Association between Atherogenic Index of Plasma and Non-alcoholic Fatty Liver Disease: Based on Results from Fasa Adults Cohort Study(FACS)

Association between Atherogenic Index of Plasma and Non-alcoholic Fatty Liver Disease: Based on Results from Fasa Adults Cohort Study(FACS) | 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 Association between Atherogenic Index of Plasma and Non-alcoholic Fatty Liver Disease: Based on Results from Fasa Adults Cohort Study(FACS) Shaghayegh Nematollahi, Ahmad Maghsoudi, Azizallah Dehghan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7180392/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Non-alcoholic fatty liver disease (NAFLD) affects approximately 25% of the global population, with prevalence rising alongside obesity, type 2 diabetes, and metabolic syndrome. The Atherogenic Index of Plasma (AIP) has recently garnered interest as a potential marker in NAFLD pathogenesis. This study aimed to investigate the association between AIP and NAFLD. Methods This cross-sectional study utilized data from the Fasa Adults Cohort Study, comprising approximately 10,000 individuals, of whom 2,599 were diagnosed with NAFLD based on clinical evaluation. Baseline data—including biochemical profiles, medical history, and lifestyle factors—were extracted. AIP was calculated from triglyceride and HDL cholesterol levels. Statistical analyses included descriptive statistics, chi-square tests, t-tests, and multivariate logistic regression, with significance set at p < 0.05. Sensitivity and specificity analyses assessed AIP’s predictive value for NAFLD. Results Among 9,587 participants, 2,599 (27.1%) had NAFLD, with a higher prevalence in females (30.9%) compared to males (21.9%) (p < 0.001). Those with NAFLD exhibited elevated triglycerides, total cholesterol, systolic blood pressure, and anthropometric measurements, alongside reduced High-density lipoprotein (HDL) cholesterol(HDL-C). Notably, AIP was strongly associated with NAFLD (p < 0.001). The predictive performance of AIP was confirmed with a sensitivity of 74.04% and specificity of 73.61%. Conclusion AIP demonstrates substantial potential as a non-invasive predictor for NAFLD, which could facilitate early diagnosis and intervention. Further prospective studies are recommended to validate these findings. Atherogenic Index of Plasma Non-alcoholic Fatty Liver Cohort study Triglycerides HDL Figures Figure 1 Figure 2 Background Non-alcoholic fatty liver disease (NAFLD) has emerged as a significant global health concern, characterized by the abnormal accumulation of fat in the liver in the absence of excessive alcohol consumption ( 1 ). It encompasses a spectrum of hepatic conditions, ranging from simple steatosis, known as non-alcoholic fatty liver, to more advanced forms such as non-alcoholic steatohepatitis (NASH), which involves inflammation and hepatocyte injury ( 2 ). The prevalence of NAFLD is alarmingly high, affecting approximately 25% of the global population, with higher rates observed among individuals with obesity and type 2 diabetes ( 3 ). The pathogenesis of NAFLD is closely linked to metabolic syndrome—a cluster of conditions including obesity, insulin resistance, dyslipidemia, and hypertension ( 4 ). These factors promote increased hepatic lipid accumulation through enhanced de novo lipogenesis and impaired fatty acid oxidation ( 5 ), rendering NAFLD often regarded as the hepatic manifestation of metabolic syndrome. Notably, NAFLD is prevalent not only among obese individuals but also in lean populations, underscoring its systemic nature ( 6 ). In recent years, the Atherogenic Index of Plasma (AIP) has gained recognition as a valuable marker for cardiovascular disease (CVD) risk, reflecting dysregulated lipid metabolism ( 7 ). It has also been associated with NAFLD in multiple studies ( 8 , 9 ). AIP is calculated as the logarithm of the ratio between triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C): AIP = log 10 (TG / HDL-C) . This index captures the balance between atherogenic and anti-atherogenic lipoproteins, offering insights into lipid-driven atherogenesis and coronary artery disease risk ( 10 – 12 ). Emerging evidence suggests that elevated AIP levels are correlated with increased hepatic fat deposition and NAFLD progression, indicating its potential as a sensitive marker of lipid dysregulation in hepatic steatosis ( 13 – 15 ). While some studies report sex-specific differences—showing males with NAFLD exhibit higher AIP values than females—the predictive accuracy (area under the curve, AUC) appears comparable across genders (males: 0.761; females: 0.733) ( 7 ). As research advances, AIP may serve as a practical, non-invasive tool for screening and early diagnosis of NAFLD, ultimately aiding in prevention strategies and management of related complications ( 16 ). Despite the growing interest, there is a paucity of large-scale studies investigating the association between AIP and NAFLD. Therefore, this study aims to evaluate the relationship between AIP and NAFLD within the Fasa cohort population and to assess the potential of AIP as an independent risk factor for NAFLD. Materials and Methods Study design and participants This cross-sectional study utilized data from the Fasa PERSIAN cohort study, which enrolled approximately 10,000 individuals aged 35 to 70 years from the Fasa adult population in southern Iran ( 17 ). A schematic overview of the study design is presented in Fig. 1 . Assessment of NAFLD The Fatty Liver Index (FLI) was employed as a non-invasive tool to estimate the likelihood of NAFLD. The FLI is calculated using the following formula: $$\:FLI=\frac{{(\varvec{e}}^{0.953\text{*}{\mathbf{log}}_{\varvec{e}}\left(\varvec{triglyceride}\right)+0.139\text{*}\varvec{B}\varvec{M}\varvec{I}+.718\text{*}{\mathbf{log}}_{\varvec{e}}\left(\varvec{G}\varvec{G}\varvec{T}\right)+0.053\text{*}\varvec{W}\varvec{a}\varvec{i}\varvec{s}\varvec{t}\:\varvec{C}\varvec{i}\varvec{r}\varvec{c}\varvec{u}\varvec{m}\varvec{f}\varvec{e}\varvec{r}\varvec{e}\varvec{n}\varvec{c}\varvec{e}-15745})}{(1+{(\varvec{e}}^{0.953\text{*}{\mathbf{log}}_{\varvec{e}}\left(\varvec{triglyceride}\right)+0.139\text{*}\varvec{B}\varvec{M}\varvec{I}+.718\text{*}{\mathbf{log}}_{\varvec{e}}\left(\varvec{G}\varvec{G}\varvec{T}\right)+0.053\text{*}\varvec{W}\varvec{a}\varvec{i}\varvec{s}\varvec{t}\:\varvec{C}\varvec{i}\varvec{r}\varvec{c}\varvec{u}\varvec{m}\varvec{f}\varvec{e}\varvec{r}\varvec{e}\varvec{n}\varvec{c}\varvec{e}-15745}}$$ Scores range from 0 to 100, with a cutoff score of 30 indicating low probability of NAFLD, and a score of 60 or higher suggesting the presence of NAFLD ( 18 ). Calculation of Atherogenic Index of Plasma (AIP) AIP was computed using the formula: AIP = log₁₀ (Triglycerides / HDL-C) Based on AIP values, risk stratification was as follows: 0.24 corresponds to high risk ( 10 ). Data Collection Participants completed structured questionnaires approved by the PERSIAN cohort consortium, covering demographic data (sex, age, marital status, education level) and clinical information (history of chronic diseases, medication use). Trained personnel measured anthropometric parameters including weight, height, waist circumference, hip circumference, and wrist circumference. Body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were also recorded. Blood samples were obtained from all participants for biochemical analyses, including fasting measurements of low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides (TG), and total cholesterol. Information regarding smoking habits and medication use was documented. Statistical Analysis Descriptive statistics—including means and standard deviations for continuous variables, and frequencies and percentages for categorical variables—were used to summarize the data. Inferential analyses included Pearson’s correlation coefficient, independent two-sample t-tests, chi-square tests, and multivariate logistic regression to assess associations between variables. The diagnostic performance of AIP for NAFLD was evaluated through sensitivity and specificity analyses. All statistical analyses were performed using SPSS version 27, with a two-sided P-value < 0.05 considered statistically significant. Ethical Considerations This study was conducted in accordance with the Helsinki Declaration. The protocol was approved by the Ethics Committee of Fasa University of Medical Sciences (approval code: IR.FUMS.REC.1403.056) and the Research Board of Fasa University of Medical Sciences (approval code: 402056). Written informed consent was obtained from all participants prior to their inclusion in the cohort study. Results A total of 9,587 participants were included in the analysis, of whom 2,599 (27.1%) were diagnosed with NAFLD. The affected group consisted of 891 males (21.9%) and 1,708 females (30.9%), indicating a significant gender difference in NAFLD prevalence (p < 0.001). Sociodemographic Factors Table 1 provides a comprehensive overview of the sociodemographic factors and their associations with NAFLD. Table 1 Association of Sociodemographic Factors with NAFLD. Sociodemographic factors NAFLD Total Chi-Square No Yes Education level Illiterate 1807 (72.3%) 691 (27.7%) 2498 0.001 Primary education 2960 (71.7%) 1171 (28.3%) 4131 Middle school 1324 (73.8%) 470 (26.2%) 1794 Middle school 715 (75.8%) 228 (24.2%) 943 Academic 182 (82.4%) 39 (17.6%) 221 Gender Male 3172 (78.1%) 891 (21.9%) 4063 < 0.001 Female 3816 (69.1%) 1708 (30.9%) 5524 Marital Status Single 291 (85.6%) 49 (14.4%) 340 < 0.001 Married 6204 (72.9%) 2303 (27.1%) 8507 Widowed 417 (64.9%) 226 (35.1%) 643 Divorced 76 (78.4%) 21 (21.6%) 97 Total 6988 (72.9%) 2599 (27.1%) 9587 Education Level : There was a significant association between education and NAFLD prevalence (p = 0.001). Among illiterate participants, 1,807 (72.3%) did not have NAFLD, while 691 (27.7%) were diagnosed with it. In the primary education group, 2,960 (71.7%) were unaffected, with 1,171 (28.3%) affected. Middle school graduates showed similar trends: 1,324 (73.8%) without NAFLD versus 470 (26.2%) affected. High school graduates had a prevalence of 715 (75.8%) unaffected and 228 (24.2%) affected. Participants with higher educational attainment had a lower prevalence of NAFLD, with 182 (82.4%) without and 39 (17.6%) with the disease. Gender The prevalence of NAFLD was higher among females (30.9%) compared to males (21.9%) (p < 0.001). Marital Status : Significant differences were observed across marital categories (p < 0.001). Among singles, 291 (85.6%) did not have NAFLD, while 49 (14.4%) were affected. Married individuals had a prevalence similar to the overall population: 6,204 (72.9%) unaffected and 2,303 (27.1%) affected. Widowed participants exhibited a prevalence of 417 (64.9%) unaffected and 226 (35.1%) affected, while divorced participants showed 76 (78.4%) without and 21 (21.6%) with NAFLD. Clinical and Health-Related Factors Table 2 delineates the prevalence of NAFLD in association with various comorbid conditions, presenting their distribution within the study population. Table 2 Prevalence of NAFLD Associated with Comorbid Conditions Comorbid Conditions NAFLD Total Chi-Square No Yes Diabetes No 6295 (75.2%) 2080 (24.8%) 8375 < 0.001 Yes 693 (57.2%) 519 (42.8%) 1212 Hypertension No 5844 (76.9%) 1756 (23.1%) 7600 < 0.001 Yes 1144 (57.6%) 843 (42.4%) 1987 Cardiac Disease No 6298 (73.8%) 2231 (26.2%) 8529 < 0.001 Yes 690 (65.2%) 368 (34.8%) 1058 MI No 6880 (73.0%) 2544 (27.0%) 9424 0.055 Yes 108(66.3%) 55 (33.7%) 163 Stroke No 6914 (73.0%) 2556 (27.0%) 9470 0.018 Yes 74 (63.2%) 43 (36.8%) 117 Smoke No 1888 (80.8%) 448 (19.2%) 2336 < 0.001 Yes 5100 (70.3%) 2151 (29.7%) 7251 Use Drugs No 5573 (70.7%) 2307 (29.3%) 7880 < 0.001 Yes 1415 (82.9%) 292 (17.1%) 1707 Diabetes and Hypertension : Among diabetic individuals, 693 (57.2%) were free of NAFLD, whereas 519 (42.8%) had the condition (p < 0.001). Hypertensive participants showed similar patterns: 1,144 (57.6%) unaffected versus 843 (42.4%) affected (p < 0.001). Cardiovascular Disease : Those with CVD had a lower proportion of unaffected individuals (690; 65.2%) and 368 (34.8%) with NAFLD (p < 0.001). For myocardial infarction (MI), 108 (66.3%) were unaffected, with 55 (33.7%) affected (p = 0.055). Stroke history was associated with higher NAFLD prevalence: 74 (63.2%) unaffected versus 43 (36.8%) affected (p = 0.018). Behavioral Factors Among smokers, 5,100 (70.3%) did not have NAFLD, while 2,151 (29.7%) did (p < 0.001). Drug users showed a higher proportion of unaffected individuals (1,415; 82.9%) compared to affected (292; 17.1%) (p < 0.001). Paraclinical parameters Table 3 presents a comparative analysis of various Anthropometric measurements and paraclinical parameters between patients diagnosed with NAFLD and those without the condition. Table 3 Comparison of Anthropometric and Paraclinical Parameters Between Patients With and Without NAFLD variable NAFLD N Mean (SD) t-test AIP No 6972 0.30 (0.25) < 0.001 Yes 2596 0.55 (0.27) Age No 6988 48.75 (9.70) < 0.001 Yes 2599 49.32 (9.13) TG No 6972 110.24 (52.53) < 0.001 Yes 2596 188.94 (114.07) CHOL No 6972 180.13 (36.91) < 0.001 Yes 2596 200.36 (41.32) HDL.C No 6972 52.28 (16.18) 0.003 Yes 2596 48.74 (15.10) LDL No 6972 105.77 (31.46) < 0.001 Yes 2596 113.80 (35.83) MET Final No 6988 42.22 (11.55) < 0.001 Yes 2599 39.10 (9.26) Energy No 6988 2942.24 (1142.89) 0.735 Yes 2599 2913.02 (1153.44) DPB No 6988 73.40 (11.53) 0.116 Yes 2598 78.65 (12.16) SPB No 6988 109.49 (17.78) < 0.001 Yes 2598 117.53 (19.05) Height No 6988 161.59 (8.87) 0.047 Yes 2599 160.46 (8.67) Weight No 6988 62.08 (10.11) < 0.001 Yes 2599 80.01 (11.54) Waist Circumference No 6988 88.72 (8.92) 0.002 Yes 2599 106.07 (8.71) Hip Circumference No 6988 96.52 (6.74) < 0.001 Yes 2599 108.25 (8.27) Wrist Circumference No 6987 16.37 (1.16) < 0.001 Yes 2599 17.62 (1.36) BMI No 6988 23.79 (3.48) 0.032 Yes 2599 31.08 (3.83) AIP was significantly higher in participants with NAFLD (mean = 0.55 ± 0.27) compared to those without the condition (p < 0.001). Participants with NAFLD also had a slightly higher mean age (49.32 ± 9.13 years) than unaffected individuals (p < 0.001). Biochemically, TG levels were elevated in the NAFLD group (188.94 ± 114.07 mg/dL) relative to the non-NAFLD group (p < 0.001). Total cholesterol was similarly higher among participants with fatty liver (200.36 ± 41.32 mg/dL; p < 0.001), while HDL cholesterol was significantly lower in the NAFLD group (48.74 ± 15.10 mg/dL; p = 0.003). LDL cholesterol also showed a significant increase in the NAFLD group (113.80 ± 35.83 mg/dL; p < 0.001). The MET final score was notably lower in individuals with NAFLD (39.10 ± 9.26) compared to those without (p < 0.001). Energy intake did not differ significantly between groups, with the NAFLD group averaging 2913.02 ± 1153.44 kcal (p = 0.735). Conversely, systolic blood pressure was significantly higher among NAFLD cases (117.53 ± 19.05 mmHg; p < 0.001), whereas diastolic blood pressure showed no significant difference (p = 0.116). Anthropometric measurements were significantly greater in the NAFLD group, including height (p = 0.047), weight (p < 0.001), waist circumference (p = 0.002), hip circumference (p < 0.001), wrist circumference (p < 0.001), and BMI (p = 0.032) (Table 3 ). Multivariate Analysis Multivariate logistic regression identified several independent risk factors for NAFLD. Significant predictors included: AIP (p < 0.001), gender (p < 0.001), waist circumference (p < 0.001), hip circumference (p < 0.001), BMI (p < 0.001), and diabetes status (p = 0.01) (Table 4 ). Table 4 Multivariate Logistic Regression Analysis of Factors Associated with NAFLD p.value Exp(B) 95% C.I for EXP(B) Lower Upper AIP < 0.001 2183.109 1313.09 3629.59 Gender < 0.001 0.459 0.34 0.61 Age 0.06 1.012 1.00 1.023 Waist Circumference < 0.001 1.252 1.22 1.282 Hip Circumference < 0.001 0.952 0.93 0.976 Wrist Circumference 0.76 0.984 0.89 1.092 BMI < 0.001 2.051 1.93 2.182 Use Drugs 0.20 0.808 0.58 1.12 Smoke Cigarette 0.39 0.886 0.67 1.167 MET Final 0.60 0.997 0.99 1.007 Diabetes 0.01 1.39 1.07 1.799 Hypertension 0.05 1.268 1.00 1.608 Cardiac Disease 0.70 1.062 0.78 1.448 MI 0.34 0.712 0.36 1.42 Stroke 0.95 0.977 0.47 2.02 Sensitivity and specificity Analysis Finally, the predictive performance of AIP for NAFLD diagnosis was evaluated, demonstrating good accuracy with a sensitivity of 74.04% and specificity of 73.61% (p < 0.001), as shown in Fig. 2 . Discussion This study aimed to evaluate the association between the AIP and non-alcoholic NAFLD. Our findings indicate that NAFLD prevalence is significantly associated with demographic and clinical variables, including education level, gender, marital status, TG, total cholesterol (CHOL), HDL-C, waist and hip circumferences, weight, and BMI. These associations were observed relative to non-affected individuals, with notable differences in individuals with diabetes and hypertension within the Fasa PERSIAN cohort population. Moreover, the results suggest that AIP can serve as a predictive marker for NAFLD with an accuracy exceeding 70%. Early diagnosis of NAFLD, particularly using AIP, is crucial as it may substantially reduce morbidity and mortality associated with liver pathology ( 19 , 20 ). Globally, NAFLD remains a leading cause of chronic liver disease, closely linked to the rising prevalence of obesity and metabolic syndrome in industrialized nations ( 21 , 22 ). Further analysis demonstrated that elevated AIP values are significantly associated with increased probability of NAFLD, especially among patients with type 2 diabetes mellitus (T2DM). NAFLD and T2DM share common pathophysiological mechanisms, such as insulin resistance and systemic inflammation—factors contributing to both conditions ( 23 ). The predictive utility of AIP in diabetic populations underscores its potential role in early detection and management of NAFLD among these patients ( 24 ). Supporting this, a study of 586 pregnant women identified a positive linear relationship between AIP and NAFLD, noting an 18.8% prevalence of fatty liver, with higher risk in the top tertile of AIP and consistent findings across various subgroups and sensitivity analyses ( 8 ). Similarly, another investigation confirmed the strong association between elevated plasma AIP and NAFLD, with individuals in the high-risk category exhibiting a 5.37-fold increased risk compared to low-risk counterparts ( 9 ). The correlation between NAFLD and CVD is well-established, with NAFLD patients showing increased atherosclerosis and cardiovascular complications linked to shared risk factors such as obesity and dyslipidemia ( 15 , 25 ). Elevated AIP levels have been associated with heightened cardiovascular risk, positioning AIP as a dual biomarker for liver and cardiovascular health ( 13 , 26 ). Hypertension, frequently co-occurring with NAFLD, may stem from inflammatory and metabolic disturbances intrinsic to hepatic steatosis, emphasizing the importance of managing hepatic fat accumulation to control blood pressure ( 27 , 28 ). Lifestyle factors further influence the NAFLD paradigm. Smoking and alcohol consumption are known exacerbators of liver damage and cardiovascular risk, complicating disease management and highlighting the need for comprehensive lifestyle interventions ( 29 , 30 ). Additionally, anthropometric parameters such as BMI, waist, hip, and wrist circumferences have shown promise as accessible, non-invasive screening tools for NAFLD, particularly in high-risk populations characterized by obesity and metabolic disorders ( 31 – 34 ). These measurements could facilitate early intervention efforts to prevent disease progression. Lipid profile abnormalities—specifically elevated triglycerides, altered cholesterol levels, and reduced HDL-C are integral to NAFLD pathophysiology and contribute to increased cardiovascular risk ( 35 – 39 ). Regular monitoring of lipid parameters remains essential for risk stratification and therapeutic targeting in affected individuals. The significant association between AIP and NAFLD underscores the potential of this marker for early diagnosis and risk stratification. AIP not only reflects lipid-related metabolic disturbances but also correlates with broader health conditions such as diabetes, cardiovascular disease, hypertension, and lifestyle factors like smoking and alcohol use. Future prospective studies are necessary to elucidate causal relationships and assess the impact of interventions on AIP and NAFLD outcomes. This research, based on a large, well-characterized cohort of approximately 10,000 individuals from a rural area in southern Iran, offers robust statistical evidence. Nonetheless, limitations related to regional specificity and variable selection warrant caution when generalizing these findings to other populations. Conclusion AIP demonstrates a strong association with NAFLD and holds promise as a non-invasive predictive marker. Combining AIP with anthropometric measurements, including waist and hip circumferences, weight, and BMI, can enhance diagnostic accuracy and aid in effective screening and early intervention strategies for NAFLD. Abbreviations NAFLD Non—Alcoholic Fatty Liver Disease AIP Atherogenic Index of Plasma TG Triglycerides CHOL Cholesterol HDL C—High—Density Lipoprotein Cholesterol LDL Low—Density Lipoprotein DBP Diastolic Blood Pressure SBP Systolic Blood Pressure BMI Body Mass Index Declarations Acknowledgements Vice Chancellor for Research and Technology, Fasa University of Medical Sciences, for approving and supporting this research project. Funding None. Data Availability Data underlying this study are available upon reasonable request from the corresponding author. Ethics Statements This study was approved by the Ethics Committee of Fasa University of Medical Sciences (Approval Code: IR.FUMS.REC.1403.056) and the Research Board of Fasa University of Medical Sciences (Approval Code: 402056). All procedures conformed to ethical standards. Consent for publication Not applicable. Competing interests The authors declare no competing interests Author Contributions S.N. and A.D. designed the study, conducted data collection, and drafted the manuscript. A.D. and A.M. conceived the study, supervised the research, performed data analysis, and contributed to manuscript preparation. All authors reviewed and approved the final version of the manuscript. Conflict of Interest The authors declare no conflicts of interest. References Kudaravalli P, John S. Nonalcoholic Fatty Liver. StatPearls. Treasure Island (FL) ineligible companies. 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Sci Rep. 2024;14(1):12829. Lee M-S, Felipe-Dimog EB, Yang J-F, Chen Y-Y, Wu K-T, Kuo H-J, et al. The Efficacy of Anthropometric Indicators in Predicting Non-Alcoholic Fatty Liver Disease Using FibroScan® CAP Values among the Taiwanese Population. Biomedicines. 2023;11(9):2518. Hoekstra M, Van Eck M. High-density lipoproteins and non-alcoholic fatty liver disease. Atheroscler Plus. 2023;53:33–41. Tomizawa M, Kawanabe Y, Shinozaki F, Sato S, Motoyoshi Y, Sugiyama T, et al. Triglyceride is strongly associated with nonalcoholic fatty liver disease among markers of hyperlipidemia and diabetes. Biomed Rep. 2014;2(5):633–6. DeFilippis AP, Blaha MJ, Martin SS, Reed RM, Jones SR, Nasir K, et al. Nonalcoholic fatty liver disease and serum lipoproteins: the Multi-Ethnic Study of Atherosclerosis. Atherosclerosis. 2013;227(2):429–36. Cohen DE, Fisher EA. Lipoprotein metabolism, dyslipidemia, and nonalcoholic fatty liver disease. Semin Liver Dis. 2013;33(4):380–8. Chatrath H, Vuppalanchi R, Chalasani N. Dyslipidemia in patients with nonalcoholic fatty liver disease. Semin Liver Dis. 2012;32(1):22–9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 31 Aug, 2025 Reviewers agreed at journal 30 Aug, 2025 Reviewers invited by journal 22 Aug, 2025 Editor invited by journal 24 Jul, 2025 Editor assigned by journal 23 Jul, 2025 Submission checks completed at journal 23 Jul, 2025 First submitted to journal 21 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7180392","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508128912,"identity":"116c69fd-1475-4acd-b398-773a62b57d52","order_by":0,"name":"Shaghayegh Nematollahi","email":"","orcid":"","institution":"Fasa University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shaghayegh","middleName":"","lastName":"Nematollahi","suffix":""},{"id":508128913,"identity":"35a74ea0-f54e-4a0c-9ed6-8d475274cbfc","order_by":1,"name":"Ahmad Maghsoudi","email":"","orcid":"","institution":"Kerman University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Maghsoudi","suffix":""},{"id":508128914,"identity":"7d61658c-0ada-44f2-bc75-727282485995","order_by":2,"name":"Azizallah Dehghan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYDCCAzwMzGAGewOQMLAgRQvPAZAWCVK0SCSAScI6+I73Hn5dUHNHXn7m86sbfhRIMPC3dyfg1SJ55lya9YxjzwwbZ+eU3ewBOkzizNkNeLUY3MgxM+ZhO8zYLJ2TdoMHqMVAIpeAlvtvgFr+HbZvkzyTdvMPUVpu8Bg/5m07nNgjwX7sNlG2SJ7JMWPm7TucPIMnh+22jIEED0G/8B0/Y/yZ59th2/ntx5/dfPPHRo6/vRe/FiBgg8YFjwGYJKQcBJg/QGj2B8SoHgWjYBSMghEIAIRsSiauClSaAAAAAElFTkSuQmCC","orcid":"","institution":"Fasa University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Azizallah","middleName":"","lastName":"Dehghan","suffix":""}],"badges":[],"createdAt":"2025-07-21 19:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7180392/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7180392/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90316516,"identity":"bfcb7b98-b2a4-4745-a182-b1e0f4d0bc2b","added_by":"auto","created_at":"2025-09-01 10:21:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31568,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of Study Workflow\u003c/p\u003e","description":"","filename":"Onlinefloatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-7180392/v1/bec4f9eae24cb51c181b89b6.png"},{"id":90316521,"identity":"de2c42ac-fc46-4da0-9a60-2b86711221c3","added_by":"auto","created_at":"2025-09-01 10:21:37","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52804,"visible":true,"origin":"","legend":"\u003cp\u003eAUROC curve for AIP.\u003cstrong\u003e \u003c/strong\u003eA. Sensitivity 71.69 and Specificity 67.17. B. Sensitivity 67.89 and Specificity 69.86. C. Sensitivity 74.04 and Specificity 73.61\u003c/p\u003e","description":"","filename":"groupimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7180392/v1/3baf72c1b5c0432125791274.jpeg"},{"id":90320453,"identity":"61dcf678-1305-46c9-b05d-2c967b77055f","added_by":"auto","created_at":"2025-09-01 10:45:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":969323,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7180392/v1/a736bfed-bfdb-4ca0-ac82-fffba56cba12.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Atherogenic Index of Plasma and Non-alcoholic Fatty Liver Disease: Based on Results from Fasa Adults Cohort Study(FACS)","fulltext":[{"header":"Background","content":"\u003cp\u003eNon-alcoholic fatty liver disease (NAFLD) has emerged as a significant global health concern, characterized by the abnormal accumulation of fat in the liver in the absence of excessive alcohol consumption (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It encompasses a spectrum of hepatic conditions, ranging from simple steatosis, known as non-alcoholic fatty liver, to more advanced forms such as non-alcoholic steatohepatitis (NASH), which involves inflammation and hepatocyte injury (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The prevalence of NAFLD is alarmingly high, affecting approximately 25% of the global population, with higher rates observed among individuals with obesity and type 2 diabetes (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The pathogenesis of NAFLD is closely linked to metabolic syndrome\u0026mdash;a cluster of conditions including obesity, insulin resistance, dyslipidemia, and hypertension (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). These factors promote increased hepatic lipid accumulation through enhanced de novo lipogenesis and impaired fatty acid oxidation (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), rendering NAFLD often regarded as the hepatic manifestation of metabolic syndrome. Notably, NAFLD is prevalent not only among obese individuals but also in lean populations, underscoring its systemic nature (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn recent years, the Atherogenic Index of Plasma (AIP) has gained recognition as a valuable marker for cardiovascular disease (CVD) risk, reflecting dysregulated lipid metabolism (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). It has also been associated with NAFLD in multiple studies (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). AIP is calculated as the logarithm of the ratio between triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C):\u003c/p\u003e\u003cp\u003e\u003cb\u003eAIP\u0026thinsp;=\u0026thinsp;log\u003c/b\u003e\u003csub\u003e\u003cb\u003e10\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(TG / HDL-C)\u003c/b\u003e. This index captures the balance between atherogenic and anti-atherogenic lipoproteins, offering insights into lipid-driven atherogenesis and coronary artery disease risk (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEmerging evidence suggests that elevated AIP levels are correlated with increased hepatic fat deposition and NAFLD progression, indicating its potential as a sensitive marker of lipid dysregulation in hepatic steatosis (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). While some studies report sex-specific differences\u0026mdash;showing males with NAFLD exhibit higher AIP values than females\u0026mdash;the predictive accuracy (area under the curve, AUC) appears comparable across genders (males: 0.761; females: 0.733) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). As research advances, AIP may serve as a practical, non-invasive tool for screening and early diagnosis of NAFLD, ultimately aiding in prevention strategies and management of related complications (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the growing interest, there is a paucity of large-scale studies investigating the association between AIP and NAFLD. Therefore, this study aims to evaluate the relationship between AIP and NAFLD within the Fasa cohort population and to assess the potential of AIP as an independent risk factor for NAFLD.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eStudy design and participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis cross-sectional study utilized data from the Fasa PERSIAN cohort study, which enrolled approximately 10,000 individuals aged 35 to 70 years from the Fasa adult population in southern Iran (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). A schematic overview of the study design is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssessment of NAFLD\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Fatty Liver Index (FLI) was employed as a non-invasive tool to estimate the likelihood of NAFLD. The FLI is calculated using the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:FLI=\\frac{{(\\varvec{e}}^{0.953\\text{*}{\\mathbf{log}}_{\\varvec{e}}\\left(\\varvec{triglyceride}\\right)+0.139\\text{*}\\varvec{B}\\varvec{M}\\varvec{I}+.718\\text{*}{\\mathbf{log}}_{\\varvec{e}}\\left(\\varvec{G}\\varvec{G}\\varvec{T}\\right)+0.053\\text{*}\\varvec{W}\\varvec{a}\\varvec{i}\\varvec{s}\\varvec{t}\\:\\varvec{C}\\varvec{i}\\varvec{r}\\varvec{c}\\varvec{u}\\varvec{m}\\varvec{f}\\varvec{e}\\varvec{r}\\varvec{e}\\varvec{n}\\varvec{c}\\varvec{e}-15745})}{(1+{(\\varvec{e}}^{0.953\\text{*}{\\mathbf{log}}_{\\varvec{e}}\\left(\\varvec{triglyceride}\\right)+0.139\\text{*}\\varvec{B}\\varvec{M}\\varvec{I}+.718\\text{*}{\\mathbf{log}}_{\\varvec{e}}\\left(\\varvec{G}\\varvec{G}\\varvec{T}\\right)+0.053\\text{*}\\varvec{W}\\varvec{a}\\varvec{i}\\varvec{s}\\varvec{t}\\:\\varvec{C}\\varvec{i}\\varvec{r}\\varvec{c}\\varvec{u}\\varvec{m}\\varvec{f}\\varvec{e}\\varvec{r}\\varvec{e}\\varvec{n}\\varvec{c}\\varvec{e}-15745}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eScores range from 0 to 100, with a cutoff score of 30 indicating low probability of NAFLD, and a score of 60 or higher suggesting the presence of NAFLD (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCalculation of Atherogenic Index of Plasma (AIP)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAIP was computed using the formula:\u003c/p\u003e\u003cp\u003eAIP\u0026thinsp;=\u0026thinsp;log₁₀ (Triglycerides / HDL-C)\u003c/p\u003e\u003cp\u003eBased on AIP values, risk stratification was as follows: \u0026lt;0.11 indicates low cardiovascular risk, 0.11\u0026ndash;0.24 signifies moderate risk, and \u0026gt;\u0026thinsp;0.24 corresponds to high risk (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eParticipants completed structured questionnaires approved by the PERSIAN cohort consortium, covering demographic data (sex, age, marital status, education level) and clinical information (history of chronic diseases, medication use). Trained personnel measured anthropometric parameters including weight, height, waist circumference, hip circumference, and wrist circumference. Body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were also recorded.\u003c/p\u003e\u003cp\u003eBlood samples were obtained from all participants for biochemical analyses, including fasting measurements of low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides (TG), and total cholesterol. Information regarding smoking habits and medication use was documented.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics\u0026mdash;including means and standard deviations for continuous variables, and frequencies and percentages for categorical variables\u0026mdash;were used to summarize the data. Inferential analyses included Pearson\u0026rsquo;s correlation coefficient, independent two-sample t-tests, chi-square tests, and multivariate logistic regression to assess associations between variables. The diagnostic performance of AIP for NAFLD was evaluated through sensitivity and specificity analyses. All statistical analyses were performed using SPSS version 27, with a two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEthical Considerations\u003c/b\u003e\u003c/p\u003e\u003cp\u003e This study was conducted in accordance with the Helsinki Declaration. The protocol was approved by the Ethics Committee of Fasa University of Medical Sciences (approval code: IR.FUMS.REC.1403.056) and the Research Board of Fasa University of Medical Sciences (approval code: 402056). Written informed consent was obtained from all participants prior to their inclusion in the cohort study.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 9,587 participants were included in the analysis, of whom 2,599 (27.1%) were diagnosed with NAFLD. The affected group consisted of 891 males (21.9%) and 1,708 females (30.9%), indicating a significant gender difference in NAFLD prevalence (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSociodemographic Factors\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a comprehensive overview of the sociodemographic factors and their associations with NAFLD.\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 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of Sociodemographic Factors with NAFLD.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eSociodemographic factors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eNAFLD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChi-Square\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eEducation level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIlliterate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1807 (72.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e691 (27.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2960 (71.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1171 (28.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1324 (73.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e470 (26.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1794\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e715 (75.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e228 (24.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e943\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAcademic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e182 (82.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 (17.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e221\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3172 (78.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e891 (21.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3816 (69.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1708 (30.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5524\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eMarital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e291 (85.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (14.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6204 (72.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2303 (27.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8507\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e417 (64.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e226 (35.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e643\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76 (78.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (21.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6988 (72.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2599 (27.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEducation Level\u003c/b\u003e: There was a significant association between education and NAFLD prevalence (p\u0026thinsp;=\u0026thinsp;0.001). Among illiterate participants, 1,807 (72.3%) did not have NAFLD, while 691 (27.7%) were diagnosed with it. In the primary education group, 2,960 (71.7%) were unaffected, with 1,171 (28.3%) affected. Middle school graduates showed similar trends: 1,324 (73.8%) without NAFLD versus 470 (26.2%) affected. High school graduates had a prevalence of 715 (75.8%) unaffected and 228 (24.2%) affected. Participants with higher educational attainment had a lower prevalence of NAFLD, with 182 (82.4%) without and 39 (17.6%) with the disease.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003cp\u003eThe prevalence of NAFLD was higher among females (30.9%) compared to males (21.9%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e: Significant differences were observed across marital categories (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among singles, 291 (85.6%) did not have NAFLD, while 49 (14.4%) were affected. Married individuals had a prevalence similar to the overall population: 6,204 (72.9%) unaffected and 2,303 (27.1%) affected. Widowed participants exhibited a prevalence of 417 (64.9%) unaffected and 226 (35.1%) affected, while divorced participants showed 76 (78.4%) without and 21 (21.6%) with NAFLD.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical and Health-Related Factors\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e delineates the prevalence of NAFLD in association with various comorbid conditions, presenting their distribution within the study population.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrevalence of NAFLD Associated with Comorbid Conditions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eComorbid Conditions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eNAFLD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eChi-Square\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6295 (75.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2080 (24.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e693 (57.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e519 (42.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1212\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5844 (76.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1756 (23.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1144 (57.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e843 (42.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1987\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCardiac Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6298 (73.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2231 (26.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e690 (65.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e368 (34.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6880 (73.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2544 (27.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108(66.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55 (33.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eStroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6914 (73.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2556 (27.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74 (63.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43 (36.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSmoke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1888 (80.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e448 (19.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5100 (70.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2151 (29.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7251\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eUse Drugs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5573 (70.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2307 (29.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1415 (82.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e292 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1707\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\u003cb\u003eDiabetes and Hypertension\u003c/b\u003e: Among diabetic individuals, 693 (57.2%) were free of NAFLD, whereas 519 (42.8%) had the condition (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Hypertensive participants showed similar patterns: 1,144 (57.6%) unaffected versus 843 (42.4%) affected (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCardiovascular Disease\u003c/b\u003e: Those with CVD had a lower proportion of unaffected individuals (690; 65.2%) and 368 (34.8%) with NAFLD (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For myocardial infarction (MI), 108 (66.3%) were unaffected, with 55 (33.7%) affected (p\u0026thinsp;=\u0026thinsp;0.055). Stroke history was associated with higher NAFLD prevalence: 74 (63.2%) unaffected versus 43 (36.8%) affected (p\u0026thinsp;=\u0026thinsp;0.018).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eBehavioral Factors\u003c/strong\u003e\u003cp\u003eAmong smokers, 5,100 (70.3%) did not have NAFLD, while 2,151 (29.7%) did (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Drug users showed a higher proportion of unaffected individuals (1,415; 82.9%) compared to affected (292; 17.1%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eParaclinical parameters\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents a comparative analysis of various Anthropometric measurements and paraclinical parameters between patients diagnosed with NAFLD and those without the condition.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Anthropometric and Paraclinical Parameters Between Patients With and Without NAFLD\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003evariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNAFLD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-test\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAIP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30 (0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.55 (0.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.75 (9.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.32 (9.13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110.24 (52.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e188.94 (114.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCHOL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e180.13 (36.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e200.36 (41.32)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHDL.C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.28 (16.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.74 (15.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLDL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e105.77 (31.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113.80 (35.83)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMET Final\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.22 (11.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.10 (9.26)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEnergy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2942.24 (1142.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.735\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2913.02 (1153.44)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDPB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73.40 (11.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e78.65 (12.16)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSPB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109.49 (17.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e117.53 (19.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHeight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e161.59 (8.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e160.46 (8.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWeight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.08 (10.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80.01 (11.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWaist Circumference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.72 (8.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e106.07 (8.71)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHip Circumference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.52 (6.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e108.25 (8.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWrist Circumference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.37 (1.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.62 (1.36)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.79 (3.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.08 (3.83)\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\u003eAIP was significantly higher in participants with NAFLD (mean\u0026thinsp;=\u0026thinsp;0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27) compared to those without the condition (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Participants with NAFLD also had a slightly higher mean age (49.32\u0026thinsp;\u0026plusmn;\u0026thinsp;9.13 years) than unaffected individuals (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eBiochemically, TG levels were elevated in the NAFLD group (188.94\u0026thinsp;\u0026plusmn;\u0026thinsp;114.07 mg/dL) relative to the non-NAFLD group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Total cholesterol was similarly higher among participants with fatty liver (200.36\u0026thinsp;\u0026plusmn;\u0026thinsp;41.32 mg/dL; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while HDL cholesterol was significantly lower in the NAFLD group (48.74\u0026thinsp;\u0026plusmn;\u0026thinsp;15.10 mg/dL; p\u0026thinsp;=\u0026thinsp;0.003). LDL cholesterol also showed a significant increase in the NAFLD group (113.80\u0026thinsp;\u0026plusmn;\u0026thinsp;35.83 mg/dL; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eThe MET final score was notably lower in individuals with NAFLD (39.10\u0026thinsp;\u0026plusmn;\u0026thinsp;9.26) compared to those without (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Energy intake did not differ significantly between groups, with the NAFLD group averaging 2913.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1153.44 kcal (p\u0026thinsp;=\u0026thinsp;0.735). Conversely, systolic blood pressure was significantly higher among NAFLD cases (117.53\u0026thinsp;\u0026plusmn;\u0026thinsp;19.05 mmHg; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas diastolic blood pressure showed no significant difference (p\u0026thinsp;=\u0026thinsp;0.116).\u003c/p\u003e\u003cp\u003eAnthropometric measurements were significantly greater in the NAFLD group, including height (p\u0026thinsp;=\u0026thinsp;0.047), weight (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), waist circumference (p\u0026thinsp;=\u0026thinsp;0.002), hip circumference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), wrist circumference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and BMI (p\u0026thinsp;=\u0026thinsp;0.032) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMultivariate Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMultivariate logistic regression identified several independent risk factors for NAFLD. Significant predictors included: AIP (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), gender (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), waist circumference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hip circumference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and diabetes status (p\u0026thinsp;=\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\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\u003eMultivariate Logistic Regression Analysis of Factors Associated with NAFLD\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep.value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eExp(B)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e95% C.I for EXP(B)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUpper\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2183.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1313.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3629.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaist Circumference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.282\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHip Circumference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.976\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWrist Circumference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.092\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.182\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUse Drugs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke Cigarette\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMET Final\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.799\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.608\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiac Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.448\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.02\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\u003cb\u003eSensitivity and specificity Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFinally, the predictive performance of AIP for NAFLD diagnosis was evaluated, demonstrating good accuracy with a sensitivity of 74.04% and specificity of 73.61% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to evaluate the association between the AIP and non-alcoholic NAFLD. Our findings indicate that NAFLD prevalence is significantly associated with demographic and clinical variables, including education level, gender, marital status, TG, total cholesterol (CHOL), HDL-C, waist and hip circumferences, weight, and BMI. These associations were observed relative to non-affected individuals, with notable differences in individuals with diabetes and hypertension within the Fasa PERSIAN cohort population. Moreover, the results suggest that AIP can serve as a predictive marker for NAFLD with an accuracy exceeding 70%.\u003c/p\u003e\u003cp\u003eEarly diagnosis of NAFLD, particularly using AIP, is crucial as it may substantially reduce morbidity and mortality associated with liver pathology (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Globally, NAFLD remains a leading cause of chronic liver disease, closely linked to the rising prevalence of obesity and metabolic syndrome in industrialized nations (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurther analysis demonstrated that elevated AIP values are significantly associated with increased probability of NAFLD, especially among patients with type 2 diabetes mellitus (T2DM). NAFLD and T2DM share common pathophysiological mechanisms, such as insulin resistance and systemic inflammation\u0026mdash;factors contributing to both conditions (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The predictive utility of AIP in diabetic populations underscores its potential role in early detection and management of NAFLD among these patients (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Supporting this, a study of 586 pregnant women identified a positive linear relationship between AIP and NAFLD, noting an 18.8% prevalence of fatty liver, with higher risk in the top tertile of AIP and consistent findings across various subgroups and sensitivity analyses (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Similarly, another investigation confirmed the strong association between elevated plasma AIP and NAFLD, with individuals in the high-risk category exhibiting a 5.37-fold increased risk compared to low-risk counterparts (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe correlation between NAFLD and CVD is well-established, with NAFLD patients showing increased atherosclerosis and cardiovascular complications linked to shared risk factors such as obesity and dyslipidemia (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Elevated AIP levels have been associated with heightened cardiovascular risk, positioning AIP as a dual biomarker for liver and cardiovascular health (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Hypertension, frequently co-occurring with NAFLD, may stem from inflammatory and metabolic disturbances intrinsic to hepatic steatosis, emphasizing the importance of managing hepatic fat accumulation to control blood pressure (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLifestyle factors further influence the NAFLD paradigm. Smoking and alcohol consumption are known exacerbators of liver damage and cardiovascular risk, complicating disease management and highlighting the need for comprehensive lifestyle interventions (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Additionally, anthropometric parameters such as BMI, waist, hip, and wrist circumferences have shown promise as accessible, non-invasive screening tools for NAFLD, particularly in high-risk populations characterized by obesity and metabolic disorders (\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). These measurements could facilitate early intervention efforts to prevent disease progression.\u003c/p\u003e\u003cp\u003eLipid profile abnormalities\u0026mdash;specifically elevated triglycerides, altered cholesterol levels, and reduced HDL-C are integral to NAFLD pathophysiology and contribute to increased cardiovascular risk (\u003cspan additionalcitationids=\"CR36 CR37 CR38\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Regular monitoring of lipid parameters remains essential for risk stratification and therapeutic targeting in affected individuals.\u003c/p\u003e\u003cp\u003eThe significant association between AIP and NAFLD underscores the potential of this marker for early diagnosis and risk stratification. AIP not only reflects lipid-related metabolic disturbances but also correlates with broader health conditions such as diabetes, cardiovascular disease, hypertension, and lifestyle factors like smoking and alcohol use. Future prospective studies are necessary to elucidate causal relationships and assess the impact of interventions on AIP and NAFLD outcomes.\u003c/p\u003e\u003cp\u003eThis research, based on a large, well-characterized cohort of approximately 10,000 individuals from a rural area in southern Iran, offers robust statistical evidence. Nonetheless, limitations related to regional specificity and variable selection warrant caution when generalizing these findings to other populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAIP demonstrates a strong association with NAFLD and holds promise as a non-invasive predictive marker. Combining AIP with anthropometric measurements, including waist and hip circumferences, weight, and BMI, can enhance diagnostic accuracy and aid in effective screening and early intervention strategies for NAFLD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNAFLD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNon\u0026mdash;Alcoholic Fatty Liver Disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAIP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAtherogenic Index of Plasma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTriglycerides\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCHOL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHDL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eC\u0026mdash;High\u0026mdash;Density Lipoprotein Cholesterol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLDL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLow\u0026mdash;Density Lipoprotein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDiastolic Blood Pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSystolic Blood Pressure\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVice Chancellor for Research and Technology, Fasa University of Medical Sciences, for approving and supporting this research project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData underlying this study are available upon reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Fasa University of Medical Sciences (Approval Code: IR.FUMS.REC.1403.056) and the Research Board of Fasa University of Medical Sciences (Approval Code: 402056). All procedures conformed to ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.N. and A.D. designed the study, conducted data collection, and drafted the manuscript. A.D. and A.M. conceived the study, supervised the research, performed data analysis, and contributed to manuscript preparation. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKudaravalli P, John S. Nonalcoholic Fatty Liver. StatPearls. Treasure Island (FL) ineligible companies. Disclosure: Savio John declares no relevant financial relationships with ineligible companies.: StatPearls Publishing Copyright \u0026copy; 2025. StatPearls Publishing LLC.; 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrander C, Grabherr F, Tilg H. Non-alcoholic fatty liver disease: pathophysiological concepts and treatment options. Cardiovasc Res. 2023;119(9):1787\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePowell EE, Wong VW, Rinella M. Non-alcoholic fatty liver disease. Lancet. 2021;397(10290):2212\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAntunes C, Azadfard M, Hoilat GJ, Gupta M, Fatty Liver. StatPearls. Treasure Island (FL) ineligible companies. Disclosure: Mohammadreza Azadfard declares no relevant financial relationships with ineligible companies. Disclosure: Gilles Hoilat declares no relevant financial relationships with ineligible companies. Disclosure: Mohit Gupta declares no relevant financial relationships with ineligible companies.: StatPearls Publishing Copyright \u0026copy;. 2025, StatPearls Publishing LLC.; 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaurice J, Manousou P. Non-alcoholic fatty liver disease. Clin Med (Lond). 2018;18(3):245\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePouwels S, Sakran N, Graham Y, Leal A, Pintar T, Yang W, et al. Non-alcoholic fatty liver disease (NAFLD): a review of pathophysiology, clinical management and effects of weight loss. BMC Endocr Disord. 2022;22(1):63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDobi\u0026aacute;sov\u0026aacute; M, Frohlich J. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FER(HDL)). Clin Biochem. 2001;34(7):583\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIsmaiel A, Ciobanu OS, Ismaiel M, Leucuta DC, Popa SL, David L et al. Atherogenic Index of Plasma in Non-Alcoholic Fatty Liver Disease: Systematic Review and Meta-Analysis. Biomedicines. 2022;10(9).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXie F, Zhou H, Wang Y. Atherogenic index of plasma is a novel and strong predictor associated with fatty liver: a cross-sectional study in the Chinese Han population. 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Med (Baltim). 2024;103(8):e37152.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHomayounfar R, Farjam M, Bahramali E, Sharafi M, Poustchi H, Malekzadeh R, Mansoori Y, Naghizadeh MM, Vakil MK, Dehghan A. Cohort Profile: the Fasa adults Cohort Study (FACS): a prospective study of non-communicable diseases risks. Int J Epidemiol. 2023;52(3):e172\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBedogni G, Bellentani S, Miglioli L, Masutti F, Passalacqua M, Castiglione A, et al. The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol. 2006;6:33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShuai R, He Y, Yang D, Zhang Y, Zhang L. Association between the atherogenic index of plasma and non-alcoholic fatty liver disease in Korean pregnant women: secondary analysis of a prospective cohort study. 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Nonalcoholic fatty liver disease and serum lipoproteins: the Multi-Ethnic Study of Atherosclerosis. Atherosclerosis. 2013;227(2):429\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCohen DE, Fisher EA. Lipoprotein metabolism, dyslipidemia, and nonalcoholic fatty liver disease. Semin Liver Dis. 2013;33(4):380\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChatrath H, Vuppalanchi R, Chalasani N. Dyslipidemia in patients with nonalcoholic fatty liver disease. Semin Liver Dis. 2012;32(1):22\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Atherogenic Index of Plasma, Non-alcoholic Fatty Liver, Cohort study, Triglycerides, HDL","lastPublishedDoi":"10.21203/rs.3.rs-7180392/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7180392/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eNon-alcoholic fatty liver disease (NAFLD) affects approximately 25% of the global population, with prevalence rising alongside obesity, type 2 diabetes, and metabolic syndrome. The Atherogenic Index of Plasma (AIP) has recently garnered interest as a potential marker in NAFLD pathogenesis. This study aimed to investigate the association between AIP and NAFLD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis cross-sectional study utilized data from the Fasa Adults Cohort Study, comprising approximately 10,000 individuals, of whom 2,599 were diagnosed with NAFLD based on clinical evaluation. Baseline data\u0026mdash;including biochemical profiles, medical history, and lifestyle factors\u0026mdash;were extracted. AIP was calculated from triglyceride and HDL cholesterol levels. Statistical analyses included descriptive statistics, chi-square tests, t-tests, and multivariate logistic regression, with significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Sensitivity and specificity analyses assessed AIP\u0026rsquo;s predictive value for NAFLD.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong 9,587 participants, 2,599 (27.1%) had NAFLD, with a higher prevalence in females (30.9%) compared to males (21.9%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Those with NAFLD exhibited elevated triglycerides, total cholesterol, systolic blood pressure, and anthropometric measurements, alongside reduced High-density lipoprotein (HDL) cholesterol(HDL-C). Notably, AIP was strongly associated with NAFLD (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The predictive performance of AIP was confirmed with a sensitivity of 74.04% and specificity of 73.61%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eAIP demonstrates substantial potential as a non-invasive predictor for NAFLD, which could facilitate early diagnosis and intervention. Further prospective studies are recommended to validate these findings.\u003c/p\u003e","manuscriptTitle":"Association between Atherogenic Index of Plasma and Non-alcoholic Fatty Liver Disease: Based on Results from Fasa Adults Cohort Study(FACS)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 10:21:32","doi":"10.21203/rs.3.rs-7180392/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-08-31T15:23:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85907532768096229277351648708637845988","date":"2025-08-30T17:37:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-22T11:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-24T12:35:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-23T09:43:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-23T09:42:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Gastroenterology","date":"2025-07-21T19:05:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-gastroenterology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmge","sideBox":"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmge/default.aspx","title":"BMC Gastroenterology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4534511a-2d32-49f2-abd4-ae6362149698","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T10:21:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 10:21:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7180392","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7180392","identity":"rs-7180392","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}