Assessment Of Prevalence Of Fibrosis In Metabolic Dysfunction-Associated Steatotic Liver Disease Using FibroScan® In A Tertiary Care Hospital In Andhra Pradesh

Assessment Of Prevalence Of Fibrosis In Metabolic Dysfunction-Associated Steatotic Liver Disease Using FibroScan® In A Tertiary Care Hospital In Andhra Pradesh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment Of Prevalence Of Fibrosis In Metabolic Dysfunction-Associated Steatotic Liver Disease Using FibroScan® In A Tertiary Care Hospital In Andhra Pradesh Bokka Pradeepthi, Nisar Ahmed, Jakkula supriya, Allavarapu Manogna, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6604350/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as MAFLD, has emerged as a major public health concern in South Asia, driven by rising obesity, diabetes, and sedentary lifestyles. Despite its high burden, fibrosis— the strongest predictor of liver-related morbidity and mortality— remains underdiagnosed, particularly in resource-limited regions like Andhra Pradesh, India. Objective To assess the prevalence of hepatic fibrosis and its association with metabolic and lifestyle risk factors in MASLD patients using FibroScan® in a tertiary care hospital in Andhra Pradesh. Methods A prospective observational study was conducted among 194 patients diagnosed with MASLD. Liver steatosis and fibrosis were assessed non-invasively using FibroScan®. Clinical, demographic, anthropometric, and lifestyle data were collected through structured interviews and medical record reviews. Statistical analyses included Chi-square tests and regression models to evaluate associations between risk factors and fibrosis. Results Hepatic fibrosis was identified in 160 of 194 MASLD patients (82.5%). Elevated BMI (> 23) was significantly associated with fibrosis (p = 0.0389), with 83.1% of fibrosis cases occurring in overweight or obese individuals. Although physical inactivity (78.1%), hypertension (27.5%), and diabetes mellitus (21.9%) were common in fibrosis patients, these associations were not statistically significant. Non-vegetarian diets were significantly associated with steatosis (p = 0.0035) but not with fibrosis. Gender analysis revealed that while males constituted the majority of fibrosis cases, females had a disproportionately higher rate relative to their group size. Conclusion This study highlights a substantial burden of hepatic fibrosis among MASLD patients in Andhra Pradesh, with BMI > 23 as the strongest independent predictor. These findings underscore the need for early, BMI-based risk stratification and integration of non-invasive diagnostics like FibroScan® into routine care. Public health interventions should prioritize weight management, lifestyle modification, and region-specific risk assessment to curb fibrosis progression in high-risk populations. MASLD fibrosis BMI FibroScan South Asia liver disease metabolic syndrome public health Figures Figure 1 Figure 2 Figure 3 Introduction Obesity and metabolic syndrome have driven a rapid rise in fatty liver disease worldwide. In 2023, an international consensus unified non-alcoholic fatty liver disease (NAFLD) and its successor term metabolic dysfunction-associated fatty liver disease (MAFLD) under the globally accepted nomenclature of metabolic dysfunction-associated steatotic liver disease (MASLD). This shift replaced exclusionary criteria (e.g., alcohol use thresholds) with positive diagnostic criteria based on cardiometabolic risk factors, such as obesity, diabetes, or metabolic syndrome 1 , 2 . MASLD now represents the most prevalent chronic liver disorder, affecting roughly one-third of adults worldwide and over 38% of adults in high-risk regions like South Asia. In India, MASLD prevalence has surged dramatically, especially in urban populations where rates reach 40–50%, with Andhra Pradesh emerging as a critical hotspot driven by escalating central obesity (48%), diabetes (22%), and physical inactivity (65%).Recognizing its metabolic roots, an international consensus in 2020 formally shifted the diagnostic criteria: hepatic steatosis is now diagnosed based on the presence of obesity, type 2 diabetes, or other metabolic abnormalities, rather than by excluding mild alcohol use or other liver diseases. In essence, MASLD is considered the hepatic manifestation of systemic metabolic syndrome. 3 – 7 Importantly, MASLD is not a benign fat-only condition. A significant subset of patients develops steatohepatitis (NASH) and progressive hepatic fibrosis, which has emerged as the strongest predictor of liver-related outcomes and mortality. Advanced fibrosis (F3–F4) increases liver-related deaths by nearly tenfold. Despite this, hepatic fibrosis remains underdiagnosed, particularly in low-resource settings like South India, where unique regional factors—such as high-carbohydrate diets, rising environmental pollution, and genetic predispositions (e.g., PNPLA3 variants)—amplify disease severity. 8 – 10 The introduction of non-invasive modalities such as vibration-controlled transient elastography (FibroScan®) has revolutionized the detection of hepatic fibrosis. FibroScan® offers a rapid, reliable, and non-invasive assessment of both liver stiffness (fibrosis) and steatosis, enabling early identification of patients with significant (≥ F2) or advanced fibrosis. In South Asian populations, FibroScan® has demonstrated an accuracy of approximately 89% for detecting ≥ F2 fibrosis, making it particularly valuable in resource-limited settings. Recent clinical trials further underscore the urgency of early detection, showing that achieving ≥ 15% weight loss through therapies such as GLP-1 receptor agonists or novel antifibrotic agents can reverse early-stage fibrosis in a substantial proportion of patients, thereby preventing progression to cirrhosis. 11 , 12 Emerging evidence also underscores important sex-specific differences in fibrosis progression, with post-menopausal women facing higher risks due to hormonal changes and increased visceral adiposity. Meanwhile, men tend to experience faster progression during early disease stages. Furthermore, environmental exposures such as elevated PM2.5 levels and dietary advanced glycation end-products (AGEs) in Andhra Pradesh exacerbate oxidative stress and gut-liver axis dysfunction, further accelerating fibrosis progression. 13 , 14 Despite the growing burden, India’s National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases, and Stroke (NPCDCS) currently lacks MASLD-specific screening protocols, resulting in delayed diagnoses and a rising burden of cirrhosis-related mortality. Recent global guidelines, including those from AASLD, EASL, and WHO, advocate for integrating non-invasive screening tools like FibroScan® into primary healthcare systems to bridge this critical gap, particularly in high-burden states like Andhra Pradesh. 15 Against this backdrop, the present study aims to assess the prevalence and determinants of hepatic fibrosis among MASLD patients at a tertiary care hospital in Andhra Pradesh. By correlating fibrosis severity with metabolic, genetic, and environmental risk factors, this study seeks to generate the first region-specific data on the MASLD fibrosis burden in urbanising South India. The findings are intended to contribute to the growing body of evidence needed to support future efforts in prioritizing early fibrosis detection within state and national non-communicable disease (NCD) control strategies. In doing so, this research hopes to aid broader public health goals, including the reduction of cirrhosis-related mortality and progress toward achieving the United Nations Sustainable Development Goal 3.4, which aims for a 30% reduction in premature mortality from NCDs by 2030. 16 – 18 Methods Patients This hospital-based prospective observational study was conducted over six months at the Department of Gastroenterology and Hepatology in a Tertiary Care Hospital, Kakinada. Patients were enrolled consecutively after obtaining informed consent. The study aimed to collect data on patients diagnosed with MASLD, including demographic details such as age, gender, occupation, BMI, medical history, social history, medication history, Fibroscan imaging data, laboratory results, and dietary and lifestyle habits. Data collections and patient education Data were collected prospectively on a daily basis using a structured data collection form. The final sample included 194 patients, comprising 160 with hepatic fibrosis and 34 without, and 154 with hepatic steatosis and 40 without. The sample size exceeded the initial target calculated using Cochran’s formula at a 95% confidence level and 5% margin of error (assuming maximum variability at p = 0.5), enhancing the study’s statistical power. Eligible participants were adults diagnosed with metabolic dysfunction-associated steatotic liver disease (MASLD), based on clinical evaluation, biochemical markers, or imaging (including FibroScan®) findings. All participants had provided written informed consent prior to enrolment. Exclusion criteria included a history of alternative liver pathologies such as viral hepatitis, alcoholic liver disease, established cirrhosis, or hepatocellular carcinoma, as well as those unwilling or unable to provide informed consent or with comorbidities impairing their ability to participate meaningfully in the study. Following data collection, all patients received structured counselling on MASLD management. This included personalized guidance on dietary modification, physical activity, and risk factor control. Educational pamphlets—containing culturally adapted lifestyle recommendations and evidence-based strategies for disease mitigation—were distributed to support long-term behavioural change. Statistical analysis The data were analysed statistically using Chi-square tests, linear regression analysis, and descriptive statistics, as referenced in Tables 1 and 2 , with Microsoft Excel and GraphPad Prism 8.0.2. Table 01 – Grade of steatosis with ultrasound attenuation parameter values Grade of steatosis UAP (db/m) Normal 295 Table 02 Stage of fibrosis with liver stiffness measurement values Stage of fibrosis LSM (Kpa) F0-F1 17.5 Results During the study period, approximately 750 patients with suspected MASLD (formerly termed MAFLD) visited the Department of Gastroenterology and Hepatology at the tertiary care hospital. Based on the inclusion and exclusion criteria, a total of 194 patients were enrolled. Among them, 161 (83%) were male and 33 (17%) were female, with a mean age of 44.15 years. Liver steatosis was identified in 154 patients using the Ultrasound Attenuation Parameter (UAP), and liver stiffness was assessed using FibroScan®-based Liver Stiffness Measurement (LSM), revealing hepatic fibrosis in 160 patients. This highlights a high burden of MASLD and associated fibrosis in this clinical population. The distribution of fibrosis stages based on LSM values is presented in Table 2 , with a substantial proportion of patients falling within the F2–F4 range, consistent with progressive liver fibrosis requiring early intervention. 3.1 Risk Factors of MASLD in the Sample Population In the study cohort ( n = 194 ), medical history and anthropometric assessments revealed multiple contributing risk factors for MAFLD. Hypertension was observed in 36 patients and showed a significant association with steatosis ( p = 0.0157 ). Type 2 diabetes mellitus was reported in 32 patients, with a higher prevalence noted in both fibrosis and steatosis groups, though the associations did not reach statistical significance ( p = 0.1603 for both). Metabolic syndrome was present in 09 patients, predominantly among those with fibrosis (11 vs. 1), again with a non-significant p -value ( p = 0.1603 ). Physical inactivity was the most prevalent lifestyle-related risk factor, affecting 133 patients, and was notably associated with both fibrosis and steatosis, showing a significant association in the latter ( p = 0.0157 ). Anthropometric evaluation revealed that 156 patients (80.4%) had a BMI greater than 23, in alignment with the Asian criteria for overweight. Among these, 133 individuals (68.5%) were in the fibrosis group ( p = 0.0389 ), suggesting a significant link between elevated BMI and fibrosis severity. A similar pattern was observed in relation to steatosis, with 125 patients exhibiting BMI > 23 compared to 31 in the non-steatosis group, although this association was not statistically significant ( p = 0.6024 ). Dietary patterns further contributed to disease expression; non-vegetarian diets were strongly associated with steatosis (114 vs. 26, p = 0.0035 ), whereas vegetarian diets were more commonly observed in patients without steatosis (24 vs. 40). 3.2 Prevalence of MASLD and Major Risk Factors in the Sample Population Hepatic steatosis, as assessed by FibroScan, was identified in 154 of 194 patients (79.4%), underscoring the substantial burden of MASLD in this tertiary care cohort. Key risk factors included physical inactivity, reported in 133 of 154 patients with steatosis (86.4%; p = 0.0157), and non-vegetarian dietary habits, observed in 114 of 154 cases (74.0%; p = 0.0035). Although BMI > 23 was prevalent in 125 of 154 steatosis patients (81.2%), this association was not statistically significant (p = 0.6024), in contrast to its significant relationship with fibrosis (p = 0.0389).Subgroup analyses revealed that 125 of 156 overweight or obese patients (80.1%) and 32 of 41 individuals with type 2 diabetes (78.0%) had hepatic steatosis. Physical inactivity emerged as the most prominent behavioral factor, with 133 of 151 inactive individuals (88.1%) exhibiting steatosis.(fig-1) Gender-based disparities were also evident. Males predominated in both steatosis (128/154; 83.1%) and fibrosis (130/160; 81.3%) groups. While females were underrepresented in the steatosis cohort (26/154; 16.9%), they constituted a higher proportion of fibrosis cases (30/160; 18.8%) compared to those without fibrosis (3/34; 8.8%; p = 0.168). Stratification by gender and BMI showed that males with BMI > 23 accounted for the majority of steatosis cases (125/154; 81.2%), while females with BMI > 23 contributed to 26 of 154 cases (16.9%).These findings highlight the importance of early identification and lifestyle interventions in high-risk MAFLD subgroups, particularly targeting physical inactivity and non-vegetarian diets. The stronger association between elevated BMI and fibrosis—rather than steatosis alone—suggests its potential role as a predictive marker for progressive liver disease.(table-3) Table 03 Risk factors for MAFLD: PARAMETER NO STEATOSIS STEATOSIS P VALUE 40 154 Ø Male 33 128 0.923 Ø Female 7 26 AGE – Ø 60 4 10 RISK FACTORS History of Hypertension 16 36 0.0157 History of Diabetes mellitus 9 32 Metabolic Syndrome 3 9 Physical Inactivity 18 133 BMI Ø 23 31 125 DIET Ø Non vegetarian 26 114 0.0035 Ø vegetarian 24 40 3.3 Risk Factors of Fibrosis in the Sample Population Risk factors for fibrosis were assessed in the cohort (n = 194). BMI > 23 demonstrated the strongest association with fibrosis, observed in 133 out of 160 patients (83.1%, p = 0.0389). Other metabolic comorbidities included hypertension (44/160, 27.5%) and type 2 diabetes (35/160, 21.9%), while metabolic syndrome was identified in 11/160 (6.9%) of fibrosis cases. Physical inactivity, though prevalent in 125/160 (78.1%) fibrosis patients, did not reach statistical significance (p = 0.1603). Gender disparities were prominent, with males constituting the majority of fibrosis cases (130/160, 81.3%). While females represented only 18.8% (30/160) of fibrosis cases, their proportion was notably higher compared to the no-fibrosis group (3/34, 8.8%; p = 0.168).These findings underscore BMI > 23 as a critical independent predictor of hepatic fibrosis in MAFLD, emphasizing the need for early risk stratification and weight management interventions in overweight/obese populations. 3.4 Prevalence of Fibrosis and Major Risk Factors in the Sample Population Hepatic fibrosis was identified in 160 out of 194 patients (82.5%) using liver stiffness measurement (LSM) values obtained via FibroScan®. Among them, significant fibrosis (≥ F2) was observed in 133 patients (68.6%), highlighting a substantial burden of progressive liver disease in this tertiary care population. Although the dataset did not include stage-specific fibrosis classifications (F2, F3, F4), the overall prevalence underscores the need for early risk assessment in MAFLD management.(table-4) A BMI greater than 23 was the only risk factor significantly associated with fibrosis, observed in 133 out of 160 patients (83.1%, p = 0.0389). Subgroup analysis revealed that fibrosis was present in 133 out of 156 overweight or obese individuals (85.3%), further supporting the link between elevated BMI and liver fibrosis. Other metabolic comorbidities included hypertension, seen in 44 out of 160 fibrosis cases (27.5%), and type 2 diabetes mellitus, identified in 35 out of 160 patients (21.9%). Although these conditions were common among fibrosis patients, neither reached statistical significance. Physical inactivity was another prevalent risk factor, recorded in 125 out of 160 fibrosis patients (78.1%, p = 0.1603), yet the association was not statistically significant. Gender distribution analysis showed that males constituted the majority of fibrosis cases, accounting for 130 out of 160 patients (81.3%), while females represented a smaller proportion. However, no statistically significant association was found between gender and fibrosis (p = 0.168).(fig-2) Overall, the data point to a strong association between elevated BMI and hepatic fibrosis in MASLD, emphasizing BMI > 23 as a reliable predictor for disease progression. Although other metabolic and lifestyle-related risk factors were highly prevalent, BMI remains the most statistically robust marker for fibrosis in this cohort. Table 4 Risk factors for fibrosis: PARAMETER NO FIBROSIS FIBROSIS P VALUE 34 160 Ø Male 31 130 0.168 Ø Female 3 30 Age Ø 60 6 9 Risk factors History of Hypertension 8 44 0.1603 History of Diabetes mellitus 6 35 Metabolic Syndrome 1 11 Physical Inactivity 8 125 BMI Ø 23 23 133 Diet Non vegetarian 28 133 0.9715 Vegetarian 6 28 DISCUSSION This study reveals a markedly high prevalence of hepatic fibrosis (82.5%) among MASLD patients attending a tertiary care hospital in Andhra Pradesh, India. Notably, BMI > 23 emerged as the strongest independent predictor of fibrosis ( p = 0.0389), reinforcing adiposity’s pivotal role in hepatic fibrogenesis. While these findings broadly align with global trends, they also emphasize regional variations in risk profiles, disease progression, and clinical expression of MASLD in South Asia. 4.1 Fibrosis Prevalence and Risk Factors The observed fibrosis prevalence (82.5%) significantly exceeds estimates from both urban North Indian cohorts (20–35%) and Western populations (25–40%) 19 , 20 . This discrepancy likely reflects the distinct metabolic and lifestyle characteristics of the Andhra Pradesh population, where central obesity (48%), sedentary behavior (65%), and high carbohydrate consumption are prevalent. Additionally, genetic predispositions—particularly the PNPLA3 rs738409 variant—are common in South Indian populations and may contribute to accelerated fibrogenesis 21 . Our finding that 83.1% of fibrosis cases occurred in individuals with BMI > 23 supports global literature linking adiposity to fibrosis via insulin resistance and systemic inflammation 22 . However, the stronger BMI-fibrosis association observed in our cohort compared to international data may be attributed to the “thin-fat” phenotype observed in South Asians. This phenotype is characterized by disproportionate visceral fat accumulation at lower BMIs, predisposing individuals to hepatic injury despite relatively normal anthropometric profiles 23 . In contrast to Western and East Asian studies, metabolic comorbidities such as type 2 diabetes (21.9%) and hypertension (27.5%) were not significantly associated with fibrosis in our population ( p > 0.05) 23 . This may reflect the relatively young mean age of our participants (44 years), wherein chronic metabolic conditions may not have yet exerted full hepatic effects. Additionally, physical inactivity—though widespread (78.1%)—did not correlate significantly with fibrosis ( p = 0.1603), despite being significantly associated with steatosis ( p = 0.0157). These findings suggest that sedentariness may initiate hepatic fat accumulation but may not directly influence fibrogenesis, consistent with mechanistic models where mitochondrial dysfunction plays a more central role in early MASLD progression. 4.2 Gender Disparities Male predominance was observed in both steatosis (83.1%) and fibrosis (81.3%) cases, aligning with global trends that associate male sex with greater hepatic risk due to androgen-induced visceral adiposity and heightened insulin resistance 24 . However, among female participants, the proportion with fibrosis (18.8%) was notably higher compared to those without fibrosis (8.8%). This supports emerging evidence suggesting accelerated fibrosis progression in postmenopausal women, potentially driven by estrogen withdrawal and loss of its hepatoprotective effects 25 .(fig-3) 4.3 Dietary and Regional Determinants A significant association was found between non-vegetarian diets and steatosis (74.0%, p = 0.0035), likely attributable to the saturated fats and advanced glycation end-products (AGEs) prevalent in the meat-heavy cuisine of Andhra Pradesh 26 .This finding aligns with global research linking red meat consumption to hepatic lipotoxicity, oxidative stress, and progression of fatty liver disease 27 .However, dietary patterns did not show a statistically significant association with fibrosis in this study, suggesting that while diet contributes to steatosis, fibrosis progression may be more strongly governed by systemic metabolic dysregulation and genetic factors. 4.4 Clinical Implications The high fibrosis burden observed—especially with 68.6% exhibiting ≥ F2 fibrosis—underscores the importance of timely diagnosis and intervention in MASLD management. Our findings highlight FibroScan® as a critical non-invasive diagnostic modality, particularly in resource-limited settings. Prior studies have demonstrated up to 89% accuracy for detecting ≥ F2 fibrosis in South Asian populations 12 . Integrating FibroScan® into India’s National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke (NPCDCS) is therefore essential and aligns with recommendations from both the WHO and AASLD 28 . 4.5 Limitations This study has several limitations. First, its single-center design may limit generalizability across diverse south Indian subpopulations. Lastly, reliance on self-reported dietary and activity data introduces potential recall and social desirability biases, warranting future studies using validated dietary assessments and accelerometer-based activity tracking. CONCLUSION This study underscores an alarmingly high prevalence of hepatic fibrosis (82.5%) among patients with metabolic dysfunction-associated steatotic liver disease (MASLD) in Andhra Pradesh, India. Among the various risk factors examined, a body mass index (BMI) greater than 23 emerged as the most robust and statistically significant predictor of fibrosis, emphasizing the central role of adiposity-driven metabolic dysfunction in hepatic fibrogenesis. This association is particularly relevant in South Asian populations, where visceral fat accumulation occurs even at comparatively lower BMI thresholds. The disproportionate burden of fibrosis observed in this cohort reflects the convergence of regional risk factors—sedentary lifestyles, carbohydrate-dense diets, and genetic susceptibility (e.g., PNPLA3 polymorphisms)—compounded by systemic barriers to early screening and intervention. Although non-vegetarian diets were significantly associated with hepatic steatosis, progression to fibrosis appeared to be more strongly linked to underlying metabolic dysregulation than to dietary factors alone. Gender-based analysis further highlighted the need for sex-specific clinical strategies: while males accounted for the majority of cases, the relative proportion of fibrosis among females was notably higher, suggesting potentially accelerated fibrotic progression in postmenopausal women. The effectiveness of FibroScan® as a non-invasive and scalable diagnostic modality was reaffirmed in this study, supporting its integration into India’s National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke (NPCDCS) to facilitate timely fibrosis detection in high-risk groups. These findings call for urgent public health action centered around BMI-based risk stratification in primary care to enable early identification of high-risk MASLD patients. The routine use of FibroScan® in state-wide metabolic health screenings could help mitigate delayed diagnoses and facilitate earlier interventions. Targeted lifestyle modifications—particularly addressing physical inactivity and high intake of red meat and saturated fats—should be prioritized. Furthermore, gender-responsive strategies are essential to account for unique risk trajectories in men and postmenopausal women, including visceral adiposity and hormonal changes. Despite its strengths, this study is limited by its single-center design and reliance on self-reported lifestyle data, which may introduce recall and reporting biases. Multicenter studies using objective measures such as accelerometers and validated dietary tools are warranted to confirm these findings. Longitudinal research is also needed to elucidate the temporal dynamics between metabolic comorbidities and fibrosis progression. Moreover, examining gene–environment interactions, particularly involving PNPLA3 variants and dietary advanced glycation end-products (AGEs), could yield novel insights into pathogenesis and therapeutic targeting. Declarations Authors and Their Contributions The research study was conducted with contributions from multiple authors, each playing a crucial role. Bokka Pradeepthi, Jakkula Supriya, Nisar Ahmed, and Allavarapu Manogna , as students, were responsible for data collection. Among them, Bokka Pradeepthi took the lead in analyzing the collected data. Nisar Ahmed contributed significantly to the study by conducting an extensive literature review and writing the manuscript. Dr. K. Kiranmai , a dietitian at the hospital, provided valuable insights in designing diet plans to educate patients. Dr. R. Srinivasa Murty , the Head of the Department under whom the study was conducted, meticulously reviewed the entire work. Additionally, Ratnakumari Padamati served as the academic guide, providing necessary guidance throughout the research process. Each author’s contribution was instrumental in ensuring the study’s successful completion. Conflict of Interest The authors declare that they have no conflicts of interest related to this study. Ethical Approval This prospective, hospital-based observational study was conducted in accordance with the ethical guidelines and standards for research involving human participants. Ethical approval was obtained from the Institutional Review Board (IRB) of Aditya Pharmacy College (Ref. No. JNTUK-IRB/PharmD/2023-24/2 ). Human Ethics and Consent to Participate All procedures involving human participants complied with the ethical standards of the Institutional Review Board and the 1964 Declaration of Helsinki and its later amendments. Written informed consent was obtained from all participants before their participation in the study. Informed Consent Informed consent was obtained from all individual participants included in the study, ensuring their voluntary participation. Clinical Trial Registration Clinical trial number: not applicable. Funding No funding was received for this study. Availability of Data and Materials The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request. References Eslam, M.; Newsome, P. N.; Sarin, S. K.; Anstee, Q. M.; Targher, G.; Romero-Gomez, M.; Zelber-Sagi, S.; Wong, V. W.-S.; Dufour, J.-F.; Schattenberg, J. M.; Kawaguchi, T.; Arrese, M.; Valenti, L.; Shiha, G.; Tiribelli, C.; Yki-Järvinen, H.; Fan, J.-G.; Grønbæk, H.; Yilmaz, Y.; Cortez-Pinto, H.; Oliveira, C. P.; Bedossa, P.; Adams, L. A.; Zheng, M.-H.; Fouad, Y.; Chan, W.-K.; Mendez-Sanchez, N.; Ahn, S. H.; Castera, L.; Bugianesi, E.; Ratziu, V.; George, J. 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Prevalence and Risk Factors of MASLD and Liver Fibrosis amongst the Penitentiary Population in Catalonia: The PRISONAFLD Study. J. Clin. Med. 2023 , 12 (23), 7276. https://doi.org/10.3390/jcm12237276. Singh Thakur, J.; Nangia, R.; Singh, S. Progress and Challenges in Achieving Noncommunicable Diseases Targets for the Sustainable Development Goals. FASEB BioAdvances 2021 , 3 (8), 563–568. https://doi.org/10.1096/fba.2020-00117. Watkins, D. A.; Msemburi, W. T.; Pickersgill, S. J.; Kawakatsu, Y.; Gheorghe, A.; Dain, K.; Johansson, K. A.; Said, S.; Renshaw, N.; Tolla, M. T.; Twea, P. D.; Varghese, C.; Chalkidou, K.; Ezzati, M.; Norheim, O. F. NCD Countdown 2030: Efficient Pathways and Strategic Investments to Accelerate Progress towards the Sustainable Development Goal Target 3.4 in Low-Income and Middle-Income Countries. The Lancet 2022 , 399 (10331), 1266–1278. https://doi.org/10.1016/S0140-6736(21)02347-3. Younossi, Z.; Anstee, Q. M.; Marietti, M.; Hardy, T.; Henry, L.; Eslam, M.; George, J.; Bugianesi, E. Global Burden of NAFLD and NASH: Trends, Predictions, Risk Factors and Prevention. Nat. Rev. Gastroenterol. Hepatol. 2018 , 15 (1), 11–20. https://doi.org/10.1038/nrgastro.2017.109. Asadullah, M.; Shivashankar, R.; Shalimar, null; Kandasamy, D.; Kondal, D.; Rautela, G.; Peerzada, A.; Grover, B.; Amarchand, R.; Nayak, B.; Sharma, R.; Ramakrishnan, L.; Prabhakaran, D.; Krishnan, A.; Tandon, N. Rural-Urban Differentials in Prevalence, Spectrum and Determinants of Non-Alcoholic Fatty Liver Disease in North Indian Population. PloS One 2022 , 17 (2), e0263768. https://doi.org/10.1371/journal.pone.0263768. Narayanasamy, K.; Karthick, R.; Panneerselvam, P.; Mohan, N.; Ramachandran, A.; Prakash, R.; Rajaram, M. Association of Metabolic Syndrome and Patatin-like Phospholipase 3 - Rs738409 Gene Variant in Non-Alcoholic Fatty Liver Disease among a Chennai-Based South Indian Population. J. Gene Med. 2020 , 22 (4), e3160. https://doi.org/10.1002/jgm.3160. De, A.; Bhagat, N.; Mehta, M.; Singh, P.; Rathi, S.; Verma, N.; Taneja, S.; Premkumar, M.; Duseja, A. Central Obesity Is an Independent Determinant of Advanced Fibrosis in Lean Patients With Nonalcoholic Fatty Liver Disease. J. Clin. Exp. Hepatol. 2025 , 15 (1), 102400. https://doi.org/10.1016/j.jceh.2024.102400. Iliodromiti, S.; McLaren, J.; Ghouri, N.; Miller, M. R.; Dahlqvist Leinhard, O.; Linge, J.; Ballantyne, S.; Platt, J.; Foster, J.; Hanvey, S.; Gujral, U. P.; Kanaya, A.; Sattar, N.; Lumsden, M. A.; Gill, J. M. R. Liver, Visceral and Subcutaneous Fat in Men and Women of South Asian and White European Descent: A Systematic Review and Meta-Analysis of New and Published Data. Diabetologia 2023 , 66 (1), 44–56. https://doi.org/10.1007/s00125-022-05803-5. Lonardo, A.; Nascimbeni, F.; Ballestri, S.; Fairweather, D.; Win, S.; Than, T. A.; Abdelmalek, M. F.; Suzuki, A. Sex Differences in NAFLD: State of the Art and Identification of Research Gaps. Hepatol. Baltim. Md 2019 , 70 (4), 1457–1469. https://doi.org/10.1002/hep.30626. Menopause Raises the Risk of Fatty Liver Disease. Changing Hormones May Be the Explanation . Verywell Health. https://www.verywellhealth.com/menopause-fatty-liver-disease-risk-11709466 (accessed 2025-05-01). Leung, C.; Herath, C. B.; Jia, Z.; Andrikopoulos, S.; Brown, B. E.; Davies, M. J.; Rivera, L. R.; Furness, J. B.; Forbes, J. M.; Angus, P. W. Dietary Advanced Glycation End-Products Aggravate Non-Alcoholic Fatty Liver Disease. World J. Gastroenterol. 2016 , 22 (35), 8026–8040. https://doi.org/10.3748/wjg.v22.i35.8026. Hashemian, M.; Merat, S.; Poustchi, H.; Jafari, E.; Radmard, A.-R.; Kamangar, F.; Freedman, N.; Hekmatdoost, A.; Sheikh, M.; Boffetta, P.; Sinha, R.; Dawsey, S. M.; Abnet, C. C.; Malekzadeh, R.; Etemadi, A. Red Meat Consumption and Risk of Nonalcoholic Fatty Liver Disease in a Population with Low Meat Consumption: The Golestan Cohort Study. Am. J. Gastroenterol. 2021 , 116 (8), 1667–1675. https://doi.org/10.14309/ajg.0000000000001229. Duseja, A.; Singh, S. P.; De, A.; Madan, K.; Rao, P. N.; Shukla, A.; Choudhuri, G.; Saigal, S.; Shalimar; Arora, A.; Anand, A. C.; Das, A.; Kumar, A.; Eapen, C. E.; Devadas, K.; Shenoy, K. T.; Panigrahi, M.; Wadhawan, M.; Rathi, M.; Kumar, M.; Choudhary, N. S.; Saraf, N.; Nath, P.; Kar, S.; Alam, S.; Shah, S.; Nijhawan, S.; Acharya, S. K.; Aggarwal, V.; Saraswat, V. A.; Chawla, Y. K. Indian National Association for Study of the Liver (INASL) Guidance Paper on Nomenclature, Diagnosis and Treatment of Nonalcoholic Fatty Liver Disease (NAFLD). J. Clin. Exp. Hepatol. 2023 , 13 (2), 273–302. https://doi.org/10.1016/j.jceh.2022.11.014. 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-6604350","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452918329,"identity":"65b32d7b-30b3-48bb-aee2-d321b5fbdbfa","order_by":0,"name":"Bokka Pradeepthi","email":"","orcid":"","institution":"Aditya pharmacy college","correspondingAuthor":false,"prefix":"","firstName":"Bokka","middleName":"","lastName":"Pradeepthi","suffix":""},{"id":452918330,"identity":"055b477b-2153-4871-b517-6318e91be923","order_by":1,"name":"Nisar Ahmed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDACZgiVACIkPgAJNnZStEjOAGlhJtIysBZpHiRDcAJzdvaHnysY7uTxzz6deNvm1zZ5PmYGxg8fc3BrsWzmMZY8w/CsWOJc7mbr3L7bhm3MDMySM7fh1mJwmIdBsoHhcGLDGd5t0rk9txmBWtiYefFqYX/8E6RlPkiLZc9teyK0MJiBbdkA0sLw43YiEVp4zCwbDJ4lbjzDu9myt+F2chszYzN+v5w//vhmQ8WdxHlneDfe+PHntu389uaDHz7i0QLVeABCM7aByQZC6kEAqoXhDzGKR8EoGAWjYKQBAI3DUW4dxvLEAAAAAElFTkSuQmCC","orcid":"","institution":"Aditya pharmacy college","correspondingAuthor":true,"prefix":"","firstName":"Nisar","middleName":"","lastName":"Ahmed","suffix":""},{"id":452918331,"identity":"50710346-bce3-423b-af36-76fdecd93e9f","order_by":2,"name":"Jakkula supriya","email":"","orcid":"","institution":"Aditya pharmacy college","correspondingAuthor":false,"prefix":"","firstName":"Jakkula","middleName":"","lastName":"supriya","suffix":""},{"id":452918332,"identity":"75895a43-646f-4b7e-888d-b0db5b850ba1","order_by":3,"name":"Allavarapu Manogna","email":"","orcid":"","institution":"Aditya pharmacy college","correspondingAuthor":false,"prefix":"","firstName":"Allavarapu","middleName":"","lastName":"Manogna","suffix":""},{"id":452918333,"identity":"8dffca70-a88b-4c93-878f-5fa7cb91da1d","order_by":4,"name":"Ratnakumari Padamati","email":"","orcid":"","institution":"Aditya pharmacy college","correspondingAuthor":false,"prefix":"","firstName":"Ratnakumari","middleName":"","lastName":"Padamati","suffix":""},{"id":452918334,"identity":"c7f273ba-3d84-4222-83b7-8f4b9c534ea3","order_by":5,"name":"R Srinivasa Murty","email":"","orcid":"","institution":"Trust Multispeciality hospitals","correspondingAuthor":false,"prefix":"","firstName":"R","middleName":"Srinivasa","lastName":"Murty","suffix":""},{"id":452918335,"identity":"11dfbef3-6bbe-4742-967d-6522d20237a6","order_by":6,"name":"K Kiranmai","email":"","orcid":"","institution":"Trust Multispeciality hospitals","correspondingAuthor":false,"prefix":"","firstName":"K","middleName":"","lastName":"Kiranmai","suffix":""}],"badges":[],"createdAt":"2025-05-06 14:53:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6604350/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6604350/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82358974,"identity":"4a7366fb-e0f5-4195-bf93-662d8310f7e8","added_by":"auto","created_at":"2025-05-09 11:26:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92111,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution among males and females according to the BMI in MAFLD patients\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6604350/v1/b43108ba9bc8e6006e2201ca.png"},{"id":82359907,"identity":"cdc36aed-ddd7-403b-959d-d790c22ae1e2","added_by":"auto","created_at":"2025-05-09 11:34:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":90941,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution among males and females according to the BMI in MAFLD patients\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6604350/v1/a7fdf62076f3b22aba4f5b58.png"},{"id":82359908,"identity":"93f484a6-0de1-4ec8-aa8a-860b97f02e3f","added_by":"auto","created_at":"2025-05-09 11:34:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":289555,"visible":true,"origin":"","legend":"\u003cp\u003eComparative distribution of key fibrosis risk factors among MASLD patients (n = 194).\u003c/p\u003e\n\u003cp\u003ePanels depict differences between patients with and without fibrosis based on: (A) Gender, (B) Age, (C) Risk factor prevalence, (D) BMI categories, and (E) Dietary patterns. Only elevated BMI (\u0026gt;23) was significantly associated with fibrosis (p = 0.0389). Other variables showed no statistically significant differences. Results underscore the role of adiposity as a major determinant of fibrosis in this population.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6604350/v1/430d9132de7e3e50eb87cbe1.png"},{"id":90507396,"identity":"3007886c-c38c-46bd-90f8-70ad5cdccaf5","added_by":"auto","created_at":"2025-09-03 12:54:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1521856,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6604350/v1/841b9c9b-8bfc-4386-85a1-44f3cd305078.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment Of Prevalence Of Fibrosis In Metabolic Dysfunction-Associated Steatotic Liver Disease Using FibroScan® In A Tertiary Care Hospital In Andhra Pradesh","fulltext":[{"header":"Introduction","content":" \u003cp\u003eObesity and metabolic syndrome have driven a rapid rise in fatty liver disease worldwide. In 2023, an international consensus unified non-alcoholic fatty liver disease (NAFLD) and its successor term metabolic dysfunction-associated fatty liver disease (MAFLD) under the globally accepted nomenclature of metabolic dysfunction-associated steatotic liver disease (MASLD). This shift replaced exclusionary criteria (e.g., alcohol use thresholds) with positive diagnostic criteria based on cardiometabolic risk factors, such as obesity, diabetes, or metabolic syndrome \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. MASLD now represents the most prevalent chronic liver disorder, affecting roughly one-third of adults worldwide and over 38% of adults in high-risk regions like South Asia. In India, MASLD prevalence has surged dramatically, especially in urban populations where rates reach 40\u0026ndash;50%, with Andhra Pradesh emerging as a critical hotspot driven by escalating central obesity (48%), diabetes (22%), and physical inactivity (65%).Recognizing its metabolic roots, an international consensus in 2020 formally shifted the diagnostic criteria: hepatic steatosis is now diagnosed based on the presence of obesity, type 2 diabetes, or other metabolic abnormalities, rather than by excluding mild alcohol use or other liver diseases. In essence, MASLD is considered the hepatic manifestation of systemic metabolic syndrome.\u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eImportantly, MASLD is not a benign fat-only condition. A significant subset of patients develops steatohepatitis (NASH) and progressive hepatic fibrosis, which has emerged as the strongest predictor of liver-related outcomes and mortality. Advanced fibrosis (F3\u0026ndash;F4) increases liver-related deaths by nearly tenfold. Despite this, hepatic fibrosis remains underdiagnosed, particularly in low-resource settings like South India, where unique regional factors\u0026mdash;such as high-carbohydrate diets, rising environmental pollution, and genetic predispositions (e.g., PNPLA3 variants)\u0026mdash;amplify disease severity.\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe introduction of non-invasive modalities such as vibration-controlled transient elastography (FibroScan\u0026reg;) has revolutionized the detection of hepatic fibrosis. FibroScan\u0026reg; offers a rapid, reliable, and non-invasive assessment of both liver stiffness (fibrosis) and steatosis, enabling early identification of patients with significant (\u0026ge;\u0026thinsp;F2) or advanced fibrosis. In South Asian populations, FibroScan\u0026reg; has demonstrated an accuracy of approximately 89% for detecting\u0026thinsp;\u0026ge;\u0026thinsp;F2 fibrosis, making it particularly valuable in resource-limited settings. Recent clinical trials further underscore the urgency of early detection, showing that achieving\u0026thinsp;\u0026ge;\u0026thinsp;15% weight loss through therapies such as GLP-1 receptor agonists or novel antifibrotic agents can reverse early-stage fibrosis in a substantial proportion of patients, thereby preventing progression to cirrhosis.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eEmerging evidence also underscores important sex-specific differences in fibrosis progression, with post-menopausal women facing higher risks due to hormonal changes and increased visceral adiposity. Meanwhile, men tend to experience faster progression during early disease stages. Furthermore, environmental exposures such as elevated PM2.5 levels and dietary advanced glycation end-products (AGEs) in Andhra Pradesh exacerbate oxidative stress and gut-liver axis dysfunction, further accelerating fibrosis progression.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDespite the growing burden, India\u0026rsquo;s National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases, and Stroke (NPCDCS) currently lacks MASLD-specific screening protocols, resulting in delayed diagnoses and a rising burden of cirrhosis-related mortality. Recent global guidelines, including those from AASLD, EASL, and WHO, advocate for integrating non-invasive screening tools like FibroScan\u0026reg; into primary healthcare systems to bridge this critical gap, particularly in high-burden states like Andhra Pradesh.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAgainst this backdrop, the present study aims to assess the prevalence and determinants of hepatic fibrosis among MASLD patients at a tertiary care hospital in Andhra Pradesh. By correlating fibrosis severity with metabolic, genetic, and environmental risk factors, this study seeks to generate the first region-specific data on the MASLD fibrosis burden in urbanising South India. The findings are intended to contribute to the growing body of evidence needed to support future efforts in prioritizing early fibrosis detection within state and national non-communicable disease (NCD) control strategies. In doing so, this research hopes to aid broader public health goals, including the reduction of cirrhosis-related mortality and progress toward achieving the United Nations Sustainable Development Goal 3.4, which aims for a 30% reduction in premature mortality from NCDs by 2030.\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003ePatients\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis hospital-based prospective observational study was conducted over six months at the Department of Gastroenterology and Hepatology in a Tertiary Care Hospital, Kakinada. Patients were enrolled consecutively after obtaining informed consent. The study aimed to collect data on patients diagnosed with MASLD, including demographic details such as age, gender, occupation, BMI, medical history, social history, medication history, Fibroscan imaging data, laboratory results, and dietary and lifestyle habits.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData collections and patient education\u003c/b\u003e \u003c/p\u003e \u003cp\u003eData were collected prospectively on a daily basis using a structured data collection form. The final sample included 194 patients, comprising 160 with hepatic fibrosis and 34 without, and 154 with hepatic steatosis and 40 without. The sample size exceeded the initial target calculated using Cochran\u0026rsquo;s formula at a 95% confidence level and 5% margin of error (assuming maximum variability at p\u0026thinsp;=\u0026thinsp;0.5), enhancing the study\u0026rsquo;s statistical power.\u003c/p\u003e \u003cp\u003eEligible participants were adults diagnosed with metabolic dysfunction-associated steatotic liver disease (MASLD), based on clinical evaluation, biochemical markers, or imaging (including FibroScan\u0026reg;) findings. All participants had provided written informed consent prior to enrolment. Exclusion criteria included a history of alternative liver pathologies such as viral hepatitis, alcoholic liver disease, established cirrhosis, or hepatocellular carcinoma, as well as those unwilling or unable to provide informed consent or with comorbidities impairing their ability to participate meaningfully in the study.\u003c/p\u003e \u003cp\u003eFollowing data collection, all patients received structured counselling on MASLD management. This included personalized guidance on dietary modification, physical activity, and risk factor control. Educational pamphlets\u0026mdash;containing culturally adapted lifestyle recommendations and evidence-based strategies for disease mitigation\u0026mdash;were distributed to support long-term behavioural change.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe data were analysed statistically using Chi-square tests, linear regression analysis, and descriptive statistics, as referenced in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, with Microsoft Excel and GraphPad Prism 8.0.2.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 01\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003e\u0026ndash;\u003c/b\u003e Grade of steatosis with ultrasound attenuation parameter values\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\u003eGrade of steatosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUAP (db/m)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240\u0026ndash;265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e266\u0026ndash;295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;295\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 02\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStage of fibrosis with liver stiffness measurement values\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage of fibrosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLSM (Kpa)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF0-F1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.3\u0026ndash;9.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF2-F3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.8\u0026ndash;12.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF3-F4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.5\u0026ndash;17.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;17.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDuring the study period, approximately 750 patients with suspected MASLD (formerly termed MAFLD) visited the Department of Gastroenterology and Hepatology at the tertiary care hospital. Based on the inclusion and exclusion criteria, a total of 194 patients were enrolled. Among them, 161 (83%) were male and 33 (17%) were female, with a mean age of 44.15 years.\u003c/p\u003e \u003cp\u003eLiver steatosis was identified in 154 patients using the Ultrasound Attenuation Parameter (UAP), and liver stiffness was assessed using FibroScan\u0026reg;-based Liver Stiffness Measurement (LSM), revealing hepatic fibrosis in 160 patients. This highlights a high burden of MASLD and associated fibrosis in this clinical population. The distribution of fibrosis stages based on LSM values is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, with a substantial proportion of patients falling within the F2\u0026ndash;F4 range, consistent with progressive liver fibrosis requiring early intervention.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Risk Factors of MASLD in the Sample Population\u003c/h2\u003e \u003cp\u003eIn the study cohort (\u003cem\u003en\u0026thinsp;=\u0026thinsp;194\u003c/em\u003e), medical history and anthropometric assessments revealed multiple contributing risk factors for MAFLD. Hypertension was observed in 36 patients and showed a significant association with steatosis (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.0157\u003c/em\u003e). Type 2 diabetes mellitus was reported in 32 patients, with a higher prevalence noted in both fibrosis and steatosis groups, though the associations did not reach statistical significance (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.1603\u003c/em\u003e for both). Metabolic syndrome was present in 09 patients, predominantly among those with fibrosis (11 vs. 1), again with a non-significant \u003cem\u003ep\u003c/em\u003e-value (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.1603\u003c/em\u003e). Physical inactivity was the most prevalent lifestyle-related risk factor, affecting 133 patients, and was notably associated with both fibrosis and steatosis, showing a significant association in the latter (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.0157\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eAnthropometric evaluation revealed that 156 patients (80.4%) had a BMI greater than 23, in alignment with the Asian criteria for overweight. Among these, 133 individuals (68.5%) were in the fibrosis group (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.0389\u003c/em\u003e), suggesting a significant link between elevated BMI and fibrosis severity. A similar pattern was observed in relation to steatosis, with 125 patients exhibiting BMI\u0026thinsp;\u0026gt;\u0026thinsp;23 compared to 31 in the non-steatosis group, although this association was not statistically significant (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.6024\u003c/em\u003e). Dietary patterns further contributed to disease expression; non-vegetarian diets were strongly associated with steatosis (114 vs. 26, \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.0035\u003c/em\u003e), whereas vegetarian diets were more commonly observed in patients without steatosis (24 vs. 40).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Prevalence of MASLD and Major Risk Factors in the Sample Population\u003c/h2\u003e \u003cp\u003eHepatic steatosis, as assessed by FibroScan, was identified in 154 of 194 patients (79.4%), underscoring the substantial burden of MASLD in this tertiary care cohort. Key risk factors included physical inactivity, reported in 133 of 154 patients with steatosis (86.4%; p\u0026thinsp;=\u0026thinsp;0.0157), and non-vegetarian dietary habits, observed in 114 of 154 cases (74.0%; p\u0026thinsp;=\u0026thinsp;0.0035). Although BMI\u0026thinsp;\u0026gt;\u0026thinsp;23 was prevalent in 125 of 154 steatosis patients (81.2%), this association was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.6024), in contrast to its significant relationship with fibrosis (p\u0026thinsp;=\u0026thinsp;0.0389).Subgroup analyses revealed that 125 of 156 overweight or obese patients (80.1%) and 32 of 41 individuals with type 2 diabetes (78.0%) had hepatic steatosis. Physical inactivity emerged as the most prominent behavioral factor, with 133 of 151 inactive individuals (88.1%) exhibiting steatosis.(fig-1)\u003c/p\u003e \u003cp\u003eGender-based disparities were also evident. Males predominated in both steatosis (128/154; 83.1%) and fibrosis (130/160; 81.3%) groups. While females were underrepresented in the steatosis cohort (26/154; 16.9%), they constituted a higher proportion of fibrosis cases (30/160; 18.8%) compared to those without fibrosis (3/34; 8.8%; p\u0026thinsp;=\u0026thinsp;0.168). Stratification by gender and BMI showed that males with BMI\u0026thinsp;\u0026gt;\u0026thinsp;23 accounted for the majority of steatosis cases (125/154; 81.2%), while females with BMI\u0026thinsp;\u0026gt;\u0026thinsp;23 contributed to 26 of 154 cases (16.9%).These findings highlight the importance of early identification and lifestyle interventions in high-risk MAFLD subgroups, particularly targeting physical inactivity and non-vegetarian diets. The stronger association between elevated BMI and fibrosis\u0026mdash;rather than steatosis alone\u0026mdash;suggests its potential role as a predictive marker for progressive liver disease.(table-3)\u003c/p\u003e \u003cp\u003e \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 03\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk factors for MAFLD:\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePARAMETER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO STEATOSIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTEATOSIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP VALUE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e154\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026Oslash; Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026Oslash; Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGE \u0026ndash;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026Oslash; \u0026lt;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.4625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026Oslash; 31\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026Oslash; 41\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026Oslash; 51\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026Oslash; \u0026gt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRISK FACTORS\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of Hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.0157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of Diabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic Syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Inactivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026Oslash; \u0026lt;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.6024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026Oslash; \u0026gt;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIET\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026Oslash; Non vegetarian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.0035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026Oslash; vegetarian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Risk Factors of Fibrosis in the Sample Population\u003c/h2\u003e \u003cp\u003eRisk factors for fibrosis were assessed in the cohort (n\u0026thinsp;=\u0026thinsp;194). BMI\u0026thinsp;\u0026gt;\u0026thinsp;23 demonstrated the strongest association with fibrosis, observed in 133 out of 160 patients (83.1%, p\u0026thinsp;=\u0026thinsp;0.0389). Other metabolic comorbidities included hypertension (44/160, 27.5%) and type 2 diabetes (35/160, 21.9%), while metabolic syndrome was identified in 11/160 (6.9%) of fibrosis cases. Physical inactivity, though prevalent in 125/160 (78.1%) fibrosis patients, did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.1603).\u003c/p\u003e \u003cp\u003eGender disparities were prominent, with males constituting the majority of fibrosis cases (130/160, 81.3%). While females represented only 18.8% (30/160) of fibrosis cases, their proportion was notably higher compared to the no-fibrosis group (3/34, 8.8%; p\u0026thinsp;=\u0026thinsp;0.168).These findings underscore BMI\u0026thinsp;\u0026gt;\u0026thinsp;23 as a critical independent predictor of hepatic fibrosis in MAFLD, emphasizing the need for early risk stratification and weight management interventions in overweight/obese populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Prevalence of Fibrosis and Major Risk Factors in the Sample Population\u003c/h2\u003e \u003cp\u003eHepatic fibrosis was identified in 160 out of 194 patients (82.5%) using liver stiffness measurement (LSM) values obtained via FibroScan\u0026reg;. Among them, significant fibrosis (\u0026ge;\u0026thinsp;F2) was observed in 133 patients (68.6%), highlighting a substantial burden of progressive liver disease in this tertiary care population. Although the dataset did not include stage-specific fibrosis classifications (F2, F3, F4), the overall prevalence underscores the need for early risk assessment in MAFLD management.(table-4)\u003c/p\u003e \u003cp\u003eA BMI greater than 23 was the only risk factor significantly associated with fibrosis, observed in 133 out of 160 patients (83.1%, p\u0026thinsp;=\u0026thinsp;0.0389). Subgroup analysis revealed that fibrosis was present in 133 out of 156 overweight or obese individuals (85.3%), further supporting the link between elevated BMI and liver fibrosis. Other metabolic comorbidities included hypertension, seen in 44 out of 160 fibrosis cases (27.5%), and type 2 diabetes mellitus, identified in 35 out of 160 patients (21.9%). Although these conditions were common among fibrosis patients, neither reached statistical significance. Physical inactivity was another prevalent risk factor, recorded in 125 out of 160 fibrosis patients (78.1%, p\u0026thinsp;=\u0026thinsp;0.1603), yet the association was not statistically significant. Gender distribution analysis showed that males constituted the majority of fibrosis cases, accounting for 130 out of 160 patients (81.3%), while females represented a smaller proportion. However, no statistically significant association was found between gender and fibrosis (p\u0026thinsp;=\u0026thinsp;0.168).(fig-2)\u003c/p\u003e \u003cp\u003eOverall, the data point to a strong association between elevated BMI and hepatic fibrosis in MASLD, emphasizing BMI\u0026thinsp;\u0026gt;\u0026thinsp;23 as a reliable predictor for disease progression. Although other metabolic and lifestyle-related risk factors were highly prevalent, BMI remains the most statistically robust marker for fibrosis in this cohort.\u003c/p\u003e \u003cp\u003e \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\u003eRisk factors for fibrosis:\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePARAMETER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO FIBROSIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFIBROSIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP VALUE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026Oslash; Male\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026Oslash; Female\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026Oslash; \u0026lt;30\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.1631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026Oslash; 31\u0026ndash;40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026Oslash; 41\u0026ndash;50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026Oslash; 51\u0026ndash;60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026Oslash; \u0026gt;60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRisk factors\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistory of Hypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.1603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistory of Diabetes mellitus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetabolic Syndrome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical Inactivity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026Oslash; \u0026lt;23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026Oslash; \u0026gt;23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiet\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon vegetarian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.9715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVegetarian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study reveals a markedly high prevalence of hepatic fibrosis (82.5%) among MASLD patients attending a tertiary care hospital in Andhra Pradesh, India. Notably, BMI\u0026thinsp;\u0026gt;\u0026thinsp;23 emerged as the strongest independent predictor of fibrosis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0389), reinforcing adiposity\u0026rsquo;s pivotal role in hepatic fibrogenesis. While these findings broadly align with global trends, they also emphasize regional variations in risk profiles, disease progression, and clinical expression of MASLD in South Asia.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Fibrosis Prevalence and Risk Factors\u003c/h2\u003e \u003cp\u003eThe observed fibrosis prevalence (82.5%) significantly exceeds estimates from both urban North Indian cohorts (20\u0026ndash;35%) and Western populations (25\u0026ndash;40%) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. This discrepancy likely reflects the distinct metabolic and lifestyle characteristics of the Andhra Pradesh population, where central obesity (48%), sedentary behavior (65%), and high carbohydrate consumption are prevalent. Additionally, genetic predispositions\u0026mdash;particularly the PNPLA3 rs738409 variant\u0026mdash;are common in South Indian populations and may contribute to accelerated fibrogenesis \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur finding that 83.1% of fibrosis cases occurred in individuals with BMI\u0026thinsp;\u0026gt;\u0026thinsp;23 supports global literature linking adiposity to fibrosis via insulin resistance and systemic inflammation \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, the stronger BMI-fibrosis association observed in our cohort compared to international data may be attributed to the \u0026ldquo;thin-fat\u0026rdquo; phenotype observed in South Asians. This phenotype is characterized by disproportionate visceral fat accumulation at lower BMIs, predisposing individuals to hepatic injury despite relatively normal anthropometric profiles\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn contrast to Western and East Asian studies, metabolic comorbidities such as type 2 diabetes (21.9%) and hypertension (27.5%) were not significantly associated with fibrosis in our population (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05)\u003csup\u003e23\u003c/sup\u003e. This may reflect the relatively young mean age of our participants (44 years), wherein chronic metabolic conditions may not have yet exerted full hepatic effects. Additionally, physical inactivity\u0026mdash;though widespread (78.1%)\u0026mdash;did not correlate significantly with fibrosis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1603), despite being significantly associated with steatosis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0157). These findings suggest that sedentariness may initiate hepatic fat accumulation but may not directly influence fibrogenesis, consistent with mechanistic models where mitochondrial dysfunction plays a more central role in early MASLD progression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Gender Disparities\u003c/h2\u003e \u003cp\u003eMale predominance was observed in both steatosis (83.1%) and fibrosis (81.3%) cases, aligning with global trends that associate male sex with greater hepatic risk due to androgen-induced visceral adiposity and heightened insulin resistance \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. However, among female participants, the proportion with fibrosis (18.8%) was notably higher compared to those without fibrosis (8.8%). This supports emerging evidence suggesting accelerated fibrosis progression in postmenopausal women, potentially driven by estrogen withdrawal and loss of its hepatoprotective effects \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.(fig-3)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Dietary and Regional Determinants\u003c/h2\u003e \u003cp\u003eA significant association was found between non-vegetarian diets and steatosis (74.0%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0035), likely attributable to the saturated fats and advanced glycation end-products (AGEs) prevalent in the meat-heavy cuisine of Andhra Pradesh \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.This finding aligns with global research linking red meat consumption to hepatic lipotoxicity, oxidative stress, and progression of fatty liver disease\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.However, dietary patterns did not show a statistically significant association with fibrosis in this study, suggesting that while diet contributes to steatosis, fibrosis progression may be more strongly governed by systemic metabolic dysregulation and genetic factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Clinical Implications\u003c/h2\u003e \u003cp\u003eThe high fibrosis burden observed\u0026mdash;especially with 68.6% exhibiting\u0026thinsp;\u0026ge;\u0026thinsp;F2 fibrosis\u0026mdash;underscores the importance of timely diagnosis and intervention in MASLD management. Our findings highlight FibroScan\u0026reg; as a critical non-invasive diagnostic modality, particularly in resource-limited settings. Prior studies have demonstrated up to 89% accuracy for detecting\u0026thinsp;\u0026ge;\u0026thinsp;F2 fibrosis in South Asian populations \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Integrating FibroScan\u0026reg; into India\u0026rsquo;s National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke (NPCDCS) is therefore essential and aligns with recommendations from both the WHO and AASLD \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Limitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, its single-center design may limit generalizability across diverse south Indian subpopulations. Lastly, reliance on self-reported dietary and activity data introduces potential recall and social desirability biases, warranting future studies using validated dietary assessments and accelerometer-based activity tracking.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study underscores an alarmingly high prevalence of hepatic fibrosis (82.5%) among patients with metabolic dysfunction-associated steatotic liver disease (MASLD) in Andhra Pradesh, India. Among the various risk factors examined, a body mass index (BMI) greater than 23 emerged as the most robust and statistically significant predictor of fibrosis, emphasizing the central role of adiposity-driven metabolic dysfunction in hepatic fibrogenesis. This association is particularly relevant in South Asian populations, where visceral fat accumulation occurs even at comparatively lower BMI thresholds. The disproportionate burden of fibrosis observed in this cohort reflects the convergence of regional risk factors\u0026mdash;sedentary lifestyles, carbohydrate-dense diets, and genetic susceptibility (e.g., PNPLA3 polymorphisms)\u0026mdash;compounded by systemic barriers to early screening and intervention.\u003c/p\u003e \u003cp\u003eAlthough non-vegetarian diets were significantly associated with hepatic steatosis, progression to fibrosis appeared to be more strongly linked to underlying metabolic dysregulation than to dietary factors alone. Gender-based analysis further highlighted the need for sex-specific clinical strategies: while males accounted for the majority of cases, the relative proportion of fibrosis among females was notably higher, suggesting potentially accelerated fibrotic progression in postmenopausal women. The effectiveness of FibroScan\u0026reg; as a non-invasive and scalable diagnostic modality was reaffirmed in this study, supporting its integration into India\u0026rsquo;s National Programme for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke (NPCDCS) to facilitate timely fibrosis detection in high-risk groups.\u003c/p\u003e \u003cp\u003eThese findings call for urgent public health action centered around BMI-based risk stratification in primary care to enable early identification of high-risk MASLD patients. The routine use of FibroScan\u0026reg; in state-wide metabolic health screenings could help mitigate delayed diagnoses and facilitate earlier interventions. Targeted lifestyle modifications\u0026mdash;particularly addressing physical inactivity and high intake of red meat and saturated fats\u0026mdash;should be prioritized. Furthermore, gender-responsive strategies are essential to account for unique risk trajectories in men and postmenopausal women, including visceral adiposity and hormonal changes.\u003c/p\u003e \u003cp\u003eDespite its strengths, this study is limited by its single-center design and reliance on self-reported lifestyle data, which may introduce recall and reporting biases. Multicenter studies using objective measures such as accelerometers and validated dietary tools are warranted to confirm these findings. Longitudinal research is also needed to elucidate the temporal dynamics between metabolic comorbidities and fibrosis progression. Moreover, examining gene\u0026ndash;environment interactions, particularly involving PNPLA3 variants and dietary advanced glycation end-products (AGEs), could yield novel insights into pathogenesis and therapeutic targeting.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors and Their Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The research study was conducted with contributions from multiple authors, each playing a crucial role. \u003cstrong\u003eBokka Pradeepthi, Jakkula Supriya, Nisar Ahmed, and Allavarapu Manogna\u003c/strong\u003e, as students, were responsible for data collection. Among them, \u003cstrong\u003eBokka Pradeepthi\u003c/strong\u003e took the lead in analyzing the collected data. \u003cstrong\u003eNisar Ahmed\u003c/strong\u003e contributed significantly to the study by conducting an extensive literature review and writing the manuscript. \u003cstrong\u003eDr. K. Kiranmai\u003c/strong\u003e, a dietitian at the hospital, provided valuable insights in designing diet plans to educate patients. \u003cstrong\u003eDr. R. Srinivasa Murty\u003c/strong\u003e, the Head of the Department under whom the study was conducted, meticulously reviewed the entire work. Additionally, \u003cstrong\u003eRatnakumari Padamati\u003c/strong\u003e served as the academic guide, providing necessary guidance throughout the research process. Each author\u0026rsquo;s contribution was instrumental in ensuring the study\u0026rsquo;s successful completion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest related to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis prospective, hospital-based observational study was conducted in accordance with the ethical guidelines and standards for research involving human participants. Ethical approval was obtained from the Institutional Review Board (IRB) of Aditya Pharmacy College (Ref. No. \u003cstrong\u003eJNTUK-IRB/PharmD/2023-24/2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures involving human participants complied with the ethical standards of the Institutional Review Board and the 1964 Declaration of Helsinki and its later amendments. Written informed consent was obtained from all participants before their participation in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study, ensuring their voluntary participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEslam, M.; Newsome, P. 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Hepatol.\u003c/em\u003e \u003cstrong\u003e2023\u003c/strong\u003e, \u003cem\u003e13\u003c/em\u003e (2), 273\u0026ndash;302. https://doi.org/10.1016/j.jceh.2022.11.014.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"MASLD, fibrosis, BMI, FibroScan, South Asia, liver disease, metabolic syndrome, public health","lastPublishedDoi":"10.21203/rs.3.rs-6604350/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6604350/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eMetabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as MAFLD, has emerged as a major public health concern in South Asia, driven by rising obesity, diabetes, and sedentary lifestyles. Despite its high burden, fibrosis\u0026mdash; the strongest predictor of liver-related morbidity and mortality\u0026mdash; remains underdiagnosed, particularly in resource-limited regions like Andhra Pradesh, India.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003e To assess the prevalence of hepatic fibrosis and its association with metabolic and lifestyle risk factors in MASLD patients using FibroScan\u0026reg; in a tertiary care hospital in Andhra Pradesh.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA prospective observational study was conducted among 194 patients diagnosed with MASLD. Liver steatosis and fibrosis were assessed non-invasively using FibroScan\u0026reg;. Clinical, demographic, anthropometric, and lifestyle data were collected through structured interviews and medical record reviews. Statistical analyses included Chi-square tests and regression models to evaluate associations between risk factors and fibrosis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHepatic fibrosis was identified in 160 of 194 MASLD patients (82.5%). Elevated BMI (\u0026gt;\u0026thinsp;23) was significantly associated with fibrosis (p\u0026thinsp;=\u0026thinsp;0.0389), with 83.1% of fibrosis cases occurring in overweight or obese individuals. Although physical inactivity (78.1%), hypertension (27.5%), and diabetes mellitus (21.9%) were common in fibrosis patients, these associations were not statistically significant. Non-vegetarian diets were significantly associated with steatosis (p\u0026thinsp;=\u0026thinsp;0.0035) but not with fibrosis. Gender analysis revealed that while males constituted the majority of fibrosis cases, females had a disproportionately higher rate relative to their group size.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study highlights a substantial burden of hepatic fibrosis among MASLD patients in Andhra Pradesh, with BMI\u0026thinsp;\u0026gt;\u0026thinsp;23 as the strongest independent predictor. These findings underscore the need for early, BMI-based risk stratification and integration of non-invasive diagnostics like FibroScan\u0026reg; into routine care. Public health interventions should prioritize weight management, lifestyle modification, and region-specific risk assessment to curb fibrosis progression in high-risk populations.\u003c/p\u003e","manuscriptTitle":"Assessment Of Prevalence Of Fibrosis In Metabolic Dysfunction-Associated Steatotic Liver Disease Using FibroScan® In A Tertiary Care Hospital In Andhra Pradesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 11:26:27","doi":"10.21203/rs.3.rs-6604350/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8fb12d92-1860-4cb9-88c0-f55f974424dd","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-03T12:53:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 11:26:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6604350","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6604350","identity":"rs-6604350","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}