Fatty Liver Index as a Predictor of Metabolic Syndrome in Adult Females in India

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Fatty liver index (FLI) is a validated model to detect MAFLD. This study aimed to evaluate the accuracy of FLI in predicting MetS in adult females in India. Methods : This cross-sectional study included 450 adult females attending a tertiary care hospital in India. Clinical examination, anthropometric measurements, and biochemical tests were conducted. FLI was calculated using the standard formula. Mets were diagnosed using harmonized criteria. Logistic regression analysis was performed to determine predictors. Results : The mean age was 44.2±7.8 years and the prevalence of MetS was 61%. Increasing the FLI category was significantly associated with a worsening metabolic profile. The odds of hypertension, diabetes, MetS, and cardiovascular disease progressively increased with higher FLI levels (p<0.001), denoting a dose-response relationship. FLI demonstrated good diagnostic accuracy for MetS with AUC 0.86 (95% CI 0.81–0.89). It showed significantly higher predictive ability compared to individual components like waist circumference and lipids. A FLI cutoff ≥30 provided an optimal balance of sensitivity (71%) and specificity (59%) for predicting MetS. Conclusion : FLI demonstrates a strong association with MetS and related comorbidities in a dose-dependent manner. It shows good diagnostic accuracy for predicting MetS, better than individual criteria. FLI can be a simple, low-cost screening tool to identify high metabolic risk individuals in resource-limited settings. Fatty liver index Non-alcoholic fatty liver disease Metabolic syndrome cardiovascular diseases Diabetes mellitus Figures Figure 1 Figure 2 Introduction Metabolic associated fatty liver disease (MAFLD) has emerged as the hepatic manifestation of metabolic syndrome (MetS) (1). It represents a spectrum ranging from simple steatosis to non-alcoholic steatohepatitis which can progress to cirrhosis and hepatocellular carcinoma (2). The global prevalence of MAFLD is estimated to be around 25%, and it is increasingly recognized in developing countries like India due to rising obesity and diabetes (3). MAFLD is considered the hepatic component of MetS, and its presence can identify individuals at higher risk of cardiovascular disease (CVD) (4). However, imaging modalities like ultrasound used to diagnose MAFLD are expensive and cumbersome for screening purposes. Simple non-invasive scores incorporating clinical and biochemical parameters can estimate the probability of hepatic steatosis. The fatty liver index (FLI), based on body mass index (BMI), waist circumference (WC), triglycerides (TG), and gamma-glutamyl transferase (GGT) is one such validated model with accuracy for detecting MAFLD (5). Higher FLI levels correlate with increasing severity of hepatic steatosis, fibrosis, and necroinflammation (6). Previous studies have reported associations between fatty liver index and metabolic syndrome as well as increased cardiovascular risk (7,8). FLI demonstrates good predictive ability for incident diabetes and hypertension (9,10). However, data regarding the utility of FLI for the prediction of MetS from India is scarce, especially among high-risk groups like females who tend to have a higher prevalence. This study aimed to determine the accuracy of the fatty liver index for detecting metabolic syndrome in adult females attending a tertiary hospital in India. We also evaluated the association of FLI with individual components of MetS and related comorbidities. Methodology Study Design: It’s a hospital based cross-sectional study Study Setting and Population: The study was conducted at a tertiary care hospital. The study population included was 450 adult females aged 18 years and above attending the outpatient department. Sample Size Calculation: The sample size is calculated based on a previous study by Dasgupta et al. that reported a 45% prevalence of metabolic syndrome in adult females in India (11). Using this prevalence, with an absolute precision of 5% and a confidence level of 95%, the minimum required sample size is calculated using the formula: n = Z 2 pq/d 2 Where, n = required sample size Z = Z statistic for the level of confidence (1.96 for 95% confidence level) p = expected prevalence q = 1 – p, d = absolute precision Thus, the sample size is calculated as: n = (1.96)2 x 0.45 x 0.55 / (0.05)2 = 380 Accounting for a non-response rate of 10%, the final sample size is 380 + 38 = 418, rounding off, the sample size is taken as 450. Sampling Technique: A consecutive sampling technique was used for recruitment. All eligible adult female patients attending the OPD was screened and those meeting the inclusion/exclusion criteria was recruited until the target sample size is achieved. Inclusion Criteria: - Adult females aged ≥18 years - Provided informed consent Exclusion Criteria: - Pregnant and lactating women - Known case of chronic liver disease - Alcoholics and drug abusers - Patients on medications affecting lipid profile Data Collection Tool and Technique: - Structured questionnaire to collect sociodemographic details and clinical history - Study Variables: Demographic variables: Age Gender (12) Anthropometric measurements: Height, weight - To calculate BMI (13) Waist circumference - Central obesity marker (13) Blood pressure: Systolic and Diastolic blood pressure (14) Biochemical parameters: Fasting blood glucose (15) HbA1c Lipid profile: Total Cholesterol, Triglycerides, HDL-C, LDL-C (16) Liver enzymes: ALT, AST (17) Fatty liver index: Calculated using formula (6) Outcome variable: Metabolic syndrome diagnosed using standard criteria (18) Data Collection Procedure: Recruitment: Participants was recruited through tertiary care hospital in India. Informed Consent: Participants were provided written informed consent after receiving a detailed explanation of the study. Baseline Data Collection: Demographic information (age) and medical history (including any chronic diseases or medications) was recorded. Anthropometric Measurements: Height, weight, waist circumference was measured following standard protocols. Blood Pressure Measurement: Systolic and Diastolic Blood pressure was measured using a calibrated sphygmomanometer. Blood Sample Collection: Fasting blood samples were collected for analysis of fasting blood glucose, HbA1c, Lipid profile (including Triglycerides, HDL-C, LDL-C, Total Cholesterol), liver function tests (ALT, AST), and other relevant biomarkers. Assessment of Fatty Liver Index (FLI): FLI was calculated using the formula: FLI = (e^0.953 × ln(triglycerides) + 0.139 × BMI + 0.718 × ln(ggt) + 0.053 × waist circumference - 15.745) / (1 + e^0.953 × ln(triglycerides) + 0.139 × BMI + 0.718 × ln(ggt) + 0.053 × waist circumference - 15.745) × 100. Participants with FLI ≥60 were considered at risk for fatty liver. Assessment of Metabolic Syndrome (MetS): MetS was diagnosed based on the presence of three or more of the following criteria: elevated waist circumference, elevated triglycerides, reduced HDL cholesterol, elevated fasting glucose, and elevated blood pressure. Data Analysis: Data was analysed using the statistical software SPSS v22. Categorical variables were expressed as proportions and continuous variables as mean ± SD. Logistic regression was used to identify factors associated with fatty liver and performance of FLI was evaluated against ultrasound. p<0.05 was considered statistically significant The collected data underwent rigorous statistical analysis to derive meaningful insights. Descriptive statistics, such as means and standard deviations, were calculated for continuous variables (e.g., age, BMI, blood pressure, glucose levels, lipid profile, liver enzymes). The prevalence of metabolic syndrome was determined as a categorical outcome To investigate the association between FLI and MetS, subjects were categorized into FLI groups based on predefined cutoffs (<20, 20-59, and ≥60). Statistical analyses, including ANOVA tests, were conducted to compare baseline characteristics across FLI categories. Additionally, logistic regression analysis was performed to determine the odds ratios (ORs) for comorbidities such as hypertension, diabetes, MetS, and cardiovascular disease across increasing FLI thresholds. Subgroup analyses were conducted to explore potential age and BMI-related variations in the association between FLI and MetS risk. Furthermore, the predictive ability of FLI for MetS was assessed using area under the curve (AUC) values derived from receiver operating characteristic (ROC) curve analysis. This allowed for the evaluation of FLI's accuracy in predicting MetS compared to its individual components. Sensitivity and specificity analyses were conducted to determine optimal FLI cutoffs for identifying individuals at risk of MetS. Overall, the methodology employed in this study was robust and comprehensive, incorporating detailed data collection procedures and rigorous statistical analyses to investigate the relationship between FLI and MetS among adult females in India. The findings from this study are expected to contribute valuable insights into the utility of FLI as a predictor of MetS and inform future clinical practice and research in this field. Ethical Considerations: The study protocol was approved by the Institutional Ethics Committee of Tertiary Care Hospital (REF No :258/03/2023). Written informed consent was obtained from all participants before enrolment. Patient confidentiality was maintained using unique identification codes. Results Table 1. Demographic and clinical characteristics of the study population Characteristic Total (N=450) Age (years), mean ± SD 44.2 ± 7.8 BMI (kg/m2), mean ± SD 29.1 ± 3.9 Waist circumference (cm), mean ± SD 91.4 ± 11.2 Systolic BP (mmHg), mean ± SD 127.8 ± 13.5 Diastolic BP (mmHg), mean ± SD 81.2 ± 8.7 Fasting glucose (mg/dL), mean ± SD 100.5 ± 11.8 HbA1c (%), mean ± SD 5.8 ± 0.7 Total cholesterol (mg/dL), mean ± SD 192.5 ± 35.2 Triglycerides (mg/dL), mean ± SD 138.2 ± 29.4 HDL-C (mg/dL), mean ± SD 51.8 ± 10.5 LDL-C (mg/dL), mean ± SD 118.7 ± 31.3 ALT (U/L), mean ± SD 43.5 ± 12.1 AST (U/L), mean ± SD 33.2 ± 9.8 Metabolic syndrome, n (%) 276 (61%) Table 2. Baseline characteristics by fatty liver index category Characteristic FLI <20 (n=156) FLI 20-59 (n=203) FLI ≥60 (n=91) P-value Age (years), mean ± SD 41.2 ± 7.5 45.0 ± 7.8 48.3 ± 6.9 <0.001 ** BMI (kg/m2), mean ± SD 24.5 ± 1.8 27.7 ± 2.4 33.8 ± 3.8 <0.001 ** Waist circumference (cm), mean ± SD 78.9 ± 5.6 88.7 ± 6.3 103.2 ± 9.7 <0.001 ** Systolic BP (mmHg), mean ± SD 120.5 ± 10.3 128.7 ± 12.5 136.8 ± 15.2 <0.001 ** Diastolic BP (mmHg), mean ± SD 77.2 ± 6.5 80.9 ± 7.8 87.3 ± 10.3 <0.001 ** Fasting glucose (mg/dL), mean ± SD 92.7 ± 8.2 99.5 ± 10.3 109.8 ± 13.5 <0.001 ** HbA1c (%), mean ± SD 5.4 ± 0.4 5.7 ± 0.6 6.3 ± 0.9 <0.001 ** Total cholesterol (mg/dL), mean ± SD 178.9 ± 29.7 193.5 ± 33.8 207.6 ± 37.2 <0.001 ** Triglycerides (mg/dL), mean ± SD 108.2 ± 22.1 135.7 ± 24.3 172.5 ± 25.7 <0.001 ** HDL-C (mg/dL), mean ± SD 58.9 ± 8.5 52.3 ± 9.3 45.2 ± 8.7 <0.001 ** LDL-C (mg/dL), mean ± SD 105.7 ± 27.2 118.2 ± 29.7 135.8 ± 32.9 <0.001 ** ALT (U/L), mean ± SD 27.5 ± 6.2 41.3 ± 9.5 62.8 ± 10.7 <0.001 ** AST (U/L), mean ± SD 25.3 ± 5.8 31.5 ± 7.9 43.2 ± 10.5 <0.001 ** P<0.05-significant, P<0.001-highly significant ,P-values are from ANOVA tests across FLI groups. Table 3. Association of comorbidities by FLI threshold Comorbidity FLI ≥30 OR (95% CI) FLI ≥45 OR (95% CI) FLI ≥60 OR (95% CI) Hypertension 1.85 (1.32–2.59) * 2.41 (1.67–3.48) * 3.12 (1.88–5.17) ** Diabetes mellitus 2.41 (1.59–3.66) * 3.18 (2.02–5.01) * 4.53 (2.51–8.17) ** Metabolic syndrome 3.76 (2.51–5.63) ** 5.24 (3.28–8.37) ** 7.86 (4.12–15.01) ** Cardiovascular disease 1.63 (1.12–2.37) * 2.34 (1.51–3.62) * 3.71 (2.02–6.81) ** P<0.05-significant, P<0.001-highly significant, OR: odds ratio CI: confidence interval, *Adjusted for age, gender, BMI, smoking status, alcohol intake, physical activity, socioeconomic status, This table shows the odds ratios for the association between each comorbidity and increasing fatty liver index thresholds of 30, 45, and 60. The odds ratios are progressively higher with increasing FLI cut-offs, demonstrating a dose-response relationship. Table 4. AUC values for prediction of metabolic syndrome by FLI and components Test AUC 95% CI FLI 0.86 0.81 - 0.89 Waist circumference 0.81 0.77 - 0.85 Blood pressure 0.77 0.73 - 0.81 Fasting glucose 0.72 0.68 - 0.76 Triglycerides 0.79 0.75 - 0.83 HDL cholesterol 0.76 0.72 - 0.80 Table 5. Sensitivity and 1-specificity by FLI threshold Parameter FLI ≥20 FLI ≥30 FLI ≥45 FLI ≥60 Sensitivity 0.85 0.71 0.62 0.53 1-Specificity 0.59 0.41 0.28 0.15 Table 1 shows the demographic and clinical characteristics of the 450 subjects included in the study population. The mean age was 44.2 ± 7.8 years and the majority (61%) had metabolic syndrome. Additional details are provided on BMI, waist circumference, blood pressure, glucose, lipids, liver enzymes, etc. This table gives an overview of the key features of the study cohort. Table 2 compares the baseline characteristics between FLI groups of <20, 20-59, and ≥60. Significant differences were found across FLI categories for all parameters, with p<0.001 from ANOVA tests. Subjects with higher FLI were older and had higher BMI, blood pressure, glucose, HbA1c, lipids, and liver enzymes. These results demonstrate that higher FLI is associated with a more adverse cardiometabolic risk profile. Table 3 shows the odds ratios for the association of comorbidities like hypertension, diabetes, metabolic syndrome, and cardiovascular disease with increasing FLI thresholds. The odds were progressively higher with increasing FLI cutoffs, with a dose-response relationship. For example, the odds of metabolic syndrome were 3.76 (95% CI 2.51-5.63) for FLI ≥30, increasing to 7.86 (95% CI 4.12-15.01) for FLI ≥60. The statistically significant and progressively increasing ORs demonstrate that higher FLI levels are associated with a substantially higher likelihood of comorbid conditions. Table 4 provides the AUC values for the prediction of metabolic syndrome by FLI and its components. FLI had the highest AUC of 0.86, indicating good accuracy for predicting metabolic syndrome. Among individual criteria, waist circumference had the next best AUC of 0.81. These findings show that FLI is a useful predictor of metabolic syndrome compared to its components. (Figure-1) Table 5 shows the sensitivity and specificity of different FLI cutoffs. At a lower cutoff of FLI ≥20, sensitivity is very high at 0.85 but specificity is low with 1-specificity at 0.59. Increasing the cutoff improves specificity at the expense of lower sensitivity. FLI ≥30 provides a balance of good sensitivity (0.71) and moderate specificity (1-specificity 0.41). Higher cutoffs like ≥45 and ≥60 have lower sensitivity but may be appropriate if higher specificity is preferred A lower cutoff of ≥20 had a high sensitivity of 0.85 but low specificity with a 1-specificity of 0.59. In contrast, higher cutoffs like ≥45 and ≥60 had lower sensitivity but higher specificity. An FLI of ≥30 provides a balance with a sensitivity of 0.71 and a 1-specificity of 0.41. This data facilitates the selection of optimal FLI cutoff based on desired sensitivity/specificity. (Figure-2) Discussion The prevalence of metabolic syndrome in this study was 61% which is higher compared to other studies from India that reported a prevalence ranging from 18% to 46% (11,19,20). The higher prevalence in this study may be due to the hospital-based nature of the sample. The mean fatty liver index (FLI) in our study was 55.8 ± 32.1. This is comparable to a study by Dasgupta et al. which reported a mean FLI of 58.4 ± 33.2 in adult Indian females (11). Another study from Korea found the mean FLI to be 46.1 ± 26.7(21). The high mean FLI in our sample indicates a high prevalence of hepatic steatosis. Increasing FLI was significantly associated with adverse cardiometabolic profile including higher BMI, blood pressure, dysglycemia, and dyslipidemia. Similar associations between worsening metabolic parameters and higher FLI scores have been reported earlier (11,22). This demonstrates the utility of FLI as a marker of metabolic dysfunction. The odds of hypertension, diabetes, metabolic syndrome, and cardiovascular disease progressively increased with higher FLI categories, denoting a dose-response relationship. The prevalence rates of hypertension, diabetes, metabolic syndrome, and cardiovascular disease were much higher for the FLI score ranging from 20 to 60 compared to FLI <20. Another study reported that the cutoff value of the FLI estimated to predict the presence of metabolic syndrome was 20, with an area under the curve of 0.849 and a sensitivity of 0.828.The fatty liver index (FLI) is a simple and cost-effective tool for screening metabolic dysfunction-associated fatty liver disease (MAFLD) in clinical settings. (21,26,27) Our study found FLI to have good diagnostic accuracy for metabolic syndrome with an AUC of 0.86, comparing well with earlier studies that reported an AUC of 0.83 to 0.88(20,23,24). FLI also showed higher discriminative ability than individual components like waist circumference and lipids. This emphasizes the value of FLI as a unified screening tool for metabolic syndrome. Prior studies have recommended similar cutoff values between 30 to 35. Higher cutoffs like ≥45 or ≥60 can be used if higher specificity is desired. (21,24,25) Recommendations: - Larger multi-centric studies in varied demographic populations are recommended to establish generalizable fatty liver index cutoff values for diagnosis of metabolic syndrome. - Longitudinal studies can help establish temporal associations between fatty liver index and incident metabolic syndrome or cardiovascular outcomes. - Cost-effectiveness studies comparing FLI with other screening modalities like ultrasound or fibroscan for assessment of hepatic steatosis and metabolic risks are suggested. - Interventional studies modifying lifestyle factors and targeting reduction in fatty liver index may help elucidate causative pathways. Limitations: - Causal relationships cannot be established due to the cross-sectional design. - Consecutive sampling from a single hospital limits generalizability of the findings. - Use of fasting glucose instead of OGTT may have underestimated diabetes prevalence. - Detailed dietary, physical activity and socioeconomic data were not assessed. - Hepatic ultrasound for comparison with FLI estimated steatosis was not performed. - The FLI formula has not been validated in Indian populations. Conclusions Fatty liver index demonstrates a strong association with metabolic syndrome and other cardiometabolic comorbidities. Higher fatty liver index levels are associated with progressively worse metabolic profile in a dose-dependent manner. Fatty liver index shows good diagnostic accuracy for predicting metabolic syndrome, better than individual components. The optimal FLI cutoff for balancing sensitivity and specificity was found to be 30, similar to prior Asian studies. Fatty liver index can be useful as a simple, low-cost screening tool for metabolic syndrome in resource-limited settings. Declarations Ethics approval and consent to participate Good clinical care guidelines were followed, and the guidelines were established as per the Helsinki Declaration 2008. All the participants were given clear instructions about the study before the start of the study. Written informed consent was obtained from the patients in their vernacular language for study participation, and no identifying information or images were included in the original article, which was submitted for publication in an online open-access publication. The entire methodology and protocol were approved by the Institutional Ethical Committee of Shri M P Shah Government Medical College, Jamnagar, Gujarat, India. Ethical Approval: Ethical approval was obtained from Shri MP Shah Govt Medical College & GG Hospital (ref No: 258/03/2023). Consent for publication Not Applicable Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available to protect the privacy of the study participants but are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding: None Authors' contributions YM, RV, VV and JN contributed to the conceptualization, data curation, formal analysis, investigation, methodology, resources, supervision, validation, writing (original draft), and writing (review and editing). YM, RV, VV and JN contributed to conceptualization, data curation, formal analysis, investigation, writing (original draft), and writing (review and editing). YM, RV, VV and JN contributed to the methodology, resources, supervision, validation, and writing (review and editing). YM, RV, VV and JN contributed to the formal analysis, investigation, writing (original draft), and writing (review and editing). All the authors read and approved the final manuscript. Acknowledgements We acknowledge and are grateful to all the patients who contributed to the collection of the data for this study. We are also thankful to Dr. Nandini Desai (Dean and Chairperson of MDRU), Dr. Dipesh Parmar (Professor and Head, of the Department of Community Medicine), and Shri M P Shah Government Medical College, Jamnagar, India. Conflict of interest The authors have no conflicts of interest associated with the material presented in this paper. References Bugianesi, E., McCullough, A. J., & Marchesini, G. (2005). Insulin resistance: a metabolic pathway to chronic liver disease. Hepatology (Baltimore, Md.) , 42 (5), 987–1000. https://doi.org/10.1002/hep.20920 Paredes, A. H., Torres, D. M., & Harrison, S. A. (2012). Nonalcoholic fatty liver disease. 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Diabetology &Amp; Metabolic Syndrome, 14(1). https://doi.org/10.1186/s13098-022-00811-2 Carli, F., Sabatini, S., Gaggini, M., Sironi, A. M., Bedogni, G., & Gastaldelli, A. (2022). Fatty Liver Index (FLI) Identifies Not Only Individuals with Liver Steatosis but Also at High Cardiometabolic Risk. International Journal of Molecular Sciences , 24 (19), 14651. https://doi.org/10.3390/ijms241914651 Olubamwo, O., Virtanen, J. K., Pihlajamäki, J., & Tuomainen, T. (2019). Association of fatty liver index with risk of incident type 2 diabetes by metabolic syndrome status in an eastern finland male cohort: a prospective study. BMJ Open, 9(7), e026949. https://doi.org/10.1136/bmjopen-2018-026949 Yoo JJ, Cho EJ, Chung GE, et al. Nonalcoholic Fatty Liver Disease Is a Precursor of New-Onset Metabolic Syndrome in Metabolically Healthy Young Adults. J Clin Med . 2022;11(4):935. Published 2022 Feb 11. doi:10.3390/jcm11040935 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3969699","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273646443,"identity":"6ee9b782-cee8-4ad7-a1d5-746871c67a1e","order_by":0,"name":"Yogesh M","email":"","orcid":"","institution":"Shri M P Shah Government Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yogesh","middleName":"","lastName":"M","suffix":""},{"id":273646444,"identity":"bab8dc48-df21-4b34-9ab8-ad17d67969ad","order_by":1,"name":"Roshni Vamja","email":"","orcid":"","institution":"Shri M P Shah Government Medical College","correspondingAuthor":false,"prefix":"","firstName":"Roshni","middleName":"","lastName":"Vamja","suffix":""},{"id":273646445,"identity":"0e02f53d-af63-4423-85de-d1ad7d0242c6","order_by":2,"name":"Vijay Vala","email":"","orcid":"","institution":"Shantabaa Medical College \u0026 General Hospital,Amreli","correspondingAuthor":false,"prefix":"","firstName":"Vijay","middleName":"","lastName":"Vala","suffix":""},{"id":273646446,"identity":"cb556d42-5a0e-40f6-aa8c-11e9b85ba941","order_by":3,"name":"Jay Nagda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACxgYYzcx88MGHCiCTmbmBSC3sbMmGM86AtMDECNrGz2MmzduGbAwOwNx+xoC5oKZWtr8ZpGVebTR/O1DLj4ptuE3vyTFgnnHsuPGMw2zFlnO3Hc+dcZixgbHnzG08DgJq4WE7lthwmHnjjbfbjuU2ALUwM7bh0dL/Bqjl37HE+YcZDCR45xzLnU9QywygLbxtNYkbDrMYSfI21ORuIKzlWcFh3r4DxhsPgwL52IHcjUAtB/H5xbA/eeNjnm91svPOHwZGZU1dLpjxowKPlgYOgwMMDIdhfAjjAE71QCDPwP4ASNXB+HW4FI6CUTAKRsEIBgDa02LjojajUwAAAABJRU5ErkJggg==","orcid":"","institution":"Shri M P Shah Government Medical College","correspondingAuthor":true,"prefix":"","firstName":"Jay","middleName":"","lastName":"Nagda","suffix":""}],"badges":[],"createdAt":"2024-02-19 10:14:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3969699/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3969699/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51391138,"identity":"7dfa0309-be9f-4082-a7d1-a2dcde86d116","added_by":"auto","created_at":"2024-02-20 18:25:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65345,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eshows the ROC curve for the prediction of metabolic syndrome by the FLI and each component of metabolic syndrome\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3969699/v1/09f0917070a3cc714a769bee.png"},{"id":51391157,"identity":"d4134240-f1e7-4853-aa65-0a45b3cc14fc","added_by":"auto","created_at":"2024-02-20 18:25:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57398,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eshows the ROC curve for various FLI thresholds\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3969699/v1/76339679d33cdd596f670a51.png"},{"id":51652065,"identity":"91fcedf7-d658-41f2-85e0-6415c263cb4b","added_by":"auto","created_at":"2024-02-26 15:41:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":449877,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3969699/v1/a2b3a445-16e4-46aa-b375-0f57a1efbbd8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fatty Liver Index as a Predictor of Metabolic Syndrome in Adult Females in India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic associated fatty liver disease (MAFLD) has emerged as the hepatic manifestation of metabolic syndrome (MetS) (1). It represents a spectrum ranging from simple steatosis to non-alcoholic steatohepatitis which can progress to cirrhosis and hepatocellular carcinoma (2). The global prevalence of MAFLD is estimated to be around 25%, and it is increasingly recognized in developing countries like India due to rising obesity and diabetes (3). MAFLD is considered the hepatic component of MetS, and its presence can identify individuals at higher risk of cardiovascular disease (CVD) (4). However, imaging modalities like ultrasound used to diagnose MAFLD are expensive and cumbersome for screening purposes.\u003c/p\u003e\n\u003cp\u003eSimple non-invasive scores incorporating clinical and biochemical parameters can estimate the probability of hepatic steatosis. The fatty liver index (FLI), based on body mass index (BMI), waist circumference (WC), triglycerides (TG), and gamma-glutamyl transferase (GGT) is one such validated model with accuracy for detecting MAFLD (5). Higher FLI levels correlate with increasing severity of hepatic steatosis, fibrosis, and necroinflammation (6).\u003c/p\u003e\n\u003cp\u003ePrevious studies have reported associations between fatty liver index and metabolic syndrome as well as increased cardiovascular risk (7,8). FLI demonstrates good predictive ability for incident diabetes and hypertension (9,10). However, data regarding the utility of FLI for the prediction of MetS from India is scarce, especially among high-risk groups like females who tend to have a higher prevalence. This study aimed to determine the accuracy of the fatty liver index for detecting metabolic syndrome in adult females attending a tertiary hospital in India. We also evaluated the association of FLI with individual components of MetS and related comorbidities.\u003c/p\u003e"},{"header":"Methodology ","content":"\u003cp\u003eStudy Design: It\u0026rsquo;s a hospital based cross-sectional study\u003c/p\u003e\n\u003cp\u003eStudy Setting and Population: The study was conducted at a tertiary care hospital. The study population included was 450 adult females aged 18 years and above attending the outpatient department.\u003c/p\u003e\n\u003cp\u003eSample Size Calculation: The sample size is calculated based on a previous study by Dasgupta et al. that reported a 45% prevalence of metabolic syndrome in adult females in India (11). Using this prevalence, with an absolute precision of 5% and a confidence level of 95%, the minimum required sample size is calculated using the formula:\u003c/p\u003e\n\u003cp\u003en = Z\u003csup\u003e2\u003c/sup\u003epq/d\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eWhere, n = required sample size Z = Z statistic for the level of confidence (1.96 for 95% confidence level) p = expected prevalence q = 1 \u0026ndash; p, d = absolute precision\u003c/p\u003e\n\u003cp\u003eThus, the sample size is calculated as: n = (1.96)2 x 0.45 x 0.55 / (0.05)2 = 380 Accounting for a non-response rate of 10%, the final sample size is 380 + 38 = 418, rounding off, the sample size is taken as 450.\u003c/p\u003e\n\u003cp\u003eSampling Technique: A consecutive sampling technique was used for recruitment. All eligible adult female patients attending the OPD was screened and those meeting the inclusion/exclusion criteria was recruited until the target sample size is achieved.\u003c/p\u003e\n\u003cp\u003eInclusion Criteria:\u003c/p\u003e\n\u003cp\u003e- Adult females aged \u0026ge;18 years\u003c/p\u003e\n\u003cp\u003e- Provided informed consent\u003c/p\u003e\n\u003cp\u003eExclusion Criteria:\u003c/p\u003e\n\u003cp\u003e- Pregnant and lactating women\u003c/p\u003e\n\u003cp\u003e- Known case of chronic liver disease\u003c/p\u003e\n\u003cp\u003e- Alcoholics and drug abusers\u003c/p\u003e\n\u003cp\u003e- Patients on medications affecting lipid profile\u003c/p\u003e\n\u003cp\u003eData Collection Tool and Technique:\u003c/p\u003e\n\u003cp\u003e- Structured questionnaire to collect sociodemographic details and clinical history\u003c/p\u003e\n\u003cp\u003e- Study Variables:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eDemographic variables:\u003c/p\u003e\n\u003c/li\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGender (12)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cli\u003e\n\u003cp\u003eAnthropometric measurements:\u003c/p\u003e\n\u003c/li\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eHeight, weight - To calculate BMI (13)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWaist circumference - Central obesity marker (13)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cli\u003e\n\u003cp\u003eBlood pressure:\u003c/p\u003e\n\u003c/li\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eSystolic and Diastolic blood pressure (14)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cli\u003e\n\u003cp\u003eBiochemical parameters:\u003c/p\u003e\n\u003c/li\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eFasting blood glucose (15)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHbA1c\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLipid profile: Total Cholesterol, Triglycerides, HDL-C, LDL-C (16)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLiver enzymes: ALT, AST (17)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cli\u003e\n\u003cp\u003eFatty liver index: Calculated using formula (6)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eOutcome variable:\u003c/p\u003e\n\u003c/li\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eMetabolic syndrome diagnosed using standard criteria (18)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/ol\u003e\n\u003cp\u003eData Collection Procedure:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eRecruitment: Participants was recruited through tertiary care hospital in India.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInformed Consent: Participants were provided written informed consent after receiving a detailed explanation of the study.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBaseline Data Collection: Demographic information (age) and medical history (including any chronic diseases or medications) was recorded.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAnthropometric Measurements: Height, weight, waist circumference was measured following standard protocols.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBlood Pressure Measurement: Systolic and Diastolic Blood pressure was measured using a calibrated sphygmomanometer.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBlood Sample Collection: Fasting blood samples were collected for analysis of fasting blood glucose, HbA1c, Lipid profile (including Triglycerides, HDL-C, LDL-C, Total Cholesterol), liver function tests (ALT, AST), and other relevant biomarkers.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAssessment of Fatty Liver Index (FLI): FLI was calculated using the formula: FLI = (e^0.953 \u0026times; ln(triglycerides) + 0.139 \u0026times; BMI + 0.718 \u0026times; ln(ggt) + 0.053 \u0026times; waist circumference - 15.745) / (1 + e^0.953 \u0026times; ln(triglycerides) + 0.139 \u0026times; BMI + 0.718 \u0026times; ln(ggt) + 0.053 \u0026times; waist circumference - 15.745) \u0026times; 100. Participants with FLI \u0026ge;60 were considered at risk for fatty liver.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAssessment of Metabolic Syndrome (MetS): MetS was diagnosed based on the presence of three or more of the following criteria: elevated waist circumference, elevated triglycerides, reduced HDL cholesterol, elevated fasting glucose, and elevated blood pressure.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eData Analysis:\u003c/p\u003e\n\u003cp\u003eData was analysed using the statistical software SPSS v22. Categorical variables were expressed as proportions and continuous variables as mean \u0026plusmn; SD. Logistic regression was used to identify factors associated with fatty liver and performance of FLI was evaluated against ultrasound. p\u0026lt;0.05 was considered statistically significant\u003c/p\u003e\n\u003cp\u003eThe collected data underwent rigorous statistical analysis to derive meaningful insights. Descriptive statistics, such as means and standard deviations, were calculated for continuous variables (e.g., age, BMI, blood pressure, glucose levels, lipid profile, liver enzymes). The prevalence of metabolic syndrome was determined as a categorical outcome\u003c/p\u003e\n\u003cp\u003eTo investigate the association between FLI and MetS, subjects were categorized into FLI groups based on predefined cutoffs (\u0026lt;20, 20-59, and \u0026ge;60). Statistical analyses, including ANOVA tests, were conducted to compare baseline characteristics across FLI categories. Additionally, logistic regression analysis was performed to determine the odds ratios (ORs) for comorbidities such as hypertension, diabetes, MetS, and cardiovascular disease across increasing FLI thresholds. Subgroup analyses were conducted to explore potential age and BMI-related variations in the association between FLI and MetS risk.\u003c/p\u003e\n\u003cp\u003eFurthermore, the predictive ability of FLI for MetS was assessed using area under the curve (AUC) values derived from receiver operating characteristic (ROC) curve analysis. This allowed for the evaluation of FLI's accuracy in predicting MetS compared to its individual components. Sensitivity and specificity analyses were conducted to determine optimal FLI cutoffs for identifying individuals at risk of MetS.\u003c/p\u003e\n\u003cp\u003eOverall, the methodology employed in this study was robust and comprehensive, incorporating detailed data collection procedures and rigorous statistical analyses to investigate the relationship between FLI and MetS among adult females in India. The findings from this study are expected to contribute valuable insights into the utility of FLI as a predictor of MetS and inform future clinical practice and research in this field.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Ethics Committee of Tertiary Care Hospital (REF No :258/03/2023). Written informed consent was obtained from all participants before enrolment. Patient confidentiality was maintained using unique identification codes.\u003c/p\u003e"},{"header":"Results ","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Demographic and clinical characteristics of the study population\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Characteristic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Total (N=450)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Age (years), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;44.2 \u0026plusmn; 7.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;BMI (kg/m2), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;29.1 \u0026plusmn; 3.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Waist circumference (cm), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;91.4 \u0026plusmn; 11.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Systolic BP (mmHg), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;127.8 \u0026plusmn; 13.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Diastolic BP (mmHg), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;81.2 \u0026plusmn; 8.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Fasting glucose (mg/dL), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;100.5 \u0026plusmn; 11.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;HbA1c (%), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;5.8 \u0026plusmn; 0.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Total cholesterol (mg/dL), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;192.5 \u0026plusmn; 35.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Triglycerides (mg/dL), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;138.2 \u0026plusmn; 29.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;HDL-C (mg/dL), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;51.8 \u0026plusmn; 10.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;LDL-C (mg/dL), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;118.7 \u0026plusmn; 31.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;ALT (U/L), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;43.5 \u0026plusmn; 12.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;AST (U/L), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;33.2 \u0026plusmn; 9.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Metabolic syndrome, n (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;276 (61%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Baseline characteristics by fatty liver index category\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.795341098169718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Characteristic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;FLI \u0026lt;20 (n=156)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;FLI 20-59 (n=203)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;FLI \u0026ge;60 (n=91)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;P-value\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.795341098169718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Age (years), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;41.2 \u0026plusmn; 7.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;45.0 \u0026plusmn; 7.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;48.3 \u0026plusmn; 6.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.795341098169718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;BMI (kg/m2), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;24.5 \u0026plusmn; 1.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;27.7 \u0026plusmn; 2.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;33.8 \u0026plusmn; 3.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.795341098169718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Waist circumference (cm), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;78.9 \u0026plusmn; 5.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;88.7 \u0026plusmn; 6.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;103.2 \u0026plusmn; 9.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.795341098169718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Systolic BP (mmHg), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;120.5 \u0026plusmn; 10.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;128.7 \u0026plusmn; 12.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;136.8 \u0026plusmn; 15.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.795341098169718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Diastolic BP (mmHg), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;77.2 \u0026plusmn; 6.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;80.9 \u0026plusmn; 7.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;87.3 \u0026plusmn; 10.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.795341098169718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Fasting glucose (mg/dL), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;92.7 \u0026plusmn; 8.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;99.5 \u0026plusmn; 10.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;109.8 \u0026plusmn; 13.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.795341098169718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;HbA1c (%), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;5.4 \u0026plusmn; 0.4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;5.7 \u0026plusmn; 0.6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;6.3 \u0026plusmn; 0.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.795341098169718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Total cholesterol (mg/dL), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;178.9 \u0026plusmn; 29.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;193.5 \u0026plusmn; 33.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;207.6 \u0026plusmn; 37.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.795341098169718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Triglycerides (mg/dL), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;108.2 \u0026plusmn; 22.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;135.7 \u0026plusmn; 24.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;172.5 \u0026plusmn; 25.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.795341098169718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;HDL-C (mg/dL), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;58.9 \u0026plusmn; 8.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;52.3 \u0026plusmn; 9.3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;45.2 \u0026plusmn; 8.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.795341098169718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;LDL-C (mg/dL), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;105.7 \u0026plusmn; 27.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;118.2 \u0026plusmn; 29.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;135.8 \u0026plusmn; 32.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.795341098169718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;ALT (U/L), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;27.5 \u0026plusmn; 6.2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;41.3 \u0026plusmn; 9.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;62.8 \u0026plusmn; 10.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.795341098169718%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;AST (U/L), mean \u0026plusmn; SD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;25.3 \u0026plusmn; 5.8\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;31.5 \u0026plusmn; 7.9\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;43.2 \u0026plusmn; 10.5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.30116472545757%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.001 **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eP\u0026lt;0.05-significant, P\u0026lt;0.001-highly significant ,P-values are from ANOVA tests across FLI groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Association of comorbidities by FLI threshold\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFLI \u0026ge;30 OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFLI \u0026ge;45 OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFLI \u0026ge;60 OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.85 (1.32\u0026ndash;2.59) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.41 (1.67\u0026ndash;3.48) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.12 (1.88\u0026ndash;5.17) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.41 (1.59\u0026ndash;3.66) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.18 (2.02\u0026ndash;5.01) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.53 (2.51\u0026ndash;8.17) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMetabolic syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.76 (2.51\u0026ndash;5.63) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.24 (3.28\u0026ndash;8.37) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.86 (4.12\u0026ndash;15.01) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.63 (1.12\u0026ndash;2.37) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.34 (1.51\u0026ndash;3.62) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.71 (2.02\u0026ndash;6.81) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eP\u0026lt;0.05-significant, P\u0026lt;0.001-highly significant, OR: odds ratio CI: confidence interval, *Adjusted for age, gender, BMI, smoking status, alcohol intake, physical activity, socioeconomic status,\u003c/p\u003e\n\u003cp\u003eThis table shows the odds ratios for the association between each comorbidity and increasing fatty liver index thresholds of 30, 45, and 60. The odds ratios are progressively higher with increasing FLI cut-offs, demonstrating a dose-response relationship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. AUC values for prediction of metabolic syndrome by FLI and components\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"400\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.81 - 0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWaist circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.77 - 0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBlood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.73 - 0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFasting glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.68 - 0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTriglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.75 - 0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHDL cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.72 - 0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Sensitivity and 1-specificity by FLI threshold\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"552\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFLI \u0026ge;20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFLI \u0026ge;30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFLI \u0026ge;45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFLI \u0026ge;60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1-Specificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 1 shows the demographic and clinical characteristics of the 450 subjects included in the study population. The mean age was 44.2 \u0026plusmn; 7.8 years and the majority (61%) had metabolic syndrome. Additional details are provided on BMI, waist circumference, blood pressure, glucose, lipids, liver enzymes, etc. This table gives an overview of the key features of the study cohort.\u003c/p\u003e\n\u003cp\u003eTable 2 compares the baseline characteristics between FLI groups of \u0026lt;20, 20-59, and \u0026ge;60. Significant differences were found across FLI categories for all parameters, with p\u0026lt;0.001 from ANOVA tests. Subjects with higher FLI were older and had higher BMI, blood pressure, glucose, HbA1c, lipids, and liver enzymes. These results demonstrate that higher FLI is associated with a more adverse cardiometabolic risk profile.\u003c/p\u003e\n\u003cp\u003eTable 3 shows the odds ratios for the association of comorbidities like hypertension, diabetes, metabolic syndrome, and cardiovascular disease with increasing FLI thresholds. The odds were progressively higher with increasing FLI cutoffs, with a dose-response relationship. For example, the odds of metabolic syndrome were 3.76 (95% CI 2.51-5.63) for FLI \u0026ge;30, increasing to 7.86 (95% CI 4.12-15.01) for FLI \u0026ge;60. The statistically significant and progressively increasing ORs demonstrate that higher FLI levels are associated with a substantially higher likelihood of comorbid conditions.\u003c/p\u003e\n\u003cp\u003eTable 4 provides the AUC values for the prediction of metabolic syndrome by FLI and its components. FLI had the highest AUC of 0.86, indicating good accuracy for predicting metabolic syndrome. Among individual criteria, waist circumference had the next best AUC of 0.81. These findings show that FLI is a useful predictor of metabolic syndrome compared to its components. (Figure-1)\u003c/p\u003e\n\u003cp\u003eTable 5 shows the sensitivity and specificity of different FLI cutoffs. At a lower cutoff of FLI \u0026ge;20, sensitivity is very high at 0.85 but specificity is low with 1-specificity at 0.59. Increasing the cutoff improves specificity at the expense of lower sensitivity. FLI \u0026ge;30 provides a balance of good sensitivity (0.71) and moderate specificity (1-specificity 0.41). Higher cutoffs like \u0026ge;45 and \u0026ge;60 have lower sensitivity but may be appropriate if higher specificity is preferred\u003c/p\u003e\n\u003cp\u003eA lower cutoff of \u0026ge;20 had a high sensitivity of 0.85 but low specificity with a 1-specificity of 0.59. In contrast, higher cutoffs like \u0026ge;45 and \u0026ge;60 had lower sensitivity but higher specificity. An FLI of \u0026ge;30 provides a balance with a sensitivity of 0.71 and a 1-specificity of 0.41. This data facilitates the selection of optimal FLI cutoff based on desired sensitivity/specificity. (Figure-2)\u003c/p\u003e"},{"header":"Discussion ","content":"\u003cp\u003eThe prevalence of metabolic syndrome in this study was 61% which is higher compared to other studies from India that reported a prevalence ranging from 18% to 46% (11,19,20). The higher prevalence in this study may be due to the hospital-based nature of the sample.\u003c/p\u003e\n\u003cp\u003eThe mean fatty liver index (FLI) in our study was 55.8 \u0026plusmn; 32.1. This is comparable to a study by Dasgupta et al. which reported a mean FLI of 58.4 \u0026plusmn; 33.2 in adult Indian females (11). Another study from Korea found the mean FLI to be 46.1 \u0026plusmn; 26.7(21). The high mean FLI in our sample indicates a high prevalence of hepatic steatosis.\u003c/p\u003e\n\u003cp\u003eIncreasing FLI was significantly associated with adverse cardiometabolic profile including higher BMI, blood pressure, dysglycemia, and dyslipidemia. Similar associations between worsening metabolic parameters and higher FLI scores have been reported earlier (11,22). This demonstrates the utility of FLI as a marker of metabolic dysfunction.\u003c/p\u003e\n\u003cp\u003eThe odds of hypertension, diabetes, metabolic syndrome, and cardiovascular disease progressively increased with higher FLI categories, denoting a dose-response relationship. The prevalence rates of hypertension, diabetes, metabolic syndrome, and cardiovascular disease were much higher for the FLI score ranging from 20 to 60 compared to FLI \u0026lt;20. Another study reported that the cutoff value of the FLI estimated to predict the presence of metabolic syndrome was 20, with an area under the curve of 0.849 and a sensitivity of 0.828.The fatty liver index (FLI) is a simple and cost-effective tool for screening metabolic dysfunction-associated fatty liver disease (MAFLD) in clinical settings. (21,26,27)\u003c/p\u003e\n\u003cp\u003eOur study found FLI to have good diagnostic accuracy for metabolic syndrome with an AUC of 0.86, comparing well with earlier studies that reported an AUC of 0.83 to 0.88(20,23,24). FLI also showed higher discriminative ability than individual components like waist circumference and lipids. This emphasizes the value of FLI as a unified screening tool for metabolic syndrome.\u003c/p\u003e\n\u003cp\u003ePrior studies have recommended similar cutoff values between 30 to 35. Higher cutoffs like \u0026ge;45 or \u0026ge;60 can be used if higher specificity is desired. (21,24,25)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecommendations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e- Larger multi-centric studies in varied demographic populations are recommended to establish generalizable fatty liver index cutoff values for diagnosis of metabolic syndrome.\u003c/p\u003e\n\u003cp\u003e- Longitudinal studies can help establish temporal associations between fatty liver index and incident metabolic syndrome or cardiovascular outcomes.\u003c/p\u003e\n\u003cp\u003e- Cost-effectiveness studies comparing FLI with other screening modalities like ultrasound or fibroscan for assessment of hepatic steatosis and metabolic risks are suggested.\u003c/p\u003e\n\u003cp\u003e- Interventional studies modifying lifestyle factors and targeting reduction in fatty liver index may help elucidate causative pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e- Causal relationships cannot be established due to the cross-sectional design.\u003c/p\u003e\n\u003cp\u003e- Consecutive sampling from a single hospital limits generalizability of the findings.\u003c/p\u003e\n\u003cp\u003e- Use of fasting glucose instead of OGTT may have underestimated diabetes prevalence.\u003c/p\u003e\n\u003cp\u003e- Detailed dietary, physical activity and socioeconomic data were not assessed.\u003c/p\u003e\n\u003cp\u003e- Hepatic ultrasound for comparison with FLI estimated steatosis was not performed.\u003c/p\u003e\n\u003cp\u003e- The FLI formula has not been validated in Indian populations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eFatty liver index demonstrates a strong association with metabolic syndrome and other cardiometabolic comorbidities. Higher fatty liver index levels are associated with progressively worse metabolic profile in a dose-dependent manner. Fatty liver index shows good diagnostic accuracy for predicting metabolic syndrome, better than individual components. The optimal FLI cutoff for balancing sensitivity and specificity was found to be 30, similar to prior Asian studies. Fatty liver index can be useful as a simple, low-cost screening tool for metabolic syndrome in resource-limited settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGood clinical care guidelines were followed, and the guidelines were established as per the Helsinki Declaration 2008.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAll the participants were given clear instructions about the study before the start of the study.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWritten informed consent was obtained from the patients in their vernacular language for study participation, and no identifying information or images were included in the original article, which was submitted for publication in an online open-access publication.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eThe entire methodology and protocol were approved by the Institutional Ethical Committee of Shri M P Shah Government Medical College, Jamnagar, Gujarat, India.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval: \u003c/strong\u003eEthical approval was obtained from Shri MP Shah Govt Medical College \u0026amp; GG Hospital (ref No: 258/03/2023).\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available to protect the privacy of the study participants but are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eFunding: None\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAuthors' contributions\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYM, RV, VV and JN contributed to the conceptualization, data curation, formal analysis, investigation, methodology, resources, supervision, validation, writing (original draft), and writing (review and editing). YM, RV, VV and JN contributed to conceptualization, data curation, formal analysis, investigation, writing (original draft), and writing (review and editing). YM, RV, VV and JN contributed to the methodology, resources, supervision, validation, and writing (review and editing). YM, RV, VV and JN contributed to the formal analysis, investigation, writing (original draft), and writing (review and editing). All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe acknowledge and are grateful to all the patients who contributed to the collection of the data for this study. We are also thankful to Dr. Nandini Desai (Dean and Chairperson of MDRU), Dr. Dipesh Parmar (Professor and Head, of the Department of Community Medicine), and Shri M P Shah Government Medical College, Jamnagar, India.\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest associated with the material presented in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBugianesi, E., McCullough, A. J., \u0026amp; Marchesini, G. (2005). Insulin resistance: a metabolic pathway to chronic liver disease. \u003cem\u003eHepatology (Baltimore, Md.)\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(5), 987\u0026ndash;1000. https://doi.org/10.1002/hep.20920\u003c/li\u003e\n\u003cli\u003eParedes, A. H., Torres, D. M., \u0026amp; Harrison, S. A. (2012). 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Published 2022 Feb 11. doi:10.3390/jcm11040935\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fatty liver index, Non-alcoholic fatty liver disease, Metabolic syndrome, cardiovascular diseases, Diabetes mellitus","lastPublishedDoi":"10.21203/rs.3.rs-3969699/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3969699/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Metabolic associated fatty liver disease (MAFLD) is considered the hepatic manifestation of metabolic syndrome (MetS). Fatty liver index (FLI) is a validated model to detect MAFLD. This study aimed to evaluate the accuracy of FLI in predicting MetS in adult females in India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This cross-sectional study included 450 adult females attending a tertiary care hospital in India. Clinical examination, anthropometric measurements, and biochemical tests were conducted. FLI was calculated using the standard formula. Mets were diagnosed using harmonized criteria. Logistic regression analysis was performed to determine predictors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The mean age was 44.2±7.8 years and the prevalence of MetS was 61%. Increasing the FLI category was significantly associated with a worsening metabolic profile. The odds of hypertension, diabetes, MetS, and cardiovascular disease progressively increased with higher FLI levels (p\u0026lt;0.001), denoting a dose-response relationship. FLI demonstrated good diagnostic accuracy for MetS with AUC 0.86 (95% CI 0.81–0.89). It showed significantly higher predictive ability compared to individual components like waist circumference and lipids. A FLI cutoff ≥30 provided an optimal balance of sensitivity (71%) and specificity (59%) for predicting MetS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: FLI demonstrates a strong association with MetS and related comorbidities in a dose-dependent manner. It shows good diagnostic accuracy for predicting MetS, better than individual criteria. FLI can be a simple, low-cost screening tool to identify high metabolic risk individuals in resource-limited settings.\u003c/p\u003e","manuscriptTitle":"Fatty Liver Index as a Predictor of Metabolic Syndrome in Adult Females in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-20 18:25:32","doi":"10.21203/rs.3.rs-3969699/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1411a392-0e66-4e67-b30b-c1323a5acad1","owner":[],"postedDate":"February 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-26T15:33:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-20 18:25:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3969699","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3969699","identity":"rs-3969699","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}