Noninvasive Tests to Identify Liver Fibrosis in Metabolic Dysfunction-Associated Steatotic Liver Disease Are Affected by Race

Abstract

Objective

To assess the performance of noninvasive tests (NITs) across different racial/ethnic groups in a large multiethnic cohort.

Methods

Data were derived from the National Health and Nutrition Examination Surveys 2017–2020. Participants without valid transient elastography (TE) measurements or with alternative etiologies of liver steatosis disease were excluded from the study.

Results

Among the 6,359 adults included in the study, FLI and NAFLD liver fat scores performed well for the prediction of MASLD without significant changes across racial/ethnic groups. However, significant differences were observed across racial/ethnic groups for the prediction of advanced fibrosis and cirrhosis. Fibrosis-4 index (FIB-4), AST-to-platelet ratio index (APRI), and NAFLD fibrosis score underperformed in non-Hispanic Black subjects for the detection of cirrhosis. For the detection of advanced fibrosis their performance was also numerically worse in non-Hispanic Black patients, but only reached statistical significance for APRI. Using a cut-off point of 12kPa for advanced fibrosis both APRI and FIB-4 performed significantly worse in non-Hispanic Black subjects.

Conclusion

In a large diverse national cohort, the performance of NITs was overall poor compared to TE, and they showed differences across racial/ethnic groups. Given the widespread use of NITs, it is imperative that the scores are equitable across racial/ethnic groups.

Nonalcoholic fatty liver disease (NAFLD) has reached epidemic proportions, affecting ~25% of the overall population (1). In order to decrease stigmatization, it has recently been proposed to change its nomenclature to metabolic dysfunction-associated steatotic liver disease (MASLD) and adapt its definition to include the presence of at least one cardiometabolic risk factor (2). The overlap between these 2 entities have been shown to be close to 99% (3).

With some degree of variability, it has been shown that MASLD affect all racial groups worldwide (4). However, the development and progression of MASLD appear to be significantly influenced by race (5). Indeed, non-Hispanic Black patients have a lower prevalence of MASLD than non-Hispanic White patients (6), while Hispanic patients appear to be particularly affected by this condition (7, 8). At least in part, these differences may be driven by differential distribution of important underlying risk factors, such as obesity and diabetes (7). Among non-Hispanic Asian patients, metabolic abnormalities and MASLD appear to develop at a lower threshold of BMI (9). Whether these differences translate into a faster progression to advanced fibrosis or cirrhosis is currently unknown. However, there is evidence that all races, even non-Hispanic Black patients, are susceptible to developing steatohepatitis, liver fibrosis, and cirrhosis (10, 11). In line with differences in MASLD prevalence, racial groups have shown different metabolic profiles, with non-Hispanic Black patients consistently found to have a better lipid profile than non-Hispanic White patients (i.e., lower plasma triglyceride and higher HDL-C levels) (10), and Hispanic patients showing the opposite trend (12). However, when matched for BMI, there were no differences in metabolic parameters between Hispanic and Caucasian patients (8).

Despite clear racial differences in the development and progression of MASLD, current guidelines do not distinguish between races when recommending diagnostic or therapeutic approaches. The most recent American Association of Clinical Endocrinology (AACE) and American Association for the Study of Liver Diseases (AASLD) guidelines for NAFLD, which can be applied to patients with MASLD due to high overlap between both populations (13, 14) recommend the use of NITs, specifically FIB-4 index, for the initial screening of advanced fibrosis in at-risk patients, followed by transient elastography, ELF and/or magnetic resonance elastography. However, many of these diagnostic tools have been developed in predominantly non-Hispanic White populations (15, 16), and their performance in other ethnicities, especially non-Hispanic Black subjects, is not well known.

Therefore, the aim of this study was to assess the performance of NITs (FIB-4, APRI, and NFS) for the diagnosis of advanced fibrosis and cirrhosis based on vibration-controlled transient elastography (VCTE or Fibroscan^®) across different racial groups in a large, unselected population of the U.S. (NHANES 2017–2020).

Methods

Study design and subjects

This cross-sectional study was based on data from the 2017 to 2020 National Health and Nutrition Examination Survey (NHANES) (17). Briefly, the NHANES uses a complex, multistage, probability sampling design, in order to select a representative sample of the general non-institutionalized U.S. population of all ages. It involves a structured interview, and a standardized health examination (i.e., physical examination and laboratory tests). The original survey was approved by the Centers for Disease Control and Prevention Research Ethics Review Board and written informed consent was obtained from all adult participants. All research procedures were conducted in accordance with both the Declarations of Helsinki and Istanbul. While typically NHANES data are compiled and released in 2-year cycles as public-use data files, owing to the coronavirus disease 2019 (COVID-19) pandemic, the NHANES program suspended field operations in March 2020. Data collected from 2019 to March 2020 were combined with the NHANES 2017–2018 cycle, in order to form a nationally representative sample (i.e., 2017 – 03/2020). Blood samples and VCTE were obtained at the same appointment at the NHANES mobile examination centers. Adults subjects (≥18 years old) with reliable vibration-controlled transient elastography (VCTE) and controlled attenuation parameter (CAP) examinations were included in the analysis. VCTE and CAP were performed by NHANES health technicians according to the manufacturer’s guidelines after at least 3 hours of fasting. They were trained to take 10 valid measurements with an interquartile range / median ratio of less than 30%. Both M and XL probes were available.

Patients were excluded if they reported any prior history of chronic liver disease other than MASLD or metabolic dysfunction-associated steatohepatitis (MASH) or significant alcohol consumption, if they had serological evidence of hepatitis B or C, or if they used any medication known to be associated with hepatic steatosis (e.g., amiodarone, glucocorticoids, methotrexate, and valproic acid) or if they were diagnosed with cryptogenic steatotic liver disease. A daily average consumption of alcohol >30 grams (men) and >20 grams (women) was considered significant. This was estimated based on self-reported data regarding the amount and frequency of ‘standard drinks’ consumed within the prior 12 months. The grams of alcohol consumed were estimated assuming 14 grams per standard drink as previously done (18). Patients self-reported as multi-racial or those who did not provide a specific race (i.e., other) were excluded from the study. Figure 1 provides a diagram with numbers of patients included and reasons for exclusion.

Figure 1.

Diagram explaining patients excluded and reasons for exclusion.

Definitions

Presence of MASLD was defined as CAP ≥ 288 dB/m based on previous reports (19) and it required the presence of at least 1 cardiometabolic risk factor as recently defined for MASLD (2). Of note, only 0.3% (n=7) of patients with a diagnosis of NAFLD did not have any cardiometabolic risk factor, and thus did not fulfill criteria for MASLD (i.e., they were diagnosed with cryptogenic steatotic liver disease instead as excluded from analysis). Advanced fibrosis and cirrhosis were defined based on a liver stiffness measurement (LSM) ≥ 9.7 and ≥ 13.6 kPa, respectively (20). Other cut-off points to define MASLD (i.e., 274 and 302 dB/m), significant fibrosis (i.e., 8.2 kPa and 12.0 kPa) and cirrhosis (i.e., 14 kPa and 20 kPa) were also used as sensitivity analyses. Presence of diabetes was defined based on the presence of self-reported prior history, hemoglobin A1c ≥ 6.5%, fasting plasma glucose ≥ 126mg/dl, or use of any glucose-lowering agent. Prediabetes was defined based on the presence of fasting plasma glucose of 100–125mg/dl or A1c between 5.7–6.4%. Metabolic syndrome was defined according to the updated ATPIII definition (21). Racial and ethnic group were based on self-reporting, and included: Mexican-American, other Hispanic origin, non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, and other race including multiracial. Mexican-American and Other Hispanic were combined for the purpose of this analysis. Patients who self-identified as multiracial or as other race were excluded. Presence of overweight and obesity were defined as body mass index (BMI) ≥ 25 kg/m² and BMI ≥ 30 kg/m², respectively, except among non-Hispanic Asians, where different BMI cut-off points were used (i.e., BMI ≥ 23 kg/m² and BMI ≥ 25 kg/m², respectively). Increased waist circumference was defined as ≥94 cm (males) and ≥80 cm (females), except among non-Hispanic Asians where cut-offs of ≥90 cm (males) and ≥80 cm (females) were used based on the new MASLD definition (2). However, higher cut-off points were used for non-Asian patients for the definition of metabolic syndrome as specified by ATPIII criteria (21).

Anthropometric and analytical measurements, and clinical score calculations

Anthropometric and laboratory (i.e., alanine aminotransferase [ALT], aspartate aminotransferase [AST], γ-glutamyltranspeptidase [GGT], albumin, platelets, lipid panel, fasting glucose, fasting insulin, and hemoglobin A1c) measurements were performed as previously described based on NHANES procedural manuals(17). These measurements were used to calculate clinical scores for the prediction of liver fibrosis, including the FIB-4, NFS, and APRI, as well as the fatty liver index (FLI) and NAFLD liver fat score for the prediction of hepatic steatosis. All NITs were calculated from results of the same samples. These scores were calculated as previously described (22).

Statistical analysis

Data are presented as percentages (categorical variables) or as mean ± SD (continuous variables). Differences among groups were estimated by one-way ANOVA or chi-square test depending on the type of variables. Receiver operating characteristic (ROC) curves for the different scores were plotted and areas under the curve (AUC) were calculated to represent their performance in predicting binary outcomes. AUCs comparisons were performed using the roccomp command (test of equality of ROC areas) in Stata. A two-tailed value of P <0.05 was considered to indicate statistical significance. Analyses were performed using Stata 11.1 (StataCorp LP, College Station, TX, USA) and graphs were generated with Prism 6.0 (GraphPad Software, Inc., La Jolla, CA, USA).

Results

Baseline characteristics and prevalence of MASLD and advanced fibrosis

Table 1 summarizes the patients’ characteristics based on race/ethnicity (n = 6,359). As can be observed, non-Hispanic White subjects were significantly older, although from a clinical standpoint, without major differences among groups (means from 45 to 52 years). Sex distributions among racial groups were similar. BMI was highest among non-Hispanic Black patients, and lowest among non-Hispanic Asian individuals. Rates of obesity ranged between 41–52% among different racial/ethnic groups and prevalence of diabetes was ~17–21%. Non-Hispanic Black patients had lower triglyceride levels and higher HDL cholesterol compared to other racial groups. Moreover, non-Hispanic Black patients had lower serum AST and ALT levels. In Figure 2 we have summarized the prevalence of MASLD, advanced liver fibrosis, and cirrhosis based on the use of CAP or VCTE across racial/ethnic groups. As evidenced in the figure, non-Hispanic Black and non-Hispanic Asian patients had the lowest rates of MASLD, while Hispanic patients showed the highest prevalence. Regarding advanced liver fibrosis, non-Hispanic Asian and non-Hispanic-Black patients also showed the lowest prevalence of advanced fibrosis, with a narrow range of prevalence among the other racial/ethnic groups (from 5.6 to 6.6%). Prevalence of cirrhosis among racial/ethnic groups ranged from 1.2 to 2.8%, with significantly lower levels in non-Hispanic Black and Asians, especially when compared to non-Hispanic White subjects. Supplemental Table 1 shows patient’s characteristics based on the presence or absence of advanced liver fibrosis. As expected, patients with advanced fibrosis were older, had higher BMI, higher prevalence of diabetes, and an overall worse metabolic profile.

Table 1.

Patients’ demographic, clinical and biochemical characteristics based on racial group.

	Hispanic (n=1,568)	Non-Hispanic White (n=2,242)	Non-Hispanic Black (n=1,714)	Non-Hispanic Asian (n=835)	p value
Age, years	45 ± 17	52 ± 20	48 ± 18	46 ± 16	<0.001
Gender, male/female %	48/52%	50/50%	47/53%	49/51%	0.41
Weight, kg	80 ± 19	84 ± 22	88 ± 24	69 ± 16	<0.001
Body mass index, kg/m2	30.0 ± 6.2	29.7 ± 7.2	31.1 ± 8.3	25.8 ± 4.8	<0.001
Presence of diabetes, %	20.9%	17.1%	20.7%	17.7%	0.004
A1c, %	5.9 ±1.3	5.7 ± 0.9	5.9 ± 1.2	5.8 ± 0.9	<0.001
Fasting plasma glucose, mg/dL	107 ± 43	102 ± 33	102 ± 36	101 ± 29	<0.001
Fasting plasma insulin, μU/mL	16 ± 17	15 ± 30	14 ± 17	12 ± 12	0.038
Total cholesterol, mg/dL	186 ± 39	185 ± 41	180 ± 39	192 ± 41	<0.001
LDL-C, mg/dL	111 ± 35	107 ± 35	107 ± 36	112 ± 35	0.012
HDL-C, mg/dL	50 ± 13	53 ± 15	55 ± 15	54 ± 16	<0.001
Triglycerides, mg/dL	152 ± 140	133 ± 92	96 ± 65	144 ± 116	<0.001
Statin use, %	16.1%	25.3%	18.3%	18.7%	<0.001
Presence of cardiometabolic risk factors (based on MASLD definition)
- High BMI or WC	86%	83%	81%	77%	<0.001
- High Glucose/A1c	54%	51%	57%	54%	0.003
- High BP	43%	52%	59%	43%	<0.001
- High triglycerides	27%	34%	23%	31%	<0.001
- Low HDL	51%	51%	43%	45%	<0.001
Aspartate aminotransferase (AST), U/L	22 ± 14	21 ± 11	20 ± 9	21 ± 9	<0.001
AST ≥ 30 among those with VCTE ≥ 9.7 kPa	31%	19%	7%	38%	<0.001
Alanine aminotransferase, U/L	25 ± 19	21 ± 16	19 ± 13	22 ± 15	<0.001
ALT ≥ 30 among those with VCTE ≥ 9.7 kPa	47%	33%	16%	46%	<0.001
CAP, dB/m	273 ± 62	266 ± 63	251 ± 61	258 ± 59	<0.001
Liver stiffness by VCTE, kPa	5.6 ± 4.0	5.9 ± 5.2	5.7 ± 3.8	5.0 ± 2.1	<0.001

Figure 2.

Prevalence of MASLD based on controlled attenuation parameter (panel A) and liver advanced fibrosis (panel B) and cirrhosis (panel C) based on vibration-controlled transient elastography among racial/ethnic groups. ^$ p<0.05 compared to non-Hispanic White subjects. ^@ p<0.05 vs. Hispanic subjects.

Among patients with a diagnosis of diabetes, prevalence of MASLD, advanced fibrosis and cirrhosis were significantly higher (Supplemental Figure 1), but the overall distribution among racial/ethnic groups was not significantly changed. Using different cut off points to define MASLD, advanced fibrosis or cirrhosis did not affect the distribution across racial/ethnic groups either (Supplemental Figure 2). Among all racial/ethnic groups prevalence of MASLD was lower in female compared to male subjects, except among non-Hispanic Blacks, where both sexes had similar rates of MASLD (26.3% vs. 25.7%, p=0.80, respectively).

Performance of clinical scores for the prediction of MASLD across the different racial/ethnic groups

We assessed the performance of the FLI and the NAFLD liver fat score to predict the presence of MASLD compared to CAP. In Table 2 we have compared how these scores performed in the entire population and in each racial/ethnic group. As can be observed, they performed well, and overall, similarly across all racial groups. In non-Hispanic White patients, the FLI appeared to outperform the NAFLD liver fat score for the prediction of MASLD. However, as summarized in Supplemental Figure 3, the proportion of patients with hepatic steatosis based on typically used cut-off points of FLI (≥60) and NAFLD liver fat score (≥−0.640) resulted in an overestimation of hepatic steatosis compared to results from CAP≥288 dB/m in all racial/ethnic groups (except FLI among non-Hispanic Asians). Using these cut-off points, the overall agreement was 73.4% between CAP and FLI and 73.2% between CAP and NAFLD liver fat score. Sensitivity analyses using different CAP cut-off points for the diagnosis of MASLD (i.e., 274 or 302 dB/m) did not change the overall results of these analyses. However, the performance of FLI to identify MASLD based on CAP of 274 dB/m showed a strong trend towards better performance among non-Hispanic White patients (Hispanic: 0.83 [0.81–0.85]; non-Hispanic White: 0.85 [0.84–0.87]; non-Hispanic Black: 0.82 [0.80–0.84]; vs. non-Hispanic Asian: 0.82 [0.79–0.85], p=0.051). Performance of NAFLD liver fat score to identify MASLD based on a CAP of 302 dB/m was also significantly different across ethnic groups (Hispanic: 0.84 [0.81–0.87]; non-Hispanic White: 0.81 [0.78–0.84]; non-Hispanic Black: 0.80 [0.76–0.84]; vs. non-Hispanic Asian: 0.88 [0.84–0.92], p=0.016).

Table 2.

Performance of clinical scores to predict MASLD (CAP ≥ 288 dB/m with at least 1 cardiometabolic risk factor) among different racial/ethnic groups in the overall population.

	Hispanic	Non-Hispanic White	Non-Hispanic Black	Non-Hispanic Asian	p value#	Entire cohort
FLI	0.83 (0.81–0.85)	0.85 (0.83–0.86)	0.83 (0.81–0.86)	0.84 (0.81–0.87)	0.50	0.84 (0.82–0.85)
NAFLD LFS	0.84 (0.81–0.87)	0.80 (0.77–0.83)	0.81 (0.77–0.84)	0.85 (0.81–0.90)	0.112	0.82 (0.80–0.84)
p value*	0.75	<0.001	0.21	0.53		0.014

^*p value comparing FLI vs. NAFLD liver fat score.

^#p value comparing performance of each clinical score among different racial/ethnic groups.

Performance of clinical scores for the prediction of advanced liver fibrosis and cirrhosis across the different racial/ethnic groups in all patients (“universal screening”)

When noninvasive scores were assessed for the detection of advanced liver fibrosis (F3-F4) in the entire cohort, the overall performances of all scores were poor (Table 3), but significantly better for NFS compared to FIB-4 and APRI (AUCs: 0.77 [0.74–0.80] vs. 0.63 [0.60–0.66] and 0.61 [0.58–0.65], respectively, p<0.001). When assessing the performance based on racial/ethnic groups, we observed a better performance of noninvasive scores among non-Hispanic Asian patients and worse performance among non-Hispanic Black patients, although these differences were only statistically significant for APRI. Using 8.2 kPa as a cut-off point to define advanced fibrosis, no differences were observed in the performance of the NITs. However, using a cut-off of 12 kPa as proposed by AACE guidelines (13), the performance of FIB-4 and APRI were significantly worse in non-Hispanic Black compared to other racial/ethnic groups (for FIB-4: Hispanic: 0.74 [0.66–0.82]; non-Hispanic White: 0.63 [0.56–0.70]; non-Hispanic Black: 0.53 [0.43–0.64]; vs. non-Hispanic Asian: 0.77 [0.64–0.90], p=0.004; and for APRI: Hispanic: 0.66 [0.57–0.76]; non-Hispanic White: 0.66 [0.59–0.72]; non-Hispanic Black: 0.48 [0.38–0.58]; vs. non-Hispanic Asian: 0.85 [0.77–0.93], p<0.001).

Table 3.

Performance of clinical scores to predict advanced liver fibrosis (VCTE ≥ 9.7 kPa) and cirrhosis (VCTE ≥ 13.6 kPa) among different racial/ethnic groups in the overall population.

	Hispanic	Non-Hispanic White	Non-Hispanic Black	Non-Hispanic Asian	p value#	Entire cohort
Advanced Fibrosis (F3–4 vs. F0–2)
FIB-4	0.66 (0.60–0.73)	0.63 (0.57–0.68)	0.59 (0.52–0.65)	0.70 (0.59–0.81)	0.25	0.63 (0.60–0.67)
APRI	0.62 (0.55–0.69)	0.63 (0.58–0.68)	0.50 @,$,^ (0.44–0.57)	0.72 (0.60–0.84)	0.004	0.60 (0.57–0.64)
NFS	0.78 (0.73–0.83)	0.76 (0.72–0.80)	0.74 (0.68–0.80)	0.83 (0.74–0.92)	0.36	0.77 (0.74–0.80)
p value*	<0.001	<0.001	<0.001	0.026		<0.001
Cirrhosis (F4 vs. F0–3)
FIB-4	0.78 (0.69–0.87)	0.64 @,^ (0.56–0.72)	0.52 @,^ (0.39–0.65)	0.83 (0.72–0.95)	<0.001	0.67 (0.62–0.72)
APRI	0.65 (0.53–0.77)	0.67 ^ (0.60–0.75)	0.52 $,^ (0.41–0.62)	0.84 @ (0.76–0.93)	<0.001	0.65 (0.60–0.70)
NFS	0.85 (0.79–0.92)	0.78 ^ (0.72–0.84)	0.76 ^ (0.64–0.87)	0.91 (0.83–0.99)	0.046	0.81 (0.77–0.85)
p value*	0.002	<0.001	<0.001	0.34		<0.001

^*p value comparing FIB-4, APRI and NFS.

^#p value comparing performance of each clinical score among different racial/ethnic groups.

^@p<0.05 vs. Hispanic subjects

^$p<0.05 vs. non-Hispanic White subjects

^{^}p<0.05 vs. non-Hispanic Asian subjects

When used for the prediction of cirrhosis (F4), all scores significantly underperformed in non-Hispanic Black patients when compared to other racial groups (Table 3), although this was slightly less pronounced for the NFS. Similar to what was observed for the detection of F3-F4, NFS performed significantly better than FIB-4 and APRI for the detection of F4 when applied to the entire population. As age has been reported to influence the performance of NITs, we repeated the above analyses only in subjects between 35 and 64 years old. As can be observed in Table 4, even when limiting the analysis to this age subgroup, FIB-4 index and APRI performed significantly worse for the detection of advanced fibrosis (F3-F4) and cirrhosis (F4) among non-Hispanic Black subjects compared to other racial/ethnic groups. No racial/ethnic differences were observed with NFS in this age group. All NITs performed similarly across racial/ethnic groups when comparing female and male subjects (data not shown).

Table 4.

Performance of clinical scores to predict advanced liver fibrosis (VCTE ≥ 9.7 kPa) and cirrhosis (VCTE ≥ 13.6 kPa) among different racial/ethnic groups in patients between 35 and 64 years old.

	Hispanic	Non-Hispanic White	Non-Hispanic Black	Non-Hispanic Asian	p value#	Entire cohort
Advanced Fibrosis (F3–4 vs. F0–2)
FIB-4	0.61 (0.51–0.70)	0.51 ^ (0.43–0.60)	0.48 ^ (0.38–0.58)	0.72 (0.57–0.86)	0.029	0.55 (0.49–0.60)
APRI	0.63 (0.54–0.73)	0.57 ^ (0.48–0.65)	0.46 @,^ (0.37–0.55)	0.74 (0.60–0.89)	0.005	0.57 (0.52–0.62)
NFS	0.79 (0.73–0.86)	0.78 (0.71–0.84)	0.70 (0.60–0.79)	0.81 (0.70–0.93)	0.37	0.76 (0.72–0.80)
p value*	<0.001	<0.001	<0.001	0.38		<0.001
Cirrhosis (F4 vs. F0–3)
FIB-4	0.70 (0.57–0.84)	0.57 ^ (0.43–0.71)	0.44 @,^ (0.26–0.63)	0.88 @ (0.80–0.96)	<0.001	0.60 (0.51–0.69)
APRI	0.63 (0.48–0.79)	0.60 ^ (0.47–0.74)	0.46 ^ (0.30–0.62)	0.84 @ (0.73–0.95)	<0.001	0.60 (0.51–0.68)
NFS	0.85 (0.77–0.93)	0.85 (0.77–0.92)	0.82 (0.67–0.97)	0.86 (0.69–1.00)	0.99	0.84 (0.78–0.89)
p value*	0.041	<0.001	<0.001	0.83		<0.001

^*p value comparing FIB-4, APRI and NFS.

^#p value comparing performance of each clinical score among different racial/ethnic groups.

^@p<0.05 vs. Hispanic subjects

^$p<0.05 vs. non-Hispanic White subjects

^{^}p<0.05 vs. non-Hispanic Asian subjects

Performance of clinical scores for the prediction of advanced liver fibrosis and cirrhosis across the different racial/ethnic groups in patients pre-selected based on high-risk of liver fibrosis

Based on recent guidelines (13, 14), we selected patients ‘at risk’ of liver disease based on presence of prediabetes or type 2 diabetes, elevated AST or ALT (>30IU/L), or ≥ 2 cardiometabolic risk factors (e.g., increased waist circumference, high triglycerides [≥150mg/dl or receiving treatment], low HDL-C [<40mg/dl or <50mg/dl for males and females, or receiving treatment], high systolic or diastolic blood pressure [≥130 or ≥85 mmHg, or receiving treatment], high C-reactive protein [≥2 mg/L], and high HOMA-IR [≥ 3]). A total of 5,007 (78.7%) patients were in this ‘at risk’ category. Noninvasive clinical scores showed overall similar results when applied to only the ‘at risk’ population compared to their performance in the entire population (Table 5). Briefly, noninvasive scores had numerically worse AUCs in non-Hispanic Black patients, although these differences only reached statistical significance for APRI for the detection of F3–4 and F4, and for FIB-4 for the detection of F4. NFS performed better than the other scores in the ‘at risk’ population.

Table 5.

Performance of clinical scores to predict advanced liver fibrosis (VCTE ≥ 9.7 kPa) and cirrhosis (VCTE ≥ 13.6 kPa) among different racial/ethnic groups in patients at risk for liver disease.

	Hispanic	Non-Hispanic White	Non-Hispanic Black	Non-Hispanic Asian	p value#	Entire cohort
Advanced Fibrosis (F3–4 vs. F0–2)
FIB-4	0.64 (0.57–0.70)	0.61 (0.55–0.66)	0.56 (0.49–0.63)	0.67 (0.55–0.78)	0.33	0.61 (0.58–0.64)
APRI	0.61 (0.54–0.68)	0.62 (0.57–0.67)	0.50 @,$,^ (0.44–0.57)	0.70 (0.58–0.82)	0.010	0.59 (0.56–0.63)
NFS	0.76 (0.70–0.81)	0.73 (0.69–0.78)	0.72 (0.65–0.78)	0.81 (0.71–0.91)	0.43	0.74 (0.71–0.77)
p value*	<0.001	<0.001	<0.001	0.032		<0.001
Cirrhosis (F4 vs. F0–3)
FIB-4	0.76 (0.67–0.86)	0.62 @,^ (0.54–0.71)	0.51 @,^ (0.38–0.64)	0.81 (0.68–0.94)	0.002	0.66 (0.60–0.71)
APRI	0.64 (0.52–0.76)	0.67 ^ (0.59–0.74)	0.51 $,^ (0.40–0.62)	0.82 @ (0.72–0.91)	<0.001	0.64 (0.59–0.70)
NFS	0.85 (0.78–0.92)	0.76 (0.70–0.83)	0.76 (0.65–0.87)	0.89 (0.79–0.99)	0.079	0.80 (0.76–0.84)
p value*	0.002	<0.001	<0.001	0.38		<0.001

^*p value comparing FIB-4, APRI and NFS.

^#p value comparing performance of each clinical score among different racial/ethnic groups.

^@p<0.05 vs. Hispanic subjects

^$p<0.05 vs. non-Hispanic White subjects

^{^}p<0.05 vs. non-Hispanic Asian subjects

Differences in plasma ALT and AST in patients of different racial/ethnic background with liver disease

Among patients with advanced fibrosis, the rate of patients with abnormal plasma aminotransferases was different across the racial/ethnic groups (p<0.001; Table 1), with non-Hispanic Black patients showing the lowest rates of AST elevation (≥30 IU/L). Similar results were observed when ALT levels were used (Table 1). No significant differences were observed in platelets levels among racial groups in patients with advanced fibrosis. FIB-4 index was numerically lower in non-Hispanic Black patients with advanced fibrosis compared to other racial groups, but this difference did not reach statistical significance (p=0.066). The difference was even more pronounced in patients with cirrhosis, but also did not reach statistical significance (p=0.115).

Overall agreement between NITs and VCTE

If sequential testing is performed as proposed in the AACE guidelines (13, 14) (FIB-4 first, followed by VCTE only in those with FIB-4 index >1.3), this would result in missing 56% and 48% of patients with advanced fibrosis and cirrhosis, respectively, compared to VCTE. This degree of disagreement did not improve when the tests were targeted to the ‘at-risk’ population as suggested in the guidelines: 56% and 46% of all patients with advanced fibrosis and cirrhosis based on VCTE, respectively, were missed with this approach.

Using the typical low and high cut-off points for FIB-4 (1.30 and 2.67), APRI (0.5 and 1.0) and NAFLD fibrosis score (−1.455 and 0.676) we assessed the prevalence of liver fibrosis compared to VCTE (Figure 3). As can be observed, NAFLD fibrosis score overestimated the prevalence of advanced fibrosis even at the higher cut-off point (i.e., 0.676) in non-Hispanic White and Black subjects. Using a low cut-off point for FIB-4 (i.e., 1.30) also resulted in an overestimation of the rate of liver fibrosis compared to VCTE ≥ 9.7 kPa, but this was not the case with the higher cut-off point (i.e., 2.67). APRI underestimated the prevalence of liver fibrosis compared to VCTE ≥ 9.7 kPa even at the low cut-off point (i.e., 0.5).

Figure 3.

Prevalence of liver fibrosis based on VCTE ≥ 9.7 kPa (black columns), and using previously reported low (grey columns) and high (white columns) cut-off points for FIB-4 (≥1.30 and ≥2.67), APRI (≥0.5 and ≥1.0) and NAFLD fibrosis score (≥−1.455 and ≥0.676).

Discussion

In recent years, there has been increasing interest in finding a simple screening tool for primary care providers to identify patients at risk for clinically significant liver fibrosis to target referral and/or therapeutics. In recent guidelines, FIB-4 index and VCTE have gained acceptance above other NITs due to their simplicity, low cost, and widespread availability (13, 14). This widespread screening recommendation is coming pre-emptively as pharmacotherapies are expected for MASH with liver fibrosis in the coming years (23). Whether subspecialty care and/or biopsy will be required for the use of these agents has yet to be determined, but there is some thought that noninvasive tests may be sufficient to gain access to these highly coveted drugs. However, the performance of noninvasive tests in different races/ethnic groups had not been comprehensively assessed before. Because MASLD appears to have significant differences across racial and ethnic groups (7), it was important to assess if diagnostic tools performed similarly among these groups.

The present study represents a comprehensive assessment of the use of noninvasive scores to predict advanced liver fibrosis against VCTE across different racial and ethnic groups in an unselected, diverse, national database. We hypothesized that these noninvasive tests would likely perform worse in non-White subjects given that the tests were originally developed and validated in predominantly non-Hispanic White populations (24). For example, the study by Angulo et al that described the NAFLD fibrosis score included 90% of Caucasian subjects (16). FIB-4 was originally developed for patients with HIV/HCV co-infection and 77% and 82% of patients were Caucasian in the training and validation sets, respectively (25). When applied for the prediction of advanced fibrosis in a large cohort of patients with NAFLD, ~75% were non-Hispanic White subjects, without description of the distribution of the rest of the patients (15).

Overall, our data shows that all NITs used for the prediction of liver fibrosis performed significantly different when compared to VCTE across all racial/ethnic groups. Specifically, APRI performed significantly worse among non-Hispanic Black subjects for the prediction of advanced fibrosis or cirrhosis, whether applied to the general population or only to patients at high-risk of liver disease. Racial differences in the performance of FIB-4 were only evident when it was used to predict cirrhosis, showing worse performance among non-Hispanic Black subjects compared to other racial/ethnic groups. Our results also evidenced a large disagreement between FIB-4 and VCTE, two of the most widely supported screening tools in current guidelines (13, 14). This is concerning because it can result in a significant number of patients at risk of liver disease being misdiagnosed. Moreover, racial/ethnic differences observed in the performance of NITs suggest that race/ethnicity may need to be taken into account when applying NITs for the prediction of liver fibrosis. For example, if using APRI to detect F3-F4 or FIB-4 to detect F4, non-Hispanic Black subjects, who are also at risk for significant liver disease (10), may be under-identified and under-treated, further worsening health disparities in this population (26). It is likely that a generic, “one size fits all” diagnostic approach may not be feasible across all races/ethnicities.

Differences across races in the prevalence, severity, and biochemical characteristics of MASLD have been extensively reported (6, 7, 10). In accordance with prior results, we showed in an unselected population, that the prevalence of MASLD was significantly lower in non-Hispanic Black subjects despite their higher BMI. Hispanic patients showed the highest prevalence of MASLD as previously reported (27). However, when assessing the prevalence of advanced fibrosis based on VCTE, non-Hispanic White patients showed the highest prevalence of advanced fibrosis (6.6%) and non-Hispanic Asian patients had the lowest prevalence (3.4%). These results may suggest that the mechanisms leading to intrahepatic triglyceride accumulation and those driving liver fibrosis may be independent. In accordance with prior reports, we observed that despite lower prevalence of MASLD, non-Hispanic Black patients still have a significant proportion of advanced fibrosis (28, 29). Indeed, MASLD has been found to be the second leading cause of cirrhosis in non-Hispanic Black patients (30). Of note, the presence of diabetes increased the prevalence of MASLD and advanced liver fibrosis in all ethnic groups proportionally.

Overall, the performance of NITs compared to VCTE was poor, with a significantly low agreement between the two approaches. Despite wide promotion of FIB-4 in recent guidelines, there remains several questions regarding the best screening strategy (31). Moreover, several studies have shown the limitations of this tool to identify patients with advanced fibrosis in large samples (22, 32). Our study confirmed an overall poor performance of FIB-4 to identify patients with ≥F3, but at least for the detection of F3-F4 the performance was similar across races/ethnicities. The use of APRI to detect F3-F4 or FIB-4 to detect F4 resulted in significant differences across different racial/ethnic groups. While the reasons for this differential performance remain a mystery, we observed a dissociation between the presence of liver fibrosis and elevations in serum aminotransferase levels in certain racial/ethnic groups. Specifically, only 7% of non-Hispanic Black patients with advanced fibrosis had elevated plasma AST levels and this was significantly lower than in other racial/ethnic groups. This may explain why NITs that rely heavily on AST and/or ALT may underperform in certain racial/ethnic groups. Race-specific NITs or race-adjusted NITs may be needed in order to improve the diagnostic performance of these tools in clinical practice.

The main limitation of this study is the use of VCTE as the gold-standard to identify liver fibrosis. Although controversial, liver biopsy remains the gold standard for the diagnosis of liver fibrosis and cirrhosis, but was not performed in this population. VCTE is currently one of the most widely used modalities to assess liver fibrosis and is relatively inexpensive and noninvasive as compared to magnetic resonance elastography and liver biopsy. However, compared to liver biopsy, the performance of VCTE is only ~0.80–0.85 (33), which may have affected the results of our study. While this may limit the interpretation of the performance of NITs in absolute terms, assuming that VCTE performs similarly on all racial/ethnic groups, the comparison of NITs performance among racial/ethnic groups should not be significantly affected by this. In the setting of obesity, accuracy of CAP and VCTE is significantly reduced (34), and significant hepatic steatosis can also lead to an overestimation of liver fibrosis by VCTE (35). This may explain the better performance observed for the NAFLD fibrosis score, which relies heavily on BMI. Because increasing BMI can lead to overdiagnosis of liver fibrosis by VCTE, and NAFLD fibrosis score relies heavily on BMI to estimate fibrosis risk, the performance of this score can be overestimated when using VCTE as the comparator. Of note, the use of NAFLD fibrosis score has fallen out of favor due to its overdiagnosis of liver fibrosis among patients with obesity and diabetes (36, 37) with current cut-off points, as also shown in our study (Figure 3). Another limitation of the study is inherent to assessing racial/ethnic groups by self-reporting. Race/ethnicity is a social construct and not a biological category, and thus this stratification is subject to bias.

In summary, we have shown that among patients at risk of liver fibrosis (i.e., those recommended for liver fibrosis screening with FIB-4 with sequential VCTE testing based on recent guidelines), there is a large disagreement between FIB-4 and VCTE, which can lead to significant misdiagnoses. Non-Hispanic Black subjects appear to be particularly underdiagnosed if using the APRI score or if using FIB-4 for the detection of cirrhosis. While the performance of FIB-4 was overall poor to detect advanced liver fibrosis by VCTE, performance across racial/ethnic groups was not different when applied to the ‘at risk’ population, following current guidelines (13, 14). As race plays an important role in the development and progression of MASLD, it makes sense that race-targeted NITs may provide a more accurate estimate regarding the presence of liver fibrosis. Due to the large disagreement between FIB-4 and VCTE observed in this large population, a screening strategy based on the sequential use of these techniques may not be an accurate approach. In an era of upcoming highly awaited treatment options for MASH with fibrosis, recommended noninvasive tests for screening and treatment need to be equitable across different racial and ethnic groups. Further studies are warranted to verify these findings against liver biopsy.

What is already known about this subject?

The development and progression of metabolic dysfunction-associated steatotic liver disease is affected by race/ethnicity.

Non-invasive tests (NITs) have been broadly recommended for screening of liver fibrosis, independently of race/ethnicity.

What are the new findings in your manuscript?

In a large, unselected population, the performance of NITs was significantly affected by race/ethnicity.

Specifically, NITs used for the prediction of liver fibrosis underperformed in Non-Hispanic Black patients, and this may be related to lower serum AST levels in Non-Hispanic Black patients despite presence of advanced fibrosis.

How might your results change the direction of research or the focus of clinical practice?

Race and ethnicity need to be taken into consideration when applying NITs for the prediction of liver fibrosis.

It seems that a generic, “one size fits all” diagnostic approach will not be feasible, and that race/ethnicity will need to be included as part of the NITs’ calculations.

Abbreviations

NITs

noninvasive tests

MASLD

metabolic dysfunction-associated steatotic liver disease

NAFLD

nonalcoholic fatty liver disease

NHANES

National Health and Nutrition Examination Survey

TE

transient elastography

CAP

controlled attenuation parameter

FIB-4

fibrosis-4 index

APRI

AST to platelet ratio index

NFS

NAFLD fibrosis score

AACE

American Association of Clinical Endocrinology

AASLD

American Association for the Study of Liver Diseases

COVID-19

coronavirus disease 2019

VCTE

vibration-controlled transient elastography

MASH

metabolic dysfunction-associated steatohepatitis

BMI

body mass index

ALT

alanine aminotransferase

AST

aspartate aminotransferase

GGT

γ-glutamyl transpeptidase

FLI

fatty liver index

ROC

receiver operating characteristic

AUC

area under the curve

HOMA-IR

homeostatic model assessment of insulin resistance

Data Availability:

Data available upon request to corresponding author.