Steatosis Non-Invasive Tests Accurately Predict Metabolic Dysfunction-Associated Steatotic Liver Disease, While Fibrosis Non-Invasive Tests Fall Short: Validation in U.S. Adult Population

Steatosis Non-Invasive Tests Accurately Predict Metabolic Dysfunction-Associated Steatotic Liver Disease, While Fibrosis Non-Invasive Tests Fall Short: Validation in U.S. Adult Population | 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 Steatosis Non-Invasive Tests Accurately Predict Metabolic Dysfunction-Associated Steatotic Liver Disease, While Fibrosis Non-Invasive Tests Fall Short: Validation in U.S. Adult Population Ayesha Sualeheen, Sze-Yen Tan, Robin M. Daly, Ekavi Georgousopoulou, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5456895/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction Metabolic dysfunction associated steatotic liver disease (MASLD) has replaced NAFLD as the diagnostic standard. This study aimed to validate steatosis and fibrosis non-invasive tests (NITs) used for NAFLD in predicting MASLD and advanced fibrosis, compared to transient elastography (TE), respectively. Methods This cross-sectional study used the NHANES database (2017–2020). The Dallas steatosis index (DSI), fatty liver index (FLI), Framingham steatosis index (FSI) and hepatic steatosis index (HSI) were assessed against TE diagnosed MASLD. The NAFLD fibrosis score (NFS), fibrosis-4 (FIB-4), APRI (The aspartate aminotransferase to platelet ratio index), and BARD (body mass index, aspartate aminotransferase/alanine aminotransferase ratio diabetes score) were assessed against TE diagnosed advanced fibrosis. The diagnostic accuracy evaluated with the weighted ROC analysis. Results The study included 5,399 participants (51% female), with an estimated MASLD prevalence of 42.9%, and 10.6% indicated advanced fibrosis as assessed by TE. Steatosis NITs showed good diagnostic accuracy for predicting MASLD (AUROC 0.835 to 0.862), with FLI having the maximum Youden index (0.55). Fibrosis NITs indicated poor to fair diagnostic performance for predicting advanced fibrosis (AUROC 0.572 to 0.699) but had high NPV (89%-94%). In an age categorized subgroup analysis fibrosis NITs indicated poor performance in those aged ≤ 35 years and exhibited unacceptably low specificity to exclude fibrosis in those aged ≥ 65 years. Conclusion In this population-based cohort of U.S. adults, all steatosis NITs demonstrated good diagnostic accuracy for MASLD. However, the fibrosis NITs showed limited diagnostic ability and were influenced by age, suggesting they should be used with caution in the general population. Validation MASLD NAFLD advanced fibrosis NITs transient elastography FLI HSI NFS FIB-4 Figures Figure 1 Figure 2 Introduction Metabolic dysfunction associated fatty liver disease (MASLD), formerly known as NAFLD, is the new nomenclature and diagnostic criteria introduced by the American Association for the Study of Liver Disease (AASLD) [ 1 ]. Unlike NAFLD, which excludes other steatogenic liver diseases (SLDs), MASLD allows the co-existence of other SLDs along with the presence of at least one metabolic disorder [ 1 ]. In 2022, the global prevalence for NAFLD was estimated to be 32.4% [ 2 ], which surpassed the global prevalence of 25% in 2016 [ 3 ]. There is no global data for reporting MASLD prevalence, however it can be assumed that MASLD prevalence is higher compared to NAFLD due to the inclusivity of other steatogenic liver diseases. Obesity and insulin resistance are major factors contributing to the onset of MASLD [ 3 ], with prevalence rates as high as 50–80% among the MASLD population [ 3 ]. The presence of metabolic dysregulation accompanied with hepatic steatosis has been linked to adverse liver-related outcomes such as cirrhosis and hepatocellular carcinoma, and/or cardiovascular events [ 4 ]. This evidence justifies the inclusion of metabolic risk factors in the new definition and highlights that they are high-risk individuals requiring more robust disease management. MASLD is often asymptomatic until more advanced stages and usually detected when other comorbidities are diagnosed. Transient elastography (TE) is a simple, widely available non-invasive technique that applies ultrasound and low- frequency elastic waves to detect fibrosis and intrahepatic fat content. TE has demonstrated good overall diagnostic performance, with sensitivity of 80% and 71% to detect hepatic steatosis and advanced fibrosis, respectively, in a population with SLDs when compared with the gold standard, liver biopsy [ 5 ]. It has also been adopted in national level health examination surveys such as the National Health and Nutrition Examination Survey (NHANES) from the United States [ 6 ]. Current clinical practice guidelines, especially the AASLD, do not advise population level screening for MASLD using imaging and/or ultrasound-based modalities such as TE in the absence of cost-effectiveness data [ 7 ]. Instead, simpler, more cost-effective, and accessible non-invasive tests (NITs) are typically used to predict hepatic steatosis and advanced fibrosis (stage 3–4). The Dallas steatosis index (DSI) [ 8 ], the fatty liver index (FLI) [ 9 ], the Framingham steatosis index (FSI) [ 10 ] and the hepatic steatosis index (HSI) [ 11 ] are validated NITs reported throughout the literature which are calculated from readily available clinical and laboratory variables and can be utilized when biopsy or imaging modalities are inaccessible. Likewise, the aspartate aminotransferase to platelet ratio index (APRI) [ 12 ], the body mass index (BMI), aspartate aminotransferase/alanine aminotransferase ratio diabetes score (BARD) [ 13 ], the fibrosis-4 (FIB-4) [ 14 ] and the NAFLD fibrosis score (NFS) [ 15 ] are widely utilized to predict advanced fibrosis and have shown acceptable diagnostic performance against the gold standard, liver biopsy [ 14 , 15 ]. However, the diagnostic performance of these steatosis and fibrosis NITs varies depending on disease prevalence, clinical biomarkers, and presence of metabolic dysregulations [ 16 , 17 ]. In contrast to NAFLD, the new MASLD diagnostic criteria does not exclude secondary causes of liver disease except for excessive alcohol intake and instead requires the presence of metabolic dysregulation(s). Thus, these steatosis and fibrosis NITs require evaluation using the MASLD criteria. Hence, the aim of this study was to validate the diagnostic accuracy for DSI, FLI, FSI and HSI in detecting TE diagnosed hepatic steatosis attributed to MASLD in a large nationally representative population-based cohort, the NHANES 2017–2020 cycle. A secondary aim was to validate the diagnostic accuracy of APRI, BARD, FIB-4 and NFS for predicting advanced fibrosis among those with established MASLD. Methods This cross-sectional study represents secondary data analysis from the 2017–2020 NHANES cycle as this was the only cycle with TE available. The NHANES is a nationally representative survey of the U.S general population conducted by the National Centre for Health Statistics, a part of the Centre for Disease Control and Prevention. The methodology and data collection protocols and data files of this national survey are publicly available ( www.cdc.gov/nchs/nhanes/index.htm ). Briefly, NHANES data consists of demographic, socioeconomic, health-related questionnaires, and dietary data as well as medical examinations including anthropometric, laboratory and physical assessments. For the current study, adults aged ≥ 18 years with completed TE imaging were initially shortlisted (n = 7,768). Participants with incomplete or invalid TE (n = 1,925) or with excessive alcohol consumption (> 30g/day for men and > 20g/day for women) (n = 778) or consuming hepatotoxic drugs (n = 101) were excluded. Participants with missing data relevant to identify excessive alcohol intake (n = 614), metabolic dysregulations (n = 1,137) and variables relevant to compute steatosis (n = 780) and fibrosis NITs (n = 620) were further excluded. Some participants had missing data for more than one variable resulting in overlapping of the numbers reported above. In total, 5,399 participants met the inclusion criteria with complete data and were included in this analysis. The NHANES is approved by the National Centre for Health Statistics review board, and all study participants provided written consent. Sociodemographic Information and Interview Information about participants’ age, sex and ethnicity/race were obtained from the demographic questionnaire. Self-reported diabetes status and prescription medication data were acquired from the health examination questionnaires. Alcohol intake was calculated using day 1 from the 24-hour recalls where total nutrient intake was available. This was selected as there were a greater number of participants available with fewer missing values as compared to day 2. Transient Elastography (TE) TE data was extracted for all participants from the NHANES 2017–2020 cycle. This data was used to identify participants with hepatic steatosis and fibrosis through controlled attenuation parameter (CAP) and liver stiffness measurement (LSM) respectively, computed by FibroScan® model 502 V2 Touch. An optimal CAP cutoff ≥ 274 dB/m reflective of ≥ 5% of intrahepatic fat content was used to detect individuals with fatty liver. Additionally, a more robust cutoff of 302 dB/m was also utilized to assess intrahepatic fat [ 5 ]. Likewise, a validated LSM cutoff ≥ 9.7 kPa was used to identify advanced fibrosis (F3-F4). Covariables Trained NHANES technicians measured height, weight, and waist circumference using standard procedures. Body mass index (BMI) was calculated by dividing weight in kilograms (kg) by height in meters square (m 2 ). Systolic and diastolic blood pressure data consist of three consecutive measurements, with an average of these measurements used for analysis. Serum biomarkers included fasting glucose and insulin, glycated hemoglobin (HbA1C), triglycerides, total cholesterol, high density lipoprotein cholesterol (HDL) and plasma high sensitivity C-reactive protein (Hs-CRP). Additionally, serum biomarkers used to assess liver function included alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma glutamyl transferase (GGT), platelet count and albumin. Detailed descriptions regarding the data collection protocol and analytical guidelines for these biomarkers were available on the NHANES website ( www.cdc.gov/nchs/nhanes/index.htm ). Identification of MASLD MASLD was confirmed by the presence of hepatic steatosis detected by the CAP values ≥ 274 dB/m, plus the presence of at least one of five cardiometabolic risk factors including: being overweight or obese, presence of diabetes, hypertension, hypertriglyceridemia, or low HDL levels [ 1 ]. Cardiometabolic risk factor criteria The cardiometabolic risk factor criteria utilized in this study were as follows: overweight was defined as BMI ≥ 25 kg/m 2 (≥ 23 kg/m 2 for Asians) and obesity as BMI ≥ 30 kg/m 2 (≥ 27.5kg/m 2 for Asians); abdominal obesity as waist circumference ≥ 94 cm for males and ≥ 80 cm for females (≥ 90 cm males and ≥ 80 cm females for Asians); type 2 diabetes (T2DM) status was determined by self-report and those who did not report T2DM but had fasting HbA1C levels ≥ 6.5% or who were taking antidiabetic drugs [ 18 ]. A HbA1C level of 5.7% was used to determine prediabetes for MASLD criteria as described previously [ 1 ]. Hypertension was defined as systolic blood pressure ≥ 130 mmHg and/or diastolic blood pressure ≥ 85 mmHg or on antihypertensive drugs. Hypertriglyceridemia was defined as serum triglycerides levels ≥ 150mg/dL or on lipid lowering drugs; low HDL levels as ≤ 40mg/dL for males and ≤ 50mg/dL for females or on lipid lowering drugs. The homeostasis model assessment of insulin resistance (HOMA-IR) was used to assess insulin resistance and was calculated as fasting glucose (mmol) × fasting insulin (moly/L)/ 22.5. Biochemical NITs to predict steatosis and advanced fibrosis The hepatic steatosis and fibrosis NITs with their formulae and scoring cutoffs provided in Table 1 . Table 1 Scoring NITs to predict hepatic steatosis and advanced fibrosis NITs Developed and validated Variables Formulae Score cutoffs Steatosis NITs DSI [8] Multiethnic population-based cohort, the Dallas heart study[ 8 ]. ALT, BMI, age, sex, triglycerides, glucose, diabetes, hypertension, and ethnicity. DSI LOGIT = − 9.4 + 0.316 (if age ≥ 50 and female) + 2.4 (if known DM) + 0.02 ∗ (equals 0 if DM; if not diabetic equals the glucose concentration in mg/dL) + 0.3 (if known HTN) + 0.5 (if Hispanic/Asian/Other race) + Ln (TGs in mg/dL) + 0.4 (if ALT 13.5 − 19.49 IU/L) + 1.1 (if ALT 19.5–40 IU/L) + 1.5 (if ALT > 40 IU/L) + 0.7 (if not black and BMI 25 − 27.49 kg/m 2 and) + 1.4 (if not black and BMI 27.5 − 34.9 kg/m 2 ) + 1.9 (if not black and BMI 35 − 37.49 kg/m 2 ) + 2.6 (if not black and BMI > 37.5kg/m 2 ) − 0.2 (if black and BMI 25 − 27.49 kg/m 2 and) + 0.8 (if black and BMI 27.5 − 34.9kg/m 2 ) + 0.8 (if black and BMI 35 − 37.49 kg/m 2 ) + 1.8 (if black and BMI > 37.5kg/m 2 ) ≥ 0 FLI [9] Italian case control study[ 9 ]. BMI, waist circumference, triglycerides, and GGT. e x / (1 + e x ), where X = 0.953* ln (TGs) + 0.139 * BMI + 0.718 * ln (GGT) + 0.053 * WC – 15.745) < 30 / ≥ 60 FSI [10] Framingham heart study third generation cohort and NHANES III study[ 10 ]. Age, sex, BMI, triglycerides, hypertension, diabetes, and ALT:AST ratio. e x / (1 + e x ), where X = -7.981 + 0.011 * age (years)− 0.146 * sex (female = 1, male = 0) + 0.173 * BMI + 0.007 * TGs + 0.593 * HTN (yes = 1, no = 0) + 0.789 * DM (yes = 1, no = 0) + 1.1 * ALT/AST ratio ≥ 1.33 (yes = 1, no = 0) ≥ 23 HSI [11] Asian case control study[ 11 ]. ALT/AST ratio, BMI, diabetes, and sex HSI = 8 × ALT/AST ratio + BMI (+ 2, if DM; +2, if female) 36 Fibrosis NITs APRI [12] Patients with chronic hepatitis C virus [ 12 ]. AST and platelets AST level (/ULN)/Platelet counts (10 9 /L) * 100 ≤ 0.50 / >1.50 BARD [13] Non-diabetic NAFLD cohort [ 13 ]. BMI, ALT, AST, and diabetes status AST/ALT ratio ≥ 0.8 sums 2 points; BMI ≥ 28 kg/m 2 sums 1 point; presence of DM sums 1 point. ≥ 2 FIB-4 [14] Patients with human immunodeficiency virus and hepatitis C virus coinfection and biopsy confirmed NAFLD cohort [ 14 ]. Age, ALT, AST, and platelets [Age (years) * AST (U/L) ] / [Platelet [10 9 /L] * √ALT (U/L) ] ≤ 1.30 / ≥ 2.67 NFS [15] Biopsy confirmed NAFLD cohort [ 15 ]. Age, BMI, diabetes status, AST:ALT ratio, platelets, and albumin -1.675 + [0.037 * age (years) ] + [0.094 * BMI (kg/m2) ] + [1.13 * IFG/DM (yes=1, no=0) ] + [0.99 * AST/ALT ratio] – [0.013 * platelet (*10 9 /l)] – [0.66 * albumin (g/dl) ] 0.676 Abbreviations ALT, alanine amino transferase; AST, aspartate aminotransferase; APRI, AST to platelet ratio index; BARD, BMI, AST/ALT ratio and DM; BMI, body mass index; DM, diabetes mellitus; DSI, Dallas Steatosis Index; FLI, fatty liver index; FIB-4, fibrosis-4; FSI, Framingham risk score; GGT, gamma-glutamyl transferase; HTN, hypertension; IFG, impaired fasting glucose; NFS, NAFLD fibrosis score; TGs, triglycerides; ULN, upper limit of normal, WC, waist circumference. Statistical Analysis The normal distribution of variables was tested by visual inspection of histograms and P-P plots. Population characteristics are presented as estimated mean (95% CI) for continuous variables and as proportions for categorical variables. Diagnostic accuracy for biochemical NITs were determined from the receiver operator curve (ROC) analysis by observing area under ROC (AUROC). Sensitivity, specificity, positive predicted value (PPV), negative predicted values (NPV) and Youden index were calculated for each model with the established cutoffs from the original article and were analyzed using MASLD criteria. Positive and negative likelihood ratios, which show the ability of a test to transform the pre-test probability of the target condition into a post-test probability, were calculated. The AUROC for steatosis NITs were compared with CAP diagnosed with MASLD and the AUROC for fibrosis NITs were compared with LSM diagnosed advanced fibrosis in the MASLD group. The validated cutoffs for steatosis and fibrosis NITs are reported in Table 1 . Kappa-statistics were used to determine interrater agreement between the CAP and LSM diagnosed liver outcomes and biochemical NITs diagnosed outcomes i.e., MASLD and advanced fibrosis. The strength of agreement was interpreted based on the following kappa coefficients: <0 none, 0-0.20 slight, 0.21–0.39 fair, 0.40–0.59 moderate, 0.61–0.80 substantial and ≥ 0.81 almost perfect agreement [ 19 ]. Given the evidence that fibrosis NITs could be less reliable in individuals aged ≤ 35 and ≥ 65 years [ 16 ], a subgroup analysis of fibrosis NITs was conducted across the following age groups: ≤35 years, 36–64 years, and ≥ 65 years in the MASLD cohort. All analysis were conducted using survey weights, which is recommended for NHANES data analysis [ 20 ]. These weights used to account for complex survey design, nonresponse issues, post-stratification, and oversampling. Weighting transformed this survey data into a reflection of the broader U.S. non-institutionalized population, ensuring its representativeness. All statistical analyses were conducted using STATA version 17.0, and p-values < 0.05 were considered statistically significant. Results Characteristics of study population As shown in Table 2 , 5,399 participants were included in the analysis with estimated mean age of 47.4 years and 49.1% being males. The estimated mean BMI was 29.6kg/m 2 , with 32.3% classified as overweight and 42.4% as obese. The estimated prevalence of T2DM and hypertension was 14.9% and 41.8%, respectively. The estimated MASLD prevalence was 42.9% as assessed by a CAP value ≥ 274 dB/m and 28.4% with a more conservative, validated CAP cutoff ≥ 302 dB/m. Among those with established MASLD, 10.6% were estimated to have advanced fibrosis (stage 3–4). Participants with MASLD were older and included a higher proportion of males and Hispanics and a lower proportion of non-Hispanics Blacks compared to the non-MASLD group. The demographic and metabolic characteristics of participants classified by MASLD status reported in Table 2 . As expected, people with MASLD had higher mean BMI, exhibited dyslipidemia, and had increased levels of liver enzymes, inflammation (Hs-CRP), and insulin resistance (HOMA-IR). Table 2 Weighted demographic and clinical characteristics of the NHANES population overall and by MASLD status Variables Overall n = 5,399 MASLD n = 2,307 Non-MASLD n = 3,092 P value Mean (95% CI) unless otherwise reported Age (years) 47.4 (45.9–48.4) 51.0 (49.4–52.6) 44.7 (43.3–46.0) < 0.001 Gender, male, % 49.1 54.7 44.9 < 0.001 Ethnicity, % Non-Hispanic Whites 62.9 63.1 62.8 0.006 Hispanics 16.4 18.7 14.6 Non-Hispanics Blacks 10.9 8.5 12.7 Asians 5.7 5.0 6.2 Others 4.1 4.7 3.7 BMI (kg/m 2 ) 29.6 (29.2–30.0) 33.8 (33.2– 34.4) 26.5 (26.1–26.9) < 0.001 Waist circumference (cm) 100 (99–101) 111 (110–112) 91 (91– 92) < 0.001 Males 102 (101– 103) 112 (110–113) 93 (92–94) < 0.001 Females 98 (96–99) 110 (108– 112) 90 (89–91) < 0.001 Hypertension, % 41.8 56.9 43.1 < 0.001 Diabetes, % 14.9 74.6 25.4 < 0.001 Triglycerides mg/dL 138 (133–143) 175 (166–184) 109 (106–113) < 0.001 Total cholesterol mg/dL 185 (185–190) 188 (184–192) 183 (181–186) 0.009 HDL-C mg/dL 52.4 (51.6–53.2) 46.9 (46.1–47.7) 56.5 (55.6–57.5) < 0.001 Males 46.8 (45.8–47.8) 43.0 (41.6–44.4) 50.3 (49.4–51.1) < 0.001 Females 57.8 (56.9–58.8) 51.6 (50.7–52.6) 61.7 (60.1–63.2) < 0.001 ALT U/L 22.6 (22.0–23.1) 26.9 (25.8–28.0) 19.3 (18.8–19.7) < 0.001 AST U/L 21.4 (21.0–21.8) 22.4 (21.7–23.1) 20.5 (20.1–21.0) < 0.001 GGT IU/L 27.4 (26.2–28.7) 33.9 (31.9–35.9) 22.5 (21.4–23.6) < 0.001 Platelets 1000 cell/uL 247 (243–251) 251 (245–256) 244 (239–249) 0.038 Albumin g/L 4.1 (4.1–4.1) 4.0 (4.0–4.1) 4.2 (4.1–4.2) 0.001 hs-CRP mg/L 3.7 (3.5–4.1) 4.8 (4.4–5.2) 3.0 (2.6–3.5) < 0.001 HOMA-IR 4.3 (3.8–4.7) 6.5 (5.4–7.5) 2.7 (2.4–2.9) < 0.001 Glycohemoglobin % 5.7 (5.7–5.8) 6.0 (5.9–6.1) 5.5 (5.4–5.5) < 0.001 CAP dB/m 263 (260–266) 323 (320–326) 218 (216–220) < 0.001 LSM kPa 5.8 (5.5–6.0) 6.9 (6.4–7.3) 5.0 (4.8–5.1) < 0.001 Note MASLD was determined by the presence of hepatic steatosis detected by the CAP values ≥ 274 dB/m, plus the presence of at least one of five cardiometabolic risk factors including: being overweight or obese, presence of diabetes, hypertension, hypertriglyceridemia, or low HDL levels. T-test was used to compare between groups. Values are reported as means (95% CI) for continuous variables and in proportions for categorical variable, P value > 0.05 considered as significant. Abbreviations ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; CAP, controlled attenuation parameter; GGT, gamma-glutamyl transferase; HDL-C, high density lipoprotein-cholesterol; HOMA-IR, homeostatic model assessment for insulin resistance; hs-CRP, high sensitivity-C reactive protein; LSM, liver stiffness measurement. MASLD prediction in the NHANES cohort All four steatosis NITs indicated good diagnostic accuracy for identifying MASLD with marginal differences between the AUROC (range 0.835 to 0.862) as illustrated in Fig. 1 . The overall accuracy of previously validated cutoffs is summarized in Table 3 . A higher FLI cutoff (≥ 60) provided a sensitivity of 79.6% and specificity of 75.1%, resulting in a maximum Youden index value (0.55), closely followed by FSI (0.54). The HSI cutoff of > 30 achieved the highest sensitivity (98.9%) but exhibited the poorest specificity (21.2%), resulting in the lowest Youden index value (0.20). The DSI identified a modest proportion of true positive cases (62.3%) but had the highest proportion of true negative cases (87.1%) and the highest positive likelihood ratio (4.83) ( Table 3 ). Table 3 Weighted diagnostic accuracy of hepatic steatosis NITs to predict MASLD against controlled attenuation parameter Biochemical model AUROC (95% CI) Score cutoff Sensitivity (%) Specificity (%) PPV (%) NPV (%) LR+ LR- Youden Index DSI 0.854 (0.839–0.868) 0 62.3 87.1 78.4 75.5 4.83 0.43 0.49 FLI 0.859 (0.845–0.873) 30 95.1 50.6 59.1 93.2 1.92 0.09 0.46 60 79.6 75.1 70.6 83.0 3.20 0.27 0.55 FSI 0.862 (0.848–0.875) 23 82.7 71.7 68.7 84.7 2.92 0.24 0.54 HSI 0.835 (0.819–0.850) 30 98.9 21.2 48.5 96.2 1.25 0.05 0.20 36 87.3 62.7 63.8 86.8 2.34 0.20 0.50 Abbreviations AUROC, Area under the receiver operating curve; DSI, Dallas steatosis index; FLI, fatty liver index; FSI, Framingham steatosis index; HSI, Hepatic steatosis index; LR, likelihood ratio; PPV; positive predicted value; NPV, negative predicted value. The weighted MASLD prevalence as per the thresholds to predict hepatic steatosis were 34.1%, 48.3%, 51.6 and 58.7% for DSI, FLI, FSI, HSI, respectively. The weighted MASLD prevalence reported by FLI (48.3%) was closest to that of MASLD prevalence assessed through CAP (42.9%), whereas other NITs either under- or over-estimated MASLD prevalence. The level of agreement between CAP and biochemical NITs-diagnosed MASLD assessed through Kappa-statistics were moderate: 0.47 for DSI, 0.49 for FLI, 0.47 for FSI and 0.44 for HSI. Advanced fibrosis prediction in the NHANES MASLD cohort We evaluated the diagnostic accuracy of fibrosis NITs within the MASLD group (n = 2,307). The NFS demonstrated the highest AUROC (0.699), followed by APRI (0.607), FIB-4 (0.576), and BARD (0.572). However, all NITs exhibited suboptimal performance in discriminating against advanced fibrosis ( Fig. 2 ). The overall accuracy of previously validated cutoffs is summarized in Table 4 . For NFS, a cutoff of 0.676 showed high specificity (92.5%) but unacceptably low sensitivity (28.6%). Both FIB-4 and APRI thresholds for ruling in and out advanced fibrosis indicated extremely low sensitivities ( 90%) ( Table 4 ). The BARD score > 2 differentiated advanced fibrosis with modest sensitivity (70.9%) but exhibited poor specificity (27.9%). All fibrosis NITs exhibited notably high NPV ranging from 89.0–94.9%. Table 4 Weighted diagnostic accuracy of fibrosis NITs to predict advanced fibrosis in the MASLD group against liver stiffness measurement Biochemical model AUROC (95% CI) Score cutoff Sensitivity (%) Specificity (%) PPV (%) NPV (%) LR+ LR- Youden Index APRI 0.607 (0.545– 0.668) 0.50 11.4 97.9 40.0 90.3 5.42 0.90 0.09 0.29 32.0 86.6 22.1 91.5 2.38 0.78 0.19 1.50 1.1 100.0 100.0 89.4 - 0.98 0.01 BARD 0.572 (0.505– 0.639) 2 70.9 27.9 10.4 89.0 0.98 1.04 -0.01 3 56.9 48.2 11.5 90.4 1.09 0.89 0.05 FIB-4 0.576 (0.513– 0.639) 1.30 35.6 76.9 15.4 90.9 1.54 0.83 0.13 1.24 41.2 75.1 16.3 91.4 1.65 0.78 0.16 2.67 7.0 98.9 42.1 90.0 6.36 0.94 0.06 NFS 0.699 (0.639–0.759) -1.455 76.7 51.7 15.8 94.9 1.58 0.45 0.28 -0.895 66.3 66.2 18.9 94.2 1.96 0.50 0.32 0.676 28.6 92.5 31.2 91.6 3.81 0.77 0.21 Abbreviations AUROC, area under the receiving operating curve; APRI aspartate aminotransferase to platelet ratio index; BARD, body mass index, AST ratio and diabetes status; FIB-4, fibrosis-4; NFS, Nafld fibrosis score; LR, likelihood ratio; PPV; positive predicted value; NPV, negative predicted value. We explored optimal cutoffs for advanced fibrosis NITs by maximizing the Youden value. The cutoffs achieved were 0.29 for APRI, 3 for BARD, 1.24 for FIB-4, and − 0.895 for NFS. However, the Youden index value for these newly generated cutoffs did not exceed 0.50 for any of the fibrosis NITs ( Table 4 ). The prevalence of advanced fibrosis among those with established MASLD according to fibrosis NITs were 0.1%, 1.7%, 9.7% and 72% for APRI, FIB-4, NFS and BARD, respectively. The weighted advanced fibrosis prevalence by NFS (9.7%) was closest to the prevalence assessed through TE, LSM (10.6%). The level of agreement between LSM and biochemical NITs-diagnosed advanced fibrosis were slight to fair for all NITs. The APRI (0.04), BARD (0.01) and FIB-4 (0.12) indicated slight agreement, whereas the NFS indicated fair (0.21) agreement. Subgroup analysis of fibrosis NITs across age groups ≤ 35, 36–64 and ≥ 65 years in the NHANES MASLD cohort To assess the accuracy of fibrosis NITs across different age groups (≤ 35 years, 36–64 years, and ≥ 65 years), age categorized subgroup analysis was conducted. The NFS showed improved accuracy with increasing age, rising from 0.635 AUROC in those ≤ 35 years to 0.793 AUROC in those ≥ 65 years (Supplementary Table 1). For NFS, the upper cutoff of 0.676 in people aged ≥ 65 years showed modest sensitivity (64.8%) and high specificity (80.3%), resulting in the highest Youden index value (0.45) compared to other NITs (Supplementary Table 2). FIB-4 performed poorly in the younger age groups (≤ 35 years: 0.542 AUROC and 36–64 years: 0.511 AUROC) but showed fair accuracy in those aged ≥ 65 years. None of the individuals in the ≤ 35 age group scored FIB-4 above 1.26, which prevented the analysis for validated cutoffs. APRI showed marginally fair accuracy in the ≤ 35 years, performed poorly in those 36–64 years, and improved in those aged ≥ 65 years. The sensitivity of APRI was notably low in individuals aged < 65 years but was high in those aged ≥ 65 years at the lower cutoff of 0.50. None of the participants in ≤ 35 years group scored APRI ≥ 1.50, which prevented the analysis for the upper cutoff. The BARD index showed poor performance in the younger age groups (< 65) but achieved fair accuracy in individuals ≥ 65 years. The sensitivity for BARD increased from 41.0% in those aged ≤ 35 to 94.8% in those aged ≥ 65 but resulted in higher rates of false positives (92.1%) in those aged ≥ 65 years (Supplementary Table 2). Discussion This study evaluated the diagnostic performance of commonly utilized biochemical NITs against CAP-diagnosed MASLD and LSM diagnosed advanced fibrosis in the 2017–2020 NHANES cohort. The main findings were that more than 40% of the US population had MASLD and that all four steatosis NITs provided good diagnostic accuracy for predicting MASLD. This suggests that any of these NITs are acceptable for assessing liver fat in clinical and research settings. Moreover, fibrosis NITs exhibited poor to fair diagnostic ability but had high true negative rates and NPV typically due to low prevalence of advanced fibrosis cases in general population. This characteristic makes them clinically beneficial for use in primary care settings, where clinicians can exclude individuals without significant fibrosis, potentially sparing them from referral to secondary or tertiary care clinics. In the subgroup analysis, it was found that age influenced the discriminative ability of fibrosis NITs. These NITs tended to under- and overestimate advanced fibrosis in individuals aged ≤ 35 and ≥ 65 years, respectively, but performed similarly in the overall MASLD cohort and in those aged 36–64 years and hence should be used with caution in general population. MASLD prediction in the NHANES cohort In our study, the AUROC reported for DSI (0.854), FLI (0.859), FSI (0.862) HSI (0.835) were analogous to that reported in the previously published validation studies using the NAFLD criteria (i.e., 0.82 for DSI [ 8 ], 0.84 for FLI [ 9 ], 0.84 for FSI [ 10 ], and 0.812 [ 11 ] for HSI). The performance of steatosis NITs did not differ with the new MASLD criteria probably because higher BMI, insulin resistance and/or dyslipidemia which are components of these hepatic formulae and prerequisites for the MASLD criteria are intrinsic characteristics of the NAFLD population [ 3 ]. However, concerning the applicability of these NITs, the FLI and HSI are more user-friendly with less complex formulae and have fewer biomarkers compared to DSI or FSI, hence making them more practical to use in primary care clinics. Among all the steatosis NITs, the FLI is the most extensively used and externally validated index and has been shown to accurately identify people with increase cardiometabolic disease risk and mortality [ 21 , 22 ]. A FLI cutoff ≥ 60 showed the highest maximum Youden index (0.55) in our study, which is a marker to assess the optimal threshold from a tested model. Indeed, FLI has been recommended by both the European Association for the Study of the Liver (EASL) [ 23 ] and Asian Pacific Association for the Study of the Liver (APASL) [ 24 ] guidelines as the ideal tool to predict metabolic dysfunction related hepatic steatosis. The MASLD prevalence from steatosis NITs varied between 34.1% − 58.7% in our study, which was lowest from DSI and highest from HSI. However, the level of agreement based on Kappa statistics was deemed moderate between TE and steatosis NITs diagnosed-MASLD. The variation in prevalence could be attributed to the population’s metabolic characteristics and the diagnostic thresholds used to predict hepatic steatosis. For instance, the HSI had been developed and validated in an Asian population with an average BMI of 24.1 kg/m 2 and with a majority of male (70%) participants [ 11 ]. In contrast, our study was dominated by Caucasians (63%) and Hispanics (16%) with a higher mean BMI of 29.6 kg/m 2 and with equal proportions of male and females. Moreover, there is evidence suggesting varying ethnic susceptibilities to hepatic fat accumulation [ 25 ]. Thus, the validated HSI cut-off > 36 overestimated MASLD prevalence and showed modest specificity (62.7%) in our study. This had been previously confirmed in other studies, highlighting the discriminative ability of biochemical NITs is dependent upon population’s demographic and metabolic traits [ 26 ]. Advanced fibrosis prediction in the NHANES MASLD cohort We observed that the available fibrosis NITs performed inadequately in discriminating advanced fibrosis diagnosed through LSM in the MASLD group. Our findings align with published literature indicating that these NITs exhibit limited ability to classify true positive cases but demonstrated high rates of true negatives and NPV [ 27 ]. Despite this limitation, they can be clinically useful for excluding moderate and advanced fibrosis, potentially sparing patients from further invasive testing, which has been recommended by the updated EASL guidelines for non-invasive assessments [ 28 ]. The presence of diabetes, obesity, and metabolic dysregulations, which closely aligns with MASLD, consistently linked with an increased risk of advancement to fibrosis [ 29 ]. Indeed, liver fibrosis is the major prognostic predictor for all-cause mortality, liver related mortality and morbidity in MASLD [ 30 ]. Thus, surveillance of individuals with MASLD related fibrosis is highly relevant for targeted interventions to prevent adverse hepatic and extrahepatic outcomes in this population. In our study, NFS outperformed the other three NITs based on the highest AUROC which corresponds with published literature from NHANES [ 31 ], but exhibit low sensitivity and PPV with the cutoff to rule-in advanced fibrosis. The overall accuracy of NFS in our study (0.699) was identical to that reported in a recent multicenter biopsy confirmed MASLD cohort (0.666) but improved (0.735) after excluding people with BMI > 40 kg/m 2 [ 27 ]. The study indicated higher AUROC for APRI (0.820 vs 0.607) and FIB-4 (0.845 vs 0.576) for differentiating advanced fibrosis compared to our study, which could be attributed to elevated liver enzyme profile in this study [ 27 ]. In our study, ALT and AST levels in the MASLD group were below the upper normal limits (ALT > 40 IU/L, AST > 35 IU/L), potentially explaining the poor performance of FIB-4 and APRI. Indeed, FIB-4 performs better with elevated liver enzyme levels [ 31 ]. Our findings for FIB-4 and APRI align with a recent study using the NHANES 2017–2020 MASLD cohort, which demonstrated suboptimal performance of these NITs in detecting TE-diagnosed liver fibrosis [ 32 ]. The AUROC of BARD was lowest compared to APRI, FIB-4, and NFS in our study. Additionally, 72% of MASLD patients were misclassified with advanced fibrosis using a BARD score ≥ 2, possibly due to BMI and diabetes status components overlapping with MASLD criteria, resulting in high false positives and the lowest PPV. Despite incorporating metabolic risk factors, BARD score does not include age which is also the major prognostic factor for advanced liver disease stage. Therefore, BARD score may not effectively predict advanced fibrosis in MASLD patients. Several studies have confirmed that fibrosis NITs exhibit higher efficiency in predicting advanced fibrosis in biopsy-proven MASLD. This may be attributed to the progressive disease staging that reflects severe metabolic abnormalities and a high prevalence of advanced fibrosis (> 30%) in these studies [ 33 , 34 ], which is not the case in our general population cohort. The new cutoffs for fibrosis NITs, while optimized for the Youden index, fell short of the acceptable threshold (> 0.50) and were not ideal for clinical application. A highly specific test is valuable in clinical settings to avoid costly and risky liver biopsies, as those with intermediate or higher test scores are usually referred for further evaluation per clinical guidelines [ 7 , 23 ]. Furthermore, sequential combination of NITs with TE has shown increased specificity, allowing 60–80% of patients to avoid biopsy by ruling out advanced fibrosis or confirming cirrhosis [ 35 ]. This approach offers a reliable, non-invasive alternative for fibrosis assessment in people MASLD. Subgroup analysis of fibrosis NITs across age groups ≤ 35, 36–64 and ≥ 65 years Confounding factors, such as age, might influence the diagnostic efficiency of fibrosis NITs. [ 16 ]. Our analysis further confirmed these findings that all fibrosis NITs underperformed in those aged ≤ 35 years and showed fair to acceptable performance in those aged ≥ 65 years. The poor performance in people ≤ 35 years could be attributed to the low prevalence of advanced fibrosis cases (7%) in this age group. Age is included in both the NFS and FIB-4, which leads to false inflation of scores in individuals aged ≥ 65 years in our study. Additionally, the age-related decline in ALT and platelet levels leading to an increase in AST/ALT ratio, as observed in our analysis (Supplementary Tables 1 and 2) further contributes to the higher rates of false positives in people aged ≥ 65 in our study. For APRI, the marginally higher AUROC observed in the ≤ 35 years could be attributed to the variability in AST levels and platelet counts, impacting the discriminative ability of APRI in this age group. This also suggests that the performance of APRI may not be impacted by younger age [ 36 ]. In BARD, the prevalence of diabetes and elevated AST/ALT levels increases in people aged ≥ 65 years, leading to a high sensitivity but also to increased rates of false positives (92.1%). Our analysis indicated that these NITs had the potential to both under- and overestimate fibrosis in younger (≤ 35 years) and older (≥ 65 years) age groups, while performing similarly in those aged 36–64 years compared to the overall MASLD cohort. Hence, the application of liver fibrosis NITs in isolation should be used with caution in general population. Despite limitations, fibrosis NITs offer significant value with their high NPV and capacity to predict severe liver outcomes, providing a viable alternative in settings with limited resources [ 37 ]. Alternately, sequential combination of NITs with TE enhances specificity by effectively identifying adverse liver outcomes, allowing patients to avoid costly biopsies [ 35 ]. This approach would reduce the need for invasive procedures, improve fibrosis management in primary care, and decrease reliance on specialist referrals, which is crucial for enhancing patient care in resource-limited environments. Our findings are valuable as they demonstrated the accuracy of NITs against TE in a general population cohort. This approach reduces selection bias, enabling the pragmatic use of NITs by primary care physicians, who may not have access to TE or liver biopsy for all patients. The results support the broader application of NITs for assessing liver health, providing an accessible, non-invasive option for early diagnosis and management for MASLD in diverse populations. The study has several strengths. First, it was the first to validate hepatic steatosis and fibrosis NITs against TE using the MASLD criteria in a nationally representative population. Second, analysis was robust including application of weighted ROC analysis by incorporating appropriate survey weights, critical for NHANES data analysis. Third, we have utilized TE and both CAP and LSM as the reference standard and which has shown accurate diagnostic performance against the gold standard liver biopsy [ 5 ]. Fourth, we further tested the fibrosis NITs across different age groups to examine the influence of age as a confounding factor, further enhancing the quality of our analysis. This study however has several limitations. Although we have utilized TE as the reference standard, there are discrepancies in the utilization of CAP and LSM cutoffs to identify steatosis and advanced fibrosis. To overcome this, we utilized a CAP cutoff of 274 dB/m and a LSM cutoff of 9.7 kPa to identify steatosis and advanced fibrosis respectively, which have shown acceptable diagnostic performance against the liver biopsy and have been previously utilized to assess NAFLD prevalence in NHANES [ 6 ]. Although TE is a well validated and an accurate imaging modality, the best approach to validate liver fibrosis predicting NITs should be against the gold standard liver biopsy which was not available in the NHANES cohort, an outcome which is neither an ethical nor feasible option for a population-based cohort. Lastly, our study group consistently predominantly of Caucasians (62.9%), which limits the generalizability of the findings to other populations. Indeed, MASLD prevalence varies across the globe and certain ethnic groups such as Hispanic people have higher rates of hepatic steatosis, whereas Africans have the lowest rates. Conclusion Our findings indicate that all steatosis NITs assessed (DSI, FLI, FSI, and HSI) were acceptable predictors of MASLD. However, FLI emerged as the most practical and accurate tool for predicting MASLD in the US adult population. The fibrosis NITs (APRI, BARD score, FIB-4, and NFS) demonstrated limited diagnostic accuracy but showed notably high NPV for advanced fibrosis in people with MASLD, with age influencing their performance. Therefore, fibrosis NITs should be used with caution in general population. Despite these limitations, they remain worthwhile in identifying those without significant fibrosis and could be utilized in resource-limited settings. Declarations Financial support Ayesha Sualeheen received Deakin University Postgraduate Research Scholarship while conducting this study Conflict of Interest All authors declare no conflict of interest. Data availability statement The data for this study are available from the corresponding author, Elena S. George, upon request. Authors contribution Ayesha Sualeheen : Data curation (lead); Investigation (equal); formal analysis (equal); writing – original draft (lead). Sze-Yen Tan: Writing – Review & Editing (equal); formal analysis (equal). Robin M. Daly : Writing – Review & Editing (equal); formal analysis (equal). Ekavi Georgousopoulou : Investigation (equal); formal analysis (equal). Stuart K. Roberts: Writing – Review & Editing (equal); formal analysis (equal). Gavin Abbott: Investigation (equal); formal analysis (equal). Elena S. George : Conceptualization (lead); writing – review and editing (equal); formal analysis (equal); Supervision (lead). References Rinella, M.E., et al., A multi-society Delphi consensus statement on new fatty liver disease nomenclature . Ann Hepatol, 2023: p. 101133. Riazi, K., et al., The prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis . Lancet Gastroenterol Hepatol, 2022. 7(9): p. 851–861. Younossi, Z.M., et al., Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes . Hepatology, 2016. 64(1): p. 73–84. Jarvis, H., et al., Metabolic risk factors and incident advanced liver disease in non-alcoholic fatty liver disease (NAFLD): A systematic review and meta-analysis of population-based observational studies . PLoS Med, 2020. 17(4): p. e1003100. Eddowes, P.J., et al., Accuracy of FibroScan Controlled Attenuation Parameter and Liver Stiffness Measurement in Assessing Steatosis and Fibrosis in Patients With Nonalcoholic Fatty Liver Disease . Gastroenterology, 2019. 156(6): p. 1717–1730. Ciardullo, S., et al., Nonalcoholic Fatty Liver Disease, Liver Fibrosis and Cardiovascular Disease in the Adult US Population . Front Endocrinol (Lausanne), 2021. 12: p. 711484. Rinella, M.E., et al., AASLD Practice Guidance on the clinical assessment and management of nonalcoholic fatty liver disease . Hepatology, 2023. 77(5): p. 1797–1835. McHenry, S., et al., Dallas Steatosis Index Identifies Patients With Nonalcoholic Fatty Liver Disease . Clin Gastroenterol Hepatol, 2020. 18(9): p. 2073–2080.e7. Bedogni, G., et al., The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population . BMC Gastroenterol, 2006. 6: p. 33. Long, M.T., et al., Development and Validation of the Framingham Steatosis Index to Identify Persons With Hepatic Steatosis . Clin Gastroenterol Hepatol, 2016. 14(8): p. 1172–1180.e2. Lee, J.H., et al., Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease . Dig Liver Dis, 2010. 42(7): p. 503–8. Wai, C.T., et al., A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C . Hepatology, 2003. 38(2): p. 518–26. Harrison, S.A., et al., Development and validation of a simple NAFLD clinical scoring system for identifying patients without advanced disease . Gut, 2008. 57(10): p. 1441–7. Shah, A.G., et al., Comparison of Noninvasive Markers of Fibrosis in Patients With Nonalcoholic Fatty Liver Disease . Clinical Gastroenterology and Hepatology, 2009. 7(10): p. 1104–1112. Angulo, P., et al., The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD . Hepatology, 2007. 45(4): p. 846–54. McPherson, S., et al., Age as a Confounding Factor for the Accurate Non-Invasive Diagnosis of Advanced NAFLD Fibrosis . Official journal of the American College of Gastroenterology | ACG, 2017. 112(5). Bertot, L.C., et al., Diabetes impacts prediction of cirrhosis and prognosis by non-invasive fibrosis models in non-alcoholic fatty liver disease . Liver International, 2018. 38(10): p. 1793–1802. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021 . Diabetes Care, 2021. 44(Suppl 1): p. S15-s33. Viera, A.J. and J.M. Garrett, Understanding interobserver agreement: the kappa statistic . Fam Med, 2005. 37(5): p. 360–3. National Health and Nutrition Examination Survey , 2017 –March 2020 Prepandemic File: Sample Design, Estimation, and Analytic Guidelines , in Vital and health statistics. Series 2, Data Evaluation and Methods Research; no. 190 , S. National Center for Health, Editor. 2022, https://dx.doi.org/10.15620/cdc:115434 : Hyattsville, MD. Calori, G., et al., Fatty liver index and mortality: the Cremona study in the 15th year of follow-up. Hepatology, 2011. 54(1): p. 145 – 52. Zou, B., et al., Fatty Liver Index and Development of Cardiovascular Disease: Findings from the UK Biobank . Digestive Diseases and Sciences, 2021. 66(6): p. 2092–2100. European Association for the Study of the, L., D. European Association for the Study of, and O. European Association for the Study of, EASL-EASD-EASO Clinical Practice Guidelines for the Management of Non-Alcoholic Fatty Liver Disease . Obes Facts, 2016. 9(2): p. 65–90. Eslam, M., et al., The Asian Pacific Association for the Study of the Liver clinical practice guidelines for the diagnosis and management of metabolic associated fatty liver disease . Hepatol Int, 2020. 14(6): p. 889–919. Rich, N.E., et al., Racial and Ethnic Disparities in Nonalcoholic Fatty Liver Disease Prevalence, Severity, and Outcomes in the United States: A Systematic Review and Meta-analysis . Clinical Gastroenterology and Hepatology, 2018. 16(2): p. 198–210.e2. Liu, Y., et al., Validation of five hepatic steatosis algorithms in metabolic-associated fatty liver disease: A population based study . J Gastroenterol Hepatol, 2022. 37(5): p. 938–945. Kouvari, M., et al., Liver biopsy-based validation, confirmation and comparison of the diagnostic performance of established and novel non-invasive steatotic liver disease indexes: Results from a large multi-center study . Metabolism, 2023. 147: p. 155666. EASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis – 2021 update . J Hepatol, 2021. 75(3): p. 659–689. Pais, R., et al., A systematic review of follow-up biopsies reveals disease progression in patients with non-alcoholic fatty liver . Journal of Hepatology, 2013. 59(3): p. 550–556. Taylor, R.S., et al., Association Between Fibrosis Stage and Outcomes of Patients With Nonalcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis . Gastroenterology, 2020. 158(6): p. 1611–1625 e12. Chen, X., et al., Validation of Non-invasive Fibrosis Scores for Predicting Advanced Fibrosis in Metabolic-associated Fatty Liver Disease . J Clin Transl Hepatol, 2022. 10(4): p. 589–594. Chen, R., et al., Comparison of blood-based liver fibrosis scores in the Mount Sinai Health System, MASLD Registry, and NHANES 2017–2020 study . Hepatology Communications, 2024. 8(9): p. e0515. McPherson, S., et al., Simple non-invasive fibrosis scoring systems can reliably exclude advanced fibrosis in patients with non-alcoholic fatty liver disease . Gut, 2010. 59(9): p. 1265–9. Siddiqui, M.S., et al., Diagnostic Accuracy of Noninvasive Fibrosis Models to Detect Change in Fibrosis Stage . Clinical Gastroenterology and Hepatology, 2019. 17(9): p. 1877–1885.e5. Mózes, F.E., et al., Diagnostic accuracy of non-invasive tests for advanced fibrosis in patients with NAFLD: an individual patient data meta-analysis . Gut, 2022. 71(5): p. 1006. Goh, G.B., et al., Age impacts ability of aspartate-alanine aminotransferase ratio to predict advanced fibrosis in nonalcoholic Fatty liver disease . Dig Dis Sci, 2015. 60(6): p. 1825–31. Vaz, K., et al., Validation of serum non-invasive tests of liver fibrosis as prognostic markers of clinical outcomes in people with fatty liver disease in Australia . J Gastroenterol Hepatol, 2024. Additional Declarations No competing interests reported. Supplementary Files HepaticSteatosisNITsAccuratelyPredictMASLDGraphicalAbstract.png Graphical Abstract Abbreviations APRI, AST to platelet ratio index; AUROC, area under the receiving operating curve; BARD, BMI, AST/ALT ratio and DM; BMI, body mass index; DM, diabetes mellitus; DSI, Dallas Steatosis Index; FLI, fatty liver index; FIB-4, fibrosis-4; FSI, Framingham risk score; MASLD, metabolic dysfunction associated steatotic liver disease; NHANES, National Health and Nutrition Examination Survey; NFS, NAFLD fibrosis score; NITs, non-invasive tests; TE, transient elastography. 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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-5456895","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":381720499,"identity":"4346be13-0db1-4b38-a6a2-3518adab230b","order_by":0,"name":"Ayesha Sualeheen","email":"","orcid":"","institution":"Deakin University","correspondingAuthor":false,"prefix":"","firstName":"Ayesha","middleName":"","lastName":"Sualeheen","suffix":""},{"id":381720500,"identity":"398511d6-59b7-43e6-a46a-cabcfce8595b","order_by":1,"name":"Sze-Yen Tan","email":"","orcid":"","institution":"Deakin University","correspondingAuthor":false,"prefix":"","firstName":"Sze-Yen","middleName":"","lastName":"Tan","suffix":""},{"id":381720501,"identity":"ac53ef5a-bd67-46f2-aa38-c185eb4b9dbb","order_by":2,"name":"Robin M. 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George","email":"data:image/png;base64,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","orcid":"","institution":"Deakin University","correspondingAuthor":true,"prefix":"","firstName":"Elena","middleName":"S.","lastName":"George","suffix":""}],"badges":[],"createdAt":"2024-11-15 01:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5456895/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5456895/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71809117,"identity":"919fe2c8-1111-44df-9ac0-45dfb17055a3","added_by":"auto","created_at":"2024-12-18 18:06:20","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":100620,"visible":true,"origin":"","legend":"\u003cp\u003eArea under the receiver operating curve for steatosis NITs for predicting MASLD. \u003cstrong\u003eAbbreviations \u003c/strong\u003eDSI, Dallas steatosis index; FLI, Fatty liver index; FSI, Framingham steatosis index; HSI, Hepatic steatosis index.\u003c/p\u003e","description":"","filename":"Advancefibrosisgraph600dpiFigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5456895/v1/ec2c231bdc9263590a7c334b.jpg"},{"id":71809118,"identity":"ada3ce4c-2f06-4d26-99ba-4d3e0c316a38","added_by":"auto","created_at":"2024-12-18 18:06:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94783,"visible":true,"origin":"","legend":"\u003cp\u003eArea under the receiver operating curve for fibrosis NITs for predicting advanced fibrosis in the MASLD group. \u003cstrong\u003eAbbreviations \u003c/strong\u003eAPRI aspartate aminotransferase to platelet ratio index; BARD, body mass index, AST ratio and diabetes status; FIB-4, fibrosis-4; NFS, Nafld fibrosis score.\u003c/p\u003e","description":"","filename":"Hepaticsteatosisgraph600dpiFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5456895/v1/614fb35f4d557d885e8cf318.jpg"},{"id":74700820,"identity":"abed7f25-e876-4d33-bb34-a0b5a1ea589a","added_by":"auto","created_at":"2025-01-24 23:31:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1587991,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5456895/v1/416bab30-4f7c-4677-b816-4a71928478d8.pdf"},{"id":71810177,"identity":"f830df75-66f5-4d40-938b-358b67ebbcbd","added_by":"auto","created_at":"2024-12-18 18:14:20","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":288394,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations \u003c/strong\u003eAPRI, AST to platelet ratio index; AUROC, area under the receiving operating curve; BARD, BMI, AST/ALT ratio and DM; BMI, body mass index; DM, diabetes mellitus; DSI, Dallas Steatosis Index; FLI, fatty liver index; FIB-4, fibrosis-4; FSI, Framingham risk score; MASLD, metabolic dysfunction associated steatotic liver disease; NHANES, National Health and Nutrition Examination Survey; NFS, NAFLD fibrosis score; NITs, non-invasive tests; TE, transient elastography.\u003c/p\u003e","description":"","filename":"HepaticSteatosisNITsAccuratelyPredictMASLDGraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-5456895/v1/eaeb4824eed4035a9e74d008.png"},{"id":71810178,"identity":"6a5e9b90-1a69-48ec-98f9-cedbc0c0fb5f","added_by":"auto","created_at":"2024-12-18 18:14:20","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23368,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-5456895/v1/1c5a6a118c267b56f06d00b5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Steatosis Non-Invasive Tests Accurately Predict Metabolic Dysfunction-Associated Steatotic Liver Disease, While Fibrosis Non-Invasive Tests Fall Short: Validation in U.S. Adult Population","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic dysfunction associated fatty liver disease (MASLD), formerly known as NAFLD, is the new nomenclature and diagnostic criteria introduced by the American Association for the Study of Liver Disease (AASLD) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Unlike NAFLD, which excludes other steatogenic liver diseases (SLDs), MASLD allows the co-existence of other SLDs along with the presence of at least one metabolic disorder [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In 2022, the global prevalence for NAFLD was estimated to be 32.4% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], which surpassed the global prevalence of 25% in 2016 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. There is no global data for reporting MASLD prevalence, however it can be assumed that MASLD prevalence is higher compared to NAFLD due to the inclusivity of other steatogenic liver diseases. Obesity and insulin resistance are major factors contributing to the onset of MASLD [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], with prevalence rates as high as 50\u0026ndash;80% among the MASLD population [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The presence of metabolic dysregulation accompanied with hepatic steatosis has been linked to adverse liver-related outcomes such as cirrhosis and hepatocellular carcinoma, and/or cardiovascular events [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This evidence justifies the inclusion of metabolic risk factors in the new definition and highlights that they are high-risk individuals requiring more robust disease management.\u003c/p\u003e \u003cp\u003eMASLD is often asymptomatic until more advanced stages and usually detected when other comorbidities are diagnosed. Transient elastography (TE) is a simple, widely available non-invasive technique that applies ultrasound and low- frequency elastic waves to detect fibrosis and intrahepatic fat content. TE has demonstrated good overall diagnostic performance, with sensitivity of 80% and 71% to detect hepatic steatosis and advanced fibrosis, respectively, in a population with SLDs when compared with the gold standard, liver biopsy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It has also been adopted in national level health examination surveys such as the National Health and Nutrition Examination Survey (NHANES) from the United States [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrent clinical practice guidelines, especially the AASLD, do not advise population level screening for MASLD using imaging and/or ultrasound-based modalities such as TE in the absence of cost-effectiveness data [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Instead, simpler, more cost-effective, and accessible non-invasive tests (NITs) are typically used to predict hepatic steatosis and advanced fibrosis (stage 3\u0026ndash;4). The Dallas steatosis index (DSI) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], the fatty liver index (FLI) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], the Framingham steatosis index (FSI) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and the hepatic steatosis index (HSI) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] are validated NITs reported throughout the literature which are calculated from readily available clinical and laboratory variables and can be utilized when biopsy or imaging modalities are inaccessible. Likewise, the aspartate aminotransferase to platelet ratio index (APRI) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], the body mass index (BMI), aspartate aminotransferase/alanine aminotransferase ratio diabetes score (BARD) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], the fibrosis-4 (FIB-4) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and the NAFLD fibrosis score (NFS) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] are widely utilized to predict advanced fibrosis and have shown acceptable diagnostic performance against the gold standard, liver biopsy [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, the diagnostic performance of these steatosis and fibrosis NITs varies depending on disease prevalence, clinical biomarkers, and presence of metabolic dysregulations [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast to NAFLD, the new MASLD diagnostic criteria does not exclude secondary causes of liver disease except for excessive alcohol intake and instead requires the presence of metabolic dysregulation(s). Thus, these steatosis and fibrosis NITs require evaluation using the MASLD criteria. Hence, the aim of this study was to validate the diagnostic accuracy for DSI, FLI, FSI and HSI in detecting TE diagnosed hepatic steatosis attributed to MASLD in a large nationally representative population-based cohort, the NHANES 2017\u0026ndash;2020 cycle. A secondary aim was to validate the diagnostic accuracy of APRI, BARD, FIB-4 and NFS for predicting advanced fibrosis among those with established MASLD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis cross-sectional study represents secondary data analysis from the 2017\u0026ndash;2020 NHANES cycle as this was the only cycle with TE available. The NHANES is a nationally representative survey of the U.S general population conducted by the National Centre for Health Statistics, a part of the Centre for Disease Control and Prevention. The methodology and data collection protocols and data files of this national survey are publicly available (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003c/span\u003e). Briefly, NHANES data consists of demographic, socioeconomic, health-related questionnaires, and dietary data as well as medical examinations including anthropometric, laboratory and physical assessments. For the current study, adults aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years with completed TE imaging were initially shortlisted (n\u0026thinsp;=\u0026thinsp;7,768). Participants with incomplete or invalid TE (n\u0026thinsp;=\u0026thinsp;1,925) or with excessive alcohol consumption (\u0026gt;\u0026thinsp;30g/day for men and \u0026gt;\u0026thinsp;20g/day for women) (n\u0026thinsp;=\u0026thinsp;778) or consuming hepatotoxic drugs (n\u0026thinsp;=\u0026thinsp;101) were excluded. Participants with missing data relevant to identify excessive alcohol intake (n\u0026thinsp;=\u0026thinsp;614), metabolic dysregulations (n\u0026thinsp;=\u0026thinsp;1,137) and variables relevant to compute steatosis (n\u0026thinsp;=\u0026thinsp;780) and fibrosis NITs (n\u0026thinsp;=\u0026thinsp;620) were further excluded. Some participants had missing data for more than one variable resulting in overlapping of the numbers reported above. In total, 5,399 participants met the inclusion criteria with complete data and were included in this analysis. The NHANES is approved by the National Centre for Health Statistics review board, and all study participants provided written consent.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eSociodemographic Information and Interview\u003c/h2\u003e\n\u003cp\u003eInformation about participants\u0026rsquo; age, sex and ethnicity/race were obtained from the demographic questionnaire. Self-reported diabetes status and prescription medication data were acquired from the health examination questionnaires. Alcohol intake was calculated using day 1 from the 24-hour recalls where total nutrient intake was available. This was selected as there were a greater number of participants available with fewer missing values as compared to day 2.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eTransient Elastography (TE)\u003c/h3\u003e\n\u003cp\u003eTE data was extracted for all participants from the NHANES 2017\u0026ndash;2020 cycle. This data was used to identify participants with hepatic steatosis and fibrosis through controlled attenuation parameter (CAP) and liver stiffness measurement (LSM) respectively, computed by FibroScan\u0026reg; model 502 V2 Touch. An optimal CAP cutoff\u0026thinsp;\u0026ge;\u0026thinsp;274 dB/m reflective of \u0026ge;\u0026thinsp;5% of intrahepatic fat content was used to detect individuals with fatty liver. Additionally, a more robust cutoff of 302 dB/m was also utilized to assess intrahepatic fat [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]. Likewise, a validated LSM cutoff\u0026thinsp;\u0026ge;\u0026thinsp;9.7 kPa was used to identify advanced fibrosis (F3-F4).\u003c/p\u003e\n\u003ch3\u003eCovariables\u003c/h3\u003e\n\u003cp\u003eTrained NHANES technicians measured height, weight, and waist circumference using standard procedures. Body mass index (BMI) was calculated by dividing weight in kilograms (kg) by height in meters square (m\u003csup\u003e2\u003c/sup\u003e). Systolic and diastolic blood pressure data consist of three consecutive measurements, with an average of these measurements used for analysis. Serum biomarkers included fasting glucose and insulin, glycated hemoglobin (HbA1C), triglycerides, total cholesterol, high density lipoprotein cholesterol (HDL) and plasma high sensitivity C-reactive protein (Hs-CRP). Additionally, serum biomarkers used to assess liver function included alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma glutamyl transferase (GGT), platelet count and albumin. Detailed descriptions regarding the data collection protocol and analytical guidelines for these biomarkers were available on the NHANES website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eIdentification of MASLD\u003c/h3\u003e\n\u003cp\u003eMASLD was confirmed by the presence of hepatic steatosis detected by the CAP values\u0026thinsp;\u0026ge;\u0026thinsp;274 dB/m, plus the presence of at least one of five cardiometabolic risk factors including: being overweight or obese, presence of diabetes, hypertension, hypertriglyceridemia, or low HDL levels [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eCardiometabolic risk factor criteria\u003c/h3\u003e\n\u003cp\u003eThe cardiometabolic risk factor criteria utilized in this study were as follows: overweight was defined as BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e (\u0026ge;\u0026thinsp;23 kg/m\u003csup\u003e2\u003c/sup\u003e for Asians) and obesity as BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e (\u0026ge;\u0026thinsp;27.5kg/m\u003csup\u003e2\u003c/sup\u003e for Asians); abdominal obesity as waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;94 cm for males and \u0026ge;\u0026thinsp;80 cm for females (\u0026ge;\u0026thinsp;90 cm males and \u0026ge;\u0026thinsp;80 cm females for Asians); type 2 diabetes (T2DM) status was determined by self-report and those who did not report T2DM but had fasting HbA1C levels\u0026thinsp;\u0026ge;\u0026thinsp;6.5% or who were taking antidiabetic drugs [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. A HbA1C level of 5.7% was used to determine prediabetes for MASLD criteria as described previously [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. Hypertension was defined as systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg and/or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;85 mmHg or on antihypertensive drugs. Hypertriglyceridemia was defined as serum triglycerides levels\u0026thinsp;\u0026ge;\u0026thinsp;150mg/dL or on lipid lowering drugs; low HDL levels as \u0026le;\u0026thinsp;40mg/dL for males and \u0026le;\u0026thinsp;50mg/dL for females or on lipid lowering drugs. The homeostasis model assessment of insulin resistance (HOMA-IR) was used to assess insulin resistance and was calculated as fasting glucose (mmol) \u0026times; fasting insulin (moly/L)/ 22.5.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eBiochemical NITs to predict steatosis and advanced fibrosis\u003c/h2\u003e\n\u003cp\u003eThe hepatic steatosis and fibrosis NITs with their formulae and scoring cutoffs provided in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eScoring NITs to predict hepatic steatosis and advanced fibrosis\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNITs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDeveloped and validated\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFormulae\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eScore cutoffs\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eSteatosis NITs\u003c/em\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDSI \u003csub\u003e[8]\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMultiethnic population-based cohort, the Dallas heart study[\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALT, BMI, age, sex, triglycerides, glucose, diabetes, hypertension, and ethnicity.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDSI \u003csup\u003eLOGIT\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;9.4\u0026thinsp;+\u0026thinsp;0.316 (if age\u0026thinsp;\u0026ge;\u0026thinsp;50 and female)\u0026thinsp;+\u0026thinsp;2.4 (if known DM)\u0026thinsp;+\u0026thinsp;0.02 \u0026lowast; (equals 0 if DM; if not diabetic equals the glucose concentration in mg/dL)\u0026thinsp;+\u0026thinsp;0.3 (if known HTN)\u0026thinsp;+\u0026thinsp;0.5 (if Hispanic/Asian/Other race)\u0026thinsp;+\u0026thinsp;Ln (TGs in mg/dL)\u0026thinsp;+\u0026thinsp;0.4 (if ALT 13.5\u0026thinsp;\u0026minus;\u0026thinsp;19.49 IU/L)\u0026thinsp;+\u0026thinsp;1.1 (if ALT 19.5\u0026ndash;40 IU/L)\u0026thinsp;+\u0026thinsp;1.5 (if ALT\u0026thinsp;\u0026gt;\u0026thinsp;40 IU/L)\u0026thinsp;+\u0026thinsp;0.7 (if not black and BMI 25\u0026thinsp;\u0026minus;\u0026thinsp;27.49 kg/m\u003csup\u003e2\u003c/sup\u003e and)\u0026thinsp;+\u0026thinsp;1.4 (if not black and BMI 27.5\u0026thinsp;\u0026minus;\u0026thinsp;34.9 kg/m\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;+\u0026thinsp;1.9 (if not black and BMI 35\u0026thinsp;\u0026minus;\u0026thinsp;37.49 kg/m\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;+\u0026thinsp;2.6 (if not black and BMI\u0026thinsp;\u0026gt;\u0026thinsp;37.5kg/m\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;\u0026minus;\u0026thinsp;0.2 (if black and BMI 25\u0026thinsp;\u0026minus;\u0026thinsp;27.49 kg/m\u003csup\u003e2\u003c/sup\u003e and)\u0026thinsp;+\u0026thinsp;0.8 (if black and BMI 27.5\u0026thinsp;\u0026minus;\u0026thinsp;34.9kg/m\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;+\u0026thinsp;0.8 (if black and BMI 35\u0026thinsp;\u0026minus;\u0026thinsp;37.49 kg/m\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;+\u0026thinsp;1.8 (if black and BMI\u0026thinsp;\u0026gt;\u0026thinsp;37.5kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFLI\u003csub\u003e[9]\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eItalian case control study[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI, waist circumference, triglycerides, and GGT.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ee\u003csup\u003ex\u003c/sup\u003e / (1\u0026thinsp;+\u0026thinsp;e\u003csup\u003ex\u003c/sup\u003e), where X\u0026thinsp;=\u0026thinsp;0.953* ln (TGs)\u0026thinsp;+\u0026thinsp;0.139 * BMI\u0026thinsp;+\u0026thinsp;0.718 * ln (GGT)\u0026thinsp;+\u0026thinsp;0.053 * WC \u0026ndash; 15.745)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;30 / \u0026ge; 60\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFSI\u003csub\u003e[10]\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFramingham heart study third generation cohort and NHANES III study[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, sex, BMI, triglycerides, hypertension, diabetes, and ALT:AST ratio.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ee\u003csup\u003ex\u003c/sup\u003e / (1\u0026thinsp;+\u0026thinsp;e\u003csup\u003ex\u003c/sup\u003e), where X = -7.981\u0026thinsp;+\u0026thinsp;0.011 * age\u003csub\u003e(years)\u0026minus;\u003c/sub\u003e0.146 * sex (female\u0026thinsp;=\u0026thinsp;1, male\u0026thinsp;=\u0026thinsp;0)\u0026thinsp;+\u0026thinsp;0.173 * BMI\u0026thinsp;+\u0026thinsp;0.007 * TGs\u0026thinsp;+\u0026thinsp;0.593 * HTN (yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0)\u0026thinsp;+\u0026thinsp;0.789 * DM (yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0)\u0026thinsp;+\u0026thinsp;1.1 * ALT/AST ratio\u0026thinsp;\u0026ge;\u0026thinsp;1.33 (yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;23\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHSI\u003csub\u003e[11]\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsian case control study[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eALT/AST ratio, BMI, diabetes, and sex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHSI\u0026thinsp;=\u0026thinsp;8 \u0026times; ALT/AST ratio\u0026thinsp;+\u0026thinsp;BMI (+\u0026thinsp;2, if DM; +2, if female)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;30 / \u0026gt; 36\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFibrosis NITs\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAPRI\u003csub\u003e[12]\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePatients with chronic hepatitis C virus [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAST and platelets\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAST level (/ULN)/Platelet counts (10\u003csup\u003e9\u003c/sup\u003e/L) * 100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026le;\u0026thinsp;0.50 / \u0026gt;1.50\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBARD\u003csub\u003e[13]\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-diabetic NAFLD cohort [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI, ALT, AST, and diabetes status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAST/ALT ratio\u0026thinsp;\u0026ge;\u0026thinsp;0.8 sums 2 points; BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u003csup\u003e2\u003c/sup\u003e sums 1 point; presence of DM sums 1 point.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFIB-4\u003csub\u003e[14]\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePatients with human immunodeficiency virus and hepatitis C virus coinfection and biopsy confirmed NAFLD cohort [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, ALT, AST, and platelets\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[Age \u003csub\u003e(years)\u003c/sub\u003e * AST\u003csub\u003e(U/L)\u003c/sub\u003e] / [Platelet \u003csub\u003e[10\u003c/sub\u003e\u003csup\u003e9\u003c/sup\u003e\u003csub\u003e/L]\u003c/sub\u003e * \u0026radic;ALT\u003csub\u003e(U/L)\u003c/sub\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026le;\u0026thinsp;1.30 / \u0026ge; 2.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNFS\u003csub\u003e[15]\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBiopsy confirmed NAFLD cohort [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, BMI, diabetes status, AST:ALT ratio, platelets, and albumin\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.675 + [0.037 * age \u003csub\u003e(years)\u003c/sub\u003e] + [0.094 * BMI \u003csub\u003e(kg/m2)\u003c/sub\u003e] + [1.13 * IFG/DM \u003csub\u003e(yes=1, no=0)\u003c/sub\u003e] + [0.99 * AST/ALT ratio] \u0026ndash; [0.013 * platelet (*10\u003csup\u003e9\u003c/sup\u003e/l)] \u0026ndash; [0.66 * albumin \u003csub\u003e(g/dl)\u003c/sub\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;1.455 / \u0026gt; 0.676\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e ALT, alanine amino transferase; AST, aspartate aminotransferase; APRI, AST to platelet ratio index; BARD, BMI, AST/ALT ratio and DM; BMI, body mass index; DM, diabetes mellitus; DSI, Dallas Steatosis Index; FLI, fatty liver index; FIB-4, fibrosis-4; FSI, Framingham risk score; GGT, gamma-glutamyl transferase; HTN, hypertension; IFG, impaired fasting glucose; NFS, NAFLD fibrosis score; TGs, triglycerides; ULN, upper limit of normal, WC, waist circumference.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eThe normal distribution of variables was tested by visual inspection of histograms and P-P plots. Population characteristics are presented as estimated mean (95% CI) for continuous variables and as proportions for categorical variables. Diagnostic accuracy for biochemical NITs were determined from the receiver operator curve (ROC) analysis by observing area under ROC (AUROC). Sensitivity, specificity, positive predicted value (PPV), negative predicted values (NPV) and Youden index were calculated for each model with the established cutoffs from the original article and were analyzed using MASLD criteria. Positive and negative likelihood ratios, which show the ability of a test to transform the pre-test probability of the target condition into a post-test probability, were calculated. The AUROC for steatosis NITs were compared with CAP diagnosed with MASLD and the AUROC for fibrosis NITs were compared with LSM diagnosed advanced fibrosis in the MASLD group. The validated cutoffs for steatosis and fibrosis NITs are reported in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Kappa-statistics were used to determine interrater agreement between the CAP and LSM diagnosed liver outcomes and biochemical NITs diagnosed outcomes i.e., MASLD and advanced fibrosis. The strength of agreement was interpreted based on the following kappa coefficients: \u0026lt;0 none, 0-0.20 slight, 0.21\u0026ndash;0.39 fair, 0.40\u0026ndash;0.59 moderate, 0.61\u0026ndash;0.80 substantial and \u0026ge;\u0026thinsp;0.81 almost perfect agreement [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Given the evidence that fibrosis NITs could be less reliable in individuals aged\u0026thinsp;\u0026le;\u0026thinsp;35 and \u0026ge;\u0026thinsp;65 years [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e], a subgroup analysis of fibrosis NITs was conducted across the following age groups: \u0026le;35 years, 36\u0026ndash;64 years, and \u0026ge;\u0026thinsp;65 years in the MASLD cohort. All analysis were conducted using survey weights, which is recommended for NHANES data analysis [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. These weights used to account for complex survey design, nonresponse issues, post-stratification, and oversampling. Weighting transformed this survey data into a reflection of the broader U.S. non-institutionalized population, ensuring its representativeness. All statistical analyses were conducted using STATA version 17.0, and p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of study population\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, 5,399 participants were included in the analysis with estimated mean age of 47.4 years and 49.1% being males. The estimated mean BMI was 29.6kg/m\u003csup\u003e2\u003c/sup\u003e, with 32.3% classified as overweight and 42.4% as obese. The estimated prevalence of T2DM and hypertension was 14.9% and 41.8%, respectively. The estimated MASLD prevalence was 42.9% as assessed by a CAP value\u0026thinsp;\u0026ge;\u0026thinsp;274 dB/m and 28.4% with a more conservative, validated CAP cutoff\u0026thinsp;\u0026ge;\u0026thinsp;302 dB/m. Among those with established MASLD, 10.6% were estimated to have advanced fibrosis (stage 3\u0026ndash;4). Participants with MASLD were older and included a higher proportion of males and Hispanics and a lower proportion of non-Hispanics Blacks compared to the non-MASLD group. The demographic and metabolic characteristics of participants classified by MASLD status reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As expected, people with MASLD had higher mean BMI, exhibited dyslipidemia, and had increased levels of liver enzymes, inflammation (Hs-CRP), and insulin resistance (HOMA-IR).\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\u003eWeighted demographic and clinical characteristics of the NHANES population overall and by MASLD status\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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;5,399\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMASLD\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;2,307\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-MASLD\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;3,092\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMean (95% CI) \u003cem\u003eunless otherwise reported\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.4 (45.9\u0026ndash;48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.0 (49.4\u0026ndash;52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.7 (43.3\u0026ndash;46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, male, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNon-Hispanic Whites\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHispanics\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNon-Hispanics Blacks\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAsians\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOthers\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.6 (29.2\u0026ndash;30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.8 (33.2\u0026ndash; 34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.5 (26.1\u0026ndash;26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (99\u0026ndash;101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111 (110\u0026ndash;112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 (91\u0026ndash; 92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (101\u0026ndash; 103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (110\u0026ndash;113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (92\u0026ndash;94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFemales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (96\u0026ndash;99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (108\u0026ndash; 112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (89\u0026ndash;91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138 (133\u0026ndash;143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175 (166\u0026ndash;184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (106\u0026ndash;113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185 (185\u0026ndash;190)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188 (184\u0026ndash;192)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e183 (181\u0026ndash;186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.4 (51.6\u0026ndash;53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.9 (46.1\u0026ndash;47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.5 (55.6\u0026ndash;57.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.8 (45.8\u0026ndash;47.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.0 (41.6\u0026ndash;44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.3 (49.4\u0026ndash;51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFemales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.8 (56.9\u0026ndash;58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.6 (50.7\u0026ndash;52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.7 (60.1\u0026ndash;63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.6 (22.0\u0026ndash;23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.9 (25.8\u0026ndash;28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.3 (18.8\u0026ndash;19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.4 (21.0\u0026ndash;21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.4 (21.7\u0026ndash;23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.5 (20.1\u0026ndash;21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT IU/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.4 (26.2\u0026ndash;28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.9 (31.9\u0026ndash;35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.5 (21.4\u0026ndash;23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets 1000 cell/uL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247 (243\u0026ndash;251)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e251 (245\u0026ndash;256)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e244 (239\u0026ndash;249)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1 (4.1\u0026ndash;4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 (4.0\u0026ndash;4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2 (4.1\u0026ndash;4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.7 (3.5\u0026ndash;4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8 (4.4\u0026ndash;5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0 (2.6\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3 (3.8\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5 (5.4\u0026ndash;7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7 (2.4\u0026ndash;2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycohemoglobin %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.7 (5.7\u0026ndash;5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (5.9\u0026ndash;6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5 (5.4\u0026ndash;5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAP dB/m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e263 (260\u0026ndash;266)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e323 (320\u0026ndash;326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e218 (216\u0026ndash;220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSM kPa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.8 (5.5\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9 (6.4\u0026ndash;7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0 (4.8\u0026ndash;5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNote\u003c/b\u003e MASLD was determined by the presence of hepatic steatosis detected by the CAP values\u0026thinsp;\u0026ge;\u0026thinsp;274 dB/m, plus the presence of at least one of five cardiometabolic risk factors including: being overweight or obese, presence of diabetes, hypertension, hypertriglyceridemia, or low HDL levels. T-test was used to compare between groups. Values are reported as means (95% CI) for continuous variables and in proportions for categorical variable, P value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 considered as significant.\u003c/p\u003e \u003cp\u003e\u003cb\u003eAbbreviations\u003c/b\u003e ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; CAP, controlled attenuation parameter; GGT, gamma-glutamyl transferase; HDL-C, high density lipoprotein-cholesterol; HOMA-IR, homeostatic model assessment for insulin resistance; hs-CRP, high sensitivity-C reactive protein; LSM, liver stiffness measurement.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMASLD prediction in the NHANES cohort\u003c/h2\u003e \u003cp\u003eAll four steatosis NITs indicated good diagnostic accuracy for identifying MASLD with marginal differences between the AUROC (range 0.835 to 0.862) as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The overall accuracy of previously validated cutoffs is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. A higher FLI cutoff (\u0026ge;\u0026thinsp;60) provided a sensitivity of 79.6% and specificity of 75.1%, resulting in a maximum Youden index value (0.55), closely followed by FSI (0.54). The HSI cutoff of \u0026gt;\u0026thinsp;30 achieved the highest sensitivity (98.9%) but exhibited the poorest specificity (21.2%), resulting in the lowest Youden index value (0.20). The DSI identified a modest proportion of true positive cases (62.3%) but had the highest proportion of true negative cases (87.1%) and the highest positive likelihood ratio (4.83) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeighted diagnostic accuracy of hepatic steatosis NITs to predict MASLD against controlled attenuation parameter\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiochemical\u003c/p\u003e \u003cp\u003emodel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUROC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003cp\u003ecutoff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLR+\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLR-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eYouden Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDSI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.854 (0.839\u0026ndash;0.868)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eFLI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.859 (0.845\u0026ndash;0.873)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFSI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.862 (0.848\u0026ndash;0.875)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eHSI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.835 (0.819\u0026ndash;0.850)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e96.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbbreviations\u003c/b\u003e AUROC, Area under the receiver operating curve; DSI, Dallas steatosis index; FLI, fatty liver index; FSI, Framingham steatosis index; HSI, Hepatic steatosis index; LR, likelihood ratio; PPV; positive predicted value; NPV, negative predicted value.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe weighted MASLD prevalence as per the thresholds to predict hepatic steatosis were 34.1%, 48.3%, 51.6 and 58.7% for DSI, FLI, FSI, HSI, respectively. The weighted MASLD prevalence reported by FLI (48.3%) was closest to that of MASLD prevalence assessed through CAP (42.9%), whereas other NITs either under- or over-estimated MASLD prevalence. The level of agreement between CAP and biochemical NITs-diagnosed MASLD assessed through Kappa-statistics were moderate: 0.47 for DSI, 0.49 for FLI, 0.47 for FSI and 0.44 for HSI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAdvanced fibrosis prediction in the NHANES MASLD cohort\u003c/h2\u003e \u003cp\u003eWe evaluated the diagnostic accuracy of fibrosis NITs within the MASLD group (n\u0026thinsp;=\u0026thinsp;2,307). The NFS demonstrated the highest AUROC (0.699), followed by APRI (0.607), FIB-4 (0.576), and BARD (0.572). However, all NITs exhibited suboptimal performance in discriminating against advanced fibrosis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e The overall accuracy of previously validated cutoffs is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. For NFS, a cutoff of \u0026lt;-1.455 showed the highest sensitivity (76.7%) compared to other NITs, but with low specificity (51.7%). In contrast, the upper cutoff of \u0026gt;\u0026thinsp;0.676 showed high specificity (92.5%) but unacceptably low sensitivity (28.6%). Both FIB-4 and APRI thresholds for ruling in and out advanced fibrosis indicated extremely low sensitivities (\u0026lt;\u0026thinsp;50%) but very high specificities (\u0026gt;\u0026thinsp;90%) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e The BARD score\u0026thinsp;\u0026gt;\u0026thinsp;2 differentiated advanced fibrosis with modest sensitivity (70.9%) but exhibited poor specificity (27.9%). All fibrosis NITs exhibited notably high NPV ranging from 89.0\u0026ndash;94.9%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeighted diagnostic accuracy of fibrosis NITs to predict advanced fibrosis in the MASLD group against liver stiffness measurement\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiochemical model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUROC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003cp\u003ecutoff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLR+\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLR-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYouden Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eAPRI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.607 (0.545\u0026ndash; 0.668)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eBARD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.572 (0.505\u0026ndash; 0.639)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e89.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eFIB-4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.576 (0.513\u0026ndash; 0.639)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eNFS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.699 (0.639\u0026ndash;0.759)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbbreviations\u003c/b\u003e AUROC, area under the receiving operating curve; APRI aspartate aminotransferase to platelet ratio index; BARD, body mass index, AST ratio and diabetes status; FIB-4, fibrosis-4; NFS, Nafld fibrosis score; LR, likelihood ratio; PPV; positive predicted value; NPV, negative predicted value.\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\u003eWe explored optimal cutoffs for advanced fibrosis NITs by maximizing the Youden value. The cutoffs achieved were 0.29 for APRI, 3 for BARD, 1.24 for FIB-4, and \u0026minus;\u0026thinsp;0.895 for NFS. However, the Youden index value for these newly generated cutoffs did not exceed 0.50 for any of the fibrosis NITs \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe prevalence of advanced fibrosis among those with established MASLD according to fibrosis NITs were 0.1%, 1.7%, 9.7% and 72% for APRI, FIB-4, NFS and BARD, respectively. The weighted advanced fibrosis prevalence by NFS (9.7%) was closest to the prevalence assessed through TE, LSM (10.6%). The level of agreement between LSM and biochemical NITs-diagnosed advanced fibrosis were slight to fair for all NITs. The APRI (0.04), BARD (0.01) and FIB-4 (0.12) indicated slight agreement, whereas the NFS indicated fair (0.21) agreement.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSubgroup analysis of fibrosis NITs across age groups\u0026thinsp;\u0026le;\u0026thinsp;35, 36\u0026ndash;64 and \u0026ge;\u0026thinsp;65 years in the NHANES MASLD cohort\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo assess the accuracy of fibrosis NITs across different age groups (\u0026le;\u0026thinsp;35 years, 36\u0026ndash;64 years, and \u0026ge;\u0026thinsp;65 years), age categorized subgroup analysis was conducted. The NFS showed improved accuracy with increasing age, rising from 0.635 AUROC in those\u0026thinsp;\u0026le;\u0026thinsp;35 years to 0.793 AUROC in those\u0026thinsp;\u0026ge;\u0026thinsp;65 years \u003cb\u003e(Supplementary Table\u0026nbsp;1).\u003c/b\u003e For NFS, the upper cutoff of 0.676 in people aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years showed modest sensitivity (64.8%) and high specificity (80.3%), resulting in the highest Youden index value (0.45) compared to other NITs \u003cb\u003e(Supplementary Table\u0026nbsp;2).\u003c/b\u003e FIB-4 performed poorly in the younger age groups (\u0026le;\u0026thinsp;35 years: 0.542 AUROC and 36\u0026ndash;64 years: 0.511 AUROC) but showed fair accuracy in those aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years. None of the individuals in the \u0026le;\u0026thinsp;35 age group scored FIB-4 above 1.26, which prevented the analysis for validated cutoffs. APRI showed marginally fair accuracy in the \u0026le;\u0026thinsp;35 years, performed poorly in those 36\u0026ndash;64 years, and improved in those aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years. The sensitivity of APRI was notably low in individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;65 years but was high in those aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years at the lower cutoff of 0.50. None of the participants in \u0026le;\u0026thinsp;35 years group scored APRI\u0026thinsp;\u0026ge;\u0026thinsp;1.50, which prevented the analysis for the upper cutoff. The BARD index showed poor performance in the younger age groups (\u0026lt;\u0026thinsp;65) but achieved fair accuracy in individuals\u0026thinsp;\u0026ge;\u0026thinsp;65 years. The sensitivity for BARD increased from 41.0% in those aged\u0026thinsp;\u0026le;\u0026thinsp;35 to 94.8% in those aged\u0026thinsp;\u0026ge;\u0026thinsp;65 but resulted in higher rates of false positives (92.1%) in those aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years \u003cb\u003e(Supplementary Table\u0026nbsp;2).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the diagnostic performance of commonly utilized biochemical NITs against CAP-diagnosed MASLD and LSM diagnosed advanced fibrosis in the 2017\u0026ndash;2020 NHANES cohort. The main findings were that more than 40% of the US population had MASLD and that all four steatosis NITs provided good diagnostic accuracy for predicting MASLD. This suggests that any of these NITs are acceptable for assessing liver fat in clinical and research settings. Moreover, fibrosis NITs exhibited poor to fair diagnostic ability but had high true negative rates and NPV typically due to low prevalence of advanced fibrosis cases in general population. This characteristic makes them clinically beneficial for use in primary care settings, where clinicians can exclude individuals without significant fibrosis, potentially sparing them from referral to secondary or tertiary care clinics. In the subgroup analysis, it was found that age influenced the discriminative ability of fibrosis NITs. These NITs tended to under- and overestimate advanced fibrosis in individuals aged\u0026thinsp;\u0026le;\u0026thinsp;35 and \u0026ge;\u0026thinsp;65 years, respectively, but performed similarly in the overall MASLD cohort and in those aged 36\u0026ndash;64 years and hence should be used with caution in general population.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMASLD prediction in the NHANES cohort\u003c/h2\u003e \u003cp\u003eIn our study, the AUROC reported for DSI (0.854), FLI (0.859), FSI (0.862) HSI (0.835) were analogous to that reported in the previously published validation studies using the NAFLD criteria (i.e., 0.82 for DSI [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], 0.84 for FLI [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], 0.84 for FSI [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and 0.812 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] for HSI). The performance of steatosis NITs did not differ with the new MASLD criteria probably because higher BMI, insulin resistance and/or dyslipidemia which are components of these hepatic formulae and prerequisites for the MASLD criteria are intrinsic characteristics of the NAFLD population [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, concerning the applicability of these NITs, the FLI and HSI are more user-friendly with less complex formulae and have fewer biomarkers compared to DSI or FSI, hence making them more practical to use in primary care clinics. Among all the steatosis NITs, the FLI is the most extensively used and externally validated index and has been shown to accurately identify people with increase cardiometabolic disease risk and mortality [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A FLI cutoff\u0026thinsp;\u0026ge;\u0026thinsp;60 showed the highest maximum Youden index (0.55) in our study, which is a marker to assess the optimal threshold from a tested model. Indeed, FLI has been recommended by both the European Association for the Study of the Liver (EASL) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and Asian Pacific Association for the Study of the Liver (APASL) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] guidelines as the ideal tool to predict metabolic dysfunction related hepatic steatosis.\u003c/p\u003e \u003cp\u003eThe MASLD prevalence from steatosis NITs varied between 34.1% \u0026minus;\u0026thinsp;58.7% in our study, which was lowest from DSI and highest from HSI. However, the level of agreement based on Kappa statistics was deemed moderate between TE and steatosis NITs diagnosed-MASLD. The variation in prevalence could be attributed to the population\u0026rsquo;s metabolic characteristics and the diagnostic thresholds used to predict hepatic steatosis. For instance, the HSI had been developed and validated in an Asian population with an average BMI of 24.1 kg/m\u003csup\u003e2\u003c/sup\u003e and with a majority of male (70%) participants [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In contrast, our study was dominated by Caucasians (63%) and Hispanics (16%) with a higher mean BMI of 29.6 kg/m\u003csup\u003e2\u003c/sup\u003e and with equal proportions of male and females. Moreover, there is evidence suggesting varying ethnic susceptibilities to hepatic fat accumulation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Thus, the validated HSI cut-off \u0026gt;\u0026thinsp;36 overestimated MASLD prevalence and showed modest specificity (62.7%) in our study. This had been previously confirmed in other studies, highlighting the discriminative ability of biochemical NITs is dependent upon population\u0026rsquo;s demographic and metabolic traits [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAdvanced fibrosis prediction in the NHANES MASLD cohort\u003c/h2\u003e \u003cp\u003eWe observed that the available fibrosis NITs performed inadequately in discriminating advanced fibrosis diagnosed through LSM in the MASLD group. Our findings align with published literature indicating that these NITs exhibit limited ability to classify true positive cases but demonstrated high rates of true negatives and NPV [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Despite this limitation, they can be clinically useful for excluding moderate and advanced fibrosis, potentially sparing patients from further invasive testing, which has been recommended by the updated EASL guidelines for non-invasive assessments [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The presence of diabetes, obesity, and metabolic dysregulations, which closely aligns with MASLD, consistently linked with an increased risk of advancement to fibrosis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Indeed, liver fibrosis is the major prognostic predictor for all-cause mortality, liver related mortality and morbidity in MASLD [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Thus, surveillance of individuals with MASLD related fibrosis is highly relevant for targeted interventions to prevent adverse hepatic and extrahepatic outcomes in this population.\u003c/p\u003e \u003cp\u003eIn our study, NFS outperformed the other three NITs based on the highest AUROC which corresponds with published literature from NHANES [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], but exhibit low sensitivity and PPV with the cutoff to rule-in advanced fibrosis. The overall accuracy of NFS in our study (0.699) was identical to that reported in a recent multicenter biopsy confirmed MASLD cohort (0.666) but improved (0.735) after excluding people with BMI\u0026thinsp;\u0026gt;\u0026thinsp;40 kg/m\u003csup\u003e2\u003c/sup\u003e [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The study indicated higher AUROC for APRI (0.820 \u003cem\u003evs\u003c/em\u003e 0.607) and FIB-4 (0.845 \u003cem\u003evs\u003c/em\u003e 0.576) for differentiating advanced fibrosis compared to our study, which could be attributed to elevated liver enzyme profile in this study [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In our study, ALT and AST levels in the MASLD group were below the upper normal limits (ALT\u0026thinsp;\u0026gt;\u0026thinsp;40 IU/L, AST\u0026thinsp;\u0026gt;\u0026thinsp;35 IU/L), potentially explaining the poor performance of FIB-4 and APRI. Indeed, FIB-4 performs better with elevated liver enzyme levels [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Our findings for FIB-4 and APRI align with a recent study using the NHANES 2017\u0026ndash;2020 MASLD cohort, which demonstrated suboptimal performance of these NITs in detecting TE-diagnosed liver fibrosis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The AUROC of BARD was lowest compared to APRI, FIB-4, and NFS in our study. Additionally, 72% of MASLD patients were misclassified with advanced fibrosis using a BARD score\u0026thinsp;\u0026ge;\u0026thinsp;2, possibly due to BMI and diabetes status components overlapping with MASLD criteria, resulting in high false positives and the lowest PPV. Despite incorporating metabolic risk factors, BARD score does not include age which is also the major prognostic factor for advanced liver disease stage. Therefore, BARD score may not effectively predict advanced fibrosis in MASLD patients. Several studies have confirmed that fibrosis NITs exhibit higher efficiency in predicting advanced fibrosis in biopsy-proven MASLD. This may be attributed to the progressive disease staging that reflects severe metabolic abnormalities and a high prevalence of advanced fibrosis (\u0026gt;\u0026thinsp;30%) in these studies [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], which is not the case in our general population cohort.\u003c/p\u003e \u003cp\u003eThe new cutoffs for fibrosis NITs, while optimized for the Youden index, fell short of the acceptable threshold (\u0026gt;\u0026thinsp;0.50) and were not ideal for clinical application. A highly specific test is valuable in clinical settings to avoid costly and risky liver biopsies, as those with intermediate or higher test scores are usually referred for further evaluation per clinical guidelines [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, sequential combination of NITs with TE has shown increased specificity, allowing 60\u0026ndash;80% of patients to avoid biopsy by ruling out advanced fibrosis or confirming cirrhosis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This approach offers a reliable, non-invasive alternative for fibrosis assessment in people MASLD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis of fibrosis NITs across age groups\u0026thinsp;\u0026le;\u0026thinsp;35, 36\u0026ndash;64 and \u0026ge;\u0026thinsp;65 years\u003c/h2\u003e \u003cp\u003eConfounding factors, such as age, might influence the diagnostic efficiency of fibrosis NITs. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Our analysis further confirmed these findings that all fibrosis NITs underperformed in those aged\u0026thinsp;\u0026le;\u0026thinsp;35 years and showed fair to acceptable performance in those aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years. The poor performance in people\u0026thinsp;\u0026le;\u0026thinsp;35 years could be attributed to the low prevalence of advanced fibrosis cases (7%) in this age group. Age is included in both the NFS and FIB-4, which leads to false inflation of scores in individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years in our study. Additionally, the age-related decline in ALT and platelet levels leading to an increase in AST/ALT ratio, as observed in our analysis \u003cb\u003e(Supplementary Tables\u0026nbsp;1 and 2)\u003c/b\u003e further contributes to the higher rates of false positives in people aged\u0026thinsp;\u0026ge;\u0026thinsp;65 in our study. For APRI, the marginally higher AUROC observed in the \u0026le;\u0026thinsp;35 years could be attributed to the variability in AST levels and platelet counts, impacting the discriminative ability of APRI in this age group. This also suggests that the performance of APRI may not be impacted by younger age [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In BARD, the prevalence of diabetes and elevated AST/ALT levels increases in people aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, leading to a high sensitivity but also to increased rates of false positives (92.1%). Our analysis indicated that these NITs had the potential to both under- and overestimate fibrosis in younger (\u0026le;\u0026thinsp;35 years) and older (\u0026ge;\u0026thinsp;65 years) age groups, while performing similarly in those aged 36\u0026ndash;64 years compared to the overall MASLD cohort. Hence, the application of liver fibrosis NITs in isolation should be used with caution in general population.\u003c/p\u003e \u003cp\u003eDespite limitations, fibrosis NITs offer significant value with their high NPV and capacity to predict severe liver outcomes, providing a viable alternative in settings with limited resources [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Alternately, sequential combination of NITs with TE enhances specificity by effectively identifying adverse liver outcomes, allowing patients to avoid costly biopsies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This approach would reduce the need for invasive procedures, improve fibrosis management in primary care, and decrease reliance on specialist referrals, which is crucial for enhancing patient care in resource-limited environments. Our findings are valuable as they demonstrated the accuracy of NITs against TE in a general population cohort. This approach reduces selection bias, enabling the pragmatic use of NITs by primary care physicians, who may not have access to TE or liver biopsy for all patients. The results support the broader application of NITs for assessing liver health, providing an accessible, non-invasive option for early diagnosis and management for MASLD in diverse populations.\u003c/p\u003e \u003cp\u003eThe study has several strengths. First, it was the first to validate hepatic steatosis and fibrosis NITs against TE using the MASLD criteria in a nationally representative population. Second, analysis was robust including application of weighted ROC analysis by incorporating appropriate survey weights, critical for NHANES data analysis. Third, we have utilized TE and both CAP and LSM as the reference standard and which has shown accurate diagnostic performance against the gold standard liver biopsy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Fourth, we further tested the fibrosis NITs across different age groups to examine the influence of age as a confounding factor, further enhancing the quality of our analysis. This study however has several limitations. Although we have utilized TE as the reference standard, there are discrepancies in the utilization of CAP and LSM cutoffs to identify steatosis and advanced fibrosis. To overcome this, we utilized a CAP cutoff of 274 dB/m and a LSM cutoff of 9.7 kPa to identify steatosis and advanced fibrosis respectively, which have shown acceptable diagnostic performance against the liver biopsy and have been previously utilized to assess NAFLD prevalence in NHANES [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although TE is a well validated and an accurate imaging modality, the best approach to validate liver fibrosis predicting NITs should be against the gold standard liver biopsy which was not available in the NHANES cohort, an outcome which is neither an ethical nor feasible option for a population-based cohort. Lastly, our study group consistently predominantly of Caucasians (62.9%), which limits the generalizability of the findings to other populations. Indeed, MASLD prevalence varies across the globe and certain ethnic groups such as Hispanic people have higher rates of hepatic steatosis, whereas Africans have the lowest rates.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings indicate that all steatosis NITs assessed (DSI, FLI, FSI, and HSI) were acceptable predictors of MASLD. However, FLI emerged as the most practical and accurate tool for predicting MASLD in the US adult population. The fibrosis NITs (APRI, BARD score, FIB-4, and NFS) demonstrated limited diagnostic accuracy but showed notably high NPV for advanced fibrosis in people with MASLD, with age influencing their performance. Therefore, fibrosis NITs should be used with caution in general population. Despite these limitations, they remain worthwhile in identifying those without significant fibrosis and could be utilized in resource-limited settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFinancial support\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAyesha Sualeheen received Deakin University Postgraduate Research Scholarship while conducting this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study are available from the corresponding author, Elena S. George, upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAyesha Sualeheen\u003c/strong\u003e: Data curation (lead); Investigation (equal); formal analysis (equal); writing \u0026ndash; original draft (lead). \u003cstrong\u003eSze-Yen Tan:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; Review \u0026amp; Editing (equal); formal analysis (equal). \u003cstrong\u003eRobin M. Daly\u003c/strong\u003e: Writing \u0026ndash; Review \u0026amp; Editing (equal); formal analysis (equal). \u003cstrong\u003eEkavi Georgousopoulou\u003c/strong\u003e: Investigation (equal); formal analysis (equal). \u003cstrong\u003eStuart K. Roberts:\u0026nbsp;\u003c/strong\u003eWriting \u0026ndash; Review \u0026amp; Editing (equal); formal analysis (equal). \u003cstrong\u003eGavin Abbott:\u003c/strong\u003e Investigation (equal); formal analysis (equal).\u003cstrong\u003e\u0026nbsp;Elena S. George\u003c/strong\u003e: Conceptualization (lead); writing \u0026ndash; review and editing (equal); formal analysis (equal); Supervision (lead).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRinella, M.E., et al., \u003cem\u003eA multi-society Delphi consensus statement on new fatty liver disease nomenclature\u003c/em\u003e. Ann Hepatol, 2023: p. 101133.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiazi, K., et al., \u003cem\u003eThe prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis\u003c/em\u003e. Lancet Gastroenterol Hepatol, 2022. 7(9): p. 851\u0026ndash;861.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYounossi, Z.M., et al., \u003cem\u003eGlobal epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes\u003c/em\u003e. Hepatology, 2016. 64(1): p. 73\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJarvis, H., et al., \u003cem\u003eMetabolic risk factors and incident advanced liver disease in non-alcoholic fatty liver disease (NAFLD): A systematic review and meta-analysis of population-based observational studies\u003c/em\u003e. PLoS Med, 2020. 17(4): p. e1003100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEddowes, P.J., et al., \u003cem\u003eAccuracy of FibroScan Controlled Attenuation Parameter and Liver Stiffness Measurement in Assessing Steatosis and Fibrosis in Patients With Nonalcoholic Fatty Liver Disease\u003c/em\u003e. Gastroenterology, 2019. 156(6): p. 1717\u0026ndash;1730.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCiardullo, S., et al., \u003cem\u003eNonalcoholic Fatty Liver Disease, Liver Fibrosis and Cardiovascular Disease in the Adult US Population\u003c/em\u003e. Front Endocrinol (Lausanne), 2021. 12: p. 711484.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRinella, M.E., et al., \u003cem\u003eAASLD Practice Guidance on the clinical assessment and management of nonalcoholic fatty liver disease\u003c/em\u003e. Hepatology, 2023. 77(5): p. 1797\u0026ndash;1835.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcHenry, S., et al., \u003cem\u003eDallas Steatosis Index Identifies Patients With Nonalcoholic Fatty Liver Disease\u003c/em\u003e. Clin Gastroenterol Hepatol, 2020. 18(9): p. 2073\u0026ndash;2080.e7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBedogni, G., et al., \u003cem\u003eThe Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population\u003c/em\u003e. BMC Gastroenterol, 2006. 6: p. 33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLong, M.T., et al., \u003cem\u003eDevelopment and Validation of the Framingham Steatosis Index to Identify Persons With Hepatic Steatosis\u003c/em\u003e. Clin Gastroenterol Hepatol, 2016. 14(8): p. 1172\u0026ndash;1180.e2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, J.H., et al., \u003cem\u003eHepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease\u003c/em\u003e. Dig Liver Dis, 2010. 42(7): p. 503\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWai, C.T., et al., \u003cem\u003eA simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C\u003c/em\u003e. Hepatology, 2003. 38(2): p. 518\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrison, S.A., et al., \u003cem\u003eDevelopment and validation of a simple NAFLD clinical scoring system for identifying patients without advanced disease\u003c/em\u003e. Gut, 2008. 57(10): p. 1441\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah, A.G., et al., \u003cem\u003eComparison of Noninvasive Markers of Fibrosis in Patients With Nonalcoholic Fatty Liver Disease\u003c/em\u003e. Clinical Gastroenterology and Hepatology, 2009. 7(10): p. 1104\u0026ndash;1112.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAngulo, P., et al., \u003cem\u003eThe NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD\u003c/em\u003e. Hepatology, 2007. 45(4): p. 846\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcPherson, S., et al., \u003cem\u003eAge as a Confounding Factor for the Accurate Non-Invasive Diagnosis of Advanced NAFLD Fibrosis\u003c/em\u003e. Official journal of the American College of Gastroenterology | ACG, 2017. 112(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBertot, L.C., et al., \u003cem\u003eDiabetes impacts prediction of cirrhosis and prognosis by non-invasive fibrosis models in non-alcoholic fatty liver disease\u003c/em\u003e. Liver International, 2018. 38(10): p. 1793\u0026ndash;1802.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003e2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021\u003c/em\u003e. Diabetes Care, 2021. 44(Suppl 1): p. S15-s33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViera, A.J. and J.M. Garrett, \u003cem\u003eUnderstanding interobserver agreement: the kappa statistic\u003c/em\u003e. Fam Med, 2005. 37(5): p. 360\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Health \u003cem\u003eand Nutrition Examination Survey\u003c/em\u003e, 2017\u003cem\u003e\u0026ndash;March 2020 Prepandemic File: Sample Design, Estimation, and Analytic Guidelines\u003c/em\u003e, in \u003cem\u003eVital and health statistics. Series 2, Data Evaluation and Methods Research; no. 190\u003c/em\u003e, S. National Center for Health, Editor. 2022, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dx.doi.org/10.15620/cdc:115434\u003c/span\u003e\u003cspan address=\"10.15620/cdc:115434\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e: Hyattsville, MD.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalori, G., et al., \u003cem\u003eFatty liver index and mortality: the Cremona study in the 15th year of follow-up.\u003c/em\u003e Hepatology, 2011. 54(1): p. 145\u0026thinsp;\u0026ndash;\u0026thinsp;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou, B., et al., \u003cem\u003eFatty Liver Index and Development of Cardiovascular Disease: Findings from the UK Biobank\u003c/em\u003e. Digestive Diseases and Sciences, 2021. 66(6): p. 2092\u0026ndash;2100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuropean Association for the Study of the, L., D. European Association for the Study of, and O. European Association for the Study of, \u003cem\u003eEASL-EASD-EASO Clinical Practice Guidelines for the Management of Non-Alcoholic Fatty Liver Disease\u003c/em\u003e. Obes Facts, 2016. 9(2): p. 65\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEslam, M., et al., \u003cem\u003eThe Asian Pacific Association for the Study of the Liver clinical practice guidelines for the diagnosis and management of metabolic associated fatty liver disease\u003c/em\u003e. Hepatol Int, 2020. 14(6): p. 889\u0026ndash;919.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRich, N.E., et al., \u003cem\u003eRacial and Ethnic Disparities in Nonalcoholic Fatty Liver Disease Prevalence, Severity, and Outcomes in the United States: A Systematic Review and Meta-analysis\u003c/em\u003e. Clinical Gastroenterology and Hepatology, 2018. 16(2): p. 198\u0026ndash;210.e2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, Y., et al., \u003cem\u003eValidation of five hepatic steatosis algorithms in metabolic-associated fatty liver disease: A population based study\u003c/em\u003e. J Gastroenterol Hepatol, 2022. 37(5): p. 938\u0026ndash;945.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKouvari, M., et al., \u003cem\u003eLiver biopsy-based validation, confirmation and comparison of the diagnostic performance of established and novel non-invasive steatotic liver disease indexes: Results from a large multi-center study\u003c/em\u003e. Metabolism, 2023. 147: p. 155666.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eEASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis \u0026ndash;\u0026thinsp;2021 update\u003c/em\u003e. J Hepatol, 2021. 75(3): p. 659\u0026ndash;689.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePais, R., et al., \u003cem\u003eA systematic review of follow-up biopsies reveals disease progression in patients with non-alcoholic fatty liver\u003c/em\u003e. Journal of Hepatology, 2013. 59(3): p. 550\u0026ndash;556.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor, R.S., et al., \u003cem\u003eAssociation Between Fibrosis Stage and Outcomes of Patients With Nonalcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis\u003c/em\u003e. Gastroenterology, 2020. 158(6): p. 1611\u0026ndash;1625 e12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, X., et al., \u003cem\u003eValidation of Non-invasive Fibrosis Scores for Predicting Advanced Fibrosis in Metabolic-associated Fatty Liver Disease\u003c/em\u003e. J Clin Transl Hepatol, 2022. 10(4): p. 589\u0026ndash;594.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, R., et al., \u003cem\u003eComparison of blood-based liver fibrosis scores in the Mount Sinai Health System, MASLD Registry, and NHANES 2017\u0026ndash;2020 study\u003c/em\u003e. Hepatology Communications, 2024. 8(9): p. e0515.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcPherson, S., et al., \u003cem\u003eSimple non-invasive fibrosis scoring systems can reliably exclude advanced fibrosis in patients with non-alcoholic fatty liver disease\u003c/em\u003e. Gut, 2010. 59(9): p. 1265\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiddiqui, M.S., et al., \u003cem\u003eDiagnostic Accuracy of Noninvasive Fibrosis Models to Detect Change in Fibrosis Stage\u003c/em\u003e. Clinical Gastroenterology and Hepatology, 2019. 17(9): p. 1877\u0026ndash;1885.e5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026oacute;zes, F.E., et al., \u003cem\u003eDiagnostic accuracy of non-invasive tests for advanced fibrosis in patients with NAFLD: an individual patient data meta-analysis\u003c/em\u003e. Gut, 2022. 71(5): p. 1006.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoh, G.B., et al., \u003cem\u003eAge impacts ability of aspartate-alanine aminotransferase ratio to predict advanced fibrosis in nonalcoholic Fatty liver disease\u003c/em\u003e. Dig Dis Sci, 2015. 60(6): p. 1825\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaz, K., et al., \u003cem\u003eValidation of serum non-invasive tests of liver fibrosis as prognostic markers of clinical outcomes in people with fatty liver disease in Australia\u003c/em\u003e. J Gastroenterol Hepatol, 2024.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Validation, MASLD, NAFLD, advanced fibrosis, NITs, transient elastography, FLI, HSI, NFS, FIB-4","lastPublishedDoi":"10.21203/rs.3.rs-5456895/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5456895/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMetabolic dysfunction associated steatotic liver disease (MASLD) has replaced NAFLD as the diagnostic standard. This study aimed to validate steatosis and fibrosis non-invasive tests (NITs) used for NAFLD in predicting MASLD and advanced fibrosis, compared to transient elastography (TE), respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study used the NHANES database (2017–2020). The Dallas steatosis index (DSI), fatty liver index (FLI), Framingham steatosis index (FSI) and hepatic steatosis index (HSI) were assessed against TE diagnosed MASLD. The NAFLD fibrosis score (NFS), fibrosis-4 (FIB-4), APRI (The aspartate aminotransferase to platelet ratio index), and BARD (body mass index, aspartate aminotransferase/alanine aminotransferase ratio diabetes score) were assessed against TE diagnosed advanced fibrosis. The diagnostic accuracy evaluated with the weighted ROC analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included 5,399 participants (51% female), with an estimated MASLD prevalence of 42.9%, and 10.6% indicated advanced fibrosis as assessed by TE. Steatosis NITs showed good diagnostic accuracy for predicting MASLD (AUROC 0.835 to 0.862), with FLI having the maximum Youden index (0.55). Fibrosis NITs indicated poor to fair diagnostic performance for predicting advanced fibrosis (AUROC 0.572 to 0.699) but had high NPV (89%-94%). In an age categorized subgroup analysis fibrosis NITs indicated poor performance in those aged ≤ 35 years and exhibited unacceptably low specificity to exclude fibrosis in those aged ≥ 65 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this population-based cohort of U.S. adults, all steatosis NITs demonstrated good diagnostic accuracy for MASLD. However, the fibrosis NITs showed limited diagnostic ability and were influenced by age, suggesting they should be used with caution in the general population.\u003c/p\u003e","manuscriptTitle":"Steatosis Non-Invasive Tests Accurately Predict Metabolic Dysfunction-Associated Steatotic Liver Disease, While Fibrosis Non-Invasive Tests Fall Short: Validation in U.S. Adult Population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 18:06:15","doi":"10.21203/rs.3.rs-5456895/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":"7f3b32f2-eb31-452d-9a88-0bca016854a7","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-24T23:23:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-18 18:06:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5456895","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5456895","identity":"rs-5456895","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}