Association between nutrition-related indicators and non-alcoholic fatty liver disease and advanced liver fibrosis: evidence from NHANES 2017-2020

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A critical marker for the progression of NAFLD is advanced hepatic fibrosis (AHF), which can result in cirrhosis and hepatocellular carcinoma. This study sought to investigate the potential associations between five nutrition-related indices—the geriatric nutritional risk index (GNRI), prognostic nutritional index (PNI), controlled nutritional score (CONUT), triglyceride-total cholesterol-body mass index (TCBI), and albumin/globulin ratio (AGR)—and the progression of NAFLD and AHF. Methods : This study analyzed the association between five nutrition-related indicators and the occurrence of NAFLD and AHF by utilizing data from the National Health and Nutrition Examination Survey (NHANES) from 2017 to 2020.03. After adjusting for demographic, lifestyle, clinical, and laboratory-related factors, the associations were examined employing various statistical techniques, including multivariate logistic regression, subgroup analysis, and smooth curve fitting analysis. Results : This study included 5,514 subjects, of whom 2,088 were diagnosed with NAFLD and 359 with AHF. After adjusting for potential confounding factors using multivariate logistic regression analysis, the results revealed that GNRI, PNI, and TCBI were positively associated with the incidence of NAFLD, while CONUT and AGR demonstrated negative associations with NAFLD. Additionally, GNRI, CONUT, and TCBI were positively associated with the incidence of AHF, while AGR demonstrated a negative association with AHF. Conclusions : The study demonstrates that nutritional status plays a dual role in the progression of NAFLD and AHF, serving as both a risk and protective factor. These findings offer a scientific foundation for the early identification of high-risk populations and the development of individualized nutritional intervention strategies. NAFLD AHF nutritional status NHANES cross-sectional study Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction NAFLD is one of the most common chronic liver diseases worldwide, with an estimated prevalence of approximately 25.24% 1 . With the continued rise in global obesity and diabetes rates, NAFLD has emerged as a major non-communicable disease, presenting a significant challenge to global public health 2 . NAFLD is characterized by excessive fat accumulation in the liver without significant alcohol consumption. As the disease progresses, it can progress to liver fibrosis, cirrhosis, and even hepatocellular carcinoma (HCC) 3 . The pathophysiological mechanisms of NAFLD are highly complex and involve processes such as insulin resistance, oxidative stress, lipid peroxidation, inflammation, and hepatocyte apoptosis 4 . AHF is regarded as a key marker of disease progression in the natural history of NAFLD. It is a pathological process triggered by chronic inflammation and tissue damage, marked by the excessive deposition of collagen and other extracellular matrix in the liver. As fibrosis progresses, the liver's structure becomes increasingly disrupted, potentially leading to cirrhosis 5 . The onset of cirrhosis signals the progression of NAFLD to an irreversible stage, significantly increasing the risk of liver failure and HCC. Currently, there is no specific treatment for NAFLD, making in-depth research into its risk factors essential for its prevention and management. Previous studies have shown that nutritional imbalance, oxidative stress, and immune function are closely associated with the progression of NAFLD 6,7 . Nutritional imbalance is a primary driver of NAFLD development, contributing to disorders in lipid metabolism and oxidative stress 8 . Oxidative stress plays a key role in the development and progression of NAFLD by inducing hepatocellular injury, inflammation, and fibrogenesis 9 . The liver, rich in immune cells such as macrophages, lymphocytes, and natural killer cells, plays a vital role in immune homeostasis and pathogen defense. In patients with NAFLD, the function of immune cells is often impaired, resulting in a dysregulated immune response that exacerbates liver inflammation and promotes fibrosis 10 . In recent years, several novel laboratory-based nutritional indicators have emerged for identifying malnutrition risk, including the Geriatric Nutritional Risk Index (GNRI), Prognostic Nutritional Index (PNI), Controlled Nutritional Score (CONUT), Triglyceride-Total Cholesterol-Body Mass Index (TCBI), and Albumin-to-Globulin Ratio (AGR). These indices incorporate a range of test parameters, such as albumin, lymphocyte count, body weight, total cholesterol, and triglycerides, providing insight into the nutritional and immune status of patients. They have been widely used in research on tumors, cardiovascular diseases, and chronic obstructive pulmonary disease, but their application in NAFLD patients remains underexplored. This study used data from the National Health and Nutrition Examination Survey (NHANES) from 2017 to 2020.03, incorporating demographic, lifestyle, and disease factors to explore the associations between five nutrition-related indicators, NAFLD, and AHF. The results of the study offer new insights into NAFLD and may inform the formulation of public health policies. Methods NHANES is a nationally representative survey administered by the National Center for Health Statistics (NCHS) within the U.S. Centers for Disease Control and Prevention (CDC), designed to assess the nutrition and health status of the U.S. population. The survey maintains a high level of transparency regarding both its data and methodological design. All publicly available data and detailed survey methodologies can be accessed via the NHANES official website. NHANES has received approval from the CDC Institutional Review Board and adheres to strict guidelines for safeguarding participant safety and privacy. All participants provided written informed consent prior to participation. The data used in this study were sourced from NHANES between 2017 and March 2020, including a total of 15,560 participants. Exclusion criteria included: (1) age < 20 years (n = 6328), (2) missing nutritional status index (n = 1489), (3) missing transient elastography data (n = 431), (4) positive for serum hepatitis B surface antigen (n = 41), hepatitis C virus RNA (n = 83), and hepatitis C antibody (n = 88), (5) cancer diagnosis (n = 706), and (6) missing other covariates (n = 880). A total of 5514 participants were included in the final analysis. Of these participants, 2666 were male and 2848 were female. The detailed screening process is illustrated in Figure 1. 2.1 Assessment of NAFLD and AHF Although liver biopsy remains the gold standard for diagnosing NAFLD, its invasiveness and high cost render it impractical for large-scale population studies. CAP, measured by FibroScan—an emerging technology based on transient elastography using ultrasound—offers a quantitative diagnosis of fatty liver disease with accuracy comparable to liver biopsy in detecting NAFLD. Previous studies have indicated that a CAP value of ≥ 285 dB/m is indicative of NAFLD, while an LSM value of ≥ 9.7 kPa corresponds to AHF 11 . 2.2 Assessment of Nutritional Status This study utilized five nutritional indicators to assess the population's nutritional status: GNRI, PNI, CONUT, TCBI, and AGR. The specific calculation methods for each indicator are outlined in Table 1. Based on previous research, GNRI is classified into a risk-free cohort (GNRI ≥ 98) and a risk cohort (GNRI < 98). CONUT is categorized into normal nutrition (0–1), mild malnutrition (2–4), and moderate to severe malnutrition (5–12). PNI, TCBI, and AGR are classified by quartiles, with participants in the first quartile at higher risk of malnutrition and those in the fourth quartile at lower risk. 2.3 Covariates To minimize potential confounders in our research outcomes, a comprehensive literature review was performed to identify and incorporate relevant covariates in our analysis. The sociodemographic factors included age, sex, race/ethnicity, educational attainment, marital status, and poverty-to-income ratio (PIR). Lifestyle factors comprised smoking status, alcohol intake, and physical activity level. Health-related covariates considered were body mass index (BMI), history of stroke, lung disease, heart disease, chronic kidney disease (CKD), hypertension, and diabetes. Laboratory markers included alanine aminotransferase (ALT), alkaline phosphatase (ALP), aspartate aminotransferase (AST), and gamma-glutamyl transferase (GGT).Race/ethnicity categories encompassed Mexican-American, non-Hispanic Black, non-Hispanic White, other Hispanic, and other racial groups. Marital status was categorized into married/cohabiting, divorced, separated, widowed, or never married. Educational levels were classified as less than high school, high school graduate, and college degree or above. PIR was stratified into low (<1.3), medium (1.3–3.5), and high income (≥3.5) groups.Smoking status was categorized as never smokers, ex-smokers, and current smokers, based on lifetime smoking behavior (≥100 cigarettes). Alcohol consumption was classified into three groups: never drinkers, moderate drinkers (1–2 drinks/day for men, 1 drink/day for women), and heavy drinkers (exceeding 2 drinks/day for men or 1 drink/day for women). Physical activity was quantified using the weekly frequency, duration, and the corresponding metabolic equivalent of task (MET). Following the World Health Organization (WHO) Guidelines on Physical Activity and Sedentary Behavior, activity intensity was classified into low-to-moderate (<1,200 MET-min/week) and high (≥1,200 MET-min/week).BMI was categorized as underweight/normal (29.9 kg/m²). Stroke diagnosis was based on self-reported history. Heart disease was defined by a confirmed diagnosis of congestive heart failure, coronary artery disease, myocardial infarction, or angina pectoris. Lung disease included self-reported diagnoses of asthma, chronic obstructive pulmonary disease (COPD), or emphysema. CKD was characterized by an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m², using the Modification of Diet in Renal Disease (MDRD) study equation. Hypertension was identified through: (1) confirmed medical diagnosis, (2) mean systolic blood pressure (SBP) ≥130 mmHg or mean diastolic blood pressure (DBP) ≥80 mmHg, or (3) use of antihypertensive medications. Diabetes was defined by: (1) a confirmed diagnosis, (2) hemoglobin A1c (HbA1c) level ≥6.5%, or (3) use of glucose-lowering medication or insulin. 2.4 Statistical Analysis Due to the COVID-19 pandemic, NHANES field operations were halted in March 2020. Consequently, data collected from 2019 to March 2020 were combined with the 2017–2018 NHANES cycle to reconstruct a comprehensive database representing the health status of the U.S. population. Continuous variables are reported as means ± standard deviation (SD), while categorical variables are presented as percentages. Statistical significance was assessed using t-tests for continuous variables and chi-square tests for categorical variables.The Geriatric Nutritional Risk Index (GNRI) was divided into "risk-free" and "at-risk" categories. The Controlling Nutritional Status (CONUT) score was classified into normal nutrition, mild malnutrition, and moderate to severe malnutrition. Prognostic Nutritional Index (PNI), Total Cholesterol to Albumin Ratio (TCBI), and Albumin-to-Globulin Ratio (AGR) were divided into quartiles, with Q1 serving as the reference group. To explore the associations between nutrition-related indicators and non-alcoholic fatty liver disease (NAFLD) or advanced hepatic fibrosis (AHF), a multivariate logistic regression model was employed. Model 1 was unadjusted; Model 2 adjusted for age, sex, and race; and Model 3 further adjusted for educational level, marital status, PIR, smoking status, alcohol consumption, physical activity, BMI, history of stroke, pulmonary disease, cardiovascular disease, CKD, hypertension, diabetes, as well as ALT, ALP, AST, and GGT levels. Results are presented as weighted odds ratios (OR) with 95% confidence intervals (95% CI).Additionally, the dose-response relationship between nutritional indicators and NAFLD/AHF was evaluated using smooth curve fitting, and stratified analyses were performed to verify the robustness of the findings. All statistical analyses were conducted using R software (version 4.4.1, http://www.R-project.org) and EmpowerStats software (versions 2.0 and 4.2, http://www.empowerstats.com). A p-value < 0.05 was considered indicative of statistical significance. Results 3.1 Participant characteristics A total of 5,514 participants were included in this study. The mean age of the participants was 48.67 ± 16.80 years, with 48.35% being male, as shown in Table 2. Among the participants, 2088 were diagnosed with NAFLD, and 359 were diagnosed with AHF. The mean values of GNRI, PNI, CONUT, TCBI, and AGR were 117.36 ± 13.48, 51.81 ± 5.15, 1.38 ± 0.76, 2335.01 ± 2414.36, and 1.35 ± 0.24, respectively. Compared with the non-NAFLD group, participants in the NAFLD group were characterized by older age, male sex, Mexican American ethnicity, lower education level, being married or cohabiting, obesity, presence of lung disease, heart disease, hypertension, diabetes, chronic kidney disease, lower activity level, former smoking status, elevated ALT, ALP, AST, and GGT levels, and higher nutritional indices such as GNRI, PNI, and TCBI, but lower CONUT and AGR. Compared with the non-AHF group, participants in the AHF group were characterized by older age, male sex, middle income, obesity, presence of lung disease, heart disease, hypertension, diabetes, lower activity level, former smoking status, never drinking status, elevated ALT, ALP, AST, and GGT levels, and higher nutritional indices such as GNRI, CONUT, and TCBI, but lower PNI and AGR. 3.2 Association of nutrition-related indices with NAFLD and AHF Table 3 presents the results of the multiple linear regression analysis assessing the association between five nutrition-related indicators and NAFLD. To enhance interpretability, TCBI values were divided by 100. The indicators were evaluated both as continuous and categorical variables, and multiple models with varying adjustments for covariates were employed. In the fully adjusted model (Model 3), when considered as continuous variables, GNRI, PNI, and TCBI were significantly positively correlated with NAFLD, whereas CONUT and AGR showed a significant negative correlation with NAFLD.For every one-unit increase in GNRI, the risk of NAFLD increased by 0.054 (OR = 1.054 [1.045, 1.063]). Similarly, for every unit increase in PNI, the risk of NAFLD increased by 0.02 (OR = 1.020 [1.007, 1.034]). For every unit increase in TCBI/100, the risk of NAFLD increased by 0.021 (OR = 1.021 [1.017, 1.025]). For every one-unit increase in CONUT, the risk of NAFLD decreased by 0.101 (OR = 0.899 [0.821, 0.983]). For every one-unit increase in AGR, the risk of NAFLD decreased by 0.412 (OR = 0.588 [0.431, 0.801]). When the nutrition-related indicators were considered as categorical variables, a significant dose-response relationship was observed between PNI, CONUT, TCBI, AGR, and NAFLD (P for trend < 0.05). Compared to the lowest group, the risk of NAFLD in the highest GNRI group was elevated by 1.487 (OR = 2.487 [1.238, 4.996]), the risk of NAFLD in the highest PNI group rose by 0.351 (OR = 1.351 [1.115, 1.638]), the highest TCBI/100 group exhibited a 2.751-fold increased risk of NAFLD (OR = 3.751 [2.982, 4.718]), and the highest AGR group demonstrated a 0.237-fold decreased risk of NAFLD (OR = 0.763 [0.622, 0.936]). Table 4 presents the results of the multiple linear regression analysis between nutrition-related indicators and AHF. To emphasize the effect, TCBI was divided by 100.The indicators were categorized as continuous and categorical variables, and different models were analyzed after adjusting for multiple covariates. The results indicate that in the fully adjusted model (Model 3), when the nutrition-related indicators were treated as continuous variables, there was a significant positive correlation between GNRI, CONUT, and TCBI and AHF, and a significant negative correlation between AGR and AHF. For every one-unit increase in GNRI, the risk of AHF increased by 0.074 (OR = 1.074 [1.062, 1.086]). For every unit increase in CONUT, the risk of AHF increased by 0.219 (OR = 1.219 [1.068, 1.391]). For every unit increase in TCBI/100, the risk of AHF increased by 0.004 (OR = 1.004 [1.000, 1.008]). For each unit increase in AGR, the risk of AHF decreased by 0.589 (OR = 0.411 [0.235, 0.716]). When the nutrition-related indicators were considered as categorical variables, a significant dose-response relationship was observed between CONUT, TCBI, AGR, and AHF (P for trend < 0.05). Compared with the lowest group, the risk of AHF in the highest AGR group decreased by 0.343 (OR = 0.657 [0.459, 0.940]). 3.3 Subgroup Analysis This study performed subgroup analyses based on variables such as age, gender, race, hypertension, diabetes, alcohol consumption, and smoking to explore potential differences in the associations between five nutrition-related indicators and NAFLD/AHF across different population subgroups. Figure 2 illustrates the subgroup analysis results for NAFLD. Significant differences were observed in the association between GNRI and NAFLD across all subgroups. Notably, the relationship between PNI and NAFLD varied significantly in the race and smoking subgroups, while the association between CONUT and NAFLD remained consistent across all subgroups. TCBI demonstrated significant differences in its association with NAFLD in the gender and smoking subgroups, and AGR showed a significant relationship with NAFLD within the smoking subgroup.Figure 3 displays the subgroup analysis for AHF. Significant associations were found between GNRI and AHF across all subgroups. The relationship between PNI and AHF was significant in the gender and hypertension subgroups. Differences in the association between CONUT and AHF were observed in the age and drinking subgroups. TCBI was significantly associated with AHF in the race and hypertension subgroups, while AGR exhibited significant associations with AHF in the age and smoking subgroups. 3.4 Smooth curve fitting and threshold effect analysis Smooth curve fitting was employed in the study to assess the non-linear correlation between nutrition-related indicators and NAFLD and AHF. To emphasize the effect, TCBI was divided by 100. As shown in Figure 4, there was a significant non-linear correlation between GNRI and NAFLD and AHF, and between TCBI/100 and NAFLD, with inflection points of 141.16, 120.17, and 31.20, respectively. When GNRI was less than 141.16, for every unit increase in GNRI, the risk of NAFLD increased by 0.054, and the relationship was not statistically significant when GNRI exceeded 141.16. When GNRI was less than 120.17, the relationship between GNRI and AHF was not statistically significant. When GNRI exceeded 120.17, for every unit increase in GNRI, the risk of AHF increased by 0.083. When TCBI/100 was less than 31.20, the risk of NAFLD increased by 0.052 for every unit increase in TCBI/100. When TCBI/100 exceeded 31.20, the risk of NAFLD increased by 0.005 for every unit increase in TCBI/100. (Table 5). Discussion The increasing prevalence of metabolic syndrome and obesity has led to a global rise in non-alcoholic fatty liver disease (NAFLD) and advanced hepatic fibrosis (AHF), both of which have become major public health issues. Nutritional indices such as GNRI, PNI, CONUT, TCBI and AGR are widely used in clinical practice to evaluate nutritional status and prognosis, particularly in patients with cancer, cardiovascular diseases, and other severe conditions. Despite their clinical utility, our study is the first to comprehensively analyze the association between these five nutritional markers and NAFLD/AHF.Using data from 5,514 participants collected from the NHANES database between 2017 and March 2020, our retrospective analysis demonstrated that malnutrition is significantly associated with the incidence of both NAFLD and AHF after adjusting for all potential confounding factors. Specifically, GNRI, PNI, and TCBI showed positive associations with NAFLD, whereas CONUT and AGR exhibited negative associations. Furthermore, GNRI, CONUT, and TCBI were positively linked to AHF, while AGR was negatively correlated with AHF. The GNRI combines serum albumin levels and body weight data to assess the prognosis of patients with various diseases. Zhao et al. 12 analyzed 12,058 patients with acute kidney injury (AKI) from the eICU Collaborative Research Database and found that a nutritional risk (GNRI < 98) was significantly associated with increased in-hospital mortality among AKI patients in the intensive care unit. Another cohort study from China demonstrated that GNRI was negatively correlated with the risk of all-cause mortality in patients with atrial fibrillation, and that a higher GNRI acted as a protective factor in this population. Our study found that GNRI was positively correlated with the risk of NAFLD and AHF. This may be due to GNRI being composed of two components: serum albumin and current body weight/standard body weight. Albumin, one of the primary plasma proteins in the human body, plays a crucial role in maintaining plasma colloid osmotic pressure and transporting endogenous and exogenous substances 13 . Recent studies indicate that a reduction in albumin levels is closely related to the progression of NAFLD. In patients with NAFLD, low albumin levels are often associated with aggravated liver fibrosis, likely due to chronic inflammation, oxidative stress, and diminished albumin synthesis resulting from liver dysfunction 14 . In patients with AHF, hypoalbuminemia is more pronounced, reflecting severe impairment of liver synthetic function and closely correlating with further disease progression and an adverse prognosis. A study by Khanna et al. 15 demonstrated that a sustained reduction in albumin levels may predict liver failure and an increased incidence of complications, including ascites, hepatic encephalopathy, and infection. In contrast to the protective effect of albumin, obesity is a major risk factor for the development of NAFLD and AHF. Kuang et al. demonstrated that obesity-related indices such as BMI, A Body Shape Index (ABSI), Anthropometric Risk Index (ARI), and waist circumference were significantly and positively correlated with the risk of NAFLD. Obesity is associated with lipid metabolism disorders and excessive fat accumulation in the liver. This steatosis induces liver cell stress, oxidative damage, and non-alcoholic steatohepatitis (NASH), ultimately resulting in liver cell damage and the initiation of fibrosis 16 . Lipid metabolism disorders and chronic low-grade inflammation promote abnormal liver collagen deposition and accelerate fibrosis formation 17 . Additionally, obesity is closely linked to metabolic syndrome (e.g., insulin resistance, hypertension, and dyslipidemia), which is a key risk factor for the progression of NAFLD to AHF 18 . The PNI comprises serum albumin levels and lymphocyte counts and is used to predict the risk of malnutrition and postoperative complications. It is widely used in assessing the survival prognosis of cancer patients. A PNI value below 40 typically indicates poor nutritional status. Pinato et al. 19 analyzed clinical data from 112 liver cancer patients and found that PNI is an independent predictor of poor prognosis. Another cohort study demonstrated that PNI is an important predictor of recurrence and survival after radical surgery for early-stage hepatocellular carcinoma 20 . The pathogenesis of NAFLD involves complex metabolic disorders and chronic low-grade inflammation, where lymphocytes play a dual role. Activated T lymphocytes, such as helper T cells 1 (Th1) and 17 (Th17), secrete pro-inflammatory cytokines like interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α), exacerbating liver cell damage and apoptosis, stimulating hepatic stellate cell activation, and leading to excessive collagen deposition 21 . Conversely, regulatory T cells (Treg) and helper T cells 22 (Th22) inhibit excessive immune responses by secreting anti-inflammatory cytokines (IL-10, IL-22), thereby reducing liver damage and exerting an anti-fibrosis effect 22 . Additionally, IgG autoantibodies produced by B lymphocytes can accelerate the progression of NAFLD fibrosis. As NAFLD progresses to advanced liver fibrosis, lymphocytes increasingly promote fibrosis 23 . Our study found that PNI was significantly positively correlated with the risk of NAFLD, while no significant association was found with AHF. A retrospective study from the same NHANES cohort showed that an increase in PNI was positively correlated with an increased risk of NAFLD and negatively correlated with an increased risk of AHF. This may be attributed to differing inclusion and exclusion criteria for the samples and varying covariates 11 . The CONUT score comprises serum albumin, total lymphocyte count, and total cholesterol scores, which collectively assess the patient’s protein-energy malnutrition and immune function status. Low serum albumin levels reflect decreased liver function and impaired protein synthesis. The total lymphocyte count is a key indicator of immune system function, reflecting the patient’s current immune response capacity. Total cholesterol levels are closely linked to energy metabolism and nutritional status, with lower cholesterol levels generally indicating malnutrition 24 . Patients with NAFLD experience lipid metabolism disorders, characterized by elevated serum cholesterol levels that result in excessive cholesterol accumulation in hepatocytes. Excessive intrahepatic cholesterol induces mitochondrial damage and oxidative stress, subsequently activating hepatic stellate cells (HSCs) and promoting liver fibrosis 25 . In the early stages of the disease, elevated total cholesterol is closely associated with hepatic steatosis and inflammation. However, as the disease progresses to advanced liver fibrosis, liver synthetic function becomes impaired, and cholesterol synthesis and metabolism decline, potentially leading to lower serum total cholesterol levels 26 . This dynamic shift in cholesterol levels reflects the pathophysiological progression of NAFLD to AHF. Our study found that the CONUT score was negatively correlated with the incidence of NAFLD and positively correlated with the incidence of AHF. A possible explanation is that in the early stages of NAFLD, patients are often overweight or obese, typically associated with better nutritional reserves and higher protein levels. As a result, despite the formation of fatty liver, the CONUT score remains low. However, as the disease progresses to advanced fibrosis, liver synthetic function becomes impaired, protein metabolism is disrupted, immune function declines, and cholesterol synthesis decreases, leading to an increase in the CONUT score. Miano et al. 27 found that an elevated CONUT score is closely associated with severe hepatic steatosis but not significantly correlated with moderate steatosis. Severe hepatic steatosis is often accompanied by significant liver fibrosis, supporting our findings to some extent. TCBI is a novel and straightforward nutritional tool that evaluates a patient's nutritional and metabolic health status, primarily based on triglyceride levels, total cholesterol, and body weight. A retrospective observational cohort study from Japan demonstrated that a lower TCBI score was significantly associated with an increased risk of all-cause mortality, cardiovascular disease mortality, and cancer mortality 28 . Another recent study found that TCBI was independently and negatively associated with stroke prevalence, particularly in hypertensive patients under 60 years of age 29 . Our study found that TCBI was significantly positively correlated with the risk of NAFLD and AHF, likely due to the combined effects of triglycerides, total cholesterol, and body weight. Triglycerides are the primary form of fat storage in the liver. Elevated serum triglyceride levels typically reflect abnormal fat metabolism, and excessive fat accumulation in the liver contributes to the development of NAFLD. Insulin resistance is a core pathological mechanism of NAFLD. Insulin resistance decreases the liver's sensitivity to insulin, leading to increased fatty acid mobilization and enhanced triglyceride synthesis, which further promotes intrahepatic fat accumulation 30 . Additionally, excessive triglycerides activate immune cells, such as Kupffer cells, which release inflammatory factors like TNF-α and IL-1β, promoting the progression of NAFLD to AHF 31 . AGR is the ratio of serum albumin to globulin and reflects both liver function and the systemic inflammatory state. A low AGR typically indicates chronic inflammation, impaired liver function, or protein malnutrition, all of which are risk factors for poor prognosis. Wen et al. 32 demonstrated that in diabetic patients, higher serum AGR levels were significantly and linearly associated with reduced mortality from all causes, cancer, and cardiovascular diseases. Albumin, produced by the liver, has antioxidant, anti-inflammatory, and immune-modulating functions. Higher albumin levels are typically associated with better liver function and overall health 33 . Impaired liver function in NAFLD patients can lead to decreased albumin production and reduced serum albumin levels. Globulin plays a key role in the immune response and is a potential predictor of inflammation and cancer 34 . NAFLD development is often accompanied by chronic inflammation and immune system activation, prompting the body to produce more immunoglobulins 35 . As liver fibrosis progresses and liver cell function further deteriorates, immune and inflammatory responses become more pronounced, causing globulin levels to rise. AGR is typically low when albumin levels are low and globulin levels are high. The protective effect of albumin diminishes, while the pathological increase in globulin reflects chronic inflammation and poor liver function. Our results indicate that AGR is negatively correlated with the risk of NAFLD and AHF. Nutritional indicators such as GNRI, PNI, CONUT, TCBI, and AGR can reflect an individual's current nutritional status to a certain extent. Previous studies have typically identified nutrition as a protective factor against disease, with high nutritional status often indicating a lower disease prevalence and longer survival. Our study demonstrates that the relationship between nutritional status and NAFLD and AHF is complex. On the one hand, high nutritional status can lead to excessive fat accumulation in the liver, causing fat degeneration and inflammation, potentially increasing the risk of NAFLD. Persistent inflammatory stimuli and nutritional metabolites, such as cholesterol, exacerbate the progression of NAFLD to AHF. Conversely, nutrients like albumin can protect the liver through anti-inflammatory and antioxidant effects, improve the liver microenvironment, and delay disease progression. When liver cirrhosis progresses to liver failure, better nutritional status can enhance patient survival 36 . Therefore, maintaining a proper balance of nutrient intake, reducing excessive calories and fat, and limiting sugars and simple carbohydrates can help control body weight and improve insulin resistance. Additionally, increasing the intake of high-quality protein can aid in repairing damaged liver cells and promote liver function recovery. Overall, an individualized nutritional intervention strategy is crucial for the prevention and management of NAFLD and AHF, and warrants clinical attention. This study has several strengths. By utilizing all available continuous NHANES data cycles, it includes a large, nationally representative sample from across the United States, thereby enhancing the robustness of the statistical analysis and improving the generalizability of the findings. The study design accounted for a wide range of potential confounding factors, such as sociodemographic characteristics, lifestyle habits, health status, and laboratory parameters, with adjustments made through multivariate analysis to ensure accuracy and reliability. Importantly, this is the first comprehensive study to examine the association between nutritional indices and the risk of NAFLD and AHF, providing new insights into the potential role of nutritional status in these conditions. These findings contribute to more precise identification of high-risk populations and support the development of personalized intervention strategies. This study has certain limitations. First, its cross-sectional design prevents the establishment of a causal relationship between nutritional status and the risk of NAFLD and AHF. Second, although numerous confounding factors were adjusted for, some external variables may not have been fully accounted for, potentially affecting the accuracy of the results. Furthermore, as the study is based on a representative sample from the United States, the generalizability of the findings to other populations may be limited. Therefore, further research and additional data are required to more thoroughly explore the relationship between nutritional status and NAFLD/AHF. Conclusion In summary, our study identified significant associations between nutritional status indicators and both NAFLD and AHF. Specifically, GNRI, PNI, and TCBI were positively associated with NAFLD incidence, whereas CONUT and AGR were negatively associated. For AHF, GNRI, CONUT, and TCBI showed positive associations, while AGR exhibited a negative association. These findings underscore the complex relationship between nutritional status and the development of NAFLD and AHF. Clinically, individualized nutritional interventions should be tailored based on specific indicators to prevent or delay the onset and progression of these conditions. Further large-scale studies are needed to elucidate the mechanistic pathways linking these nutritional markers to NAFLD and AHF, providing a scientific basis for improved prevention and treatment strategies. Declarations Acknowledgments Our team sincerely appreciates all the staff and participants whose invaluable contributions have significantly enriched the NHANES data collection. Author contributions Conceptualization, Shouxin Wei. and Sijia Yu.; methodology, Shouxin Wei.; software, Shouxin Wei.; validation, Yingdong Jia. and Ningbo Yang.; formal analysis, Shouxin Wei.; resources,Shouxin Wei.; data curation, Shouxin Wei.; writing—original draft preparation, Shouxin Wei.; writing—review and editing, Yunshen Lan.; visualization, Shouxin Wei.; supervision, Sijia Yu.; project administration, Yindong Jia.; All authors have read and agreed to the published version of the manuscript. Data Availability Statement The datasets generated and/or analyzed during the current study are available in the NHANES repository, [https://www.cdc.gov/nchs/nhanes/]. Conflicts of Interest The authors declare no conflicts of interest. References Blüher, M. Obesity: global epidemiology and pathogenesis. Nature reviews. Endocrinology 15 , 288-298, doi:10.1038/s41574-019-0176-8 (2019). Younossi, Z. M. et al. Epidemiology of chronic liver diseases in the USA in the past three decades. 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Annals of surgical oncology 25 , 3647-3659, doi:10.1245/s10434-018-6715-z (2018). Tables Table 1 Details of the nutritional indices used in the study. Nutrition-related indicators Calculation formula Reference GNRI 1.489 × serum albumin (g/L) + 41.7 × (current weight/ideal weight) Men: Ideal body weight = Height (cm) − 100 − (Height (cm) − 150)/4 Women: Ideal body weight = Height (cm) − 100 − (Height (cm) − 150)/2.5 37 PNI Albumin (g/L) + 5 × total lymphocyte count (10 9 /L) 38 CONUT Serum albumin score + total lymphocyte count score + total cholesterol score Albumin score: 0, 2, 4 and 6 points are assigned when the albumin level is ≥3.5, 3.0-3.49, 2.5-2.99 and <2.5 g/dL, respectively. Lymphocyte total score: 0, 1, 2 and 3 points are awarded for total lymphocyte counts of ≥1,600, 1,200-1,599, 800-1,199 and <800/mm 3 , respectively. Total cholesterol score: When the total cholesterol level is ≥180, 140-179, 100-139 and <100 mg/dL, the corresponding scores are 0, 1, 2 and 3 points, respectively. 39 TCBI Triglycerides (mg/dL) × Total cholesterol (mg/dL) × Weight (kg)/1,000 40 AGR Albumin (g/L)/Globulin (g/L) 41 Table 2 Characteristics of the study cohort. Characteristics Total (n = 5514) NAFLD AHF No(n = 3426) Yes(n = 2088) p-value No(n = 5155) Yes(n = 359) p-value Age (years) 48.67± 16.80 47.23 ± 17.50 51.04 ± 15.29 <0.001 48.23 ± 16.82 54.99 ± 15.24 <0.001 Sex, n (%) <0.001 0.005 Male 2666(48.35%) 1506 (43.96%) 1160 (55.56%) 2467 (47.86%) 199 (55.43%) Female 2848(51.65%) 1920 (56.04%) 928 (44.44%) 2688 (52.14%) 160 (44.57%) Race, n (%) <0.001 0.062 Mexican American 685(12.42%) 318 (9.28%) 367 (17.58%) 634 (12.30%) 51 (14.21%) Other Hispanic 575(10.43%) 361 (10.54%) 214 (10.25%) 536 (10.40%) 39 (10.86%) Non-Hispanic White 1876(34.02%) 1136 (33.16%) 740 (35.44%) 1738 (33.71%) 138 (38.44%) Non-Hispanic Black 1400(25.39%) 957 (27.93%) 443 (21.22%) 1315 (25.51%) 85 (23.68%) Other Race - Including Multi-Racial 978(17.74%) 654 (19.09%) 324 (15.52%) 932 (18.08%) 46 (12.81%) Education level, n (%) 0.007 0.092 Below high school 957(17.36%) 565 (16.49%) 392 (18.77%) 889 (17.25%) 68 (18.94%) High school 1311(23.78%) 789 (23.03%) 522 (25.00%) 1212 (23.51%) 99 (27.58%) Above high school 3246(59.45%) 2072 (60.48%) 1174 (56.23%) 3054 (59.24%) 192 (53.48%) Marital status , n (%) <0.001 0.06 Married or living with partner 3278(48.35%) 1926 (56.22%) 1352 (64.75%) 3059 (59.34%) 219 (61.00%) Divorced, separated, or widowed 1128(20.46%) 706 (20.61%) 422 (20.21%) 1044 (20.25%) 84 (23.40%) Never married 1108(20.09%) 794 (23.18%) 314 (15.04%) 1052 (20.41%) 56 (15.60%) PIR, n (%) 0.3 0.002 <1.3 1547(28.06%) 973 (28.40%) 574 (27.49%) 1447 (28.07%) 100 (27.86%) 1.3-3.5 2149(38.97%) 1308 (38.18%) 841 (40.28%) 1981 (38.43%) 168 (46.80%) ≥3.5 1818(32.97%) 1145 (33.42%) 673 (32.23%) 1727 (33.50%) 91 (25.35%) BMI <0.001 <0.001 29.9 kg/m² 2390(43.34%) 963 (28.11%) 1427 (68.34%) 2099 (40.72%) 291 (81.06%) Stroke,n (%) 0.421 0.066 Yes 221(4.01%) 143 (4.17%) 78 (3.74%) 200 (3.88%) 21 (5.85%) NO 5293(95.99%) 3283 (95.83%) 2010 (96.26%) 4955 (96.12%) 338 (94.15%) Pulmonary disease, n (%) 0.003 0.003 Yes 1099(19.93%) 640 (18.68%) 459 (21.98%) 1006 (19.52%) 93 (25.91%) NO 4415(80.07%) 2786 (81.32%) 1629 (78.02%) 4149 (80.48%) 266 (74.09%) Heart disease, n (%) <0.001 <0.001 Yes 380(6.89%) 194 (5.66%) 186 (8.91%) 322 (6.25%) 58 (16.16%) NO 5134(93.11%) 3232 (94.34%) 1902 (91.09%) 4833 (93.75%) 301 (83.84%) Hypertension, n (%) <0.001 <0.001 Yes 2980(54.04%) 1563 (45.62%) 1417 (67.86%) 2699 (52.36%) 281 (78.27%) NO 2534(45.96%) 1863 (54.38%) 671 (32.14%) 2456 (47.64%) 78 (21.73%) Diabetes, n (%) <0.001 <0.001 Yes 986(17.88%) 364 (10.62%) 622 (29.79%) 812 (15.75%) 174 (48.47%) NO 4528(82.12%) 3062 (89.38%) 1466 (70.21%) 4343 (84.25%) 185 (51.53%) Intensity of activity, n (%) <0.001 <0.001 Moderate to low 2530(45.88%) 1506 (43.96%) 1024 (49.04%) 2330 (45.20%) 200 (55.71%) High 2984(54.12%) 1920 (56.04%) 1064 (50.96%) 2825 (54.80%) 159 (44.29%) Smoking status, n (%) <0.001 <0.001 Former 1252(22.71%) 675 (19.70%) 577 (27.63%) 1132 (21.96%) 120 (33.43%) Current 958(17.37%) 622 (18.16%) 336 (16.09%) 910 (17.65%) 48 (13.37%) Never 3304(59.92%) 2129 (62.14%) 1175 (56.27%) 3113 (60.39%) 191 (53.20%) Drinking status, n (%) 0.257 0.005 Moderate 1899(34.44%) 1179 (34.41%) 720 (34.48%) 1767 (34.28%) 132 (36.77%) Heavy 1958(35.51%) 1241 (36.22%) 717 (34.34%) 1858 (36.04%) 100 (27.86%) Never 1657(30.05%) 1006 (29.36%) 651 (31.18%) 1530 (29.68%) 127 (35.38%) Chronic kidney disease <0.001 0.296 Yes 1037(18.81%) 596 (17.40%) 441 (21.12%) 962 (18.66%) 75 (20.89%) NO 4477(81.19%) 2830 (82.60%) 1647 (78.88%) 4193 (81.34%) 284 (79.11%) ALT 22.17± 16.27 18.81 ± 12.10 27.67 ± 20.25 <0.001 21.46 ± 14.72 32.34 ± 29.11 <0.001 ALP 77.00± 25.73 74.41 ± 26.00 81.25 ± 24.71 <0.001 76.16 ± 24.83 89.06 ± 34.15 <0.001 AST 21.55± 12.50 20.43 ± 10.17 23.39 ± 15.43 <0.001 20.96 ± 9.97 30.06 ± 29.97 <0.001 GGT 31.63± 53.53 26.54 ± 54.61 39.98 ± 50.63 <0.001 29.20 ± 38.19 66.53 ± 147.71 <0.001 GNRI 117.36± 13.48 112.87 ± 11.30 124.72 ± 13.54 <0.001 116.31 ± 12.29 132.38 ± 19.58 <0.001 PNI 51.81± 5.15 51.63 ± 5.11 52.10 ± 5.22 <0.001 51.87 ± 5.10 50.90 ± 5.76 <0.001 CONUT 1.38± 0.76 1.40 ± 0.77 1.34 ± 0.73 0.002 1.37 ± 0.73 1.58 ± 1.02 <0.001 TCBI 2335.01± 2414.36 1729.94 ± 1541.31 3327.82 ± 3148.38 <0.001 2252.99 ± 2297.38 3512.78 ± 3506.11 <0.001 AGR 1.35± 0.24 1.37 ± 0.24 1.32 ± 0.24 <0.001 1.36 ± 0.24 1.27 ± 0.26 <0.001 Continuous variables were expressed as mean ± standard deviation (SD), while categorical variables were expressed as percentages. The significance of the data was evaluated using the t-test and the chi-square test. ALT: alanine aminotransferase; ALP: alkaline phosphatase; AST: aspartate aminotransferase; GGT: gamma glutamyl transferase; PIR: Poverty Income Ratio; BMI: body mass index; GNRI: Geriatric Nutritional Risk Index; PNI: Prognostic Nutritional Index; CONUT: Control Nutritional Status Score; TCBI: Triglycerides × Total Cholesterol × Body Weight Index; AGR: Albumin-to-globulin ratio.n: number of study samples. Table 3 Association between nutrition-related indices and NAFLD . Characteristics Model 1 Model 2 Model 3 GNRI continuous 1.086 (1.080, 1.093) *** 1.099 (1.092, 1.106) *** 1.054 (1.045, 1.063) *** GNRI binary < 98 1[Reference] 1[Reference] 1[Reference] ≥ 98 12.814 (6.774, 24.242)*** 12.370 (6.513, 23.492) *** 2.487 (1.238, 4.996)* PNI continuous 1.018 (1.007, 1.029) *** 1.022 (1.011, 1.034)* 1.020 (1.007, 1.034)** PNI quartiles Quartile 1 1[Reference] 1[Reference] 1[Reference] Quartile 2 1.090 (0.929, 1.280) 1.116 (0.946, 1.317) 1.156 (0.955, 1.400) Quartile 3 1.048 (0.893, 1.229) 1.104 (0.935, 1.305) 1.179 (0.971, 1.431) Quartile 4 1.271 (1.091, 1.481) ** 1.343 (1.141, 1.581)*** 1.351 (1.115, 1.638)** P for trend 0.004 <0.001 0.003 CONUT continuous 0.891 (0.827, 0.960)** 0.849 (0.785, 0.917) *** 0.899 (0.821, 0.983) * CONUT ternary 0-1 1[Reference] 1[Reference] 1[Reference] 2-4 0.789 (0.696, 0.894) *** 0.733 (0.644, 0.835) *** 0.873 (0.748, 1.019) 5-12 0.651 (0.321, 1.321) 0.540 (0.262, 1.113) 0.476 (0.209, 1.083) P for trend <0.001 <0.001 0.032 TCBI/100 continuous 1.049 (1.044, 1.053) *** 1.046 (1.042, 1.051)*** 1.021 (1.017, 1.025)* TCBI/100 quartiles Quartile 1 1[Reference] 1[Reference] 1[Reference] Quartile 2 2.766 (2.266, 3.376) *** 2.608 (2.131, 3.193)*** 1.547 (1.236, 1.935) *** Quartile 3 6.083 (5.017, 7.376) *** 5.525 (4.542, 6.720) *** 2.347 (1.882, 2.928) *** Quartile 4 13.539 (11.135, 16.462) *** 12.198 (9.990, 14.894)*** 3.751 (2.982, 4.718) *** P for trend <0.001 <0.001 <0.001 AGR continuous 0.434 (0.345, 0.546) *** 0.255 (0.197, 0.330)*** 0.588 (0.431, 0.801)*** AGR quartiles Quartile 1 1[Reference] 1[Reference] 1[Reference] Quartile 2 0.784 (0.673, 0.912) ** 0.685 (0.584, 0.803) *** 0.887 (0.738, 1.066) Quartile 3 0.698 (0.598, 0.815) *** 0.540 (0.457, 0.637) *** 0.759 (0.624, 0.923)** Quartile 4 0.583 (0.500, 0.681) *** 0.418 (0.353, 0.496) *** 0.763 (0.622, 0.936)* P for trend <0.001 <0.001 0.004 OR: odds ratio; 95% CI: 95% confidence interval.GNRI: Geriatric Nutritional Risk Index; PNI: Prognostic Nutritional Index; CONUT: Control Nutritional Status Score; TCBI: Triglycerides × Total Cholesterol × Body Weight Index; AGR: Albumin-to-globulin ratio.Model 1: No covariates were adjusted. Model 2: Age, gender and race were adjusted. Model 3: Age, gender, race, education level, marital status, PIR, smoking, drinking, activity level, BMI, stroke, lung disease, heart disease, chronic kidney disease, hypertension, diabetes, as well as ALT, ALP, AST, and GGT were adjusted.*p < 0.05, **p < 0.01, ***p < 0.001; P < 0.05 is considered statistically significant. Table 4 Association between nutrition-related indices and AHF . Characteristics Model 1 Model 2 Model 3 GNRI continuous 1.071 (1.063, 1.079) *** 1.087 (1.078, 1.096) *** 1.074 (1.062, 1.086) ** GNRI binary < 98 1[Reference] 1[Reference] 1[Reference] ≥ 98 2.051 (0.958, 4.388) 1.973 (0.917, 4.245) 0.544 (0.184, 1.604) PNI continuous 0.961 (0.940, 0.983)*** 0.975 (0.953, 0.998) * 0.984 (0.961, 1.008) PNI quartiles Quartile 1 1[Reference] 1[Reference] 1[Reference] Quartile 2 0.632 (0.468, 0.854) ** 0.676 (0.498, 0.917) * 0.784 (0.563, 1.093) Quartile 3 0.600 (0.443, 0.812) *** 0.689 (0.505, 0.940) * 0.812 (0.577, 1.141) Quartile 4 0.658 (0.495, 0.874)** 0.793 (0.588, 1.069) 0.875 (0.628, 1.219) P for trend 0.005 0.154 0.493 CONUT continuous 1.351 (1.206, 1.513) *** 1.265 (1.125, 1.422) *** 1.219 (1.068, 1.391) ** CONUT ternary 0-1 1[Reference] 1[Reference] 1[Reference] 2-4 1.398 (1.111, 1.759) ** 1.244 (0.983, 1.573) 1.274 (0.980, 1.658) 5-12 3.105 (1.283, 7.516)* 2.430 (0.990, 5.965) 2.097 (0.778, 5.655) P for trend <0.001 0.021 0.033 TCBI/100 continuous 1.012 (1.009, 1.016) *** 1.012 (1.009, 1.015) *** 1.004 (1.000, 1.008) * TCBI/100 quartiles Quartile 1 1[Reference] 1[Reference] 1[Reference] Quartile 2 1.650 (1.063, 2.562) * 1.883 (1.216, 2.915) ** 0.995 (0.616, 1.606) Quartile 3 2.979 (1.984, 4.473) *** 3.508 (2.345, 5.247) *** 1.193 (0.756, 1.882) Quartile 4 4.993 (3.369, 7.399) *** 5.603 (3.805, 8.251)*** 1.453 (0.923, 2.287) P for trend <0.001 <0.001 0.022 AGR continuous 0.189 (0.118, 0.304) *** 0.133 (0.080, 0.220) *** 0.411 (0.235, 0.716) ** AGR quartiles Quartile 1 1[Reference] 1[Reference] 1[Reference] Quartile 2 0.480 (0.360, 0.640) *** 0.438 (0.326, 0.588)*** 0.617 (0.447, 0.852) ** Quartile 3 0.464 (0.345, 0.623) *** 0.389 (0.285, 0.530) *** 0.645 (0.457, 0.909)* Quartile 4 0.405 (0.301, 0.547) *** 0.325 (0.236, 0.448) *** 0.657 (0.459, 0.940)* P for trend <0.001 <0.001 0.019 OR: odds ratio; 95% CI: 95% confidence interval.GNRI: Geriatric Nutritional Risk Index; PNI: Prognostic Nutritional Index; CONUT: Control Nutritional Status Score; TCBI: Triglycerides × Total Cholesterol × Body Weight Index; AGR: Albumin-to-globulin ratio.Model 1: No covariates were adjusted. Model 2: Age, gender and race were adjusted. Model 3: Age, gender, race, education level, marital status, PIR, smoking, drinking, activity level, BMI, stroke, lung disease, heart disease, chronic kidney disease, hypertension, diabetes, as well as ALT, ALP, AST, and GGT were adjusted.*p < 0.05, **p < 0.01, ***p < 0.001; P < 0.05 is considered statistically significant. Table 5 Analysis of threshold effects between GNRI and NAFLD and AHF, and between TCBI/100 and NAFLD. Characteristics OR (95% CI) GNRI and NAFLD Fitting by standard linear model 1.054 (1.045, 1.063) Fitting by two-piecewise linear model Inflection point 141.16 141.16 1.016 (0.996, 1.036) Log likelihood ratio <0.001 GNRI and AHF Fitting by standard linear model 1.074 (1.062, 1.086) Fitting by two-piecewise linear model Inflection point 120.17 120.17 1.083 (1.070, 1.096) Log likelihood ratio <0.001 TCBI/100 and NAFLD Fitting by standard linear model 1.021 (1.017, 1.025) Fitting by two-piecewise linear model Inflection point 31.20 31.20 1.005 (1.001, 1.010) Log likelihood ratio <0.001 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-5188054","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":366762761,"identity":"fe923b16-b1f3-4154-9960-581996b776b2","order_by":0,"name":"Shouxin Wei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACNmb+jw8+/rGR45c/fIA4LXzsDcaGMxvSjCVnsCUQp0WO54CZNGfDoUSDGTwGRDpMIiFNmnHHgQQD6Z6PN94w2MnpNhDWcti68MydPHOZs5st5zAkG5sdIKglsfH2DLZnxZYNudukeRgOJG4jrCWZQZqH7XDihgM5z4jUwnOMSZq3DajlRg4bkVrYe5gNZ5wBBnLPMWPLOQZE+EW+mYfxwYcKYFSyNz+88abCTo6gFhQgQWzUIGshVccoGAWjYBSMCAAAU41EHy9hGC0AAAAASUVORK5CYII=","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Shouxin","middleName":"","lastName":"Wei","suffix":""},{"id":366762762,"identity":"471d5828-a82e-4289-9d6e-1d0bd8df8583","order_by":1,"name":"Sijia Yu","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sijia","middleName":"","lastName":"Yu","suffix":""},{"id":366762763,"identity":"fe34cc90-4d53-4047-bae0-134316a383e9","order_by":2,"name":"Yunsheng Lan","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yunsheng","middleName":"","lastName":"Lan","suffix":""},{"id":366762764,"identity":"a241f28e-fdfb-415c-9740-22ca515a6f9d","order_by":3,"name":"Yingdong Jia","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yingdong","middleName":"","lastName":"Jia","suffix":""},{"id":366762765,"identity":"9b8bcd80-2f2c-48f3-9adc-224a464046f6","order_by":4,"name":"Ningbo Yang","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ningbo","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-10-01 14:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5188054/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5188054/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66966788,"identity":"39eb0829-3f98-4f60-a534-d9a142319d59","added_by":"auto","created_at":"2024-10-18 14:08:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":263718,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5188054/v1/99a1dccbf5d9b3bcdf64256b.png"},{"id":66966790,"identity":"be3aefb6-d258-4907-9d74-b3bf17b5075d","added_by":"auto","created_at":"2024-10-18 14:08:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":795929,"visible":true,"origin":"","legend":"\u003cp\u003eStratified analysis of the association between nutrition-related indicators and NAFLD in the 2017–2020.03 US National Health and Nutrition Examination Survey (NHANES). This analysis considered factors such as age, gender, race, education level, marital status, PIR, smoking, drinking, activity level, BMI, stroke, lung disease, heart disease, chronic kidney disease, hypertension, diabetes, and ALT, ALP, AST, and GGT. (A) Stratified analysis of the association between GNRI and NAFLD. (B) Stratified analysis of the association between PNI and NAFLD. (C) Stratified analysis of the association between CONUT and NAFLD. (D) Stratified analysis of the association between TCBI and NAFLD. (E) Stratified analysis of the association between AGR and NAFLD.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5188054/v1/c4857da266b0bac9cd19d38e.png"},{"id":66966791,"identity":"178e7cee-31eb-4d11-a635-4fea68828d89","added_by":"auto","created_at":"2024-10-18 14:08:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":878081,"visible":true,"origin":"","legend":"\u003cp\u003eStratified analysis of the association between nutrition-related indicators and AHF in the 2017–2020.03 US National Health and Nutrition Examination Survey (NHANES).This analysis considered factors such as age, gender, race, education level, marital status, PIR, smoking, drinking, activity level, BMI, stroke, lung disease, heart disease, chronic kidney disease, hypertension, diabetes, and ALT, ALP, AST, and GGT. (A) Stratified analysis of the association between GNRI and AHF. (B) Stratified analysis of the association between PNI and AHF. (C) Stratified analysis of the association between CONUT and AHF. (D) Stratified analysis of the association between TCBI and AHF. (E) Stratified analysis of the association between AGR and AHF.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5188054/v1/e0990770e38d0e7bb91c967c.png"},{"id":66966789,"identity":"a8ab4365-1387-455d-9936-adfa33d3a57f","added_by":"auto","created_at":"2024-10-18 14:08:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":154396,"visible":true,"origin":"","legend":"\u003cp\u003eThe dose-response relationship between GNRI and NAFLD and AHF, and between TCBI/100 and NAFLD. Adjusted for age, sex, race, education, marital status, PIR, smoking, drinking, activity level, BMI, stroke, lung disease, heart disease, chronic kidney disease, hypertension, diabetes, and ALT, ALP, AST, and GGT. (A) GNRI and NAFLD. (B) GNRI and AHF. (C) TCBI/100 and NAFLD\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5188054/v1/1b06814426bdf05cf7b11580.png"},{"id":67182991,"identity":"710f14f7-332b-4e5c-ada6-86d249cff1cc","added_by":"auto","created_at":"2024-10-22 06:40:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3194611,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5188054/v1/1964d2a8-97cf-45f4-bfeb-e22e196b69e3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between nutrition-related indicators and non-alcoholic fatty liver disease and advanced liver fibrosis: evidence from NHANES 2017-2020","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNAFLD is one of the most common chronic liver diseases worldwide, with an estimated prevalence of approximately 25.24%\u0026nbsp;\u003csup\u003e1\u003c/sup\u003e. With the continued rise in global obesity and diabetes rates, NAFLD has emerged as a major non-communicable disease, presenting a significant challenge to global public health\u0026nbsp;\u003csup\u003e2\u003c/sup\u003e. NAFLD is characterized by excessive fat accumulation in the liver without significant alcohol consumption. As the disease progresses, it can progress to liver fibrosis, cirrhosis, and even hepatocellular carcinoma (HCC)\u0026nbsp;\u003csup\u003e3\u003c/sup\u003e. The pathophysiological mechanisms of NAFLD are highly complex and involve processes such as insulin resistance, oxidative stress, lipid peroxidation, inflammation, and hepatocyte apoptosis\u0026nbsp;\u003csup\u003e4\u003c/sup\u003e. AHF is regarded as a key marker of disease progression in the natural history of NAFLD. It is a pathological process triggered by chronic inflammation and tissue damage, marked by the excessive deposition of collagen and other extracellular matrix in the liver. As fibrosis progresses, the liver\u0026apos;s structure becomes increasingly disrupted, potentially leading to cirrhosis\u0026nbsp;\u003csup\u003e5\u003c/sup\u003e. The onset of cirrhosis signals the progression of NAFLD to an irreversible stage, significantly increasing the risk of liver failure and HCC. Currently, there is no specific treatment for NAFLD, making in-depth research into its risk factors essential for its prevention and management.\u003c/p\u003e\n\u003cp\u003ePrevious studies have shown that nutritional imbalance, oxidative stress, and immune function are closely associated with the progression of NAFLD\u0026nbsp;\u003csup\u003e6,7\u003c/sup\u003e. Nutritional imbalance is a primary driver of NAFLD development, contributing to disorders in lipid metabolism and oxidative stress\u0026nbsp;\u003csup\u003e8\u003c/sup\u003e. Oxidative stress plays a key role in the development and progression of NAFLD by inducing hepatocellular injury, inflammation, and fibrogenesis\u0026nbsp;\u003csup\u003e9\u003c/sup\u003e. The liver, rich in immune cells such as macrophages, lymphocytes, and natural killer cells, plays a vital role in immune homeostasis and pathogen defense. In patients with NAFLD, the function of immune cells is often impaired, resulting in a dysregulated immune response that exacerbates liver inflammation and promotes fibrosis\u0026nbsp;\u003csup\u003e10\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn recent years, several novel laboratory-based nutritional indicators have emerged for identifying malnutrition risk, including the Geriatric Nutritional Risk Index (GNRI), Prognostic Nutritional Index (PNI), Controlled Nutritional Score (CONUT), Triglyceride-Total Cholesterol-Body Mass Index (TCBI), and Albumin-to-Globulin Ratio (AGR). These indices incorporate a range of test parameters, such as albumin, lymphocyte count, body weight, total cholesterol, and triglycerides, providing insight into the nutritional and immune status of patients. They have been widely used in research on tumors, cardiovascular diseases, and chronic obstructive pulmonary disease, but their application in NAFLD patients remains underexplored.\u003c/p\u003e\n\u003cp\u003eThis study used data from the National Health and Nutrition Examination Survey (NHANES) from 2017 to 2020.03, incorporating demographic, lifestyle, and disease factors to explore the associations between five nutrition-related indicators, NAFLD, and AHF. The results of the study offer new insights into NAFLD and may inform the formulation of public health policies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eNHANES is a nationally representative survey administered by the National Center for Health Statistics (NCHS) within the U.S. Centers for Disease Control and Prevention (CDC), designed to assess the nutrition and health status of the U.S. population. The survey maintains a high level of transparency regarding both its data and methodological design. All publicly available data and detailed survey methodologies can be accessed via the NHANES official website. NHANES has received approval from the CDC Institutional Review Board and adheres to strict guidelines for safeguarding participant safety and privacy. All participants provided written informed consent prior to participation. The data used in this study were sourced from NHANES between 2017 and March 2020, including a total of 15,560 participants. Exclusion criteria included: (1) age \u0026lt; 20 years (n = 6328), (2) missing nutritional status index (n = 1489), (3) missing transient elastography data (n = 431), (4) positive for serum hepatitis B surface antigen (n = 41), hepatitis C virus RNA (n = 83), and hepatitis C antibody (n = 88), (5) cancer diagnosis (n = 706), and (6) missing other covariates (n = 880). A total of 5514 participants were included in the final analysis. Of these participants, 2666 were male and 2848 were female. The detailed screening process is illustrated in Figure 1.\u003c/p\u003e\n\u003ch2\u003e2.1\u0026nbsp; \u0026nbsp; \u0026nbsp;Assessment of NAFLD and AHF\u003c/h2\u003e\n\u003cp\u003eAlthough liver biopsy remains the gold standard for diagnosing NAFLD, its invasiveness and high cost render it impractical for large-scale population studies. CAP, measured by FibroScan\u0026mdash;an emerging technology based on transient elastography using ultrasound\u0026mdash;offers a quantitative diagnosis of fatty liver disease with accuracy comparable to liver biopsy in detecting NAFLD. Previous studies have indicated that a CAP value of\u0026nbsp;\u0026ge;\u0026nbsp;285 dB/m is indicative of NAFLD, while an LSM value of\u0026nbsp;\u0026ge;\u0026nbsp;9.7 kPa corresponds to AHF\u0026nbsp;\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003e2.2\u0026nbsp; \u0026nbsp; \u0026nbsp;Assessment of Nutritional Status\u003c/h2\u003e\n\u003cp\u003eThis study utilized five nutritional indicators to assess the population\u0026apos;s nutritional status: GNRI, PNI, CONUT, TCBI, and AGR. The specific calculation methods for each indicator are outlined in Table 1. Based on previous research, GNRI is classified into a risk-free cohort (GNRI\u0026nbsp;\u0026ge;\u0026nbsp;98) and a risk cohort (GNRI \u0026lt; 98). CONUT is categorized into normal nutrition (0\u0026ndash;1), mild malnutrition (2\u0026ndash;4), and moderate to severe malnutrition (5\u0026ndash;12). PNI, TCBI, and AGR are classified by quartiles, with participants in the first quartile at higher risk of malnutrition and those in the fourth quartile at lower risk.\u003c/p\u003e\n\u003ch2\u003e2.3\u0026nbsp; \u0026nbsp; \u0026nbsp;Covariates\u003c/h2\u003e\n\u003cp\u003eTo minimize potential confounders in our research outcomes, a comprehensive literature review was performed to identify and incorporate relevant covariates in our analysis. The sociodemographic factors included age, sex, race/ethnicity, educational attainment, marital status, and poverty-to-income ratio (PIR). Lifestyle factors comprised smoking status, alcohol intake, and physical activity level. Health-related covariates considered were body mass index (BMI), history of stroke, lung disease, heart disease, chronic kidney disease (CKD), hypertension, and diabetes. Laboratory markers included alanine aminotransferase (ALT), alkaline phosphatase (ALP), aspartate aminotransferase (AST), and gamma-glutamyl transferase (GGT).Race/ethnicity categories encompassed Mexican-American, non-Hispanic Black, non-Hispanic White, other Hispanic, and other racial groups. Marital status was categorized into married/cohabiting, divorced, separated, widowed, or never married. Educational levels were classified as less than high school, high school graduate, and college degree or above. PIR was stratified into low (\u0026lt;1.3), medium (1.3\u0026ndash;3.5), and high income (\u0026ge;3.5) groups.Smoking status was categorized as never smokers, ex-smokers, and current smokers, based on lifetime smoking behavior (\u0026ge;100 cigarettes). Alcohol consumption was classified into three groups: never drinkers, moderate drinkers (1\u0026ndash;2 drinks/day for men, 1 drink/day for women), and heavy drinkers (exceeding 2 drinks/day for men or 1 drink/day for women). Physical activity was quantified using the weekly frequency, duration, and the corresponding metabolic equivalent of task (MET). Following the World Health Organization (WHO) Guidelines on Physical Activity and Sedentary Behavior, activity intensity was classified into low-to-moderate (\u0026lt;1,200 MET-min/week) and high (\u0026ge;1,200 MET-min/week).BMI was categorized as underweight/normal (\u0026lt;25.0 kg/m\u0026sup2;), overweight (25.0\u0026ndash;29.9 kg/m\u0026sup2;), and obese (\u0026gt;29.9 kg/m\u0026sup2;). Stroke diagnosis was based on self-reported history. Heart disease was defined by a confirmed diagnosis of congestive heart failure, coronary artery disease, myocardial infarction, or angina pectoris. Lung disease included self-reported diagnoses of asthma, chronic obstructive pulmonary disease (COPD), or emphysema. CKD was characterized by an estimated glomerular filtration rate (eGFR) \u0026lt;60 mL/min/1.73 m\u0026sup2;, using the Modification of Diet in Renal Disease (MDRD) study equation. Hypertension was identified through: (1) confirmed medical diagnosis, (2) mean systolic blood pressure (SBP)\u0026nbsp;\u0026ge;130 mmHg or mean diastolic blood pressure (DBP)\u0026nbsp;\u0026ge;80 mmHg, or (3) use of antihypertensive medications. Diabetes was defined by: (1) a confirmed diagnosis, (2) hemoglobin A1c (HbA1c) level\u0026nbsp;\u0026ge;6.5%, or (3) use of glucose-lowering medication or insulin.\u003c/p\u003e\n\u003ch2\u003e2.4\u0026nbsp; \u0026nbsp; \u0026nbsp;Statistical Analysis\u003c/h2\u003e\n\u003cp\u003eDue to the COVID-19 pandemic, NHANES field operations were halted in March 2020. Consequently, data collected from 2019 to March 2020 were combined with the 2017\u0026ndash;2018 NHANES cycle to reconstruct a comprehensive database representing the health status of the U.S. population. Continuous variables are reported as means \u0026plusmn; standard deviation (SD), while categorical variables are presented as percentages. Statistical significance was assessed using t-tests for continuous variables and chi-square tests for categorical variables.The Geriatric Nutritional Risk Index (GNRI) was divided into \u0026quot;risk-free\u0026quot; and \u0026quot;at-risk\u0026quot; categories. The Controlling Nutritional Status (CONUT) score was classified into normal nutrition, mild malnutrition, and moderate to severe malnutrition. Prognostic Nutritional Index (PNI), Total Cholesterol to Albumin Ratio (TCBI), and Albumin-to-Globulin Ratio (AGR) were divided into quartiles, with Q1 serving as the reference group. To explore the associations between nutrition-related indicators and non-alcoholic fatty liver disease (NAFLD) or advanced hepatic fibrosis (AHF), a multivariate logistic regression model was employed. Model 1 was unadjusted; Model 2 adjusted for age, sex, and race; and Model 3 further adjusted for educational level, marital status, PIR, smoking status, alcohol consumption, physical activity, BMI, history of stroke, pulmonary disease, cardiovascular disease, CKD, hypertension, diabetes, as well as ALT, ALP, AST, and GGT levels. Results are presented as weighted odds ratios (OR) with 95% confidence intervals (95% CI).Additionally, the dose-response relationship between nutritional indicators and NAFLD/AHF was evaluated using smooth curve fitting, and stratified analyses were performed to verify the robustness of the findings. All statistical analyses were conducted using R software (version 4.4.1, http://www.R-project.org) and EmpowerStats software (versions 2.0 and 4.2, http://www.empowerstats.com). A p-value \u0026lt; 0.05 was considered indicative of statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e3.1\u0026nbsp; \u0026nbsp; \u0026nbsp;Participant characteristics\u003c/h2\u003e\n\u003cp\u003eA total of 5,514 participants were included in this study. The mean age of the participants was 48.67\u0026nbsp;\u0026plusmn;\u0026nbsp;16.80 years, with 48.35% being male, as shown in Table\u0026nbsp;2. Among the participants, 2088 were diagnosed with NAFLD, and 359 were diagnosed with AHF. The mean values of GNRI, PNI, CONUT, TCBI, and AGR were 117.36\u0026nbsp;\u0026plusmn;\u0026nbsp;13.48, 51.81\u0026nbsp;\u0026plusmn;\u0026nbsp;5.15, 1.38\u0026nbsp;\u0026plusmn;\u0026nbsp;0.76, 2335.01\u0026nbsp;\u0026plusmn;\u0026nbsp;2414.36, and 1.35\u0026nbsp;\u0026plusmn;\u0026nbsp;0.24, respectively.\u003c/p\u003e\n\u003cp\u003eCompared with the non-NAFLD group, participants in the NAFLD group were characterized by older age, male sex, Mexican American ethnicity, lower education level, being married or cohabiting, obesity, presence of lung disease, heart disease, hypertension, diabetes, chronic kidney disease, lower activity level, former smoking status, elevated ALT, ALP, AST, and GGT levels, and higher nutritional indices such as GNRI, PNI, and TCBI, but lower CONUT and AGR.\u003c/p\u003e\n\u003cp\u003eCompared with the non-AHF group, participants in the AHF group were characterized by older age, male sex, middle income, obesity, presence of lung disease, heart disease, hypertension, diabetes, lower activity level, former smoking status, never drinking status, elevated ALT, ALP, AST, and GGT levels, and higher nutritional indices such as GNRI, CONUT, and TCBI, but lower PNI and AGR.\u003c/p\u003e\n\u003ch2\u003e3.2\u0026nbsp; \u0026nbsp; \u0026nbsp;Association of nutrition-related indices with NAFLD and AHF\u003c/h2\u003e\n\u003cp\u003eTable 3 presents the results of the multiple linear regression analysis assessing the association between five nutrition-related indicators and NAFLD. To enhance interpretability, TCBI values were divided by 100. The indicators were evaluated both as continuous and categorical variables, and multiple models with varying adjustments for covariates were employed. In the fully adjusted model (Model 3), when considered as continuous variables, GNRI, PNI, and TCBI were significantly positively correlated with NAFLD, whereas CONUT and AGR showed a significant negative correlation with NAFLD.For every one-unit increase in GNRI, the risk of NAFLD increased by 0.054 (OR = 1.054 [1.045, 1.063]). Similarly, for every unit increase in PNI, the risk of NAFLD increased by 0.02 (OR = 1.020 [1.007, 1.034]). For every unit increase in TCBI/100, the risk of NAFLD increased by 0.021 (OR = 1.021 [1.017, 1.025]). For every one-unit increase in CONUT, the risk of NAFLD decreased by 0.101 (OR = 0.899 [0.821, 0.983]). For every one-unit increase in AGR, the risk of NAFLD decreased by 0.412 (OR = 0.588 [0.431, 0.801]). When the nutrition-related indicators were considered as categorical variables, a significant dose-response relationship was observed between PNI, CONUT, TCBI, AGR, and NAFLD (P for trend \u0026lt; 0.05). Compared to the lowest group, the risk of NAFLD in the highest GNRI group was elevated by 1.487 (OR = 2.487 [1.238, 4.996]), the risk of NAFLD in the highest PNI group rose by 0.351 (OR = 1.351 [1.115, 1.638]), the highest TCBI/100 group exhibited a 2.751-fold increased risk of NAFLD (OR = 3.751 [2.982, 4.718]), and the highest AGR group demonstrated a 0.237-fold decreased risk of NAFLD (OR = 0.763 [0.622, 0.936]).\u003c/p\u003e\n\u003cp\u003eTable 4 presents the results of the multiple linear regression analysis between nutrition-related indicators and AHF. To emphasize the effect, TCBI was divided by 100.The indicators were categorized as continuous and categorical variables, and different models were analyzed after adjusting for multiple covariates. The results indicate that in the fully adjusted model (Model 3), when the nutrition-related indicators were treated as continuous variables, there was a significant positive correlation between GNRI, CONUT, and TCBI and AHF, and a significant negative correlation between AGR and AHF. For every one-unit increase in GNRI, the risk of AHF increased by 0.074 (OR = 1.074 [1.062, 1.086]). For every unit increase in CONUT, the risk of AHF increased by 0.219 (OR = 1.219 [1.068, 1.391]). For every unit increase in TCBI/100, the risk of AHF increased by 0.004 (OR = 1.004 [1.000, 1.008]). For each unit increase in AGR, the risk of AHF decreased by 0.589 (OR = 0.411 [0.235, 0.716]). When the nutrition-related indicators were considered as categorical variables, a significant dose-response relationship was observed between CONUT, TCBI, AGR, and AHF (P for trend \u0026lt; 0.05). Compared with the lowest group, the risk of AHF in the highest AGR group decreased by 0.343 (OR = 0.657 [0.459, 0.940]).\u003c/p\u003e\n\u003ch2\u003e3.3\u0026nbsp; \u0026nbsp; \u0026nbsp;Subgroup Analysis\u003c/h2\u003e\n\u003cp\u003eThis study performed subgroup analyses based on variables such as age, gender, race, hypertension, diabetes, alcohol consumption, and smoking to explore potential differences in the associations between five nutrition-related indicators and NAFLD/AHF across different population subgroups. Figure 2 illustrates the subgroup analysis results for NAFLD. Significant differences were observed in the association between GNRI and NAFLD across all subgroups. Notably, the relationship between PNI and NAFLD varied significantly in the race and smoking subgroups, while the association between CONUT and NAFLD remained consistent across all subgroups. TCBI demonstrated significant differences in its association with NAFLD in the gender and smoking subgroups, and AGR showed a significant relationship with NAFLD within the smoking subgroup.Figure 3 displays the subgroup analysis for AHF. Significant associations were found between GNRI and AHF across all subgroups. The relationship between PNI and AHF was significant in the gender and hypertension subgroups. Differences in the association between CONUT and AHF were observed in the age and drinking subgroups. TCBI was significantly associated with AHF in the race and hypertension subgroups, while AGR exhibited significant associations with AHF in the age and smoking subgroups.\u003c/p\u003e\n\u003ch2\u003e3.4\u0026nbsp; \u0026nbsp; \u0026nbsp;Smooth curve fitting and threshold effect analysis\u003c/h2\u003e\n\u003cp\u003eSmooth curve fitting was employed in the study to assess the non-linear correlation between nutrition-related indicators and NAFLD and AHF. To emphasize the effect, TCBI was divided by 100. As shown in Figure 4, there was a significant non-linear correlation between GNRI and NAFLD and AHF, and between TCBI/100 and NAFLD, with inflection points of 141.16, 120.17, and 31.20, respectively. When GNRI was less than 141.16, for every unit increase in GNRI, the risk of NAFLD increased by 0.054, and the relationship was not statistically significant when GNRI exceeded 141.16. When GNRI was less than 120.17, the relationship between GNRI and AHF was not statistically significant. When GNRI exceeded 120.17, for every unit increase in GNRI, the risk of AHF increased by 0.083. When TCBI/100 was less than 31.20, the risk of NAFLD increased by 0.052 for every unit increase in TCBI/100. When TCBI/100 exceeded 31.20, the risk of NAFLD increased by 0.005 for every unit increase in TCBI/100. (Table 5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe increasing prevalence of metabolic syndrome and obesity has led to a global rise in non-alcoholic fatty liver disease (NAFLD) and advanced hepatic fibrosis (AHF), both of which have become major public health issues. Nutritional indices such as GNRI, PNI, CONUT, TCBI and AGR are widely used in clinical practice to evaluate nutritional status and prognosis, particularly in patients with cancer, cardiovascular diseases, and other severe conditions. Despite their clinical utility, our study is the first to comprehensively analyze the association between these five nutritional markers and NAFLD/AHF.Using data from 5,514 participants collected from the NHANES database between 2017 and March 2020, our retrospective analysis demonstrated that malnutrition is significantly associated with the incidence of both NAFLD and AHF after adjusting for all potential confounding factors. Specifically, GNRI, PNI, and TCBI showed positive associations with NAFLD, whereas CONUT and AGR exhibited negative associations. Furthermore, GNRI, CONUT, and TCBI were positively linked to AHF, while AGR was negatively correlated with AHF.\u003c/p\u003e\n\u003cp\u003eThe GNRI combines serum albumin levels and body weight data to assess the prognosis of patients with various diseases. Zhao et al.\u0026nbsp;\u003csup\u003e12\u003c/sup\u003e analyzed 12,058 patients with acute kidney injury (AKI) from the eICU Collaborative Research Database and found that a nutritional risk (GNRI \u0026lt; 98) was significantly associated with increased in-hospital mortality among AKI patients in the intensive care unit. Another cohort study from China demonstrated that GNRI was negatively correlated with the risk of all-cause mortality in patients with atrial fibrillation, and that a higher GNRI acted as a protective factor in this population. Our study found that GNRI was positively correlated with the risk of NAFLD and AHF. This may be due to GNRI being composed of two components: serum albumin and current body weight/standard body weight. Albumin, one of the primary plasma proteins in the human body, plays a crucial role in maintaining plasma colloid osmotic pressure and transporting endogenous and exogenous substances\u0026nbsp;\u003csup\u003e13\u003c/sup\u003e. Recent studies indicate that a reduction in albumin levels is closely related to the progression of NAFLD. In patients with NAFLD, low albumin levels are often associated with aggravated liver fibrosis, likely due to chronic inflammation, oxidative stress, and diminished albumin synthesis resulting from liver dysfunction\u0026nbsp;\u003csup\u003e14\u003c/sup\u003e. In patients with AHF, hypoalbuminemia is more pronounced, reflecting severe impairment of liver synthetic function and closely correlating with further disease progression and an adverse prognosis. A study by Khanna et al.\u0026nbsp;\u003csup\u003e15\u003c/sup\u003edemonstrated that a sustained reduction in albumin levels may predict liver failure and an increased incidence of complications, including ascites, hepatic encephalopathy, and infection. In contrast to the protective effect of albumin, obesity is a major risk factor for the development of NAFLD and AHF. Kuang et al. demonstrated that obesity-related indices such as BMI, A Body Shape Index (ABSI), Anthropometric Risk Index (ARI), and waist circumference were significantly and positively correlated with the risk of NAFLD. Obesity is associated with lipid metabolism disorders and excessive fat accumulation in the liver. This steatosis induces liver cell stress, oxidative damage, and non-alcoholic steatohepatitis (NASH), ultimately resulting in liver cell damage and the initiation of fibrosis\u0026nbsp;\u003csup\u003e16\u003c/sup\u003e. Lipid metabolism disorders and chronic low-grade inflammation promote abnormal liver collagen deposition and accelerate fibrosis formation\u0026nbsp;\u003csup\u003e17\u003c/sup\u003e. Additionally, obesity is closely linked to metabolic syndrome (e.g., insulin resistance, hypertension, and dyslipidemia), which is a key risk factor for the progression of NAFLD to AHF\u0026nbsp;\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe PNI comprises serum albumin levels and lymphocyte counts and is used to predict the risk of malnutrition and postoperative complications. It is widely used in assessing the survival prognosis of cancer patients. A PNI value below 40 typically indicates poor nutritional status. Pinato et al.\u0026nbsp;\u003csup\u003e19\u003c/sup\u003eanalyzed clinical data from 112 liver cancer patients and found that PNI is an independent predictor of poor prognosis. Another cohort study demonstrated that PNI is an important predictor of recurrence and survival after radical surgery for early-stage hepatocellular carcinoma\u0026nbsp;\u003csup\u003e20\u003c/sup\u003e. The pathogenesis of NAFLD involves complex metabolic disorders and chronic low-grade inflammation, where lymphocytes play a dual role. Activated T lymphocytes, such as helper T cells 1 (Th1) and 17 (Th17), secrete pro-inflammatory cytokines like interferon-gamma (IFN-\u0026gamma;) and tumor necrosis factor-alpha (TNF-\u0026alpha;), exacerbating liver cell damage and apoptosis, stimulating hepatic stellate cell activation, and leading to excessive collagen deposition\u0026nbsp;\u003csup\u003e21\u003c/sup\u003e. Conversely, regulatory T cells (Treg) and helper T cells 22 (Th22) inhibit excessive immune responses by secreting anti-inflammatory cytokines (IL-10, IL-22), thereby reducing liver damage and exerting an anti-fibrosis effect\u0026nbsp;\u003csup\u003e22\u003c/sup\u003e. Additionally, IgG autoantibodies produced by B lymphocytes can accelerate the progression of NAFLD fibrosis. As NAFLD progresses to advanced liver fibrosis, lymphocytes increasingly promote fibrosis\u0026nbsp;\u003csup\u003e23\u003c/sup\u003e. Our study found that PNI was significantly positively correlated with the risk of NAFLD, while no significant association was found with AHF. A retrospective study from the same NHANES cohort showed that an increase in PNI was positively correlated with an increased risk of NAFLD and negatively correlated with an increased risk of AHF. This may be attributed to differing inclusion and exclusion criteria for the samples and varying covariates\u0026nbsp;\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe CONUT score comprises serum albumin, total lymphocyte count, and total cholesterol scores, which collectively assess the patient\u0026rsquo;s protein-energy malnutrition and immune function status. Low serum albumin levels reflect decreased liver function and impaired protein synthesis. The total lymphocyte count is a key indicator of immune system function, reflecting the patient\u0026rsquo;s current immune response capacity. Total cholesterol levels are closely linked to energy metabolism and nutritional status, with lower cholesterol levels generally indicating malnutrition\u0026nbsp;\u003csup\u003e24\u003c/sup\u003e. Patients with NAFLD experience lipid metabolism disorders, characterized by elevated serum cholesterol levels that result in excessive cholesterol accumulation in hepatocytes. Excessive intrahepatic cholesterol induces mitochondrial damage and oxidative stress, subsequently activating hepatic stellate cells (HSCs) and promoting liver fibrosis\u0026nbsp;\u003csup\u003e25\u003c/sup\u003e. In the early stages of the disease, elevated total cholesterol is closely associated with hepatic steatosis and inflammation. However, as the disease progresses to advanced liver fibrosis, liver synthetic function becomes impaired, and cholesterol synthesis and metabolism decline, potentially leading to lower serum total cholesterol levels\u0026nbsp;\u003csup\u003e26\u003c/sup\u003e. This dynamic shift in cholesterol levels reflects the pathophysiological progression of NAFLD to AHF. Our study found that the CONUT score was negatively correlated with the incidence of NAFLD and positively correlated with the incidence of AHF. A possible explanation is that in the early stages of NAFLD, patients are often overweight or obese, typically associated with better nutritional reserves and higher protein levels. As a result, despite the formation of fatty liver, the CONUT score remains low. However, as the disease progresses to advanced fibrosis, liver synthetic function becomes impaired, protein metabolism is disrupted, immune function declines, and cholesterol synthesis decreases, leading to an increase in the CONUT score. Miano et al.\u0026nbsp;\u003csup\u003e27\u003c/sup\u003e found that an elevated CONUT score is closely associated with severe hepatic steatosis but not significantly correlated with moderate steatosis. Severe hepatic steatosis is often accompanied by significant liver fibrosis, supporting our findings to some extent.\u003c/p\u003e\n\u003cp\u003eTCBI is a novel and straightforward nutritional tool that evaluates a patient\u0026apos;s nutritional and metabolic health status, primarily based on triglyceride levels, total cholesterol, and body weight. A retrospective observational cohort study from Japan demonstrated that a lower TCBI score was significantly associated with an increased risk of all-cause mortality, cardiovascular disease mortality, and cancer mortality\u0026nbsp;\u003csup\u003e28\u003c/sup\u003e. Another recent study found that TCBI was independently and negatively associated with stroke prevalence, particularly in hypertensive patients under 60 years of age\u0026nbsp;\u003csup\u003e29\u003c/sup\u003e. Our study found that TCBI was significantly positively correlated with the risk of NAFLD and AHF, likely due to the combined effects of triglycerides, total cholesterol, and body weight. Triglycerides are the primary form of fat storage in the liver. Elevated serum triglyceride levels typically reflect abnormal fat metabolism, and excessive fat accumulation in the liver contributes to the development of NAFLD. Insulin resistance is a core pathological mechanism of NAFLD. Insulin resistance decreases the liver\u0026apos;s sensitivity to insulin, leading to increased fatty acid mobilization and enhanced triglyceride synthesis, which further promotes intrahepatic fat accumulation\u0026nbsp;\u003csup\u003e30\u003c/sup\u003e. Additionally, excessive triglycerides activate immune cells, such as Kupffer cells, which release inflammatory factors like TNF-\u0026alpha;\u0026nbsp;and IL-1\u0026beta;, promoting the progression of NAFLD to AHF\u0026nbsp;\u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAGR is the ratio of serum albumin to globulin and reflects both liver function and the systemic inflammatory state. A low AGR typically indicates chronic inflammation, impaired liver function, or protein malnutrition, all of which are risk factors for poor prognosis. Wen et al.\u0026nbsp;\u003csup\u003e32\u003c/sup\u003e demonstrated that in diabetic patients, higher serum AGR levels were significantly and linearly associated with reduced mortality from all causes, cancer, and cardiovascular diseases. Albumin, produced by the liver, has antioxidant, anti-inflammatory, and immune-modulating functions. Higher albumin levels are typically associated with better liver function and overall health\u0026nbsp;\u003csup\u003e33\u003c/sup\u003e. Impaired liver function in NAFLD patients can lead to decreased albumin production and reduced serum albumin levels. Globulin plays a key role in the immune response and is a potential predictor of inflammation and cancer\u0026nbsp;\u003csup\u003e34\u003c/sup\u003e. NAFLD development is often accompanied by chronic inflammation and immune system activation, prompting the body to produce more immunoglobulins\u0026nbsp;\u003csup\u003e35\u003c/sup\u003e. As liver fibrosis progresses and liver cell function further deteriorates, immune and inflammatory responses become more pronounced, causing globulin levels to rise. AGR is typically low when albumin levels are low and globulin levels are high. The protective effect of albumin diminishes, while the pathological increase in globulin reflects chronic inflammation and poor liver function. Our results indicate that AGR is negatively correlated with the risk of NAFLD and AHF.\u003c/p\u003e\n\u003cp\u003eNutritional indicators such as GNRI, PNI, CONUT, TCBI, and AGR can reflect an individual\u0026apos;s current nutritional status to a certain extent. Previous studies have typically identified nutrition as a protective factor against disease, with high nutritional status often indicating a lower disease prevalence and longer survival. Our study demonstrates that the relationship between nutritional status and NAFLD and AHF is complex. On the one hand, high nutritional status can lead to excessive fat accumulation in the liver, causing fat degeneration and inflammation, potentially increasing the risk of NAFLD. Persistent inflammatory stimuli and nutritional metabolites, such as cholesterol, exacerbate the progression of NAFLD to AHF. Conversely, nutrients like albumin can protect the liver through anti-inflammatory and antioxidant effects, improve the liver microenvironment, and delay disease progression. When liver cirrhosis progresses to liver failure, better nutritional status can enhance patient survival\u0026nbsp;\u003csup\u003e36\u003c/sup\u003e. Therefore, maintaining a proper balance of nutrient intake, reducing excessive calories and fat, and limiting sugars and simple carbohydrates can help control body weight and improve insulin resistance. Additionally, increasing the intake of high-quality protein can aid in repairing damaged liver cells and promote liver function recovery. Overall, an individualized nutritional intervention strategy is crucial for the prevention and management of NAFLD and AHF, and warrants clinical attention.\u003c/p\u003e\n\u003cp\u003eThis study has several strengths. By utilizing all available continuous NHANES data cycles, it includes a large, nationally representative sample from across the United States, thereby enhancing the robustness of the statistical analysis and improving the generalizability of the findings. The study design accounted for a wide range of potential confounding factors, such as sociodemographic characteristics, lifestyle habits, health status, and laboratory parameters, with adjustments made through multivariate analysis to ensure accuracy and reliability. Importantly, this is the first comprehensive study to examine the association between nutritional indices and the risk of NAFLD and AHF, providing new insights into the potential role of nutritional status in these conditions. These findings contribute to more precise identification of high-risk populations and support the development of personalized intervention strategies.\u003c/p\u003e\n\u003cp\u003eThis study has certain limitations. First, its cross-sectional design prevents the establishment of a causal relationship between nutritional status and the risk of NAFLD and AHF. Second, although numerous confounding factors were adjusted for, some external variables may not have been fully accounted for, potentially affecting the accuracy of the results. Furthermore, as the study is based on a representative sample from the United States, the generalizability of the findings to other populations may be limited. Therefore, further research and additional data are required to more thoroughly explore the relationship between nutritional status and NAFLD/AHF.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our study identified significant associations between nutritional status indicators and both NAFLD and AHF. Specifically, GNRI, PNI, and TCBI were positively associated with NAFLD incidence, whereas CONUT and AGR were negatively associated. For AHF, GNRI, CONUT, and TCBI showed positive associations, while AGR exhibited a negative association. These findings underscore the complex relationship between nutritional status and the development of NAFLD and AHF. Clinically, individualized nutritional interventions should be tailored based on specific indicators to prevent or delay the onset and progression of these conditions. Further large-scale studies are needed to elucidate the mechanistic pathways linking these nutritional markers to NAFLD and AHF, providing a scientific basis for improved prevention and treatment strategies.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur team sincerely appreciates all the staff and participants whose invaluable contributions have significantly enriched the NHANES data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Shouxin Wei. and Sijia Yu.; methodology, Shouxin Wei.; software, Shouxin Wei.; validation, Yingdong Jia. and Ningbo Yang.; formal analysis, Shouxin Wei.; resources,Shouxin Wei.; data curation, Shouxin Wei.; writing\u0026mdash;original draft preparation, Shouxin Wei.; writing\u0026mdash;review and editing, Yunshen Lan.; visualization, Shouxin Wei.; supervision, Sijia Yu.; project administration, Yindong Jia.; \u0026nbsp;All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the NHANES repository, [https://www.cdc.gov/nchs/nhanes/].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBl\u0026uuml;her, M. 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Insulin resistance and reduced metabolic flexibility: cause or consequence of NAFLD? \u003cem\u003eClinical science (London, England : 1979)\u003c/em\u003e \u003cstrong\u003e131\u003c/strong\u003e, 2701-2704, doi:10.1042/cs20170987 (2017).\u003c/li\u003e\n\u003cli\u003eMoreno-Vedia, J.\u003cem\u003e et al.\u003c/em\u003e Lipidomics of triglyceride-rich lipoproteins derived from hyperlipidemic patients on inflammation. \u003cem\u003eEuropean journal of clinical investigation\u003c/em\u003e \u003cstrong\u003e54\u003c/strong\u003e, e14132, doi:10.1111/eci.14132 (2024).\u003c/li\u003e\n\u003cli\u003eWen, H.\u003cem\u003e et al.\u003c/em\u003e Association of Serum AGR With All-Cause and Cause-Specific Mortality Among Individuals With Diabetes. \u003cem\u003eThe Journal of clinical endocrinology and metabolism\u003c/em\u003e, doi:10.1210/clinem/dgae215 (2024).\u003c/li\u003e\n\u003cli\u003eDuran-G\u0026uuml;ell, M.\u003cem\u003e et al.\u003c/em\u003e Albumin protects the liver from tumor necrosis factor \u0026alpha;-induced immunopathology. \u003cem\u003eFASEB journal : official publication of the Federation of American Societies for Experimental Biology\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, e21365, doi:10.1096/fj.202001615RRR (2021).\u003c/li\u003e\n\u003cli\u003eYang, M.\u003cem\u003e et al.\u003c/em\u003e Prognostic Roles of Inflammation- and Nutrition-Based Indicators for Female Patients with Cancer. \u003cem\u003eJournal of inflammation research\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 3573-3586, doi:10.2147/jir.S361300 (2022).\u003c/li\u003e\n\u003cli\u003ePatrick-Melin, A. J.\u003cem\u003e et al.\u003c/em\u003e Nonalcoholic fatty liver disease: biochemical and therapeutic considerations. \u003cem\u003eUkrains\u0026apos;kyi biokhimichnyi zhurnal (1999 )\u003c/em\u003e \u003cstrong\u003e81\u003c/strong\u003e, 16-25 (2009).\u003c/li\u003e\n\u003cli\u003eSakamoto, N., Suda, G., Morikawa, K. \u0026amp; Ogawa, K. Nutrition is often ignored in management of chronic liver diseases. \u003cem\u003eJournal of gastroenterology and hepatology\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 1127-1128, doi:10.1111/jgh.14776 (2019).\u003c/li\u003e\n\u003cli\u003eBouillanne, O.\u003cem\u003e et al.\u003c/em\u003e Geriatric Nutritional Risk Index: a new index for evaluating at-risk elderly medical patients. \u003cem\u003eThe American journal of clinical nutrition\u003c/em\u003e \u003cstrong\u003e82\u003c/strong\u003e, 777-783, doi:10.1093/ajcn/82.4.777 (2005).\u003c/li\u003e\n\u003cli\u003eOnodera, T., Goseki, N. \u0026amp; Kosaki, G. [Prognostic nutritional index in gastrointestinal surgery of malnourished cancer patients]. \u003cem\u003eNihon Geka Gakkai zasshi\u003c/em\u003e \u003cstrong\u003e85\u003c/strong\u003e, 1001-1005 (1984).\u003c/li\u003e\n\u003cli\u003eBuzby, G. P., Mullen, J. L., Matthews, D. C., Hobbs, C. L. \u0026amp; Rosato, E. F. Prognostic nutritional index in gastrointestinal surgery. \u003cem\u003eAmerican journal of surgery\u003c/em\u003e \u003cstrong\u003e139\u003c/strong\u003e, 160-167, doi:10.1016/0002-9610(80)90246-9 (1980).\u003c/li\u003e\n\u003cli\u003eDoi, S.\u003cem\u003e et al.\u003c/em\u003e A novel and simply calculated nutritional index serves as a useful prognostic indicator in patients with coronary artery disease. \u003cem\u003eInternational journal of cardiology\u003c/em\u003e \u003cstrong\u003e262\u003c/strong\u003e, 92-98, doi:10.1016/j.ijcard.2018.02.039 (2018).\u003c/li\u003e\n\u003cli\u003eLi, X.\u003cem\u003e et al.\u003c/em\u003e Prognostic Significance of Albumin-Globulin Score in Patients with Operable Non-Small-Cell Lung Cancer. \u003cem\u003eAnnals of surgical oncology\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 3647-3659, doi:10.1245/s10434-018-6715-z (2018).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Details of the nutritional indices used in the study.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003eNutrition-related indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.3061%;\"\u003e\n \u003cp\u003eCalculation formula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003eGNRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.3061%;\"\u003e\n \u003cp\u003e1.489 \u0026times; serum albumin (g/L) + 41.7 \u0026times; (current weight/ideal weight)\u003c/p\u003e\n \u003cp\u003eMen: Ideal body weight = Height (cm) \u0026minus; 100 \u0026minus; (Height (cm) \u0026minus; 150)/4\u003c/p\u003e\n \u003cp\u003eWomen: Ideal body weight = Height (cm) \u0026minus; 100 \u0026minus; (Height (cm) \u0026minus; 150)/2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003e\u003csup\u003e37\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003ePNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.3061%;\"\u003e\n \u003cp\u003eAlbumin (g/L) + 5 \u0026times; total lymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003e\u003csup\u003e38\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003eCONUT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.3061%;\"\u003e\n \u003cp\u003eSerum albumin score + total lymphocyte count score + total cholesterol score\u003c/p\u003e\n \u003cp\u003eAlbumin score: 0, 2, 4 and 6 points are assigned when the albumin level is \u0026ge;3.5, 3.0-3.49, 2.5-2.99 and \u0026lt;2.5 g/dL, respectively.\u003cbr\u003eLymphocyte total score: 0, 1, 2 and 3 points are awarded for total lymphocyte counts of \u0026ge;1,600, 1,200-1,599, 800-1,199 and \u0026lt;800/mm\u003csup\u003e3\u003c/sup\u003e, respectively.\u003cbr\u003e\u0026nbsp;Total cholesterol score: When the total cholesterol level is \u0026ge;180, 140-179, 100-139 and \u0026lt;100 mg/dL, the corresponding scores are 0, 1, 2 and 3 points, respectively.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003e\u003csup\u003e39\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003eTCBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.3061%;\"\u003e\n \u003cp\u003eTriglycerides (mg/dL) \u0026times; Total cholesterol (mg/dL) \u0026times; Weight (kg)/1,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003e\u003csup\u003e40\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.4898%;\"\u003e\n \u003cp\u003eAGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.3061%;\"\u003e\n \u003cp\u003eAlbumin (g/L)/Globulin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2041%;\"\u003e\n \u003cp\u003e\u003csup\u003e41\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e Characteristics of the study cohort.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (n\u0026thinsp;=\u0026thinsp;5514)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 32%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNAFLD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 32%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAHF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003eNo(n\u0026thinsp;=\u0026thinsp;3426)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003eYes(n\u0026thinsp;=\u0026thinsp;2088)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003eNo(n\u0026thinsp;=\u0026thinsp;5155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003eYes(n\u0026thinsp;=\u0026thinsp;359)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e48.67\u0026plusmn; 16.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e47.23 \u0026plusmn; 17.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e51.04 \u0026plusmn; 15.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e48.23 \u0026plusmn; 16.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e54.99 \u0026plusmn; 15.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2666(48.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1506 (43.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1160 (55.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2467 (47.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e199 (55.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2848(51.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1920 (56.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e928 (44.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2688 (52.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e160 (44.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e685(12.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e318 (9.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e367 (17.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e634 (12.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e51 (14.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e575(10.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e361 (10.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e214 (10.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e536 (10.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e39 (10.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1876(34.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1136 (33.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e740 (35.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1738 (33.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e138 (38.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1400(25.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e957 (27.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e443 (21.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1315 (25.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e85 (23.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e978(17.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e654 (19.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e324 (15.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e932 (18.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e46 (12.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eBelow high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e957(17.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e565 (16.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e392 (18.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e889 (17.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e68 (18.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1311(23.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e789 (23.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e522 (25.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1212 (23.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e99 (27.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eAbove high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e3246(59.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2072 (60.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1174 (56.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e3054 (59.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e192 (53.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status , n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eMarried or living with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e3278(48.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1926 (56.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1352 (64.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e3059 (59.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e219 (61.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eDivorced, separated, or widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1128(20.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e706 (20.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e422 (20.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1044 (20.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e84 (23.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1108(20.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e794 (23.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e314 (15.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1052 (20.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e56 (15.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIR, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u0026lt;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1547(28.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e973 (28.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e574 (27.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1447 (28.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e100 (27.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e1.3-3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2149(38.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1308 (38.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e841 (40.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1981 (38.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e168 (46.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u0026ge;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1818(32.97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1145 (33.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e673 (32.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1727 (33.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e91 (25.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;25.0 kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1404(25.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1278 (37.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e126 (6.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1386 (26.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e18 (5.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e25.0\u0026ndash;29.9 kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1720(31.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1185 (34.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e535 (25.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1670 (32.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e50 (13.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u0026gt;29.9 kg/m\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2390(43.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e963 (28.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1427 (68.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2099 (40.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e291 (81.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStroke,n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e221(4.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e143 (4.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e78 (3.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e200 (3.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e21 (5.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e5293(95.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e3283 (95.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2010 (96.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e4955 (96.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e338 (94.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePulmonary disease, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1099(19.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e640 (18.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e459 (21.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1006 (19.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e93 (25.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e4415(80.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2786 (81.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1629 (78.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e4149 (80.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e266 (74.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeart disease, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e380(6.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e194 (5.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e186 (8.91%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e322 (6.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e58 (16.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e5134(93.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e3232 (94.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1902 (91.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e4833 (93.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e301 (83.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2980(54.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1563 (45.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1417 (67.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2699 (52.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e281 (78.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2534(45.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1863 (54.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e671 (32.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2456 (47.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e78 (21.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e986(17.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e364 (10.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e622 (29.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e812 (15.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e174 (48.47%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e4528(82.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e3062 (89.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1466 (70.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e4343 (84.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e185 (51.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntensity of activity, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eModerate to low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2530(45.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1506 (43.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1024 (49.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2330 (45.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e200 (55.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2984(54.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1920 (56.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1064 (50.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2825 (54.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e159 (44.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1252(22.71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e675 (19.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e577 (27.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1132 (21.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e120 (33.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e958(17.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e622 (18.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e336 (16.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e910 (17.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e48 (13.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e3304(59.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2129 (62.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1175 (56.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e3113 (60.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e191 (53.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking status, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1899(34.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1179 (34.41%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e720 (34.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1767 (34.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e132 (36.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eHeavy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1958(35.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1241 (36.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e717 (34.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1858 (36.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e100 (27.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1657(30.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1006 (29.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e651 (31.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1530 (29.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e127 (35.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic kidney disease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1037(18.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e596 (17.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e441 (21.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e962 (18.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e75 (20.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e4477(81.19%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2830 (82.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1647 (78.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e4193 (81.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e284 (79.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eALT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e22.17\u0026plusmn; 16.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e18.81 \u0026plusmn; 12.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e27.67 \u0026plusmn; 20.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e21.46 \u0026plusmn; 14.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e32.34 \u0026plusmn; 29.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eALP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e77.00\u0026plusmn; 25.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e74.41 \u0026plusmn; 26.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e81.25 \u0026plusmn; 24.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e76.16 \u0026plusmn; 24.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e89.06 \u0026plusmn; 34.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e21.55\u0026plusmn; 12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e20.43 \u0026plusmn; 10.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e23.39 \u0026plusmn; 15.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e20.96 \u0026plusmn; 9.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e30.06 \u0026plusmn; 29.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGGT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e31.63\u0026plusmn; 53.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e26.54 \u0026plusmn; 54.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e39.98 \u0026plusmn; 50.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e29.20 \u0026plusmn; 38.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e66.53 \u0026plusmn; 147.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGNRI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e117.36\u0026plusmn; 13.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e112.87 \u0026plusmn; 11.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e124.72 \u0026plusmn; 13.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e116.31 \u0026plusmn; 12.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e132.38 \u0026plusmn; 19.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePNI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e51.81\u0026plusmn; 5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e51.63 \u0026plusmn; 5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e52.10 \u0026plusmn; 5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e51.87 \u0026plusmn; 5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e50.90 \u0026plusmn; 5.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCONUT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.38\u0026plusmn; 0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.40 \u0026plusmn; 0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.34 \u0026plusmn; 0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.37 \u0026plusmn; 0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.58 \u0026plusmn; 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTCBI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2335.01\u0026plusmn; 2414.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1729.94 \u0026plusmn; 1541.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e3327.82 \u0026plusmn; 3148.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2252.99 \u0026plusmn; 2297.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e3512.78 \u0026plusmn; 3506.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAGR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.35\u0026plusmn; 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.37 \u0026plusmn; 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.32 \u0026plusmn; 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.36 \u0026plusmn; 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.27 \u0026plusmn; 0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eContinuous variables were expressed as mean \u0026plusmn; standard deviation (SD), while categorical variables were expressed as percentages. The significance of the data was evaluated using the t-test and the chi-square test. ALT: alanine aminotransferase; ALP: alkaline phosphatase; AST: aspartate aminotransferase; GGT: gamma glutamyl transferase; PIR: Poverty Income Ratio; BMI: body mass index; GNRI: Geriatric Nutritional Risk Index; PNI: Prognostic Nutritional Index; CONUT: Control Nutritional Status Score; TCBI: Triglycerides \u0026times; Total Cholesterol \u0026times; Body Weight Index; AGR: Albumin-to-globulin ratio.n: number of study samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e Association between nutrition-related indices and NAFLD .\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGNRI continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.086 (1.080, 1.093) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.099 (1.092, 1.106) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.054 (1.045, 1.063) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGNRI binary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge; 98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.814 (6.774, 24.242)***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.370 (6.513, 23.492) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.487 (1.238, 4.996)*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePNI continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.018 (1.007, 1.029) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.022 (1.011, 1.034)*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.020 (1.007, 1.034)**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePNI quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.090 (0.929, 1.280)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.116 (0.946, 1.317)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.156 (0.955, 1.400)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.048 (0.893, 1.229)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.104 (0.935, 1.305)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.179 (0.971, 1.431)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.271 (1.091, 1.481) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.343 (1.141, 1.581)***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.351 (1.115, 1.638)**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCONUT continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.891 (0.827, 0.960)**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.849 (0.785, 0.917) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.899 (0.821, 0.983) *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCONUT ternary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e0-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.789 (0.696, 0.894) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.733 (0.644, 0.835) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.873 (0.748, 1.019)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e5-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.651 (0.321, 1.321)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.540 (0.262, 1.113)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.476 (0.209, 1.083)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTCBI/100 continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.049 (1.044, 1.053) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.046 (1.042, 1.051)***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.021 (1.017, 1.025)*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTCBI/100 quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.766 (2.266, 3.376) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.608 (2.131, 3.193)***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.547 (1.236, 1.935) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.083 (5.017, 7.376) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.525 (4.542, 6.720) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.347 (1.882, 2.928) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.539 (11.135, 16.462) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.198 (9.990, 14.894)***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.751 (2.982, 4.718) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAGR continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.434 (0.345, 0.546) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.255 (0.197, 0.330)***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.588 (0.431, 0.801)***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAGR quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.784 (0.673, 0.912) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.685 (0.584, 0.803) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.887 (0.738, 1.066)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.698 (0.598, 0.815) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.540 (0.457, 0.637) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.759 (0.624, 0.923)**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.583 (0.500, 0.681) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.418 (0.353, 0.496) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.763 (0.622, 0.936)*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOR: odds ratio; 95% CI: 95% confidence interval.GNRI: Geriatric Nutritional Risk Index; PNI: Prognostic Nutritional Index; CONUT: Control Nutritional Status Score; TCBI: Triglycerides \u0026times; Total Cholesterol \u0026times; Body Weight Index; AGR: Albumin-to-globulin ratio.Model 1: No covariates were adjusted. Model 2: Age, gender and race were adjusted. Model 3: Age, gender, race, education level, marital status, PIR, smoking, drinking, activity level, BMI, stroke, lung disease, heart disease, chronic kidney disease, hypertension, diabetes, as well as ALT, ALP, AST, and GGT were adjusted.*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001; P \u0026lt; 0.05 is considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e Association between nutrition-related indices and\u0026nbsp;AHF\u0026nbsp;.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGNRI continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.071 (1.063, 1.079) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.087 (1.078, 1.096) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.074 (1.062, 1.086) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGNRI binary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; 98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge; 98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.051 (0.958, 4.388)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.973 (0.917, 4.245)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.544 (0.184, 1.604)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePNI continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.961 (0.940, 0.983)***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.975 (0.953, 0.998) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.984 (0.961, 1.008)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePNI quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.632 (0.468, 0.854) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.676 (0.498, 0.917) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.784 (0.563, 1.093)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.600 (0.443, 0.812) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.689 (0.505, 0.940) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.812 (0.577, 1.141)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.658 (0.495, 0.874)**\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.793 (0.588, 1.069)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.875 (0.628, 1.219)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCONUT continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.351 (1.206, 1.513) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.265 (1.125, 1.422) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.219 (1.068, 1.391) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCONUT ternary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e0-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.398 (1.111, 1.759) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.244 (0.983, 1.573)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.274 (0.980, 1.658)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e5-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.105 (1.283, 7.516)*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.430 (0.990, 5.965)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.097 (0.778, 5.655)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTCBI/100 continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.012 (1.009, 1.016) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.012 (1.009, 1.015) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.004 (1.000, 1.008) *\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTCBI/100 quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.650 (1.063, 2.562) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.883 (1.216, 2.915) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.995 (0.616, 1.606)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.979 (1.984, 4.473) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.508 (2.345, 5.247) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.193 (0.756, 1.882)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.993 (3.369, 7.399) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.603 (3.805, 8.251)***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.453 (0.923, 2.287)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAGR continuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.189 (0.118, 0.304) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.133 (0.080, 0.220) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.411 (0.235, 0.716) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAGR quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1[Reference]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.480 (0.360, 0.640) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.438 (0.326, 0.588)***\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.617 (0.447, 0.852) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.464 (0.345, 0.623) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.389 (0.285, 0.530) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.645 (0.457, 0.909)*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQuartile 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.405 (0.301, 0.547) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.325 (0.236, 0.448) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.657 (0.459, 0.940)*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOR: odds ratio; 95% CI: 95% confidence interval.GNRI: Geriatric Nutritional Risk Index; PNI: Prognostic Nutritional Index; CONUT: Control Nutritional Status Score; TCBI: Triglycerides \u0026times; Total Cholesterol \u0026times; Body Weight Index; AGR: Albumin-to-globulin ratio.Model 1: No covariates were adjusted. Model 2: Age, gender and race were adjusted. Model 3: Age, gender, race, education level, marital status, PIR, smoking, drinking, activity level, BMI, stroke, lung disease, heart disease, chronic kidney disease, hypertension, diabetes, as well as ALT, ALP, AST, and GGT were adjusted.*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001; P \u0026lt; 0.05 is considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e Analysis of threshold effects between GNRI and NAFLD and AHF, and between TCBI/100 and NAFLD.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003eOR (95% CI)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003eGNRI and NAFLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003eFitting by standard linear model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e1.054 (1.045, 1.063)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003eFitting by two-piecewise linear model\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003eInflection point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e141.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003e\u0026lt; 141.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e1.066 (1.054, 1.077)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003e\u0026gt; 141.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e1.016 (0.996, 1.036)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003eLog likelihood ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003eGNRI and AHF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003eFitting by standard linear model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e1.074 (1.062, 1.086)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003eFitting by two-piecewise linear model\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003eInflection point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e120.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003e\u0026lt; 120.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e0.992 (0.953, 1.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003e\u0026gt; 120.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e1.083 (1.070, 1.096)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003eLog likelihood ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003eTCBI/100 and NAFLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003eFitting by standard linear model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e1.021 (1.017, 1.025)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003eFitting by two-piecewise linear model\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003eInflection point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e31.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003e\u0026lt; 31.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e1.052 (1.042, 1.061)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003e\u0026gt; 31.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e1.005 (1.001, 1.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 63.6364%;\"\u003e\n \u003cp\u003eLog likelihood ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"NAFLD, AHF, nutritional status, NHANES, cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-5188054/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5188054/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases worldwide, and its incidence has been rising in parallel with the prevalence of obesity and diabetes. A critical marker for the progression of NAFLD is advanced hepatic fibrosis (AHF), which can result in cirrhosis and hepatocellular carcinoma. This study sought to investigate the potential associations between five nutrition-related indices—the geriatric nutritional risk index (GNRI), prognostic nutritional index (PNI), controlled nutritional score (CONUT), triglyceride-total cholesterol-body mass index (TCBI), and albumin/globulin ratio (AGR)—and the progression of NAFLD and AHF.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This study analyzed the association between five nutrition-related indicators and the occurrence of NAFLD and AHF by utilizing data from the National Health and Nutrition Examination Survey (NHANES) from 2017 to 2020.03. After adjusting for demographic, lifestyle, clinical, and laboratory-related factors, the associations were examined employing various statistical techniques, including multivariate logistic regression, subgroup analysis, and smooth curve fitting analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: This study included 5,514 subjects, of whom 2,088 were diagnosed with NAFLD and 359 with AHF. After adjusting for potential confounding factors using multivariate logistic regression analysis, the results revealed that GNRI, PNI, and TCBI were positively associated with the incidence of NAFLD, while CONUT and AGR demonstrated negative associations with NAFLD. Additionally, GNRI, CONUT, and TCBI were positively associated with the incidence of AHF, while AGR demonstrated a negative association with AHF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: The study demonstrates that nutritional status plays a dual role in the progression of NAFLD and AHF, serving as both a risk and protective factor. These findings offer a scientific foundation for the early identification of high-risk populations and the development of individualized nutritional intervention strategies.\u003c/p\u003e","manuscriptTitle":"Association between nutrition-related indicators and non-alcoholic fatty liver disease and advanced liver fibrosis: evidence from NHANES 2017-2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-18 14:08:05","doi":"10.21203/rs.3.rs-5188054/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":"2b61123b-0cfa-483e-8cd3-86c9ecfec19d","owner":[],"postedDate":"October 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-24T09:08:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-18 14:08:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5188054","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5188054","identity":"rs-5188054","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}