Differential effects of systemic immune inflammation indices on hepatic steatosis and hepatic fibrosis: evidence from NHANES 1999-2018

doi:10.21203/rs.3.rs-5010932/v1

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Differential effects of systemic immune inflammation indices on hepatic steatosis and hepatic fibrosis: evidence from NHANES 1999-2018

https://doi.org/10.21203/rs.3.rs-5010932/v1

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Background

Several studies have demonstrated that systemic immune inflammation index (SII) has a positive relationship with hepatic steatosis. However, it is lack of system evidence for the correlation between SII and hepatic fibrosis. The objective of this study was to evaluate the relationships between SII and hepatic steatosis or hepatic fibrosis.

Methods

A cross-sectional analysis was performed of 21833 subjects aged over 20 from the National Health and Nutrition Examination Survey (NHANES). Fibrosis-4 index (FIB-4), NAFLD fibrosis score (NFS) and hepamet fibrosis score (HFS) were the indicators for hepatic fibrosis; fatty liver index (FLI), NAFLD liver fat score (LFS) and Framingham steatosis index (FSI) were the indicators for hepatic steatosis. Pearson’s test, generalized linear model (GLM) and restricted cubic splines (RCS) were used to analyze associations of SII with hepatic fibrosis and hepatic steatosis.

Results

Pearson’s test and GLM revealed that there were negative relationships between SII and hepatic fibrosis (FIB-4, NFS and HFS), while positive relationships between SII and hepatic steatosis (FLI, LFS and FSI). The corresponding β (95%CI) of SII and hepatic fibrosis were − 0.35(-0.46, -0.24), -0.67(-0.71, -0.63) and − 0.10(-0.12, -0.09), respectively. The corresponding β (95%CI) of SII and hepatic steatosis were 6.12(4.75, 7.50), 0.22(0.12, 0.31) and 0.27(0.20, 0.34), respectively. Statistically significant non-linear association were found in SII with hepatic fibrosis and hepatic steatosis in RCS model (all P < 0.001).

Conclusion

There was a negative significant association between SII and hepatic fibrosis, while a positive significant association between SII and hepatic steatosis.

Systemic Immune Inflammation Index

hepatic steatosis

hepatic fibrosis

Nonalcoholic fatty liver disease (NAFLD) is a main cause of liver disease worldwide, which warrants the attention of related medical workers and health policy makers[1]. With the rapid societal development and lifestyle changes, NAFLD has been gradually increased. Several studies showed that prevalence exceeding 20% in developed countries[1] as well as a global prevalence of 25%[2]. Additionally, health care utilization and expenditure are particularly high among NAFLD patients those requiring hospital admission[3]. One study suggests that overnutrition is the primary driver of NAFLD, which could induce expansion of fat reservoirs and accumulation of ectopic fat[2]. Another study also indicates that fructose could induce to ATP depletion and suppression of mitochondrial fatty acid oxidation, leading to increased production of reactive oxygen species[4]. Numerous studies have also indicated that inflammatory probably play a crucial role in disease progression[5, 6].

Systemic immune inflammation index (SII) is constructed based on platelet counts, lymphocyte and neutrophil, which is a comprehensive biomarker and reflects local immune response and systemic inflammation of the whole body[7]. Several studies have demonstrated that SII is an independent predictor of patients with hepatocellular carcinoma, sepsis and colorectal cancer[7–10]. SII can objectively reflects the severity of inflammation as well as immune pathways in the host[8, 11].

More and more biomarkers about the NAFLD have been studied. On the one hand, these are hepatic fibrosis related biomarkers, such as fibrosis-4 index (FIB-4)[12], NAFLD fibrosis score (NFS)[13] and hepamet fibrosis score (HFS)[14]. On the other hand, these are hepatic steatosis related biomarkers, such as fatty liver index (FLI)[15], NAFLD liver fat score (LFS)[16] and Framingham steatosis index (FSI)[17]. Numerous studies have proved that inflammation is an important factor in hepatic steatosis[18–20]. In addition, one study has suggested that SII is not association with hepatic fibrosis[21]. Furthermore, there is still a lack of a systematic investigation to explore the association of SII with hepatic steatosis and hepatic fibrosis.

Thus, in this study, we aim to assess the association of SII with hepatic steatosis and hepatic fibrosis, using a large sample of adults from the National Health and Nutrition Examination Survey (NHANES).

2.1. Data and sample source

NHANES was a large population-based cross-sectional survey conducted by the National Center for Health Statistics (NCHS) in the United States. The Details of the NHANES design and data publicly available had been described at https://www.cdc.gov/nchs/nhanes/. Briefly, NHANES was a stratified, multistage probability approach, which surveyed about 5000 participants from across the country every year. The data included demographic data, questionnaire data, examination data, laboratory data and limited access data. The survey had been conducted over 10 cycles (1999–2018).

There were 101316 subjects in NHANES 1999–2018. The 18576 participants were excluded for missing SII; the 19158 subjects were excluded for imponderable FIB-4; the 2789 participants were excluded for imponderable FLI; the 31478 subjects were excluded for missing FSI; the 264 participants were excluded for imponderable LFS. There were 6838 with age < 20, 119 with HBV and 261 with HCV. At last, 21833 subjects were included in this study (Fig. 1).

2.2. Systemic immune-inflammation biomarkers detection

The SII was derived from complete blood count. The standardized protocols for measuring blood count were provided by Laboratory Procedures Manual of NHANES. The details can be found at https://www.cdc.gov/nchs/nhanes/biospecimens/serum_plasma_urine.htm. In this study, the following equation was utilized to calculate SII = platelet counts × neutrophil counts/lymphocyte counts[7]. The SII optimal cutoff point was 330.

2.3. Hepatic fibrosis-related biomarkers

The FIB-4 was an index, which can accurately predict fibrosis in patients coinfected with hepatitis C virus or human immunodeficiency virus. The overall accuracy was 87%[12]. The equation for FIB-4 was:

NFS was constructed from laboratory variables and routine clinical and could accurately predict fibrosis in NAFLD. The formulas included age, hyperglycemia, BMI, platelet count, albumin and AST/ALT ratio. The area under the receiver operating characteristic curve (ROC curve) was 0.82[13]. The formulas for NFS were:

HFS was constructed based on age, sex, diabetes, homeostatic model assessment (HOMA), AST, albumin and platelets. The area under the ROC curve was 0.85[14]. The equation for HFS was:

2.4. Hepatic steatosis biomarker detection

FLI was used to predict liver fat and was simple to perform by body mass index (BMI), waist circumference (WC), triglycerides (TG) and gamma-glutamyl-transferase (GGT), which had an accuracy of 0.84 (95%CI 0.81–0.87)[15]. The formulas for FLI were:

LFS was used to identify hepatic steatosis based on metabolic syndrome (MS), type 2 diabetes (T2DM), insulin, aspartate aminotransferase (AST) and the ratio of AST/alanine aminotransferase (ALT). The area under the receiver operating characteristic curve of LFS was 0.86 with proton magnetic resonance spectroscopy as the standard for diagnosing fatty liver[16]. The equation for LFS was:

FSI was used to estimate patients with liver fat with a c-statistic value of 0.845. The FSI formulas were based on age, gender, BMI, TG, hypertension (HTN), hyperglycemia and ALT/AST[17]. The formulas for FSI were:

2.5. Covariates

The baseline characteristics included demographic factors (age, gender, education, marital status, ratio of family income to poverty (PIR) and race), behavior factors (drug, smoke, drink and physical activity (PA)) and health risk factors (HTN and T2DM).

2.6. Statistical analysis

All the participants were divided into healthy control group and SII group. Continuous and categorical baseline characters were presented as mean ± standard deviation (SD) and number (percentage), respectively. The distribution differences of variables were compared by weighted student's t test for continuous data and weighted chi-square test for categorical data, respectively.

The SII data was unevenly distribution and skewed to right. Based on this, the SII data need to be ln-transformed. The Pearson’s test was used to analyze the correlation between ln-SII and NAFLD related biomarkers values. Three models were developed to assess associations of ln-SII and NAFLD related biomarkers values by using weighted generalized linear model (GLM): crude model was not adjusted; model 1 adjusted for age, gender, education, marital status, PIR and race. model 2 adjusted for age, gender, education, marital status, PIR, race, drug, smoke, drink, physical activity, HTN and T2DM. Furthermore, the weighted restricted cubic splines (RCS) were used to explore the non-linear relationships between ln-SII and NAFLD related biomarkers values.

All data were analyzed by using R version 4.2.2 and all the statistical significance was set p-value < 0.05 and all statistical test was two tails.

3.1. Study population characteristics

A total of 21833 participants were included in this study. After weighted, there were 2015554456 subjects. Overall, a total of 1568281782 subjects had a high level of SII. The age of mean ± SD for individuals with SII and healthy control were 46.83 ± 16.66 and 45.09 ± 16.94, respectively. Except for PIR and Drug, different distributions in the other selected variables were found between individuals with SII and healthy control (All p < 0.05). Participants’ demographic factors, behavior factors and health risk factors were shown in Table 1.

Table 1

Weighted demographic characteristics of participants (n = 20451) in the NHANES 1999–2018
Variables	Total (N = 2015554456)	Healthy control (N = 447272674)	SII (N = 1568281782)	t/c²	P
Age (mean ± SD)	46.45 ± 16.74	45.09 ± 16.94	46.83 ± 16.66	4.6584	<0.001
Gender (%)				66.248	<0.001
Men	971664879(48.21)	245800566(54.96)	725864313(46.28)
Women	1043889577(51.79)	201472108(45.04)	842417469(53.72)
Education (%)				6.331	0.013
High school or below	828596365(41.11)	174284177(38.97)	654312187(41.72)
College graduate or above	1186958091(58.89)	272988497(61.03)	913969594(58.28)
Marital status (%)				12.374	<0.001
Married/Living with partner	1303333702(64.66)	286995069(64.17)	1016338633(64.81)
Never married	343833355(17.06)	66148055(14.79)	277685301(17.71)
Widowed/Divorced/Separated	345234038(17.13)	89851139(20.09)	255382900(16.28)
PIR (%)				1.3843	0.254
<2	637661336(31.64)	146611896(32.78)	491049440(31.31)
2–4	566662080(28.11)	122935507(27.49)	443726573(28.29)
>4	678909954(33.68)	147994835(33.09)	530915119(33.85)
Race (%)				65.301	<0.001
Mexican American	166457513(8.26)	38484880(8.6)	127972632(8.16)
Other Hispanic	113572019(5.63)	23613121(5.28)	89958898(5.74)
Non-Hispanic White	1384000248(68.67)	263652878(58.95)	1120347370(71.44)
Non-Hispanic Black	215326154(10.68)	82549318(18.46)	132776837(8.47)
Other Race	136198522(6.76)	38972477(8.71)	97226045(6.2)
Drug (%)				0.826	0.3645
No	999466032(49.59)	224618703(50.22)	774847329(49.41)
Yes	713136318(35.38)	166331840(37.19)	546804478(34.87)
PA (%)				42.535	<0.001
Inactive	842368918(41.79)	164863472(36.86)	677505446(43.2)
Moderate	494552596(24.54)	121788888(27.23)	372763708(23.77)
Active	416970957(20.69)	119662167(26.75)	297308790(18.96)
Smoke (%)				11.834	<0.001
No	1087542971(53.96)	263001982(58.8)	824540988(52.58)
Yes	926643515(45.97)	183648904(41.06)	742994610(47.38)
Drink (%)				1.2984	<0.001
No	429467843(21.31)	93436135(20.89)	336031707(21.43)
Yes	1488221947(73.84)	328000781(73.33)	1160221165(73.98)
HTN (%)				19.835	<0.001
No	1197775212(59.43)	283864142(63.47)	913911070(58.27)
Yes	778903608(38.64)	155724339(34.82)	623179269(39.74)
T2DM (%)				4.17	0.043
No	1081461358(53.66)	247829497(55.41)	833631860(53.16)
Yes	933963420(46.34)	199443177(44.59)	734520243(46.84)
ln-transformed SII (mean ± SD)	6.17 ± 0.52	5.49 ± 0.33	6.36 ± 0.39	/	/
Hepatic fibrosis
FIB-4 (mean ± SD)	1.03 ± 0.75	1.27 ± 1.13	0.96 ± 0.58	-12.183	<0.001
NFS (mean ± SD)	-0.51 ± 1.29	-0.07 ± 1.22	-0.63 ± 1.28	-20.028	<0.001
HFS (mean ± SD)	0.77 ± 0.40	0.85 ± 0.34	0.75 ± 0.44	-14.692	<0.001
Hepatic steatosis
LFS (mean ± SD)	-0.18 ± 2.65	-0.38 ± 2.35	-0.12 ± 2.73	5.6485	<0.001
FLI (mean ± SD)	50.03 ± 32.70	45.32 ± 32.60	52.37 ± 32.61	9.2829	<0.001
FSI (mean ± SD)	-0.92 ± 1.89	-1.18 ± 1.87	-0.84 ± 1.89	8.4289	<0.001

3.2. Associations of SII with NAFLD related biomarkers values.

The Pearson’s correlation analysis was constructed to explore the correlation between ln-transformed SII with NAFLD related biomarkers values. As shown in Fig. 2, there were negative correlation between ln-transformed SII and hepatic fibrosis biomarkers value (FIB-4 (r=-0.25), NFS (r=-0.26) and HFS (r=-0.09), all p < 0.001); there were positive correlation between ln-transformed SII and hepatic steatosis biomarkers value (FLI (r = 0.08), LFS (r = 0.04) and FSI (r = 0.07), all p < 0.001).

Figure 3 suggested that ln-transformed SII were negatively related to hepatic fibrosis biomarkers value (FIB-4, NFS and HFS) in the three models (all P < 0.001). After adjusting for the potential confounding factors, the corresponding β (95%CI) between ln-transformed SII and hepatic fibrosis biomarkers value were − 0.35(-0.46, -0.24), -0.67(-0.71, -0.63) and − 0.10(-0.12, -0.09), respectively.

Figure 4 exhibited that ln-transformed SII were positively related to hepatic steatosis biomarkers value (FLI, LFS and FSI) in the three models (all P < 0.001). After adjusting for the potential confounding factors, the corresponding β (95%CI) between ln-transformed SII and hepatic steatosis biomarkers value were 6.12(4.75, 7.50), 0.22(0.12, 0.31) and 0.27(0.20, 0.34), respectively.

3.3. The non-linear association of SII with NAFLD related biomarkers values.

The nonlinear relationship was explored between ln-transformed SII and β (the association of ln-transformed SII value with NAFLD related biomarkers value) by RCS. Figure 5 and Fig. 6 showed statistically significant non-linear association was found in ln-transformed SII with β (all P < 0.001). With the ln-transformed SII increasing, the β (association of ln-transformed SII value with hepatic fibrosis biomarkers value) gradually decreases. However, with the ln-transformed SII increasing, the β (association of ln-transformed SII value with hepatic steatosis biomarkers value) gradually increases.

In this study, Pearson’s correlation analysis, GLM and RCS were constructed to reveal the associations between SII and NAFLD related biomarkers values. There was negative significant association between SII and hepatic fibrosis biomarkers value (FIB-4, NFS and HFS). In contrast, there was positive significant association between SII and hepatic steatosis biomarkers value (FLI, LFS and FSI).

SII, as a novel biomarker, can be easily obtained from routine complete blood count tests (platelet counts, neutrophil, and lymphocyte) and reflect the overall status of the immune response and systemic inflammation of the subjects. In the study, SII was constructed to predict the recurrence and survival for hepatocellular carcinoma patients[7]. In the recent years, more and more studies use SII to explore the association of systemic inflammation with the subjects’ healthy status (lupus nephritis[22], psoriasis[23], arthritis[24] and NAFLD[20, 25, 26]). One study, based on NHANES, revealed that ln-transformed SII was an independent risk factor for NAFLD risk (defined by a US Fatty Liver Index (USFLI) score exceeding 30), and the corresponding OR and 95%CI was 1.46 (1.27, 1.69)[25]. Another study also found that there was a significant positive association between SII and hepatic steatosis index (HSI) and OR (95%CI) was 1.30 (1.10, 1.52)[20]. Although above studies used the different biomarkers to detect the hepatic steatosis, the result also indicted that there was a positive association between SII and hepatic steatosis, which were similar with our study.

Besides the association between SII and hepatic steatosis, two studies also explored the relationship between SII and hepatic fibrosis[21, 27]. The first study implied that there was no link between SII and liver fibrosis (based on liver stiffness measurement), and the corresponding β (95%CI) and P value were 0.000 (-0.000, 0.001) and 0.263[21]. While the second research suggested that there was a negative association between SII and advanced liver fibrosis (according to FibroScan model) in the individuals with diabetes mellitus, and the OR (95%CI) was 0.33 (0.15, 0.70)[27]. In this study, there was negative significant association between SII and hepatic fibrosis (FIB-4, NFS and HFS). The inconsistencies in the results of these studies may stem from differences in the included population and the biological markers of liver fibrosis.

The underling mechanism of relationship between SII and hepatic steatosis was not yet completed understood. Evidences suggested that immune-inflammation response played an important role in hepatic steatosis. Cell death caused by inflammasome pathways played a key role in the pathogenesis of NAFLD, and interleukin (IL)-1 and IL-18 released after cell death can cause a strong inflammatory process and induce the recruitment of inflammatory cells[28, 29]. Another theory was that nutrient overload was the primary cause of hepatic steatosis. The excess visceral fat could cause macrophage infiltration and result in a pro-inflammatory state, which induced insulin resistance. Inappropriate lipolysis resulted in aberrant fatty acid transport to the hepatic cell in the state of insulin resistance, which could induce a decrease in metabolic capacity. Lipotoxic lipids were formed as a result of lipid metabolic imbalances that lead to cellular stress and inflammasome activation[5, 30].

The mechanism underlying the relationship between SII and hepatic fibrosis remained unclear. Based on previous describe, we postulated that SII may play a pivotal role in both the initiation and progression of hepatic steatosis. However, as the disease progresses toward hepatic fibrosis, there was a discernible decline in complete blood count, predominantly lymphocytes. This could be attributed to a series of pathophysiological evolutions, such as hypersplenism, bone marrow haematopoietic inhibition, and endocrine shifts within the organism. Taken together, these observations suggested that lymphocytes, as a component of the SII formula, have different developmental trends in hepatic steatosis and hepatic fibrosis stages, and may explain the phenomenon of SII different trends in hepatic steatosis and hepatic fibrosis[27].

It was important to emphasize some limitations of this study. Firstly, the NHANES study was a cross-sectional design, which limits its ability to make established causal conclusions. Secondly, even with consideration of several influencing factors, the potential impact of hidden variables was a concern. Thirdly, due to limited information in the data set, the exclusion of subjects with incomplete data may introduce bias into the analysis. Fourthly, the current findings may not be persuasive for other racial groups, as non-Hispanic whites predominate among NHANES. In addition, utilizing the NHANES database inevitably introduces the possibility of imprecise data capture and recall bias. Although these limitations existed in this study, to some extent, the relatively large epidemiological study also could reflect the associations between SII and hepatic fibrosis or hepatic steatosis.

The results reveal there was a negative significant association between SII and hepatic fibrosis, while a positive significant association between SII and hepatic steatosis. SII was a cost-effective, credible, and easily obtained from routine complete blood count tests for discerning the presence of hepatic steatosis or hepatic fibrosis. Moreover, more extensive prospective studies are required for comprehensive validation.

ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; FIB-4, fibrosis-4 index; FLI, fatty liver index; FSI, Framingham steatosis index; HFS, hepamet fibrosis score; HOMA, homeostatic model assessment; HTN, hypertension; GGT, gamma-glutamyl-transferase; GLM, generalized linear model; LFS, liver fat score; MS, syndrome; NAFLD, nonalcoholic fatty liver disease; NFS, NAFLD fibrosis score; NHANES, National Health and Nutrition Examination Survey; NCHS, National Center for Health Statistics; PA, physical activity; PIR, ratio of family income to poverty; RCS, restricted cubic splines; ROC curve, receiver operating characteristic curve; SD, standard deviation; SII, systemic immune inflammation index; TG, triglycerides; T2DM, type 2 diabetes; WC, waist circumference.

Funding

This study was not supported by any funding.

CRediT authorship contribution statement

Shunyin Duan: Writing – review & editing, Writing – original draft, Data curation, Formal analysis, Conceptualization. Zhanwen Tu: Writing – review & editing, Formal analysis, Data curation. Lijuan Duan: Writing – review & editing. Runqi Tu: Writing – review & editing, Data curation, Formal analysis, Conceptualization.

Conflict of interest

All authors had declared no conflict of interest.

Acknowledgements

The authors thank all the staff and participants in NHANES for their contribution of donation, collection and sharing data.

Ethics approval and consent to participate

This study was reviewed and approved by NCHS Ethics Review Board. The patients/participants provided their written informed consent to participate in this study.

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: www.cdc.gov/nchs/nhanes/.

Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M: Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 2016, 64(1):73-84.
Powell EE, Wong VW, Rinella M: Non-alcoholic fatty liver disease. Lancet 2021, 397(10290):2212-2224.
Younossi ZM, Tampi R, Priyadarshini M, Nader F, Younossi IM, Racila A: Burden of Illness and Economic Model for Patients With Nonalcoholic Steatohepatitis in the United States. Hepatology 2019, 69(2):564-572.
Softic S, Cohen DE, Kahn CR: Role of Dietary Fructose and Hepatic De Novo Lipogenesis in Fatty Liver Disease. Dig Dis Sci 2016, 61(5):1282-1293.
Sanyal AJ: Past, present and future perspectives in nonalcoholic fatty liver disease. Nat Rev Gastroenterol Hepatol 2019, 16(6):377-386.
Miura K, Yang L, van Rooijen N, Ohnishi H, Seki E: Hepatic recruitment of macrophages promotes nonalcoholic steatohepatitis through CCR2. Am J Physiol Gastrointest Liver Physiol 2012, 302(11):G1310-1321.
Hu B, Yang XR, Xu Y, Sun YF, Sun C, Guo W, Zhang X, Wang WM, Qiu SJ, Zhou J et al: Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res 2014, 20(23):6212-6222.
Wang BL, Tian L, Gao XH, Ma XL, Wu J, Zhang CY, Zhou Y, Guo W, Yang XR: Dynamic change of the systemic immune inflammation index predicts the prognosis of patients with hepatocellular carcinoma after curative resection. Clin Chem Lab Med 2016, 54(12):1963-1969.
Nakamoto S, Ohtani Y, Sakamoto I, Hosoda A, Ihara A, Naitoh T: Systemic Immune-Inflammation Index Predicts Tumor Recurrence after Radical Resection for Colorectal Cancer. Tohoku J Exp Med 2023, 261(3):229-238.
Mangalesh S, Dudani S, Malik A: The systemic immune-inflammation index in predicting sepsis mortality. Postgrad Med 2023, 135(4):345-351.
Dong M, Shi Y, Yang J, Zhou Q, Lian Y, Wang D, Ma T, Zhang Y, Mi Y, Gu X et al: Prognostic and clinicopathological significance of systemic immune-inflammation index in colorectal cancer: a meta-analysis. Ther Adv Med Oncol 2020, 12:1758835920937425.
Sterling RK, Lissen E, Clumeck N, Sola R, Correa MC, Montaner J, M SS, Torriani FJ, Dieterich DT, Thomas DL et al: Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology 2006, 43(6):1317-1325.
Angulo P, Hui JM, Marchesini G, Bugianesi E, George J, Farrell GC, Enders F, Saksena S, Burt AD, Bida JP et al: The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD. Hepatology 2007, 45(4):846-854.
Ampuero J, Pais R, Aller R, Gallego-Duran R, Crespo J, Garcia-Monzon C, Boursier J, Vilar E, Petta S, Zheng MH et al: Development and Validation of Hepamet Fibrosis Scoring System-A Simple, Noninvasive Test to Identify Patients With Nonalcoholic Fatty Liver Disease With Advanced Fibrosis. Clin Gastroenterol Hepatol 2020, 18(1):216-225 e215.
Bedogni G, Bellentani S, Miglioli L, Masutti F, Passalacqua M, Castiglione A, Tiribelli C: The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol 2006, 6:33.
Kotronen A, Peltonen M, Hakkarainen A, Sevastianova K, Bergholm R, Johansson LM, Lundbom N, Rissanen A, Ridderstrale M, Groop L et al: Prediction of non-alcoholic fatty liver disease and liver fat using metabolic and genetic factors. Gastroenterology 2009, 137(3):865-872.
Long MT, Pedley A, Colantonio LD, Massaro JM, Hoffmann U, Muntner P, Fox CS: Development and Validation of the Framingham Steatosis Index to Identify Persons With Hepatic Steatosis. Clin Gastroenterol Hepatol 2016, 14(8):1172-1180 e1172.
Song Y, Zhang J, Wang H, Guo D, Yuan C, Liu B, Zhong H, Li D, Li Y: A novel immune-related genes signature after bariatric surgery is histologically associated with non-alcoholic fatty liver disease. Adipocyte 2021, 10(1):424-434.
Barrea L, Di Somma C, Muscogiuri G, Tarantino G, Tenore GC, Orio F, Colao A, Savastano S: Nutrition, inflammation and liver-spleen axis. Crit Rev Food Sci Nutr 2018, 58(18):3141-3158.
Song Y, Guo W, Li Z, Guo D, Li Z, Li Y: Systemic immune-inflammation index is associated with hepatic steatosis: Evidence from NHANES 2015-2018. Front Immunol 2022, 13:1058779.
Xie R, Xiao M, Li L, Ma N, Liu M, Huang X, Liu Q, Zhang Y: Association between SII and hepatic steatosis and liver fibrosis: A population-based study. Front Immunol 2022, 13:925690.
Rabrenovic V, Petrovic M, Rabrenovic M, Pilcevic D, Rancic N: The significance of biomarkers of inflammation in predicting the activity of Lupus nephritis. J Med Biochem 2024, 43(1):116-125.
Zhao X, Li J, Li X: Association between systemic immune-inflammation index and psoriasis: a population-based study. Front Immunol 2024, 15:1305701.
Zhao H, Chen X, Ni J, Fang L, Chen Y, Ma Y, Cai G, Pan F: Associations of perchlorate, nitrate, and thiocyanate exposure with arthritis and inflammation indicators in young and middle-aged adults, NHANES 2005-2016. Front Immunol 2024, 15:1318737.
Liu K, Tang S, Liu C, Ma J, Cao X, Yang X, Zhu Y, Chen K, Liu Y, Zhang C et al: Systemic immune-inflammatory biomarkers (SII, NLR, PLR and LMR) linked to non-alcoholic fatty liver disease risk. Front Immunol 2024, 15:1337241.
Wang G, Zhao Y, Li Z, Li D, Zhao F, Hao J, Yang C, Song J, Gu X, Huang R: Association between novel inflammatory markers and non-alcoholic fatty liver disease: a cross-sectional study. Eur J Gastroenterol Hepatol 2024, 36(2):203-209.
Chen G, Fan L, Yang T, Xu T, Wang Z, Wang Y, Kong L, Sun X, Chen K, Xie Q et al: Prognostic nutritional index (PNI) and risk of non-alcoholic fatty liver disease and advanced liver fibrosis in US adults: Evidence from NHANES 2017-2020. Heliyon 2024, 10(4):e25660.
Colak Y, Hasan B, Erkalma B, Tandon K, Zervos X, Menzo EL, Erim T: Pathogenetic mechanisms of nonalcoholic fatty liver disease and inhibition of the inflammasome as a new therapeutic target. Clin Res Hepatol Gastroenterol 2021, 45(4):101710.
Lozano-Ruiz B, Gonzalez-Navajas JM: The Emerging Relevance of AIM2 in Liver Disease. Int J Mol Sci 2020, 21(18).
Friedman SL, Neuschwander-Tetri BA, Rinella M, Sanyal AJ: Mechanisms of NAFLD development and therapeutic strategies. Nat Med 2018, 24(7):908-922.

No competing interests reported.

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Differential effects of systemic immune inflammation indices on hepatic steatosis and hepatic fibrosis: evidence from NHANES 1999-2018

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

1. Introduction

2. Material and Methods

2.1. Data and sample source

2.2. Systemic immune-inflammation biomarkers detection

2.3. Hepatic fibrosis-related biomarkers

2.4. Hepatic steatosis biomarker detection

2.5. Covariates

2.6. Statistical analysis

3. Results

3.1. Study population characteristics

3.2. Associations of SII with NAFLD related biomarkers values.

3.3. The non-linear association of SII with NAFLD related biomarkers values.

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1