Diabetes Metab J > Epub ahead of print
Han, Kim, Jang, and Kim: Albuminuria Is Associated with Steatosis Burden in Patients with Type 2 Diabetes Mellitus and Nonalcoholic Fatty Liver Disease

Abstract

Background

This study aimed to investigate the association between hepatic steatosis burden and albuminuria in Korean patients with type 2 diabetes mellitus (T2DM) and nonalcoholic fatty liver disease (NAFLD).

Methods

We recruited 100 patients with both T2DM and NAFLD, but without chronic kidney disease. Albuminuria was defined as a spot urinary albumin-to-creatinine ratio (ACR) ≥30 mg/g. Transient elastography was performed, and the steatosis burden was quantified by controlled attenuation parameter (CAP) with significant steatosis defined as CAP >302 dB/m.

Results

The prevalence of significant steatosis and albuminuria was 56.0% and 21.0%, respectively. Subjects with significant steatosis were significantly younger and had a significantly shorter duration of T2DM, greater waist circumference, and higher body mass index, total cholesterol, triglyceride, and low density lipoprotein cholesterol levels, than subjects without severe NAFLD (all P<0.05). Albuminuria was higher in patients with significant steatosis than in patients without significant steatosis (32.1% vs. 6.8%, P=0.002). Urinary ACR showed a correlation with CAP (r=0.331, P=0.001), and multiple linear regression analysis revealed a significant association between a high degree of albuminuria and high CAP value (r=0.321, P=0.001). Additionally, multivariate logistic regression analysis demonstrated the independent association between urinary ACR and significant steatosis after adjustment for confounding factors including age, body mass index, duration of T2DM, low density lipoprotein level, and renin-angiotensin system blocker use (odds ratio, 1.88; 95% confidence interval, 1.31 to 2.71; P=0.001).

Conclusion

T2DM patients with NAFLD had a higher prevalence of albuminuria, which correlated with their steatosis burden.

INTRODUCTION

Due to the increasing prevalence of obesity and type 2 diabetes mellitus (T2DM), nonalcoholic fatty liver disease (NAFLD) has emerged as one of the most common chronic liver diseases, with an estimated worldwide prevalence of 25.2% [1]. The prevalence of NAFLD in patients with T2DM is much greater than in the general population and is reported to be 55.5% [2]. As NAFLD shows a 3% rise in the annual incidence rate, the prevalence of NAFLD is expected to rise as individuals with obesity and T2DM age [3]. NAFLD is defined by the presence of fat accumulation in hepatocytes, after the exclusion of excessive alcohol use, hepatitis virus infection, iron overloading, autoimmune disease, or medications, which can cause chronic liver disease. NAFLD can advance to non-alcoholic steatohepatitis (NASH) that can progress to cirrhosis, end-stage liver disease, and finally, hepatocellular carcinoma (HCC) [4]. Moreover, NAFLD, per se, accounts for the most predominant cause of HCC, even without cirrhotic progression [5]. The risk of HCC rises at least two-fold in populations with T2DM and obesity [6].
Albuminuria is an independent risk factor for cardiovascular and renal diseases [7,8]. Additionally, it is a marker of insulin resistance and systemic inflammation. Consequently, an association between albuminuria and other metabolic diseases has been reported [9,10]. Although NAFLD and albuminuria are independently associated with the aggravation of coexisting metabolic diseases, the interactive influence of NALFD and albuminuria is not fully determined, especially in subjects with normal kidney function. Therefore, we aimed to investigate the association between the degree of steatosis and albuminuria, independent of other metabolic factors. Furthermore, we investigated whether albuminuria is associated with the hepatic burden in NAFLD.

METHODS

Study population

In this cross-sectional study, a total of 100 Korean subjects, aged 18 years or above, with T2DM were recruited at Keimyung University Dongsan Hospital, a tertiary university hospital in Korea. T2DM was defined according to the American Diabetes Association guidelines [11]. This study complied with the Declaration of Helsinki and Good Clinical Practice guidelines. The study protocol received ethical approval from the Institutional Review Board at the Keimyung University School of Medicine (2017-09-045). Informed consent was obtained from the patients prior to each examination.
All patients had NAFLD, as diagnosed using abdominal ultrasound or computed tomography and confirmed by a radiologist. Patients were excluded if they fulfilled any one of the following criteria: (1) diagnosis of type 1 diabetes mellitus, gestational diabetes mellitus, or any other form of diabetes other than T2DM; (2) history of addiction to alcohol, heavy alcohol consumption (≥210 g/week for men or ≥140 g/week for women); (3) other causes of liver disease (e.g., active viral or autoimmune hepatitis), liver cirrhosis, or HCC; (4) estimated glomerular filtration rate (eGFR) <45 mL/min/1.73 m2; (5) medications associated with fatty liver disease (e.g., amiodarone, methotrexate, tamoxifen, or valproate); and (6) pregnant or nursing women.

Measurement of steatosis burden and albuminuria

All patients underwent physical examination and clinical laboratory tests after an overnight (over 8 hours) fasting period for baseline parameters. Liver fat content was assessed via the controlled attenuation parameter (CAP), and liver fibrotic burden was assessed via the liver stiffness measurement (LSM). Both CAP and LSM measure ultrasonic attenuations at 3.5 MHz, using signals acquired by transient liver elastography (FibroScan; Echosens, Paris, France) with XL probe. To achieve an accurate CAP value, we calculated only ultrasonic attenuation when the matched LSM was valid while attempting to collect at least 10 valid LSMs. A success rate of ≥60% and a ratio for the interquartile range (IQR) to the median value of LSM of ≤30% were considered reliable and used for the final analysis [12]. The final CAP value was the median of each individual CAP value using the same valid measurements [13,14]. The steatosis grade (S) was determined using the following CAP-cutoff values that have been reported earlier: 236 dB/m for S ≥1, 270 dB/m for S ≥2, and 302 dB/m for S ≥3. We characterized significant steatosis when CAP was over 302 dB/m [15-17].
Urinary albumin and creatinine levels were measured in a fasting, morning spot urine sample obtained from each participant. Urinary albumin levels were expressed as the urinary albumin-to-creatinine ratio (ACR) to minimize the influence of variations in kidney function. The urine albumin level was measured by an immunoturbidimetric method using an AU680 automated chemistry analyzer (Beckman Coulter Inc., Brea, CA, USA). The urine creatinine level was also measured using the AU680 analyzer (Beckman Coulter Inc.) by the kinetic Jaffe method. Albuminuria was defined as urinary ACR ≥30 mg/g.

Clinical parameters and biochemical analysis

Body weight, waist circumference, blood pressure, glycemic parameters (i.e., fasting plasma glucose and glycosylated hemoglobin [HbA1c]), lipids (i.e., total cholesterol, high density lipoprotein cholesterol [HDL-C], low density lipoprotein cholesterol [LDL-C], and triglycerides), and liver enzymes (i.e., aspartate aminotransferase [AST], alanine transaminase [ALT], and gamma-glutamyl transferase [γ-GT]) were measured. An eGFR was calculated using the Modification of Diet in Renal Disease equation [18]. The homeostasis model assessment of insulin resistance (HOMA-IR) was calculated as follows: fasting glucose (mg/dL)×fasting insulin (μIU/L)/405. Subjects were considered as obese when their body mass index (BMI) was ≥25 kg/m2. Central obesity was defined on the basis of the waist circumference criteria from the Korean Society for the Study of Obesity (≥90 cm for men and ≥85 cm for women) [19]. Regular exercise was defined as more than 20 minutes per session and at least three times per week. Hypertension was defined as systolic pressure over 140 mm Hg and diastolic pressure over 90 mm Hg, or if the subject was currently prescribed antihypertensive medications. Cardiovascular disease was defined as any prior history of acute coronary syndromes, cerebrovascular accidents, or peripheral arterial diseases. The presence of diabetic neuropathy and diabetic retinopathy was defined by the patient’s medical record diagnosed by a neurologist/ophthalmologist, or if the patient was currently prescribed medication for neuropathy/retinopathy.

Statistical analysis

Data are presented as mean±standard deviation for parametric variables and as median (IQR) for non-parametric variables or percent for categorical variables. We analyzed differences in the patient characteristics between the two groups using paired t-tests for parametric continuous variables and chi-square tests for categorical variables. The urinary ACR, total cholesterol, triglyceride, HDL-C, LDL-C, AST, ALT, γ-GT, insulin, and HOMA-IR values were not normally distributed; analyses, therefore, were performed using the Mann-Whitney U test. The association between NAFLD and albuminuria was evaluated using the chi-square test after transforming the variables into tertiles. Correlations between urinary ACR, CAP, and other parameters were analyzed using Spearman’s correlation coefficients. Backward multiple linear regression analysis was performed on CAP to analyze the relationship between CAP and demographic and laboratory parameters. The relative risk factors for severe NAFLD were obtained using multiple logistic regression, and the risk was reported as odds ratios (ORs) with 95% confidence intervals (CIs). All statistical analyses were performed using SPSS version 25.0 (IBM Corp., Armonk, NY, USA). For all tests, P<0.05 was considered as statistically significant.

RESULTS

Baseline characteristics of the study population

Baseline characteristics according to the status of NAFLD are shown in Table 1. The prevalence of significant steatosis and albuminuria was 56.0% and 21.0%, respectively. Subjects with significant steatosis were significantly younger and had a significantly shorter duration of T2DM. They had greater waist circumferences, higher BMI, total cholesterol, triglycerides, and LDL-C, than subjects without severe NAFLD (all P<0.05). Furthermore, the proportion of central obesity and obesity was significantly higher in subjects with significant steatosis, whereas the proportion of hypertension and cardiovascular diseases was similar between the two groups. Liver function tests, including ALT, AST, γ-GT levels, were mostly within the normal range for both groups, with no significant difference between the values. The prevalence of albuminuria was higher in patients with significant steatosis than in subjects without significant steatosis (32.1% vs. 6.8%, P=0.002). Ninety-seven patients were prescribed metformin, and 35 patients were using angiotensin-converting enzyme (ACE) inhibitors or angiotensin II receptor blockers (ARB), and the proportion of anti-diabetic medications and ACE inhibitors/ARB was similar between the severe and non-severe NAFLD groups. In addition, the fibrotic burden, LSM was comparable between the two groups.

Correlation between CAP and urinary ACR

After stratifying into CAP and urinary ACR tertiles (<2.39, 2.39 to 11.41, >11.41 mg/g), individuals with a higher CAP had a higher urinary ACR than those with a lower CAP (Fig. 1A). When stratified by severity of NAFLD (S0, S1, S2, and S3 stages assigned to 12, 14, 18, and 56 subjects, respectively), the proportion of the highest urinary ACR tertile and significant albuminuria gradually increased and was the highest in subjects with S3 stage NAFLD (Fig. 1B and C). To assess the association between CAP and other metabolic components, we performed a linear correlation analysis. As shown in Table 2, BMI, waist circumference, ALT, γ-GT, and urinary ACR positively correlated with CAP, displaying statistical significance, whereas age and duration of T2DM showed a negative correlation with CAP. Furthermore, to determine which factors were significantly associated with CAP, we performed multiple linear analyses (Table 3). After adjusting for age and sex in Model 1, BMI, waist circumference, and urinary ACR were independently associated with CAP. In Model 2, adjusting further for the geodemographic parameters, including duration of T2DM, diabetic retinopathy, and ACE inhibitors/ARB use, led to a significant association of triglyceride level, BMI, waist circumference, and urinary ACR with CAP. On further adjusting Model 2 for laboratory findings (HbA1c, ALT, γ-GT, and triglyceride)(Model 3), BMI, waist circumference, and urinary ACR remained independently associated factors. Finally, Model 3 adjusted for BMI and waist circumference (Model 4) showed a significant association between urinary ACR and CAP.

Association between significant steatosis and urinary ACR

We demonstrated a relationship between CAP and urinary ACR; likewise, we analyzed the clinical and demographical factors related to significant steatosis. Table 4 shows that age, BMI, waist circumference, duration of T2DM, triglycerides, LDL-C, and urinary ACR demonstrated a significant association with significant steatosis. Similar to the linear association, younger age and shorter duration of T2DM was associated with a higher risk of significant steatosis ([OR, 0.97; 95% CI, 0.94 to 0.99; P=0.030 for age], [OR, 0.94; 95% CI, 0.89 to 0.99; P=0.034 for the duration of T2DM]), along with higher BMI, waist circumference, triglycerides, LDL-C, and urinary ACR (OR ranges 1.02 to 3.79; all P<0.05). To determine the independence of these risk factors, we performed multivariable logistic regression analysis. After adjusting covariate factors including age, duration of T2DM, BMI, use of ACE inhibitors or ARB, LDL-C, and BMI, LDL-C, urinary ACR still remained statistically significant ([OR, 1.34; 95% CI, 1.15 to 1.57; P< 0.001 for BMI], [OR, 4.33; 95% CI, 1.20 to 15.63; P=0.025 for LDL-C], [OR, 1.88; 95% CI, 1.31 to 2.71; P=0.001 for urinary ACR]).

DISCUSSION

This prospective and cross-sectional study demonstrates a significant association between CAP and urinary ACR, and an increased risk of significant steatosis in subjects with albuminuria and T2DM. Urinary ACR showed a significant positive correlation with CAP and independently predicted fatty burden of NAFLD after adjustment for clinical variables, including age, sex, waist circumference, BMI, duration of T2DM, presence of diabetic retinopathy and the use of ACE inhibitor/ARB, along with laboratory test results including HbA1c, ALT, γ-GT, triglycerides, and LDL-C. After further adjustments, we observed an approximately 2-fold rise in the adjusted risk of significant steatosis in patients with albuminuria and T2DM (OR, 1.88; 95% CI, 1.31 to 2.71; P=0.001).
There have been several attempts made to elucidate the association between NAFLD and albuminuria. Although previous studies consistently indicated a strong association between NAFLD and chronic kidney disease (CKD) [20,21], the results from investigations regarding NAFLD and albuminuria are conflicting; especially in populations with T2DM. A meta-analysis study showed a 2.25-fold elevated risk of albuminuria among patients with NAFLD without diabetes; however, they could not find any significant association between albuminuria and NAFLD among subjects with diabetes [22]. A Chinese cross-sectional study of 1,763 patients with T2DM and NAFLD (ascertained using transient elastography), showed only a significant association between fibrotic burden and albuminuria, but not between steatosis burden and albuminuria, in subjects with T2DM [23]. Compared to the Chinese study, our study population was relatively younger (mean age 53.0±15.5 years vs. 60.7±11.5 years in the Chinese study), and had a shorter duration of T2DM (7.1±7.3 years vs. 10.8±8.5 years in the Chinese study). Moreover, we excluded patients with liver cirrhosis, splenomegaly, or chronic liver diseases other than NAFLD, based on imaging studies, along with subjects with CKD to clarify the association between the degree of steatosis and albuminuria. These factors could account for the discrepancies between the two studies. In concordance with our results, studies have confirmed the association between NAFLD and albuminuria in prediabetic patients and those diagnosed recently or with a shorter duration of T2DM [24,25]. As albuminuria is regarded as an early clinical manifestation of diabetic kidney disease (DKD), and fatty burden accumulation precedes fibrotic change, the association between the degree of steatosis and albuminuria might emerge in the early course of T2DM. This finding is clinically relevant to identify high-risk patients of NAFLD for further screening and evaluation.
With respect to risk factors for NAFLD in populations with T2DM, traditional metabolic risk factors such as BMI, waist circumference, and dyslipidemia were independent variables associated with CAP and the aggravation of NAFLD in our study. In addition to these traditional markers, albuminuria showed at least a comparable potential to predict NAFLD severity. In other words, patients with T2DM and additional metabolic diseases are at a higher risk of developing advanced NAFLD and may benefit from a liver assessment. Moreover, it is important to note that among patients with T2DM, urinary ACR test may be useful for screening NAFLD as well as DKD. Among current valid liver assessments, transient elastography has the advantage of providing quantitative measurements, with validations done in different populations, showing high reproducibility [17,26]. In contrast to the scoring system, confounding factors such as age and liver enzymes do not influence the results of transient elastography [27-29].
The possible mechanisms for the association between albuminuria and NAFLD might be explained by insulin resistance and systemic inflammation. Albuminuria reflects insulin resistance, even in a healthy population, and the reduced insulin sensitivity promotes hepatic steatosis [30,31]. An increase in systemic inflammatory markers (interleukin 6, tumor necrosis factor α, fibrinogen, monocyte chemoattractant protein-1) is observed in subjects with albuminuria, attributing to NAFLD, and its progression [32-34]. Recently, the concept of crosstalk between the liver and kidney has emerged, involving the renin-angiotensin system. Adipocytes (fat cells) express components of the renin-angiotensin system, impacting the ACE (angiotensin II) angiotensin II type 1 receptor axis, which is also expressed in the liver and kidney, leading to organ damage [35]. In addition, the altered activation of nutrient/energy sensors sirtuin-1 and adenosine monophosphate-activated kinase, and impaired antioxidant defense mediated by nuclear factor erythroid 2-related factor-2 (NRF2), support the crosstalk between liver and kidney [36]. Patatin-like phospholipase domain-containing protein 3 (PNPLA3) gene, which is expressed in the liver, adipose tissue, and kidney, is also linked with the crosstalk. PNPLA3 is a lipid droplet-associated protein that has hydrolase activity toward triglycerides and retinyl esters, leading to hepatic steatosis. Recent studies have also reported the association of PNPLA3 polymorphism with albuminuria [37,38].
Previous studies reported the increased prevalence of NAFLD with age [39,40]. However, recent studies have also demonstrated an increased presence of NAFLD in young adults with T2DM [41,42]. A Danish cross-sectional study showed that the risk of NAFLD nearly doubled in patients diagnosed with T2DM before the age of 40 [41]. In addition, NAFLD and NASH have shown an increased prevalence in young adults, and, if unchecked, could lead to liver transplantation [43]. In agreement with recent studies, we observed that patients with significant steatosis were younger and more obese than those without significant steatosis. However, after adjusting for age and BMI, this association was dismissed. A selection bias might have affected the results achieved in our study since we actively performed imaging studies in young and recently diagnosed T2DM patients. However, such patients usually do not show any symptoms of NAFLD, and are, therefore, left treated. Screening and early identification of NAFLD should, therefore, not be neglected in this population, and screening should be emphasized as for older patients with T2DM.
We acknowledge that our study has some limitations. Firstly, despite the acceptance of transient elastography for detecting NAFLD, this diagnostic tool does not replace liver biopsy, which is the gold standard for detecting NAFLD. Liver biopsy is regarded as a precise diagnostic tool for evaluating NAFLD and NASH. However, owing to its complications, non-invasive diagnostic devices, including transient elastography, have been widely utilized. Additionally, although we applied XL probe in the current study to enhance its accuracy, CAP could be overestimated in patients with morbid obesity. Secondly, since this was a cross-sectional study, we could not elucidate a causal relationship between the observed findings. Thirdly, since this study was based on a population visiting a single tertiary university hospital, our results may not be reflective of the entire Korean population. In addition, selection bias could be present and might have affected the results. Further multi-center, large-scale, longitudinal studies are required to show a clear association between albuminuria and hepatic burden in T2DM.
In conclusion, our results show that patients with T2DM and NAFLD frequently have albuminuria, which is associated with a higher risk of significant steatosis. Additionally, we showed that the level of urinary ACR was associated with an increased hepatic steatosis burden in NAFLD patients. Hepatic complication should be kept in mind in T2DM patients with albuminuria. Further studies with a larger number of patients and longer periods of observation, along with liver biopsy, are needed to elucidate the association more clearly between albuminuria occurrence and NAFLD progression.

NOTES

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

AUTHOR CONTRIBUTIONS

Conception or design: E.H., H.S.K.

Acquisition, analysis, or interpretation of data: E.H., M.K.K., B.K.J., H.S.K.

Drafting the work or revising: E.H., M.K.K., H.S.K.

Final approval of the manuscript: H.S.K.

FUNDING

This study was supported by a grant (Eugene Han, 2017F-5) from the Korean Diabetes Association. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.

ACKNOWLEDGMENTS

None

Fig. 1.
Association between albuminuria and steatosis burden. (A) Association of urinary albumin-to-creatinine ratio (ACR) with controlled attenuation parameter (CAP) by tertiles. T1 for the lowest tertile, T2 for middle tertile, and T3 for the highest tertile group. (B) Association of urinary ACR tertiles according to the steatosis grade. T1 for the lowest tertile, T2 for middle tertile, and T3 for the highest tertile group. (C) Proportion of albuminuria (urinary ACR ≥30 mg/g) according to the steatosis grade. The steatosis grade (S) was determined using the following CAP-cutoff values: 236 dB/m for S ≥1, 270 dB/m for S ≥2, and 302 dB/m for S ≥3.
dmj-2020-0118f1.jpg
Table 1.
Baseline characteristics of study population
Characteristic Total (n=100) Patients without significant steatosis (n=44) Patients with significant steatosis (n=56) P value
Age, yr 53.0±15.5 56.8±14.5 49.9±15.7 0.026
Male sex 53 (53.0) 23 (52.3) 30 (53.6) 0.897
Duration of T2DM, yr 7.1±7.3 8.9±8.7 5.7±5.9 0.036
Body mass index, kg/m2 26.5±4.3 24.6±3.4 28.1±4.2 <0.001
Obesity 59 (59.0) 16 (36.4) 43 (76.8) <0.001
Waist circumference, cm 90.7±10.5 87.4±9.7 93.3±10.4 0.005
Central obesity 60 (60.0) 21 (47.7) 39 (69.6) 0.026
Current smoking 23 (23.0) 15 (34.1) 8 (14.8) 0.064
Regular exercise 33 (33.0) 14 (28.0) 19 (38.0) 0.288
SBP, mm Hg 126.7±12.5 125.1±13.0 128.0±12.0 0.229
DBP, mm Hg 72.8±11.7 70.1±14.0 74.9±9.0 0.056
FPG, mg/dL 136.5±50.1 142.8±58.8 131.5±42.0 0.267
HbA1c, % 7.9±2.0 8.0±2.2 7.9±2.0 0.986
PPPG, mg/dL 186.2±65.2 184.0±61.5 188.0±68.6 0.757
BUN, mg/dL 14.9±5.3 15.9±6.5 14.2±3.9 0.097
Creatinine, mg/dL 0.8±0.2 0.8±0.2 0.8±0.2 0.371
eGFR, mL/min/1.73 m2 98.5±25.1 93.4±22.6 102.5±26.4 0.069
Albumin, mg/dL 4.6±0.4 4.5±0.4 4.6±0.4 0.602
Total cholesterol, mg/dL 150.0 (126.0-181.0) 142.5 (119.5-170.3) 159.0 (136.0-195.0) 0.019
Triglyceride, mg/dL 124.0 (101-177.7) 117.2 (90.4-157.6) 141.0 (111.0-185.0) 0.034
HDL-C, mg/dL 47.1 (36.4-53.4) 48.5 (35.3-58.0) 46.1 (40.1-52.1) 0.726
LDL-C, mg/dL 74.0 (54.5-94.0) 61.5 (47.0-81.0) 80.7 (63.3-100.1) 0.006
AST, IU/L 25.0 (19.0-37.0) 25.0 (19.0-40.8) 24.5 (20.0-36.3) 0.622
ALT, IU/L 29.0 (17.0-51.0) 28.5 (16.3-51.8) 29.5 (19.5-50.5) 0.391
γ-GT, IU/L 35.0 (21.0-55.0) 35.0 (17.3-52.8) 33.0 (23.0-61.0) 0.428
HOMA-IR 2.1 (1.2-4.0) 1.8 (1.3-3.8) 2.2 (1.4-4.3) 0.242
CAP, dB/m 302.3±47.2 260.3±29.9 335.3±28.2 <0.001
Liver stiffness measurement, kPa 6.1±2.6 5.7±2.7 6.4±2.5 0.194
Urinary ACR, mg/g creatinine 5.0 (1.7-17.2) 2.8 (1.1-8.0) 8.8 (2.4-50.8) 0.002
Albuminuria 21 (21.0) 3 (6.8) 18 (32.1) 0.002
Comorbidities
 Hypertension 36 (35.0) 18 (40.9) 18 (32.1) 0.365
 Cardiovascular disease 10 (10.0) 6 (13.6) 4 (7.1) 0.283
 Diabetic retinopathy 6 (6.0) 4 (9.1) 2 (3.0) 0.249
 Diabetic neuropathy 16 (16.0) 9 (20.5) 7 (12.5) 0.281
Medications
 Metformin 97 (97.0) 43 (97.7) 54 (96.4) 0.706
 Sulfonylurea 30 (30.0) 14 (31.8) 16 (28.6) 0.725
 Thiazolidinedione 7 (7.0) 1 (2.3) 6 (10.7) 0.101
 Insulin 24 (24.0) 9 (20.5) 15 (26.8) 0.462
 Lipid lowering agents 70 (70.0) 29 (65.9) 41 (73.2) 0.429
 ACE inhibitor/ARB 35 (35.0) 18 (40.9) 17 (30.4) 0.272

Values are presented as mean±standard deviation, number (%), or median (interquartile range).

T2DM, type 2 diabetes mellitus; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; HbA1c, glycosylated hemoglobin; PPPG, postprandial plasma glucose; BUN, blood urea nitrogen; eGFR, estimated glomerular filtration rate; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol; AST, aspartate transaminase; ALT, alanine transferase; γ-GT, gamma glutamyltransferase; HOMA-IR, homeostatic model assessment of insulin resistance; CAP, controlled attenuation parameter; ACR, albumin-to-creatinine ratio; ACE, angiotensin-converting enzyme; ARB, angiotensin II receptor blocker.

Table 2.
Correlation between CAP and other parameters
Parameter r P value
Age, yr -0.321 0.001
BMI, kg/m2 0.449 <0.001
Waist circumference, cm 0.339 0.001
Duration of T2DM, yr -0.266 0.007
Presence of diabetic retinopathy -0.081 0.421
ACE inhibitor/ARB use 0.018 0.858
FPG, mg/dL -0.101 0.316
HbA1c, % -0.065 0.521
PPPG, mg/dL -0.035 0.730
AST, IU/La 0.192 0.056
ALT, IU/La 0.319 0.001
γ-GT, IU/La 0.281 0.005
Triglyceride, mg/dLa 0.281 0.005
LDL-C, mg/dLa 0.101 0.322
HOMA-IRa 0.113 0.301
eGFR, mL/min/1.73 m2 0.166 0.099
Urinary ACR, mg/g creatininea 0.331 0.001

CAP, controlled attenuation parameter; BMI, body mass index; T2DM, type 2 diabetes mellitus; ACE, angiotensin-converting enzyme; ARB, angiotensin II receptor blocker; FPG, fasting plasma glucose; HbA1c, glycosylated hemoglobin; PPPG, postprandial plasma glucose; AST, aspartate transaminase; ALT, alanine transferase; γ-GT, gamma glutamyltransferase; LDL-C, low density lipoprotein cholesterol; HOMA-IR, homeostatic model assessment of insulin resistance; eGFR, estimated glomerular filtration rate; ACR, albumin-to-creatinine ratio.

a Log transformed.

Table 3.
Multiple linear regression analysis for CAP
Variable Model 1
Model 2
Model 3
Model 4
STD β P value STD β P value STD β P value STD β P value
Duration of T2DM, yr -0.160 0.135 - - - - - -
Diabetic retinopathy -0.414 0.680 - - - - - -
ACE inhibitor/ARB use 0.382 0.704 - - - - - -
HbA1c, % -0.177 0.083 -0.157 0.131 - - - -
ALT, IU/La 0.153 0.119 0.195 0.081 - - - -
γ-GT, IU/La 0.096 0.344 0.060 0.574 - - - -
Triglyceride, mg/dLa 0.198 0.062 0.213 0.047 - - - -
BMI, kg/m2 0.391 0.001 0.380 <0.001 0.364 <0.001 - -
Waist circumference, cm 0.287 0.003 0.284 0.006 0.300 0.003 - -
Urinary ACR, mg/g creatininea 0.305 0.001 0.321 0.001 0.299 0.004 0.321 0.001

Model 1: adjusted for age and sex; Model 2: adjusted for Model 1+duration of T2DM, diabetic retinopathy and ACE inhibitor/ARB use; Model 3: adjusted for Model 2+glycated hemoglobin, alanine aminotransferasea, gamma glutamyl transferasea and triglyceridea; Model 4: adjusted for Model 3+body mass index and waist circumference.

CAP, controlled attenuation parameter; STD, standardized coefficient; T2DM, type 2 diabetes mellitus; ACE, angiotensin-converting enzyme; ARB, angiotensin II receptor blocker; HbA1c, glycosylated hemoglobin; ALT, alanine transferase; γ-GT, gamma glutamyltransferase; BMI, body mass index; ACR, albumin-to-creatinine ratio.

a Log transformed.

Table 4.
Logistic regression analysis for significant steatosis
Parameter Odds ratio (95% CI) P value
Univariate logistic regression analysis
 Age, yr 0.97 (0.94-0.99) 0.030
 Male sex 1.05 (0.48-2.32) 0.897
 BMI, kg/m2 1.29 (1.13-1.48) <0.001
 Waist circumference, cm 1.02 (1.02-1.11) 0.008
 Duration of T2DM, yr 0.94 (0.89-0.99) 0.034
 ACE inhibitors or ARB use 0.27 (0.28-1.44) 0.273
 HbA1c, % 1.00 (0.83-1.21) 0.986
 HOMA-IRa 1.32 (0.85-2.05) 0.223
 AST, IU/La 1.22 (0.52-2.86) 0.643
 ALT, IU/La 1.36 (0.74-2.51) 0.328
 γ-GT, IU/La 1.33 (0.76-2.32) 0.317
 Triglyceride, mg/dLa 2.43 (1.04-5.68) 0.040
 LDL-C, mg/dLa 3.79 (1.36-10.55) 0.011
 Urinary ACR, mg/g creatininea 1.54 (1.17-2.03) 0.002
Multivariable logistic regression analysis
 Age, yr 1.02 (0.98-1.06) 0.443
 BMI, kg/m2 1.34 (1.15-1.57) <0.001
 Duration of T2DM, yr 0.97 (0.89-1.05) 0.475
 ACE inhibitors or ARB use 0.38 (0.12-1.14) 0.085
 LDL-C, mg/dLa 4.33 (1.20-15.63) 0.025
 Urinary ACR, mg/g creatininea 1.88 (1.31-2.71) 0.001

CI, confidence interval; BMI, body mass index; T2DM, type 2 diabetes mellitus; ACE, angiotensin-converting enzyme; ARB, angiotensin II receptor blocker; HbA1c, glycosylated hemoglobin; HOMA-IR, homeostatic model assessment of insulin resistance; AST, aspartate transaminase; ALT, alanine transferase; γ-GT, gamma glutamyltransferase; LDL-C, low density lipoprotein cholesterol; ACR, albumin-to-creatinine ratio.

a Log transformed.

REFERENCES

1. Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. Global epidemiology of nonalcoholic fatty liver disease: meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 2016;64:73-84.
crossref pmid
2. Younossi ZM, Golabi P, de Avila L, Paik JM, Srishord M, Fukui N, et al. The global epidemiology of NAFLD and NASH in patients with type 2 diabetes: a systematic review and meta-analysis. J Hepatol 2019;71:793-801.
crossref pmid
3. Kanwal F, Kramer JR, Duan Z, Yu X, White D, El-Serag HB. Trends in the burden of nonalcoholic fatty liver disease in a United States cohort of veterans. Clin Gastroenterol Hepatol 2016;14:301-8.
crossref pmid
4. Lee YH, Cho Y, Lee BW, Park CY, Lee DH, Cha BS, et al. Nonalcoholic fatty liver disease in diabetes. Part I: epidemiology and diagnosis. Diabetes Metab J 2019;43:31-45.
crossref pmid
5. Torres DM, Harrison SA. Nonalcoholic steatohepatitis and noncirrhotic hepatocellular carcinoma: fertile soil. Semin Liver Dis 2012;32:30-8.
crossref pmid
6. Yu J, Shen J, Sun TT, Zhang X, Wong N. Obesity, insulin resistance, NASH and hepatocellular carcinoma. Semin Cancer Biol 2013;23(6 Pt B):483-91.
crossref
7. Yoo JS, Lee YM, Lee EH, Kim JW, Lee SY, Jeong KC, et al. Serum cystatin C reflects the progress of albuminuria. Diabetes Metab J 2011;35:602-9.
crossref pmid pmc
8. Yun KJ, Kim HJ, Kim MK, Kwon HS, Baek KH, Roh YJ, et al. Risk factors for the development and progression of diabetic kidney disease in patients with type 2 diabetes mellitus and advanced diabetic retinopathy. Diabetes Metab J 2016;40:473-81.
crossref pmid pmc
9. Han E, Cho NH, Kim MK, Kim HS. Lower leg fat depots are associated with albuminuria independently of obesity, insulin resistance, and metabolic syndrome (Korea National Health and Nutrition Examination Surveys 2008 to 2011). Diabetes Metab J 2019;43:461-73.
crossref pmid pmc
10. Han E, Lee YH, Kim G, Kim SR, Lee BW, Kang ES, et al. Sarcopenia is associated with albuminuria independently of hypertension and diabetes: KNHANES 2008-2011. Metabolism 2016;65:1531-40.
crossref pmid
11. American Diabetes Association. 2. Classification and diagnosis of diabetes: standards of medical care in diabetes-2020. Diabetes Care 2020;43(Suppl 1):S14-31.
crossref
12. Lee YH, Kim JH, Kim SR, Jin HY, Rhee EJ, Cho YM, et al. Lobeglitazone, a novel thiazolidinedione, improves non-alcoholic fatty liver disease in type 2 diabetes: its efficacy and predictive factors related to responsiveness. J Korean Med Sci 2017;32:60-9.
crossref pmid
13. Han E, Lee YH, Lee BW, Kang ES, Cha BS. Ipragliflozin additively ameliorates non-alcoholic fatty liver disease in patients with type 2 diabetes controlled with metformin and pioglitazone: a 24-week randomized controlled trial. J Clin Med 2020;9:259.
crossref pmc
14. Chon YE, Jung KS, Kim SU, Park JY, Park YN, Kim DY, et al. Controlled attenuation parameter (CAP) for detection of hepatic steatosis in patients with chronic liver diseases: a prospective study of a native Korean population. Liver Int 2014;34:102-9.
crossref pmid
15. Eddowes PJ, Sasso M, Allison M, Tsochatzis E, Anstee QM, Sheridan D, et al. Accuracy of fibroscan controlled attenuation parameter and liver stiffness measurement in assessing steatosis and fibrosis in patients with nonalcoholic fatty liver disease. Gastroenterology 2019;156:1717-30.
crossref
16. Imajo K, Kessoku T, Honda Y, Tomeno W, Ogawa Y, Mawatari H, et al. Magnetic resonance imaging more accurately classifies steatosis and fibrosis in patients with nonalcoholic fatty liver disease than transient elastography. Gastroenterology 2016;150:626-37.
crossref pmid
17. Bellan M, Colletta C, Barbaglia MN, Salmi L, Clerici R, Mallela VR, et al. Severity of nonalcoholic fatty liver disease in type 2 diabetes mellitus: relationship between nongenetic factors and PNPLA3/HSD17B13 polymorphisms. Diabetes Metab J 2019;43:700-10.
crossref
18. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med 1999;130:461-70.
crossref pmid
19. Oh SW. Obesity and metabolic syndrome in Korea. Diabetes Metab J 2011;35:561-6.
crossref pmid pmc
20. Han E, Lee YH. Non-alcoholic fatty liver disease: the emerging burden in cardiometabolic and renal diseases. Diabetes Metab J 2017;41:430-7.
crossref
21. Targher G, Bertolini L, Rodella S, Lippi G, Zoppini G, Chonchol M. Relationship between kidney function and liver histology in subjects with nonalcoholic steatohepatitis. Clin J Am Soc Nephrol 2010;5:2166-71.
crossref
22. Wijarnpreecha K, Thongprayoon C, Boonpheng B, Panjawatanan P, Sharma K, Ungprasert P, et al. Nonalcoholic fatty liver disease and albuminuria: a systematic review and meta-analysis. Eur J Gastroenterol Hepatol 2018;30:986-94.
crossref pmid
23. Yeung MW, Wong GL, Choi KC, Luk AO, Kwok R, Shu SS, et al. Advanced liver fibrosis but not steatosis is independently associated with albuminuria in Chinese patients with type 2 diabetes. J Hepatol 2018;68:147-56.
crossref
24. Hwang ST, Cho YK, Yun JW, Park JH, Kim HJ, Park DI, et al. Impact of non-alcoholic fatty liver disease on microalbuminuria in patients with prediabetes and diabetes. Intern Med J 2010;40:437-42.
crossref pmid
25. Jia G, Di F, Wang Q, Shao J, Gao L, Wang L, et al. Non-alcoholic fatty liver disease is a risk factor for the development of diabetic nephropathy in patients with type 2 diabetes mellitus. PLoS One 2015;10:e0142808.
crossref pmid pmc
26. Fraquelli M, Rigamonti C, Casazza G, Conte D, Donato MF, Ronchi G, et al. Reproducibility of transient elastography in the evaluation of liver fibrosis in patients with chronic liver disease. Gut 2007;56:968-73.
crossref pmid pmc
27. McPherson S, Hardy T, Dufour JF, Petta S, Romero-Gomez M, Allison M, et al. Age as a confounding factor for the accurate non-invasive diagnosis of advanced NAFLD fibrosis. Am J Gastroenterol 2017;112:740-51.
crossref pmid
28. Chang PE, Lui HF, Chau YP, Lim KH, Yap WM, Tan CK, et al. Prospective evaluation of transient elastography for the diagnosis of hepatic fibrosis in Asians: comparison with liver biopsy and aspartate transaminase platelet ratio index. Aliment Pharmacol Ther 2008;28:51-61.
crossref pmid
29. McPherson S, Anstee QM, Henderson E, Day CP, Burt AD. Are simple noninvasive scoring systems for fibrosis reliable in patients with NAFLD and normal ALT levels? Eur J Gastroenterol Hepatol 2013;25:652-8.
crossref pmid
30. Pilz S, Rutters F, Nijpels G, Stehouwer CD, Hojlund K, Nolan JJ, et al. Insulin sensitivity and albuminuria: the RISC study. Diabetes Care 2014;37:1597-603.
crossref pmid
31. Kim KS, Lee BW, Kim YJ, Lee DH, Cha BS, Park CY. Nonalcoholic fatty liver disease and diabetes. Part II: treatment. Diabetes Metab J 2019;43:127-43.
crossref pmid pmc
32. Gupta J, Mitra N, Kanetsky PA, Devaney J, Wing MR, Reilly M, et al. Association between albuminuria, kidney function, and inflammatory biomarker profile in CKD in CRIC. Clin J Am Soc Nephrol 2012;7:1938-46.
crossref pmid pmc
33. Haukeland JW, Damas JK, Konopski Z, Loberg EM, Haaland T, Goverud I, et al. Systemic inflammation in nonalcoholic fatty liver disease is characterized by elevated levels of CCL2. J Hepatol 2006;44:1167-74.
crossref pmid
34. Eardley KS, Zehnder D, Quinkler M, Lepenies J, Bates RL, Savage CO, et al. The relationship between albuminuria, MCP-1/CCL2, and interstitial macrophages in chronic kidney disease. Kidney Int 2006;69:1189-97.
crossref pmid
35. Matthew Morris E, Fletcher JA, Thyfault JP, Rector RS. The role of angiotensin II in nonalcoholic steatohepatitis. Mol Cell Endocrinol 2013;378:29-40.
crossref pmid
36. Musso G, Cassader M, Cohney S, Pinach S, Saba F, Gambino R. Emerging liver-kidney interactions in nonalcoholic fatty liver disease. Trends Mol Med 2015;21:645-62.
crossref
37. Sun DQ, Zheng KI, Xu G, Ma HL, Zhang HY, Pan XY, et al. PNPLA3 rs738409 is associated with renal glomerular and tubular injury in NAFLD patients with persistently normal ALT levels. Liver Int 2020;40:107-19.
crossref pmid
38. Dong XC. PNPLA3: a potential therapeutic target for personalized treatment of chronic liver disease. Front Med (Lausanne) 2019;6:304.
crossref pmid pmc
39. Hu X, Huang Y, Bao Z, Wang Y, Shi D, Liu F, et al. Prevalence and factors associated with nonalcoholic fatty liver disease in Shanghai work-units. BMC Gastroenterol 2012;12:123.
crossref
40. Wong VW, Chu WC, Wong GL, Chan RS, Chim AM, Ong A, et al. Prevalence of non-alcoholic fatty liver disease and advanced fibrosis in Hong Kong Chinese: a population study using proton-magnetic resonance spectroscopy and transient elastography. Gut 2012;61:409-15.
crossref pmid
41. Mor A, Svensson E, Rungby J, Ulrichsen SP, Berencsi K, Nielsen JS, et al. Modifiable clinical and lifestyle factors are associated with elevated alanine aminotransferase levels in newly diagnosed type 2 diabetes patients: results from the nationwide DD2 study. Diabetes Metab Res Rev 2014;30:707-15.
crossref pmid
42. Doycheva I, Cui J, Nguyen P, Costa EA, Hooker J, Hofflich H, et al. Non-invasive screening of diabetics in primary care for NAFLD and advanced fibrosis by MRI and MRE. Aliment Pharmacol Ther 2016;43:83-95.
crossref pmid
43. Doycheva I, Watt KD, Alkhouri N. Nonalcoholic fatty liver disease in adolescents and young adults: the next frontier in the epidemic. Hepatology 2017;65:2100-9.
crossref


ABOUT
BROWSE ARTICLES
FOR CONTRIBUTORS
Editorial Office
101-2104, Lotte Castle President, 109 Mapo-daero, Mapo-gu, Seoul 04146, Korea​
Tel: +82-2-714-9064    Fax: +82-2-714-9084    E-mail: diabetes@kams.or.kr                

Copyright © 2021 by Korean Diabetes Association. All rights reserved.

Developed in M2PI

Close layer