Association between neutrophil to high-density lipoprotein cholesterol ratio and risk of non-alcoholic fatty liver disease and liver fibrosis: A cross-sectional study

Association between neutrophil to high-density lipoprotein cholesterol ratio and risk of non-alcoholic fatty liver disease and liver fibrosis: A cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between neutrophil to high-density lipoprotein cholesterol ratio and risk of non-alcoholic fatty liver disease and liver fibrosis: A cross-sectional study Na Zhu, Yanyan Li, Yingying Lin, XinYu Cui, Xin Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5308727/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Non-alcoholic fatty liver disease (NAFLD) is closely associated with chronic inflammation and lipid metabolism disorders. The neutrophil-to-high-density lipoprotein cholesterol ratio (NHR) is an integrative marker reflecting inflammatory responses and lipid metabolism disorders. It has been associated with the prognosis of several diseases. This study aimed to investigate the relationship between NHR and the risk of NAFLD and liver fibrosis. Methods We conducted a cross-sectional study using data from the 2017–2020 National Health and Nutrition Examination Survey (NHANES).weighted multivariate regression was used to investigate the association of NHR with NAFLD and liver fibrosis. and restricted cubic spline model was used to explore potential non-linear relationships. Subgroup analyses were used to verify the stability of the relationship of NHR with NAFLD and liver fibrosis in different populations. Results A total of 6526 participants were included in the study. After adjusting for confounders, the elevated NHR levels were positively associated with the risk of NAFLD. for every unit increase in NHR, there was a 2.5 dB/m increase in the controlled attenuation parameter (CAP) (β = 2.5; P = 0.019) and an 11% increase in NAFLD prevalence (OR = 1.11; P < 0.05). Participants in the highest quartile of NHR had a twofold increased risk of developing NAFLD compared with those in the lowest quartile (OR = 2.00; P < 0.001). However, after adjusting for confounders, the association between NHR and liver fibrosis was not statistically significant. RCS analyses showed that the risk of NAFLD increased with increasing NHR water at NHR values below 3.013. The risk of developing liver fibrosis was significantly increased at NHR above 3.013. Subgroup analyses showed that the positive association between NHR and NAFLD was more pronounced in women and participants without diabetes or hypertension. Conclusion Elevated NHR levels are positively correlated with the risk of NAFLD, particularly in women and individuals without diabetes or hypertension. and the risk of developing liver fibrosis significantly increases at NHR values above 3.013. which can help in the early detection of NAFLD and liver fibrosis and timely intervention. Non-alcoholic fatty liver disease(NAFLD) neutrophil-to-high-density lipoprotein cholesterol ratio inflammation Lipid metabolism disorders liver fibrosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction With the increasing prevalence of metabolic conditions such as obesity and type 2 diabetes, non-alcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease in the world, affecting approximately 25% of the global population ( 1 ). The pathological features of NAFLD include hepatocellular steatosis, inflammatory cell infiltration, and different degrees of hepatic fibrosis( 2 ). In its early stages, NAFLD may present as simple fatty liver or non-alcoholic steatohepatitis (NASH). As the disease progresses, it may evolve into liver fibrosis, cirrhosis, and hepatocellular carcinoma( 3 , 4 ). For patients with NAFLD who have not progressed to liver fibrosis, cardiovascular disease, and extrahepatic malignancies are the leading causes of death. However, for those patients who have progressed to advanced hepatic fibrosis, the leading cause of death is liver-related diseases( 5 , 6 ). Therefore, early detection of NAFLD and liver fibrosis is critical for timely intervention and effective management. Inflammatory response and abnormal lipid metabolism are critical in causing and developing NAFLD( 7 ). As an essential component of the innate immune system, neutrophils play a central role in fighting infections. They are closely associated with various chronic inflammatory diseases, especially metabolic diseases such as obesity, type 2 diabetes, and NAFLD( 8 ). Several studies have shown that the degree of neutrophil infiltration in liver tissue of NAFLD patients is positively correlated with the severity of the lesion. Neutrophil infiltration can directly lead to hepatocellular injury and accelerate the progression of NAFLD to hepatic fibrosis and cirrhosis by promoting fibrosis( 7 – 9 ). NAFLD begins with excessive accumulation of triglycerides in the hepatocytes and is accompanied by a reduction in plasma cholesterol levels associated with antiatherosclerosis high-density lipoproteins( 10 ). HDL-C is critical in developing NAFLD through lipid metabolism modulation and reverse cholesterol promotion. HDL-C exerts anti-inflammatory and antioxidant effects by regulating lipid metabolism, promoting reverse cholesterol transport, and reducing the production of inflammatory mediators( 11 ). Therefore, reducing HDL-C levels in NAFLD may weaken its anti-inflammatory and antioxidant functions and thus exacerbate the occurrence and progression of NAFLD( 12 ). In recent years, NHR has been proposed as a comprehensive indicator of inflammation and lipid metabolic status. It has been shown that NHR is significantly associated with cardiovascular disease, metabolic syndrome, hepatocellular carcinoma, and Parkinson's disease( 13 – 16 ), but its potential relationship with NAFLD has not been fully investigated. This study used data from the 2017–2020 National Health and Nutrition Examination Survey (NHANES) in a cross-sectional analysis to investigate the association between NHR and both NAFLD and liver fibrosis. Materials and methods study population NHANES is a long-term, large-scale health survey program initiated and conducted every two years by the National Center for Health Statistics (NCHS), a division of the Centers for Disease Control and Prevention (CDC). Through a complex, multistage sampling design, NHANES collects health and nutrition data representative of the United States population, including personal interviews, physical examinations, and laboratory test results. NHANES data are widely used to study public health trends, assess the burden of disease and nutritional status, and are freely available to researchers worldwide. This study analyzed pre-epidemic data from the 2017–2020 NHANES survey, which initially included 15,560 participants. From this cohort, 8,317 individuals aged 18 years and older with vibration-controlled transient elastography (VCTE) results were selected. We excluded 219 participants with unreliable VCTE measurements (liver stiffness quartile/median ratio ≥ 30%), 283 with hepatitis B or C, 821 with excessive alcohol intake (defined as more than two standard drinks per day for women and more than three for men), and those with missing data on neutrophil or high-density lipoprotein cholesterol (HDL-C). Consequently, 6526 participants were included in the final analysis. Measurement of hepatic steatosis and hepatic fibrosis NHANES staff use the FibroScan 502 Touch device to assess liver stiffness and fat content. The device measures liver elasticity and stiffness through vibration-controlled transient elastography (VCTE) technology to help determine the extent of liver fibrosis. At the same time, the device measures hepatic steatosis by ultrasound attenuation and records the Controlled Attenuation Parameter (CAP) as an indicator of hepatic fat content. Previous studies define a CAP value of ≥ 274 dB/m as a diagnostic criterion for NAFLD. A CAP value of ≥ 302 dB/m indicates severe hepatic steatosis( 17 , 18 ). In addition, liver stiffness measurements (LSM) of ≥ 8.2 kPa, ≥ 9.7 kPa, and ≥ 13.7 kPa represented the F2, F3, and F4 stages of liver fibrosis, respectively( 19 ). Variable Demographic and clinical data were extracted from the NHANES database. Age, sex, race, educational level, body mass index (BMI), diabetes, hypertension, history of cardiovascular disease, smoking status, and laboratory variables were included. Diabetes mellitus was defined as HbA1c ≥ 6.5% or fasting glucose ≥ 126 mg/dL; in addition, participants had diabetes if they answered, “yes” to any of the following questions: “Do you use insulin?” or “Has your doctor told you that you have diabetes?” or “Do you take glucose-lowering medication?”, Hypertension was defined as a mean systolic blood pressure ≥ 140 mmHg or a mean diastolic blood pressure ≥ 90 mmHg on three consecutive measurements, and participants who responded to the questions “Have you been told you have high blood pressure on two or more occasions” or “Do you have to take prescription medication for high blood pressure?” A “yes” response was also defined as hypertension. A history of cardiovascular disease was described as a response confirming a physician's diagnosis of myocardial infarction, angina pectoris, coronary heart disease, congestive heart failure, or stroke. Smoking status was categorized as a smoker or never smoker based on having smoked fewer than 100 cigarettes in their lifetime. Laboratory tests included measurements of alanine aminotransferase (ALT), aspartate aminotransferase (AST), total cholesterol (TC), triglycerides (TG), uric acid, albumin (Alb), glycosylated hemoglobin (HbA1c), γ-glutamyltranspeptidase (GGT) and high-density lipoprotein cholesterol (HDL-C). The neutrophil-to-high-density lipoprotein cholesterol ratio (NHR) was calculated by dividing the neutrophil count by the HDL-C level. Statistical Analyses Considering NHANES's complicated multistage sampling design, sample weights were applied in all analyses to ensure that the results were representative of the US population. Participants were divided into four groups according to NHR quartiles. Continuous variables are presented as weighted means with standard errors, while categorical variables are presented as unweighted counts and weighted percentages. One-way ANOVA for continuous variables and weighted chi-squared tests for categorical variables were used to compare differences between NHR quartiles. NHR was analyzed as a continuous and categorical variable, with exposure variables grouped by quartiles (the first quartile served as the reference group). Outcome variables included liver steatosis parameters (CAP), NAFLD, liver stiffness measurements (LSM), and liver fibrosis. We used weighted linear regression and weighted logistic regression models for the analyses. In addition, we assessed potential non-linear associations between NHR and the prevalence of NAFLD and liver fibrosis using restricted cubic spline (RCS) analysis. The RCS model was adjusted for several confounders, including age, sex, ethnicity, smoking history, diabetes, hypertension, cardiovascular disease (CVD), body mass index (BMI), total cholesterol (TC), alanine aminotransferase (ALT), and uric acid. Subgroup analyses were conducted by stratifying participants according to age, gender, BMI, presence of hypertension, diabetes, and history of cardiovascular disease (CVD). All data analyses were done using R software (version 4.4.0), and the statistical significance level was set at P < 0.05. Results Baseline characteristics of study participants This study included 6526 participants. The mean age of the participants was 47.81 ± 17.82 years, with 49.6% males and 50.4% females. The prevalence of NAFLD and liver fibrosis was 44.1% and 8.9%, respectively. Table 1 presents the baseline characteristics of the study population, grouped according to quartiles of NHR. Table 1 Baseline characteristics of study participants (grouped according to NHR quartile). Variables Total (N = 6526) Q1 (N = 1631, NHR < 2.07) Q2 (N = 1637, 2.07 ≤ NHR < 3) Q3 (N = 1627, 3 ≤ NHR < 4.2) Q4 (N = 1631, NHR ≥ 4.2) P value Age(year) 47.81 ± 17.82 49.24 ± 17.68 48.48 ± 17.91 47.61 ± 18.02 46.14 ± 17.52 0.008 Gender(%) Male female 3251(49.6%) 3275(50.4%) 641(36.3%) 990(63.7%) 776(46.1%) 861(53.9%) 878(53.8%) 749(46.2%) 956(60.0%) 675(40.0%) < 0.001 Race(%) Non-Hispanic White Non-Hispanic Black Hispanic Others 2210(61.2%) 1610(10.8%) 711(7.9%) 1995(20.1%) 422(56.9%) 679(20.5%) 134(6.4%) 396(16.2%) 537(61.0%) 408(11.3%) 160(7.2%) 532(20.5%) 590(63.5%) 309(7.5%) 220(9.1%) 508(19.9) 661(62.8%) 214(5.4%) 197(8.6%) 559(23.3%) < 0.001 Education level(%) Less than high school High school or above high school Others 1215(11.6%) 4977(85.2%) 334(3.2%) 211(7.0%) 1345(90.5%) 75(2.5%) 312(12.0%) 1243(85.1%) 82(2.9%) 331(12.1%) 1207(83.7%) 89(4.1%) 361(14.8%) 1182(82.0%) 88(3.2%) < 0.001 Hypertension(%) Yes No Diabetes(%) Yes No History of CVD(%) Yes No 2700(36%) 3826(64%) 1321(15.7%) 5205(84.3%) 715(9.1%) 5811(90.9%) 597(28.6%) 1034(71.4%) 178(6.7%) 1453(93.3%) 140(6.0%) 1491(94.0%) 674(34.6%) 963(65.4%) 295(11.4%) 1342(88.6%) 158(8.1%) 1479(91.9%) 711(37.4%) 916(62.6%) 371(17.7%) 1256(82.3%) 192(10.0%) 1435(90.0%) 718(42.5%) 913(57.5%) 477(25.6%) 1154(74.4%) 225(11.8%) 1406(88.2%) < 0.001 < 0.001 0.003 Smoking status(%) Yes No BMI (kg/m2),(%) < 25 25–30 ≥ 30 ALT(U/L) AST(U/L) TG(mmol/L) TC(mmol/L) Uric acid (mg/dl) Alb(g/L) HbA1c(%) GGT(U/L) HDL-C(mmol/L) NHR CAP(dB/m) LSM(kPa) NAFLD,n(%) Severe steatosis Liver fibrosis,(%) F2 F3 F4 2578(41.4%) 3948(58.6%) 1752(27.2%) 2105(31.6%) 2669(41.2%) 22.42 ± 16.91 21.46 ± 11.36 1.61 ± 1.20 4.83 ± 1.05 5.35 ± 1.43 41.09 ± 3.22 5.69 ± 0.96 28.88 ± 37.46 1.37 ± 0.40 3.45 ± 1.85 264.85 ± 63.13 5.78 ± 4.69 2839(44.1%) 1850(29.0%) 610(8.9%) 233(3.4%) 214(3.0%) 163(2.5%) 520(32.1%) 1111(67.9%) 697(48.2%) 511(32.0%) 423(19.8%) 19.49 ± 16.52 22.23 ± 13.88 1.07 ± 0.63 4.99 ± 1.03 4.93 ± 1.37 41.17 ± 3.08 5.48 ± 0.63 26.23 ± 38.25 1.77 ± 0.42 1.57 ± 0.36 235.40 ± 53.65 5.17 ± 2.72 427(24.2%) 224(12.5%) 82(4.0%) 33(1.5%) 27(1.3%) 22(1.2%) 615(39.0%) 1022(61.0%) 487(29.6%) 580(35.9%) 570(34.5%) 20.87 ± 14.81 21.11 ± 10.24 1.34 ± 0.69 4.81 ± 1.01 5.21 ± 1.33 41.31 ± 3.15 5.61 ± 0.84 27.09 ± 43.05 1.45 ± 0.31 2.54 ± 0.27 253.76 ± 57.30 5.26 ± 3.57 615(37.3%) 359(21.0%) 126(6.1%) 55(2.9%) 41(2.1%) 30(1.1%) 670(44.5%) 957(55.5%) 343(20.3%) 542(30.5%) 742(49.2%) 23.24 ± 15.92 21.27 ± 10.79 1.68 ± 1.18 4.78 ± 1.01 5.48 ± 1.43 41.18 ± 3.23 5.70 ± 0.90 29.70 ± 36.37 1.26 ± 0.24 3.57 ± 0.34 274.95 ± 62.05 5.98 ± 5.06 804(50.1%) 549(34.6%) 177(10.6%) 74(4.5%) 55(2.6%) 48(3.6%) 773(48.8%) 858(51.2%) 225(13.6%) 472(28.2%) 934(58.2%) 25.62 ± 19.34 21.31 ± 10.46 2.26 ± 1.59 4.76 ± 1.11 5.73 ± 1.45 40.73 ± 3.39 5.95 ± 1.26 32.07 ± 31.33 1.06 ± 0.26 5.85 ± 1.78 290.78 ± 64.17 6.61 ± 6.22 993(61.6%) 718(45.2%) 225(13.9%) 71(4.3%) 91(5.6%) 63(3.9%) < 0.001 < 0.001 < 0.001 0.255 < 0.001 < 0.001 < 0.001 0.017 < 0.001 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.005 < 0.001 0.001 Continuous variables are presented as mean ± SE; categorical variables are presented as unweighted counts (weighted percentages). CVD, cardiovascular disease; MI, Body mass index; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase’s, Triglyceride; TC, Total cholesterol; Alb, Albumin; GGT, γ-glutamyl transpeptidase; HDL-C, High-density lipoprotein cholesterol, Neutrophil/High-Density Lipoprotein Cholesterol Ratio; CAP, Controlled attenuation parameter; HbA1c, Glycosylated hemoglobin A1c; LSM, Liver stiffness measurement; NAFLD, Nonalcoholic fatty liver disease The results showed that the prevalence of CAP, LSM, NAFLD, severe hepatic steatosis, hepatic fibrosis, and the severity of hepatic fibrosis gradually increased with increasing NHR (P < 0.05). In addition, it was found that there were significant differences between participants with different NHR levels in terms of age, gender, race, education level, hypertension, diabetes mellitus, smoking status, history of cardiovascular disease, BMI, TG, TC, ALT, Alb, GGT, HbA1c, HDL-C, and uric acid (P < 0.05). Association of NHR with NAFLD As shown in Table 2 , we analyzed the effect of NHR on CAP and NAFLD using weighted multiple regression models adjusted for all possible confounding variables (age, sex, race, smoking, diabetes, hypertension, history of CVD, BMI, TC, ALT, and uric acid). Table 2 Association of NHR with NAFLD. model1:β/OR (95% CI) P value model2: β/OR (95% CI) P value model3: β/OR (95% CI) P value CAP(dB/m) NHR 9.7(7.6–12) < 0.001 9.3(7.1–11) < 0.001 2.5(0.51–4.5) 0.019 NHR (Quartile) Q1 Reference Reference Reference Q2 18( 13 – 24 ) < 0.001 17( 12 – 22 ) < 0.001 3.0(-1.6-7.5) 0.175 Q3 40(34–46) < 0.001 38(33–43) < 0.001 11(8.1–15) < 0.001 Q4 P for trend 55(50–61) < 0.001 15( 14 – 16 ) < 0.001 54(48–59) < 0.001 14( 13 – 16 ) < 0.001 17( 12 – 22 ) < 0.001 4.8(3.4–6.1) < 0.001 NAFLD NHR NHR (Quartile) Q1 Q2 Q3 Q4 P for trend 1.35(1.28,1.43) < 0.001 Reference 1.87(1.51–2.32) < 0.001 3.14(2.58–3.83) < 0.001 5.03(4.14–6.11) < 0.001 1.52(1.45–1.59) < 0.001 1.35(1.28–1.44) < 0.001 Reference 1.84(1.44–2.35) < 0.001 3.15(2.59–3.82) < 0.001 5.14(4.04–6.55) < 0.001 1.53(1.45–1.62) < 0.001 1.11(1.01–1.22) 0.029 Reference 1.24(0.94–1.63) 0.105 1.52(1.24–1.86) 0.002 2.00(1.46–2.75) < 0.001 1.20(1.11–1.30) < 0.001 CAP: Controlled attenuation parameter; NHR: neutrophil-to-high-density lipoprotein cholesterol ratio; NAFLD: non-alcoholic fatty liver disease. Model 1: no covariates were adjusted. Model 2:adjusted for age, sex, race. Model 3:adjusted for age, sex, race, smoking, diabetes, hypertension, history of CVD, BMI, TC, ALT, and uric acid. We performed weighted linear regression analyses with CAP as the outcome. When NHR was included as a continuous variable in the model for analysis, the results showed that CAP increased by 2.5 dB/m for each unit increase in NHR in the fully adjusted model (β = 2.5; 95% CI (0.51–4.5); P = 0.019). NHR was included as a categorical variable (quartiles) in the analysis model, and after adjusting for all confounding variables, CAP values increased significantly with higher levels of NHR (P for trend < 0.001), with participants in the fourth quartile group of NHR having the highest CAP values compared with the first quartile of NHR (β = 17; 95% CI ( 12 – 22 ); P < 0.001 ). Subsequently, we performed weighted logistic regression analyses with NAFLD as the outcome and showed that higher NHR was directly associated with increased prevalence of NAFLD. After fully adjusting for confounding variables, each unit increase in NHR was associated with an 11% increase in the prevalence of NAFLD (OR = 1.11; 95% CI (1.01–1.22); P < 0.05). Including NHR as a categorical variable (quartiles) in the analysis model showed that after adjusting for all confounding variables, the prevalence of NAFLD showed a gradual increase with increasing levels of NHR (P for trend < 0.001), with participants in the fourth quartile group of NHR having the highest risk of developing NAFLD compared with the first quartile of NHR (OR = 2.00; 95% CI (1.46–2.75); P < 0.001). Relationship between NHR and liver fibrosis Similarly, we analyzed the effect of NHR on LSM and liver fibrosis using weighted multivariate regression models. As presented in Table 3 , the unadjusted model indicated that each unit increase in NHR was associated with a 0.29 kPa increase in LSM (beta = 0.29; 95% CI: 0.22, 0.37; P < 0.001) and a 21% higher risk of liver fibrosis (OR = 1.21; 95% CI: 1.24, 1.29; P < 0.001). However, after adjustment for all confounders, the association between higher NHR and liver fibrosis was no longer statistically significant(Table 3 ). Table 3 Relationship between NHR and liver fibrosis. model1:β/OR (95% CI) P value model2: β/OR (95% CI) P value model3: β/OR (95% CI) P value LSM(kPa) NHR NHR (Quartile) Q1 Q2 Q3 Q4 P for trend 0.29(0.22–0.37) < 0.001 Reference 0.09(-0.26-0.43) 0.604 0.81(0.40–1.2) < 0.001 1.4(1.1–1.8) < 0.001 0.42(0.32–0.52) < 0.001 0.28(0.19–0.36) < 0.001 Reference 0.07(-0.28-0.41) 0.693 0.76(0.38–1.1) < 0.001 1.4(1.0-1.8) < 0.001 0.41(0.30–0.52) < 0.001 0.07(-0.04-0.18) 0.171 Reference -0.31(-0.64-0.01) 0.058 -0.02(-0.38-0.34) 0.896 0.27(-0.24-0.78) 0.253 0.11(-0.03-0.25) 0.10 Liver fibrosis NHR NHR (Quartile) 1.21(1.24–1.29) < 0.001 1.22(1.13–1.31) < 0.001 1.05(0.98–1.13) 0.178 Q1 Q2 Q3 Q4 P for trend Reference 1.58(1.01–2.47) 0.043 2.89(1.92–4.35) < 0.001 3.91(2.75–5.57) < 0.001 1.40(1.31–1.50) < 0.001 Reference 1.58(1.00-2.51) 0.051 2.90(1.91–4.41) < 0.001 4.01(2.77–5.80) < 0.001 1.41(1.32–1.51) < 0.001 Reference 1.07(0.60–1.90) 0.801 1.42(0.85–2.39) 0.157 1.49(0.84–2.63) 0.145 1.11(0.98–1.25) 0.086 NHR: neutrophil-to-high-density lipoprotein cholesterol ratio. Model 1: no covariates were adjusted Model 2:adjusted for age, sex, race Model 3:adjusted for age, sex, race, smoking, diabetes, hypertension, history of CVD, BMI, TC, ALT, and uric acid Potential non-linear relationship between NHR and NAFLD and liver fibrosis The potential non-linear associations of NHR with NAFLD and liver fibrosis were analyzed using the restricted cubic spline (RCS) model. As shown in Fig. 1 and Fig. 2 , there is a positive nonlinear association of NHR with both NAFLD and liver fibrosis (P-non-linear < 0.05). Figure 1 shows that the lower NHR values below 3.013 are associated with a reduced risk of developing NAFLD. In contrast. Figure 2 shows that while lower NHR levels do not significantly correlate with the risk of liver fibrosis, a notable increase in the risk of liver fibrosis was observed when NHR levels exceeded 3.013. Subgroup analyses We used stratified weighted multiple regression analyses to investigate the association of NHR with NAFLD and liver fibrosis in different population settings, dividing participants into subgroups based on gender, age, BMI, hypertension, diabetes mellitus, smoking, and history of cardiovascular disease for the analyses and the interaction tests, as displayed in Fig. 3 , among women, participants without diabetes mellitus and hypertension observed that between NHR and NAFLD There was a stronger positive correlation (P < 0.05). However, similar correlations between NHR and NAFLD were observed in different subgroups of age, smoking, BMI, and CVD. In addition, a significant correlation between NHR and liver fibrosis was observed in participants with BMI > 30(Fig. 4 ). Discussion This study evaluated the association between NHR and NAFLD and liver fibrosis in the American population. The results showed a significant positive association between NHR and NAFLD. In addition, although lower NHR levels were not significantly associated with the risk of hepatic fibrosis, the risk of hepatic fibrosis increased significantly when NHR exceeded 3.013. Subgroup analyses further revealed that the association between NHR and NAFLD was more significant in women and individuals without hypertension and diabetes, and the association between NHR and liver fibrosis was more prominent in participants with BMI > 30. Our study extends and supports previous findings. An earlier study involving 936 individuals from a Chinese population demonstrated that NHR was positively associated with the risk of ultrasound diagnosed NAFLD, suggesting that NHR may be a valid predictor of NAFLD( 20 ). Our study validated this association and assessed the prevalence and severity of NAFLD using data from a large-scale U.S. general population, employing the VCTE technology of the FibroScan device. Studies have shown that the accuracy of VCTE in diagnosing hepatic steatosis and fibrosis is comparable to liver biopsy( 21 , 22 ). This enhances the broad applicability and reliability of the results. The main features of non-alcoholic fatty liver disease (NAFLD) include hepatic lipid accumulation, inflammatory response, fibrosis formation, and hepatocyte injury( 2 ). Hepatic steatosis is the pathological basis for the progression of NAFLD, often accompanied by the onset of chronic inflammatory responses( 23 ). Studies have shown that chronic inflammation plays a vital role in developing NAFLD( 24 , 25 ). An analysis based on NHANES 2017–2018 showed that the neutrophil-to-albumin ratio (NPAR), a systemic marker of inflammation, was significantly associated with NAFLD and advanced liver fibrosis( 26 ). When immune cells such as neutrophils and lymphocytes are activated, they release pro-inflammatory cytokines that promote the development of NAFLD( 27 ). Neutrophils are the first immune cells to respond to inflammation, producing cytokines to promote lymphocyte activation and recruit macrophages, ultimately leading to chronic inflammation( 28 , 29 ). In addition, HDL-C reduces neutrophil activation, adhesion, spreading, and migration, inhibiting oxidized LDL production and exerting anti-inflammatory and antioxidant effects( 30 , 31 ). Therefore, NHR, as a combination of neutrophil numbers and HDL-C levels, may reflect the state of chronic inflammation and oxidative stress and serve as a sensitive indicator of the pathological process of NAFLD. Recent studies have shown that NHR is associated with the progression of several diseases, particularly cardiovascular and metabolic diseases( 32 – 34 ). Our analysis revealed a non-linear association of NHR with NAFLD and liver fibrosis, which may be related to the complex interaction of neutrophils and HDL-C in the development of metabolic diseases. In in vitro experiments, mice fed a high-fat diet showed increased neutrophil infiltration in the liver, accompanied by the development of hepatic steatosis and inflammation. Neutrophil depletion in mice using the 1A8 antibody significantly reduced liver triglyceride accumulation, hepatic inflammation, and fibrosis( 35 ). Thus, NHR may play an essential role in the progression of NAFLD, with high NHR levels strongly associated with increased severity of hepatic steatosis and fibrosis. In particular, the risk of liver fibrosis increases significantly when the NHR exceeds 3.013, probably due to exacerbation of chronic inflammation. Neutrophil accumulation has been linked to the progression of liver fibrosis and cirrhosis. During the development of liver inflammation and fibrosis, immature neutrophils with pro-inflammatory properties are released into the circulation, further exacerbating the inflammatory response and liver fibrosis( 36 ). In addition, several studies have shown that neutrophil elastase (NE), neutrophil granule protein (PR3), tissue protease G (CSTG), and other neutrophil-derived proteases play a critical role in the progression of hepatic steatosis and inflammation in NAFLD( 37 – 39 ). The role of myeloperoxidase (MPO) in the progression of liver fibrosis has also been demonstrated( 40 ). These mechanisms may explain the association between NHR and hepatic steatosis and fibrosis. Study strengths and limitations The main strength of this study is the use of large-scale data from the general population of the United States, with a large and nationally representative sample size. In addition, we used VCTE to assess hepatic steatosis and hepatic fibrosis, which provided greater diagnostic accuracy. However, this study has some limitations. Firstly, as a cross-sectional study, it was impossible to establish a causal relationship between NHR and hepatic steatosis and fibrosis. Second, although we adjusted for confounders as much as possible, there may still be potential confounding variables that were not considered. In addition, although VCTE demonstrated high accuracy in non-invasive diagnosis, its diagnostic accuracy compared to liver biopsy requires further validation. Future studies should adopt a longitudinal design to assess the long-term association between NHR and NAFLD progression and validate its predictive value. Conclusions In conclusion, this study found that higher NHR levels are significantly associated with an increased risk of NAFLD, especially among women and those without diabetes or hypertension. Furthermore, the risk of liver fibrosis rises markedly when NHR exceeds 3.013. Thus, NHR may serve as a valuable marker for detecting hepatic steatosis and fibrosis, facilitating early diagnosis and intervention for NAFLD. Abbreviations NAFLD Non-alcoholic fatty liver disease NHR Neutrophil-to-high-density lipoprotein cholesterol ratio NHANES National Health and Nutrition Examination Survey VCTE Vibration-controlled transient elastography CAP Controlled attenuation parameter LSM Liver stiffness measurement CVD Cardiovascular disease BMI Body mass index ALT Alanine aminotransferase AST Aspartate aminotransferase TG Triglyceride TC Total cholesterol Alb Albumin GGT γ-glutamyl transpeptidase HDL-C High-density lipoprotein cholesterol HbA1c Glycosylated hemoglobin A1c Declarations Ethics approval and consent to participate The NHANES database was approved by the National Center for Health Statistics (NCHS) Ethics Review Board, and all participants provided written informed consent. Competing interest The authors declare that they have no conflict of interest. Funding This work was supported by Capital’s Funds for Health Improvement and Research (2024-1-1203); Dengfeng Talent Support Program of Beijing Municipal Administration of Hospitals (No.DFL20221601); High-level Public Health Technical Personnel Construction Project (Subject leaders-03-21). Author Contribution ZN and LX contributed to the study's conception and design. ZN, LYY, LYY, and CXY performed data extraction and assembly. ZN, LYY, and LX analyzed and interpreted the data. ZN prepared figures 1-4. ZN and LX were responsible for writing and revising the manuscript. All authors (ZN, LX, LYY, CXY) reviewed and approved the final manuscript. Acknowledgments We are grateful for the dedication of the NHANES team and the valuable participation of all survey participants. Data Availability This research utilized the publicly accessible dataset from the National Health and Nutrition Examination Survey, which is available at the following link (https://wwwn.cdc.gov/nchs/nhanes/Default.aspx). References Huang DQ, El-Serag HB, Loomba R. Global epidemiology of NAFLD-related HCC: trends, predictions, risk factors and prevention. Nature reviews Gastroenterology & hepatology. 2021;18(4):223. doi: 10.1038/s41575-020-00381-6 . Friedman SL, Neuschwander-Tetri BA, Rinella M, Sanyal AJ. Mechanisms of NAFLD development and therapeutic strategies. Nat Med. 2018;24(7):908–922. doi: 10.1038/s41591-018-0104-9 . Perumpail BJ, Khan MA, Yoo ER, Cholankeril G, Kim D, Ahmed A. Clinical epidemiology and disease burden of nonalcoholic fatty liver disease. World J Gastroenterol. 2017;23(47):8263–8276. doi: 10.3748/wjg.v23.i47.8263 Shah PA, Patil R, Harrison SA. NAFLD-related hepatocellular carcinoma: The growing challenge. Hepatology (Baltimore, Md). 2023;77(1):323. doi: 10.1002/hep.32542 Simon TG, Roelstraete B, Khalili H, Hagström H, Ludvigsson JF. Mortality in Biopsy-Confirmed Nonalcoholic Fatty Liver Disease. Gut. 2021;70(7):1375–1382. doi: 10.1136/gutjnl-2020-322786 . Targher G, Byrne CD, Tilg H. NAFLD and increased risk of cardiovascular disease: clinical associations, pathophysiological mechanisms and pharmacological implications. Gut. 2020;69(9):1691–1705. doi: 10.1136/gutjnl-2020-320622 Luci C, Bourinet M, Leclère PS, Anty R, Gual P. Chronic Inflammation in Non-Alcoholic Steatohepatitis: Molecular Mechanisms and Therapeutic Strategies. Front Endocrinol (Lausanne). 2020;11:597648. doi: 10.3389/fendo.2020.597648 Herrero-Cervera A, Soehnlein O, Kenne E. Neutrophils in chronic inflammatory diseases. Cell Mol Immunol. 2022;19(2):177–191. doi: 10.1038/s41423-021-00832-3 Antonucci L, Porcu C, Timperi E, Santini SJ, Iannucci G, Balsano C. Circulating Neutrophils of Nonalcoholic Steatohepatitis Patients Show an Activated Phenotype and Suppress T Lymphocytes Activity. J Immunol Res. 2020;2020:4570219. doi: 10.1155/2020/4570219 Karami S, Poustchi H, Sarmadi N, et al. Association of anti-oxidative capacity of HDL with subclinical atherosclerosis in subjects with and without non-alcoholic fatty liver disease. Diabetol Metab Syndr. 2021;13:121. doi: 10.1186/s13098-021-00741-5 Deprince A, Haas JT, Staels B. Dysregulated lipid metabolism links NAFLD to cardiovascular disease. Mol Metab. 2020;42:101092. doi: 10.1016/j.molmet.2020.101092 Li R, Kong D, Ye Z, et al. Correlation of multiple lipid and lipoprotein ratios with nonalcoholic fatty liver disease in patients with newly diagnosed type 2 diabetic mellitus: A retrospective study. Front Endocrinol (Lausanne). 2023;14:1127134. doi: 10.3389/fendo.2023.1127134 Kou T, Luo H, Yin L. Relationship between neutrophils to HDL-C ratio and severity of coronary stenosis. BMC Cardiovasc Disord. 2021;21:127. doi: 10.1186/s12872-020-01771-z Chen T, Chen H, Xiao H, et al. Comparison of the Value of Neutrophil to High-Density Lipoprotein Cholesterol Ratio and Lymphocyte to High-Density Lipoprotein Cholesterol Ratio for Predicting Metabolic Syndrome Among a Population in the Southern Coast of China. Diabetes Metab Syndr Obes. 2020;13:597–605. doi: 10.2147/DMSO.S238990 Shi K, Hou J, Zhang Q, Bi Y, Zeng X, Wang X. Neutrophil-to-high-density-lipoprotein-cholesterol ratio and mortality among patients with hepatocellular carcinoma. Frontiers in Nutrition. 2023;10. doi: 10.3389/fnut.2023.1127913 Liu Z, Fan Q, Wu S, Wan Y, Lei Y. Compared with the monocyte to high-density lipoprotein ratio (MHR) and the neutrophil to lymphocyte ratio (NLR), the neutrophil to high-density lipoprotein ratio (NHR) is more valuable for assessing the inflammatory process in Parkinson’s disease. Lipids Health Dis. 2021;20:35. doi: 10.1186/s12944-021-01462-4 Xie R, Xiao M, Li L, et al. Association between SII and hepatic steatosis and liver fibrosis: A population-based study. Front Immunol. 2022;13:925690. doi: 10.3389/fimmu.2022.925690 Xie R, Liu M. Relationship Between Non-Alcoholic Fatty Liver Disease and Degree of Hepatic Steatosis and Bone Mineral Density. Front Endocrinol (Lausanne). 2022;13:857110. doi: 10.3389/fendo.2022.857110 Eddowes PJ, Sasso M, Allison M, et al. Accuracy of FibroScan Controlled Attenuation Parameter and Liver Stiffness Measurement in Assessing Steatosis and Fibrosis in Patients with Nonalcoholic Fatty Liver Disease. Gastroenterology. 2019;156(6):1717–1730. doi: 10.1053/j.gastro.2019.01.042 Jia Z, Li Z, Chen S. The Association Between Neutrophil/High-Density Lipoprotein Cholesterol Ratio and Non-Alcoholic Fatty Liver Disease in a Healthy Population. Diabetes, Metabolic Syndrome and Obesity. 2024;17:2597. doi: 10.2147/DMSO.S464406 Selvaraj EA, Mózes FE, Jayaswal ANA, et al. Diagnostic accuracy of elastography and magnetic resonance imaging in patients with NAFLD: A systematic review and meta-analysis. Journal of Hepatology. 2021;75(4):770–785. doi: 10.1016/j.jhep.2021.04.044 Boursier J, Hagström H, Ekstedt M, et al. Non-invasive tests accurately stratify patients with NAFLD based on their risk of liver-related events. Journal of Hepatology. 2022;76(5):1013–1020. doi: 10.1016/j.jhep.2021.12.031 Bessone F, Razori MV, Roma MG. Molecular pathways of nonalcoholic fatty liver disease development and progression. Cell Mol Life Sci. 2018;76(1):99–128. doi: 10.1007/s00018-018-2947-0 Petermann-Rocha F, Wirth MD, Boonpor J, et al. Associations between an inflammatory diet index and severe non-alcoholic fatty liver disease: a prospective study of 171,544 UK Biobank participants. BMC Med. 2023;21:123. doi: 10.1186/s12916-023‐02793‐y Gong H, He Q, Zhu L, et al. Associations between systemic inflammation indicators and nonalcoholic fatty liver disease: evidence from a prospective study. Front Immunol. 2024;15:1389967. doi: 10.3389/fimmu.2024.1389967 Liu CF, Chien LW. Predictive Role of Neutrophil-Percentage-to-Albumin Ratio (NPAR) in Nonalcoholic Fatty Liver Disease and Advanced Liver Fibrosis in Nondiabetic US Adults: Evidence from NHANES 2017–2018. Nutrients. 2023;15(8):1892. doi: 10.3390/nu15081892 Hwang S, Yun H, Moon S, Cho YE, Gao B. Role of Neutrophils in the Pathogenesis of Nonalcoholic Steatohepatitis. Front Endocrinol (Lausanne). 2021;12:751802. doi: 10.3389/fendo.2021.751802 Mantovani A, Cassatella MA, Costantini C, Jaillon S. Neutrophils in the activation and regulation of innate and adaptive immunity. Nat Rev Immunol. 2011;11(8):519–531. doi: 10.1038/nri3024 Borregaard N. Neutrophils, from Marrow to Microbes. Immunity. 2010;33(5):657–670. doi: 10.1016/j.immuni.2010.11.011 Murphy AJ, Woollard KJ, Suhartoyo A, et al. Neutrophil Activation Is Attenuated by High-Density Lipoprotein and Apolipoprotein A-I in In Vitro and In Vivo Models of Inflammation. ATVB. 2011;31(6):1333–1341. doi: 10.1161/ATVBAHA.111.226258 Curcic S, Holzer M, Frei R, et al. Neutrophil effector responses are suppressed by secretory phospholipase A2 modified HDL. Biochimica et biophysica acta. 2015;1851(2):184. doi: 10.1016/j.bbalip.2014.11.010 Huang JB, Chen YS, Ji HY, et al. Neutrophil to high-density lipoprotein ratio has a superior prognostic value in elderly patients with acute myocardial infarction: a comparison study. Lipids in Health and Disease. doi: 10.1186/s12944-020-01238-2 Kou T, Luo H, Yin L. Relationship between neutrophils to HDL-C ratio and severity of coronary stenosis. BMC Cardiovascular Disorders. doi: 10.1186/s12872-020-01771-z Chen T, Chen H, Xiao H, et al. Comparison of the Value of Neutrophil to High-Density Lipoprotein Cholesterol Ratio and Lymphocyte to High-Density Lipoprotein Cholesterol Ratio for Predicting Metabolic Syndrome Among a Population in the Southern Coast of China. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy. 2020;13:597. doi: 10.2147/DMSO.S238990 Ou R, Liu J, Lv M, et al. Neutrophil depletion improves diet-induced non-alcoholic fatty liver disease in mice. Endocrine. 2017;57(1):72–82. doi: 10.1007/s12020-017-1323-4 Maretti-Mira AC, Salomon MP, Chopra S, Yuan L, Golden-Mason L. Circulating Neutrophil Profiles Undergo a Dynamic Shift during Metabolic Dysfunction-Associated Steatohepatitis (MASH) Progression. Biomedicines. 2024;12(5):1105. doi: 10.1155/2020/4570219 Talukdar S, Oh DY, Bandyopadhyay G, et al. Neutrophils mediate insulin resistance in high fat diet fed mice via secreted elastase. Nat Med. 2012;18(9):1407–1412. doi: 10.1038/nm.2885 Mirea AM, Toonen EJM, van den Munckhof I, et al. Increased proteinase 3 and neutrophil elastase plasma concentrations are associated with non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes. Mol Med. 2019;25:16. doi: 10.1186/s10020-019-0084-3 Toonen EJ, Mirea AM, Tack CJ, et al. Activation of Proteinase 3 Contributes to Nonalcoholic Fatty Liver Disease and Insulin Resistance. Mol Med. 2016;22:202–214. doi: 10.2119/molmed.2016.00033 Pulli B, Ali M, Iwamoto Y, et al. Myeloperoxidase–Hepatocyte–Stellate Cell Cross Talk Promotes Hepatocyte Injury and Fibrosis in Experimental Nonalcoholic Steatohepatitis. Antioxid Redox Signal. 2015;23(16):1255–1269. doi: 10.1089/ars.2014.6108 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-5308727","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":369815716,"identity":"132842c2-9d83-4a0a-83f5-9c391533479b","order_by":0,"name":"Na Zhu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Zhu","suffix":""},{"id":369815717,"identity":"b744c173-3b66-4fe3-afa0-7f293bd9f21a","order_by":1,"name":"Yanyan Li","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanyan","middleName":"","lastName":"Li","suffix":""},{"id":369815718,"identity":"d684e13f-2661-40f9-b1b8-81ca9ef99376","order_by":2,"name":"Yingying Lin","email":"","orcid":"","institution":"Peking University Ditan Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yingying","middleName":"","lastName":"Lin","suffix":""},{"id":369815719,"identity":"dc66d70e-0aaf-48f7-87dd-6cec182e3ddf","order_by":3,"name":"XinYu Cui","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"XinYu","middleName":"","lastName":"Cui","suffix":""},{"id":369815720,"identity":"b0e69a9e-215e-4ea5-9c8d-be5e6220c56f","order_by":4,"name":"Xin Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYBACPhCRwGAD5bIRoYUNoiWNVC0MDIdJ0cLe/Ezi4Y7z8vzTzhgwfCg7zMA/u4GAFp5jxgaJZ24bzridY8A449xhBok7BwhokUgwfJDYdjvBQDrHgJm37TCDgUQCAS3yzz8cSGw7B9HylygtEjwgWw5AtDASpYUnp9ggsS0Z6Je0goM959J5JG4Q0MLPfnyb5M82O3n+2ckbH/wos5bjn0FACwo4AMQ8JKgfBaNgFIyCUYALAACrATyfQApZYwAAAABJRU5ErkJggg==","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xin","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-10-22 05:53:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5308727/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5308727/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67614163,"identity":"ba34d758-67da-4657-be88-944c70e8e336","added_by":"auto","created_at":"2024-10-28 06:18:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":175105,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline (RCS) plot of the non-linear relationship between NHR and NAFLD risk. Nonlinear relationships were detected after age, sex, race, smoking, diabetes, hypertension, history of CVD, BMI, TC, ALT, and uric acid. (NHR: neutrophil-to-high-density lipoprotein cholesterol ratio; NAFLD: non-alcoholic fatty liver disease; CVD, cardiovascular disease; BMI, Body mass index)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5308727/v1/94378f8ba131bfde57d71de6.png"},{"id":67614162,"identity":"274679d1-eb88-4985-9e5f-cd13d181cae4","added_by":"auto","created_at":"2024-10-28 06:18:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":186439,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline (RCS) plot of the non-linear relationship between NHR and risk of liver fibrosis. Nonlinear relationships were detected after age, sex, race, smoking, diabetes, hypertension, history of CVD, BMI, TC, ALT, and uric acid. (NHR: neutrophil-to-high-density lipoprotein cholesterol ratio; NAFLD: non-alcoholic fatty liver disease; CVD, cardiovascular disease; BMI, Body mass index)\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5308727/v1/6fa1167d791b002165f2c8f8.png"},{"id":67614166,"identity":"2691dc39-e25b-4512-bbf3-7677ea19fe42","added_by":"auto","created_at":"2024-10-28 06:18:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":697036,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of the association between NHR and NAFLD. (NHR: neutrophil-to-high-density lipoprotein cholesterol ratio; NAFLD: non-alcoholic fatty liver disease; CVD, cardiovascular disease; BMI, Body mass index)\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5308727/v1/434968a533d036e92cac46b9.png"},{"id":67616159,"identity":"06669162-e216-4785-a9b9-9a6edddeddc5","added_by":"auto","created_at":"2024-10-28 06:26:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":716101,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of the association between NHR and liver fibrosis. (NHR: neutrophil-to-high-density lipoprotein cholesterol ratio; CVD, cardiovascular disease; BMI, Body mass index.)\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5308727/v1/0d9a0eed449c6fb9e2e9301f.png"},{"id":67616786,"identity":"1e1333a0-6716-41fc-9c65-12d6da0a4727","added_by":"auto","created_at":"2024-10-28 06:34:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2464734,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5308727/v1/14c63e3d-4fc6-403c-9f28-37fb9c2190c1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between neutrophil to high-density lipoprotein cholesterol ratio and risk of non-alcoholic fatty liver disease and liver fibrosis: A cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith the increasing prevalence of metabolic conditions such as obesity and type 2 diabetes, non-alcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease in the world, affecting approximately 25% of the global population (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The pathological features of NAFLD include hepatocellular steatosis, inflammatory cell infiltration, and different degrees of hepatic fibrosis(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In its early stages, NAFLD may present as simple fatty liver or non-alcoholic steatohepatitis (NASH). As the disease progresses, it may evolve into liver fibrosis, cirrhosis, and hepatocellular carcinoma(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). For patients with NAFLD who have not progressed to liver fibrosis, cardiovascular disease, and extrahepatic malignancies are the leading causes of death. However, for those patients who have progressed to advanced hepatic fibrosis, the leading cause of death is liver-related diseases(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Therefore, early detection of NAFLD and liver fibrosis is critical for timely intervention and effective management.\u003c/p\u003e \u003cp\u003eInflammatory response and abnormal lipid metabolism are critical in causing and developing NAFLD(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). As an essential component of the innate immune system, neutrophils play a central role in fighting infections. They are closely associated with various chronic inflammatory diseases, especially metabolic diseases such as obesity, type 2 diabetes, and NAFLD(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Several studies have shown that the degree of neutrophil infiltration in liver tissue of NAFLD patients is positively correlated with the severity of the lesion. Neutrophil infiltration can directly lead to hepatocellular injury and accelerate the progression of NAFLD to hepatic fibrosis and cirrhosis by promoting fibrosis(\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). NAFLD begins with excessive accumulation of triglycerides in the hepatocytes and is accompanied by a reduction in plasma cholesterol levels associated with antiatherosclerosis high-density lipoproteins(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). HDL-C is critical in developing NAFLD through lipid metabolism modulation and reverse cholesterol promotion. HDL-C exerts anti-inflammatory and antioxidant effects by regulating lipid metabolism, promoting reverse cholesterol transport, and reducing the production of inflammatory mediators(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Therefore, reducing HDL-C levels in NAFLD may weaken its anti-inflammatory and antioxidant functions and thus exacerbate the occurrence and progression of NAFLD(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In recent years, NHR has been proposed as a comprehensive indicator of inflammation and lipid metabolic status. It has been shown that NHR is significantly associated with cardiovascular disease, metabolic syndrome, hepatocellular carcinoma, and Parkinson's disease(\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), but its potential relationship with NAFLD has not been fully investigated.\u003c/p\u003e \u003cp\u003eThis study used data from the 2017\u0026ndash;2020 National Health and Nutrition Examination Survey (NHANES) in a cross-sectional analysis to investigate the association between NHR and both NAFLD and liver fibrosis.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003estudy population\u003c/h2\u003e \u003cp\u003eNHANES is a long-term, large-scale health survey program initiated and conducted every two years by the National Center for Health Statistics (NCHS), a division of the Centers for Disease Control and Prevention (CDC). Through a complex, multistage sampling design, NHANES collects health and nutrition data representative of the United States population, including personal interviews, physical examinations, and laboratory test results. NHANES data are widely used to study public health trends, assess the burden of disease and nutritional status, and are freely available to researchers worldwide.\u003c/p\u003e \u003cp\u003eThis study analyzed pre-epidemic data from the 2017\u0026ndash;2020 NHANES survey, which initially included 15,560 participants. From this cohort, 8,317 individuals aged 18 years and older with vibration-controlled transient elastography (VCTE) results were selected. We excluded 219 participants with unreliable VCTE measurements (liver stiffness quartile/median ratio\u0026thinsp;\u0026ge;\u0026thinsp;30%), 283 with hepatitis B or C, 821 with excessive alcohol intake (defined as more than two standard drinks per day for women and more than three for men), and those with missing data on neutrophil or high-density lipoprotein cholesterol (HDL-C). Consequently, 6526 participants were included in the final analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurement of hepatic steatosis and hepatic fibrosis\u003c/h3\u003e\n\u003cp\u003eNHANES staff use the FibroScan 502 Touch device to assess liver stiffness and fat content. The device measures liver elasticity and stiffness through vibration-controlled transient elastography (VCTE) technology to help determine the extent of liver fibrosis. At the same time, the device measures hepatic steatosis by ultrasound attenuation and records the Controlled Attenuation Parameter (CAP) as an indicator of hepatic fat content. Previous studies define a CAP value of \u0026ge;\u0026thinsp;274 dB/m as a diagnostic criterion for NAFLD. A CAP value of \u0026ge;\u0026thinsp;302 dB/m indicates severe hepatic steatosis(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In addition, liver stiffness measurements (LSM) of \u0026ge;\u0026thinsp;8.2 kPa, \u0026ge;\u0026thinsp;9.7 kPa, and \u0026ge;\u0026thinsp;13.7 kPa represented the F2, F3, and F4 stages of liver fibrosis, respectively(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eVariable\u003c/h3\u003e\n\u003cp\u003eDemographic and clinical data were extracted from the NHANES database. Age, sex, race, educational level, body mass index (BMI), diabetes, hypertension, history of cardiovascular disease, smoking status, and laboratory variables were included. Diabetes mellitus was defined as HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5% or fasting glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL; in addition, participants had diabetes if they answered, \u0026ldquo;yes\u0026rdquo; to any of the following questions: \u0026ldquo;Do you use insulin?\u0026rdquo; or \u0026ldquo;Has your doctor told you that you have diabetes?\u0026rdquo; or \u0026ldquo;Do you take glucose-lowering medication?\u0026rdquo;, Hypertension was defined as a mean systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or a mean diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg on three consecutive measurements, and participants who responded to the questions \u0026ldquo;Have you been told you have high blood pressure on two or more occasions\u0026rdquo; or \u0026ldquo;Do you have to take prescription medication for high blood pressure?\u0026rdquo; A \u0026ldquo;yes\u0026rdquo; response was also defined as hypertension. A history of cardiovascular disease was described as a response confirming a physician's diagnosis of myocardial infarction, angina pectoris, coronary heart disease, congestive heart failure, or stroke. Smoking status was categorized as a smoker or never smoker based on having smoked fewer than 100 cigarettes in their lifetime.\u003c/p\u003e \u003cp\u003eLaboratory tests included measurements of alanine aminotransferase (ALT), aspartate aminotransferase (AST), total cholesterol (TC), triglycerides (TG), uric acid, albumin (Alb), glycosylated hemoglobin (HbA1c), γ-glutamyltranspeptidase (GGT) and high-density lipoprotein cholesterol (HDL-C). The neutrophil-to-high-density lipoprotein cholesterol ratio (NHR) was calculated by dividing the neutrophil count by the HDL-C level.\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eConsidering NHANES's complicated multistage sampling design, sample weights were applied in all analyses to ensure that the results were representative of the US population. Participants were divided into four groups according to NHR quartiles. Continuous variables are presented as weighted means with standard errors, while categorical variables are presented as unweighted counts and weighted percentages. One-way ANOVA for continuous variables and weighted chi-squared tests for categorical variables were used to compare differences between NHR quartiles.\u003c/p\u003e \u003cp\u003eNHR was analyzed as a continuous and categorical variable, with exposure variables grouped by quartiles (the first quartile served as the reference group). Outcome variables included liver steatosis parameters (CAP), NAFLD, liver stiffness measurements (LSM), and liver fibrosis. We used weighted linear regression and weighted logistic regression models for the analyses. In addition, we assessed potential non-linear associations between NHR and the prevalence of NAFLD and liver fibrosis using restricted cubic spline (RCS) analysis. The RCS model was adjusted for several confounders, including age, sex, ethnicity, smoking history, diabetes, hypertension, cardiovascular disease (CVD), body mass index (BMI), total cholesterol (TC), alanine aminotransferase (ALT), and uric acid. Subgroup analyses were conducted by stratifying participants according to age, gender, BMI, presence of hypertension, diabetes, and history of cardiovascular disease (CVD). All data analyses were done using R software (version 4.4.0), and the statistical significance level was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of study participants\u003c/h2\u003e \u003cp\u003eThis study included 6526 participants. The mean age of the participants was 47.81\u0026thinsp;\u0026plusmn;\u0026thinsp;17.82 years, with 49.6% males and 50.4% females. The prevalence of NAFLD and liver fibrosis was 44.1% and 8.9%, respectively. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of the study population, grouped according to quartiles of NHR.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of study participants (grouped according to NHR quartile).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;6526)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1631,\u003c/p\u003e \u003cp\u003eNHR\u0026thinsp;\u0026lt;\u0026thinsp;2.07)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1637,\u003c/p\u003e \u003cp\u003e2.07\u0026thinsp;\u0026le;\u0026thinsp;NHR\u0026thinsp;\u0026lt;\u0026thinsp;3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1627,\u003c/p\u003e \u003cp\u003e3\u0026thinsp;\u0026le;\u0026thinsp;NHR\u0026thinsp;\u0026lt;\u0026thinsp;4.2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1631,\u003c/p\u003e \u003cp\u003eNHR\u0026thinsp;\u0026ge;\u0026thinsp;4.2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e47.81\u0026thinsp;\u0026plusmn;\u0026thinsp;17.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e49.24\u0026thinsp;\u0026plusmn;\u0026thinsp;17.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e48.48\u0026thinsp;\u0026plusmn;\u0026thinsp;17.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e47.61\u0026thinsp;\u0026plusmn;\u0026thinsp;18.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e46.14\u0026thinsp;\u0026plusmn;\u0026thinsp;17.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender(%)\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3251(49.6%)\u003c/p\u003e \u003cp\u003e3275(50.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e641(36.3%)\u003c/p\u003e \u003cp\u003e990(63.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e776(46.1%)\u003c/p\u003e \u003cp\u003e861(53.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e878(53.8%)\u003c/p\u003e \u003cp\u003e749(46.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e956(60.0%)\u003c/p\u003e \u003cp\u003e675(40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace(%)\u003c/p\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2210(61.2%)\u003c/p\u003e \u003cp\u003e1610(10.8%)\u003c/p\u003e \u003cp\u003e711(7.9%)\u003c/p\u003e \u003cp\u003e1995(20.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e422(56.9%)\u003c/p\u003e \u003cp\u003e679(20.5%)\u003c/p\u003e \u003cp\u003e134(6.4%)\u003c/p\u003e \u003cp\u003e396(16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e537(61.0%)\u003c/p\u003e \u003cp\u003e408(11.3%)\u003c/p\u003e \u003cp\u003e160(7.2%)\u003c/p\u003e \u003cp\u003e532(20.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e590(63.5%)\u003c/p\u003e \u003cp\u003e309(7.5%)\u003c/p\u003e \u003cp\u003e220(9.1%)\u003c/p\u003e \u003cp\u003e508(19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e661(62.8%)\u003c/p\u003e \u003cp\u003e214(5.4%)\u003c/p\u003e \u003cp\u003e197(8.6%)\u003c/p\u003e \u003cp\u003e559(23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level(%)\u003c/p\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003cp\u003eHigh school or above high school\u003c/p\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1215(11.6%)\u003c/p\u003e \u003cp\u003e4977(85.2%)\u003c/p\u003e \u003cp\u003e334(3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e211(7.0%)\u003c/p\u003e \u003cp\u003e1345(90.5%)\u003c/p\u003e \u003cp\u003e75(2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e312(12.0%)\u003c/p\u003e \u003cp\u003e1243(85.1%)\u003c/p\u003e \u003cp\u003e82(2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e331(12.1%)\u003c/p\u003e \u003cp\u003e1207(83.7%)\u003c/p\u003e \u003cp\u003e89(4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e361(14.8%)\u003c/p\u003e \u003cp\u003e1182(82.0%)\u003c/p\u003e \u003cp\u003e88(3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension(%)\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eDiabetes(%)\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eHistory of CVD(%)\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2700(36%)\u003c/p\u003e \u003cp\u003e3826(64%)\u003c/p\u003e \u003cp\u003e1321(15.7%)\u003c/p\u003e \u003cp\u003e5205(84.3%)\u003c/p\u003e \u003cp\u003e715(9.1%)\u003c/p\u003e \u003cp\u003e5811(90.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e597(28.6%)\u003c/p\u003e \u003cp\u003e1034(71.4%)\u003c/p\u003e \u003cp\u003e178(6.7%)\u003c/p\u003e \u003cp\u003e1453(93.3%)\u003c/p\u003e \u003cp\u003e140(6.0%)\u003c/p\u003e \u003cp\u003e1491(94.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e674(34.6%)\u003c/p\u003e \u003cp\u003e963(65.4%)\u003c/p\u003e \u003cp\u003e295(11.4%)\u003c/p\u003e \u003cp\u003e1342(88.6%)\u003c/p\u003e \u003cp\u003e158(8.1%)\u003c/p\u003e \u003cp\u003e1479(91.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e711(37.4%)\u003c/p\u003e \u003cp\u003e916(62.6%)\u003c/p\u003e \u003cp\u003e371(17.7%)\u003c/p\u003e \u003cp\u003e1256(82.3%)\u003c/p\u003e \u003cp\u003e192(10.0%)\u003c/p\u003e \u003cp\u003e1435(90.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e718(42.5%)\u003c/p\u003e \u003cp\u003e913(57.5%)\u003c/p\u003e \u003cp\u003e477(25.6%)\u003c/p\u003e \u003cp\u003e1154(74.4%)\u003c/p\u003e \u003cp\u003e225(11.8%)\u003c/p\u003e \u003cp\u003e1406(88.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status(%)\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eBMI (kg/m2),(%)\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003cp\u003e25\u0026ndash;30\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003cp\u003eALT(U/L)\u003c/p\u003e \u003cp\u003eAST(U/L)\u003c/p\u003e \u003cp\u003eTG(mmol/L)\u003c/p\u003e \u003cp\u003eTC(mmol/L)\u003c/p\u003e \u003cp\u003eUric acid (mg/dl)\u003c/p\u003e \u003cp\u003eAlb(g/L)\u003c/p\u003e \u003cp\u003eHbA1c(%)\u003c/p\u003e \u003cp\u003eGGT(U/L)\u003c/p\u003e \u003cp\u003eHDL-C(mmol/L)\u003c/p\u003e \u003cp\u003eNHR\u003c/p\u003e \u003cp\u003eCAP(dB/m)\u003c/p\u003e \u003cp\u003eLSM(kPa)\u003c/p\u003e \u003cp\u003eNAFLD,n(%)\u003c/p\u003e \u003cp\u003eSevere steatosis\u003c/p\u003e \u003cp\u003eLiver fibrosis,(%)\u003c/p\u003e \u003cp\u003eF2\u003c/p\u003e \u003cp\u003eF3\u003c/p\u003e \u003cp\u003eF4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2578(41.4%)\u003c/p\u003e \u003cp\u003e3948(58.6%)\u003c/p\u003e \u003cp\u003e1752(27.2%)\u003c/p\u003e \u003cp\u003e2105(31.6%)\u003c/p\u003e \u003cp\u003e2669(41.2%)\u003c/p\u003e \u003cp\u003e22.42\u0026thinsp;\u0026plusmn;\u0026thinsp;16.91\u003c/p\u003e \u003cp\u003e21.46\u0026thinsp;\u0026plusmn;\u0026thinsp;11.36\u003c/p\u003e \u003cp\u003e1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003cp\u003e4.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003cp\u003e5.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e \u003cp\u003e41.09\u0026thinsp;\u0026plusmn;\u0026thinsp;3.22\u003c/p\u003e \u003cp\u003e5.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003cp\u003e28.88\u0026thinsp;\u0026plusmn;\u0026thinsp;37.46\u003c/p\u003e \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003cp\u003e3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e \u003cp\u003e264.85\u0026thinsp;\u0026plusmn;\u0026thinsp;63.13\u003c/p\u003e \u003cp\u003e5.78\u0026thinsp;\u0026plusmn;\u0026thinsp;4.69\u003c/p\u003e \u003cp\u003e2839(44.1%)\u003c/p\u003e \u003cp\u003e1850(29.0%)\u003c/p\u003e \u003cp\u003e610(8.9%)\u003c/p\u003e \u003cp\u003e233(3.4%)\u003c/p\u003e \u003cp\u003e214(3.0%)\u003c/p\u003e \u003cp\u003e163(2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e520(32.1%)\u003c/p\u003e \u003cp\u003e1111(67.9%)\u003c/p\u003e \u003cp\u003e697(48.2%)\u003c/p\u003e \u003cp\u003e511(32.0%)\u003c/p\u003e \u003cp\u003e423(19.8%)\u003c/p\u003e \u003cp\u003e19.49\u0026thinsp;\u0026plusmn;\u0026thinsp;16.52\u003c/p\u003e \u003cp\u003e22.23\u0026thinsp;\u0026plusmn;\u0026thinsp;13.88\u003c/p\u003e \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003cp\u003e4.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003cp\u003e4.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37\u003c/p\u003e \u003cp\u003e41.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08\u003c/p\u003e \u003cp\u003e5.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003cp\u003e26.23\u0026thinsp;\u0026plusmn;\u0026thinsp;38.25\u003c/p\u003e \u003cp\u003e1.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003cp\u003e235.40\u0026thinsp;\u0026plusmn;\u0026thinsp;53.65\u003c/p\u003e \u003cp\u003e5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;2.72\u003c/p\u003e \u003cp\u003e427(24.2%)\u003c/p\u003e \u003cp\u003e224(12.5%)\u003c/p\u003e \u003cp\u003e82(4.0%)\u003c/p\u003e \u003cp\u003e33(1.5%)\u003c/p\u003e \u003cp\u003e27(1.3%)\u003c/p\u003e \u003cp\u003e22(1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e615(39.0%)\u003c/p\u003e \u003cp\u003e1022(61.0%)\u003c/p\u003e \u003cp\u003e487(29.6%)\u003c/p\u003e \u003cp\u003e580(35.9%)\u003c/p\u003e \u003cp\u003e570(34.5%)\u003c/p\u003e \u003cp\u003e20.87\u0026thinsp;\u0026plusmn;\u0026thinsp;14.81\u003c/p\u003e \u003cp\u003e21.11\u0026thinsp;\u0026plusmn;\u0026thinsp;10.24\u003c/p\u003e \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003cp\u003e4.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e \u003cp\u003e5.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e \u003cp\u003e41.31\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15\u003c/p\u003e \u003cp\u003e5.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003cp\u003e27.09\u0026thinsp;\u0026plusmn;\u0026thinsp;43.05\u003c/p\u003e \u003cp\u003e1.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003cp\u003e2.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003cp\u003e253.76\u0026thinsp;\u0026plusmn;\u0026thinsp;57.30\u003c/p\u003e \u003cp\u003e5.26\u0026thinsp;\u0026plusmn;\u0026thinsp;3.57\u003c/p\u003e \u003cp\u003e615(37.3%)\u003c/p\u003e \u003cp\u003e359(21.0%)\u003c/p\u003e \u003cp\u003e126(6.1%)\u003c/p\u003e \u003cp\u003e55(2.9%)\u003c/p\u003e \u003cp\u003e41(2.1%)\u003c/p\u003e \u003cp\u003e30(1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e670(44.5%)\u003c/p\u003e \u003cp\u003e957(55.5%)\u003c/p\u003e \u003cp\u003e343(20.3%)\u003c/p\u003e \u003cp\u003e542(30.5%)\u003c/p\u003e \u003cp\u003e742(49.2%)\u003c/p\u003e \u003cp\u003e23.24\u0026thinsp;\u0026plusmn;\u0026thinsp;15.92\u003c/p\u003e \u003cp\u003e21.27\u0026thinsp;\u0026plusmn;\u0026thinsp;10.79\u003c/p\u003e \u003cp\u003e1.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.18\u003c/p\u003e \u003cp\u003e4.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e \u003cp\u003e5.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e \u003cp\u003e41.18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003c/p\u003e \u003cp\u003e5.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e \u003cp\u003e29.70\u0026thinsp;\u0026plusmn;\u0026thinsp;36.37\u003c/p\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003cp\u003e3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003cp\u003e274.95\u0026thinsp;\u0026plusmn;\u0026thinsp;62.05\u003c/p\u003e \u003cp\u003e5.98\u0026thinsp;\u0026plusmn;\u0026thinsp;5.06\u003c/p\u003e \u003cp\u003e804(50.1%)\u003c/p\u003e \u003cp\u003e549(34.6%)\u003c/p\u003e \u003cp\u003e177(10.6%)\u003c/p\u003e \u003cp\u003e74(4.5%)\u003c/p\u003e \u003cp\u003e55(2.6%)\u003c/p\u003e \u003cp\u003e48(3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e773(48.8%)\u003c/p\u003e \u003cp\u003e858(51.2%)\u003c/p\u003e \u003cp\u003e225(13.6%)\u003c/p\u003e \u003cp\u003e472(28.2%)\u003c/p\u003e \u003cp\u003e934(58.2%)\u003c/p\u003e \u003cp\u003e25.62\u0026thinsp;\u0026plusmn;\u0026thinsp;19.34\u003c/p\u003e \u003cp\u003e21.31\u0026thinsp;\u0026plusmn;\u0026thinsp;10.46\u003c/p\u003e \u003cp\u003e2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e \u003cp\u003e4.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e \u003cp\u003e5.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e \u003cp\u003e40.73\u0026thinsp;\u0026plusmn;\u0026thinsp;3.39\u003c/p\u003e \u003cp\u003e5.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003cp\u003e32.07\u0026thinsp;\u0026plusmn;\u0026thinsp;31.33\u003c/p\u003e \u003cp\u003e1.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003cp\u003e5.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78\u003c/p\u003e \u003cp\u003e290.78\u0026thinsp;\u0026plusmn;\u0026thinsp;64.17\u003c/p\u003e \u003cp\u003e6.61\u0026thinsp;\u0026plusmn;\u0026thinsp;6.22\u003c/p\u003e \u003cp\u003e993(61.6%)\u003c/p\u003e \u003cp\u003e718(45.2%)\u003c/p\u003e \u003cp\u003e225(13.9%)\u003c/p\u003e \u003cp\u003e71(4.3%)\u003c/p\u003e \u003cp\u003e91(5.6%)\u003c/p\u003e \u003cp\u003e63(3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e0.255\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e0.017\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e0.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e0.005\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE; categorical variables are presented as unweighted counts (weighted percentages). CVD, cardiovascular disease; MI, Body mass index; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase\u0026rsquo;s, Triglyceride; TC, Total cholesterol; Alb, Albumin; GGT, γ-glutamyl transpeptidase; HDL-C, High-density lipoprotein cholesterol, Neutrophil/High-Density Lipoprotein Cholesterol Ratio; CAP, Controlled attenuation parameter; HbA1c, Glycosylated hemoglobin A1c; LSM, Liver stiffness measurement; NAFLD, Nonalcoholic fatty liver disease\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results showed that the prevalence of CAP, LSM, NAFLD, severe hepatic steatosis, hepatic fibrosis, and the severity of hepatic fibrosis gradually increased with increasing NHR (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In addition, it was found that there were significant differences between participants with different NHR levels in terms of age, gender, race, education level, hypertension, diabetes mellitus, smoking status, history of cardiovascular disease, BMI, TG, TC, ALT, Alb, GGT, HbA1c, HDL-C, and uric acid (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociation of NHR with NAFLD\u003c/h3\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, we analyzed the effect of NHR on CAP and NAFLD using weighted multiple regression models adjusted for all possible confounding variables (age, sex, race, smoking, diabetes, hypertension, history of CVD, BMI, TC, ALT, and uric acid).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of NHR with NAFLD.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emodel1:β/OR (95% CI) P value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emodel2: β/OR (95% CI) P value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emodel3: β/OR (95% CI) P value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAP(dB/m)\u003c/p\u003e \u003cp\u003eNHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.7(7.6\u0026ndash;12)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.3(7.1\u0026ndash;11)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5(0.51\u0026ndash;4.5) 0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHR (Quartile)\u003c/p\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0(-1.6-7.5) 0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(34\u0026ndash;46)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(33\u0026ndash;43)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(8.1\u0026ndash;15)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(50\u0026ndash;61)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e15(\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54(48\u0026ndash;59)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e14(\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e4.8(3.4\u0026ndash;6.1)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003cp\u003eNHR\u003c/p\u003e \u003cp\u003eNHR (Quartile)\u003c/p\u003e \u003cp\u003eQ1\u003c/p\u003e \u003cp\u003eQ2\u003c/p\u003e \u003cp\u003eQ3\u003c/p\u003e \u003cp\u003eQ4\u003c/p\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35(1.28,1.43)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eReference\u003c/p\u003e \u003cp\u003e1.87(1.51\u0026ndash;2.32)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e3.14(2.58\u0026ndash;3.83)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e5.03(4.14\u0026ndash;6.11)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e1.52(1.45\u0026ndash;1.59)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35(1.28\u0026ndash;1.44)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eReference\u003c/p\u003e \u003cp\u003e1.84(1.44\u0026ndash;2.35)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e3.15(2.59\u0026ndash;3.82)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e5.14(4.04\u0026ndash;6.55)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e1.53(1.45\u0026ndash;1.62)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11(1.01\u0026ndash;1.22) 0.029\u003c/p\u003e \u003cp\u003eReference\u003c/p\u003e \u003cp\u003e1.24(0.94\u0026ndash;1.63) 0.105\u003c/p\u003e \u003cp\u003e1.52(1.24\u0026ndash;1.86) 0.002\u003c/p\u003e \u003cp\u003e2.00(1.46\u0026ndash;2.75)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e1.20(1.11\u0026ndash;1.30)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eCAP: Controlled attenuation parameter; NHR: neutrophil-to-high-density lipoprotein cholesterol ratio; NAFLD: non-alcoholic fatty liver disease.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 1: no covariates were adjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 2:adjusted for age, sex, race.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 3:adjusted for age, sex, race, smoking, diabetes, hypertension, history of CVD, BMI, TC, ALT, and uric acid.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe performed weighted linear regression analyses with CAP as the outcome. When NHR was included as a continuous variable in the model for analysis, the results showed that CAP increased by 2.5 dB/m for each unit increase in NHR in the fully adjusted model (β\u0026thinsp;=\u0026thinsp;2.5; 95% CI (0.51\u0026ndash;4.5); P\u0026thinsp;=\u0026thinsp;0.019). NHR was included as a categorical variable (quartiles) in the analysis model, and after adjusting for all confounding variables, CAP values increased significantly with higher levels of NHR (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with participants in the fourth quartile group of NHR having the highest CAP values compared with the first quartile of NHR (β\u0026thinsp;=\u0026thinsp;17; 95% CI (\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e); P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ).\u003c/p\u003e \u003cp\u003eSubsequently, we performed weighted logistic regression analyses with NAFLD as the outcome and showed that higher NHR was directly associated with increased prevalence of NAFLD. After fully adjusting for confounding variables, each unit increase in NHR was associated with an 11% increase in the prevalence of NAFLD (OR\u0026thinsp;=\u0026thinsp;1.11; 95% CI (1.01\u0026ndash;1.22); P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Including NHR as a categorical variable (quartiles) in the analysis model showed that after adjusting for all confounding variables, the prevalence of NAFLD showed a gradual increase with increasing levels of NHR (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with participants in the fourth quartile group of NHR having the highest risk of developing NAFLD compared with the first quartile of NHR (OR\u0026thinsp;=\u0026thinsp;2.00; 95% CI (1.46\u0026ndash;2.75); P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003ch3\u003eRelationship between NHR and liver fibrosis\u003c/h3\u003e\n\u003cp\u003eSimilarly, we analyzed the effect of NHR on LSM and liver fibrosis using weighted multivariate regression models. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the unadjusted model indicated that each unit increase in NHR was associated with a 0.29 kPa increase in LSM (beta\u0026thinsp;=\u0026thinsp;0.29; 95% CI: 0.22, 0.37; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a 21% higher risk of liver fibrosis (OR\u0026thinsp;=\u0026thinsp;1.21; 95% CI: 1.24, 1.29; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, after adjustment for all confounders, the association between higher NHR and liver fibrosis was no longer statistically significant(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between NHR and liver fibrosis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emodel1:β/OR (95% CI) P value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emodel2: β/OR (95% CI) P value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emodel3: β/OR (95% CI) P value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSM(kPa)\u003c/p\u003e \u003cp\u003eNHR\u003c/p\u003e \u003cp\u003eNHR (Quartile)\u003c/p\u003e \u003cp\u003eQ1\u003c/p\u003e \u003cp\u003eQ2\u003c/p\u003e \u003cp\u003eQ3\u003c/p\u003e \u003cp\u003eQ4\u003c/p\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29(0.22\u0026ndash;0.37)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eReference\u003c/p\u003e \u003cp\u003e0.09(-0.26-0.43) 0.604\u003c/p\u003e \u003cp\u003e0.81(0.40\u0026ndash;1.2)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e1.4(1.1\u0026ndash;1.8)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e0.42(0.32\u0026ndash;0.52)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28(0.19\u0026ndash;0.36)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003eReference\u003c/p\u003e \u003cp\u003e0.07(-0.28-0.41) 0.693\u003c/p\u003e \u003cp\u003e0.76(0.38\u0026ndash;1.1)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e1.4(1.0-1.8)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e0.41(0.30\u0026ndash;0.52)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07(-0.04-0.18) 0.171\u003c/p\u003e \u003cp\u003eReference\u003c/p\u003e \u003cp\u003e-0.31(-0.64-0.01) 0.058\u003c/p\u003e \u003cp\u003e-0.02(-0.38-0.34) 0.896\u003c/p\u003e \u003cp\u003e0.27(-0.24-0.78) 0.253\u003c/p\u003e \u003cp\u003e0.11(-0.03-0.25) 0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver fibrosis\u003c/p\u003e \u003cp\u003eNHR\u003c/p\u003e \u003cp\u003eNHR (Quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21(1.24\u0026ndash;1.29)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22(1.13\u0026ndash;1.31)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05(0.98\u0026ndash;1.13) 0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003cp\u003eQ2\u003c/p\u003e \u003cp\u003eQ3\u003c/p\u003e \u003cp\u003eQ4\u003c/p\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003cp\u003e1.58(1.01\u0026ndash;2.47) 0.043\u003c/p\u003e \u003cp\u003e2.89(1.92\u0026ndash;4.35)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e3.91(2.75\u0026ndash;5.57)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e1.40(1.31\u0026ndash;1.50)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003cp\u003e1.58(1.00-2.51) 0.051\u003c/p\u003e \u003cp\u003e2.90(1.91\u0026ndash;4.41)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e4.01(2.77\u0026ndash;5.80)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e1.41(1.32\u0026ndash;1.51)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003cp\u003e1.07(0.60\u0026ndash;1.90) 0.801\u003c/p\u003e \u003cp\u003e1.42(0.85\u0026ndash;2.39) 0.157\u003c/p\u003e \u003cp\u003e1.49(0.84\u0026ndash;2.63) 0.145\u003c/p\u003e \u003cp\u003e1.11(0.98\u0026ndash;1.25) 0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNHR: neutrophil-to-high-density lipoprotein cholesterol ratio.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 1: no covariates were adjusted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 2:adjusted for age, sex, race\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 3:adjusted for age, sex, race, smoking, diabetes, hypertension, history of CVD, BMI, TC, ALT, and uric acid\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePotential non-linear relationship between NHR and NAFLD and liver fibrosis\u003c/h2\u003e \u003cp\u003eThe potential non-linear associations of NHR with NAFLD and liver fibrosis were analyzed using the restricted cubic spline (RCS) model. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, there is a positive nonlinear association of NHR with both NAFLD and liver fibrosis (P-non-linear\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that the lower NHR values below 3.013 are associated with a reduced risk of developing NAFLD. In contrast. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that while lower NHR levels do not significantly correlate with the risk of liver fibrosis, a notable increase in the risk of liver fibrosis was observed when NHR levels exceeded 3.013.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analyses\u003c/h2\u003e \u003cp\u003e We used stratified weighted multiple regression analyses to investigate the association of NHR with NAFLD and liver fibrosis in different population settings, dividing participants into subgroups based on gender, age, BMI, hypertension, diabetes mellitus, smoking, and history of cardiovascular disease for the analyses and the interaction tests, as displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, among women, participants without diabetes mellitus and hypertension observed that between NHR and NAFLD There was a stronger positive correlation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, similar correlations between NHR and NAFLD were observed in different subgroups of age, smoking, BMI, and CVD. In addition, a significant correlation between NHR and liver fibrosis was observed in participants with BMI\u0026thinsp;\u0026gt;\u0026thinsp;30(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the association between NHR and NAFLD and liver fibrosis in the American population. The results showed a significant positive association between NHR and NAFLD. In addition, although lower NHR levels were not significantly associated with the risk of hepatic fibrosis, the risk of hepatic fibrosis increased significantly when NHR exceeded 3.013. Subgroup analyses further revealed that the association between NHR and NAFLD was more significant in women and individuals without hypertension and diabetes, and the association between NHR and liver fibrosis was more prominent in participants with BMI\u0026thinsp;\u0026gt;\u0026thinsp;30.\u003c/p\u003e \u003cp\u003eOur study extends and supports previous findings. An earlier study involving 936 individuals from a Chinese population demonstrated that NHR was positively associated with the risk of ultrasound diagnosed NAFLD, suggesting that NHR may be a valid predictor of NAFLD(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Our study validated this association and assessed the prevalence and severity of NAFLD using data from a large-scale U.S. general population, employing the VCTE technology of the FibroScan device. Studies have shown that the accuracy of VCTE in diagnosing hepatic steatosis and fibrosis is comparable to liver biopsy(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This enhances the broad applicability and reliability of the results.\u003c/p\u003e \u003cp\u003eThe main features of non-alcoholic fatty liver disease (NAFLD) include hepatic lipid accumulation, inflammatory response, fibrosis formation, and hepatocyte injury(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Hepatic steatosis is the pathological basis for the progression of NAFLD, often accompanied by the onset of chronic inflammatory responses(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Studies have shown that chronic inflammation plays a vital role in developing NAFLD(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). An analysis based on NHANES 2017\u0026ndash;2018 showed that the neutrophil-to-albumin ratio (NPAR), a systemic marker of inflammation, was significantly associated with NAFLD and advanced liver fibrosis(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). When immune cells such as neutrophils and lymphocytes are activated, they release pro-inflammatory cytokines that promote the development of NAFLD(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Neutrophils are the first immune cells to respond to inflammation, producing cytokines to promote lymphocyte activation and recruit macrophages, ultimately leading to chronic inflammation(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In addition, HDL-C reduces neutrophil activation, adhesion, spreading, and migration, inhibiting oxidized LDL production and exerting anti-inflammatory and antioxidant effects(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Therefore, NHR, as a combination of neutrophil numbers and HDL-C levels, may reflect the state of chronic inflammation and oxidative stress and serve as a sensitive indicator of the pathological process of NAFLD.\u003c/p\u003e \u003cp\u003eRecent studies have shown that NHR is associated with the progression of several diseases, particularly cardiovascular and metabolic diseases(\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Our analysis revealed a non-linear association of NHR with NAFLD and liver fibrosis, which may be related to the complex interaction of neutrophils and HDL-C in the development of metabolic diseases. In in vitro experiments, mice fed a high-fat diet showed increased neutrophil infiltration in the liver, accompanied by the development of hepatic steatosis and inflammation. Neutrophil depletion in mice using the 1A8 antibody significantly reduced liver triglyceride accumulation, hepatic inflammation, and fibrosis(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Thus, NHR may play an essential role in the progression of NAFLD, with high NHR levels strongly associated with increased severity of hepatic steatosis and fibrosis.\u003c/p\u003e \u003cp\u003eIn particular, the risk of liver fibrosis increases significantly when the NHR exceeds 3.013, probably due to exacerbation of chronic inflammation. Neutrophil accumulation has been linked to the progression of liver fibrosis and cirrhosis. During the development of liver inflammation and fibrosis, immature neutrophils with pro-inflammatory properties are released into the circulation, further exacerbating the inflammatory response and liver fibrosis(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In addition, several studies have shown that neutrophil elastase (NE), neutrophil granule protein (PR3), tissue protease G (CSTG), and other neutrophil-derived proteases play a critical role in the progression of hepatic steatosis and inflammation in NAFLD(\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). The role of myeloperoxidase (MPO) in the progression of liver fibrosis has also been demonstrated(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). These mechanisms may explain the association between NHR and hepatic steatosis and fibrosis.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStudy strengths and limitations\u003c/h2\u003e \u003cp\u003eThe main strength of this study is the use of large-scale data from the general population of the United States, with a large and nationally representative sample size. In addition, we used VCTE to assess hepatic steatosis and hepatic fibrosis, which provided greater diagnostic accuracy. However, this study has some limitations. Firstly, as a cross-sectional study, it was impossible to establish a causal relationship between NHR and hepatic steatosis and fibrosis. Second, although we adjusted for confounders as much as possible, there may still be potential confounding variables that were not considered. In addition, although VCTE demonstrated high accuracy in non-invasive diagnosis, its diagnostic accuracy compared to liver biopsy requires further validation. Future studies should adopt a longitudinal design to assess the long-term association between NHR and NAFLD progression and validate its predictive value.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study found that higher NHR levels are significantly associated with an increased risk of NAFLD, especially among women and those without diabetes or hypertension. Furthermore, the risk of liver fibrosis rises markedly when NHR exceeds 3.013. Thus, NHR may serve as a valuable marker for detecting hepatic steatosis and fibrosis, facilitating early diagnosis and intervention for NAFLD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eNAFLD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003eNon-alcoholic fatty liver disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNHR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003eNeutrophil-to-high-density lipoprotein cholesterol ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNHANES \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVCTE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eVibration-controlled transient elastography\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCAP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003eControlled attenuation parameter\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLSM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003eLiver stiffness measurement\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCVD\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Cardiovascular disease\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Body mass index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eALT\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Alanine aminotransferase\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAST\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Aspartate aminotransferase\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTG\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Triglyceride\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTC\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Total cholesterol\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlb\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Albumin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGGT\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026gamma;-glutamyl transpeptidase\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHDL-C\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;High-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHbA1c\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Glycosylated hemoglobin A1c\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The NHANES database was approved by the National Center for Health Statistics (NCHS) Ethics Review Board, and all participants provided written informed consent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interest\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by Capital\u0026rsquo;s Funds for Health Improvement and Research (2024-1-1203); Dengfeng Talent Support Program of Beijing Municipal Administration of Hospitals (No.DFL20221601); High-level Public Health Technical Personnel Construction Project (Subject leaders-03-21).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZN and LX contributed to the study's conception and design. ZN, LYY, LYY, and CXY performed data extraction and assembly. ZN, LYY, and LX analyzed and interpreted the data. ZN prepared figures 1-4. ZN and LX were responsible for writing and revising the manuscript. All authors (ZN, LX, LYY, CXY) reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe are grateful for the dedication of the NHANES team and the valuable participation of all survey participants.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis research utilized the publicly accessible dataset from the National Health and Nutrition Examination Survey, which is available at the following link (https://wwwn.cdc.gov/nchs/nhanes/Default.aspx).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHuang DQ, El-Serag HB, Loomba R. Global epidemiology of NAFLD-related HCC: trends, predictions, risk factors and prevention. 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Antioxid Redox Signal. 2015;23(16):1255\u0026ndash;1269. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/ars.2014.6108\u003c/span\u003e\u003cspan address=\"10.1089/ars.2014.6108\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Non-alcoholic fatty liver disease(NAFLD), neutrophil-to-high-density lipoprotein cholesterol ratio, inflammation, Lipid metabolism disorders, liver fibrosis","lastPublishedDoi":"10.21203/rs.3.rs-5308727/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5308727/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNon-alcoholic fatty liver disease (NAFLD) is closely associated with chronic inflammation and lipid metabolism disorders. The neutrophil-to-high-density lipoprotein cholesterol ratio (NHR) is an integrative marker reflecting inflammatory responses and lipid metabolism disorders. It has been associated with the prognosis of several diseases. This study aimed to investigate the relationship between NHR and the risk of NAFLD and liver fibrosis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a cross-sectional study using data from the 2017\u0026ndash;2020 National Health and Nutrition Examination Survey (NHANES).weighted multivariate regression was used to investigate the association of NHR with NAFLD and liver fibrosis. and restricted cubic spline model was used to explore potential non-linear relationships. Subgroup analyses were used to verify the stability of the relationship of NHR with NAFLD and liver fibrosis in different populations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 6526 participants were included in the study. After adjusting for confounders, the elevated NHR levels were positively associated with the risk of NAFLD. for every unit increase in NHR, there was a 2.5 dB/m increase in the controlled attenuation parameter (CAP) (β\u0026thinsp;=\u0026thinsp;2.5; P\u0026thinsp;=\u0026thinsp;0.019) and an 11% increase in NAFLD prevalence (OR\u0026thinsp;=\u0026thinsp;1.11; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Participants in the highest quartile of NHR had a twofold increased risk of developing NAFLD compared with those in the lowest quartile (OR\u0026thinsp;=\u0026thinsp;2.00; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, after adjusting for confounders, the association between NHR and liver fibrosis was not statistically significant. RCS analyses showed that the risk of NAFLD increased with increasing NHR water at NHR values below 3.013. The risk of developing liver fibrosis was significantly increased at NHR above 3.013. Subgroup analyses showed that the positive association between NHR and NAFLD was more pronounced in women and participants without diabetes or hypertension.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eElevated NHR levels are positively correlated with the risk of NAFLD, particularly in women and individuals without diabetes or hypertension. and the risk of developing liver fibrosis significantly increases at NHR values above 3.013. which can help in the early detection of NAFLD and liver fibrosis and timely intervention.\u003c/p\u003e","manuscriptTitle":"Association between neutrophil to high-density lipoprotein cholesterol ratio and risk of non-alcoholic fatty liver disease and liver fibrosis: A cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-28 06:17:55","doi":"10.21203/rs.3.rs-5308727/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":"25a17cbc-0134-41ac-8158-4737635c2344","owner":[],"postedDate":"October 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-28T06:17:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-28 06:17:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5308727","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5308727","identity":"rs-5308727","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}