Use of noninvasive fibrosis calculators in an urban diabetes center suggests a large burden of undetected advanced liver disease

doi:10.21203/rs.3.rs-4940956/v1

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Research Article

Use of noninvasive fibrosis calculators in an urban diabetes center suggests a large burden of undetected advanced liver disease

https://doi.org/10.21203/rs.3.rs-4940956/v1

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Background

Metabolic dysfunction associated steatotic liver disease (MASLD) is prevalent in up to 60% of patients with type 2 diabetes mellitus (T2DM). T2DM accelerates the risk of hepatic fibrosis and hepatocellular carcinoma in patients with MASLD. Our goal in this study was to identify patients with suspected MASLD and hepatic fibrosis in a large T2DM clinic by using noninvasive fibrosis scoring systems.

Methods

We conducted a retrospective study of patients with T2DM seen by our endocrinologists at the Medical Faculty Associates (MFA) Diabetes Center in Washington, DC, from November 1, 2021, until November 1, 2022. We included all subjects who were over 18 years old with a hemoglobin A1c (HbA1c) of 6.5 or higher. Patients with a history of significant alcohol consumption, decompensated cirrhosis, previous bariatric surgery, or prior chronic liver disease were excluded from the study. We identified patients at risk for hepatic fibrosis by using the Fibrosis-4 (FIB-4) Index, NAFLD Fibrosis Score (NFS) and AST to Platelet Ratio Index (APRI) when lab values were available.

Results

A total of 1,411 patients were evaluated for T2DM by an endocrinology provider during the one-year period. Out of these, 336 patients met one or more of the exclusion criteria, leaving a total of 1075 patients included in the analysis. The majority were African American (n = 582, 54%), 261 were Caucasian (24.3%), and 85 were Hispanic (7.9%). Most patients were females (n = 675, 62.7%). The mean HbA1c was 8.1 ± 2.3. 643 patients (59.8%) were insulin dependent. Based on FIB-4 scores, we found that 35 (3.9%) patients had a score of > 2.67 associated with advanced fibrosis and 257 (29%) patients with scores of 1.3–2.67 had moderate fibrosis. Using the NFS calculator, there were 281 (28%) patients with values of > 0.675 consistent with F3-F4 disease. 715 (71.8%) patients with values of < .675 consistent with F0-F2 fibrosis. A total of 6(< 1%) patients met criteria for advanced fibrosis by APRI scoring.

Conclusion

In our urban Diabetes Center, utilizing the NFS calculator may detect many patients with an advanced liver disease. Further research is needed to correlate these findings with transient elastography and other imaging evidence of fatty liver disease.

Fatty Liver Disease

Liver fibrosis

Fibrosis Scoring Systems

Diabetes Mellitus

Metabolic dysfunction associated steatotic liver disease (MASLD) is characterized by the existence of hepatic steatosis in the absence of secondary factors that lead to fat accumulation in hepatocytes, such as excessive alcohol consumption, chronic viral hepatitis, or hereditary metabolic liver disorders (1). From a clinical perspective, individuals with MASLD frequently exhibit features associated with metabolic syndrome, including obesity, T2DM, hypertension and dyslipidemia. (2–4).

Epidemiologic research has demonstrated the bidirectional and reciprocal association between T2DM and MASLD, wherein T2DM increases the risk of MASLD incidence and progression to liver cirrhosis and ultimately hepatocellular carcinoma, and MASLD increases the risk of T2DM incidence (5). The possible compensatory hyperinsulinemia in response to insulin resistance leads to defective lipid metabolism and subsequent accumulation of hepatic triglycerides (TG) in MASLD, and to dysfunction of beta cells in T2DM (6, 7).

According to the CDC's National Diabetes Statistics Report, 11.6% of the U.S. population, or 38.4 million people, had diabetes in 2021(8) and MASLD as an often-overlooked complication of T2DM is prevalent in up to 60% of patients with T2DM (9)

Due to higher cardiovascular and cerebrovascular risks in diabetic patients with MASLD (10), it is important to accurately determine how prevalent MASLD is in T2DM to help providers better assess the need for hepatology referral and care.

Given the above, efforts are underway to assess liver disease in diabetes clinics all over the world (11, 12). In addition, academic institutions have started opening muti-disciplinary clinics to tackle the coordination of care necessary for these patients ensuring an early diagnosis and treatment (13).

The most reliable approach for diagnosis is histopathological examination via biopsy, which is limited due to its invasive nature, amongst other reasons (14). Although, ultrasonography is the recommended first-line screening methods in clinical practice, it is known with its limited sensitivity (15)

Other methods such as proton magnetic resonance spectroscopy (1H-MRS), magnetic resonance elastography, and vibration-controlled transient elastography could be used to assess the magnitude of liver stiffness. However, these methods are time-consuming, and not cost-effective for large-scale MASLD screening (16). Noninvasive scoring systems such as the NAFLD fibrosis score (NFS), fibrosis-4 (FIB-4) index, and aspartate aminotransferase (AST) to platelet ratio index (APRI), have been developed to predict advanced fibrosis. These scores are easy to compute using demographic and laboratory data, making them readily available for use by health care providers and clinical researchers (17–19)

The primary outcome of the study is to assess the prevalence of MASLD and the subsequent advanced liver fibrosis in T2DM patients using these noninvasive fibrosis scoring systems.

We conducted a single-center retrospective study of patients with T2DM seen by endocrinologists at the Diabetes Center of the GW Medical Faculty Associates (MFA) in Washington DC, from November 1, 2021, until November 1, 2022. We included all subjects who were over 18 years old with a hemoglobin A1c (HbA1c) of 6.5 or higher who were not previously established with hepatology care. Patients with a history of significant alcohol consumption (> 14 standard drinks/week in men, > 7 standard drinks/week in women), decompensated cirrhosis, previous bariatric surgery, previous liver biopsy, or prior chronic liver disease were excluded from the study. We identified patients at risk for hepatic fibrosis by using noninvasive fibrosis scoring systems such as Fibrosis-4 (FIB-4) Index, NAFLD Fibrosis Score (NFS) and AST to Platelet Ratio Index (APRI) when lab values were available. Additionally, all patients’ records were reviewed for any prior abdominal imaging such as ultrasonography, Magnetic Resonance Imaging or Computed Topography to assess the presence of liver fibrosis and/or steatosis over a period of 10 years from 2013–2023.

The FIB-4 Index is based on the following formula: FIB-4 Score = (Age* x AST)/ (Platelets x √(ALT)) and a score of > 2.67 is indicative of advanced fibrosis (17)

The NAFLD Fibrosis Score (NFS) is based on the following formula: NFS = -1.675 + (0.037*age [years]) + (0.094*BMI [kg/m2]) + (1.13*IFG/diabetes [yes = 1, no = 0]) + (0.99*AST/ALT ratio) – (0.013*platelet count [×109/L]) – (0.66*albumin [g/dl]) and a score of > 0.675 is indicative of advanced fibrosis (18).

The AST to Platelet Ratio Index (APRI) is based on (AST in IU/L) / (AST Upper Limit of Normal in IU/L) / (Platelets in 109/L) and score of ≥ 1.5 is indicative of advanced fibrosis (19). Low APRI score (< 0.5) and FIB − 4 score (< 1.3) had a NPV of 98% in predicting future severe liver disease related events

Statistical Methodology

A descriptive summary table was calculated for the cohort. Continuous variables were summarized using means and standard deviations, while categorical variables were summarized with percentages. One-way ANOVA was used to calculate differences in continuous laboratory variables by categorical demographic classifications.

Fisher’s Exact Tests were used to calculate the differences in the distribution of fibrosis marker values (FIB-4, NFS and APRI) by demographics (age, race, sex and ethnicity).

A binomial multivariable logistic regression model for the NAFLD fibrosis score (NFS) was developed with patient race, sex, and level of diabetes management (quantified by Hemoglobin A1C) used as categorical predictor variables. Adjusted odds ratios were calculated for each categorical predictor.

A total of 1,411 patients were evaluated for T2DM by an endocrinology provider during the one-year date range. 336 patients met one or more of the exclusion criteria, leaving a total of 1075 patients included in the analysis. The majority were African American (n = 582, 54%), 261 were Caucasian (24.3%), and 85 were Hispanic (7.9%). Most patients were females (n = 675, 62.7%). The mean BMI was 31.7. In terms of the clinical characteristics, the mean HbA1c was 8.1 ± 2.3. 643 patients (59.8%) were insulin dependent. The ALT and AST mean was 24.8 and 25.9 respectively and the platelet count mean was 268.7 (Table 1).

Based on FIB-4 scores, we found that 35 patients had a score of > 2.67 associated with high risk of liver disease and 257 patients with scores of 1.3–2.67 intermediate risk of liver disease. Using the NFS calculator, there were 281 patients (29%) with values of > 0.675 consistent with advanced fibrosis and 715 patients (71%) with values < 0.675. Using the APRI scoring, only 6 patients (< 1%) met the criteria for advanced fibrosis (Table 2).

Using the NFS score, patients who were identified with values < 0.675 (consistent with F0-F2 fibrosis) 513 (71.8%) were 65 years or younger and 202 (28.2%) were more than 65 years old. In terms of race, 403 (56.4%) were black, 168 (23.5%) were white and 144 (20.1%) were either other or unknown races. In terms of sex, 454 (64.5%) were females and 261 (36.5%) were males. 583 (81.7%) had an A1c of less than 10 and 131 (18.3%) had an A1c equal or more than 10. Of those identified with values of > 0.675 (consistent with F3-F4 disease), 153 (54.4%) were 65 or younger and 128 (45.6%) were more than 65 years old. In terms of race, 152 (54.1%) were black, 78 (27.8%) were white and 51 (18.2%) were either other or unknown races. In terms of sex, 172 (61.2%) were females and 109 (38.8%) were males. 232 (82.6%) had an A1c of less than 10 and 49 (17.4%) had an A1c equal or more than 10 (Table 3)

Using multivariable binary logistic regression analysis, adjusted odds ratios were calculated for associations of variables using the NFS score. For white patients, adjusted odds ratio was 0.945 (0.518, 1.771 CI). For black patients, the adjusted odds ratio was 0.785 (0.444, 1.432 CI). For female patients, adjusted odds ratio was 3.702e + 05 (1.558e+-42, N/A). For male patients, adjusted ratio was 3.971e + 05 (1.673e+-42, N/A). For patients with uncontrolled diabetes determined by A1c > 10, adjusted odds ratio was 0.960 (0.660, 1.379 CI). (Table 4)

In the assessment of imaging-based hepatic steatosis, evidence of fatty liver was identified in 112 out of 1075 patients using abdominal imaging studies. Specifically, 62 cases were detected via abdominal ultrasound, 5 via MRI, and 45 via CT scan.

Table 1

Population Demographics and clinical characteristics (n = 1075)
Variable	Population (n = 1075)
Age (mean ± SD)		58.05 ± 14.61
< 65 (n, %)		699 (65.0%)
≥ 65 (n, %)		376 (35.0%)
Race (n, %)
White		262 (24.3%)
Black		582 (54.1%)
Other- Asian		51 (4.7%)
Other- Native Hawaiian/Pacific Islander		2 (0.19%)
Other- American Indian/Alaskan Native		5 (0.47%)
Other- Other		93 (8.7%)
Unknown		81 (7.53%)
Ethnicity (n, %)
Hispanic/Latino		85 (7.9%)
Not Hispanic/Latino		697 (64.8%)
Unknown		293 (27.3%)
Sex (n, %)
Male		399 (37.1%)
Female		675 (62.8%)
Unknown		1 (0.1%)
BMI (mean ± SD) Aspartate aminotransferase (AST) (mean ± SD)		31.7 ± 7.8 25.9 ± 68.8
Alanine transaminase (ALT) (mean ± SD)		24.8 ± 34.8
Hemoglobin A1C (mean ± SD)		8.1 ± 2.3
Platelet count (mean ± SD)		268.7 ± 83.0

Table 2

Occurrence of Fibrosis via NFS, FIB-4 and APRI Fibrosis Scores
Fibrosis scoring system	Number of patients, mean score
NFS (n = 996)
≤.675	n = 715 (71.8%), x̄ = − 1.129
>.675	n = 281 (28.2%), x̄ = 2.006
FIB-4 (n = 880)
<1.3 (low risk)	n = 588 (66.9%), x̄ = 0.81
1.3–2.67 (indeterminate risk)	n = 257 (29.2%), x̄ = 1.73
>2.67 (high risk)	n = 35 (4.0%), x̄ = 7.69
APRI (n = 900)
< 0.5	n = 844 (93.8%), x̄ = 0.201
0.5 ≤ APRI ≤ 1.5	n = 50 (5.6%), x̄ = 0.719
> 1.5	n = 6 (0.7%), x̄ = 8.225

Table 3

Differences in covariates amongst NFS ≤ .675 and NFS > .675
Variable	NFS ≤ .675 (n = 715)	NFS > .675 (n = 281)	p-value
Age (years)	56.4	63.0	< .001
≤65	513 (71.8%)	153 (54.4%)	< .001
>65	202 (28.2%)	128 (45.6%)	< .001
Race (n, %)
White	168 (23.5%)	78 (27.8%)	.186
Black	403 (56.4%)	152 (54.1%)	.563
Other	104 (14.5%)	32 (11.4%)	.229
Unknown	40 (5.6%)	19 (6.8%)	.580
Ethnicity (n, %)
Hispanic/Latino	53 (7.4%)	23 (8.2%)	.779
Not Hispanic/Latino	489 (68.4%)	171 (60.9%)	.029
Unknown	173 (24.2%)	87 (31.0%)	.036
Sex (n, %)
Male	261 (36.5%)	109 (38.8%)	.549
Female	454 (64.5%)	172 (61.2%)	.549
Level of DM Control
A1c < 10.0	583 (81.7%)	232 (82.6%)	.807
A1c ≥ 10.0	131 (18.3%)	49 (17.4%)	.807

Table 4

Multivariable Binary Logistic Regression Analysis to Calculate Adjusted Odds Ratios for Associations of Variables with the NAFLD Fibrosis Score
Variable (Level)	Multivariable Odds Ratio (95% CI)	P-value
Race: White	.945 (.518, 1.771)	.856
Race: Black	.785 (.444, 1.432)	.416
Race: Other	.639 (.326, 1.271)	.196
Male	3.971e + 05 (1.673e+-42, N/A)	.981
Female	3.702e + 05 (1.558e+-42, N/A)	.981
Uncontrolled DM (A1C ≥ 10.0)	.960 (.660, 1.379)	.828

*Adjusted ORs were calculated using a multivariable binomial logistic regression model for the nonalcoholic fatty liver disease fibrosis score (score of ≤ .675 versus > .675 as a binary outcome variable). The multivariable binary logistic regression analysis model included race (as categorical factors), sex and level of DM control, quantified by Hemoglobin A1c (≥ 10.0 versus < 10.0).

The revised acronym "metabolic dysfunction-associated fatty liver disease" was recommended by an international group of experts from 22 nations in 2020 as a replacement for NAFLD. This phrase highlights the close connection between T2DM and MASLD. Not only do MASLD and T2DM have almost identical risk factors, but they also influence each other's disease development and consequences in a complementary way (29) Therefore, for the prevention and management of both T2DM and MASLD, frequent screening for T2DM in people with MASLD and vice versa, together with dietary and physical activity adjustments, are advised.

An optimal non-invasive method for evaluating hepatic fibrosis would be characterized by its high sensitivity and specificity, and its cost-effectiveness for patients (22). Additionally, it should apply to all types of chronic liver diseases. In the case of MASLD, this test should also have the capability to differentiate between a fatty liver and steatohepatitis.

Probably the most common reason for underdiagnosing MASLD is that primary care physicians and diabetes specialists only depend on normal liver enzymes to rule out the condition. AST and ALT in our study were within normal ranges, even though a high % of participants had MASLD suspicions.

To date, none of the presently existing tests fulfill these requirements, thus, it is important to evaluate the effectiveness of any marker by considering both the specific clinical query at hand and how well the marker performs within that clinical context (23).

A meta-analysis that was published in 2019 concluded that, among 80 studies from 20 countries, there were 49,419 individuals with T2DM (mean age 58.5 years, mean body mass index 27.9 kg/m2, and males 52.9%). The global prevalence of MASLD among patients with T2DM was 55.5% (95% CI 47.3–63.7) (24).

In our study we concluded there is a high prevalence of suspected MASLD and advanced fibrosis in T2DM patients when utilizing the various scoring systems. Around 28%, upon which the NFS calculator was run, showed a resultant score of > .675 indicating advanced fibrosis while 33.2%, upon which the FIB-4 calculation was run, showed intermediate to high risk of fibrosis. Less than 1% of whom underwent the APRI calculation met the criteria for advanced fibrosis. Additionally, we found significant variability in fibrosis percentages among different scores, highlighting the need for further validation of the scoring systems in diabetic patients. However, it is not negligible that T2DM patients face the risk of MASLD with its different degrees, as our research showed.

Further stratified analysis revealed that the majority of MASLD cases were females (n = 675, 62.7%) which is in contrary with the demographic analysis done in the metanalysis published in 2017 where the pooled prevalence of MASLD in both male and female T2DM patients was 60.11% (95% CI: 53.63–66.41%) and 59.35% (95% CI: 53.28–65.28%), respectively (26).

Based on what Shah et al concluded in their study, NFS outperforms seven other non-invasive markers of fibrosis in patients with MASLD (25), we did consider it in our study and the findings of fibrosis using NFS classification were significantly associated with the age group (< 65 Vs >-65) and ethnicity. According to a recent (National Health and Nutrition Examination Survey [NHANES] 2015-2016 database), individuals with T2DM had significant rates of hepatic steatosis and fibrosis with ethnic variations. (27). In a similar vein, certain ethnicities in our study showed a strong correlation between T2DM status and NFS and MASLD classification, whereas other ethnicities showed no such correlation—among Hispanic/Latino, for example.

The regression model we ran for NFS scoring system showed insignificant results for race, gender and uncontrolled DM indicated by A1C ≥ 10.0. Thus, we conclude that these variables are not to be considered when looking at MASLD risk for T2DM patients using the NFS scoring system.

We plan to use our results to guide us in the next step of our project, which will provide a free fibro scan screening using vibration controlled transient elastography (VCTE) and linkage to care in our hepatology clinics.

Limitations

Being a retrospective study, there is more susceptibility to biases and confounding factors than prospective designs. The results of this single-centered study are limited in their applicability to other groups and contexts. The accuracy of the MASLD risk assessment may be compromised in individuals who lack laboratory measurements, which are required to calculate the various scores employed. Individuals with other stages of hepatic fibrosis or other liver disorders may be unable to benefit from the study's findings, which were designed to identify people with a higher risk of F3/F4 fibrosis.

Endocrinologists, primary care physicians, and gastroenterologists ought to screen for MASLD in diabetic patients. Based on the conclusions drawn from our study conducted at our urban Diabetes Centers, utilizing the NFS calculator detected many patients with advanced liver disease. The obtained scores can be used to assess clinical progression. Further research is needed to correlate these findings to transient elastography. More screening for hepatitis B and C is needed to successfully rule out infectious causes of liver disease before attributing it to MASLD and other liver-associated metabolic syndromes

- The study got approved by the George Washington Institute of Review Board with a study exempt # NCR224560

- Given the study's retrospective nature, the GW IRB approved a full HIPPA waiver form. Thus, there was no need for consent from the study subject to participate or permission to publish

- We have used password-encrypted data sheets from the GW EMR systems. Only the study personnel had access to the data.

- The datasets analyzed during the current study are available from the corresponding author upon reasonable request.

Conflicts of interest:

There is no conflict of interest to be mentioned.

Funding:

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Contribution

AE, FM, VM, SP and SD contributed to the study design, data collection and manuscript writingJP, FM, AE and AA contributed the data analysisAA, ZV,SG , LJ and DE contributed to the analysis review and manuscript review and editing

Acknowledgement

The authors would like to thank Noor Abdalla at the University of Ottawa, in Canada. and Al-Hafis Adegun at the George Washington University School of Medicine and Health Sciences in Washington DC for their invaluable contribution in the data collection. Their dedication and hard work were instrumental in ensuring the accuracy and completeness of our research data.

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No competing interests reported.

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Use of noninvasive fibrosis calculators in an urban diabetes center suggests a large burden of undetected advanced liver disease

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