Advanced Lung Cancer Inflammation Index ： A Key Predictor of Hepatic Steatosis and Fibrosis Severity

doi:10.21203/rs.3.rs-4884729/v1

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Advanced Lung Cancer Inflammation Index ： A Key Predictor of Hepatic Steatosis and Fibrosis Severity

https://doi.org/10.21203/rs.3.rs-4884729/v1

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Background

Inflammation and nutrition are crucial pathogenic mechanisms in Non-alcoholic fatty liver disease (NAFLD). The Advanced Lung Cancer Inflammation Index (ALI) effectively reflects the systemic inflammation level and nutritional metabolic status of patients. However, its relationship with hepatic steatosis and hepatic fibrosis remains unclear. The study aimed to provide novel insights into the prevention of hepatic steatosis and hepatic fibrosis by examining the relationship between the ALI and Controlled Attenuation Parameter (CAP) and Liver Stiffness Measurement (LSM).

Methods

To identify the connection between ALI and hepatic steatosis and hepatic fibrosis, this study utilized descriptive analysis, multivariate linear regression, smooth curve fitting, and threshold effect analysis to investigate data from the National Health and Nutrition Examination Survey conducted in 2017–2020.

Results

The ALI levels in the NAFLD and Advanced liver fibrosis (AHF) groups were considerably elevated than the control group, with statistical significance (NAFLD: 70.980 vs. 58.680, P < 0.001; AHF: 72.420 vs. 63.360, P < 0.001). Multiple linear regression analyses indicated significant positive associations between ALI and its quartiles with both CAP and LSM levels. There was a positive smooth curve fitting relationship between ALI and the levels of CAP and LSM, with threshold effect inflection points at 88.287 and 98.420, respectively.

Conclusion

The findings suggest a positive correlation between elevated ALI levels and the levels of CAP and LSM. Maintaining ALI within an appropriate range may help mitigate the prevalence of hepatic steatosis and hepatic fibrosis.

Advanced Lung Cancer Inflammation Index

Hepatic steatosis

Hepatic fibrosis

Non-alcoholic fatty liver disease (NAFLD) is marked by widespread hepatocellular steatosis and fat accumulation within liver lobules, excluding alcohol consumption and other specific liver injuries (1). It is projected that 24% of people worldwide have NAFLD (2). With the global rise in obesity and metabolic syndrome, the prevalence of adults (≥ 15 years) with NAFLD is projected to reach 33.5% by 2030 (3, 4). Approximately 20% of NAFLD patients might eventually progress to cirrhosis and hepatocellular carcinoma. NAFLD may emerge as a major cause of end-stage liver disease, liver transplantation, and primary hepatocellular carcinoma(HCC) within the next ten years(5). Presently, the U.S. Food and Drug Administration has not sanctioned any safe and effective drugs for treating NAFLD. Consequently, lifestyle changes, such as dietary adjustments and regular exercise, remain the primary therapeutic methods, emphasizing the critical need for NAFLD prevention(6).

The Advanced Lung Cancer Inflammation Index (ALI) combines body weight, albumin levels, and the NLR to effectively reflect patients' systemic inflammation levels and nutritional metabolic status (7). In patients with lung cancer, ALI has been connected to all-cause mortality and is a critical factor in prognostic evaluation(8). Besides lung cancer, ALI is extensively utilized in other malignancies such as colorectal cancer (9), liver cancer (10), and gastric cancer (11). With the increasing focus on inflammation-related diseases, ALI's application is expanding to conditions such as Crohn's disease (12), heart failure (13), and hypertension (14). Given that inflammation is a central pathogenic mechanism in NAFLD, the potential of ALI to evaluate both inflammation and nutritional status concerning hepatic steatosis and hepatic fibrosis merits further exploration (15).

This study investigates the correlation between ALI, Controlled Attenuation Parameter (CAP), and Liver Stiffness Measurement (LSM) levels to identify biomarkers of hepatic steatosis and hepatic fibrosis development from various perspectives. The objective is to develop preventive strategies at both individual and population levels and to provide reliable references for early detection and recognition indicators of hepatic steatosis and hepatic fibrosis.

2.1. Study design and participants

The National Health and Nutrition Examination Survey (NHANES) is a national survey designed to assess the nutritional and health status of the United States population(16). In this study, we selected data from NHANES for the years 2017–2020. Inclusion criteria: registered participants aged 20 and older (N = 9232). Exclusion criteria: (1) individuals lacking CAP or LSM data (N = 1310). (2) individuals with hepatitis B or C (N = 128). (3) individuals consuming large amounts of alcohol (more than 30 grams for men and more than 20 grams for women per day) (N = 643). (4) individuals lacking ALI index (N = 560). Ultimately, 6591 participants were included in the analysis. Figure 1 shows the detailed screening process.

Figure 1. Filter flow chart

2.2 Definition of NAFLD and AHF

Transient elastography has been widely utilized to detect NAFLD in the general population(17). The CAP value threshold of 274 dB/m or above was considered as the diagnostic criterion for NAFLD, with a sensitivity of 90% in identifying various degrees of hepatic steatosis(18). In this study, the diagnosis of NAFLD was confirmed by a CAP value equal to or greater than 274 dB/m while excluding individuals with hepatitis B and C infections and alcoholic (> 30 g/day for men and > 20 g/day for women). Fibrosis staging was determined by liver stiffness, with a fibrosis threshold of 8 kPa(19, 20).

2.3 ALI Levels

ALI is calculated as Body Mass Index (BMI kg/m²) multiplied by serum albumin level (g/dL) divided by NLR(21).

2.4 Assessment of Covariates

Building on previous research on NAFLD, demographics, lifestyle, and health status were selected as covariates to be adjusted in this study(22). Demographics include age, gender, race, education, marriage, family income-to-poverty ratio (FMPIR), and BMI. Lifestyle includes alcohol consumption and smoking status. Health status includes measurements of hypertension(HTN), hyperlipidemia(HL), and diabetes mellitus(DM), which are derived from questionnaires and medical reports.

2.5 Statistical analysis

R software (version 4.2.2) and EmpowerStats (version 2.0) were used for statistical analysis. Baseline characteristics of the study population were statistically described by groups of NAFLD and AHF. If the Continuous variables conform to the normal distribution, it is represented by mean ± standard deviation (Mean ± SD), and if it is skewed, it is represented by Median (P25, P75). Categorical information was reported as rates and component ratios. Multiple linear regression analysis was used to calculate the beta coefficient (β) values and 95% confidence intervals (CI) between ALI, CAP, and LSM. Model 1 had no adjustments; Model 2 adjusted for age, gender, and race; and Model 3 further adjusted for education, marriage, FMPIR, BMI, smoking, drinking, DM, HTN, and HL. Furthermore, the association between ALI and both CAP and LSM was evaluated using smooth curve fitting and threshold effect analysis. A P-value of less than 0.050 was considered statistically significant.

3.1 General Demographic Characteristics of the Study Population

The study enrolled 6,591 participants, and their median age was 52 years. There were 51.98% female participants and 48.02% male participants. The median ALI value was 64.210.

Table 1 presents the clinical characteristics of participants stratified by NAFLD status. The ALI level was significantly higher in the NAFLD group compared to the Non-NAFLD group (70.980 vs 58.680, P < 0.001). In comparison to Non-NAFLD group, the NAFLD group had higher age and BMI. Additionally, the NAFLD group was more inclined to be male, smoking more than 100 cigarettes in life, being married or living with a partner, and those with DM, HTN, or HL. Laboratory tests showed significantly higher levels of neutrophils, lymphocytes, albumin, CAP, and LSM (P < 0.050). Please refer to Table 1 for detailed information.

Table 1

Clinical characteristics for the study participants based on the NAFLD.
Variables	Non-NAFLD (n = 3654)	NAFLD (n = 2937)	P
age	48 (33, 64)	55 (41, 65)	< 0.001
Age group			< 0.001
18–39 years	1332 (36.453)	658 (22.404)
40–59 years	1106 (30.268)	1105 (37.623)
60 + years	1216 (33.279)	1174 (39.973)
sex			< 0.001
female	2062 (56.431)	1364 (46.442)
male	1592 (43.569)	1573 (53.558)
race			< 0.001
Mexican American	337 (9.223)	493 (16.786)
Non-Hispanic Black	1061 (29.037)	607 (20.667)
Non-Hispanic White	1196 (32.731)	1046 (35.615)
Other	1060 (29.009)	791 (26.932)
education			0.010
College or above	2132 (58.347)	1623 (55.260)
High school or equivalent	876 (23.974)	714 (24.311)
Less than high school	646 (17.679)	600 (20.429)
smoking			0.033
< 100 cigarettes in life	2238 (61.248)	1722 (58.631)
≥ 100 cigarettes in life	1416 (38.752)	1215 (41.369)
Marriage			< 0.001
Married/Living with partner	2017 (55.200)	1835 (62.479)
Never married	813 (22.250)	457 (15.560)
Widowed/Divorced/Separated	824 (22.551)	645 (21.961)
Drinking			0.231
no	386 (10.564)	283 (9.636)
yes	3268 (89.436)	2654 (90.364)
FMPIR	2.200 (1.180, 4.160)	2.240 (1.200, 4.140)	0.821
FMPIR group			0.488
< 1.300	1055 (28.872)	814 (27.715)
≥ 3.500	1182 (32.348)	947 (32.244)
1.300 ≤ FMPIR < 3.500	1417 (38.779)	1176 (40.041)
BMI	26.300 (23.130, 30.000)	32.300 (28.500, 37.300)	< 0.001
BMIgroup			< 0.001
< 25	1421 (38.889)	206 (7.014)
≥ 30	933 (25.534)	1912 (65.100)
25 ≤ BMI < 30	1300 (35.577)	819 (27.886)
CAP	224 (198, 249)	316 (293, 348)	< 0.001
LSM	4.600 (3.800, 5.600)	5.600 (4.500, 7.100)	< 0.001
neutrophil	3.700 (2.800, 4.700)	4.200 (3.300, 5.300)	< 0.001
lymphocyte	2.000 (1.600, 2.500)	2.200 (1.800, 2.700)	< 0.001
albumin	4.100 (3.900, 4.300)	4.000 (3.800, 4.300)	< 0.001
HTN			< 0.001
no	3138 (85.878)	2393 (81.478)
yes	516 (14.122)	544 (18.522)
HL			< 0.001
no	3435 (94.007)	2421 (82.431)
yes	219 (5.993)	516 (17.569)
DM			< 0.001
no	3325 (90.996)	2268 (77.222)
yes	329 (9.004)	669 (22.778)
ALI	58.680 (43.170, 82.000)	70.980(51.180, 96.650)	< 0.001

Table 2 presents the clinical characteristics of participants stratified by AHF status. The ALI level was significantly higher in the AHF group compared to the Non-AHF group (72.420 vs 63.360, P < 0.001). The AHF group had a higher age and BMI. Additionally, the AHF group was more inclined to male, smoking more than 100 cigarettes in life, and those with DM, HTN, or HL. Laboratory tests revealed significantly higher levels of neutrophils, lymphocytes, albumin, CAP, and LSM in the AHF group (P < 0.050). Please refer to Table 2 for detailed information.

Table 2

Clinical characteristics for the study participants based on the AHF.
Variables	Non-AHF (n = 5855)	AHF (n = 736)	P
age	51 (35, 64)	58 (45, 67)	< 0.001
Age group			< 0.001
18–39 years	1858 (31.734)	132 (17.935)
40–59 years	1949 (33.288)	262 (35.598)
60 + years	2048 (34.979)	342 (46.467)
sex			< 0.001
female	3101 (52.963)	325 (44.158)
male	2754 (47.037)	411 (55.842)
race			0.004
Mexican American	730 (12.468)	100 (13.587)
Non-Hispanic Black	1480 (25.278)	188 (25.543)
Non-Hispanic White	1961 (33.493)	281 (38.179)
Other	1684 (28.762)	167 (22.690)
education			0.053
College or above	3365 (57.472)	390 (52.989)
High school or equivalent	1390 (23.740)	200 (27.174)
Less than high school	1100 (18.787)	146 (19.837)
smoking			0.009
< 100 cigarettes in life	3551 (60.649)	409 (55.571)
≥ 100 cigarettes in life	2304 (39.351)	327 (44.429)
Marriage			0.007
Married/Living with partner	3420 (58.412)	432 (58.696)
Never married	1155 (19.727)	115 (15.625)
Widowed/Divorced/Separated	1280 (21.862)	189 (25.679)
Drinking			0.147
no	606 (10.350)	63 (8.560)
yes	5249 (89.650)	673 (91.440)
FMPIR	2.240 (1.190, 4.190)	2.150 (1.200, 3.730)	0.328
FMPIR group			0.004
< 1.300	1666 (28.454)	203 (27.582)
≥ 3.500	1924 (32.861)	205 (27.853)
1.300 ≤ FMPIR < 3.500	2265 (38.685)	328 (44.565)
BMI	28.300(24.700, 32.800)	35.800 (29.800,41.600)	< 0.001
BMIgroup			< 0.001
< 25	1557 (26.593)	70 (9.511)
≥ 30	2296 (39.214)	549 (74.592)
25 ≤ BMI < 30	2002 (34.193)	117 (15.897)
CAP	258 (216, 301)	321 (278, 360)	< 0.001
LSM	4.800 (4.000, 5.800)	10.100(8.700, 12.500)	< 0.001
neutrophil	3.900 (3.000, 5.000)	4.300 (3.300, 5.500)	< 0.001
lymphocyte	2.100 (1.700, 2.600)	2.200 (1.700, 2.700)	0.086
albumin	4.100 (3.900, 4.300)	4.000 (3.700, 4.200)	< 0.001
HTN			< 0.001
no	4959 (84.697)	572 (77.717)
yes	896 (15.303)	164 (22.283)
HL			< 0.001
no	5240 (89.496)	616 (83.696)
yes	615 (10.504)	120 (16.304)
DM			< 0.001
no	5110 (87.276)	483 (65.625)
yes	745 (12.724)	253 (34.375)
ALI	63.360 (45.890, 87.390)	72.420(52.180, 98.660)	< 0.001

3.2 Association Between ALI and CAP, LSM

Analyzing ALI as a continuous variable, we found a positive correlation between ALI and both CAP and LSM. In Model 3, for each unit increase in ALI, CAP increased by 0.510 units (β = 0.510; 95% CI: 0.465–0.555), and LSM increased by 0.011 units (β = 0.011; 95% CI: 0.009–0.013). When analyzing ALI as a quartile variable in Model 3, compared to the lowest quartile (Q1), the CAP values for ALI quartiles Q2, Q3, and Q4 were higher by 19.847 units (β = 19.847; 95% CI: 15.974–23.720), 32.445 units (β = 32.445; 95% CI: 28.583–36.307), and 47.096 units (β = 47.096; 95% CI: 42.937–51.255), respectively. Similarly, the LSM values for ALI quartiles Q2, Q3, and Q4 were higher by 0.177 units (β = 0.177; 95% CI: 0.013–0.340), 0.573 units (β = 0.573; 95% CI: 0.410–0.736), and 1.022 units (β = 1.022; 95% CI: 0.846–1.197), respectively. The trend test was significant (P < 0.001). Please refer to Tables 3 and 4 for detailed information.

Table 3

The association between ALI and CAP
Variables/CAP	Model		Model2		Model3
	β(95%CI)	P	β (95%CI)	P	β (95%CI)	P
ALI	0.411 (0.364, 0.457)	< 0.001	0.525 (0.479, 0.571)	< 0.001	0.510 (0.465, 0.555)	< 0.001
ALI_quantile
Q1(4.1625–46.325)	referennce		referennce		referennce
Q2(46.327–64.198)	15.392 (11.226, 19.557)	< 0.001	18.761 (14.772, 22.749)	< 0.001	19.847 (15.974, 23.720)	< 0.001
Q3( 64.211–88.735)	27.953 (23.847, 32.060)	< 0.001	32.441 (28.483, 36.399)	< 0.001	32.445 (28.583, 36.307)	< 0.001
Q4(88.746–212.914)	39.122 (34.758, 43.487)	< 0.001	48.019 (43.744, 52.293)	< 0.001	47.096 (42.937, 51.255)	< 0.001
P for trend		< 0.001		< 0.001		< 0.001

Table 4

The association between ALI and LSM
Variables/LSM	Model		Model2		Model3
	β (95%CI)	P	β (95%CI)	P	β (95%CI)	P
ALI	0.009 (0.008, 0.011)	< 0.001	0.011 (0.009, 0.013)	< 0.001	0.011 (0.009, 0.013)	< 0.001
ALI_quantile
Q1(4.162–46.325)	referennce		referennce		referennce
Q2(46.327–64.198)	0.052 (-0.115, 0.219)	0.543	0.114 (-0.052, 0.281)	0.177	0.177 (0.013, 0.340)	0.034
Q3(64.211–88.735)	0.439 (0.274, 0.604)	< 0.001	0.519 (0.354, 0.684)	< 0.001	0.573 (0.410, 0.736)	< 0.001
Q4(88.746 -212.914)	0.873 (0.698, 1.048)	< 0.001	0.998 (0.820, 1.176)	< 0.001	1.022 (0.846, 1.197)	< 0.001
P for trend		< 0.001		< 0.001		< 0.001

3.3 Smoothing Curve Fitting

We utilized smoothing curve fitting to investigate the relationship between ALI and both CAP and LSM. Our analysis revealed a positive correlation with inflection points at 88.287 and 98.420, respectively. When ALI is ≤ 88.287, each unit increase in ALI results in a 0.807 dB/m rise in CAP. When ALI is > 88.287, each unit increase in SIRI corresponds to a 0.102 dB/m increase in CAP. Similarly, when ALI is ≤ 98.420, each unit increase in ALI leads to a 0.015 Kpa increase in LSM. Please refer to Figs. 2 and 3 for detailed information.

Figure 2. Smooth curve-fitting relationship between ALI and CAP

Figure 3. Smooth curve-fitting relationship between ALI and LSM

3.4 Threshold and Saturation Effect Analysis

Smoothing curve fitting and threshold effect analysis were employed to investigate the relationship between ALI and both CAP and LSM. When ALI is ≤ 88.287, each unit increase in ALI results in a 0.807 dB/m rise in CAP. When ALI is > 88.287, each unit increase in ALI leads to a 0.102 dB/m increase in CAP. Additionally, when ALI is ≤ 98.420, each unit increase in ALI results in a 0.015 Kpa increase in LSM. Please refer to Table 5 for detailed information.

Table 5

Analysis of threshold effect between ALI and both CAP and LSM
CAP(db/m)	Adjust (95%CI) P value	LSM(Kpa)	Adjust (95%CI) P value
ALI		ALI
Inflection point	88.287	Inflection point	98.420
ALI<88.287	0.807 (0.732, 0.882) <0.001	ALI<98.42	0.015 (0.013, 0.018) <0.001
ALI>88.287	0.102(0.008, 0.195) 0.033	ALI>98.42	0.003(-0.002, 0.007) 0.283
Log likelihood ratio	<0.001	Log likelihood ratio	<0.001

The results of this cross-sectional study, which included 6,591 participants, showed that the ALI levels in the AHF and NAFLD groups were considerably higher than the control group. Multiple linear regression models, analyzing ALI as both a continuous and quartile variable, revealed a positive correlation between ALI and both CAP and LSM levels. A positive smooth curve fit was observed in the relationship between ALI, CAP, and LSM levels, with threshold effect inflection points at 88.287 and 98.420, respectively. Monitoring ALI can help identify those at risk for NAFLD due to systemic inflammation and nutritional imbalances. Maintaining ALI within a desirable range may lower the prevalence of NAFLD. Significant evidence for the primary prevention of NAFLD is provided by this study.

Upon review, this study represents the first NHANES investigation to examine the connection between ALI and hepatic steatosis and fibrosis. ALI has been frequently utilized as a predictive tool for various malignancies in previous studies. A cohort study of 425 individuals revealed that ALI could serve as a prognostic indicator for both overall survival (OS) and cancer-specific survival (CSS)among hepatocellular carcinoma patients after hepatectomy (10). In a single-center retrospective study involving 65 individuals, the ALl index demonstrated superior prognostic predictive capability relative to OS among patients with advanced HCC after hepatectomy (23). A single-center retrospective study of 98 individuals found that ALI could be employed as a novel prognostic marker in patients with advanced HCC treated with immunotherapy (24). This study identified significant correlations between ALI and hepatic steatosis and hepatic fibrosis. Apart from cancerous diseases, ALI is widely used for other diseases. A cohort study comprising 1440 individuals discovered an inverse J-shaped nonlinear association between elevated ALI and all-cause mortality among stroke patients (21). A study including 3,888 participants established a significant association between ALI and long-term all-cause cardiovascular mortality among type 2 diabetes patients (25) A cross-sectional study of 15,681 individuals found that ALI was significantly associated with long-term all-cause mortality among hypertensive patients(26). Furthermore, it was determined that the use of the independent prognostic factor - can predict reoperation rates after bowel resection surgery in Crohn's disease patients.

The effect of ALI on NAFLD may stem from the involvement of multiple pathophysiological mechanisms, including lipid metabolism disorders (27), oxidative stress (28), and inflammatory response (29).ALI, a composite index, integrates BMI, albumin levels, and NLR, effectively reflecting the patient's systemic inflammation level and nutritional metabolism. Obesity triggers excessive accumulation of adipose tissue in the body, leading to abnormal release of free fatty acids due to disordered lipid metabolism. These free fatty acids are transported to the liver through the circulatory system, resulting in fat accumulation in the liver and promoting the progression of NAFLD (30). Excessive fat accumulation induces oxidative stress in the liver which triggers an inflammatory response and eventually leads to liver fibrosis (31). Albumin is a major protein synthesized in the liver that reflects hepatic cell damage and apoptosis. Its levels are closely associated with liver function and metabolic status (32). Higher neutrophil counts serve as markers for persistent destructive inflammation processes while lower lymphocyte counts indicate relatively deficient immune regulation and impaired immunity (33). Albumin, a key protein synthesized in the liver, serves as an indicator of hepatocyte damage and apoptosis. It is closely linked to liver function and nutritional status(34). A higher neutrophil count is a marker of a persistent, destructive, and non-specific inflammatory process, while a lower lymphocyte count indicates relative immunodeficiency and compromised immunity(35). The NLR combines neutrophil and lymphocyte counts to provide a comprehensive assessment of the body's immune status and inflammatory response. NLR is considered an independent predictor of major morbidity, mortality, and long-term survival in NAFLD-related liver fibrosis(36).

Strengths: Our study is the first to explore the link between ALI and hepatic steatosis and fibrosis, which provides a valuable reference for identifying risk factors for hepatic steatosis and fibrosis in clinical settings; The NHANES dataset, with its nationwide population, offers robust sample representation as a cross-sectional study. Additionally, we constructed three models to adjust for potential covariate effects on outcomes, which makes the results more reliable.

Limitations: The present study, being cross-sectional, can only explore the association of risk between ALI and hepatic steatosis and fibrosis without establishing causality; therefore, future research should focus on larger prospective cohort studies.

Furthermore, despite adjusting for multiple covariates in our analysis, potential confounding factors still exist.

This study reveals a positive association between elevated ALI levels and both CAP and LSM levels, highlighting the importance of inflammation and nutritional status in diagnosing hepatic steatosis and fibrosis. As a novel indicator, ALI could be a valuable predictor of hepatic steatosis and fibrosis progression. Managing ALI within an optimal range, through weight management, maintaining normal albumin levels, and anti-inflammatory therapy, may reduce the risk of hepatic steatosis and fibrosis. Nevertheless, further large-scale prospective studies are necessary to confirm these findings.

Data availability: The dataset supporting the conclusions of this article is available in the NHANES repository, https://www.cdc.gov/nchs/nhanes/index.htm.

Ethics Statement: The NHANES Research Project was approved by the Research Ethics Review Board (ERB) of the National Center for Health Statistics (NCHS) under Agreement No. Continuation of Protocol #2011-17; Protocol #2018-01. The study was conducted in accordance with the ethical standards set forth in the 1964 Declaration of Helsinki and its later amendments. All respondents provided written informed consent.

Funding Statement: This research was supported by the National Natural Science Foundation of China Grant (NO.81673806) and the Research Topic supported by the China Pharmaceutical Education Association (2020KTY001).

Author Contribution Statement: Yajie Liu was responsible for the writing, statistical analysis, and chart drawing of the paper; Professor Ruilin Wang is responsible for formulating writing ideas, guiding writing, data curation, and data review.

Conflict of Interest Statement: There is no conflict of interest in this article.

1. Targher G, Tilg H, Byrne CD. Non-alcoholic fatty liver disease: a multisystem disease requiring a multidisciplinary and holistic approach. Lancet Gastroenterol Hepatol. 2021 Jul;6(7):578 − 88.
2. Younossi Z, Anstee QM, Marietti M, Hardy T, Henry L, Eslam M, et al. Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention. Nat Rev Gastroenterol Hepatol. 2018 Jan;15(1):11–20.
3. Estes C, Razavi H, Loomba R, Younossi Z, Sanyal AJ. Modeling the epidemic of nonalcoholic fatty liver disease demonstrates an exponential increase in burden of disease. Hepatology. 2018 Jan;67(1):123 − 33.
4. Younossi ZM. Non-alcoholic fatty liver disease - A global public health perspective. J Hepatol. 2019 Mar;70(3):531 − 44.
5. Tan DJH, Ng CH, Lin SY, Pan XH, Tay P, Lim WH, et al. Clinical characteristics, surveillance, treatment allocation, and outcomes of non-alcoholic fatty liver disease-related hepatocellular carcinoma: a systematic review and meta-analysis. Lancet Oncol. 2022 Apr;23(4):521 − 30.
6. Powell EE, Wong VW, Rinella M. Non-alcoholic fatty liver disease. Lancet. 2021 Jun 5;397(10290):2212-24.
7. Yao J, Chen X, Meng F, Cao H, Shu X. Combined influence of nutritional and inflammatory status and depressive symptoms on mortality among US cancer survivors: Findings from the NHANES. Brain Behav Immun. 2024 Jan;115:109 − 17.
8. Song M, Zhang Q, Song C, Liu T, Zhang X, Ruan G, et al. The advanced lung cancer inflammation index is the optimal inflammatory biomarker of overall survival in patients with lung cancer. J Cachexia Sarcopenia Muscle. 2022 Oct;13(5):2504-14.
9. Kusunoki K, Toiyama Y, Okugawa Y, Yamamoto A, Omura Y, Ohi M, et al. Advanced Lung Cancer Inflammation Index Predicts Outcomes of Patients With Colorectal Cancer After Surgical Resection. Dis Colon Rectum. 2020 Sep;63(9):1242-50.
10. Wen YZ, Liu GM, Liao JP, Xu JW. Advanced lung cancer inflammation index predicts overall survival of hepatocellular carcinoma after hepatectomy. Front Oncol. 2024;14:1294253.
11. Pang H, Dai L, Chen L, Chen X, Chen Z, Zhang S, et al. Prognostic value of the advanced lung cancer inflammation index in patients with gastric cancer after radical gastrectomy: a propensity-score matching cohort study and meta-analysis. BMC Cancer. 2024 May 13;24(1):583.
12. Kusunoki K, Toiyama Y, Okugawa Y, Yamamoto A, Omura Y, Kusunoki Y, et al. The advanced lung cancer inflammation index predicts outcomes in patients with Crohn's disease after surgical resection. Colorectal Dis. 2021 Jan;23(1):84–93.
13. Yuan X, Huang B, Wang R, Tie H, Luo S. The prognostic value of advanced lung cancer inflammation index (ALI) in elderly patients with heart failure. Front Cardiovasc Med. 2022;9:934551.
14. Tu J, Wu B, Xiu J, Deng J, Lin S, Lu J, et al. Advanced lung cancer inflammation index is associated with long-term cardiovascular death in hypertensive patients: national health and nutrition examination study, 1999–2018. Front Physiol. 2023;14:1074672.
15. Tilg H, Effenberger M. From NAFLD to MAFLD: when pathophysiology succeeds. Nat Rev Gastroenterol Hepatol. 2020 Jul;17(7):387-8.
16. Hu M, Yang M, Tang H, Zhang C. The association between exposure to volatile organic chemicals and serum α-Klotho in USA middle to old aged population: A cross-sectional study from NHANES 2011–2016. Sci Total Environ. 2024 May 16:173083.
17. Eddowes PJ, Sasso M, Allison M, Tsochatzis E, Anstee QM, Sheridan D, 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 May;156(6):1717-30.
18. Siddiqui MS, Vuppalanchi R, Van Natta ML, Hallinan E, Kowdley KV, Abdelmalek M, et al. Vibration-Controlled Transient Elastography to Assess Fibrosis and Steatosis in Patients With Nonalcoholic Fatty Liver Disease. Clin Gastroenterol Hepatol. 2019 Jan;17(1):156 − 63.e2.
19. Xie R, Xiao M, Li L, Ma N, Liu M, Huang X, et al. Association between SII and hepatic steatosis and liver fibrosis: A population-based study. Front Immunol. 2022;13:925690.
20. Rinella ME, Neuschwander-Tetri BA, Siddiqui MS, Abdelmalek MF, Caldwell S, Barb D, et al. AASLD Practice Guidance on the clinical assessment and management of nonalcoholic fatty liver disease. Hepatology. 2023 May 1;77(5):1797 − 835.
21. Chen X, Hong C, Guo Z, Huang H, Ye L. Association between advanced lung cancer inflammation index and all-cause and cardiovascular mortality among stroke patients: NHANES, 1999–2018. Front Public Health. 2024;12:1370322.
22. She D, Jiang S, Yuan S. Association between serum cotinine and hepatic steatosis and liver fibrosis in adolescent: a population-based study in the United States. Sci Rep. 2024 May 19;14(1):11424.
23. Li Q, Ma F, Tsilimigras DI, Åberg F, Wang JF. The value of the Advanced Lung Cancer Inflammation Index (ALI) in assessing the prognosis of patients with hepatocellular carcinoma treated with camrelizumab: a retrospective cohort study. Ann Transl Med. 2022 Nov;10(22):1233.
24. Li Q, Ma F, Wang JF. Advanced lung cancer inflammation index predicts survival outcomes of hepatocellular carcinoma patients receiving immunotherapy. Front Oncol. 2023;13:997314.
25. Chen Y, Guan M, Wang R, Wang X. Relationship between advanced lung cancer inflammation index and long-term all-cause, cardiovascular, and cancer mortality among type 2 diabetes mellitus patients: NHANES, 1999–2018. Front Endocrinol (Lausanne). 2023;14:1298345.
26. Zhang Y, Pan Y, Tu J, Liao L, Lin S, Chen K, et al. The advanced lung cancer inflammation index predicts long-term outcomes in patients with hypertension: National health and nutrition examination study, 1999–2014. Front Nutr. 2022;9:989914.
27. Li Z, Zhang B, Liu Q, Tao Z, Ding L, Guo B, et al. Genetic association of lipids and lipid-lowering drug target genes with non-alcoholic fatty liver disease. EBioMedicine. 2023 Apr;90:104543.
28. Chen Z, Tian R, She Z, Cai J, Li H. Role of oxidative stress in the pathogenesis of nonalcoholic fatty liver disease. Free Radic Biol Med. 2020 May 20;152:116 − 41.
29. Diehl AM, Day C. Cause, Pathogenesis, and Treatment of Nonalcoholic Steatohepatitis. N Engl J Med. 2017 Nov 23;377(21):2063-72.
30. Eslam M, Newsome PN, Sarin SK, Anstee QM, Targher G, Romero-Gomez M, et al. A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement. J Hepatol. 2020 Jul;73(1):202-9.
31. Quek J, Chan KE, Wong ZY, Tan C, Tan B, Lim WH, et al. Global prevalence of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in the overweight and obese population: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2023 Jan;8(1):20–30.
32. Hajri T, Gharib M, Fungwe T, M'Koma A. Very low-density lipoprotein receptor mediates triglyceride-rich lipoprotein-induced oxidative stress and insulin resistance. Am J Physiol Heart Circ Physiol. 2024 May 24.
33. Haddad GM, Gestic MA, Utrini MP, Chaim FDM, Chaim EA, Cazzo E. DIAGNOSTIC ACCURACY OF THE NON-INVASIVE MARKERS NFLS, NI-NASH-DS, AND FIB-4 FOR ASSESSMENT OF DIFFERENT ASPECTS OF NON-ALCOHOLIC FATTY LIVER DISEASE IN INDIVIDUALS WITH OBESITY: CROSS-SECTIONAL STUDY. Arq Gastroenterol. 2024;61:e23050.
34. Parthasarathy G, Revelo X, Malhi H. Pathogenesis of Nonalcoholic Steatohepatitis: An Overview. Hepatol Commun. 2020 Apr;4(4):478 − 92.
35. Adane T, Melku M, Worku YB, Fasil A, Aynalem M, Kelem A, et al. The Association between Neutrophil-to-Lymphocyte Ratio and Glycemic Control in Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis. J Diabetes Res. 2023;2023:3117396.
36. Peng Y, Li Y, He Y, Wei Q, Xie Q, Zhang L, et al. The role of neutrophil to lymphocyte ratio for the assessment of liver fibrosis and cirrhosis: a systematic review. Expert Rev Gastroenterol Hepatol. 2018 May;12(5):503 − 13.

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Advanced Lung Cancer Inflammation Index ： A Key Predictor of Hepatic Steatosis and Fibrosis Severity

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

1. Introduction

2. Materials and methods

2.1. Study design and participants

2.2 Definition of NAFLD and AHF

2.3 ALI Levels

2.4 Assessment of Covariates

2.5 Statistical analysis

3. Results

3.1 General Demographic Characteristics of the Study Population

3.2 Association Between ALI and CAP, LSM

3.3 Smoothing Curve Fitting

3.4 Threshold and Saturation Effect Analysis

4. Discussion

5. Strengths and limitations

6. Conclusion

Declarations

References

Additional Declarations

Status:

Version 1