Abstract
Background
Metabolic dysfunction–associated steatotic liver disease (MASLD) is characterized by insulin resistance and metabolic syndrome, with type 2 diabetes mellitus (T2DM) as a major risk factor. Additionally, MASLD has been linked to liver fibrosis, which is associated with increased cardiovascular risk. Although noninvasive liver fibrosis markers are widely used, the association of these markers with atherosclerosis and continuous glucose monitoring (CGM) indices in T2DM remains unclear. We aimed to investigate the associations of the fibrosis‐4 (FIB‐4) index with atherosclerosis and CGM indices in patients with T2DM.
Methods
This study was an exploratory subanalysis of a prospective observational cohort. Among 999 outpatients with T2DM who underwent baseline CGM, 887 patients (excluding heavy alcohol consumers) were analyzed. FIB‐4, a noninvasive marker of liver fibrosis, was calculated. Associations of FIB‐4 with carotid ultrasound, vascular wave velocity tests, and CGM indices were explored.
Results
After adjusting for disease duration, body mass index (BMI), glycated hemoglobin (HbA1c), gamma‐glutamyl transferase, and complications, FIB‐4 severity category showed significant associations with mean intima‐media thickness (IMT), common carotid artery (CCA) maximum IMT, and brachial–ankle pulse wave velocity in ordinal logistic analyses. Consistent with the ordinal logistic analyses, FIB‐4 was independently associated with mean IMT in multivariable linear regression. Among the CGM indices, FIB‐4 was positively associated with both mean glucose and the standard deviation (SD) of mean glucose. Moreover, FIB‐4 was negatively associated with time in range (TIR) and positively with time above range (TAR).
Conclusions
In Japanese patients with T2DM, FIB‐4 is associated with carotid thickening and arterial stiffness. Moreover, FIB‐4 correlates with the duration of hyperglycemia, suggesting that liver fibrosis may be linked to atherosclerosis through mechanisms involving poor glycemic control. Assessment of liver fibrosis in T2DM is imperative, necessitating monitoring of atherosclerosis and cardiovascular diseases (CVDs) in cases of suspected advanced liver fibrosis.
Trial Registration
The study has been registered (UMIN000032325) in the University Hospital Medical Information Network Clinical Trials Registry, a nonprofit organization in Japan that meets the requirements of the International Committee of Medical Journal Editors.
Keywords: atherosclerosis, carotid intima-media thickness, continuous glucose monitoring, fibrosis-4 index, metabolic dysfunction-associated steatotic liver disease, type 2 diabetes
1. Introduction
Type 2 diabetes mellitus (T2DM) is strongly associated with insulin resistance, often due to obesity or metabolic syndrome. T2DM increases the risk of not only microvascular complications but also atherosclerotic diseases, such as myocardial infarction and cerebrovascular disorders [1]. Therefore, preventing the progression of atherosclerosis in such patients is crucial for extending healthy life expectancy.
With the increasing prevalence of obesity and fatty liver, the incidence of nonviral liver diseases, particularly nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH), is rapidly increasing. Recently, NAFLD and NASH have been renamed to metabolic dysfunction–associated steatotic liver disease (MASLD) and metabolic dysfunction‐associated steatohepatitis (MASH), respectively, and redefined [2]. The major underlying features of MASLD/MASH include exacerbated insulin resistance and metabolic syndrome, with T2DM being strongly associated with the development of MASLD/MASH [3]. In this context, metabolic dysfunction, characterized by insulin resistance and related cardiometabolic abnormalities, can be viewed as a common pathophysiological background linking atherosclerosis and MASLD/MASH [4, 5]. In addition, the presence of diabetes is an independent factor associated with liver fibrosis in MASLD/MASH [6]. Progression of liver fibrosis is associated with poor prognosis, including the development of cirrhosis, hepatocellular carcinoma, liver‐related mortality, and increased risk of cardiovascular events, particularly in individuals with advanced fibrosis [7, 8]. Moreover, MASLD has been proposed as a systemic metabolic disorder with cardiovascular complications, supporting the clinical relevance of evaluating liver fibrosis in relation to vascular risk [9].
Progression of liver fibrosis is clinically important. Accordingly, various noninvasive markers and clinically based scoring systems, including the fibrosis‐4 (FIB‐4) index, have been used to assess fibrosis severity [10–12]. Although there are sporadic reports on the association between noninvasive markers of liver fibrosis and atherosclerotic indices in MASLD patients, only a limited number of studies specifically focused on patients with T2DM. One such study reported a positive association between the FIB‐4 index and carotid intima‐media thickness (IMT), but the sample size in that study was small, and the data analysis did not adjust for important confounding factors [13]. Furthermore, other studies found a close association between MASLD onset and progression with impaired hepatic insulin resistance and decreased insulin clearance [14]. To our knowledge, however, the potential impact of liver fibrosis on continuous glucose monitoring (CGM)–derived metrics has not yet been investigated systematically.
CGM provides clinically useful glucose parameters, including mean glucose levels, various glycemic variability indices, such as standard deviation (SD), % coefficient of variation (%CV), and mean amplitude of glycemic excursions (MAGEs), as well as metrics like time in range (TIR), time above range (TAR), and time below range (TBR). In this regard, the Advanced Technologies and Treatments for Diabetes (ATTD) issued an international consensus in 2019 recommending TIR as a standard metric for diabetes management, underscoring its clinical value [15]. In fact, high TIR values have been reported to be significantly associated with reduced risk of both microvascular and macrovascular complications [16, 17].
The aim of the present study was to determine the associations of noninvasive markers of liver fibrosis, including the FIB‐4, with atherosclerosis and various CGM indices in Japanese patients with T2DM.
2. Materials and Methods
2.1. Participants
This study targeted Japanese patients with T2DM who regularly attended the outpatient diabetes clinics of 34 clinical institutions across Japan (Supporting Information 1: Methods), and details of the study design and inclusion/exclusion criteria have been published previously [18]. The two inclusion criteria were as follows: (1) age ≥ 30 and <80 years; (2) no change in antidiabetic medications (except insulin dosage) for ≥6 months before signing the informed consent form. The exclusion criteria were the following: (1) type 1 or secondary diabetes; (2) history of myocardial infarction, angina pectoris, stroke, cerebral infarction, or atherosclerosis obliterans; (3) current renal dialysis; (4) liver dysfunction rated as worse than mild (aspartate aminotransferase [AST] ≥ 100 IU/L); (5) moderate or severe heart failure (New York Heart Association Stage III or worse); or (6) use of a sensor‐augmented insulin pump. Patients who met the eligibility criteria were invited to participate. From May 2018 to March 2019, we enrolled 1000 outpatients with T2DM who were confirmed to have no significant changes in medications or glycated hemoglobin (HbA1c) and no history of apparent cardiovascular diseases (CVDs). One patient withdrew consent after enrollment. Of the remaining 999 participants, data of 887 were included in the analysis, after excluding 112 who were considered heavy alcohol drinkers (ethanol volume ≥ 30 and ≥20 g/day for men and women, respectively).
2.2. Study Design
This study involved exploratory subanalysis of data from an ongoing observational, prospective cohort study on the relationships between glucose fluctuations (detected using CGM) and the incidence of composite cardiovascular events during a 5‐year follow‐up [18].
2.3. Study Endpoints
The primary endpoint was the association of FIB‐4 with carotid IMT. The secondary endpoints included the association of FIB‐4 with CGM indices as well as the association of other noninvasive markers of liver fibrosis with carotid IMT.
2.4. Biochemical and Clinical Measurements
Fasting blood samples were obtained during the outpatient visits and subsequently analyzed using standard techniques for measurement of AST, alanine aminotransferase (ALT), lipids, creatinine, HbA1c (National Glycohemoglobin Standardization Program), and platelet count. Urinary albumin excretion (UAE) was measured using a latex agglutination assay on a spot urine sample. The estimated glomerular filtration rate (eGFR) was calculated according to the guidelines issued in the Statement of the Japanese Society of Nephrology: eGFR (mL/min/1.73 m2) = 194 × serum creatinine−1.094 × Age−0.287 × 0.739 (if female) [19].
2.5. Noninvasive Test of Liver Fibrosis
From the clinical laboratory data, we used the originally reported formulas to calculate FIB‐4:
FIB‐4 = (age [years] × AST [U/L])/([platelets (109/L)] × √ALT [U/L]) [11].
2.6. Measurement of Carotid IMT
Carotid ultrasound was performed by specially trained technicians using the same device for each examination in the respective environments [20, 21]. Briefly, the extracranial common carotid artery (CCA), carotid bulb, and internal carotid artery in the neck were scanned bilaterally in transverse projections and at least three different longitudinal projections. The site of greatest thickness and plaque lesions was identified along the arterial walls. IMT represents the distance between two parallel echogenic lines corresponding to the vascular lumen and the adventitial layer. To avoid variability among readers, all scanning images were electronically stored and emailed to the central office (IMT Evaluation Committee, Osaka, Japan), where an experienced reader, blinded to the clinical characteristics of the patients, read them in random order using automatic digital edge detection software (Intimascope, MediCross, Japan) [22] that calculated the average of ~200 IMT in a segment 2‐cm proximal to the dilated portion of the carotid bulb (mean IMT‐CCA); the CCA‐max‐IMT was defined as the higher value on either side.
2.7. Measurement of Brachial–Ankle Pulse‐Wave Velocity
Brachial–ankle pulse‐wave velocity (baPWV) was measured at baseline by blinded well‐trained investigators using an automatic waveform analyzer (BP‐203RPE form; Colin Medical Technology, Komaki, Japan), as described in detail previously [23], with the patients lying supine after a 5‐min rest. Occlusion and monitoring cuffs were placed snugly around the appropriate areas on the upper and lower extremities. Using the oscillometric method, the pressure waveforms from the brachial arteries were recorded simultaneously. Patients with an ankle‐brachial index of ≤0.90 were classified as having peripheral artery disease, and their baPWV data were not included in the analysis in this study.
2.8. CGM
The FreeStyle Libre Pro CGM (FLP‐CGM) device (Abbott, Japan) was used in this study [18]. No restrictions on daily activities were imposed on the participants, while they wore the FLP‐CGM. Following completion of the 14‐day recording, the CGM‐stored data were downloaded and analyzed for the following CGM parameters: average glucose, SD, %CV, and MAGE; TIR: 3.9–10.0 mmol/L, TAR: >10.0 mmol/L, TAR: >13.9 mmol/L, TBR: <3.9 mmol/L, and TBR: <3.0 mmol/L [15]. Given that the FLP‐CGM is less accurate during the first 24 h after insertion (days 1–2) and the last 4 days of its 14‐day lifetime [24], the FLP‐CGM data presented in this study are those of the middle 8‐day period.
2.9. Statistical Analysis
Data are expressed as mean ± SD or median (interquartile range) for continuous variables and as frequency (proportion) for categorical variables. Spearman’s correlation coefficient and linear regression model were used to assess the relationship between two variables. Continuous data were compared using analysis of variance and the Kruskal–Wallis test, whereas categorical data were compared using the chi‐square and Fisher’s exact tests. Based on the FIB‐4, the participants were divided into three FIB‐4 subgroups: those with low FIB‐4: <1.30, medium: 1.30 to <2.67, and high: ≥2.67 [25]. Multivariate linear regression analysis was performed to determine whether noninvasive tests for liver fibrosis were associated with IMT or baPWV. Univariate or multivariate ordinal logistic regression analysis was performed to determine whether the severity indicated by the noninvasive tests was associated with IMT or baPWV. Based on clinical judgment, potential conventional risk factors that are usually evaluated with clinical, biochemical, or metabolic testing were included in the models. For all tests, a p‐value < 0.05 was considered statistically significant. All analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC).
3. Results
3.1. Participant Demographics
Table 1 presents the baseline characteristics of the study participants (n = 887), with a mean estimated duration of diabetes of 13.0 years, HbA1c level of 7.07% (53.7 mmol/L), and FIB‐4 of 1.57. The mean indices of atherosclerosis were an IMT of 0.76 mm, a CCA‐max IMT of 1.11 mm, and a baPWV of 1700.0 cm/s. The mean CGM indices were a glucose level of 7.57 mmol/L, a CV of 26.09%, and a TIR of 79.1%. Table 1 summarizes the FIB‐4‐severity‐stratified comparisons of baseline characteristics of patients with T2DM. The low, middle, and high FIB‐4 groups accounted for 42%, 52%, and 6% of the participants, respectively. Subjects of the low FIB‐4 group were significantly younger and had significantly shorter duration of diabetes, higher body mass index (BMI), higher triglyceride levels, and higher HbA1c levels, although no significant difference was observed in the CGM‐based mean glucose level or other CGM indices. Regarding atherosclerosis indices among the three groups, the mean IMT and CCA‐max IMT were the lowest in the low FIB‐4 group, which also had a lower baPWV (Figure 1). In the analysis of antidiabetic medications, the low FIB‐4 group had higher usage rates of metformin and sodium‐glucose cotransporter 2 inhibitors but lower usage rates of dipeptidyl peptidase‐4 inhibitors.
Table 1.
Comparison of baseline characteristics of patients with type 2 diabetes by severity of FIB‐4 index.
| Variables | All | FIB‐4 index | |||
|---|---|---|---|---|---|
| Low (<1.30) | Middle (1.30 to 2.67) | High (≥2.67) | p‐Value | ||
| Number | 887 | 370 | 460 | 57 | — |
| Age (years) | 64.7 ± 9.7 | 58.1 ± 9.7 | 69.2 ± 6.1 | 72.4 ± 5.6 | <0.001 |
| Male (%) | 508 (57.3) | 203 (54.9) | 273 (59.3) | 32 (56.1) | 0.418 |
| Estimated duration of diabetes (years) | 13.0 ± 8.5 | 11.2 ± 7.8 | 14.3 ± 8.8 | 14.9 ± 9.1 | <0.001 |
| Ever smoker (%) | 449 (50.7) | 207 (55.9) | 218 (47.5) | 24 (42.1) | <0.001 |
| Alcohol drinking habits (%) | 395 (44.8) | 167 (45.5) | 203 (44.4) | 15 (43.9) | 0.404 |
| BMI (kg/m2) | 24.6 ± 3.9 | 25.5 ± 4.1 | 24.0 ± 3.6 | 23.6 ± 3.8 | <0.001 |
| Systolic BP (mmHg) | 131 ± 15 | 130 ± 15 | 132 ± 15 | 131 ± 18 | 0.378 |
| Diastolic BP (mmHg) | 75 ± 11 | 77 ± 11 | 74 ± 10 | 72 ± 11 | <0.001 |
| HbA1c (%) | 7.07 ± 0.8 | 7.14 ± 0.88 | 7.04 ± 0.71 | 6.89 ± 0.94 | 0.015 |
| HbA1c (mmol/mol) | 53.7 ± 8.8 | 54.5 ± 9.6 | 53.4 ± 7.8 | 51.8 ± 10.3 | 0.015 |
| Fasting plasma glucose (mmol/L) | 7.57 ± 1.79 | 7.66 ± 1.88 | 7.49 ± 1.64 | 7.65 ± 2.22 | 0.435 |
| Aspartate aminotransferase (U/L) | 23.1 ± 8.7 | 20.6 ± 6.6 | 24.0 ± 8.3 | 32.9 ± 13.4 | <0.001 |
| Alanine aminotransferase (U/L) | 23.5 ± 13.2 | 24.5 ± 13.8 | 22.5 ± 12.7 | 25.0 ± 12.5 | 0.005 |
| Gamma‐glutamyl transferase (U/L) | 30.2 ± 30.9 | 28.8 ± 24.5 | 29.0 ± 31.0 | 48.3 ± 53.8 | 0.103 |
| eGFR (mL/min/1.73 m2) | 72.9 ± 20.9 | 78.8 ± 22.9 | 68.1 ± 17.5 | 73.0 ± 23.1 | <0.001 |
| Total cholesterol (mmol/L) | 4.77 ± 0.81 | 4.74 ± 0.81 | 4.79 ± 0.80 | 4.76 ± 0.87 | 0.506 |
| HDL cholesterol (mmol/L) | 1.54 ± 0.40 | 1.49 ± 0.34 | 1.57 ± 0.41 | 1.68 ± 0.50 | 0.003 |
| Triglycerides (mmol/L) | 1.34 ± 0.92 | 1.43 ± 0.93 | 1.29 ± 0.95 | 1.17 ± 0.58 | 0.007 |
| Uric acid (μmol/L) | 304.6 ± 71.4 | 301.8 ± 71.8 | 308.4 ± 71.6 | 292.2 ± 65.78 | 0.188 |
| Urinary albumin excretion (mg/g・creatinine) | 103.9 ± 395.1 | 93.8 ± 337.5 | 118.0 ± 455.8 | 54.7 ± 109.0 | 0.016 |
| Leukocyte count (/μL) | 6171 ± 1693 | 6688 ± 1782 | 5870 ± 1507 | 5237 ± 1547 | <0.001 |
| Hemoglobin (g/dL) | 14.1 ± 1.4 | 14.3 ± 1.5 | 14.0 ± 1.4 | 13.6 ± 1.4 | 0.007 |
| Platelet count (104/μL) | 22.3 ± 5.5 | 26.1 ± 4.9 | 20.3 ± 3.8 | 14.2 ± 3.0 | <0.001 |
| FIB‐4 index | 1.57 ± 0.77 | 0.97 ± 0.23 | 1.81 ± 0.36 | 3.52 ± 1.11 | <0.001 |
| Ultrasonographic scans of the artery (n) | 536 | 225 | 278 | 33 | — |
| Mean IMT (mm) | 0.76 ± 0.15 | 0.72 ± 0.13 | 0.79 ± 0.16 | 0.80 ± 0.14 | <0.001 |
| CCA‐max‐IMT (mm) | 1.11 ± 0.42 | 1.03 ± 0.37 | 1.17 ± 0.45 | 1.13 ± 0.36 | <0.001 |
| Arterial stiffness (n) | 393 | 150 | 216 | 27 | — |
| baPWV (cm/s) | 1700 ± 363 | 1580 ± 308 | 1775 ± 381 | 1767 ± 336 | <0.001 |
| CGM metrics | |||||
| Mean glucose (mmol/L) | 7.57 ± 1.79 | 7.66 ± 1.88 | 7.49 ± 1.64 | 7.65 ± 2.22 | 0.869 |
| SD (mmol/L) | 2.02 ± 0.62 | 1.98 ± 0.57 | 2.04 ± 0.64 | 2.14 ± 0.70 | 0.252 |
| CV (%) | 26.09 ± 5.76 | 25.55 ± 5.27 | 26.50 ± 6.15 | 26.31 ± 5.36 | 0.078 |
| MAGE (mmol/L) | 5.38 ± 1.95 | 5.27 ± 1.84 | 5.45 ± 2.00 | 5.55 ± 2.17 | 0.370 |
| TIR (%) | 79.1 ± 18.5 | 79.4 ± 19.3 | 79.3 ± 17.1 | 74.9 ± 23.7 | 0.507 |
| TAR>10 mmol/L (%) | 18.7 ± 19.1 | 18.7 ± 19.9 | 18.2 ± 17.6 | 23.2 ± 24.8 | 0.852 |
| TAR>13.9 mmol/L (%) | 3.7 ± 9.2 | 3.6 ± 10.1 | 3.3 ± 7.2 | 7.0 ± 15.1 | 0.411 |
| TBR<3.9 mmol/L (%) | 2.2 ± 4.7 | 1.9 ± 3.8 | 2.5 ± 5.4 | 1.9 ± 3.9 | 0.281 |
| TBR<3.0 mmol/L (%) | 0.3 ± 1.6 | 0.3 ± 1.7 | 0.4 ± 1.5 | 0.2 ± 1.0 | 0.193 |
| Use of medications (%) | |||||
| Oral glucose‐lowering agents | 796 (89.7) | 335 (90.5) | 410 (89.1) | 51 (89.5) | 0.808 |
| Metformin | 490 (55.2) | 232 (62.7) | 237 (51.5) | 21 (36.8) | <0.001 |
| Sulfonylureas | 112 (12.6) | 35 (9.5) | 69 (15.0) | 8 (14.0) | 0.050 |
| Glinides | 63 (7.1) | 22 (5.9) | 39 (8.5) | 2 (3.5) | 0.239 |
| Dipeptidyl peptidase‐4 inhibitors | 521 (58.7) | 193 (52.2) | 293 (63.7) | 35 (61.4) | 0.003 |
| Sodium‐glucose cotransporter 2 inhibitors | 202 (22.8) | 107 (28.9) | 83 (18.0) | 12 (21.1) | <0.001 |
| Thiazolidinediones | 129 (14.5) | 48 (13.0) | 74 (16.1) | 7 (12.3) | 0.420 |
| α‐Glucosidase inhibitors | 158 (17.8) | 69 (18.6) | 79 (17.2) | 10 (17.5) | 0.856 |
| Glucagon‐like peptide‐1 antagonists | 70 (7.9) | 38 (10.3) | 29 (6.3) | 3 (5.3) | 0.092 |
| Insulin | 141 (15.9) | 60 (16.2) | 69 (15.0) | 12 (21.1) | 0.446 |
| Antihypertensive agents | 430 (48.5) | 171 (46.2) | 230 (50.0) | 29 (50.9) | 0.522 |
| Angiotensin‐converting enzyme inhibitors | 25 (2.8) | 10 (2.7) | 12 (2.6) | 3 (5.3) | 0.462 |
| Angiotensin II receptor blockers | 347 (39.1) | 142 (38.4) | 183 (39.8) | 22 (38.6) | 0.921 |
| Calcium channel blockers | 241 (27.2) | 98 (26.5) | 128 (27.8) | 15 (26.3) | 0.912 |
| Antihyperlipidemia agents | 545 (61.6) | 194 (52.4) | 250 (54.6) | 29 (50.9) | 0.638 |
| Statins | 473 (53.4) | 194 (52.4) | 250 (54.6) | 29 (50.9) | 0.756 |
| Antiplatelet agents | 41 (4.6) | 8 (2.2) | 27 (5.9) | 6 (10.5) | 0.003 |
| Retinopathy (%) | 77 (20.8) | 77 (20.8) | 117 (25.4) | 12 (21.1) | 0.281 |
| Nephropathy (%) | 88 (23.8) | 88 (23.8) | 140 (30.4) | 14 (24.6) | 0.092 |
| Peripheral neuropathy (%) | 92 (24.9) | 92 (24.9) | 144 (31.3) | 21 (36.8) | 0.050 |
Note: Data are the mean ± standard deviation or n (%). HbA1c, glycated hemoglobin; SD, standard deviation of mean glucose.
Abbreviations: baPWV, brachial–ankle pulse wave velocity; BMI, body mass index; BP, blood pressure; CCA, common carotid artery; CGM, continuous glucose monitoring; CV, coefficient variation; eGFR, estimated glomerular filtration rate; FIB‐4, fibrosis‐4; FIB‐4, fibrosis‐4 index; FPG, fasting plasma glucose; HDL, high‐density lipoprotein; IMT, intima‐media thickness; MAGEs, mean amplitude of glycemic excursions; TAR, time above range; TBR, time below range; TIR, time in range.
Figure 1.
Comparisons of atherosclerosis indices (mean IMT, CCA‐max‐IMT, and baPWV) based on the severity of FIB‐4. Data are mean ± SD. p‐Value by the Kruskal–Wallis test. baPWV, brachial–ankle pulse‐wave velocity; CCA, common carotid artery; FIB‐4, fibrosis‐4 index; IMT, intima‐media thickness.
3.2. Relationship Between Noninvasive Biomarkers for Liver Fibrosis and Arteriosclerosis Indices or CGM Indices in T2DM
Table S1 summarizes the correlation coefficients between the FIB‐4 index and atherosclerosis indices or CGM indices in patients with T2DM. FIB‐4 correlated with atherosclerosis indices (mean IMT, CCA‐max‐IMT, and baPWV).
3.3. Association of Arteriosclerosis Indices With FIB‐4 Severity
In the FIB‐4‐severity‐stratified subgroups, ordinal logistic regression analysis performed to examine whether the index was associated with the atherosclerosis indices (Table 2) showed that the mean IMT, CCA‐max‐IMT, and baPWV were crudely associated with FIB‐4 severity and significantly associated with FIB‐4 severity in the following four models adjusted covariates (Table 2): Model 2 adjusted for sex and positive or negative alcohol consumption; Model 3 adjusted for BMI and duration of diabetes, in addition to the variables adjusted in Model 2; Model 4 adjusted for HbA1c, systolic blood pressure (BP), gamma‐glutamyl transferase, total cholesterol, high‐density lipoprotein (HDL) cholesterol, logarithm of triglycerides, serum uric acid, eGFR, and logarithm of UAE, leukocyte count, and hemoglobin level, in addition to the variables adjusted in Model 3; Model 5 adjusted for smoking status, use of antihypertensive agents, use of antihyperlipidemia agents, use of antiplatelet agents, and presence or absence of diabetic retinopathy, in addition to the variables adjusted in Model 4. The odds ratios (95% confidence interval) of mean IMT, CCA‐max‐IMT, and baPWV were 23.652 (5.034–110.937), 1.894 (1.142–3.141), and 1.109 (1.030–1.194), respectively (Model 5 in Table 2). With age included as a covariate, the associations between FIB‐4 and the atherosclerosis indices were attenuated and became nonsignificant (Table S2).
Table 2.
Association between atherosclerosis metrics and severity of FIB‐4 index.
| Variables | Odds ratio (95% CI) | p |
|---|---|---|
| Mean IMT (1 mm increase) | ||
| Model 1 | 24.153 (7.273, 80.212) | <0.001 |
| Model 2 | 23.193 (6.877, 78.224) | <0.001 |
| Model 3 | 18.691 (5.636, 61.989) | <0.001 |
| Model 4 | 15.653 (3.508, 69.835) | <0.001 |
| Model 5 | 23.632 (5.034, 110.937) | <0.001 |
| CCA‐max‐IMT (1 mm increase) | ||
| Model 1 | 1.974 (1.310, 2.975) | 0.001 |
| Model 2 | 1.945 (1.289, 2.936) | 0.002 |
| Model 3 | 1.983 (1.301, 3.022) | 0.001 |
| Model 4 | 1.645 (1.005, 2.691) | 0.048 |
| Model 5 | 1.894 (1.142, 3.141) | 0.013 |
| baPWV (100 cm/s increase) | ||
| Model 1 | 1.149 (1.084, 1.218) | <0.001 |
| Model 2 | 1.151 (1.087, 1.220) | <0.001 |
| Model 3 | 1.130 (1.065, 1.199) | <0.001 |
| Model 4 | 1.104 (1.029, 1,185) | 0.006 |
| Model 5 | 1.109 (1.030, 1.194) | 0.006 |
Note: Model 1: crude. Model 2: adjusted for gender and positive or negative alcohol drinking. Model 3: adjusted for variables in Model 2 plus body mass index and duration of diabetes. Model 4: adjusted for variables in Model 3 plus HbA1c, systolic blood pressure, gamma‐glutamyl transferase, total cholesterol, HDL cholesterol, logarithm of triglycerides, serum uric acid, estimated glomerular filtration rate, logarithm of urinary albumin excretion, leukocyte count, and hemoglobin. Model 5: adjusted for variables in Model 4 plus smoking status (never smoker, previous smoker, or current smoker), use of antihypertensive agents, use of antihyperlipidemia agents, use of antiplatelet agents, and presence of diabetic retinopathy.
Abbreviations: baPWV, brachial–ankle pulse wave velocity; CCA, common carotid artery; CI, confidence interval; FIB‐4, fibrosis‐4; IMT, intima‐media thickness.
Similarly, linear regression analyses were conducted to examine the association of FIB‐4 with various indices of atherosclerosis (Table 3). FIB‐4 was crudely associated with mean IMT, CCA‐max‐IMT, and baPWV and, in Model 5, significantly associated with mean IMT. The CCA‐max‐IMT and baPWV were significantly associated with FIB‐4 in Models 1–3, but no significant association was observed in Models 4–5.
Table 3.
Multivariate linear regression analysis with FIB‐4 as the dependent variable in patients with type 2 diabetes.
| Variables |
Regression coefficient, β (95% CI) |
p |
|---|---|---|
| Mean IMT (1 mm increase) | ||
| Model 1 | 0.901 (0.491, 1.310) | <0.001 |
| Model 2 | 0.871 (0.455, 1.287) | <0.001 |
| Model 3 | 0.785 (0.372, 1.199) | <0.001 |
| Model 4 | 0.695 (0.220, 1.170) | 0.004 |
| Model 5 | 0.776 (0.297, 1.255) | 0.002 |
| CCA‐max‐IMT (1 mm increase) | ||
| Model 1 | 0.163 (0.014, 0.311) | 0.032 |
| Model 2 | 0.155 (0.006, 0.304) | 0.041 |
| Model 3 | 0.153 (0.005, 0.302) | 0.043 |
| Model 4 | 0.077 (−0.082, 0.236) | 0.342 |
| Model 5 | 0.115 (−0.046, 0.277) | 0.160 |
| baPWV (100 cm/s increase) | ||
| Model 1 | 0.039 (0.019, 0.060) | <0.001 |
| Model 2 | 0.039 (0.019, 0.059) | <0.001 |
| Model 3 | 0.032 (0.011, 0.053) | 0.003 |
| Model 4 | 0.024 (0.000, 0.048) | 0.052 |
| Model 5 | 0.021 (−0.003, 0.045) | 0.089 |
Note: Model 1: crude. Model 2: adjusted for gender and positive or negative alcohol drinking. Model 3: adjusted for variables in Model 2 plus body mass index and duration of diabetes. Model 4: adjusted for variables in Model 3 plus HbA1c, systolic blood pressure, gamma‐glutamyl transferase, total cholesterol, HDL cholesterol, logarithm of triglycerides, serum uric acid, estimated glomerular filtration rate, logarithm of urinary albumin excretion, leukocyte count, and hemoglobin. Model 5: adjusted for variables in Model 4 plus smoking status (never smoker, previous smoker, or current smoker), use of antihypertensive agents, use of antihyperlipidemia agents, use of antiplatelet agents, and presence of diabetic retinopathy.
Abbreviations: baPWV, brachial–ankle pulse wave velocity; CCA, common carotid artery; CI, confidence interval; FIB‐4, fibrosis‐4; IMT, intima‐media thickness.
3.4. Association of CGM Indices With FIB‐4
Linear regression analyses showed a crude association between FIB‐4 and glycemic variability indices (e.g., SD and MAGE), while a significant positive association with SD was observed in Model 5 (Table 4). In Model 5, FIB‐4 was positively associated with TAR>10 mmol/L and TAR>13.9 mmol/L but negatively with TIR.
Table 4.
Association between CGM metrics and FIB‐4 index.
| Variables |
Regression coefficient, β (95% CI) |
p |
|---|---|---|
| Mean glucose (1 mmol/L) increase) | ||
| Model 1 | 0.018 (−0.010, 0.046) | 0.211 |
| Model 2 | 0.018 (−0.010, 0.047) | 0.213 |
| Model 3 | 0.019 (−0.009, 0.047) | 0.182 |
| Model 4 | 0.091 (0.052, 0.129) | <0.001 |
| Model 5 | 0.089 (0.051, 0.128) | <0.001 |
| SD (1 mmol/L increase) | ||
| Model 1 | 0.092 (0.011, 0.174) | 0.027 |
| Model 2 | 0.092 (0.010, 0.174) | 0.028 |
| Model 3 | 0.049 (−0.034, 0.132) | 0.244 |
| Model 4 | 0.113 (0.024, 0.202) | 0.013 |
| Model 5 | 0.119 (0.029, 0.208) | 0.009 |
| CV (1% increase) | ||
| Model 1 | 0.006 (−0.002, 0.015) | 0.149 |
| Model 2 | 0.006 (−0.002, 0.015) | 0.155 |
| Model 3 | −0.001 (−0.010, 0.008) | 0.904 |
| Model 4 | −0.002 (−0.010, 0.007) | 0.685 |
| Model 5 | −0.001 (−0.009, 0.007) | 0.828 |
| MAGE (1 mmol/L increase) | ||
| Model 1 | 0.026 (0.000, 0.052) | 0.049 |
| Model 2 | 0.025 (−0.000, 0.051) | 0.057 |
| Model 3 | 0.015 (−0.010, 0.041) | 0.240 |
| Model 4 | 0.025 (−0.001, 0.051) | 0.064 |
| Model 5 | 0.025 (−0.001, 0.052) | 0.062 |
| TIR (10% increase) | ||
| Model 1 | −0.027 (−0.054, 0.001) | 0.054 |
| Model 2 | −0.027 (−0.054, 0.000) | 0.053 |
| Model 3 | −0.024 (−0.051, 0.003) | 0.087 |
| Model 4 | −0.076 (−0.111, −0.041) | <0.001 |
| Model 5 | −0.078 (−0.113, −0.043) | <0.001 |
| TAR>10 mmol/L (1% increase) | ||
| Model 1 | 0.002 (−0.000, 0.005) | 0.085 |
| Model 2 | 0.002 (−0.000, 0.005) | 0.081 |
| Model 3 | 0.002 (−0.000, 0.005) | 0.090 |
| Model 4 | 0.009 (0.005, 0.012) | <0.001 |
| Model 5 | 0.009 (0.005, 0.012) | <0.001 |
| TAR>13.9 mmol/L (1% increase) | ||
| Model 1 | 0.007 (0.002, 0.013) | 0.012 |
| Model 2 | 0.007 (0.002, 0.013) | 0.012 |
| Model 3 | 0.007 (0.002, 0.012) | 0.011 |
| Model 4 | 0.014 (0.007, 0.020) | <0.001 |
| Model 5 | 0.014 (0.008, 0.021) | <0.001 |
| TBR<3.9 mmol/L (1% increase) | ||
| Model 1 | 0.003 (−0.008, 0.014) | 0.574 |
| Model 2 | 0.003 (−0.008, 0.014) | 0.585 |
| Model 3 | −0.001 (−0.012, 0.010) | 0.864 |
| Model 4 | −0.007 (−0.017, 0.003) | 0.161 |
| Model 5 | −0.006 (−0.016, 0.004) | 0.257 |
| TBR<3.0 mmol/L (1% increase) | ||
| Model 1 | −0.004 (−0.037, 0.028) | 0.788 |
| Model 2 | −0.004 (−0.037, 0.028) | 0.791 |
| Model 3 | −0.013 (−0.045, 0.019) | 0.440 |
| Model 4 | −0.023 (−0.052, 0.006) | 0.120 |
| Model 5 | −0.022 (−0.051, 0.007) | 0.138 |
Note: Model 1: crude. Model 2: adjusted for gender and positive or negative alcohol drinking. Model 3: adjusted for variables in Model 2 plus body mass index and duration of diabetes. Model 4: adjusted for variables in Model 3 plus HbA1c, systolic blood pressure, gamma‐glutamyl transferase, total cholesterol, HDL cholesterol, logarithm of triglycerides, serum uric acid, estimated glomerular filtration rate, logarithm of urinary albumin excretion, leukocyte count, and hemoglobin. Model 5: adjusted for variables in Model 4 plus smoking status (never smoker, previous smoker, or current smoker), use of antihypertensive agents, use of antihyperlipidemia agents, use of antiplatelet agents, and presence of diabetic retinopathy.
Abbreviations: CGM, continuous glucose monitoring; CI, confidence interval; CV, coefficient variation; FIB‐4, fibrosis‐4; MAGEs, mean amplitude of glycemic excursions; SD, standard deviation; TAR, time above range; TBR, time below range; TIR, time in range.
4. Discussion
This study investigated the association of the noninvasive liver fibrosis marker FIB‐4 with atherosclerosis and CGM indices and found that FIB‐4 was associated with carotid thickening and arterial stiffness in Japanese patients with T2DM. The mean IMT, CCA‐max‐IMT, and baPWV increased with increasing severity of the FIB‐4 index. In addition, multivariate linear regression analysis showed a significant association between FIB‐4 and mean IMT. These associations remained significant even after adjustment for confounding factors, including HbA1c, BP, lipid profile, and renal function. Another novelty of our study is the focus on the FIB‐4 index and the CGM metrics, making this study highly valuable clinically in this regard.
The progression of hepatic steatosis and liver fibrosis is thought to increase cardiovascular risk through multiple mechanisms, including insulin resistance, dyslipidemia, chronic inflammation, and vascular dysfunction. Hepatic steatosis exacerbates insulin resistance and promotes dyslipidemia, which is characterized by high levels of triglycerides, low levels of HDL cholesterol, and increased levels of small, dense low‐density lipoprotein (LDL) particles [26, 27]. In patients with advanced liver fibrosis, chronic systemic inflammation is likely to occur due to elevated levels of inflammatory cytokines, such as tumor necrosis factor‐alpha (TNF‐α) and interleukin‐6 (IL‐6). Furthermore, in patients with MASLD or MASH, enhanced platelet activation and increased levels of coagulation and fibrinolysis inhibitory factors (e.g., plasminogen activator inhibitor 1, fibrinogen, and factor VII) may contribute to atherothrombosis [28, 29].
FIB‐4, a noninvasive marker of liver fibrosis, is not only simple and easy to calculate but also a useful tool for evaluating liver fibrosis [11, 12] and facilitates the prediction of liver‐related complications and mortality [30]. Recent studies have indicated that liver fibrosis is associated with CVDs in patients with MASLD [9] and that the FIB‐4 is useful for predicting the onset of CVDs [31]. However, only a few studies have investigated the association of noninvasive liver fibrosis markers and CVDs in diabetic patients. In a previous study using data on diabetic patients extracted from the database of the National Health and Nutrition Examination Survey (NHANES) conducted in the United States from 1999 to 2008, high FIB‐4 scores were associated with increased risks of all‐cause and CVD mortality [32]. In a longitudinal cohort study using data on patients with obesity or T2DM extracted from the United Kingdom Clinical Practice Research Datalink (CPRD) General Practice Online Data (GOLD) database, FIB‐4 was found to be associated with risks of liver events, cardiovascular events, and mortality [33]. Our study of Japanese patients found that FIB‐4 was associated with carotid thickening or arterial stiffness. This finding suggests a possible association between liver fibrosis markers and risk of CVDs and adds support to the results of the abovementioned studies [32, 33]. Since carotid ultrasound and baPWV can be used as prognostic tools for predicting all‐cause and cardiovascular mortality in patients with diabetes [34, 35], attention is warranted to identify the risks of not only liver fibrosis but also advanced arteriosclerosis in patients with high FIB‐4.
To the best of our knowledge, there are only two reports that examined previously the association between FIB‐4 and IMT. Cheng et al. [36] reported greater IMT in patients with MASLD (defined as the presence of fatty liver and at least one of the following three conditions: overweight/obesity, T2DM, and metabolic dysfunction) than in those with NAFLD (defined as the presence of fatty liver alone). Although their study included more than 9000 patients, the authors did not adjust for confounding factors in their analysis. The second report by Watanabe et al. [13] examined Japanese patients with T2DM and demonstrated a positive association between FIB‐4 and IMT. However, their multivariate analysis was adjusted only for BP, eGFR, and HbA1c. The strength of our study was the adjustment for various confounding factors, such as the duration of diabetes, diabetic retinopathy, and urinary microalbumin.
The secondary outcome of this study was the association of FIB‐4 with CGM indices. FIB‐4 was crudely associated with SD and MAGE. In Model 5, FIB‐4 was positively associated with SD but not significantly with MAGE or %CV. These results suggest a limited association of FIB‐4 with glucose fluctuations. In contrast, FIB‐4 was negatively associated with TIR but positively with TAR. In patients with advanced liver fibrosis, the development of hepatic insulin resistance exacerbates postprandial hyperglycemia and consequently contributes to prolonged duration of hyperglycemia [14]. Notably, a recent study has demonstrated that low TIR values are significantly associated with increased carotid IMT in patients with T2DM [17]. Given our findings that high FIB‐4 values are associated with low TIR values, which in turn have been linked to increased IMT, it is possible that the progression of atherosclerosis associated with liver fibrosis is partly mediated through low TIR. To the best of our knowledge, no study has reported the association of FIB‐4 with TIR or TAR.
This study has some limitations. First, it involved only Japanese patients; therefore, it is not clear if the study findings are applicable to European and American patients in whom obesity and insulin resistance are presumably more prevalent. Second, the presence or absence of fatty liver was not confirmed in this study by liver biopsy or abdominal ultrasonography. Accordingly, a definitive diagnosis of MASLD could not be established in the study subjects. Therefore, misclassification cannot be excluded [37]. Third, noninvasive fibrosis indices including FIB‐4 have limited diagnostic performance for advanced fibrosis and can be influenced by age and comorbidity profiles; therefore, misclassification cannot be excluded, and these indices should be interpreted with caution [38, 39]. However, despite these limitations, FIB‐4 was significantly associated with markers of atherosclerosis in patients with T2DM. These findings support the potential utility of FIB‐4 as a noninvasive cardiovascular risk assessment tool in patients with type 2 diabetes. Although age is a factor included in the formula to calculate FIB‐4, the models used in the multivariate analysis in this study were not adjusted for any factors that are included in this formula. Consequently, the impact of age was not excluded (Tables 2 and 3). As a sensitivity analysis, we re‐estimated the ordinal logistic models with age included as an additional covariate, and the associations between FIB‐4 and the atherosclerosis indices were attenuated and became nonsignificant (Table S2). These findings suggest that the contribution of the age component to the observed associations cannot be ignored, and the results should be interpreted with appropriate caution. Reduced diagnostic accuracy of FIB‐4 in younger individuals has been reported [40]; however, this was not directly assessed in our study. Fourth, the chance of false‐positive findings may increase because the multiplicity of testing for the multiple outcomes and statistical models was not adjusted in our analysis. Finally, because this study was based on subanalysis of baseline data, we were unable to determine the causal relationship between FIB‐4 and atherosclerosis. Moreover, the association of changes in FIB‐4 with changes in IMT and other indicators is unknown. Future longitudinal studies integrating hard cardiovascular outcomes, more specific fibrosis assessment (including advanced imaging and validated biomarkers), and detailed CGM metrics are warranted to clarify causality.
5. Conclusions
Our study found that FIB‐4 was associated with carotid thickening and arterial stiffness in Japanese patients with T2DM. Among the CGM indices, FIB‐4 was negatively associated with TIR and positively with TAR. Given that reduced TIR has been linked to increased carotid IMT in previous studies, the progression of atherosclerosis associated with liver fibrosis may be partly mediated by impaired glycemic control. Noninvasive liver fibrosis markers could be a useful tool for cardiovascular risk identification and personalized diabetes management interventions in T2DM, especially in patients with suspected advanced liver fibrosis. Prediction of liver fibrosis in patients with T2DM is crucial in order to prevent the progression of atherosclerosis and the development of CVDs. However, because FIB‐4 has inherent limitations and is influenced by age and comorbidity profiles, these findings should be interpreted with caution.
Author Contributions
Conceptualization: Kenichi Tanaka and Yosuke Okada. Methodology: Kenichi Tanaka and Yosuke Okada. Investigation: Kenichi Tanaka, Yosuke Okada, Tomoya Mita, Keiichi Torimoto, Fumiya Sato, Naoto Katakami, Hidenori Yoshii, Iichiro Shimomura, and Keiko Nishida. Data curation: Kenichi Tanaka, Yosuke Okada, Tomoya Mita, Keiichi Torimoto, Fumiya Sato, Naoto Katakami, Hidenori Yoshii, and Keiko Nishida. Formal analysis: Ryota Ishii and Masahiko Gosho. Writing – original draft: Kenichi Tanaka. Writing – review and editing: Yosuke Okada, Tomoya Mita, Keiichi Torimoto, Yoshiya Tanaka, and Hirotaka Watada.
Funding
This work was supported by the Japan Agency for Medical Research and Development (AMED) (to Hirotaka Watada) (Grant JP20ek0210105) and the Manpei Suzuki Diabetes Foundation (to Hirotaka Watada).
Disclosure
All authors have read and approved the final manuscript.
Ethics Statement
The study protocol was approved by the Institutional Review Board of each participating institution and conducted in accordance with the Declaration of Helsinki and current legal regulations in Japan. The study was registered in the University Hospital Medical Information Network Clinical Trials Registry (UMIN000032325). Written informed consent was obtained from all participants.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting Information
Additional supporting information can be found online in the Supporting Information section.
Supporting information
Supporting Information Methods: List of participating institutions. Table S1: Correlation coefficients between noninvasive tests for liver fibrosis and atherosclerotic index or CGM metrics in patients with type 2 diabetes. Table S2: Association between atherosclerosis metrics and severity of FIB‐4 index (including age as a covariate).
Acknowledgments
The authors thank the study investigators listed in the Supporting Information and all the participants for their contribution to this study. The authors also acknowledge the assistance of D. Takayama and H. Yamada (Soiken Holdings Inc., Tokyo, Japan) and N. Sakaguchi (University of Occupational and Environmental Health, Japan, Kitakyushu, Japan).
Tanaka, Kenichi , Okada, Yosuke , Mita, Tomoya , Torimoto, Keiichi , Sato, Fumiya , Katakami, Naoto , Yoshii, Hidenori , Nishida, Keiko , Tanaka, Yoshiya , Ishii, Ryota , Gosho, Masahiko , Shimomura, Iichiro , Watada, Hirotaka , Association of the Fibrosis‐4 Index With Indices of Atherosclerosis in Patients With Type 2 Diabetes: An Exploratory Subanalysis of a Prospective Observational Cohort Study, Journal of Diabetes Research, 2026, 4671246, 10 pages, 2026. 10.1155/jdr/4671246
Academic Editor: Hannah Wesley
Contributor Information
Yosuke Okada, Email: y-okada@med.uoeh-u.ac.jp.
Hannah Wesley, Email: hwesley@wiley.com.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
- 1. Stratton I. M., Adler A. I., and Neil H. A., et al.Association of Glycaemia With Macrovascular and Microvascular Complications of Type 2 Diabetes (UKPDS. 35): Prospective Observational Study, BMJ. (2000) 321, no. 7258, 405–412, 10.1136/bmj.321.7258.405. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Rinella M. E., Lazarus J. V., and Ratziu V., et al.A Multisociety Delphi Consensus Statement on New Fatty Liver Disease Nomenclature, Hepatology. (2023) 78, no. 6, 1966–1986, 10.1097/HEP.0000000000000520. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Bhatt H. B. and Smith R. J., Fatty Liver Disease in Diabetes Mellitus, Hepatobiliary Surgery and Nutrition. (2015) 4, no. 2, 101–108, 10.3978/j.issn.2304-3881.2015.01.03. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Muzurović E., Peng C. C.-H., Belanger M. J., Sanoudou D., Mikhailidis D. P., and Mantzoros C. S., Nonalcoholic Fatty Liver Disease and Cardiovascular Disease: A Review of Shared Cardiometabolic Risk Factors, Hypertension. (2022) 79, no. 7, 1319–1326, 10.1161/HYPERTENSIONAHA.122.17982. [DOI] [PubMed] [Google Scholar]
- 5. Younossi Z. M., Zelber-Sagi S., and Lazarus J. V., et al.Global Consensus Recommendations for Metabolic Dysfunction-Associated Steatotic Liver Disease and Steatohepatitis, Gastroenterology. (2025) 169, no. 5, 1017–1032.e2, 10.1053/j.gastro.2025.02.044. [DOI] [PubMed] [Google Scholar]
- 6. Nakahara T., Hyogo H., and Yoneda M., et al.Type 2 Diabetes Mellitus is Associated With the Fibrosis Severity in Patients With Nonalcoholic Fatty Liver Disease in a Large Retrospective Cohort of Japanese Patients, Journal of Gastroenterology. (2014) 49, no. 11, 1477–1484, 10.1007/s00535-013-0911-1, 2-s2.0-84926643436. [DOI] [PubMed] [Google Scholar]
- 7. Angulo P., Kleiner D. E., and Dam-Larsen S., et al.Liver Fibrosis, but No Other Histologic Features, is Associated With Long-Term Outcomes of Patients With Nonalcoholic Fatty Liver Disease, Gastroenterology. (2015) 149, no. 2, 389–397.e10, 10.1053/j.gastro.2015.04.043, 2-s2.0-84938057875. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Targher G., Byrne C. D., Lonardo A., Zoppini G., and Barbui C., Non-Alcoholic Fatty Liver Disease and Risk of Incident Cardiovascular Disease: A Meta-Analysis, Journal of Hepatology. (2016) 65, no. 3, 589–600, 10.1016/j.jhep.2016.05.013, 2-s2.0-84991083809. [DOI] [PubMed] [Google Scholar]
- 9. Targher G., Byrne C. D., and Tilg H., MASLD: A Systemic Metabolic Disorder With Cardiovascular and Malignant Complications, Gut. (2024) 73, no. 4, 691–702, 10.1136/gutjnl-2023-330595. [DOI] [PubMed] [Google Scholar]
- 10. Sterling R. K., Lissen E., and Clumeck N., et al.Development of a Simple Noninvasive Index to Predict Significant Fibrosis in Patients With HIV/HCV Coinfection, Hepatology. (2006) 43, no. 6, 1317–1325, 10.1002/hep.21178, 2-s2.0-33745547388. [DOI] [PubMed] [Google Scholar]
- 11. European Association for the Study of the Liver (EASL), European Association for the Study of Diabetes (EASD), and European Association for the Study of Obesity (EASO), EASL-EASD-EASO Clinical Practice Guidelines for the Management of Non-Alcoholic Fatty Liver Disease, Journal of Hepatology. (2016) 64, no. 6, 1388–1402, 10.1016/j.jhep.2015.11.004, 2-s2.0-84962661546. [DOI] [PubMed] [Google Scholar]
- 12. Chalasani N., Younossi Z., and Lavine J. E., et al.The Diagnosis and Management of Nonalcoholic Fatty Liver Disease: Practice Guidance From the American Association for the Study of Liver Diseases, Hepatology. (2018) 67, no. 1, 328–357, 10.1002/hep.29367, 2-s2.0-85030176004. [DOI] [PubMed] [Google Scholar]
- 13. Watanabe K., Takakubo N., and Saigusa T., et al.Fibrosis-4 Index is Closely Associated With Arterial Damage and Future Risk of Coronary Heart Disease in Type 2 Diabetes, International Journal of Hypertension. (2022) 2022, 10.1155/2022/2760027, 2760027. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Lee W.-H., Najjar S. M., Kahn C. R., and Hinds T. D., Hepatic Insulin Receptor: New Views on the Mechanisms of Liver Disease, Metabolism: Clinical and Experimental. (2023) 145, 10.1016/j.metabol.2023.155607, 155607. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Battelino T., Danne T., and Bergenstal R. M., et al.Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range, Diabetes Care. (2019) 42, no. 8, 1593–1603, 10.2337/dci19-0028, 2-s2.0-85066997802. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Raj R., Mishra R., Jha N., Joshi V., Correa R., and Kern P. A., Time in Range, as Measured by Continuous Glucose Monitor, as a Predictor of Microvascular Complications in Type 2 Diabetes: A Systematic Review, BMJ Open Diabetes Research & Care. (2022) 10, no. 1, 10.1136/bmjdrc-2021-002573, e002573. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Lu J., Ma X., and Shen Y., et al.Time in Range is Associated With Carotid Intima-Media Thickness in Type 2 Diabetes, Diabetes Technology & Therapeutics. (2020) 22, no. 2, 72–78, 10.1089/dia.2019.0251. [DOI] [PubMed] [Google Scholar]
- 18. Mita T., Katakami N., and Okada Y., et al.Protocol of a Prospective Observational Study on the Relationship Between Glucose Fluctuation and Cardiovascular Events in Patients With Type 2 Diabetes, Diabetes Therapy. (2019) 10, no. 5, 1565–1575, 10.1007/s13300-019-0665-8, 2-s2.0-85069658921. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Matsuo S., Imai E., and Horio M., et al.Revised Equations for Estimated GFR From Serum Creatinine in Japan, American Journal of Kidney Diseases. (2009) 53, no. 6, 982–992, 10.1053/j.ajkd.2008.12.034, 2-s2.0-65549111017. [DOI] [PubMed] [Google Scholar]
- 20. Mita T., Katakami N., and Shiraiwa T., et al.Sitagliptin Attenuates the Progression of Carotid Intima-Media Thickening in Insulin-Treated Patients With Type 2 Diabetes: The Sitagliptin Preventive Study of Intima-Media Thickness Evaluation (SPIKE): A Randomized Controlled Trial, Diabetes Care. (2016) 39, no. 3, 455–464, 10.2337/dc15-2145, 2-s2.0-84962090232. [DOI] [PubMed] [Google Scholar]
- 21. Mita T., Katakami N., and Yoshii H., et al.Alogliptin, a Dipeptidyl Peptidase 4 Inhibitor, Prevents the Progression of Carotid Atherosclerosis in Patients With Type 2 Diabetes: The Study of Preventive Effects of Alogliptin on Diabetic Atherosclerosis (SPEAD-A), Diabetes Care. (2016) 39, no. 1, 139–148, 10.2337/dc15-0781, 2-s2.0-84962052181. [DOI] [PubMed] [Google Scholar]
- 22. Yanase T., Nasu S., and Mukuta Y., et al.Evaluation of a New Carotid Intima-Media Thickness Measurement by B-Mode Ultrasonography Using an Innovative Measurement Software, Intimascope, American Journal of Hypertension. (2006) 19, no. 12, 1206–1212, 10.1016/j.amjhyper.2006.05.010, 2-s2.0-33845203468. [DOI] [PubMed] [Google Scholar]
- 23. Osonoi Y., Mita T., and Osonoi T., et al.Morningness-Eveningness Questionnaire Score and Metabolic Parameters in Patients With Type 2 Diabetes Mellitus, Chronobiology International. (2014) 31, no. 9, 1017–1023, 10.3109/07420528.2014.943843, 2-s2.0-84911966927. [DOI] [PubMed] [Google Scholar]
- 24. Boscari F., Galasso S., and Acciaroli G., et al.Head-to-Head Comparison of the Accuracy of Abbott FreeStyle Libre and Dexcom G5 Mobile, Nutrition, Metabolism and Cardiovascular Diseases. (2018) 28, no. 4, 425–427, 10.1016/j.numecd.2018.01.003, 2-s2.0-85043366777. [DOI] [PubMed] [Google Scholar]
- 25. Shah A. G., Lydecker A., Murray K., Tetri B. N., Contos M. J., and Sanyal A. J., Comparison of Noninvasive Markers of Fibrosis in Patients With Nonalcoholic Fatty Liver Disease, Clinical Gastroenterology and Hepatology. (2009) 7, no. 10, 1104–1112, 10.1016/j.cgh.2009.05.033, 2-s2.0-70349546276. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Gastaldelli A., Cusi K., and Pettiti M., et al.Relationship Between Hepatic/Visceral Fat and Hepatic Insulin Resistance in Nondiabetic and Type 2 Diabetic Subjects, Gastroenterology. (2007) 133, no. 2, 496–506, 10.1053/j.gastro.2007.04.068, 2-s2.0-34547514221. [DOI] [PubMed] [Google Scholar]
- 27. Katsiki N., Mikhailidis D. P., and Mantzoros C. S., Non-Alcoholic Fatty Liver Disease and Dyslipidemia: An Update, Metabolism-Clinical and Experimental. (2016) 65, no. 8, 1109–1123, 10.1016/j.metabol.2016.05.003, 2-s2.0-84969961723. [DOI] [PubMed] [Google Scholar]
- 28. Tripodi A., Fracanzani A. L., and Primignani M., et al.Procoagulant Imbalance in Patients With Non-Alcoholic Fatty Liver Disease, Journal of Hepatology. (2014) 61, no. 1, 148–154, 10.1016/j.jhep.2014.03.013, 2-s2.0-84902650580. [DOI] [PubMed] [Google Scholar]
- 29. Targher G., Bertolini L., and Rodella S., et al.NASH Predicts Plasma Inflammatory Biomarkers Independently of Visceral Fat in Men, Obesity (Silver Spring). (2008) 16, no. 6, 1394–1399, 10.1038/oby.2008.64, 2-s2.0-44449109670. [DOI] [PubMed] [Google Scholar]
- 30. Younes R., Caviglia G. P., and Govaere O., et al.Long-Term Outcomes and Predictive Ability of Non-Invasive Scoring Systems in Patients With Non-Alcoholic Fatty Liver Disease, Journal of Hepatology. (2021) 75, no. 4, 786–794, 10.1016/j.jhep.2021.05.008. [DOI] [PubMed] [Google Scholar]
- 31. Schonmann Y., Yeshua H., Bentov I., and Zelber-Sagi S., Liver Fibrosis Marker is an Independent Predictor of Cardiovascular Morbidity and Mortality in the General Population, Digestive and Liver Disease. (2021) 53, no. 1, 79–85, 10.1016/j.dld.2020.10.014. [DOI] [PubMed] [Google Scholar]
- 32. Guan L., Li L., Zou Y., Zhong J., and Qiu L., Association Between FIB-4, All-Cause Mortality, Cardiovascular Mortality, and Cardiovascular Disease Risk Among Diabetic Individuals: NHANES. 1999–2008, Frontiers in Cardiovascular Medicine. (2023) 10, 10.3389/fcvm.2023.1172178, 1172178. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Anstee Q. M., Berentzen T. L., and Nitze L. M., et al.Prognostic Utility of Fibrosis-4 Index for Risk of Subsequent Liver and Cardiovascular Events, and All-cause Mortality in Individuals With Obesity and/or Type 2 Diabetes: A Longitudinal Cohort Study, The Lancet Regional Health – Europe. (2024) 36, 10.1016/j.lanepe.2023.100780, 100780. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34. Hoke M., Schillinger M., Minar E., Goliasch G., Binder C. J., and Mayer F. J., Carotid Ultrasound Investigation as a Prognostic Tool for Patients With Diabetes Mellitus, Cardiovascular Diabetology. (2019) 18, no. 1, 10.1186/s12933-019-0895-0, 2-s2.0-85068969775, 90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Kim J. M., Kim S. S., and Kim I. J., et al.Arterial Stiffness is an Independent Predictor for Risk of Mortality in Patients With Type 2 Diabetes Mellitus: The REBOUND Study, Cardiovascular Diabetology. (2020) 19, no. 1, 10.1186/s12933-020-01120-6, 143. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Cheng Y.-M., Wang C.-C., and Kao J.-H., Metabolic Associated Fatty Liver Disease Better Identifying Patients at Risk of Liver and Cardiovascular Complications, Hepatology International. (2023) 17, no. 2, 350–356, 10.1007/s12072-022-10449-x. [DOI] [PubMed] [Google Scholar]
- 37. Muzurović E., Topić G., Todorović N., Rizzo M., and Zečević K., The Prevalence and Correlates of Advanced Fibrosis in Patients With and Without Diabetes Mellitus and Metabolic Dysfunction-Associated Steatotic Liver Disease: A Cross-Sectional Study, Journal of Diabetes and Its Complications. (2025) 39, no. 10, 10.1016/j.jdiacomp.2025.109147, 109147. [DOI] [PubMed] [Google Scholar]
- 38. Sualeheen A., Tan S. Y., and Daly R. M., et al.Steatosis Non-Invasive Tests Accurately Predict Metabolic Dysfunction-Associated Steatotic Liver Disease, While Fibrosis Non-Invasive Tests Fall Short: Validation in U.S. Adult Population, Clinics and Research in Hepatology and Gastroenterology. (2025) 49, no. 7, 10.1016/j.clinre.2025.102649, 102649. [DOI] [PubMed] [Google Scholar]
- 39. Bera C., Hamdan-Perez N., Kosick H. M.-K., Shengir M., Sebastiani G., and Patel K., Validation of FIB-4 for the Diagnosis of Liver Cirrhosis in Metabolic Dysfunction-Associated Steatotic Liver Disease, Canadian Liver Journal. (2025) 8, no. 2, 280–283, 10.3138/canlivj-2024-0063. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Lee Y. and Lee W., Time-Updated FIB-4 Index Predicts Coronary Artery Calcification Progression in Individuals With Metabolic Dysfunction-Associated Steatotic Liver Disease, Scientific Reports. (2026) 16, no. 1, 10.1038/s41598-025-31289-3, 1459. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supporting Information Methods: List of participating institutions. Table S1: Correlation coefficients between noninvasive tests for liver fibrosis and atherosclerotic index or CGM metrics in patients with type 2 diabetes. Table S2: Association between atherosclerosis metrics and severity of FIB‐4 index (including age as a covariate).
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.