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

doi:10.21203/rs.3.rs-5308727/v1

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

https://doi.org/10.21203/rs.3.rs-5308727/v1

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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

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.

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.

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).

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.

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.

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

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).

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No competing interests reported.

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

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Abstract

Background

Methods

Results

Conclusion

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Introduction

Materials and methods

study population

Measurement of hepatic steatosis and hepatic fibrosis

Variable

Statistical Analyses

Results

Baseline characteristics of study participants

Association of NHR with NAFLD

Relationship between NHR and liver fibrosis

Potential non-linear relationship between NHR and NAFLD and liver fibrosis

Subgroup analyses

Discussion

Study strengths and limitations

Conclusions

Abbreviations

Declarations

Funding

Author Contribution

Acknowledgments

Data Availability

References

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