technique was used to select patients who met the inclusion criteria. A total population sampling technique was employed, whereby all eligible patient records that met the inclusion criteria within the one-year timeframe were reviewed. This approach was adopted due to the relatively manageable number of hepatitis cases recorded during the period and the desire to maximize the statistical power of the study through exhaustive data inclusion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were collected manually from patient case files and laboratory registers using a structured data abstraction form developed for the study which includes. The form included fields for demographic details, hepatitis type, Medical history (diagnosis, treatment) and Laboratory results (liver enzyme levels) values for ALT, AST, ALP, and TB. The abstraction process was supervised by a clinical records officer to ensure fidelity to the original documentation and to minimize errors of transcription.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample Size Determination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince this study was retrospective in nature, the sample size was defined by the number of patients who fulfilled the study criteria within the year. Based on a preliminary survey of FMC Keffi\u0026apos;s medical records unit, an estimated 500 to 800 hepatitis cases with complete biochemical records were expected. This sample was considered adequate for observing meaningful enzyme differentials, particularly when disaggregated by hepatitis type, age, and sex.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLaboratory Analysis of Liver Enzymes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary variables of interest were the values of liver enzymes including ALT, AST, ALP, and TB. Secondary variables included age, sex, and hepatitis type. These variables were selected based on their clinical relevance to hepatic function and their ability to provide comparative insights into enzyme differentials among various patient subgroups. Liver enzyme levels were measured using a standardized assay (Selectra ProS Chemistry System). The following liver enzymes were measured: Alanine transaminase (ALT), Aspartate transaminase (AST), Alkaline phosphatase (ALP) and Total bilirubin (Tb).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis Techniques\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were analyzed using descriptive statistics, correlation analysis, and multiple regression analysis. The following statistical tests were used:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eMean and standard deviation to describe demographic and laboratory data\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eCorrelation analysis to examine the relationship between liver enzyme levels and demographic variables\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eMultiple regression analysis to examine the relationship between liver enzyme levels and demographic variables while controlling for other variables\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDescriptive statistics such as means, medians, ranges, and standard deviations were calculated for liver enzyme values. Frequency distributions were generated for categorical variables such as sex, age groups, and hepatitis type. These initial analyses provided a general overview of the study population and established baseline characteristics for further comparison.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by Ethical Board of Health Research Ethics Committee of Federal Medical Centre Keffi for data collection. Patient confidentiality and anonymity were maintained throughout the study to ensure confidentiality of data obtained. All ethical guidelines were followed during and after the research. Given the retrospective nature of the study, patient consent was waived; however, all identifiers were removed from the dataset to ensure confidentiality. Data were stored securely and used exclusively for academic purposes in line with national and institutional research guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfidentiality and Data Security\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo maintain patient confidentiality, all names, hospital numbers, and other unique identifiers were excluded from the research dataset. Hardcopy data abstraction forms were kept in a locked cabinet during the study period, and all electronic files were password-protected and accessible only to the research team.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic Characteristics of Hepatitis Patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge Group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;30 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e198 (27.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e31\u0026ndash;40 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e163 (22.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e41\u0026ndash;50 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e145 (20.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e51\u0026ndash;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108 (15.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109 (15.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e723 (100)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e390 (54.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e333 (46.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e723 (100)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHepatitis Type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatitis B\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e470 (65.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatitis C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e166 (23.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCo-infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87 (12.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e723 (100)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87 (12.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e145 (20.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e289 (0.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTertiary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e202 (27.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e723 (100\u003c/b\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTribe\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEggon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e218 (30.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHausa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e174 (24.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMada\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e104 (14.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAfo (Eloyi)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87 (12.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGwandara\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58 (8.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTiv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43 (.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (5.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e723 (100)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the demographic characteristics of the 723 hepatitis patients at Federal Medical Centre Keffi, showing a predominance of adults aged 18\u0026ndash;50 years (70.1%) and a slight male majority (54%). Hepatitis B was the most common infection (65%), followed by Hepatitis C (23%) and co-infections (12%). Educationally, the patients were diverse, with 12% having no formal education, 40% completing secondary school, and 28% attaining tertiary education. Ethnically, the cohort mainly comprised Eggon (30.2%) and Hausa (24.1%) tribes, reflecting the local population. This demographic profile highlights a broad age range affected by hepatitis, varied educational backgrounds, and ethnic diversity, all of which are important considerations for targeted healthcare interventions and interpreting liver enzyme variations in this population.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents descriptive statistics of liver enzyme levels and total bilirubin stratified by hepatitis type among 723 patients. Patients with HBV/HCV co-infection exhibited the highest mean values across all markers: ALT (210\u0026thinsp;\u0026plusmn;\u0026thinsp;90 IU/L), AST (195\u0026thinsp;\u0026plusmn;\u0026thinsp;85 IU/L), ALP (320\u0026thinsp;\u0026plusmn;\u0026thinsp;110 IU/L), and total bilirubin (5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5 \u0026micro;mol/l). Hepatitis C patients showed intermediate elevations: ALT (110\u0026thinsp;\u0026plusmn;\u0026thinsp;55 IU/L), AST (95\u0026thinsp;\u0026plusmn;\u0026thinsp;50 IU/L), ALP (140\u0026thinsp;\u0026plusmn;\u0026thinsp;65 IU/L), and bilirubin (2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 \u0026micro;mol/l). Hepatitis B patients demonstrated the lowest yet clinically significant elevations: ALT (85\u0026thinsp;\u0026plusmn;\u0026thinsp;40 IU/L), AST (78\u0026thinsp;\u0026plusmn;\u0026thinsp;35 IU/L), ALP (120\u0026thinsp;\u0026plusmn;\u0026thinsp;50 IU/L), and bilirubin (1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 mg/dL). Overall means were ALT (105\u0026thinsp;\u0026plusmn;\u0026thinsp;70 IU/L), AST (92\u0026thinsp;\u0026plusmn;\u0026thinsp;65 IU/L), ALP (145\u0026thinsp;\u0026plusmn;\u0026thinsp;90 IU/L), and bilirubin (2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 \u0026micro;mol/l).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics of Liver Enzyme Levels by Hepatitis Type\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnzyme\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHepatitis B (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHepatitis C (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCo-infection (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOverall (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT (IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e85\u0026thinsp;\u0026plusmn;\u0026thinsp;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e110\u0026thinsp;\u0026plusmn;\u0026thinsp;55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e210\u0026thinsp;\u0026plusmn;\u0026thinsp;90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e105\u0026thinsp;\u0026plusmn;\u0026thinsp;70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST (IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e78\u0026thinsp;\u0026plusmn;\u0026thinsp;35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e95\u0026thinsp;\u0026plusmn;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e195\u0026thinsp;\u0026plusmn;\u0026thinsp;85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e92\u0026thinsp;\u0026plusmn;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP (IU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e120\u0026thinsp;\u0026plusmn;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e140\u0026thinsp;\u0026plusmn;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e320\u0026thinsp;\u0026plusmn;\u0026thinsp;110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e145\u0026thinsp;\u0026plusmn;\u0026thinsp;90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Bilirubin (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e\u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis quantitative stratification confirms a severity gradient: co-infection\u0026thinsp;\u0026gt;\u0026thinsp;HCV\u0026thinsp;\u0026gt;\u0026thinsp;HBV. The 147% higher mean ALT in co-infection versus HCV (210 vs. 110 IU/L) and 247% versus HBV (210 vs. 85 IU/L) reflects substantially greater hepatocellular injury. Similarly, bilirubin levels in co-infected patients exceeded HCV by 162% (5.5 vs. 2.1 mg/dL) and HBV by 206% (5.5 vs. 1.8 mg/dL), indicating pronounced cholestatic dysfunction. These differentials align with established patterns of accelerated liver damage in co-infection contexts.\u003c/p\u003e\u003cp\u003eThe clinical severity of liver disease among the 723 hepatitis patients was categorized into mild, moderate, and severe based on clinical notes, diagnostic imaging, and fibrosis scoring systems such as APRI (Aspartate Aminotransferase to Platelet Ratio Index) and FIB-4 (Fibrosis Index Based on 4 Factors) where available. The distribution reflects the expected pattern with the majority experiencing mild to moderate disease, while a smaller but significant proportion presented with severe liver damage, including fibrosis and cirrhosis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of Clinical Severity Markers by Sex and Hepatitis Type\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverity Marker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHepatitis B (n, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHepatitis C (n, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCo-infection (n, %)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTotal (n, %)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMild\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e150 (38.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90 (24.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20 (5.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e260 (68.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e120 (36.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30 (9.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17 (5.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e167 (50.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80 (20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50 (13.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25 (6.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e155 (40.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40 (12.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e89 (26.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSevere\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20 (5.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15 (4.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30 (7.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e65 (16.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17 (5.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12 (3.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15 (4.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e44 (13.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eTotal patients\u003c/b\u003e: 723 (Male: 390, Female: 333)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the distribution of clinical severity among hepatitis patients, stratified by sex and hepatitis type, using established criteria based on Aspartate Aminotransferase to Platelet Ratio Index (APRI), Fibrosis Index Based on 4 Factors (FIB-4 scores), and imaging findings. The data reveal that mild liver disease is most prevalent, particularly among males with hepatitis B and C mono-infections, while moderate and severe disease are more frequently observed in patients with HBV/HCV co-infection across both sexes. Notably, co-infected individuals account for a disproportionately higher percentage of severe cases, underscoring the aggressive nature of dual infection. Females show a slightly lower frequency of severe disease overall, but the risk remains elevated in the presence of co-infection. These findings highlight the importance of considering both sex and hepatitis type when assessing liver disease severity and reinforce the need for vigilant monitoring and targeted interventions for high-risk groups, particularly those with co-infection (Odeghe \u003cem\u003eet al\u003c/em\u003e., 2024 ).\u003c/p\u003e\u003cp\u003eThe overall distribution of liver enzyme levels among the 723 hepatitis patients showed elevated mean values across all enzymes, consistent with liver injury. Mean ALT and AST were 105 IU/L and 92 IU/L respectively, with wider variability in co-infected patients. Mean ALP was 145 IU/L, indicating some cholestatic involvement. Total bilirubin averaged 2.3 mg/dL, with higher values in severe cases. Demographically, the sample was predominantly middle-aged adults (18\u0026ndash;50 years) with a slight male majority (54%). The images below (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003ed) include boxplots illustrating the distribution and spread of enzyme levels by hepatitis type, highlighting greater enzyme elevations in co-infected patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eANOVA tests were performed to compare liver enzyme levels across the three hepatitis groups (HBV, HCV, and co-infection). Results indicated statistically significant differences for all enzymes and total bilirubin (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Post-hoc analyses revealed that co-infected patients had significantly higher enzyme levels than both HBV and HCV mono-infected groups, while HCV patients had moderately higher levels than HBV patients.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Liver Enzyme Levels Across Hepatitis Types (ANOVA)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnzyme\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTest Statistic (F)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificant Differences (Post-hoc)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e112.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCo-infection\u0026thinsp;\u0026gt;\u0026thinsp;HCV\u0026thinsp;\u0026gt;\u0026thinsp;HBV\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e98.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCo-infection\u0026thinsp;\u0026gt;\u0026thinsp;HCV\u0026thinsp;\u0026gt;\u0026thinsp;HBV\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e145.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCo-infection\u0026thinsp;\u0026gt;\u0026thinsp;HCV\u0026thinsp;\u0026gt;\u0026thinsp;HBV\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Bilirubin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e160.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCo-infection\u0026thinsp;\u0026gt;\u0026thinsp;HCV\u0026thinsp;\u0026gt;\u0026thinsp;HBV\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that liver enzyme levels and total bilirubin differ significantly across hepatitis types, with all ANOVA tests yielding p-values less than 0.001. Post-hoc analysis reveals that patients with HBV and HCV co-infection have markedly higher levels of ALT, AST, ALP, and total bilirubin compared to those with either HBV or HCV alone. Additionally, HCV patients exhibit higher enzyme levels than HBV patients, indicating a gradient of liver injury severity from HBV to HCV to co-infection. These findings suggest that co-infection is associated with more pronounced liver dysfunction, emphasizing the need for targeted clinical management in this high-risk group.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the correlation coefficients between liver enzyme levels (ALT, AST, ALP, and total bilirubin) and key demographic and clinical variables including age, sex, and clinical severity of liver disease. The analysis reveals statistically significant positive correlations between clinical severity and all liver enzymes, indicating that higher enzyme levels are associated with more severe liver damage. Age shows a moderate positive correlation with ALP and total bilirubin, reflecting increased cholestatic and liver dysfunction markers in older patients. Sex (coded as male\u0026thinsp;=\u0026thinsp;1, female\u0026thinsp;=\u0026thinsp;0) has a weak positive correlation with ALT, suggesting slightly higher enzyme levels in males.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation Coefficients Between Liver Enzymes and Demographic/Clinical Variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAST\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eALP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal Bilirubin\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.12*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.10*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.25**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.22**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (male\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.15**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.12*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical Severity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.62**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.58**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.54**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.65**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eClinical severity shows strong positive correlations with all liver enzymes and total bilirubin, confirming that enzyme elevations correspond closely with worsening liver disease. Age is moderately correlated with ALP and bilirubin, consistent with age-related liver changes and cholestasis. The weak positive correlations between sex and certain enzymes suggest males may have slightly higher enzyme levels, possibly due to lifestyle or biological factors. These findings align with existing literature indicating liver enzyme elevations as reliable markers of liver injury severity in hepatitis patients.\u003c/p\u003e\u003cp\u003eMultiple regression models were constructed to predict liver enzyme levels (ALT, AST, ALP and total bilirubin) based on hepatitis type (with Hepatitis B as the reference category), age, sex, and clinical severity. The results (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) indicate that clinical severity is the strongest positive predictor of elevated liver enzymes across all models, followed by hepatitis type and age. Co-infection status significantly increased enzyme levels compared to HBV alone, while HCV showed moderate increases. Age was positively associated with ALP and total bilirubin levels, reflecting age-related liver changes. Male sex was a modest but significant predictor of higher ALT levels. The models explained a substantial proportion of variance in enzyme levels, with R\u0026sup2; values ranging from 0.45 to 0.62, indicating good model fit.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultiple Regression Analysis Predicting Liver Enzyme Levels\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor Variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eALT (R\u0026sup2;=0.58)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatitis Type (HCV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatitis Type (Co-inf)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (male\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical Severity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e95.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAST (R\u0026sup2;=0.55)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatitis Type (HCV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatitis Type (Co-inf)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (male\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical Severity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e88.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eALP (R\u0026sup2;=0.47)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatitis Type (HCV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatitis Type (Co-inf)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e140.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (male\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.395\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical Severity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e110.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal Bilirubin (R\u0026sup2;=0.62)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatitis Type (HCV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHepatitis Type (Co-inf)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (male\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical Severity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe multiple regression analysis demonstrates that clinical severity is the most influential predictor of elevated liver enzyme levels and bilirubin, underscoring its role as a key indicator of liver damage. Co-infection with HBV and HCV significantly increases enzyme levels compared to HBV alone, with HCV mono-infection also showing moderate effects. Age contributes modestly to enzyme elevations, particularly ALP and bilirubin, reflecting age-related liver changes. Male sex is associated with higher ALT levels but is not a significant predictor for all enzymes. Overall, the models explain a substantial proportion of variability in liver enzyme levels, confirming the combined influence of hepatitis type, clinical severity, and demographics on liver function markers.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe observed liver enzyme profiles in this study provide compelling evidence of both statistically significant and clinically meaningful distinctions across the spectrum of hepatitis infections and disease severities. Notably, individuals with concurrent hepatitis B and C virus infections consistently demonstrated the most pronounced biochemical alterations, characterized by elevated levels of transaminases, alkaline phosphatase, and total bilirubin [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These elevations are indicative of widespread hepatocellular injury and compromised biliary function, which are hallmarks of advanced or fulminant hepatic disease [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The biochemical derangement in co-infected individuals is likely reflective of the compounded virological assault on hepatic parenchyma, leading to accelerated fibrogenesis, necroinflammation, and a greater propensity for progression to cirrhosis and hepatocellular carcinoma [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This synergistic pathogenic effect has been well-documented in previous studies, and our findings add a critical dimension by linking these biochemical signatures to stratified clinical severity in a Nigerian cohort, a population often underrepresented in global hepatitis research.\u003c/p\u003e\u003cp\u003eMono-infected patients exhibited enzyme patterns consistent with moderate hepatic injury, with hepatitis C cases showing more biochemical activity compared to hepatitis B. This aligns with known viral pathophysiology; hepatitis C, lacking a DNA intermediate, often elicits a persistent low-grade inflammatory response that gradually undermines liver architecture [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In contrast, hepatitis B, especially in its immune-tolerant phase, may present with relatively mild enzyme elevations despite ongoing viral replication, underscoring the importance of interpreting laboratory data within the broader clinical and virological context [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Importantly, these enzyme profiles were closely mirrored by clinical severity classifications, reinforcing the diagnostic and prognostic utility of biochemical markers in routine hepatitis management. Beyond their value in assessing disease activity, liver enzymes can serve as early warning signals in resource-limited settings where advanced diagnostics such as elastography or biopsy may be inaccessible [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In such contexts, elevated aminotransferases and bilirubin levels, especially in the presence of co-infection, should prompt urgent clinical attention and consideration for intensified management strategies [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis pattern is consistent with both Nigerian and international studies, which have found that HBV/HCV co-infection is associated with more pronounced biochemical abnormalities, higher rates of cirrhosis, and increased risk of decompensated liver disease compared to mono-infection [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. For instance, Khan \u003cem\u003eet al.\u003c/em\u003e (2022) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] reported significantly higher ALT, AST, and ALP levels in co-infected patients, Additionally, studies in Nigeria have documented similar trends, with co-infected patients experiencing more severe clinical outcomes, including higher rates of cirrhosis and hepatocellular carcinoma [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] (Olayinka \u003cem\u003eet al.\u003c/em\u003e, 2016; Musa \u003cem\u003eet al.\u003c/em\u003e, 2015). Globally, hepatitis C is recognized as a leading cause of advanced liver disease and liver transplantation, and the synergistic effect of HBV/HCV co-infection further accelerates disease progression [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The elevated ALP levels observed in this study reflect cholestatic injury, which is often associated with advanced liver disease and bile duct involvement, as reported in the literature [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBeyond hepatitis type, other predictors such as age and sex also influenced enzyme levels, with older patients and males tending to have higher values, a finding echoed in several regional studies [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The strong correlation between clinical severity and enzyme elevation reinforces the utility of liver function tests as non-invasive markers for liver injury, supporting their use in diagnosis, staging, and monitoring of hepatitis patients [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe significant differences in liver enzyme levels among hepatitis groups observed in this study underscore the varying degrees of liver injury associated with each infection type. Co-infected patients (HBV/HCV) consistently exhibited the highest elevations in ALT, AST, ALP, and total bilirubin, indicating more severe hepatocellular and cholestatic damage compared to mono-infected individuals. This pronounced enzyme elevation in co-infection may be attributed to the synergistic effect of dual viral replication, immune-mediated cytotoxicity, and compounded hepatic inflammation, which together accelerate liver injury and fibrosis [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Previous studies have shown that co-infected patients have a higher risk of cirrhosis, hepatic decompensation, and hepatocellular carcinoma, supporting the clinical importance of early identification and aggressive management of these cases [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The higher enzyme levels in HCV mono-infected patients compared to those with HBV alone also reflect the well-documented aggressive nature of hepatitis C, which is known to cause persistent inflammation and rapid progression to fibrosis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These differential enzyme patterns provide valuable diagnostic and prognostic information, reinforcing the need for tailored clinical management strategies based on hepatitis type and infection status [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe correlation analysis further illustrates the relationships between liver enzyme levels and key demographic and clinical variables. The strong positive correlations between clinical severity and all measured liver enzymes confirm that enzyme elevations are reliable indicators of worsening liver damage, consistent with findings from both Nigerian and international cohorts [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moderate correlations with age, particularly for ALP and bilirubin, suggest that older patients may experience more pronounced cholestatic injury or impaired bilirubin clearance, possibly due to age-related hepatic changes or longer disease duration [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The weak but significant associations of male sex with certain enzymes may be linked to gender differences in liver metabolism, hormonal influences, or differences in exposure to risk factors such as alcohol or occupational hazards [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Collectively, these results align with established literature, confirming that liver enzymes are accessible and practical biomarkers for monitoring disease progression and guiding individualized treatment decisions in hepatitis patients [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan additionalcitationids=\"CR43 CR44 CR45\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths and Limitations of the Data and Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study benefits from a large sample size of 723 hepatitis patients, which enhances the robustness and representativeness of the findings within the context of the Federal Medical Centre Keffi population. The use of a structured data abstraction form ensured systematic collection of demographics, clinical, and biochemical data, contributing to data consistency and reliability. Methodologically, the application of multiple statistical techniques, including ANOVA, correlation, and regression analyses, allowed for comprehensive examination of liver enzyme differentials while adjusting for potential confounders. However, as the data were retrospectively collected from medical records, some limitations inherent to routine clinical data apply. These include potential variability in laboratory assay methods and timing, incomplete documentation, and missing data, which could introduce bias or affect the accuracy of some variables despite efforts to cross-verify laboratory results.\u003c/p\u003e\u003cp\u003eMoreover, the observational nature limits causal inference, and the single-center design limits the findings' generalisability outside of the local population. In certain situations, the depth of clinical severity assessment was diminished by missing information on specific clinical markers, such as fibrosis staging or a thorough treatment history. Selection effects may also result in biases because patients who come to the tertiary centre might be more severe or have better access to healthcare. Notwithstanding these limitations, the quality of the data was adequate to find noteworthy correlations that aligned with the body of current research. As suggested in global hepatitis surveillance and monitoring frameworks, prospective, multicenter designs and standardised data collection procedures could enhance data completeness and external validity in future research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study underscores the critical interplay between biochemical and demographic characteristics in shaping the clinical course of hepatitis among Nigerian patients. The data clearly demonstrate that individuals co-infected with both hepatitis B and C exhibit markedly elevated liver enzyme levels and more severe clinical manifestations compared to those with mono-infections. This finding highlights the synergistic pathogenicity of dual infection, reinforcing the need for early identification and aggressive management of co-infected patients. The use of liver enzymes such as ALT, AST, ALP, and total bilirubin as non-invasive markers of liver injury proved valuable in distinguishing disease severity and guiding clinical decision-making. Furthermore, the demographic patterns observed, particularly the predominance of middle-aged adults and ethnic clustering suggest that targeted screening programs could significantly enhance early detection rates in at-risk populations. Incorporating demographic profiling into hepatitis surveillance systems could allow for more efficient resource allocation and tailored health interventions, especially in resource-constrained environments. Public health strategies must prioritize widespread vaccination, especially for hepatitis B, early screening of high-risk groups, and integration of hepatitis services into primary healthcare systems. Strengthening healthcare infrastructure, expanding access to diagnostic tools, and training frontline health workers will be pivotal in reducing the burden of hepatitis. Ultimately, a comprehensive, data-driven, and equity-focused approach is essential to achieve the World Health Organization\u0026rsquo;s goal of viral hepatitis elimination by 2030 in Nigeria and similar low- and middle-income countries.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNot Applicable\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (2013 version) as adopted by the World Medical Association. Ethical approval for the study was secured from the Health Research Ethics Committee of the Federal Medical Center, Keffi (FMC, Keffi) (a fully registered committee the under National Health Research Ethics Committee)_ FMH-Ref No: C.5187/76IT, after a defence of the research proposal during the Committee\u0026rsquo;s ethical screening interview held on the 14\u003csup\u003eth\u003c/sup\u003e November, 2024.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Participation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to privacy considerations of the participants but are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors hereby declare that there are no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any specific grant from any funding institution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLIU, AIA, and YBN conceptualized and designed the study and contributed to drafting and revising the manuscript. LIU, AIA, YBN, DIU, and SOA contributed to data collection, and manuscript review, All authors participated in study design, and critically reviewed the manuscript for important intellectual content. All authors assisted with the literature review, data visualization, and preparation of initial manuscript drafts, All authors provided methodological expertise, and contributed significantly to manuscript revisions. All authors supported data acquisition and provided feedback on the manuscript drafts. All authors contributed to the manuscript structure, final proofreading, and editing for clarity and coherence; all authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors hereby acknowledge Mr. Mustapha Batutalah Usman of the Ethics and Research Department of the Federal Medical Center, Keffi, Nasarawa State, and his team were invaluable as field research assistants in the collection of data for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trials Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdemoyegun JK, Aremu SO. Socioeconomic determinants of malaria and hepatitis infections: Insights from the Federal Medical Center, Makurdi, North Central, Nigeria. BMC Public Health. 2024;24(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-024-20666-8\u003c/span\u003e\u003cspan address=\"10.1186/s12889-024-20666-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAremu DO, Maxim A, Aremu SO, Aremu DE, Terhemen YD, Itodo SO, Barkhadle AA. The interplay of socio-demographic factors and disease prevalence: insights into malaria, Hepatitis B, and Hepatitis C in Lafia, Nasarawa State, Nigeria. 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Treasure Island (FL): StatPearls Publishing; 2025 Jan-. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/books/NBK459290/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/books/NBK459290/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCornberg M, Sandmann L, Jaroszewicz J, Kennedy P, Lampertico P, Lemoine M, Lens S, Testoni B, Wong GLH, Russo FP. EASL Clinical Practice Guidelines on the management of hepatitis B virus infection. J Hepatol. 2025; [Epub ahead of print].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKamili S, Drobeniuc J, Araujo AC. Laboratory diagnostics for hepatitis. 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Journal of the International Association of Providers of AIDS Care, 23, 23259582241292511. https://doi.org/10.1177/23259582241292511.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWazir H, Abid M, Essani B, Saeed H, Ahmad Khan M, Nasrullah F, Qadeer U, Khalid A, Varrassi G, Muzammil MA, Maryam A, Syed ARS, Shah AA, Kinger S, Ullah F. Diagnosis and Treatment of Liver Disease: Current Trends and Future Directions. Cureus. 2023;15(12):e49920. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7759/cureus.49920\u003c/span\u003e\u003cspan address=\"10.7759/cureus.49920\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Viral hepatitis, Liver enzymes, Co-infection, Public health, Disease severity, Nigeria","lastPublishedDoi":"10.21203/rs.3.rs-7130610/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7130610/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e\u003cp\u003eViral hepatitis remains a significant global health concern, with hepatitis B virus (HBV) and hepatitis C virus (HCV) contributing substantially to chronic liver disease, cirrhosis, and hepatocellular carcinoma. In sub-Saharan Africa, the public health burden of hepatitis is exacerbated by late diagnosis, inadequate monitoring, and limited resources. This study aimed to evaluate liver enzyme levels and their association with demographic factors and clinical severity among hepatitis patients in a tertiary health facility in Nigeria to inform targeted interventions.\u003c/p\u003e\u003ch2\u003eMethodology\u003c/h2\u003e\u003cp\u003eA cross-sectional descriptive and analytical study was conducted involving 723 hepatitis patients at Federal Medical Centre Keffi, Nigeria. Sociodemographic data were collected alongside laboratory results for alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and total bilirubin. Patients were classified as having HBV, HCV, or HBV/HCV co-infection. Data analysis included descriptive statistics, ANOVA, correlation, and multiple regression models.\u003c/p\u003e\u003ch2\u003eResults and Discussion\u003c/h2\u003e\u003cp\u003eThe majority of patients were aged 18\u0026ndash;50 years (70.1%), with a slight male predominance (54%). Hepatitis B was the most common infection (65%), followed by HCV (23%) and co-infection (12%). Co-infected patients showed the highest mean levels of liver enzymes and total bilirubin (ALT: 210 IU/L, AST: 195 IU/L, ALP: 320 IU/L, bilirubin: 5.5 mg/dL), with statistically significant differences across groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Clinical severity was positively correlated with all liver markers (r\u0026thinsp;=\u0026thinsp;0.54\u0026ndash;0.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and multiple regression analysis confirmed that co-infection and disease severity were the strongest predictors of elevated liver enzymes. These findings align with global evidence that co-infection accelerates liver damage, and emphasize the need for sex- and age-sensitive screening and early treatment programs.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study highlights the biochemical and demographic characteristics of hepatitis patients in Nigeria, emphasizing the heightened disease severity in co-infected individuals. Integration of liver enzyme monitoring with demographic profiling can improve early diagnosis and resource allocation in hepatitis management. Strengthening public health infrastructure and implementing routine fibrosis assessment are vital to reducing hepatitis-related morbidity in resource-limited settings.\u003c/p\u003e","manuscriptTitle":"Clinical burden and biochemical profiles of viral hepatitis in a tertiary healthcare facility in North Central, Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 12:49:32","doi":"10.21203/rs.3.rs-7130610/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-15T17:40:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-15T16:29:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-14T07:18:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-12T03:36:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172782947468190194575049559149828476427","date":"2025-08-11T03:20:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265112372918628667622190081482699001220","date":"2025-08-10T11:42:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53829974840557320898260800597603185405","date":"2025-08-09T14:33:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215954540236509263664521703234198476079","date":"2025-08-08T08:32:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116262701992017069235784917490879792715","date":"2025-08-07T10:28:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-07T07:19:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304849613903946761483345197987798864252","date":"2025-08-06T22:48:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53753040684940155302384880102760813501","date":"2025-08-06T19:44:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-06T19:33:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-18T04:07:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-17T07:29:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-17T07:25:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-07-15T12:23:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1cac23c0-b983-4257-8bf8-08ef1dfc3196","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:25:08+00:00","versionOfRecord":{"articleIdentity":"rs-7130610","link":"https://doi.org/10.1186/s12879-025-11792-8","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-10-21 16:16:12","publishedOnDateReadable":"October 21st, 2025"},"versionCreatedAt":"2025-08-12 12:49:32","video":"","vorDoi":"10.1186/s12879-025-11792-8","vorDoiUrl":"https://doi.org/10.1186/s12879-025-11792-8","workflowStages":[]},"version":"v1","identity":"rs-7130610","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7130610","identity":"rs-7130610","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}