Abstract
Dietary fiber intake is linked to metabolic health, yet its association with liver fibrosis, particularly across different fiber sources, remains incompletely understood. This study aims to assess the relationship between dietary fiber from various sources and liver fibrosis in Unites States National Health And Nutrition Examination Survey adults. We used data from National Health and Nutrition Examination Survey 2017 to 2020, examining the associations between dietary fiber intake and liver fibrosis assessed by transient elastography in a nationally representative sample of United States adults. Dietary fiber intake, including total fiber and fiber from fruits, vegetables, grains, and legumes, was assessed using 24-hour dietary recalls. Liver fibrosis was evaluated using both continuous liver stiffness measurements and a categorical definition of significant fibrosis (median liver stiffness ≥ 8 kPa). Survey-weighted linear and logistic regression models were applied, adjusting for sociodemographic characteristics, lifestyle behaviors, metabolic comorbidities, and total energy intake. After multivariable adjustment, higher intake of fruit-derived dietary fiber was consistently associated with lower liver stiffness (β = −0.13, 95% confidence interval: −0.22 to −0.04). Compared with the lowest quartile, participants in the highest quartile of fruit fiber intake had significantly lower odds of significant liver fibrosis (odds ratio = 0.61, 95% confidence interval: 0.45–0.83). In contrast, total dietary fiber and fiber from vegetables, grains, and legumes showed weaker or less consistent associations, and no clear monotonic dose-response relationships were observed. These findings suggest that the source of dietary fiber, particularly fruit-derived fiber, may be more relevant than total fiber intake alone in relation to liver fibrosis risk. This study emphasizes the clinical importance of fiber sources in liver fibrosis management and supports further prospective studies to clarify causality.
Keywords: dietary fiber, fruit fiber, liver fibrosis, liver stiffness, NHANES, transient elastography
1. Introduction
Chronic liver disease constitutes a major and growing global health burden, with liver fibrosis representing a central pathological process that predicts progression to cirrhosis, hepatocellular carcinoma, and liver-related mortality.[1,2] Because early-stage fibrosis is often clinically silent yet potentially reversible, identifying modifiable lifestyle factors associated with fibrotic risk is of considerable public health importance.
Dietary factors have been increasingly recognized as key determinants of liver health. In particular, dietary fiber intake has been linked to improved metabolic profiles, reduced insulin resistance, and lower levels of systemic inflammation: pathways that are mechanistically relevant to hepatic fibrogenesis.[3,4] Experimental and clinical studies further suggest that dietary fiber may influence liver outcomes through modulation of the gut microbiota, production of short-chain fatty acids (SCFAs), and attenuation of gut-derived inflammatory signaling along the gut-liver axis.[5]
Despite these biologically plausible mechanisms, population-based epidemiologic evidence relating dietary fiber intake to liver fibrosis remains limited. Although liver fibrosis has been evaluated as an outcome in some observational studies, most prior studies have focused on total dietary fiber intake or overall dietary patterns, with inconsistent findings, and relatively few have evaluated liver fibrosis assessed by validated noninvasive methods as the primary outcome.[6,7] In contrast, population-based studies examining liver fibrosis in relation to specific dietary fiber sources are scarce. Moreover, dietary fiber is a heterogeneous exposure; fibers derived from fruits, vegetables, grains, and legumes differ substantially in physicochemical properties, fermentability, and accompanying bioactive compounds, which may lead to differential effects on liver metabolism and fibrotic processes.[8] However, population-based evidence evaluating fiber subtypes in relation to liver fibrosis is scarce.
Another limitation of previous research is the reliance on serum-based fibrosis indices or liver enzyme levels, which may inadequately capture fibrosis severity. Transient elastography provides a validated, noninvasive measure of liver stiffness and has been widely adopted for fibrosis assessment in both clinical and population-based settings.[9] The incorporation of transient elastography into recent cycles of the National Health and Nutrition Examination Survey (NHANES) offers a unique opportunity to investigate diet-fibrosis associations in a nationally representative sample of United States (U.S.) adults.
Therefore, this study aimed to examine the associations between dietary fiber intake (including total fiber and major fiber subtypes) and liver fibrosis assessed by transient elastography using data from NHANES 2017 to 2020. Liver fibrosis was evaluated using both continuous liver stiffness measurements and a clinically relevant categorical definition. By distinguishing fiber sources, this study aims to provide population-based evidence on whether specific dietary fiber subtypes are differentially associated with liver fibrosis in U.S. adults.
2. Methods
2.1. Study population
This cross-sectional study was based on data from NHANES 2017 to 2020, a nationally representative survey of the noninstitutionalized U.S. population conducted using a complex, multistage probability sampling design. Among 15,560 participants examined during the study cycles, we restricted the analysis to adults aged ≥ 18 years who completed the mobile examination center visit and had valid transient elastography measurements and dietary intake data. Participants with missing or unreliable liver stiffness measurements, missing dietary fiber intake data, or missing key covariates were excluded. Specifically, individuals with missing data on gender, age, race, poverty-income ratio (PIR), smoking status, hypertension, diabetes, or total energy intake were excluded. Participants with implausible total energy intake (< 500 or > 5000 kcal/day) were also excluded. Because alcohol consumption variables had a relatively high proportion of missing values, individuals with missing alcohol data were retained and categorized as a separate group.
After applying these exclusion criteria, a total of 6090 participants were included in the final analytic sample for complete-case analyses. The participant selection process is illustrated in Figure 1.
Figure 1.
Flowchart of participant selection from NHANES 2017 to 2020. Participants with missing alcohol consumption data were retained as a separate category. N = number of participants, NHANES = National Health and Nutrition Examination Survey, PIR = poverty-income ratio.
2.2. Dietary fiber intake
Dietary intake was assessed using up to 2 24-hour dietary recall interviews conducted by trained interviewers employing the U.S. Department of Agriculture Automated Multiple-Pass Method. Total dietary fiber intake and fiber derived from grains, fruits, vegetables, and legumes were estimated using the U.S. Department of Agriculture Food Patterns Equivalents Database (https://www.ars.usda.gov/nea/bhnrc/fsrg). Fiber intake variables were expressed as grams per day (g/day). In addition to continuous analyses, participants were categorized into quartiles according to the survey-weighted distribution of each dietary fiber subtype to reduce the influence of extreme values.
2.3. Liver fibrosis assessment
Liver stiffness was measured using transient elastography during the mobile examination center visit. Only participants with reliable measurements, according to established quality criteria, were included in the analysis. The primary continuous outcome was median liver stiffness, expressed in kilopascals (kPa). For categorical analyses, liver fibrosis was defined as median liver stiffness ≥ 8 kPa, a threshold commonly used to indicate clinically significant fibrosis in population-based studies.
2.4. Covariates
Covariates were selected a priori based on biological plausibility and previous literature. These included age (years), gender (male or female), race (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, or other), and socioeconomic status assessed using the PIR, categorized as low (≤ 1.3), middle (> 1.3 to < 3.5), or high (≥ 3.5) family income. Smoking status was classified as never, former, or current smoker. Alcohol consumption was categorized as nondrinker, light/moderate drinker, heavy drinker, or missing. Hypertension was defined as a self-reported physician diagnosis or current use of antihypertensive medication. Diabetes was defined based on self-reported diagnosis, use of glucose-lowering medication or insulin, or elevated hemoglobin A1c (≥ 6.5%). Total energy intake was calculated as the mean of 2 dietary recalls when available; otherwise, a single recall value was used.
2.5. Statistical analysis
All analyses incorporated NHANES sampling weights, strata, and primary sampling units to account for the complex survey design and to obtain nationally representative estimates. Continuous variables were summarized as survey-weighted means with 95% confidence intervals (CIs), and categorical variables were summarized as survey-weighted percentages with 95% CIs. Differences in participant characteristics by liver fibrosis status were assessed using survey-weighted linear regression for continuous variables and survey-weighted chi-square tests for categorical variables.
Associations between dietary fiber intake and median liver stiffness were examined using survey-weighted linear regression models, while associations with liver fibrosis (≥ 8 kPa) were assessed using survey-weighted logistic regression. Three models were constructed: Model 1 was unadjusted; Model 2 was adjusted for gender, age, and race; and Model 3 was further adjusted for PIR, smoking status, alcohol consumption, hypertension, diabetes, and total energy intake. Tests for trend across quartiles were performed by modeling the median value of each quartile as a continuous variable. A 2-sided P value < .05 was considered statistically significant. All statistical analyses were conducted using Empower Stats (version 4.2; X&Y Solutions Inc., Boston) and R software (version 4.2.0; R Foundation for Statistical Computing, Vienna, Austria ), with appropriate survey procedures to account for the complex sampling design.
3. Results
3.1. Study population and baseline characteristics
Among the 6090 participants included in the final analytic sample (Fig. 1), the survey-weighted prevalence of liver fibrosis, defined as median liver stiffness ≥ 8 kPa, was 9.34% (95% CI: 7.95–10.73).
Baseline characteristics are summarized in Table 1. The weighted mean age was 47.27 years (95% CI: 45.98–48.57) and 51.02% were female. Compared with participants without fibrosis, those with significant fibrosis were older (weighted mean 51.47 vs 46.84 years; P < .001), had higher mean body mass index (BMI) (35.84 vs 28.87 kg/m2; P < .001), and higher prevalences of hypertension (50.53% vs 29.17%; P < .001) and diabetes (39.35 vs 10.79%; P < .001). Total energy and total dietary fiber intake did not differ materially by fibrosis status (Table 1).
Table 1.
Characteristics of study participants according to liver fibrosis status.
| Characteristics | Overall (N = 6090) |
No fibrosis (N = 5470) | Significant fibrosis (N = 620) | P value |
|---|---|---|---|---|
| Gender, n% | < .001 | |||
| Male | 48.98 (46.95, 51.01) | 47.77 (45.89, 49.65) | 60.73 (53.38, 67.62) | |
| Female | 51.02 (48.99, 53.05) | 52.23 (50.35, 54.11) | 39.27 (32.38, 46.62) | |
| Age (yrs) | 47.27 (45.98, 48.57) | 46.84 (45.55, 48.13) | 51.47 (49.23, 53.71) | < .001 |
| Race, n% | .481 | |||
| Mexican American | 8.11 (6.08, 10.75) | 8.00 (5.97, 10.65) | 9.18 (6.35, 13.11) | |
| Other Hispanic | 6.86 (5.45, 8.61) | 6.81 (5.48, 8.44) | 7.33 (4.46, 11.81) | |
| Non-Hispanic White | 65.45 (60.41, 70.16) | 65.32 (60.62, 69.75) | 66.64 (56.98, 75.08) | |
| Non-Hispanic Black | 10.41 (7.92, 13.55) | 10.49 (8.09, 13.49) | 9.60 (6.07, 14.88) | |
| Other Race | 9.17 (7.49, 11.19) | 9.37 (7.73, 11.32) | 7.25 (4.59, 11.26) | |
| PIR | < .001 | |||
| low family income | 17.93 (16.37, 19.59) | 17.80 (16.25, 19.47) | 19.10 (15.77, 22.93) | |
| Middle family income | 33.74 (30.82, 36.80) | 32.61 (29.68, 35.68) | 44.73 (38.76, 50.87) | |
| High family income | 48.33 (44.78, 51.89) | 49.58 (45.89, 53.28) | 32.53 (27.52, 37.97) | |
| BMI, kg/m2 | 29.52 (29.18, 29.86) | 28.87 (28.51, 29.22) | 35.84 (34.83, 36.84) | < .001 |
| Smoking status, n% | .025 | |||
| Never | 58.41 (55.83, 60.94) | 58.86 (56.24, 61.42) | 54.06 (48.53, 59.49) | |
| Former | 25.94 (23.93, 28.06) | 25.36 (23.25, 27.60) | 31.50 (26.09, 37.47) | |
| Current | 15.65 (13.69, 17.84) | 15.78 (13.74, 18.06) | 14.44 (11.69, 17.70) | |
| Alcohol use, n% | .008 | |||
| nondrinker | 6.94 (6.02, 7.98) | 6.86 (6.06, 7.75) | 7.68 (4.67, 12.38) | |
| Light/Moderate | 35.94 (33.96, 37.96) | 35.66 (33.75, 37.60) | 38.69 (32.71, 45.03) | |
| Heavy | 40.97 (38.83, 43.14) | 41.77 (39.51, 44.06) | 33.21 (28.82, 37.92) | |
| Missing | 16.16 (14.92, 17.48) | 15.72 (14.48, 17.04) | 20.41 (16.93, 24.41) | |
| Hypertension, n% | < .001 | |||
| Yes | 31.17 (28.50, 33.96) | 29.17 (26.63, 31.85) | 50.53 (43.51, 57.54) | |
| No | 68.83 (66.04, 71.50) | 70.83 (68.15, 73.37) | 49.47 (42.46, 56.49) | |
| Diabetes, n% | < .001 | |||
| Yes | 13.46 (12.44, 14.55) | 10.79 (9.82, 11.84) | 39.35 (35.42, 43.42) | |
| No | 86.54 (85.45, 87.56) | 89.21 (88.16, 90.18) | 60.65 (56.58, 64.58) | |
| Total energy intake, kcal/d | 2074.95 (2049.37, 2100.53) | 2066.50 (2040.63, 2092.36) | 2156.95 (2020.02, 2293.87) | .223 |
| Total dietary fiber, g/d | 8.75 (8.54, 8.97) | 8.76 (8.55, 8.96) | 8.73 (8.13, 9.33) | .930 |
| Grain fiber, g/d | 6.28 (6.13, 6.43) | 6.27 (6.11, 6.42) | 6.47 (5.98, 6.95) | .428 |
| Fruit fiber, g/d | 0.86 (0.80, 0.92) | 0.87 (0.81, 0.94) | 0.76 (0.63, 0.90) | .186 |
| Vegetable fiber, g/d | 1.49 (1.42, 1.56) | 1.50 (1.43, 1.57) | 1.40 (1.26, 1.55) | .213 |
| Legume fiber, g/d | 0.11 (0.10, 0.13) | 0.12 (0.10, 0.13) | 0.10 (0.08, 0.12) | .043 |
| Median liver stiffness, kPa | 5.71 (5.48, 5.95) | 4.87 (4.81, 4.92) | 13.94 (12.48, 15.40) | < .001 |
Values are presented as survey-weighted means with 95% CIs for continuous variables and survey-weighted percentages with 95% CIs for categorical variables. Unweighted sample sizes (N) are shown in the column headers, whereas all percentages and means presented in the table are survey-weighted estimates. P values were calculated using survey-weighted linear regression models for continuous variables and survey-weighted chi-square tests for categorical variables, accounting for the complex multistage sampling design of NHANES. Liver fibrosis status was defined based on median liver stiffness measured by transient elastography, with significant fibrosis defined as median liver stiffness ≥ 8 kPa.
BMI = body mass index, CI = confidence interval, kPa = kilopascals, n/N = number of participants, NHANES = National Health and Nutrition Examination Survey, PIR = poverty-income ratio.
3.2. Associations of dietary fiber intake with continuous liver stiffness (median liver stiffness, kPa)
Table 2 displays survey-weighted associations between dietary fiber subtypes and continuous median liver stiffness across three models (Model 1: unadjusted; Model 2: adjusted for gender, age, and race; Model 3: fully adjusted).
Table 2.
Associations between dietary fiber intake and continuous liver stiffness measured by transient elastography.
| Exposure | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| β (95% CI) | P value | β (95% CI) | P value | β (95% CI) | P value | |
| Total dietary fiber | 0.04 (−0.01, 0.08) | .120 | 0.02 (−0.03, 0.07) | .495 | −0.04 (−0.09, 0.02) | .210 |
| Grain fiber | 0.06 (0.00, 0.12) | .058 | 0.05 (−0.02, 0.11) | .183 | −0.01 (−0.09, 0.08) | .891 |
| Fruit fiber | −0.09 (−0.20, 0.01) | .0781 | −0.14 (−0.24, −0.04) | .017 | −0.13 (−0.22, −0.04) | .024 |
| Vegetable fiber | −0.00 (−0.17, 0.17) | .9715 | −0.04 (−0.21, 0.14) | .677 | −0.07 (−0.24, 0.11) | .485 |
| Legume fiber | −0.25 (−0.45, −0.05) | .021 | −0.37 (−0.61, −0.12) | .009 | −0.39 (−0.64, −0.14) | .016 |
| Fruit fiber quartile | ||||||
| Q1 (lowest) | Reference | - | Reference | - | Reference | - |
| Q2 | −0.18 (−0.62, 0.26) | .424 | −0.18 (−0.62, 0.26) | .436 | −0.17 (−0.59, 0.26) | .472 |
| Q3 | 0.09 (−0.45, 0.64) | .747 | 0.07 (−0.50, 0.63) | .8138 | 0.07 (−0.42, 0.56) | .792 |
| Q4 (highest) | −0.40 (−0.75, −0.06) | .033 | −0.52 (−0.88, −0.16) | .013 | −0.50 (−0.81, −0.18) | .022 |
Values are survey-weighted regression coefficients (β) with 95% CI derived from survey-weighted multivariable linear regression models. Liver stiffness was assessed as median liver stiffness (kPa) and analyzed as a continuous outcome. Quartiles of dietary fiber intake were defined based on weighted population distributions.
Quartile analyses for legume fiber in relation to continuous liver stiffness did not demonstrate a monotonic dose-response relationship and are not presented.
Model 1: Unadjusted. Model 2: Adjust for gender, age and race. Model 3: Adjusted for gender, age, race, PIR, smoking status, alcohol consumption, hypertension, diabetes, and total energy intake.
CI = confidence intervals, kPa = kilopascals, PIR = poverty-income ratio.
In fully adjusted analyses (Model 3), higher fruit fiber intake was associated with lower median liver stiffness in survey-weighted analyses. (β per 1 g/day = −0.13 kPa; 95% CI, −0.22–−0.04; P = .024). Legume fiber was also inversely associated with stiffness (β per 1 g/day = −0.39 kPa; 95% CI, −0.64–−0.14; P = .016). Grain, vegetable, and total fiber were not significantly associated with continuous liver stiffness in Model 3.
Quartile analyses for the primary exposure (fruit fiber) showed a graded association: participants in the highest quartile of fruit fiber had lower median stiffness compared with those in the lowest quartile (Q4 vs Q1: β = −0.50 kPa; 95% CI, −0.81–−0.18; P = .022 for Q4). Quartile analyses for other fiber subtypes did not demonstrate monotonic dose-response relationships and therefore were not presented.
3.3. Associations of dietary fiber intake with binary liver fibrosis outcome (median liver stiffness ≥ 8 kPa)
Results from survey-weighted logistic regression for the binary outcome are shown in Table 3. In the fully adjusted model (Model 3), total dietary fiber modeled continuously was inversely associated with odds of liver fibrosis (OR per 1 g/day = 0.94; 95% CI, 0.89–0.99; P = .041). Continuous fruit, grain, and vegetable fiber were not statistically significant in Model 3; legume fiber showed a borderline inverse association (OR = 0.65; 95% CI, 0.44–0.95; P = .058).
Table 3.
Associations between dietary fiber intake and liver fibrosis defined by transient elastography (median liver stiffness ≥ 8 kPa).
| Exposure | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value | |
| Total dietary fiber | 1.00 (0.96, 1.04) | .930 | 0.98 (0.94, 1.02) | .295 | 0.94 (0.89, 0.99) | .041 |
| Grain fiber | 1.02 (0.98, 1.06) | .416 | 1.00 (0.96, 1.05) | .873 | 0.97 (0.91, 1.04) | .414 |
| Fruit fiber | 0.90 (0.77, 1.06) | .217 | 0.86 (0.72, 1.02) | .092 | 0.88 (0.74, 1.05) | .198 |
| Vegetable fiber | 0.92 (0.79, 1.06) | .236 | 0.88 (0.76, 1.02) | .118 | 0.87 (0.73, 1.04) | .164 |
| Legume fiber | 0.74 (0.55, 1.00) | .064 | 0.65 (0.47, 0.90) | .018 | 0.65 (0.44, 0.95) | .058 |
| Fruit fiber quartile | ||||||
| Q1 (lowest) | Reference | - | Reference | - | Reference | - |
| Q2 | 0.82 (0.60, 1.12) | .227 | 0.78 (0.57, 1.05) | .121 | 0.79 (0.59, 1.04) | .141 |
| Q3 | 0.68 (0.45, 1.03) | .084 | 0.62 (0.41, 0.92) | .031 | 0.61 (0.42, 0.89) | .041 |
| Q4 (highest) | 0.70 (0.50, 0.97) | .045 | 0.59 (0.42, 0.82) | .006 | 0.61 (0.45, 0.83) | .020 |
Values are survey-weighted OR with 95% CI estimated using survey-weighted multivariable logistic regression models. Liver fibrosis was defined as median liver stiffness ≥ 8 kPa. Quartiles of dietary fiber intake were categorized according to weighted population distributions.
Quartile analyses are shown for fruit fiber, which demonstrated a clear dose-response association with liver fibrosis. Quartile results for other fiber subtypes and total dietary fiber did not show consistent dose-response patterns and are presented in Table S1.
Model 1: Unadjusted. Model 2: Adjust for gender, age and race. Model 3: Adjusted for gender, age, race, PIR, smoking status, alcohol consumption, hypertension, diabetes, and total energy intake.
CI = confidence intervals, OR = odds ratios, kPa = kilopascals, PIR = poverty-income ratio.
In quartile analyses, fruit fiber showed a clear inverse association with fibrosis: compared with the lowest quartile, the third and fourth quartiles had lower odds of fibrosis (Q3 vs Q1: OR = 0.61, 95% CI 0.42–0.89, P = .041; Q4 vs Q1: OR = 0.61, 95% CI 0.45–0.83, P = .020) in multivariable-adjusted models. Quartile results for total, grain, vegetable and legume fiber are provided in Table S1.
3.4. Sensitivity analyses
Findings were generally robust in sensitivity analyses. When an alternative cutoff for fibrosis (median liver stiffness ≥ 7 kPa) was used, associations were directionally consistent (Table S2), although some estimates were attenuated and statistical significance changed for a subset of comparisons. Results were not materially altered when participants with missing alcohol data were retained as a separate category, indicating that the treatment of alcohol missingness did not drive the observed associations.
3.5. Subgroup analyses
Figure 2 presents subgroup analyses for the association of fruit fiber intake (Q4 vs Q1) with liver fibrosis stratified by gender, age, race, and BMI. Tests for interaction showed no statistically significant effect modification by any of these factors (gender: P for interaction = .574; age: P for interaction = .781; race: P for interaction = .116; BMI: P for interaction = .603).
Figure 2.
Forest plot showing the association between fruit fiber intake (highest vs lowest quartile) and liver fibrosis, defined as median liver stiffness ≥ 8 kPa, in the overall population and stratified by gender, age, race, and BMI. ORs and 95% CIs were estimated using survey-weighted logistic regression models, adjusted for gender, age, race, poverty-income ratio, smoking status, alcohol consumption, hypertension, diabetes, and total energy intake, except for the stratification variable in each subgroup analysis. P for interaction was calculated by including an interaction term between fruit fiber intake and the corresponding subgroup variable in the multivariable model. BMI = body mass index, CI = confidence intervals, kPa = kilopascals, OR = odds ratios.
In stratified analyses, the inverse association between fruit fiber intake and liver fibrosis was statistically significant among females (OR = 0.55; 95% CI: 0.37–0.83; P = .005) and obese participants (OR = 0.70; 95% CI: 0.53–0.93; P = .014), but not among males or nonobese participants. Age-stratified analyses did not reveal statistically significant associations within either age group.
In race-stratified analyses, the inverse association was statistically significant among Non-Hispanic White participants (OR = 0.49; 95% CI: 0.31–0.77; P = .002), with point estimates suggesting a potentially protective direction in Mexican American and Other Hispanic participants. No clear association was observed among Non-Hispanic Black participants. Detailed results for all subgroups are presented in Figure 2.
4. Discussion
In this nationally representative analysis of U.S. adults, higher intake of fruit-derived dietary fiber was consistently associated with lower liver stiffness and a lower prevalence of significant liver fibrosis assessed by transient elastography. These associations remained robust after multivariable adjustment for sociodemographic factors, metabolic comorbidities, lifestyle behaviors, and total energy intake. In contrast, total dietary fiber and other fiber subtypes demonstrated weaker or less consistent associations, particularly in dose-response analyses. Together, these findings suggest that the source of dietary fiber may be an important consideration, beyond total fiber intake alone, in relation to liver fibrosis risk. The apparent inconsistency between continuous and quartile-based analyses, particularly for fruit fiber in relation to the binary fibrosis outcome, may reflect a nonlinear association or a threshold effect, whereby the protective relationship becomes evident only above a certain intake level. Additionally, quartile categorization helps reduce measurement error and the influence of extreme values, which may be particularly relevant for dietary exposure data collected via 24-hour recalls. For the continuous liver stiffness outcome, however, fruit fiber showed consistent inverse associations in both analytical approaches, suggesting that the inconsistency is outcome-dependent.
Our findings extend the existing literature by systematically examining multiple fiber subtypes in relation to liver fibrosis. Previous observational studies have generally reported inverse associations between dietary fiber intake and liver-related outcomes, including nonalcoholic fatty liver disease and liver stiffness.[6,10] However, most prior research has focused on total dietary fiber or overall dietary patterns, with limited attention to fiber sources. Among studies that have examined fiber subtypes, the results have generally supported the hepatoprotective effects of fruit fiber. A large population-based study using NHANES data found that total, cereal, fruit, and vegetable fiber intakes were all inversely associated with nonalcoholic fatty liver disease risk,[10] suggesting that multiple fiber sources may confer benefits. More specifically, an intervention study in obese subjects under energy restriction reported that fruit fiber consumption led to significant improvements in liver enzymes (gamma-glutamyl transferase, aspartate aminotransferase, alanine aminotransferase) and reduced markers of fatty liver disease, highlighting the particular benefits of fruit-derived fiber.[11] These findings align with our observation that fruit fiber, but not grain fiber, was consistently associated with lower liver fibrosis risk. The consistency of fruit fiber findings across studies with different designs (cross-sectional, interventional) and outcomes (nonalcoholic fatty liver disease, liver enzymes, fibrosis) strengthens the evidence for its hepatoprotective role. The physicochemical properties of fruit fiber, rich in soluble and fermentable components, may confer distinct advantages for liver health compared to insoluble cereal fiber.[12] By simultaneously examining multiple fiber subtypes using both continuous and categorical measures of liver fibrosis, our study provides novel population-based evidence clarifying the heterogeneity of dietary fiber sources in relation to fibrotic progression.
Several biologically plausible mechanisms may explain the observed inverse association between fruit fiber intake and liver stiffness. Fruit fiber is typically enriched in soluble and highly fermentable components that are preferentially utilized by gut microbiota, leading to increased production of SCFAs, including acetate, propionate, and butyrate.[13,14] These SCFAs have been shown to improve insulin sensitivity, suppress hepatic inflammation, and modulate hepatic stellate cell activation, a central process in fibrogenesis.[15–17] Experimental studies further suggest that butyrate may directly attenuate collagen deposition and fibrotic remodeling in the liver.[18] In addition to fiber fermentability, fruits are rich sources of bioactive compounds such as polyphenols, carotenoids, and vitamin C,[19] which may act synergistically with dietary fiber to influence liver health. Polyphenols have been reported to modulate gut microbial composition, enhance intestinal barrier integrity, and reduce oxidative stress and systemic inflammation.[20–23] Improved gut barrier function may reduce the translocation of endotoxin-derived lipopolysaccharides into the portal circulation, thereby attenuating chronic hepatic inflammation and downstream fibrotic responses along the gut-liver axis.[20,24] Importantly, these mechanisms directly link our primary finding, the specific association with fruit fiber, to the gut-liver axis pathways, suggesting that the fermentability and accompanying phytochemicals of fruit fiber may be key drivers of the observed hepatoprotective effects. Although these mechanisms could not be directly evaluated in the present study, they provide a biologically coherent framework for interpreting our results.
This study has several strengths, including the use of a large, nationally representative sample, validated assessment of liver fibrosis by transient elastography, and comprehensive adjustment for potential confounders. However, several important limitations should be acknowledged. First, the cross-sectional design precludes causal inference, and reverse causation cannot be excluded. Second, dietary intake was assessed by 24-hour recalls, which may not reflect long-term habits and are subject to measurement error. Third, despite extensive adjustment, residual confounding by unmeasured factors (e.g., socioeconomic status, healthcare access) remains possible. Fourth, although we performed race-stratified analyses, sample sizes in some subgroups (e.g., other race) were limited, resulting in wide CIs and imprecise estimates. Fifth, transient elastography may involve misclassification, and NHANES lacks data on specific fruit types, precluding analysis of fruit-specific effects. Sixth, multiple subgroup comparisons (e.g., by race, gender, age, and BMI) were performed without adjustment for multiple testing, which may have increased the risk of type I error. Therefore, these exploratory stratified findings should be interpreted cautiously and require confirmation in independent cohorts.
5. Conclusion
In conclusion, higher fruit fiber intake was independently associated with lower liver stiffness and a lower prevalence of significant liver fibrosis among U.S. adults. These findings underscore the potential importance of fiber source and quality, rather than total fiber intake alone, in relation to liver fibrosis assessed by transient elastography. Prospective cohort studies and randomized controlled trials are warranted to confirm these associations and to further elucidate the underlying biological mechanisms.
Acknowledgements
We thank the participants and staff of the NHANES databases.
Author contributions
Conceptualization: Jingqian Qin.
Data curation: Jingqian Qin.
Formal analysis: Shuang Liu, Mengjie Qin.
Investigation: Shuang Liu, Mengjie Qin.
Methodology: Jingqian Qin.
Validation: Xiying Zhou, Fenghua Ai.
Visualization: Xiying Zhou, Fenghua Ai.
Writing – original draft: Jingqian Qin.
Writing – review & editing: Shuang Liu, Mengjie Qin, Xiying Zhou, Fenghua Ai, Jingqian Qin.
Abbreviations:
- BMI
- body mass index
- CI
- confidence interval
- kPa
- kilopascals
- NHANES
- National Health and Nutrition Examination Survey
- PIR
- poverty-income ratio
- SCFAs
- short-chain fatty acids
- U.S.
- United States
- USDA
- U.S. Department of Agriculture.
The present study was a secondary analysis of publicly available data from NHANES. All NHANES study protocols were approved by the National Center for Health Statistics Research Ethics Review Board, and all participants provided written informed consent. The present analysis was conducted in accordance with the ethical principles of the Declaration of Helsinki.
The authors have no funding or conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are publicly available.
Supplemental Digital Content is available in the online version of this article (http://dx.doi.org/10.1097/MD.0000000000048958).
How to cite this article: Liu S, Qin M, Zhou X, Ai F, Qin J. Association of dietary fiber sources with liver fibrosis assessed by transient elastography in U.S. adults: a cross-sectional study from NHANES 2017 to 2020. Medicine 2026;105:21(e48958).
SL and MQ contributed to this article equally.
Contributor Information
Shuang Liu, Email: 2285766467@qq.com.
Mengjie Qin, Email: qinjingqian77@163.com.
Xiying Zhou, Email: 137698399@qq.com.
Fenghua Ai, Email: 1197638718@qq.com.
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