Abstract
Non-alcoholic fatty liver disease (NAFLD) encompasses a broad spectrum of diseases and stands as the second most prevalent liver disorder in the 21st century. Advanced hepatic fibrosis (AHF) is a crucial indicator of the progression of NAFLD. Selenium (Se) is an indispensable trace element for human physiology; however, excessive intake can lead to poisoning and detrimental effects. Notably, males exhibit significantly higher serum Se levels compared to females. To investigate the correlation between serum Se levels and the prevalence of NAFLD and AHF across different genders. Utilizing data from the National Health and Nutrition Examination Survey (NHANES) 2017–2020, 7271 participants were included. Through descriptive analysis, multivariable logistic regression, subgroup analysis, interaction, and restricted cubic spline regression analysis, the relationship between serum Se levels and the prevalence of NAFLD and AHF was investigated. serum Se levels were significantly higher in both male and female NAFLD groups compared to the non-NAFLD groups (Males: 187.570 vs 183.300, Z = −16.169, P < .001; Females: 184.780 vs 180.130, Z = −4.102, P < .001). After adjusting for confounders, an increase in one quartile of serum Se was associated with a 17.60% increase in NAFLD prevalence in males (OR, 1.176; 95% CI: 1.052–1.315) and a 38.50% decrease in AHF prevalence (OR, 0.615; 95% CI: 0.479–0.789). In females, each quartile increase in serum Se was associated with a 29.10% increase in NAFLD prevalence (OR,1.291;95%CI: 1.155–1.442) and a 51.60% decrease in AHF prevalence (OR, 0.484; 95% CI: 0.344–0.682). serum Se levels are positively correlated with the prevalence of NAFLD and negatively correlated with the prevalence of AHF in both males and females.
Keywords: advanced hepatic fibrosis, non-alcoholic fatty liver disease, serum selenium
1. Introduction
Non-alcoholic fatty Liver Disease (NAFLD) is a syndrome characterized by excessive accumulation of fat in hepatocytes, excluding alcohol or other clear causes of liver injury. Its spectrum includes non-alcoholic steatohepatitis, related cirrhosis, and hepatocellular carcinoma.[1,2]Epidemiological surveys indicate that NAFLD is the most common chronic liver disease globally, affecting 25.24% of adults.[3] With improving living standards and an increasing number of individuals with obesity, the global burden of NAFLD is parallel to the increase in the global obesity rate.[4] In the United States, it estimated that over 64 million people have NAFLD, with direct healthcare costs amounting to approximately $103 billion annually.[5]
Advanced hepatic fibrosis (AHF) is a crucial indicator of the progression of NAFLD, characterized by the excessive proliferation and deposition of extracellularmatrix (ECM) within the liver tissue. Essentially, AHF represents an excessive and reversible repair response to chronic liver injury. With progressive ECM accumulation, liver parenchyma is gradually replaced by scar tissue formed by ECM, eventually leading to cirrhosis.[6] Hepatic fibrosis is one of the primary predictors of poor prognosis in NAFLD patients.[7] As fibrotic severity escalates, the risk of cirrhosis, liver failure, and hepatocellular carcinoma in NAFLD patients significantly rises.[8]
Selenium (Se) is an essential trace element acquired through dietary intake.[9] Following absorption by the gastrointestinal tract, Se is transported to the liver, which serves as the central organ for Se metabolism.[10] Se is closely related to the severity of liver injury.[11] Se exposure can induce elevated serum liver enzyme levels, activation of Kupffer cells, hepatic insulin resistance, and increased hepatic triglyceride concentrations, promoting the progression of NAFLD.[12] There are significant gender differences in serum Se levels, with males having notably higher levels than females.[13] Currently, the U.S. Food and Drug Administration has not approved any safe and effective treatment for NAFLD and AHF, and lifestyle modifications (diet and exercise) remain the primary treatment method, highlighting the importance of NAFLD and AHF prevention.[14]
Given the correlation of serum Se and the pathogenesis of NAFLD and AHF, as well as gender differences, this study categorizes by gender to investigate the correlation between serum Se and the prevalence of NAFLD and AHF, to formulate targeted prevention strategies at individual and population levels.
2. Study object and methods
2.1. Study object
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.[15] The public database website is https://www.cdc.gov/nchs/nhanes/. In this study, we selected data from NHANES for the years 2017 to 2020. All databases used in this study are available online at the NHANES website. Inclusion criteria: registered participants aged 18 and over from the NHANES database for the years 2017 to 2020 (N = 9693). Exclusion criteria: individuals lacking liver transient elastography data (N = 1376), individuals with hepatitis B or C (N = 128), individuals with significant alcohol consumption (men > 30g/day, women > 20g/day) (N = 456), and individuals lacking serum Se indicators (N = 462). Ultimately, 7271 participants were included in the analysis, divided by gender into 3517 males and 3700 females. The detailed screening process is shown in Figure 1.
Figure 1.
Filter flow chart.
2.2. Assessment of NAFLD and AHF
The controlled attenuation parameter (CAP) is a new technology based on ultrasound transient elastography for the quantitative diagnosis of fatty liver, with reliability comparable to liver biopsy for NAFLD detection.[16,17] According to the 2023 American Association for the Study of Liver Diseases NAFLD Clinical Practice Guidelines, NAFLD is diagnosed with a CAP ≥ 288db·m−1, excluding individuals with hepatitis B or C and significant drinking (men > 30g/day, women > 20g/day), and AHF is diagnosed with a Fibrosis-4 Index (FIB-4) ≥ 2.67.[18]
2.3. Serum Se levels
Serum Se levels were measured using an Inductively Coupled Plasma Dynamic Reaction Cell Mass Spectrometer at the National Center laboratory according to standard protocols.[19] The detection limit for serum Se in this study was 24.480 μg·L−1, with values below the detection limit replaced by the detection limit divided by the square root of 2.[20] FIB-4 formula is given by: FIB-4 = (age × aspartate aminotransferase [AST])/(Platelet Count × square root of alanine aminotransferase [ALT]).[21]
2.4. Covariates
Age, gender, race, marriage, education, family income and poverty-to-income ratio (FMPIR), body mass index (BMI), smoking, and drinking were extracted from the household questionnaire and used as covariates.[19] Histories of diabetes mellitus (DM), hypertension (HTN), and hyperlipidemia (HL) refer to self-reported diagnoses of these specific diseases.[22]
2.5. Statistical analysis
R4.2.2 software has been used for this study. If the Continuous variables conform to the normal distribution, it is represented by mean ± standard deviation, and if it is skewed, it is represented by M (P25, P75). Categorical data were represented by rates and proportions. Multivariable-adjusted logistic models were used to investigate the relationship between serum Se levels and NAFLD and AHF in males and females. Serum Se levels were divided into continuous variables and categorical variables (divided into quartiles, with the first quartile as reference), and odds ratios (OR) and corresponding 95% confidence intervals (95%CI) were calculated for 3 models. Model 1 did not adjust for any variables, Model 2 adjusted for age, race, education, marriage, FMPIR, BMI sociodemographic variables, and Model 3 further adjusted for smoking, drinking, DM, HTN, and HL health-related factors on the basis of Model 2. Restricted cubic spline regression analysis was used to test for non-linear correlation between serum Se and NAFLD/AHF, visualizing the dose-response relationship. Additionally, in subgroup analyses, males and females were categorized by age, race, education, marriage, FMPIR, BMI, smoking, drinking, DM, HTN, and HL to estimate prevalence interactions and detect potential differences in the relationship between serum Se and NAFLD/AHF.
3. Result
3.1. General demographic characteristics of the study object
The study included a total of 3571 male participants (1434 in the NAFLD group and 2137 in the non-NAFLD group) and 3700 female participants (1143 in the NAFLD group and 2557 in the non-NAFLD group). Serum Se levels in both male and female NAFLD groups were higher than those in the non-NAFLD group, and the difference was statistically significant. (Males: 187.570 vs 183.300, Z = −16.169, P < .001; Females: 184.780 vs 180.130, Z = −4.102, P < .001). In males, compared to the non-NAFLD group, the NAFLD group was older, had higher BMI (BMI ≥ 30), with DM, HTN, or HL, higher ALT, and AST, with a statistically significant difference (P < .050). In females, compared to the non-NAFLD group, the NAFLD group was also older, lifetime smokers of ≥ 100 cigarettes, with DM, HTN, or HL, with a higher ALT, AST, and platelet counts, with a statistically significant difference (P < .050). See Tables 1 and 2 for details.
Table 1.
Male basic characteristics of research objects.
| Characteristics | Number of cases (n = 3571) |
Non-NAFLD group (n = 2137) |
NAFLD group (n = 1434) |
Statistical values |
P |
|---|---|---|---|---|---|
| Age (yr) | 50 (33, 64) | 47 (29, 64) | 53 (39, 65) | Z = −7.398 | <.001 |
| Age group | χ2 = 87.502 | <.001 | |||
| 18–39 yr old | 1211 (33.912) | 852 (39.869) | 359 (25.035) | ||
| 40–59 yr old | 1099 (30.776) | 577 (27.000) | 522 (36.402) | ||
| ≥60 yr old | 1261 (35.312) | 708 (33.131) | 553 (38.563) | ||
| Race | χ2 = 102.458 | <.001 | |||
| Mexican-American | 462 (12.938) | 209 (9.780) | 253 (17.643) | ||
| Non-Hispanic Blacks | 872 (24.419) | 628 (29.387) | 244 (17.015) | ||
| Non-Hispanic Whites | 1250 (35.004) | 701 (32.803) | 549 (38.285) | ||
| Other | 987 (27.639) | 599 (28.030) | 388 (27.057) | ||
| Education | χ2 = 7.458 | .429 | |||
| College degree or above | 1950 (54.607) | 1176 (55.030) | 774 (53.975) | ||
| High school or equivalent | 897 (25.119) | 543 (25.409) | 354 (24.686) | ||
| Below high school | 724 (20.274) | 418 (19.560) | 306 (21.339) | ||
| Smoking | χ2 = 4.128 | .046 | |||
| Lifetime smoking<100 cigarettes | 1867 (52.282) | 1147 (53.673) | 720 (50.209) | ||
| Lifetime smoking ≥ 100cigarettes | 1704 (47.718) | 990 (46.327) | 714 (49.791) | ||
| Marriage | χ2 = 64.713 | <.001 | |||
| Married/Living with a partner | 2224 (62.279) | 1239 (57.978) | 985 (68.689) | ||
| Non-Married | 790 (22.123) | 569 (26.626) | 221 (15.411) | ||
| Widowed/divorced/separated | 557 (15.598) | 329 (15.395) | 228 (15.900) | ||
| Drinking | χ2 = 0.417 | .566 | |||
| No | 251 (7.029) | 155 (7.253) | 96 (6.695) | ||
| Yes | 3320 (92.971) | 1982 (92.747) | 1338 (93.305) | ||
| FMPIR | 2.280 (1.215, 4.200) |
2.280 (1.200, 4.210) |
2.255 (1.260, 4.190) |
Z = −0.609 | .543 |
| FMPIR | χ2 = 3.386 | .184 | |||
| <1.300 | 968 (27.107) | 600 (28.077) | 368 (25.662) | ||
| ≥3.500 | 1188 (33.268) | 713 (33.365) | 475 (33.124) | ||
| 1.300 ≤ FMPIR<3.500 | 1415 (39.625) | 824 (38.559) | 591 (41.213) | ||
| BMI (kg/m2) | 28.200 (24.900, 32.600) |
26.100 (23.300, 29.100) |
32.100 (28.600, 36.400) |
Z = −31.139 | <.001 |
| BMI | χ2 = 75.713 | <.001 | |||
| <25 | 904 (25.315) | 827 (38.699) | 77 (5.370) | ||
| ≥30 | 1381 (38.673) | 432 (20.215) | 949 (66.179) | ||
| 25 ≤ BMI<30 | 1286 (36.012) | 878 (41.086) | 408 (28.452) | ||
| Platelet count (1000 cells/µL) | 225 (193, 263) | 224 (193, 261) | 227 (194, 267) | Z = −16.178 | .122 |
| ALT (U/L) | 21 (15, 29) | 18 (14, 25) | 25 (18, 37) | Z = −17.998 | <.001 |
| AST (U/L) | 20 (17, 25) | 20 (17, 24) | 21 (18, 27) | Z = −17.161 | <.001 |
| HTN | χ2 = 39.588 | <.001 | |||
| No | 3044 (85.242) | 1887 (88.301) | 1157 (80.683) | ||
| Yes | 527 (14.758) | 250 (11.699) | 277 (19.317) | ||
| HL | χ2 = 79.569 | <.001 | |||
| No | 3068 (85.914) | 1954 (91.437) | 1114 (77.685) | ||
| Yes | 503 (14.086) | 183 (8.563) | 320 (22.315) | ||
| DM | χ2 = 87.739 | <.001 | |||
| No | 2992 (83.786) | 1922 (89.939) | 1070 (74.616) | ||
| Yes | 579 (16.214) | 215 (10.061) | 364 (25.384) | ||
| Serum Se (μg·L−1) | 184.980 (170.640, 201.495) |
183.300 (169.790, 200.370) |
187.570 (172.500, 203.358) |
Z = −16.169 | <.001 |
ALT = alanine aminotransferase, AST = aspartate aminotransferase, BMI = body mass index, DM = diabetes mellitus, FMPIR = family income and poverty-to-income ratio, HL = hyperlipidemia, HTN = hypertension, NAFLD = non-alcoholic fatty liver disease, Se = selenium.
Table 2.
Female basic characteristics of research objects.
| Characteristics | Number of cases (n = 3700) |
Non-NAFLD group (n = 2557) |
NAFLD group (n = 1143) |
Statistical value |
P |
|---|---|---|---|---|---|
| Age (yr) | 50 (34, 63) | 47 (31, 62) | 55 (42, 64) | Z = −8.429 | <.001 |
| Age group | χ2 = 102.586 | <.001 | |||
| 18–39 yr old | 1241 (33.541) | 992 (38.795) | 249 (21.785) | ||
| 40–59 yr old | 1231 (33.270) | 781 (30.544) | 450 (39.370) | ||
| ≥60 yr old | 1228 (33.189) | 784 (30.661) | 444 (38.845) | ||
| Race | χ2 = 47.687 | <.001 | |||
| Mexican-American | 463 (12.514) | 261 (10.207) | 202 (17.673) | ||
| Non-Hispanic Blacks | 980 (26.486) | 712 (27.845) | 268 (23.447) | ||
| Non-Hispanic Whites | 1215 (32.838) | 827 (32.343) | 388 (33.946) | ||
| Others | 1042 (28.162) | 757 (29.605) | 285 (24.934) | ||
| Education | χ2 = 9.632 | .008 | |||
| College Degree or above | 2169 (58.622) | 1541 (60.266) | 628 (54.943) | ||
| High school or equivalent | 882 (23.838) | 591 (23.113) | 291 (25.459) | ||
| Below High School | 649 (17.541) | 425 (16.621) | 224 (19.598) | ||
| Smoking | χ2 = 4.094 | .047 | |||
| Lifetime smoking<100 cigarettes | 2555 (69.054) | 1792 (70.082) | 763 (66.754) | ||
| Lifetime smoking ≥ 100cigarettes | 1145 (30.946) | 765 (29.918) | 380 (33.246) | ||
| Marriage | χ2 = 19.937 | <.001 | |||
| Married/Living with a partner | 1927 (52.081) | 1301 (50.880) | 626 (54.768) | ||
| Non-married | 784 (21.189) | 593 (23.191) | 191 (16.710) | ||
| Widowed/divorced/separated | 989 (26.730) | 663 (25.929) | 326 (28.521) | ||
| Drinking | χ2 = 0.217 | .960 | |||
| No | 524 (14.162) | 362 (14.157) | 162 (14.173) | ||
| Yes | 3176 (85.838) | 2195 (85.843) | 981 (85.827) | ||
| FMPIR | 2.040 (1.100, 3.950) |
2.070 (1.130, 4.040) |
1.990 (1.045, 3.690) |
Z = −1.620 | .105 |
| FMPIR | χ2 = 3.404 | .182 | |||
| <1.300 | 1167 (31.541) | 791 (30.935) | 376 (32.896) | ||
| ≥3.500 | 1104 (29.838) | 786 (30.739) | 318 (27.822) | ||
| 1.300 ≤ FMPIR<3.500 | 1429 (38.622) | 980 (38.326) | 449 (39.283) | ||
| BMI (kg/m2) | 29.100 (24.600, 34.700) |
27.100 (23.300, 31.900) |
34.000 (29.400, 39.900) |
Z = −26.068 | <.001 |
| BMI | χ2 = 97.713 | <.001 | |||
| <25 | 986 (26.649) | 918 (35.901) | 68 (5.949) | ||
| ≥30 | 1686 (45.568) | 866 (33.868) | 820 (71.741) | ||
| 25 ≤ BMI<30 | 1028 (27.784) | 773 (30.231) | 255 (22.310) | ||
| Platelet count (1000 cells/µL) | 257 (218, 301) | 253 (215, 296) | 265 (223, 312) | Z = −5.621 | <.001 |
| ALT (U/L) | 15 (11, 20) | 14 (11, 18) | 18 (13, 25) | Z = −16.283 | <.001 |
| AST (U/L) | 17 (15, 21) | 17 (15, 21) | 18 (15, 23) | Z = −4.488 | <.001 |
| HTN | χ2 = 13.433 | <.001 | |||
| No | 3083 (83.324) | 2169 (84.826) | 914 (79.965) | ||
| Yes | 617 (16.676) | 388 (15.174) | 229 (20.035) | ||
| HL | χ2 = 88.567 | <.001 | |||
| No | 3380 (91.351) | 2416 (94.486) | 964 (84.339) | ||
| Yes | 320 (8.649) | 141 (5.514) | 179 (15.661) | ||
| DM | χ2 = 87.769 | <.001 | |||
| No | 3244 (87.676) | 2361 (92.335) | 883 (77.253) | ||
| Yes | 456 (12.324) | 196 (7.665) | 260 (22.747) | ||
| Serum Se (μg·L−1) | 181.285 (166.562, 198.035) |
180.130 (165.900, 196.900) |
184.780 (168.545, 199.980) |
Z = −4.102 | <.001 |
ALT = alanine aminotransferase, AST = aspartate aminotransferase, BMI = body mass index, DM = diabetes mellitus, FMPIR = family income and poverty-to-income ratio, HL = hyperlipidemia, HTN = hypertension, NAFLD = non-alcoholic fatty liver disease, Se = selenium.
3.2. Logistic analysis of serum Se with NAFLD and AHF by gender
A multiple logistic regression model was constructed to investigate the relationship between serum Se levels and NAFLD and AHF in different genders. serum Se was analyzed as a continuous index in Model 3, for every quartile increase of male serum Se, the prevalence of NAFLD increased by 17.60% (OR, 1.176; 95% CI: 1.052–1.315) the prevalence of AHF decreased by 38.50% (OR, 0.615; 95% CI: 0.479–0.789). For every increase in female serum Se, the prevalence of NAFLD increased by 29.10% (OR,1.291;95%CI: 1.155–1.442), the prevalence of AHF decreased by 51.60% decrease in AHF prevalence (OR, 0.484; 95% CI: 0.344–0.682).
Analyzing serum Se as a quartile index, in Model 3, compared with Q1, the lowest serum Se group, the prevalence of NAFLD in male serum Se Q3 group increased by 32.80% (OR, 1.328; 95% CI: 1.048–1.684), the prevalence of AHF in serum Se Q2, Q3, and Q4 groups decreased by 56.60% (OR, 0.434; 95% CI: 0.256–0.713), 40% (OR, 0.600; 95% CI: 0.364–0.967), and 47% (OR, 0.530; 95% CI: 0.313–0.873) respectively. The prevalence of NAFLD in female serum Se Q4 groups increased by 54.10% (OR, 1.541; 95% CI: 1.225–1.942), and the prevalence of AHF in serum Se Q2, Q3, and Q4 groups decreased by 53.70% (OR, 0.463; 95% CI: 0.243–0.848), 56.10% (OR, 0.439; 95% CI: 0.222–0.826), and 67.50% (OR, 0.352; 95% CI: 0.153–0.640) respectively. See Tables 3 and 4 for details.
Table 3.
Logistic analysis of serum Se about NAFLD and AHF in males.
| Variable | Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|---|
| OR(95%CI) | P | OR(95%CI) | P | OR(95%CI) | P | ||
| NAFLD | Serum Se (per quartile spacing) |
1.237 (1.130, 1.355) | <.001 | 1.214 (1.088, 1.355) | <.001 | 1.176 (1.052, 1.315) | .004 |
| Serum Se quartile | |||||||
| Q1 | 1.000 | —— | 1.000 | —— | 1.000 | —— | |
| Q2 | 1.054 [0.870, 1.278] | .589 | 1.051 [0.830, 1.329] | .681 | 1.048 [0.826, 1.330] | .698 | |
| Q3 | 1.346 [1.113, 1.629] | .002 | 1.369 [1.084, 1.730] | .008 | 1.328 [1.048, 1.684] | .019 | |
| Q4 | 1.375 [1.137, 1.663] | .001 | 1.287 [1.021, 1.624] | .033 | 1.223 [0.967, 1.549] | .094 | |
| AHF | Serum Se (per quartile spacing) |
0.481 (0.377, 0.614) | <.001 | 0.612 (0.478, 0.783) | <.001 | 0.615 (0.479, 0.789) | <.001 |
| Serum Se quartile | |||||||
| Q1 | 1.000 | —— | 1.000 | —— | 1.000 | —— | |
| Q2 | 0.337 [0.203, 0.539] | <.001 | 0.435 [0.257, 0.713] | .001 | 0.434 [0.256, 0.713] | .001 | |
| Q3 | 0.413 [0.259, 0.643] | <.001 | 0.592 [0.360, 0.951] | .034 | 0.600 [0.364, 0.967] | .040 | |
| Q4 | 0.352 [0.214, 0.559] | <.001 | 0.523 [0.310, 0.858] | .012 | 0.530 [0.313, 0.873] | .015 | |
95%CI = 95% confidence intervals, AHF = advanced hepatic fibrosis, NAFLD = non-alcoholic fatty liver disease, OR = odds ratios, Se = selenium.
Table 4.
Logistic analysis of serum Se about NAFLD and AHF in female.
| Variable | Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|---|
| OR(95%CI) | P | OR(95%CI) | P | OR(95%CI) | P | ||
| NAFLD | Serum SE | ||||||
| (per quartile spacing) | 1.217 (1.107, 1.337) | <.001 | 1.315 (1.179, 1.467) | <.001 | 1.291 (1.155, 1.442) | <.001 | |
| Serum SE Quartile | |||||||
| Q1 | 1.000 | —— | 1.000 | —— | 1.000 | —— | |
| Q2 | 0.958 [0.781, 1.174] | .678 | 0.935 [0.740, 1.181] | .572 | 0.933 [0.735, 1.183] | .567 | |
| Q3 | 1.239 [1.016, 1.512] | .034 | 1.235 [0.982, 1.552] | .071 | 1.236 [0.980, 1.560] | .073 | |
| Q4 | 1.385 [1.138, 1.686] | .001 | 1.606 [1.281, 2.017] | <.001 | 1.541 [1.225, 1.942] | <.001 | |
| AHF | Serum SE | ||||||
| (per quartile spacing) | 0.456 (0.328,0.635) | <.001 | 0.476 (0.338, 0.67) | <.001 | 0.484 (0.344, 0.682) | ||
| Serum SE Quartile | |||||||
| Q1 | 1.000 | —— | 1.000 | —— | 1.000 | —— | |
| Q2 | 0.461 [0.246, 0.828] | .012 | 0.456 [0.240, 0.833] | .013 | 0.463 [0.243, 0.848] | .015 | |
| Q3 | 0.403 [0.208, 0.740] | .005 | 0.430 [0.218, 0.804] | .011 | 0.439 [0.222, 0.826] | .013 | |
| Q4 | 0.315 [0.152, 0.607] | .001 | 0.313 [0.148, 0.615] | .001 | 0.325 [0.153, 0.640] | .002 | |
95%CI = 95% confidence intervals, AHF = advanced hepatic fibrosis, NAFLD = non-alcoholic fatty liver disease, OR = odds ratios, Se = selenium.
3.3. Subgroup analysis and interaction of serum Se with NAFLD and AHF by gender
In males, a significant interaction between serum Se and AHF was observed in terms of age and BMI (P for interaction < 0.050). For males aged 40 to 60 and those over 60, the prevalence of AHF decreased by 62.50% (OR, 0.375; 95%CI: 0.192–0.733) and 34.90% (OR, 0.651; 95%CI: 0.486–0.873) respectively, for each quartile increase in serum Se. For males with a BMI of 25 ≤ BMI < 30 and those with a BMI ≥ 30, the prevalence of AHF decreased by 37.90% (OR, 0.621; 95%CI: 0.396–0.974) and 54.30% (OR, 0.457; 95%CI: 0.286–0.729) respectively, for each quartile increase in serum Se, with a statistically significant difference (P < .050). In the female population, a significant interaction between serum Se and AHF was observed in terms of BMI (P for interaction < 0.050). For females with a BMI < 25, the prevalence of AHF decreased by 76.20% (OR, 0.238; 95%CI: 0.127–0.443) for each quartile increase in serum Se, with a statistically significant difference (P < .01). This study did not find any interaction between serum Se and NAFLD in either males or females. See Tables 5 and 6 for details.
Table 5.
Subgroup analysis and interaction of serum Se in male subjects with NAFLD and AHF.
| Characteristics | NAFLD | AHF | ||||
|---|---|---|---|---|---|---|
| OR(95%CI) | P value |
P for interaction |
OR(95%CI) | P value |
P for interaction |
|
| Age group | ||||||
| 18–39 yr old | 1.273 (1.030, 1.572) | .025 | 0.391 | 0.273 (0.032, 2.317) | .234 | 0.008 |
| 40–60 yr old | 1.325 (1.103, 1.590) | .003 | 0.375 (0.192, 0.733) | .004 | ||
| Above 60 yr old | 1.117 (0.930, 1.341) | .236 | 0.651 (0.486, 0.873) | .004 | ||
| Race | ||||||
| Mexican-American | 1.233 (0.938, 1.620) | .133 | 0.258 | 0.368 (0.154, 0.879) | .025 | 0.400 |
| Others | 1.344 (1.091, 1.656) | .005 | 0.783 (0.463, 1.325) | .362 | ||
| Non-Hispanic Whites | 1.017 (0.839, 1.232) | .867 | 0.597 (0.403, 0.886) | .010 | ||
| Non-Hispanic Blacks | 1.137 (0.894, 1.446) | .296 | 0.707 (0.440, 1.137) | .153 | ||
| Education | ||||||
| Below High School | 1.116 (0.874, 1.424) | .381 | 0.244 | 0.495 (0.295, 0.832) | .008 | 0.217 |
| High School or equivalent | 0.993 (0.780, 1.264) | .951 | 0.891 (0.541, 1.466) | .649 | ||
| College Degree or above | 1.276 (1.100, 1.481) | .001 | 0.575 (0.402, 0.824) | .003 | ||
| Marriage | ||||||
| Married/Living with a partner | 1.139 (0.997, 1.301) | .056 | 0.526 | 0.634 (0.463, 0.869) | .005 | 0.928 |
| Non-married | 1.418 (1.050, 1.914) | .023 | 0.670 (0.282, 1.593) | .365 | ||
| Widowed/divorced/separated | 1.137 (0.848, 1.524) | .391 | 0.543 (0.323, 0.912) | .021 | ||
| FMPIR | ||||||
| <1.300 | 1.232 (0.981, 1.546) | .073 | 0.484 | 0.537 (0.337, 0.855) | .009 | 0.494 |
| 1.300 ≤ FMPIR < 3.500 | 1.071 (0.896, 1.280) | .450 | 0.636 (0.431, 0.938) | .023 | ||
| ≥3.500 | 1.249 (1.034, 1.509) | .021 | 0.631 (0.389, 1.025) | .063 | ||
| BMI | ||||||
| <25 | 1.083 (0.788, 1.488) | .624 | 0.425 | 0.763 (0.509, 1.143) | .190 | 0.001 |
| 25 ≤ BMI < 30 | 1.212 (1.024, 1.436) | .026 | 0.621 (0.396, 0.974) | .038 | ||
| ≥30 | 1.187 (1.005, 1.402) | .044 | 0.457 (0.286, 0.729) | .001 | ||
| Smoking | ||||||
| Lifetime smoking ≥ 100 cigarettes | 1.090 (0.929, 1.280) | .290 | 0.301 | 0.605 (0.441, 0.830) | .002 | 0.779 |
| Lifetime smoking<100 cigarettes | 1.258 (1.074, 1.474) | .004 | 0.611 (0.402, 0.929) | .021 | ||
| Drinking | ||||||
| Yes | 1.185 (1.056, 1.329) | .004 | 0.493 | 0.612 (0.476, 0.788) | .004 | 0.872 |
| No | 1.001 (0.617, 1.624) | .997 | 0.522 (0.976, 1.788) | .054 | ||
| DM | ||||||
| Yes | 1.193 (0.899, 1.584) | .220 | 0.784 | 0.637 (0.378, 1.073) | .090 | 0.901 |
| No | 1.166 (1.032, 1.318) | .014 | 0.612 (0.460, 0.815) | .001 | ||
| HTN | ||||||
| Yes | 1.416 (1.057, 1.897) | .020 | 0.062 | 0.453 (0.271, 0.756) | .020 | 0.149 |
| No | 1.128 (0.999, 1.275) | .052 | 0.690 (0.516, 0.922) | .012 | ||
| HL | ||||||
| Yes | 0.981 (0.749, 1.284) | .886 | 0.208 | 0.747 (0.393, 1.420) | .374 | 0.666 |
| No | 1.213 (1.073, 1.370) | .002 | 0.594 (0.453, 0.779) | <.001 | ||
95%CI = 95% confidence intervals, AHF = advanced hepatic fibrosis, BMI = body mass index, DM = diabetes mellitus, FMPIR = family income and poverty-to-income ratio, HL = hyperlipidemia, HTN = hypertension, NAFLD = non-alcoholic fatty liver disease, OR = odds ratios.
Table 6.
Subgroup analysis and interaction of serum Se in female subjects with NAFLD and AHF.
| Characteristics | NAFLD | AHF | ||||
|---|---|---|---|---|---|---|
| OR(95%CI) | P value |
P for interaction |
OR(95%CI) | P value |
P for interaction |
|
| Age group | 0.131 | 0.599 | ||||
| 18–39 yr old | 1.129 (0.899, 1.418) | .296 | 0.520 (0.114, 2.365) | .398 | ||
| 40–60 yr old | 1.312 (1.091, 1.579) | .004 | 0.573 (0.302, 1.089) | .089 | ||
| Above 60 yr old | 1.249 (1.046, 1.492) | .014 | 0.456 (0.301, 0.689) | <.001 | ||
| Race | 0.101 | 0.577 | ||||
| Mexican-American | 1.354 (1.024, 1.790) | .033 | 0.173 (0.018, 1.629) | .125 | ||
| Others | 1.610 (1.294, 2.003) | <.001 | 0.410 (0.184, 0.911) | .029 | ||
| Non-Hispanic Whites | 1.107 (0.913, 1.342) | .301 | 0.418 (0.238, 0.733) | .002 | ||
| Non-Hispanic Blacks | 1.182 (0.945, 1.480) | .144 | 0.548 (0.301, 0.997) | .049 | ||
| Education | 0.582 | 0.766 | ||||
| Below High School | 1.368 (1.044, 1.793) | .023 | 0.620 (0.265, 1.450) | .27 | ||
| High School or equivalent | 1.149 (0.915, 1.442) | .232 | 0.395 (0.207, 0.753) | .005 | ||
| College Degree or above | 1.312 (1.136, 1.515) | <.001 | 0.489 (0.307, 0.778) | .003 | ||
| Marriage | 0.405 | 0.491 | ||||
| Married/Living with a partner | 1.283 (1.102, 1.493) | .001 | 0.501 (0.295, 0.854) | .011 | ||
| Non-Married | 1.135 (0.854, 1.507) | .383 | 0.435 (0.083, 2.272) | .324 | ||
| Widowed/divorced/separated | 1.293 (1.060, 1.576) | .011 | 0.410 (0.251, 0.669) | <.001 | ||
| FMPIR | 0.825 | 0.209 | ||||
| <1.300 | 1.300 (1.065, 1.588) | .010 | 0.906 (0.466, 1.762) | .772 | ||
| 1.300 ≤ FMPIR < 3.500 | 1.319 (1.110, 1.568) | .002 | 0.445 (0.260, 0.761) | .003 | ||
| ≥3.500 | 1.206 (0.977, 1.489) | .081 | 0.320 (0.165, 0.622) | .001 | ||
| BMI | 0.735 | 0.029 | ||||
| <25 | 1.118 (0.770, 1.624) | .559 | 0.238 (0.127, 0.443) | <.001 | ||
| 25 ≤ BMI < 30 | 1.528 (1.257, 1.857) | <.001 | 0.680 (0.354, 1.308) | .248 | ||
| ≥30 | 1.148 (1.003, 1.314) | .046 | 0.710 (0.402, 1.253) | .238 | ||
| Smoking | 0.285 | 0.511 | ||||
| Lifetime smoking ≥ 100 cigarettes | 1.426 (1.166, 1.745) | .001 | 0.406 (0.234, 0.705) | .001 | ||
| Lifetime smoking<100 cigarettes |
1.242 (1.086, 1.421) | .002 | 0.512 (0.324, 0.808) | .004 | ||
| Drinking | 0.857 | 0.348 | ||||
| Yes | 1.296 (1.152, 1.459) | <.001 | 0.450 (0.309, 0.655) | <.001 | ||
| No | 1.220 (0.881, 1.691) | .231 | 0.558 (0.235, 1.324) | .186 | ||
| DM | 0.259 | 0.099 | ||||
| Yes | 1.316 (0.982, 1.764) | .066 | 0.156 (0.048, 0.504) | .002 | ||
| No | 1.265 (1.120, 1.429) | <.001 | 0.557 (0.387, 0.801) | .002 | ||
| HTN | 0.521 | 0.981 | ||||
| Yes | 1.341 (1.050, 1.712) | .019 | 0.472 (0.264, 0.845) | .012 | ||
| No | 1.273 (1.124, 1.441) | <.001 | 0.493 (0.323, 0.754) | .001 | ||
| HL | 0.234 | 0.450 | ||||
| Yes | 1.469 (1.021, 2.112) | .038 | 0.981 (0.749, 1.284) | .886 | ||
| No | 1.258 (1.119, 1.415) | <.001 | 0.495 (0.350, 0.700) | <.001 | ||
95%CI = 95% confidence intervals, AHF = advanced hepatic fibrosis, BMI = body mass index, DM = diabetes mellitus, FMPIR = family income and poverty-to-income ratio, HL = hyperlipidemia, HTN = hypertension, NAFLD = non-alcoholic fatty liver disease, OR = odds ratios.
3.4. Dose-response relationship between serum Se and NAFLD and AHF by gender
After adjusting for age, race, education, marriage, FMPIR, BMI, smoking, drinking, DM, HTN, and HL, restricted cubic spline regression analysis further revealed a positive dose-response relationship between serum Se levels and the prevalence of NAFLD in both males and females (males: P = .012, females: P < .001), which was non-linear relationship (males: Pnon-linear = 0.035, females: Pnon-linear = 0.041). For males, serum Se levels below 185.370μg·L−1 served as protective factors against NAFLD, with protection decreased as levels increased. When serum Se levels exceeded 185.370μg·L−1, they became risk factors for NAFLD. For females, serum Se levels below 181.413μg·L−1 served as protective factors against NAFLD, with protection decreased and risk increasing as levels rose above 181.413μg·L−1.
The prevalence of serum Se and AHF in both males and females had a negative dose-response relationship (males: P < .001, females: P < .001), with a non-linear relationship observed in males (Pnon-linear < 0.001) and a non-linear relationship in females (Pnon-linear = 0.015). For males, serum Se levels below 184.741μg·L−1. served as risk factors for AHF, with risk decreased as serum Se levels increased. For females, serum Se levels below 180.771μg·L−1 served as risk factors for AHF, with risk decreased as serum Se levels increased. Serum Se levels above 180.771μg·L−1 showed no correlation with the prevalence of AHF. Details are provided in Figures 2, 3, 4, and 5.
Figure 2.
Dose-response relationship between serum Se and NAFLD in males. NAFLD = non-alcoholic fatty liver disease, Se = selenium.
Figure 3.
Dose-response relationship between serum Se and NAFLD in females. NAFLD = non-alcoholic fatty liver disease, Se = selenium.
Figure 4.
Dose-response relationship between serum Se and AHF in males. AHF = advanced hepatic fibrosis, Se = selenium.
Figure 5.
Dose-response relationship between serum Se and AHF in females. AHF = advanced hepatic fibrosis, Se = selenium.
4. Discussion
Previous studies have investigated the relationship between serum Se and the prevalence of NAFLD. A cross-sectional study based on middle-aged and elderly Chinese reported a positive correlation between serum Se and NAFLD prevalence.[23] A positive correlation exists between serum Se and hepatic steatosis in NAFLD.[24,25] A study utilizing the NHANES database found a positive correlation between serum Se, ALT activity, and NAFLD prevalence.[26] Subjects with elevated dietary Se intake exhibit a higher prevalence of NAFLD.[27] The progression of NAFLD is influenced by Se through a diverse range of mechanisms. Se contributes to hepatic steatosis by inducing insulin resistance and increasing hepatic triglyceride concentrations.[28] Se, by enhancing the activities of glutathione peroxidase and protein tyrosine phosphatase, upgrades steroid regulatory element-binding protein 1 (a key molecule in hepatic lipogenesis and accumulation), thereby influencing the disease progression of NAFLD.[29] SE exerts an influence on the synthesis and breakdown of fatty acids, resulting in excessive hepatic fat deposition and thereby elevating the susceptibility to NAFLD.[30]
Se can mitigate the fibrogenic stimuli of hepatic stellate cells, reducing the proliferation and remodeling of the extracellular matrix.[31] Hepatic stellate cells play a crucial role in hepatic fibrosis, and SE can inhibit their activation by modulating various cell signaling pathways (such as the TGF-β/Smad pathway, NF-κB pathway, etc), thereby mitigating excessive extracellular matrix deposition and alleviating fibrosis.[32] SE is an essential constituent of numerous antioxidant enzymes that effectively counteract excessive reactive oxygen species within the body, thus reducing oxidative stress and impeding liver fibrosis progression.[33] Moreover, SE possesses anti-inflammatory properties and can suppress the expression of pro-inflammatory cytokines such as tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-6 (IL-6). Given that inflammation plays a pivotal role in liver fibrogenesis, SE ability to attenuate inflammation contributes to diminishing the severity of liver fibrosis.[34]
This study, categorizing by gender, similarly shows that serum Se levels in both male and female NAFLD groups were significantly higher than those in the non-NAFLD groups, with higher levels in males within the NAFLD group, suggesting that serum Se levels might be influenced by sex hormones and dietary structure. This study is the first to investigate the correlation between serum Se levels and the prevalence of NAFLD and AHF in different genders. It was speculated that individuals with advanced liver fibrosis often show lower serum Se levels, especially in males, suggesting that high serum Se levels might be an important protective factor against advanced fibrosis in NAFLD patients. The mitigation of inflammation and oxidative stress may be the potential mechanisms by which high serum Se levels reduce liver fibrosis. Furthermore, our study indicates interactions between serum Se and AHF across age and BMI in males, and between serum Se and AHF across BMI in females, suggesting a possible influence of age and BMI on serum Se levels in both genders. However, no interaction was found between serum Se levels and NAFLD in either gender. The impact mechanisms of various covariates on NAFLD remain uncertain, necessitating further investigation through rigorous animal experiments and clinical trials.
In previous studies on serum Se and NAFLD, NAFLD was diagnosed only by ALT, AST serum activity, or fatty liver index (FLI ≥ 60 or USFLI ≥ 30), yet these are not the gold standards for the diagnosis of NAFLD.[35] Up to 80% of NAFLD patients have normal ALT and AST levels.[36] The sensitivity of CAP for detecting all degrees of steatosis is 90%,[37] while FLI has a sensitivity of 84% and USFLI 80%.[38] FLI and USFLI have lower sensitivity for NAFLD diagnosis.[39] The adverse effects of Se on health depend on its dosage.[40] Given the limited epidemiological evidence on the dose-response relationship between serum Se and the prevalence of NAFLD and AHF, many studies have indicated that the serum Se level of males is significantly higher than that of females.[41] Different from the previous studies on the diagnosis of NAFLD, this study adopts liver transient elastography as the diagnosis of NAFLD, and based on the difference of serum Se Levels between males and females, it is more clinically meaningful to investigate the correlation between serum Se in different genders and the prevalence of NAFLD and AHF, as well as the dose-response relationship.
5. Strengths and limitations
The NHANES database, as a nationally representative survey, can represent the characteristics of the entire US population. The results of this study can be extended to the entire US population. However, there are also some limitations to this study. First of all, as a cross-sectional study, it can only investigate the correlation between serum Se levels and the prevalence of NAFLD and AHF, without explaining causation. larger scale and prospective cohort studies should be encouraged in the future. Secondly, some data collected by the NHANES database were obtained through questionnaires, which may bring memory bias and other confounding variables. At present, there are few studies on the correlation between serum Se and NAFLD, and AHF, and further prospective studies are necessary to verify it.
6. Conclusion
By comparing serum Se levels in males and females included in NHANES 2017–2020, high serum Se levels were identified as risk factors for increased NAFLD prevalence in both genders and as protective factors for AHF, with differences in prevalence between genders. In the prevention and clinical practice of NAFLD, monitoring serum Se levels in both males and females can facilitate early prevention and screening of high-risk groups for NAFLD and AHF.
Author contributions
Data curation: Ruilin Wang.
Formal analysis: Liu Yajie.
Investigation: Liu Yajie.
Software: Liu Yajie.
Validation: Ruilin Wang.
Visualization: Liu Yajie.
Writing – original draft: Liu Yajie.
Writing – review & editing: Ruilin Wang.
Abbreviations:
- 95%CI
- 95% confidence intervals
- AHF
- advanced hepatic fibrosis
- ALT
- alanine aminotransferase
- AST
- aspartate aminotransferase
- BMI
- body mass index
- CAP
- controlled attenuation parameter
- DM
- diabetes mellitus
- ECM
- extracellularmatrix
- FIB-4
- fibrosis-4
- FLI
- fatty liver index
- FMPIR
- family income and poverty-to-income ratio
- HL
- hyperlipidemia
- HTN
- hypertension
- NAFLD
- non-alcoholic fatty liver disease
- NHANES
- National Health and Nutrition Examination Survey
- OR
- odds ratios
- Se
- selenium
National Natural Science Foundation of China Grant (NO.81673806); Research Topic supported by the China Pharmaceutical Education Association (2020KTY001).
The NHANES Research Project was approved by the Research Ethics Review Board (ERB) of the National Center for Health Statistics (NCHS) (NCHS IRB/ERB Agreement No. Continuation of Protocol#2011-17; Protocol #2018-01), in which all respondents provided written informed consent.
The authors have no conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are publicly available.
How to cite this article: Liu Y, Wang R. Association between serum selenium and non-alcoholic fatty liver disease: Results from NHANES: An observational study. Medicine 2024;103:28(e38845).
Contributor Information
Yajie Liu, Email: lyj777_2023@163.com.
Ruilin Wang, Email: wrl7905@163.com.
References
- [1].European Association for the Study of the Liver (EASL), European Association for the Study of Diabetes (EASD), European Association for the Study of Obesity (EASO). EASL-EASD-EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease. J Hepatol. 2016;64:1388–402. [DOI] [PubMed] [Google Scholar]
- [2].Younossi ZM, Koenig AB, Abdelatif D, et al. Global epidemiology of nonalcoholic fatty liver disease-meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology (Baltimore, Md.). 2016;64:73–84. [DOI] [PubMed] [Google Scholar]
- [3].Blüher M. Obesity: global epidemiology and pathogenesis. Nat Rev Endocrinol. 2019;15:288–98. [DOI] [PubMed] [Google Scholar]
- [4].Quek J, Chan KE, Wong ZY, 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;8:20–30. [DOI] [PubMed] [Google Scholar]
- [5].Younossi ZM, Blissett D, Blissett R, et al. The economic and clinical burden of nonalcoholic fatty liver disease in the United States and Europe. Hepatology (Baltimore, Md.). 2016;64:1577–86. [DOI] [PubMed] [Google Scholar]
- [6].Diehl AM, Day C. Cause, pathogenesis, and treatment of nonalcoholic steatohepatitis. N Engl J Med. 2017;377:2063–72. [DOI] [PubMed] [Google Scholar]
- [7].Mózes FE, Lee JA, Selvaraj EA, et al. Diagnostic accuracy of non-invasive tests for advanced fibrosis in patients with NAFLD: an individual patient data meta-analysis. Gut. 2022;71:1006–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [8].Loomba R, Wong R, Fraysse J, et al. Nonalcoholic fatty liver disease progression rates to cirrhosis and progression of cirrhosis to decompensation and mortality: a real-world analysis of Medicare data. Aliment Pharmacol Ther. 2020;51:1149–59. [DOI] [PubMed] [Google Scholar]
- [9].Rayman MP. Selenium and human health. Lancet (London, England). 2012;379:1256–68. [DOI] [PubMed] [Google Scholar]
- [10].Seale LA, Ha HY, Hashimoto AC, et al. Relationship between selenoprotein P and selenocysteine lyase: Insights into selenium metabolism. Free Radic Biol Med. 2018;127:182–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11].Rua RM, Ojeda ML, Nogales F, et al. Serum selenium levels and oxidative balance as differential markers in hepatic damage caused by alcohol. Life Sci. 2014;94:158–63. [DOI] [PubMed] [Google Scholar]
- [12].Hasegawa T, Taniguchi S, Nakamuro K, et al. Toxicity and chemical form of selenium in the liver of mice orally administered selenocystine for 90 days. Arch Toxicol. 1994;68:91–5. [DOI] [PubMed] [Google Scholar]
- [13].Clark NA, Teschke K, Rideout K, et al. Trace element levels in adults from the west coast of Canada and associations with age, gender, diet, activities, and levels of other trace elements. Chemosphere. 2007;70:155–64. [DOI] [PubMed] [Google Scholar]
- [14].Sheka AC, Adeyi O, Thompson J, et al. Nonalcoholic steatohepatitis: a review. JAMA. 2020;323:1175–83. [DOI] [PubMed] [Google Scholar]
- [15].Wong T, Dang K, Ladhani S, et al. Prevalence of alcoholic fatty liver disease among adults in the United States, 2001-2016. JAMA. 2019;321:1723–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16].De Lédinghen V, Vergniol J, Foucher J, et al. Non-invasive diagnosis of liver steatosis using controlled attenuation parameter (CAP) and transient elastography. Liver Int. 2012;32:911–8. [DOI] [PubMed] [Google Scholar]
- [17].Castera L, Friedrich-Rust M, Loomba R. Noninvasive assessment of liver disease in patients with nonalcoholic Fatty liver disease. Gastroenterology. 2019;156:1264–81.e4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [18].Rinella ME, Neuschwander-Tetri BA, Siddiqui MS, et al. AASLD Practice Guidance on the clinical assessment and management of nonalcoholic fatty liver disease. Hepatology (Baltimore, Md.). 2023;77:1797–835. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Huang Z. Association between blood lead level with high blood pressure in US (NHANES 1999–2018). Front Public Health. 2022;10:836357. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [20].Deng MG, Liu F, Liang Y, et al. Associations of serum zinc, copper, and selenium with sleep disorders in the American adults: data from NHANES 2011-2016. J Affect Disord. 2023;323:378–85. [DOI] [PubMed] [Google Scholar]
- [21].Kjaergaard M, Lindvig KP, Thorhauge KH, et al. Using the ELF test, FIB-4 and NAFLD fibrosis score to screen the population for liver disease. J Hepatol. 2023;79:277–86. [DOI] [PubMed] [Google Scholar]
- [22].Peng H, Pan L, Ran S, et al. Prediction of MAFLD and NAFLD using different screening indexes: a cross-sectional study in U.S. adults. Front Endocrinol. 2023;14:1083032. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [23].Wu J, Zeng C, Yang Z, et al. Association between dietary selenium intake and the prevalence of nonalcoholic fatty liver disease: a cross-sectional study. J Am Coll Nutr. 2020;39:103–11. [DOI] [PubMed] [Google Scholar]
- [24].Spaur M, Nigra AE, Sanchez TR, et al. Association of blood manganese, selenium with steatosis, fibrosis in the National Health and Nutrition Examination Survey, 2017-18. Environ Res. 2022;213:113647. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [25].Liu J, Tan L, Liu Z, et al. The association between non-alcoholic fatty liver disease (NAFLD) and advanced fibrosis with blood selenium level based on the NHANES 2017-2018. Ann Med. 2022;54:2259–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [26].Wang X, Seo YA, Park SK. Serum selenium and non-alcoholic fatty liver disease (NAFLD) in U.S. adults: National Health and Nutrition Examination Survey (NHANES) 2011–2016. Environ Res. 2021;197:111190. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [27].Wu J, Zeng C, Yang Z, et al. Association between dietary selenium intake and the prevalence of nonalcoholic Fatty liver disease: a cross-sectional study. J Am Coll Nutr. 2020;39:103–11. [DOI] [PubMed] [Google Scholar]
- [28].Mueller AS, Klomann SD, Wolf NM, et al. Redox regulation of protein tyrosine phosphatase 1B by manipulation of dietary selenium affects the triglyceride concentration in rat liver. J Nutr. 2008;138:2328–36. [DOI] [PubMed] [Google Scholar]
- [29].Mueller A, Bosse AC, Most E, et al. Regulation of the insulin antagonistic protein tyrosine phosphatase 1B by dietary Se studied in growing rats. J Nutr Biochem. 2009;20:235–47. [DOI] [PubMed] [Google Scholar]
- [30].Dong X, Deng Y, Chen G. Selenium intake in relation to all-cause and cardiovascular mortality in individuals with nonalcoholic fatty liver disease: a nationwide study in nutrition. PLoS One. 2024;19:e0303140. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [31].Polyzos SA, Kountouras J, Goulas A, et al. Selenium and selenoprotein P in nonalcoholic fatty liver disease. Hormones (Athens, Greece). 2020;19:61–72. [DOI] [PubMed] [Google Scholar]
- [32].Lee YA, Wallace MC, Friedman SL. Pathobiology of liver fibrosis: a translational success story. Gut. 2015;64:830–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [33].Labunskyy VM, Hatfield DL, Gladyshev VN. Selenoproteins: molecular pathways and physiological roles. Physiol Rev. 2014;94:739–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [34].Tinggi U, Perkins AV. Selenium status: its interactions with dietary mercury exposure and implications in human health. Nutrients. 2022;14:5308. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [35].Yang Z, Yan C, Liu G, et al. Plasma selenium levels and nonalcoholic fatty liver disease in Chinese adults: a cross-sectional analysis. Sci Rep. 2016;6:37288. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [36].Cao YT, Xiang LL, Qi F, et al. Accuracy of controlled attenuation parameter (CAP) and liver stiffness measurement (LSM) for assessing steatosis and fibrosis in non-alcoholic fatty liver disease: a systematic review and meta-analysis. EClinicalMedicine. 2022;51:101547. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [37].Xie R, Xiao M, Li L, et al. Association between SII and hepatic steatosis and liver fibrosis: a population-based study. Front Immunol. 2022;13:925690. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [38].Ruhl CE, Everhart JE. Fatty liver indices in the multiethnic United States National Health and Nutrition Examination Survey. Aliment Pharmacol Ther. 2015;41:65–76. [DOI] [PubMed] [Google Scholar]
- [39].Papatheodoridi M, Cholongitas E. Diagnosis of non-alcoholic fatty liver disease (NAFLD): current concepts. Curr Pharm Des. 2018;24:4574–86. [DOI] [PubMed] [Google Scholar]
- [40].Vinceti M, Filippini T, Wise LA. Environmental selenium and human health: an update. Curr Environ Health Rep. 2018;5:464–85. [DOI] [PubMed] [Google Scholar]
- [41].Kafai MR, Ganji V. Sex, age, geographical location, smoking, and alcohol consumption influence serum selenium concentrations in the USA: third national health and nutrition examination survey, 1988–1994. J Trace Elements Med Biol. 2003;17:13–8. [DOI] [PubMed] [Google Scholar]