Advanced Ultrasound in Diagnosis and Therapy ›› 2025, Vol. 9 ›› Issue (2): 197-206.doi: 10.37015/AUDT.2025.250017
• Original Research • Previous Articles Next Articles
Zhang Simina, Zhou Changyua, Shi Xianquanb,*(), Huang Lizhena,*
Received:
2025-04-19
Revised:
2025-03-25
Accepted:
2025-04-29
Online:
2025-06-30
Published:
2025-07-06
Contact:
Department of Gastroenterology, China-Japan Union Hospital of Jilin University,126 Xiantai Street, Changchun, Jilin, China. e-mail: Zhang Simin, Zhou Changyu, Shi Xianquan, Huang Lizhen. Predictive Value of AIP and AGR for Non-alcoholic Fatty Liver Disease and Significant Liver Fibrosis. Advanced Ultrasound in Diagnosis and Therapy, 2025, 9(2): 197-206.
Table 1
Baseline Characteristics of the NAFLD Group and the Non-NAFLD Group"
Research variables | All patients (n = 960) | NAFLD (n = 346) | Non-NAFLD (n =614) | P value |
---|---|---|---|---|
Age (years) | 50 (28) | 55 (24) | 47(28) | < 0.001 |
Gender n(%) | 0.083 | |||
Male | 522(54.4) | 201(58.1) | 321(52.3) | |
Female | 438(45.6) | 145(41.9) | 293(47.7) | |
Race n(%) | < 0.001 | |||
Mexican Americans | 93(9.7) | 45(13.0) | 48(7.8) | |
Other Hispanics | 92(9.6) | 33(9.5) | 59(9.6) | |
Non-Hispanic Whites | 325(33.9) | 137(39.6) | 188(30.6) | |
Non-Hispanic Blacks | 239(24.9) | 72(20.8) | 167(27.2) | |
Other races-including multi-races | 211(22.0) | 59(17.1) | 152(24.8) | |
Education level n(%) | 0.134 | |||
Low | 140(14.6) | 54(15.6) | 86(14.0) | |
Medium | 199(20.7) | 82(23.7) | 117(19.1) | |
High | 621(64.7) | 210(60.7) | 411(66.9) | |
Marital status n(%) | < 0.001 | |||
Married/Cohabiting | 622(64.8) | 258(74.6) | 364(59.3) | |
Divorced/Widowed/Separated | 172(17.9) | 51(14.7) | 121(19.7) | |
Unmarried | 166(17.3) | 37(10.7) | 129(21.0) | |
Poverty Index (PIR) n(%) | 0.355 | |||
PIR ≤ 1.3 | 231(24.1) | 92(26.6) | 139(22.6) | |
1.3 < PIR ≤ 1.85 | 147(15.3) | 49(14.2) | 98(16.0) | |
1.85 < PIR | 582(60.6) | 205(59.2) | 377(61.4) | |
Smoking n(%) | 0.094 | |||
Yes | 309(32.2) | 123(35.5) | 186(30.3) | |
No | 651(67.8) | 223(64.5) | 428(69.7) | |
Diabetes n(%) | < 0.001 | |||
Yes | 201(20.9) | 126(36.4) | 75(12.2) | |
No | 290(30.2) | 45(13.0) | 245(39.9) | |
Prediabetes | 469(48.9) | 175(50.6) | 294(47.9) | |
Hypertension n(%) | < 0.001 | |||
Yes | 399(41.6) | 190(54.9) | 209(34.0) | |
No | 561(58.4) | 156(45.2) | 405(66.0) | |
Cardiovascular disease n(%) | 0.014 | |||
Yes | 73(7.6) | 36(10.4) | 37(6.0) | |
No | 887(92.4) | 310(89.6) | 577(94.0) | |
Hypercholesterolemia n(%) | 0.316 | |||
Yes | 289(30.1) | 111(32.1) | 178(29.0) | |
No | 671(69.9) | 235(67.9) | 436(71.0) | |
Hypertriglyceridemia n(%) | < 0.001 | |||
Yes | 170(17.7) | 106(30.6) | 64(10.4) | |
No | 790(82.3) | 240(69.4) | 550(89.6) | |
Exercise level n(%) | < 0.001 | |||
None or light | 417(43.4) | 184(53.2) | 233(37.9) | |
Moderate | 279(29.1) | 94(27.2) | 185(30.1) | |
Vigorous | 264(27.5) | 68(19.7) | 196(31.9) | |
Sedentary time (h) n(%) | 0.014 | |||
< 4 h | 302(31.5) | 90(26.0) | 212(34.5) | |
4-5 h | 236(24.6) | 89(25.7) | 147(23.9) | |
6-7 h | 144(15.0) | 49(14.2) | 95(15.5) | |
≥ 8h | 278(29.0) | 118(34.1) | 160(26.1) | |
BMI (kg/m2) | 28.0(8.3) | 32.4(8.7) | 26.0(6.5) | < 0.001 |
WC (cm) | 97.50(22.75) | 109.0(20.05) | 91.70(18.78) | < 0.001 |
Laboratory indicators | ||||
ALT (U/L) | 18.0(12.0) | 22.0(15.0) | 16.0(10.0) | < 0.001 |
AST (U/L) | 19.0(7.0) | 20.0(9.0) | 18.0(7.0) | < 0.001 |
GGT (U/L) | 20.0(16.0) | 24.5(19.0) | 18.0(13.0) | < 0.001 |
ALP (IU/L) | 71.0(27.00) | 75.0(28.25) | 69.0(25.00) | < 0.001 |
LDH (U/L) | 149.0(37.00) | 151.0(37.25) | 148.0(35.25) | 0.013 |
Albumin (g/L) | 40.0(4.0) | 40.0(4.0) | 41.0(4.0) | < 0.001 |
FBG (mmo/L) | 5.72(0.94) | 6.11(1.28) | 5.55(0.78) | < 0.001 |
HbA1c (%) | 5.6(0.6) | 5.8(1.0) | 5.5(0.6) | < 0.001 |
Uric acid (umol/L) | 318.2(111.5) | 345(114.5) | 303.3(101.1) | < 0.001 |
Blood urea nitrogen (mmol/L) | 5.0(2.14) | 5.36(2.15) | 4.64(1.87) | < 0.001 |
Creatinine (umol/L) | 75.14(24.75) | 76.02(25.63) | 75.14(24.75) | 0.687 |
TC (mmol/L) | 4.65(1.40) | 4.70(1.46) | 4.65(1.37) | 0.28 |
LDL (mmol/L) | 2.767(1.190) | 2.845(1.215) | 2.74(1.138) | 0.185 |
Insulin (uU/ml) | 9.81(9.94) | 15.59(13.54) | 7.60(6.68) | < 0.001 |
CRP (mg/L) | 1.54(2.96) | 2.48(4.09) | 1.18(2.05) | < 0.001 |
TBIL (umol/L) | 6.84(5.13) | 6.84(5.13) | 8.55(5.13) | 0.02 |
AGR (g/U) | 2.05(1.52) | 1.59(1.09) | 2.32(1.71) | < 0.001 |
AIP | -0.120 ± 0.321 | 0.039 ± 0.295 | -0.200 ± 0.298 | < 0.001 |
Table 2
Univariate and multivariate logistic regression analyses of NAFLD"
Variables | Univariate analysis | Multivariate analysis | |||
---|---|---|---|---|---|
OR (95% CI) | P value | OR (95% CI) | P value | ||
AGR (g/U) | 0.507 (0.439-0.585) | < 0.001 | 0.782 (0.655-0.934) | 0.007 | |
Q1 | 1 | ||||
Q2 | 0.710 (0.496-1.017) | 0.062 | |||
Q3 | 0.407 (0.281-0.589) | < 0.001 | |||
Q4 | 0.152 (0.098-0.236) | < 0.001 | |||
AIP | 17.433 (10.493-28.965) | < 0.001 | 3.549 (1.876-6.712) | < 0.001 | |
Q1 | 1 | ||||
Q2 | 2.385 (1.476-3.857) | < 0.001 | |||
Q3 | 5.923 (3.742-9.375) | < 0.001 | |||
Q4 | 10.684 (6.733-16.955) | < 0.001 | |||
Age (years) | 1.019 (1.011-1.027) | < 0.001 | |||
Gender n(%) | |||||
Male | 1.265 (0.970-1.651) | 0.083 | |||
Female | |||||
Race n(%) | < 0.001 | 0.096 | |||
Mexican Americans | |||||
Other Hispanics | 2.415 (1.457-4.005) | < 0.001 | 1.61 (0.863-3.006) | 0.135 | |
Non-Hispanic Whites | 1.441 (0.855-2.427) | 0.17 | 1.031 (0.54-1.966) | 0.927 | |
Non-Hispanic Blacks | 1.877 (1.293-2.725) | < 0.001 | 1.37 (0.848-2.213) | 0.198 | |
Other races-including multi-races | 1.111 (0.738-1.671) | 0.614 | 0.779 (0.448-1.354) | 0.376 | |
Education level n(%) | 0.134 | ||||
Low | |||||
Medium | 1.116 (0.717-1.737) | 0.626 | |||
High | 0.286 (0.557-1.188) | 0.286 | |||
Marital status n(%) | < 0.001 | 0.004 | |||
Married/Cohabiting | |||||
Divorced/Widowed/Separated | 2.471 (1.658-3.682) | < 0.001 | 2.241 (1.312-3.826) | 0.003 | |
Unmarried | 1.470 (0.900-2.400) | 0.124 | 1.416 (0.746-2.689) | 0.287 | |
Poverty Index (PIR) n(%) | 0.355 | ||||
PIR ≤ 1.3 | 1.217 (0.890-1.665) | 0.219 | |||
1.3 < PIR ≤ 1.85 | 0.920 (0.627-1.348) | 0.667 | |||
1.85 < PIR | |||||
Smoking n(%) | |||||
Yes | |||||
No | 0.095 (0.596-1.042) | 0.095 | |||
Diabetes n(%) | < 0.001 | ||||
No | |||||
Yes | 9.147 (5.965-14.026) | < 0.001 | |||
Prediabetes | 3.241 (2.241-4.686) | < 0.001 | |||
Hypertension n(%) | |||||
No | |||||
Yes | 2.360 (1.802-3.091) | < 0.001 | |||
Exercise level n(%) | < 0.001 | ||||
None or light | 1.554 (1.135-2.129) | 0.006 | |||
Moderate | 0.683 (0.471-0.990) | 0.044 | |||
Vigorous | |||||
Sedentary time (h) n(%) | 0.015 | ||||
< 4 h | 0.576 (0.409-0.810) | 0.002 | |||
4-5 h | 0.821 (0.576-1.171) | 0.276 | |||
6-7 h | 0.699 (0.460-1.063) | 0.094 | |||
≥ 8 h | |||||
Cardiovascular disease n(%) | |||||
Yes | |||||
No | 0.552 (0.342-0.892) | 0.015 | |||
Hypercholesterolemia n(%) | |||||
Yes | |||||
No | 0.864 (0.650-1.150) | 0.316 | |||
Hypertriglyceridemia n(%) | |||||
Yes | |||||
No | 0.263 (0.187-0.372) | < 0.001 | |||
BMI (kg/m²) | 1.189 (1.157-1.222) | < 0.001 | |||
WC (cm) | 1.088 (1.075-1.101) | < 0.001 | 1.066 (1.05-1.081) | < 0.001 | |
ALT (U/L) | 1.048 (1.036-1.061) | < 0.001 | |||
AST (U/L) | 1.026 (1.011-1.041) | < 0.001 | |||
ALP (U/L) | 1.013 (1.007-1.019) | < 0.001 | |||
LDH (U/L) | 1.004 (1.000-1.009) | 0.034 | |||
Urea nitrogen (mmol/L) | 1.139 (1.055-1.229) | < 0.001 | |||
Creatinine (umol/L) | 1.001 (0.997-1.006) | 0.631 | |||
FBG (mmo/L) | 1.418 (1.274-1.578) | < 0.001 | |||
HbA1c (%) | 1.903 (1.596-2.27) | < 0.001 | 1.327 (1.111-1.587) | 0.002 | |
TBIL (umol/L) | 0.96 (0.932-0.989) | 0.008 | |||
Uric acid (umol/L) | 1.006 (1.005-1.008) | < 0.001 | |||
LDL (mmol/L) | 1.119 (0.974-1.286) | 0.113 | |||
TC (mmol/L) | 1.101 (0.974-1.246) | 0.125 | |||
Insulin (uU/ml)(uU/ml) | 1.095 (1.075-1.115) | < 0.001 | 1.021 (1.004-1.039) | 0.013 | |
CRP (mg/L) | 1.081 (1.046-1.117) | < 0.001 |
[1] | Younossi ZM, Golabi P, Paik JM, Henry A, Van Dongen C, Henry L. The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review. Hepatology 2023;77:1335-1347. |
[2] | Shang Y, Akbari C, Dodd M, Nasr P, Vessby J, Rorsman F, et al. Cause of death by fibrosis stage in 959 patients with biopsy-proven NAFLD. Gut 2024;73:e30. |
[3] | Liu J, Zhou L, An Y, Wang Y, Wang G.The atherogenic index of plasma: A novel factor more closely related to non-alcoholic fatty liver disease than other lipid parameters in adults. Front Nutr 2022;9:954219. |
[4] | Alifu J, Xiang L, Zhang W, Qi P, Chen H, Liu L, et al. Association between the atherogenic index of plasma and adverse long-term prognosis in patients diagnosed with chronic coronary syndrome. Cardiovasc Diabetol 2023;22:255. |
[5] | Zhang X, Zhang X, Li X, Feng J, Chen X. Association of metabolic syndrome with atherogenic index of plasma in an urban Chinese population: A 15-year prospective study. NutrMetab Cardiovasc Dis 2019;29:1214-1219. |
[6] | Pei K, Gui T, Kan D, Feng H, Jin Y, Yang Y, et al. An Overview of Lipid Metabolism and Nonalcoholic Fatty Liver Disease. Biomed Res Int 2020;2020:4020249. |
[7] | Heredia NI, Zhang X, Balakrishnan M, Hwang JP, Thrift AP.Association of lifestyle behaviors with non-alcoholic fatty liver disease and advanced fibrosis detected by transient elastography among Hispanic/Latinos adults in the U.S. Ethn Health 2023;28:299-312. |
[8] | 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;156:1717-1730. |
[9] | Staufer K, Halilbasic E, Spindelboeck W, Eilenberg M, Prager G, Stadlbauer V, et al. Evaluation and comparison of six noninvasive tests for prediction of significant or advanced fibrosis in nonalcoholic fatty liver disease. United European Gastroenterol J 2019;7:1113-1123. |
[10] | Dobiásová M. Atherogenic index of plasma log(triglycerides/HDL-cholesterol):theoretical and practical implications. Clin Chem 2004;50:1113-1115. |
[11] | Younossi ZM, Golabi P, Price JK, Owrangi S, Gundu-Rao N, Satchi R, et al. The global epidemiology of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis among patients with type 2 diabetes. Clin Gastroenterol Hepatol 2024;22:1999-2010. |
[12] | Nakagami H. Mechanisms underlying the bidirectional association between nonalcoholic fatty liver disease and hypertension. Hypertens Res 2023;46:539-541. |
[13] | Ajmal MR, Yaccha M, Malik MA, Rabbani MU, Ahmad I, Isalm N, et al. Prevalence of nonalcoholic fatty liver disease (NAFLD) in patients of cardiovascular diseases and its association with hs-CRP and TNF-α. Indian Heart J 2014;66:574-579. |
[14] | Utzschneider K.M., Kahn S.E. The Role of Insulin Resistance in Nonalcoholic Fatty Liver Disease. J Clin Endocrinol Metab 2006;91:4753-4761. |
[15] | Zhang Y, Yang S, Zhang M, Wang Z, He X, Hou Y, et al. Glycyrrhetinic acid improves insulin-response pathway by regulating the balance between the Ras/MAPK and PI3K/Akt pathways. Nutrients 2019;11:604. |
[16] | Lund-Katz S, Phillips MC. High density lipoprotein structure-function and role in reverse cholesterol transport. Subcell Biochem 2010;51:183-227. |
[17] | Rashid S, Uffelman KD, Lewis GF. The mechanism of HDL lowering in hypertriglyceridemic, insulin-resistant states. J Diabetes Complications 2002;16:24-28. |
[18] | Ma B, Ju A, Zhang S, An Q, Xu S, Liu J, et al. Albumosomes formed by cytoplasmic pre-folding albumin maintain mitochondrial homeostasis and inhibit nonalcoholic fatty liver disease. Signal Transduct Target Ther 2023;8:229. |
[19] | Masarone M, Rosato V, Dallio M, Gravina AG, Aglitti A, Loguercio C, et al. Role of oxidative stress in pathophysiology of nonalcoholic fatty liver fisease. Oxid Med Cell Longev 2018;2018:9547613. |
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