OBJECTIVE

Most people with type 2 diabetes (T2DM) and nonalcoholic steatohepatitis (NASH) or advanced fibrosis (AF) remain undiagnosed, resulting in missed opportunities for early intervention. This multicenter, prospective study assessed the yield of using routinely available data to identify these patients.

RESEARCH DESIGN AND METHODS

A total of 713 outpatients with T2DM, screened in four diabetology clinics for nonalcoholic fatty liver disease according to American Diabetes Association criteria, were referred to hepatologists for further work-up (Fibrosis-4 and vibration-controlled transient elastography [VCTE]). A liver biopsy was proposed when ALT levels were persistently >20 IU/L in female patients or >30 IU/L in male patients, in the absence of other liver disease.

RESULTS

Liver biopsies were performed in 360 patients and considered adequate for reading after central review for 330 specimens (median patient age, 59 years; male patients, 63%; median BMI and HbA1c values, 32 and 7.5%, respectively). Prevalence of NASH, AF, and cirrhosis were 58%, 38%, and 10%, respectively. Liver lesions were independently associated with the components of metabolic syndrome but not with the micro- and macrovascular complications of T2DM. Models based on routinely available data with or without VCTE had good accuracy to predict AF (respectively: area under the receiver operating characteristic curve [AUROC], 0.84 and 0.77; and correctly classified 59% and 45%) and NASH (respectively: AUROC, 0.82 and 0.81; 44% and 42%).

CONCLUSIONS

Despite the use of a low ALT threshold, prevalence of NASH (58%) or AF (38%) was high. Routinely available data had a high yield in identifying patients with T2DM with AF and/or NASH requiring further liver assessment.

Nonalcoholic fatty liver disease (NAFLD) affects approximately 25% of the adult population worldwide (1,2). NAFLD consists of various entities, ranging from nonalcoholic fatty liver to nonalcoholic steatohepatitis (NASH). NASH, characterized by steatosis, lobular inflammation, and hepatocyte ballooning, is associated with fibrosis, progression to cirrhosis, and hepatocellular carcinoma (3). Advanced fibrosis (AF), which is severe fibrosis or cirrhosis seen in a liver biopsy specimen (often referred to as F3-F4), is the main independent predictor for liver-related outcomes and overall mortality (4).

Patients with type 2 diabetes mellitus (T2DM) have a higher prevalence of NAFLD (5) and an exacerbated risk of developing NASH and AF, compared with the general population (6). However, the real prevalence of NASH and AF in patients with T2DM remains poorly known. Population-based epidemiological studies are weakened by the absence of liver histopathology, and studies based on liver histopathology are limited by small size, spectrum bias, varying case definitions, and unclear indications for liver biopsies. Another key aspect is that, in those studies, liver biopsies were only performed in the subset of patients with elevated transaminases (i.e., ALT > 40 IU/L), according to current common practice. Such a threshold may be not sensitive enough to capture NASH and AF, resulting in underestimated prevalence (7).

Although liver biopsy is considered the gold standard for NASH and AF diagnosis, it has several well-known limitations, including invasiveness and rare but potentially life-threatening complications, limiting patients’ acceptability (8). In addition, liver biopsy is not readily accessible in most diabetes clinics for several reasons. First, it requires the expertise of a hepatologist; second, it may be difficult to perform in case of morbid obesity and/or anticoagulant therapy; and third, well-established referral pathways between diabetes and liver clinics are commonly lacking (9,10).

Noninvasive tests (NITs) for liver fibrosis assessment have gained popularity over the past decade as alternatives to liver biopsy and have changed the practice of hepatology (11). The most popular NITs include the Fibrosis-4 (FIB-4) and the NAFLD fibrosis score for serum markers and vibration-controlled transient elastography (VCTE) for liver stiffness measurement (LSM) (12). Several noninvasive and clinical models have been proposed specifically for patients with T2DM for the detection of NASH or AF (1315); In addition, the use of VCTE (16,17) alone, or in sequential combination with FIB-4, has been recommended by several guidelines for the detection of AF in patients with T2DM (1820). However, availability of VCTE is currently limited in most diabetes clinics, and awareness regarding NITs remains poor among diabetologists (21). Thus, despite these recommendations, most people with NASH and AF remain undiagnosed in diabetes and endocrinology clinics, and potential opportunities for early intervention are missed. For instance, patients with AF who are at risk for developing hepatocellular carcinoma should enter surveillance programs that perform abdominal ultrasound every 6 months (22). Additionally, although no pharmacologic treatment is currently approved by the US Food and Drug Administration with NASH as an indication, some diabetic medications, such as pioglitazone or glucagon-like peptide 1 (GLP-1) receptor agonists, have shown a beneficial effect on NASH (23,24).

To address these limitations in patient care, we performed a multicenter, prospective study in a well-characterized population of outpatients with T2DM referred by their diabetologists to hepatologists for suspected NAFLD based on steatosis on ultrasound and/or elevated ALT level. Indication of liver biopsy was not based on usual NITs (i.e., FIB-4 or VCTE) but on prespecified standardized criteria, namely elevated ALT level using a low threshold. Our aims were: 1) to assess the prevalence of histological NASH and AF: and 2) to assess the yield of routinely available data as compared with conventional NITs for identifying patients who require further liver assessment.

Study Design and Participants

This study was nested in the QUID-NASH study (ClinicalTrials.gov identifier NCT03634098), an investigator-initiated, prospective, cross-sectional, multicenter study aimed at identifying noninvasive biomarkers of the diagnosis and severity of NASH in patients with T2DM, using liver histopathology as the gold standard. The project was initiated in October 2018 in four diabetes outpatient clinics across the Paris Ile-de-France area (Hôpital Lariboisière, Groupe Hospitalier Cochin–Port Royal, Hôpital Européen Georges Pompidou and Hôpital Avicenne; Assistance Publique–Hôpitaux de Paris). The study protocol is detailed in the Supplementary Material.

Inclusion criteria into the QUID-NASH study were as follows: ≥18 years of age; able to give written informed consent; and with T2DM defined according to American Diabetes Association (ADA) or World Health Organization criteria (25,26), having an indication for liver biopsy which they had agreed to undergo.

Exclusion criteria from the QUID-NASH study were as follows: pregnant women; patients without national social security coverage; patients with other causes of chronic liver disease than NASH, including excessive alcohol consumption (>20 g/day or women; >30 g/day for men); positivity for hepatitis B surface antigen; positive anti–hepatitis C virus antibodies and hepatitis C virus-RNA; markers of autoimmune hepatitis (namely, anti-tissue antibody or antinuclear antibody >1/40 and increased serum levels of immunoglobulins); evidence of hemochromatosis; α-1 antitrypsin deficiency; Wilson disease; obstruction of liver vessels or biliary tract; patients who have undergone liver transplantation; patients with blood hemoglobin <7 g/L, or <10 g/L if they have cardiovascular or pulmonary disease; patients who refuse to undergo blood tests; patients with a possible ongoing chronic alcohol consumption as indicated by serum carbohydrate-deficient transferrin percent >2% unless explained otherwise; and patients with a confirmed diagnosis of active malignancy or other terminal diseases.

For the QUID-NASH study, during routine annual workup, outpatients unaware of having NAFLD were evaluated for NAFLD by their diabetologist according to ADA recommendations (i.e., elevated serum ALT levels or steatosis on ultrasound) (27). Patients with suspected NAFLD were referred to a hepatologist for further work-up. A liver biopsy was considered as part of standard of care based on predefined criteria: persistently elevated transaminase levels (serum ALT levels >20 IU/L in women or > 30 IU/L in men on at least two occasions) and documented absence of other causes of liver disease as detailed above.

The study was conducted in accordance with the Declaration of Helsinki and has been approved by the Research Ethics Committee (Comité de Protection Personnes Sud Méditerranée no. 18.021-2018-A00311–54). Participants who had agreed to undergo a liver biopsy as part of standard of care gave written informed consent for the present study as part of the QUID-NASH study.

The present nested study consists of the analysis of the relationship of routine clinical, imaging and laboratory data with liver biopsy specimen–based diagnosis of NASH or AF in the first 330 consecutive patients included in the QUID-NASH study.

Clinical Assessment and Complications of Diabetes

The assessment included vital signs and anthropometric measurements, medical history, known duration of diabetes, and current treatments. Of note, none of the patients were treated with SGLT2 inhibitors because this class of antidiabetic drugs was only approved in France in April 2021. Patients were also evaluated for diabetes-related complications.

Macrovascular complications were defined as ischemic heart disease and/or cerebrovascular and/or peripheral arterial disease (25). Ischemic heart disease was defined as previous myocardial infarction, coronary revascularization, or documented coronary stenosis or myocardial ischemia on invasive or noninvasive testing. Cerebrovascular arterial disease was defined as ischemic stroke or carotid revascularization. Peripheral artery disease was defined as revascularization or documented arterial stenosis >50%.

Microvascular complications were defined as diabetic retinopathy and/or nephropathy and/or neuropathy (25). Diabetic nephropathy was identified by the presence of a urine albumin to creatinine ratio >3.4 mg/mmol or estimated glomerular filtration rate <60 mL/min/1.73 m2 using the Chronic Kidney Disease Epidemiology Collaboration formula. The diagnosis of diabetic retinopathy was defined as follows: moderate or proliferative retinopathy and/or laser photocoagulation and/or clinically significant macular edema requiring laser and/or intravitreal injections. Neuropathy was assessed by the presence of paresthesia or cramps or loss of protective sensation with the 10-g Semmes-Weinstein monofilament test.

Biological and Noninvasive Fibrosis Assessment

For each patient, a 12-h fasting blood sample was collected and tested locally for laboratory parameters (detailed in Supplementary Material). Blood-based tests for liver fibrosis included FIB-4 (based on age, AST and ALT levels, and platelet count) and NAFLD fibrosis score (based on age; BMI; diabetes status; AST, ALT, and serum albumin concentrations; and platelet count), calculated according to published formulas (12).

Controlled attenuation parameter (CAP) and LSM were performed by nurses or physicians trained and certified by the manufacturer and blinded to the patient’s histological evaluation, using VCTE (FibroScan 502 Touch model; Echosens, Paris, France) equipped with both M and XL probes. The CAP and LSM results were expressed in dB/m and kPa, respectively. Only examinations with at least 10 valid individual measurements, as well as with interquartile range or median ratio ≤30%, both for LSM and CAP were deemed reliable (12).

Histopathologic Assessment

Liver biopsies were performed according to local standard procedure in the liver units of Hôpital Beaujon or Hôpital Cochin (detailed in Supplementary Material). The transcapsular route, or transjugular route in case of concomitant anticoagulant treatment or morbid obesity, was used (8). Slides were analyzed in each center as part of standard care and then centrally reviewed by a single expert pathologist (P.B.), blinded to all patient characteristics, for the readouts. NASH was diagnosed using the Fatty Liver: Inhibition of Progression (FLIP) definition (i.e., presence of steatosis, hepatocyte ballooning, and lobular inflammation with at least 1 point for each category) (28). Biopsy specimens were categorized by the pathologists as NAFL (isolated steatosis) or NASH. Fibrosis was staged according to the NASH Clinical Research Network scoring system (29) on a scale from 0 to 4, where F0 is the absence of fibrosis, F1 is perisinusoidal or portal fibrosis, F2 is perisinusoidal and portal/periportal fibrosis, F3 is septal or bridging fibrosis; and F4 is cirrhosis. AF was defined as F3 or F4. NASH diagnosis and fibrosis staging presented below correspond to those after central review.

Statistical Analysis

The analysis population was defined as all patients included in the QUID-NASH project between October 2018 and March 2021 who had an interpretable liver biopsy specimen. Qualitative data were described by absolute (n) and relative (%) frequencies. Quantitative data were described by median and interquartile range. The analyses focused on the estimations of prevalence and the search for features associated with 1) NASH and 2) AF as main outcomes.

Prevalence estimations with their 95% CIs were performed with the total cases and the data available in the analysis population, in particular patients with NASH diagnosis or with a fibrosis stage according to the studied outcome.

To identify factors associated with each outcome, bivariate analyses were performed with logistic regressions. Odds ratios (ORs) and their 95% CIs were estimated. Several models with all variables with P values <0.20 were built for each outcome to determine multivariate logistic regression models for each specific case. Because CAP and LSM using VCTE (FibroScan) may not be available in many diabetes clinics, we built models 1) without CAP and LSM; and 2) with CAP and LSM. The final models were determined with a P value cutoff of 0.05 as a stopping rule for this backward manual selection. Adjusted ORs (aORs) and their 95% CIs were computed and the area under the receiver operating characteristic curve (AUROC) and its 95% CI of each regression model were estimated. A Shiny App was developed to report multivariate results to calculate a prediction and its CI. Comparisons of AUROCs were performed using DeLong’s test for paired data. Sensitivity (Se), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) were calculated for models with and without VCTE as well as for LSM, FIB-4, AST, and ALT. Two thresholds, one with optimal Se (>0.9) and one with optimal Sp (>0.9), were determined. Patients between these two thresholds were considered in the gray zone. The number of correctly classified patients was calculated as the sum of true positive outcome and true negative outcome (according to the two optimal thresholds determined with Se > 0.9 and Sp > 0.9). All tests were two-sided and P < 0.05 was considered significant. All statistical analyses were performed using R, version 4.0.3.

Patients

Between October 2018 and March 2021, 713 outpatients with suspected NAFLD were referred by diabetologists to hepatologists for further work-up. As shown in the flowchart in Supplementary Fig. 1, 243 patients had not been considered for liver biopsy because, despite having steatosis, they had ALT levels persistently <20 IU/L in women or 30 IU/L in men or ALT above this threshold on one occasion only. Additionally, 32 patients had other causes of liver disease, and 23 patients had serious concomitant conditions. Liver biopsy was thus proposed to 415 patients, of whom 360 consented. Thirty patients were secondarily excluded due to the small size or fragmentation of liver specimens, absence of adequate coloration, absence of liver tissue, or uncovering of other liver lesions such as granulomas. In these 30 patients, compared with the 330 other patients, the liver specimen was smaller (median length, 10 mm vs. 17 mm) and the number of fragments higher (median, 4 vs. 2) (Supplementary Table 1).

Patient Characteristics at Enrollment

As shown in Table 1, among the 330 patients included in the analysis, there were 123 women and 207 men. Their median age was 59 years and median BMI was 32 kg/m2. Sixty percent of patients had at least one microvascular complication, most commonly nephropathy, and 20% had at least one macrovascular complication, most commonly ischemic heart disease. The median HbA1c was 7.5%. Most patients (97%) were taking oral antidiabetic drugs. Median AST and ALT levels were 35 IU/L and 49 IU/L, respectively. As shown in Supplementary Table 2, one-third of patients had ALT levels <40 IU/L. Most characteristics of these patients did not differ from those of patients with ALT >40 IU/L, except for sex (more were female) and γ-glutamyl transferase (GGT), bilirubin, serum albumin, and ferritin levels (all lower than in patients with ALT >40 IU/L).

Table 1

Demographic and clinical characteristics of the 330 participants

CharacteristicData valueN
Sex  
 Male 207 (63)  
 Female 123 (37)  
Age, years 59 (52–66)  
BMI, kg/m2 32 (28–36)  
WC, cm 109 (101–120) 323 
WC (men: >102 cm; women: >88 cm) 260 (81) 323 
Hypertension 222 (68) 329 
HDL cholesterol (men: <1.03 mmol/L; women: <1.29 mmol/L) 195 (59)  
Triglycerides >1.7 mmol/L 167 (51)  
Known duration of diabetes, years 9 (5–15) 329 
Retinopathy 62 (19) 326 
Neuropathy 68 (21) 322 
Nephropathy 141 (42.9) 329 
At least one microvascular complication 196 (60) 326 
Cerebrovascular disease 12 (4)  
Peripheral arterial disease 10 (3) 329 
Ischemic heart disease 50 (15) 329 
At least one macrovascular complication 66 (20) 329 
Medication   
 Oral antidiabetic drugs 320 (97)  
 Insulin without GLP-1 29 (9)  
 GLP-1 without insulin 75 (23)  
 GLP-1 and insulin 113 (34)  
 Cardiovascular drugs 240 (73)  
 Lipid-lowering drugs 213 (65)  
Platelet count, G/L 244 (198–292)  
INR 1.0 (1.0–1.04) 304 
Liver enzyme concentration, IU/L   
 AST 35 (27–46) 329 
 ALT 49 (34–70)  
 ALT >40 210 (64)  
 GGT 55 (35–86)  
Total bilirubin, µmol/L 8.9 (6.1–12.5)  
Serum albumin, g/L 43.0 (40.7–45.4) 322 
Serum ferritin, µg/L 109 (54–210) 325 
Total cholesterol, mmol/L 3.97 (3.39–4.81)  
LDL cholesterol, mmol/L 2.07 (1.55–2.66) 315 
HDL cholesterol, mmol/L 1.03 (0.89–1.24)  
Triglycerides, mmol/L 1.73 (1.23–2.38)  
Fasting blood glucose, g/L 1.51 (1.27–1.89) 329 
HbA1c, % 7.5 (6.8–8.4) 327 
Serum creatinine, µmol/L 71.0 (59.8–84.0) 328 
eGFR, mL/min 94.9 (81.3–104.0) 328 
UACR, mg/mmol 2.00 (0.98–5.78) 260 
FIB-4 1.20 (0.90–1.69) 329 
NAFLD fibrosis score −0.7 (−1.5 to 0.1) 322 
 CAP, dB/m 340 (305–372) 317 
LSM, kPa 8.3 (6.2–11.8) 322 
CharacteristicData valueN
Sex  
 Male 207 (63)  
 Female 123 (37)  
Age, years 59 (52–66)  
BMI, kg/m2 32 (28–36)  
WC, cm 109 (101–120) 323 
WC (men: >102 cm; women: >88 cm) 260 (81) 323 
Hypertension 222 (68) 329 
HDL cholesterol (men: <1.03 mmol/L; women: <1.29 mmol/L) 195 (59)  
Triglycerides >1.7 mmol/L 167 (51)  
Known duration of diabetes, years 9 (5–15) 329 
Retinopathy 62 (19) 326 
Neuropathy 68 (21) 322 
Nephropathy 141 (42.9) 329 
At least one microvascular complication 196 (60) 326 
Cerebrovascular disease 12 (4)  
Peripheral arterial disease 10 (3) 329 
Ischemic heart disease 50 (15) 329 
At least one macrovascular complication 66 (20) 329 
Medication   
 Oral antidiabetic drugs 320 (97)  
 Insulin without GLP-1 29 (9)  
 GLP-1 without insulin 75 (23)  
 GLP-1 and insulin 113 (34)  
 Cardiovascular drugs 240 (73)  
 Lipid-lowering drugs 213 (65)  
Platelet count, G/L 244 (198–292)  
INR 1.0 (1.0–1.04) 304 
Liver enzyme concentration, IU/L   
 AST 35 (27–46) 329 
 ALT 49 (34–70)  
 ALT >40 210 (64)  
 GGT 55 (35–86)  
Total bilirubin, µmol/L 8.9 (6.1–12.5)  
Serum albumin, g/L 43.0 (40.7–45.4) 322 
Serum ferritin, µg/L 109 (54–210) 325 
Total cholesterol, mmol/L 3.97 (3.39–4.81)  
LDL cholesterol, mmol/L 2.07 (1.55–2.66) 315 
HDL cholesterol, mmol/L 1.03 (0.89–1.24)  
Triglycerides, mmol/L 1.73 (1.23–2.38)  
Fasting blood glucose, g/L 1.51 (1.27–1.89) 329 
HbA1c, % 7.5 (6.8–8.4) 327 
Serum creatinine, µmol/L 71.0 (59.8–84.0) 328 
eGFR, mL/min 94.9 (81.3–104.0) 328 
UACR, mg/mmol 2.00 (0.98–5.78) 260 
FIB-4 1.20 (0.90–1.69) 329 
NAFLD fibrosis score −0.7 (−1.5 to 0.1) 322 
 CAP, dB/m 340 (305–372) 317 
LSM, kPa 8.3 (6.2–11.8) 322 

Qualitative data are reported as absolute (n) and relative frequencies (%) and quantitative data as median and interquartile range. eGFR, glomerular filtration rate; INR, international normalized ratio; UACR, urine albumin to creatinine ratio.

Prevalence of and Features Associated With AF

AF was found in 124 patients (38%; 95% CI, 32–43%), severe fibrosis (stage F3) in 91 patients (28%), and cirrhosis (stage F4) in 33 patients (10%) (Fig. 1).

Figure 1

Prevalence of NASH and AF in the 330 patients in this study.

Figure 1

Prevalence of NASH and AF in the 330 patients in this study.

Close modal

By univariate analysis, AF was significantly associated with age; BMI; waist circumference (WC); AST, ALT, and GGT levels; platelet count; FIB-4; CAP; and LSM. AF was inversely associated with serum albumin level (Supplementary Table 3).

In the multivariate analysis model without VCTE, AF was associated with WC >102 cm in men and >88 cm in women, HDL cholesterol <1.03 mmol/L in men and <1.29 mmol/L in women, increased GGT levels, and FIB-4 (Table 2). A model combining these four parameters had an AUROC of 0.77 (95% CI, 0.72–0.83) for the presence of AF (Fig. 2A) (AppShiny [version test]; https://pmn-test.shinyapps.io/quidnash-nash-f3f4/).

Table 2

Factors associated with NASH or AF in multivariate analysis

Missing data (n)Histological liver lesions severityMultivariate analysis
aOR (95% CI)Padjusted*
Without VCTE      
 NASH status  No (n = 138) Yes (n = 192) AUROC, 0.81 (0.76–0.86) (n = 312)  
  Serum albumin, g/L 43.9 (41.0–46.0) 43.0 (40.4–45.0) 0.91 (0.83–0.97) 0.01 
  Serum creatinine, µmol/L 73.0 (63.6–84.1) 70.0 (58.4–83.5) 0.987 (0.976–0.995) 0.01 
  AST, IU/L 29 (24–36) 40 (32–53) 1.07 (1.04–1.09) <0.0001 
  Hypertension 87 (63) 135 (71) 1.77 (1.02–3.11) 0.04 
  Triglycerides (>1.7 mmol/L)  56 (41) 111 (58) 2.24 (1.33–3.82) 0.003 
  WC (men: >102 cm; women: >88 cm) 99 (73) 161 (86) 2.84 (1.47–5.65) 0.002 
 AF  F0–F2 (N = 206) F3–F4 (N = 124) AUROC, 0.77 (0.72–0.83) (n = 322)  
  GGT, IU/L  47 (30–67) 74 (44–129) 1.008 (1.004–1.012) 0.0003 
  WC (men: >102; women: >88), cm 156 (77) 104 (87) 2.24 (1.15–4.59) 0.02 
  HDL-C (men: <1.03; women: <1.29), mmol/L  112 (54) 83 (67) 2.57 (1.49–4.54) 0.001 
  FIB-4 score 1.13 (0.81–1.47) 1.50 (1.09–2.08) 3.01 (1.99–4.71) <0.0001 
With VCTE      
 NASH status  No (n = 138) Yes (n = 192) AUROC, 0.82 (0.77–0.87) (n = 307)  
  Serum albumin, g/L 43.9 (41.0–46.0) 43.0 (40.4–45) 0.88 (0.81–0.95) 0.002 
  Serum creatinine, µmol/L 73.0 (63.6–84.1) 70.0 (58.4–83.5) 0.985 (0.975–0.994) 0.005 
  CAP, dB/m 13 325 (283–359) 349 (321–378) 1.014 (1.008–1.021) <0.0001 
  AST, IU/L 29 (24–36) 40 (32–53) 1.07 (1.04–1.09) <0.0001 
  Triglycerides (>1.7 mmol/L)  56 (41) 111 (58) 1.91 (1.12–3.28) 0.019 
 AF  F0–F2 (n = 206) F3–F4 (n = 124) AUROC, 0.84 (0.80–0.89) (n = 326)  
  LSM, kPa 6.9 (5.5–9.1) 12.0 (9.2–16.7) 1.21 (1.14–1.29) <0.0001 
  HDL-C (men: <1.03; women: <1.29), mmol/L  112 (54) 83 (67) 2.29 (1.31–4.10) 0.004 
  FIB-4 score 1.13 (0.81–1.47) 1.50 (1.09–2.08) 2.33 (1.50–3.69) 0.0002 
Missing data (n)Histological liver lesions severityMultivariate analysis
aOR (95% CI)Padjusted*
Without VCTE      
 NASH status  No (n = 138) Yes (n = 192) AUROC, 0.81 (0.76–0.86) (n = 312)  
  Serum albumin, g/L 43.9 (41.0–46.0) 43.0 (40.4–45.0) 0.91 (0.83–0.97) 0.01 
  Serum creatinine, µmol/L 73.0 (63.6–84.1) 70.0 (58.4–83.5) 0.987 (0.976–0.995) 0.01 
  AST, IU/L 29 (24–36) 40 (32–53) 1.07 (1.04–1.09) <0.0001 
  Hypertension 87 (63) 135 (71) 1.77 (1.02–3.11) 0.04 
  Triglycerides (>1.7 mmol/L)  56 (41) 111 (58) 2.24 (1.33–3.82) 0.003 
  WC (men: >102 cm; women: >88 cm) 99 (73) 161 (86) 2.84 (1.47–5.65) 0.002 
 AF  F0–F2 (N = 206) F3–F4 (N = 124) AUROC, 0.77 (0.72–0.83) (n = 322)  
  GGT, IU/L  47 (30–67) 74 (44–129) 1.008 (1.004–1.012) 0.0003 
  WC (men: >102; women: >88), cm 156 (77) 104 (87) 2.24 (1.15–4.59) 0.02 
  HDL-C (men: <1.03; women: <1.29), mmol/L  112 (54) 83 (67) 2.57 (1.49–4.54) 0.001 
  FIB-4 score 1.13 (0.81–1.47) 1.50 (1.09–2.08) 3.01 (1.99–4.71) <0.0001 
With VCTE      
 NASH status  No (n = 138) Yes (n = 192) AUROC, 0.82 (0.77–0.87) (n = 307)  
  Serum albumin, g/L 43.9 (41.0–46.0) 43.0 (40.4–45) 0.88 (0.81–0.95) 0.002 
  Serum creatinine, µmol/L 73.0 (63.6–84.1) 70.0 (58.4–83.5) 0.985 (0.975–0.994) 0.005 
  CAP, dB/m 13 325 (283–359) 349 (321–378) 1.014 (1.008–1.021) <0.0001 
  AST, IU/L 29 (24–36) 40 (32–53) 1.07 (1.04–1.09) <0.0001 
  Triglycerides (>1.7 mmol/L)  56 (41) 111 (58) 1.91 (1.12–3.28) 0.019 
 AF  F0–F2 (n = 206) F3–F4 (n = 124) AUROC, 0.84 (0.80–0.89) (n = 326)  
  LSM, kPa 6.9 (5.5–9.1) 12.0 (9.2–16.7) 1.21 (1.14–1.29) <0.0001 
  HDL-C (men: <1.03; women: <1.29), mmol/L  112 (54) 83 (67) 2.29 (1.31–4.10) 0.004 
  FIB-4 score 1.13 (0.81–1.47) 1.50 (1.09–2.08) 2.33 (1.50–3.69) 0.0002 

Qualitative data are reported as absolute (n) and relative frequencies (%) and quantitative data as median and interquartile range. HDL-C, high-density lipoprotein cholesterol.

*

Adjusted P value for multivariate analysis using Wald test.

Figure 2

Multivariate models for AF and NASH without and with VCTE. A: AF model without VCTE (LSM). B: AF model with VCTE (LSM). C: NASH model without VCTE (CAP). D: NASH model with VCTE (CAP). F, female; HDL-C, high-density lipoprotein cholesterol; M, male.

Figure 2

Multivariate models for AF and NASH without and with VCTE. A: AF model without VCTE (LSM). B: AF model with VCTE (LSM). C: NASH model without VCTE (CAP). D: NASH model with VCTE (CAP). F, female; HDL-C, high-density lipoprotein cholesterol; M, male.

Close modal

In the multivariate analysis model with VCTE (LSM), AF was associated with HDL cholesterol <1.03 mmol/L in men and <1.29 mmol/L in women, FIB-4, and increased LSM (Table 2). A model combining these three parameters had an AUROC of 0.84 (95% CI, 0.80–0.89) for the presence of AF (Fig. 2B). This model outperformed the model without VCTE (for models with vs. without VCTE: AUROCs, 0.85 vs. 0.77, respectively; P = 0.0007) (Supplementary Table 4).

Interestingly, when compared with individual parameters, the model without VCTE outperformed AST (AUROC, 0.78 vs. 0.70, respectively; P < 0.0001), ALT (AUROC, 0.78 vs. 0.61, respectively; P < 0.015), and FIB-4 (AUROC, 0.78 vs. 0.71, respectively; P = 0.0001), but not LSM alone, although the difference was close to statistical significance (AUROC, 0.78 vs. 0.84, respectively; P = 0.057). The model with VCTE outperformed AST (AUROC, 0.85 vs. 0.70, respectively; P < 0.0001), ALT (AUROC, 0.85 vs. 0.61, respectively; P < 0.0001), and FIB-4 (AUROC, 0.85 vs. 0.71, respectively; P < 0.0001), but not LSM alone (AUROCs 0.85 vs. 0.84, respectively; P = 0.29) (Supplementary Table 4).

The corresponding values for Se, Sp, PPV, NPV, and percentages of correctly classified patients for each model and tests are presented in Supplementary Table 4. The percentage of correctly classified patients was higher for the model with VCTE and for LSM alone than the model without VCTE, FIB-4, and AST (59%, 58%, 45%, 33%, and 30%, respectively).

Prevalence of and Features Associated With NASH

NASH was documented in 192 patients (58%; 95% CI, 53–64%) (Fig. 1). By univariate analysis, NASH was significantly associated with age; BMI; WC; total cholesterol, triglyceride, ferritin, AST, ALT, and GGT levels; fasting blood glycemia; HbA1c levels; CAP; and FIB-4 (Supplementary Table 5). NASH was inversely associated with HDL cholesterol, <1.03 mmol/L in men and <1.23 mmol/L in women, and serum creatinine levels.

In the multivariate analysis model without VCTE (Table 2), NASH was associated with hypertension, WC >102 cm in men and >88 cm in women, triglycerides levels >1.7 mmol/L, AST levels, and low serum albumin and serum creatinine levels. A model combining these six parameters had an AUROC of 0.81 (95% CI, 0.76–0.86) for the presence of NASH (Fig. 2C) (AppShiny [version test]; https://pmn-test.shinyapps.io/quidnash-nash-f3f4/).

In the multivariate analysis model including VCTE (CAP) (Table 2), NASH was associated with increased CAP, triglyceride levels >1.7 mmol/L, AST levels, and low serum albumin and serum creatinine levels. A model combining these five parameters had an AUROC of 0.82 (95% CI, 0.77–0.87) for the presence of NASH (Fig. 2D). There was no difference between the two models with and without VCTE (AUROC, 0.82 vs. 0.81, respectively; P = 0.58) (Supplementary Table 4).

Interestingly, when compared with individual parameters, the model without VCTE outperformed AST (AUROC, 0.81 vs. 0.74, respectively; P = 0.003), ALT (AUROC, 0.81 vs. 0.68, respectively; P < 0.015), and FIB-4 (AUROC, 0.81 vs. 0.62, respectively; P < 0.0001). The model with VCTE outperformed AST (AUROC, 0.82 vs. 0.74, respectively; P = 0.001), ALT (AUROC, 0.82 vs. 0.68, respectively; P < 0.0001), and FIB-4 (AUROC, 0.82 vs. 0.62, respectively; P < 0.0001) (Supplementary Table 4).

The corresponding values for Se, Sp, PPV, NPV, and correctly classified patients for each model and tests are presented in Supplementary Table 4. The percentage of correctly classified patients did not differ between the models with VCTE and without VCTE (44% vs. 42% respectively) but was higher than with AST, LSM, and FIB-4 (33%, 27%, and 20%, respectively).

To our knowledge, this multicenter prospective study is the largest to date having analyzed liver biopsy findings in a well-characterized population of outpatients with T2DM without known liver disease and with a comprehensive assessment of micro- and macrovascular complications. Our study design (multicenter study; careful exclusion of other causes of liver diseases; liver biopsy specimen indications not based on NIT results but on a low threshold of transaminases and standardized criteria, good quality specimens, and central review) offered a robust way to establish the factors associated with NASH and AF in outpatients with T2DM seen in diabetes clinics in the real world. Interestingly, despite mild liver test abnormalities, more than one-third of patients had AF and almost two-thirds had NASH. Importantly, simple, routinely available parameters were found to help to identify these high-risk patients who require further liver assessment.

The prevalence of a condition in a defined population is a major determinant of the predictive value of corresponding diagnostic tests in this population. Prevalence of AF in the present study (38%) exceeds estimates (17%) from a recent meta-analysis (5). The latter, however, was based on a limited number of studies (n = 7) with liver biopsy specimens (n = 439) showing significant heterogeneity among them, which precludes combining their data. A large part of this heterogeneity is related to differences in study populations and indications for liver biopsy. In this regard, excessive alcohol intake, a factor usually neglected as a contributor to disease progression in patients with T2DM (30), was successfully excluded by clinical screening in our study, as shown by low levels of carbohydrate-deficient transferrin. Regarding indications for liver biopsy, it is important to stress that the majority of the patients deemed to require a liver biopsy had transaminase levels within the reference range of many laboratories (usually <40 IU) (31). These findings independently confirm the relevance of choosing lower thresholds for abnormal ALT levels, specifically 20 IU/L in women and 30 IU/L in men (31,32).

The high prevalence of AF and NASH in this study may also be related to its setting (namely, tertiary referral centers), where the metabolic burden of patients with T2DM was possibly higher than in the community setting (17). Indeed, obesity and dyslipidemia, well-known associates of liver disease severity (33), were present in 65% and 59% of our patients, respectively. Moreover, the use of the transjugular route in patients with morbid obesity and/or anticoagulant therapy, regarded as not eligible for percutaneous liver biopsy in other studies (16,17,3436), enabled us to capture the whole spectrum of NAFLD severity regardless of the feasibility of percutaneous liver biopsy.

Last, in most previous studies (16,17,34,36,37), diagnosis of AF in patients with T2DM was based on LSM, using VCTE, yielding an observed prevalence of AF in the range of 12.6% to 21.0%. The prevalence of AF documented in this study (38%) in patients not selected on the basis of LSM is in line with that (36–50%) of those patients who underwent liver biopsy in these studies based on elevated LSM. This is even more striking because only 4.9% to 15.4% of reported patients with elevated LSM underwent liver biopsy in these studies. For instance, in the recent study by Lomonaco et al. (17), only 21 of 526 patients underwent liver biopsy. Altogether, this suggests that prevalence of AF may have been underestimated in previous studies using VCTE without liver biopsy as a reference or only using percutaneous liver biopsy specimens, which may not be feasible in the most severe population.

In the present study, AF was independently associated with low HDL cholesterol (aOR, 2.29; 95% CI, 1.31–4.10), increased FIB-4 (aOR, 2.33; 95% CI, 1.50–3.70), and LSM (aOR, 1.21; 95% CI, 1.14–1.29). A simple model combining these three variables was associated with AF with a good accuracy (AUROC, 0.85) in our population. However, for such a model to be used widely, VCTE must be made available to all diabetes clinics, which is not yet the case. Therefore, we developed a model without VCTE, combining simple parameters widely available in diabetes clinics, that had good accuracy (AUROC, 0.77) for predicting AF. Indeed, this model included WC, GGT levels, FIB-4 (age, AST and ALT levels, and platelets), and HDL cholesterol levels and may help identify these high-risk patients who require further liver assessment. Interestingly, this model outperformed FIB-4 alone, as well as AST and ALT levels that had been reported to be suboptimal in patients with T2DM (15).

WC, hypertension, and hypertriglyceridemia, all components of the metabolic syndrome, were also independently associated with NASH. This cross-sectional survey was not designed to investigate the possible causal relationship with underlying mechanisms (e.g., insulin resistance). The independent association of NASH with high AST and low serum creatinine levels found here provide external confirmation for those of a recent report in NAFLD populations with varying proportions of patients with T2DM (38). The high AUROC of a model for NASH including only these above simple parameters (0.81) is consistent with that reported by Bazick et al. (13) and is encouraging for building and validating an accessible tool for diagnosing NASH, currently a major unmet need in patients with T2DM, as exemplified here (AppShiny [version test]; https://pmn-test.shinyapps.io/quidnash-nash-f3f4/).

There are limitations in interpreting our findings. ADA recommendations were used by diabetologists for referring patients with suspected NAFLD to hepatologists. Accordingly, the design of this study did not include a referral of consecutive outpatients with T2DM; thus the epidemiological information yielded cannot be readily considered unbiased estimates. Because this study focused on a population of patients with T2DM with ultrasound evidence for NAFLD and persistently elevated ALT levels, our findings cannot be directly extrapolated to all diabetic populations. Validation studies are needed in other subpopulations of patients with diabetes, such as those with normal ALT levels, to see if the tested associations and predictive values of routinely available data found in the present study would be maintained in a population with different a priori risks. However, performing liver biopsies in patients with ALT <20 IU/L in women or 30 IU/L in men may be challenging and raise ethical issues. Also, it is possible that our population differs from those seen in primary care because almost 45% of our patients were treated with insulin. There is well-known intra- and interobserver variability in interpreting the histological data in NAFLD (39). However, this was accounted for by a central reading by a highly experienced pathologist using prespecified criteria for liver-sample quality. Last, the models proposed for predicting AF and NASH require validation in independent populations of patients.

In conclusion, patients with T2DM and NAFLD attending outpatient diabetes clinics have a high prevalence of AF and NASH despite mild liver-test abnormalities, which emphasizes the relevance of choosing lower thresholds for abnormal ALT levels, namely 20 IU/L in women and 30 IU/L in men (31,32). Micro- and macrovascular complications of T2DM are not independently related to liver lesions in such patients. Routinely available parameters enable many of these high-risk patients requiring further liver assessment to be identified.

Clinical trial reg. no. NCT03634098, clinicaltrials.gov

See accompanying article, p. 1332.

This article contains supplementary material online at https://doi.org/10.2337/figshare.22220875.

This article is featured in a podcast available at diabetesjournals.org/journals/pages/diabetes-core-update-podcasts.

Acknowledgments. The authors are grateful to the participants listed in Supplementary Material for their contribution to the QUID-NASH project.

Funding. The RHU QUID-NASH Project is funded by Agence Nationale de la Recherche programme Investissements d'Avenir (grant ANR-17-T171105J-RHUS-0009 to D.V.). The RHU QUID NASH is implemented by Institut National de la Recherche Medicale, Paris Descartes University, Université Paris Cité, Centre National de la Recherche Scientifique, Centre de l'Energie Atomique, Servier, Biopredictive, and Assistance Publique-Hôpitaux de Paris under the coordination of D.V. and project leader A.B.

Duality of Interest. L.C. has received a grant from Gilead; consulting fees from Echosens, Madrigal, MSD, Novo Nordisk, Pfizer, and Sagimet; and lecture fees from Echosens and Novo Nordisk. A.V.-P. has received lecture fees from AbbVie and Gilead. V.P. has received consulting fees from Inventiva and lectures fees from Novartis. H.B. has received lecture fees from AbbVie. T.P. is founder of Biopredictive. S.C. has received consulting fees from Boehringer Ingelheim, Janssen, Novartis, Novo Nordisk, Eli Lilly and Company, and Servier. S.P. received lecture fees from Janssen, Gilead, MSD, AbbVie, Biotest, Shinogui, Viiv, and LFB. P.B. is founder of Liverpat. J.-F.G. has received lecture fees from AstraZeneca, Bayer, Bristol-Myers Squibb, Eli Lilly and Company, Gilead, Novo Nordisk, Pfizer, and Sanofi. D.V. has received lecture fees from Intercept. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. L.C., D.V., S.C., S.P., and J.-F.G. contributed to critical revision of the manuscript for important intellectual content. D.V., C.L., N.G., V.P., L.C., T.P., and J.-F.G. contributed to the study concept and design. T.V.-T., A.V.-P., S.C., L.C., D.V., E.L., C.B., S.P., D.R., H.L., J.-B.J., J.-F.G., V.P., B.T., P.B., and A.R. contributed to patient recruitment and acquisition of data. C.L., P.M., L.C., D.V., S.C., S.P., and J.-.F.G. contributed to analysis and interpretation of data. L.C., D.V., C.L., P.M., and J.-F.G. drafted the manuscript. P.M. and C.L. performed the statistical analysis. A.B., D.V., C.L., N.G., C.B., J.-F.G., and V.P. obtained funding for this work. D.V., C.L., N.G., and V.P. supervised the study. L.C. and D.V. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

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