Is the metabolic dysfunction-associated steatotic liver disease a predictor of chronic kidney disease?

Abstract

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) is increasingly recognized as a multisystem disorder strongly associated with metabolic dysfunction. Emerging evidence suggests a close association between MASLD and chronic kidney disease (CKD). However, the magnitude and determinants of renal involvement remain inconsistent across studies, particularly in relation to diabetes mellitus (DM) and hepatic fibrosis.

AIM

To assess the association between CKD and MASLD and identify the predictors of renal impairment in patients with MASLD.

METHODS

A case-control study was conducted in the Internal Medicine Department, Faculty of Medicine, Assiut University, Egypt, between March 2024 and March 2025. It included 150 participants recruited from outpatient clinics. Participants were divided into three groups: Those with MASLD and type 2 DM (n = 50), those with MASLD without DM (n = 50), and healthy controls (n = 50). All participants underwent clinical and laboratory evaluation. Additionally, they were assessed for insulin resistance using Homeostasis Model Assessment of Insulin Resistance, abdominal ultrasound, and FibroScan. CKD was defined based on the estimated glomerular filtration rate and albuminuria. Multivariate logistic regression was used to assess the factors associated with CKD in patients with MASLD.

RESULTS

The three groups were similar in terms of mean age (P = 0.102) and gender (P = 0.553) distribution of the participants. However, the incidence of hypertension and ischemic heart disease was significantly higher in patients with MASLD than in those without. Patients with MASLD exhibited significantly higher serum creatinine, urea, and albuminuria levels, along with lower estimated glomerular filtration rate (P < 0.001). Advanced hepatic fibrosis was more prevalent in MASLD with DM, with F3-F4 fibrosis observed in 50% of patients compared to 14% in those with MASLD without DM. The severity of fibrosis and steatosis increased progressively with advancing CKD stage (P < 0.001). Factors associated with CKD included hepatic fibrosis score [odds ratio (OR) = 5.61], steatosis score (OR = 4.17), Homeostasis Model Assessment of Insulin Resistance (OR = 4.15), DM (OR = 3.10), and obesity (OR = 2.37).

CONCLUSION

MASLD is associated with CKD, particularly in patients with DM and advanced hepatic fibrosis. Incorporating non-invasive liver fibrosis assessment may aid in the early identification of patients with MASLD who are at a high risk of renal disease.

Key Words: Chronic kidney disease; Diabetes mellitus; Hepatic fibrosis; Kidney injury; Metabolic dysfunction-associated steatotic liver disease; Obesity

Core Tip: Metabolic dysfunction-associated steatotic liver disease (MASLD) is now recognized as a multisystem disease. It is closely linked to obesity, insulin resistance, type-2 diabetes mellitus (DM), dyslipidemia, and hypertension. The present case-control study revealed that MASLD is strongly associated with chronic kidney disease, particularly in patients with DM and advanced hepatic fibrosis. The factors associated with chronic kidney disease included hepatic fibrosis score, steatosis score, Homeostasis Model Assessment of Insulin Resistance, DM, and obesity. Incorporating liver fibrosis assessment using FibroScan may aid in the early identification of patients with MASLD who are at high risk for renal disease.

INTRODUCTION

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common chronic liver disorder worldwide. It is now recognized as a multisystem disease rather than a condition confined to the liver alone. It is closely linked to metabolic dysfunction, including obesity, insulin resistance, type-2 diabetes mellitus (DM), dyslipidemia, and hypertension[1-4]. Chronic kidney disease (CKD) is a growing global health burden characterized by a progressive decline in renal function and an increased risk of end-stage renal disease and cardiovascular events. Increasing evidence suggests a strong association between MASLD and CKD, independent of traditional risk factors[5]. However, the magnitude of this association varies substantially among studies, and many findings remain inconsistent due to differences in population characteristics, diagnostic criteria, and assessment methods. The updated MASLD definition, which incorporates metabolic dysfunction, may further strengthen the observed relationship with renal disease; however, local data remain limited[6]. The proposed mechanisms underlying this association include systemic inflammation, insulin resistance, oxidative stress, endothelial dysfunction, activation of the renin–angiotensin–aldosterone system, and atherogenic dyslipidemia. These pathophysiological pathways highlight the potential role of MASLD as an independent risk factor for renal impairment[7]. The present study aimed to assess the association of CKD with MASLD and to identify the factors associated with it in patients with MASLD, either with or without DM.

MATERIALS AND METHODS

Study design and settings

A case-control study was conducted on patients diagnosed with MASLD with or without type-2 DM. The study was conducted in the Outpatient Clinics of the Internal Medicine Department, Faculty of Medicine, from March 2024 to March 2025.

Patient selection criteria

Patients with MASLD who met at least one of the following metabolic criteria and had hepatic steatosis (identified by imaging, blood biomarkers, or liver histology) were included in the study: Body mass index (BMI) ≥ 25 kg/m² type-2 DM, metabolic dysregulation without obesity or DM, patients with stage II, III, and IV CKD, and type-2 DM based on the American Diabetes Association criteria[8]. The inclusion of cases with CKD was aimed at case enrichment rather than estimating epidemiological frequency. Patients were excluded if they had acute kidney injury, non-alcoholic fatty liver disease, alcoholic fatty liver disease, liver cirrhosis, type-1 DM, malignancy, chronic viral hepatitis (hepatitis B virus or hepatitis C virus infection), autoimmune diseases, or uncontrolled DM or blood pressure.

Sample size calculation and participants’ groups

The sample size was calculated using OpenEpi version 3, based on the association of CKD with MASLD[9], a 95% confidence interval, and an 80% power; a total of 150 cases were eligible for the study. The participants were divided into three groups based on MASLD and type-2 DM: The first group included 50 patients with both MASLD and type-2 DM; the second group included 50 patients with MASLD but without type-2 DM; and the third group consisted of 50 healthy participants without MASLD or DM, serving as the control group. Controls were recruited from the relatives of patients and healthy individuals who presented for regular medical checkups. They underwent the same basic and specific assessments as the patients in the first and second groups (Figure 1).

Figure 1 Flowchart assessment of patients with metabolic dysfunction-associated steatotic disease. The basic assessment helped differentiate patients into those with and without type-2 diabetes mellitus. Additionally, specific assessments exhibited a significant association between metabolic dysfunction-associated steatotic disease, diabetes mellitus, and increased chronic kidney disease stage. MASLD: Metabolic dysfunction-associated steatotic disease; DM: Diabetes mellitus; CKD: Chronic kidney disease; HOMA-IR: Homeostasis Model Assessment of Insulin Resistance.

Assessment workups

All participants underwent a comprehensive clinical evaluation, including a full medical history and thorough physical examination. The physical examination included general, chest, cardiac, neurological, and lower limb assessments. A detailed medical profile was obtained from each individual, including the type and duration of DM, current antidiabetic therapy (insulin and/or oral agents), and the presence of related microvascular complications, such as nephropathy, neuropathy, and retinopathy, to evaluate the medical conditions of CKD, DM, and MASLD.

The investigations included complete blood count, prothrombin time and concentration, international normalized ratio, liver function, serum creatinine, blood urea nitrogen, and lipid profile. CKD stage was assessed using the Chronic Kidney Disease Epidemiology Collaboration equation-2021 to estimate the glomerular filtration rate (eGFR)[10]. Additionally, serial serum creatinine and urinary albumin-to-creatinine ratio values were collected from patients with a previous diagnosis. However, they were repeated within 3 months for newly diagnosed patients to confirm CKD. The latter was defined as the presence of a reduced eGFR < 60 mL/minute/1.73 m², with or without a urinary albumin-to-creatinine ratio > 30 mg/g for at least 3 months. CKD stages (Table 1) were defined based on eGFR reduction and proteinuria[10].

Table 1 Classification of chronic kidney disease into five stages based on the glomerular filtration rate reduction and degree of proteinuria[10].

Stage	Description of the stage	eGFR (mL/minute/1.73 m2)
1	Kidney damage with normal or increased GFR	≥ 90
2	Kidney damage with mildly decreased GFR	60-90
3	Moderately decreased GFR	30-59
4	Severely decreased GFR	15-29
5	End-stage renal disease	< 15 or dialysis

eGFR: Estimated glomerular filtration rate.

Homeostasis Model Assessment of Insulin Resistance

Venous blood samples were collected after an overnight fast of at least 8 hours. Fasting plasma glucose and fasting serum insulin levels were measured using standardized laboratory assays. The Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) index was calculated using the following equation: Fasting insulin (μIU/mL) × fasting plasma glucose (mg/dL)/405. Based on previously validated thresholds, insulin resistance was defined as a HOMA-IR value ≥ 2.5[11].

Ultrasound assessment and diagnosis of MASLD

Ultrasonographic evaluation was performed after fasting for 8 hours. This scan aimed to identify the presence and assess the severity of MASLD, using a real-time electronic 3.75 MHz convex-type scanner attached to a high-resolution ultrasound machine (Aplio; Toshiba Medical Systems Corporation, Tochigi, Japan). Two physicians performed all the scans to minimize interobserver variability. In case of disagreement, a senior physician evaluated the patient. The physicians were blinded to the patient’s prior medical history or laboratory data. The steatosis grade (mild, moderate, or severe) was also assessed[12].

FibroScan assessment

Liver stiffness was measured using a specialized ultrasound device (FibroScan^® 502 Touch, Echosens, Paris, France). An experienced physician performed all the procedures. Liver stiffness measurements were taken in the right lobe of the liver through the intercostal spaces while the patient was in the supine position, with the right arm maximally abducted, after fasting for 3 hours. Measurements were recorded in kilopascals (kPa) following standard procedures. The cutoff value for limited fibrosis (≥ F2) is 7.1 kPa, and for cirrhosis (F4), it is 12.5 kPa[13].

Study outcomes

The primary outcome was the difference in steatosis and FibroScan parameters in patients and controls. The secondary outcome was eGFR in patients and controls.

Ethical considerations

The proposal of this study was approved by the Ethics Committee of the Faculty of Medicine, Assiut University, Egypt, on November 9, 2023. The institutional review board number is No. 04-2023-200487.

Statistical analysis

Recorded data were analyzed using the Statistical Package for the Social Sciences, version 20.0 (SPSS Inc., Chicago, IL, United States). Quantitative data were expressed as mean ± SD and compared with the Student t-test. Qualitative data were expressed as frequencies and percentages and compared using the χ² test. The correlation between the fibrosis score and other variables was determined using Pearson’s correlation. Multivariate regression analysis was employed to identify the risk factors associated with MASLD-related CKD in patients with MASLD. The accuracy of the association between CKD and MASLD was assessed using receiver operating characteristic curves. The level of confidence was set at 95%; therefore, a P-value of < 0.05 was considered statistically significant.

RESULTS

Baseline characteristics of patients in the study groups

The present study included 50 patients in each group. Females accounted for only 31 of the 150 participants. In both groups of patients with and without DM, the mean BMI was significantly higher than that of the control group (P < 0.001). Other characteristics showed insignificant differences between the patient groups and controls (Table 2).

Table 2 Demographic and baseline clinical characteristics of patients and control groups, mean ± SD/n (%).

Variables	MASLD with DM (n = 50)	MASLD without DM (n = 50)	Control group (n = 50)	P value
Age (years)	46.53 ± 3.89	42.65 ± 13.90	43.98 ± 12.01	0.104
Sex				0.550
Male	34 (68)	38 (74)	37 (74)
Female	16 (32)	12 (24)	13 (26)
BMI (kg/m2)	28.18 ± 3.22	27.01 ± 4.30	22.10 ± 3.33	< 0.001
Residence				0.511
Rural	30 (60)	34 (68)	35 (70)
Urban	20 (40)	16 (32)	15 (30)
Smoking status				0.856
Smoker	9 (18)	10 (20)	12 (24)
Ex-smoker	15 (30)	15 (30)	8 (16)
Passive smoker	7 (14)	10 (20)	9 (18)
None	19 (38)	15 (30)	21 (42)
Hypertension	12 (24)	3 (6)	0	< 0.001
IHD	2 (4)	0	0	< 0.001
Pulse (b/minute)	83.09 ± 12.45	80.09 ± 10.34	80 ± 8.76	0.406
SBP (mmHg)	121.76 ± 18.89	118.90 ± 22.18	117 ± 14.67	0.076
DBP (mmHg)	75.40 ± 8.55	71.18 ± 7.19	70.13 ± 6.66	0.562

BMI: Body mass index; DBP: Diastolic blood pressure; DM: Diabetes mellitus; IHD: Ischemic heart disease; MASLD: Metabolic dysfunction-associated steatotic disease; SBP: Systolic blood pressure.

Laboratory characteristics

Patients with MASLD (with or without DM) had significantly higher neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and lipid profile and liver enzyme levels than those in the control group. HOMA-IR was also significantly higher in patients with MASLD and DM (2.89 ± 1.21) than in those with MASLD without DM (1.34 ± 0.87) and in the control group (1.31 ± 0.23) (Table 3). DM was significantly associated with CKD in patients with MASLD when comparing patients with and without DM (28% vs 12%) (Table 4).

Table 3 Laboratory characteristics of patient and control groups, mean ± SD.

Variables	MASLD with DM (n = 50)	MASLD without DM (n = 50)	Control group (n = 50)	P value
Hb (g/dL)	12.03 ± 1.23	12.09 ± 2.87	12.45 ± 2.42	0.542
Platelets (103/uL)	290.10 ± 55.90	312.98 ± 46.99	302.08 ± 67.54	0.206
Leucocytes (103/uL)	6.67 ± 2.01	7.617 ± 2.35	7.01 ± 2.98	0.105
PLR	208.45 ± 45.78	201.21 ± 55.09	134.29 ± 32.90	< 0.001
NLR	2.39 ± 0.98	2.22 ± 0.76	1.20 ± 0.50	< 0.001
FBS (mg/dL)	228.17 ± 40.45	158.22 ± 39.10	148.76 ± 54.56	< 0.001
Glycosylated Hb (%)	7.67 ± 0.27	5.17 ± 0.24	5.20 ± 0.36	< 0.001
INR	1.02 ± 0.01	1.01 ± 0.01	1.01 ± 0.01	0.395
Sodium (mmol/L)	134 ± 0.19	133.5 ± 0.18	133.10 ± 0.04	0.085
Potassium (mmol/L)	3.98 ± 0.57	4.24 ± 0.55	4.01 ±0.30	0.212
Cholesterol (mg/dL)	221.09 ± 22.05	210.45 ± 18.98	154.23 ± 34.09	< 0.001
Triglycerides (mg/dL)	134.30 ± 29.76	114.10 ± 21.10	101.22 ± 19.14	< 0.001
LDL (mg/dL)	128.19 ± 38.19	123.11 ± 33.47	98.10 ± 29.50	0.011
HDL (mg/dL)	47.80 ± 4.59	48.79 ± 4.12	50.56 ± 5.42	0.761
AST (u/L)	89.18 ± 15.43	79.18 ± 10.45	22.70 ± 3.87	< 0.001
ALT (u/L)	78.11 ± 6.66	68.11 ± 7.69	27.56 ± 5.10	< 0.001
Albumin (mg/dL)	3.79 ± 0.59	3.80 ± 0.36	3.98 ± 0.40	0.198
Bilirubin (mg/dL)	1.09 ± 0.12	1.18 ± 0.18	0.99 ± 0.11	0.464
CRP (mg/dL)	2.45 ± 2.45	2.12 ± 0.68	2 ± 1.20	0.173
ESR (mL/hour)	15.28 ± 3.01	11.28 ± 2.10	6.70 ± 2.18	0.472
HOMA-IR	2.89 ± 1.21	1.34 ± 0.87	1.31 ± 0.23	< 0.001

ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; CRP: C-reactive protein; DM: Diabetes mellitus; ESR: Erythrocyte sedimentation rate; FBS: Fasting blood sugar; Hb: Hemoglobin; HDL: High density lipoproteins; HOMA-IR: Homeostasis Model Assessment of Insulin Resistance; INR: International normalized ratio; LDL: Low density lipoproteins; MASLD: Metabolic dysfunction-associated steatotic liver disease; NLR: Neutrophil/Lymphocyte ratio; PLR: Platelets/Lymphocyte ratio.

Table 4 Kidney function tests in patients with and without diabetes mellitus and control groups, mean ± SD.

Variables	MASLD with DM (n = 50)	MASLD without DM (n = 50)	Control group (n = 50)	P value
Urea (mg/dL)	21.19 ± 8.97	16.19 ± 3.11	10.24 ± 3.49	< 0.001
Creatinine (mg/dL)	1.75 ± 0.60	1.35 ± 0.76	0.79 ± 0.13	< 0.001
eGFR (mL/minute)	79.09 ± 7.98	85.98 ± 8.70	101.76 ± 2.34	< 0.001
Albuminuria (mg/day)	78.17 ± 12.87	34.19 ± 5.55	12.90 ± 4.12	< 0.001

eGFR: Estimated glomerular filtration rate; DM: Diabetes mellitus; MASLD: Metabolic dysfunction-associated Steatotic liver disease.

Liver assessment by FibroScan

The MASLD with DM group had more advanced fibrosis, as assessed by Fibrosis 4 Score (FIB-4), aspartate aminotransferase/platelet count ratio index (APRI), and FibroScan, and the degree of steatosis compared to the other groups. Additionally, the fibrosis and steatosis parameters were significantly higher in the MASLD without DM group than in the control group (Table 5, Figure 2).

Figure 2 Pathological degrees in patients and controls. A: Fibrosis-4 index and aspartate aminotransferase to platelet ratio index in patients and controls; B: Degree of fibrosis in patients and controls; C: Degree of steatosis in patients and control groups. MASLD: Metabolic dysfunction-associated steatotic disease; DM: Diabetes mellitus; F: Fibrosis; S: Steatosis; FIB-4: Fibrosis-4 index; APIR: Aspartate aminotransferase to platelet ratio index.

Table 5 Fibrosis parameters and FibroScan assessment in patients and control groups, mean ± SD/n (%).

Variables	MASLD with DM (n = 50)	MASLD without DM (n = 50)	Control group (n = 50)	P value
FIB-4	3.49 ± 1.09	2.80 ± 0.87	1.29 ± 0.40	< 0.001
APRI	1.55 ± 0.29	1.41 ± 0.34	1.04 ± 0.39	< 0.001
FibroScan score	13.01 ± 3.49	10.69 ± 5.44	4.76 ± 0.27	< 0.001
Degree of fibrosis				0.001
F0	5 (10)	13 (26)	45 (90)
F1	9 (18)	20 (40)	5 (10)
F2	11 (22)	10 (20)	0
F3	12 (24)	5 (10)	0
F4	13 (26)	2 (4)	0
Steatosis score	282.11 ± 45.67	208.33 ± 55.90	178.09 ± 23.10	< 0.001
Degree of steatosis				0.010
S0	3 (6)	8 (16)	39 (78)
S1	11 (22)	13 (26)	11 (22)
S2	15 (30)	13 (26)	0
S3	21 (22)	16 (32)	0

APRI: Aspartate aminotransferase to platelet ratio index; FIB-4: Fibrosis-4 index; MASLD: Metabolic dysfunction-associated steatotic liver disease; DM: Diabetes mellitus; F: Fibrosis; S: Steatosis.

Degree of fibrosis and steatosis based on the stages of CKD

It was observed that the degree of fibrosis (FIB-4, APRI, FibroScan) and the degree of steatosis increased with the increasing stages of CKD, where stage-III had more advanced fibrosis and steatosis (Table 6).

Table 6 Degree of fibrosis and steatosis based on the stage of chronic kidney disease, mean ± SD/n (%).

Variables	CKD stage-I (n = 9)	CKD stage-II (n = 6)	CKD stage-III (n = 5)	P value
FIB-4	2.55 ± 0.65	3.01 ± 0.68	3.60 ± 0.78	< 0.001
APRI	1.22 ± 0.39	1.51 ± 0.80	1.85 ± 0.40	< 0.001
FibroScan score	8.01 ± 3.90	11.33 ± 4.19	16.76 ± 3.33	< 0.001
Degree of fibrosis				0.001
F0	2 (22.2)	1 (16.7)	0
F1	4 (44.4)	2 (33.3)	0
F2	3 (33.4)	1 (16.7)	1 (20)
F3	0	1 (16.7)	2 (40)
F4	0	1 (16.7)	2 (40)
Steatosis score	218.09 ± 44.40	235.90 ± 78.70	292.21 ± 66.89	< 0.001
Degree of steatosis				0.013
S0	3 (33.4)	1 (16.7)	0
S1	2 (22.2)	2 (33.3)	0
S2	2 (22.2)	2 (33.3)	2 (40)
S3	2 (22.2)	1 (16.7)	3 (60)

APRI: Aspartate aminotransferase to platelet ratio index; CKD: Chronic kidney disease; FIB-4: Fibrosis-4 index; F: Fibrosis; S: Steatosis.

Predictors of CKD in MASLD

Based on the present study, the predictors for CKD included obesity, DM, PLR, NLR, HOMA-IR, FIB-4, APRI, fibrosis score, and steatosis score. The highest odds ratios were observed for the fibrosis score, steatosis score, and HOMA-IR (Table 7).

Table 7 Multivariate regression analysis of the risk factors of chronic kidney disease in patients with metabolic dysfunction-associated steatotic liver disease.

Variables	Odds ratio	95%CI	P value
Old age (years)	1.19	0.45-2.78	0.402
Sex	1.03	0.90-3.14	0.316
Obesity	2.37	1.67-7.89	0.012
Diabetes mellitus	3.10	2.39-8.19	< 0.001
PLR (> 150)	2.90	1.18-5.17	0.010
NLR (> 3)	2.17	1.22-6.01	< 0.001
HOMA-IR	4.15	2.90-11.19	< 0.001
FIB-4 (> 3.25)	3.56	2.78-9.18	< 0.001
Fibrosis score > F2	5.61	3.45-13.90	< 0.001
Steatosis score > S3	4.17	2.23-10.56	< 0.001

CI: Confidence interval; FIB-4: Fibrosis-4 index; HOMA-IR: Homeostasis Model Assessment of Insulin Resistance; NLR: Neutrophil/Lymphocyte ratio; PLR: Platelets/Lymphocyte ratio; F: Fibrosis; S: Steatosis.

DISCUSSION

Recently, scientists have become interested in studying the potential relationship between MASLD and CKD. The early identification of kidney disease and the selection of medications that target both liver and kidney disease, with potentially advantageous preventive and therapeutic implications, would be made possible by establishing an association between liver and kidney injury[14]. Therefore, the present study was conducted to assess the association between renal affection and MASLD and to explore the influencing clinical, laboratory, and metabolic factors in a tertiary care setting. This study revealed significant associations between the parameters of MASLD assessment scores and CKD, especially in patients with diabetes.

Baseline demographic and clinical characteristics demonstrated that patients with MASLD frequently exhibited features of metabolic dysfunction, including an increased BMI and a higher prevalence of DM, hypertension, and dyslipidemia. These findings are consistent with the diagnostic framework of MASLD and reflect the strong metabolic background of this disease. The presence of multiple metabolic risk factors likely contributes to systemic inflammation and endothelial dysfunction, predisposing patients to renal impairment[15].

In a recent Egyptian study, Semeya et al[16] found that patients with MASLD had a higher BMI and waist circumference and were more likely to exhibit features consistent with metabolic dysfunction than the control group, underscoring the role of obesity and visceral adiposity in the pathogenesis of MASLD compared to the control group[16]. The current results are consistent with this finding and with those of many previous studies[17-19].

Laboratory analysis in the present study demonstrated that patients with MASLD exhibited adverse metabolic and inflammatory profiles, characterized by dyslipidemia, elevated fasting blood glucose levels, increased insulin resistance, and more pronounced liver enzyme abnormalities. These metabolic disturbances are implicated in renal injury through mechanisms such as oxidative stress, glomerular hyperfiltration, and activation of profibrotic pathways, supporting a shared pathophysiological link between MASLD and renal damage[17-19]. In agreement with the present study, Seo et al[20] stated that patients with MASLD had a poorer metabolic profile with higher BMI, waist circumference, systolic blood pressure, high-sensitivity C-reactive protein levels, total cholesterol levels, triglyceride levels, and low-density lipoprotein cholesterol levels, and lower high-density lipoprotein cholesterol levels, with higher aspartate aminotransferase and alanine aminotransferase levels, either with DM or without DM[20].

In the present study, HOMA-IR values were significantly higher in patients with MASLD, regardless of the presence of DM. Consistent with these findings, a meta-analysis reported a substantial increase in HOMA-IR in individuals with MASLD compared to healthy controls, with a weighted mean difference of 1.28. These results reinforce the concept that insulin resistance is a central pathogenic factor in the development of MASLD. Moreover, HOMA-IR has been demonstrated to be a reliable surrogate marker of insulin resistance and shows a strong association with MASLD risk across diverse populations[21]. Furthermore, Zeng et al[22] found that HOMA-IR was significantly higher in MASLD patients without DM compared to the control group[21]. Additionally, this discovery aligns with the findings of previous studies, including those on non-diabetic patients with biopsy-confirmed MASLD. These studies demonstrated that HOMA-IR independently predicts the presence of MASLD[23-25].

Assessment of renal parameters in the present study revealed a notable association between CKD and albuminuria among patients with MASLD. Both reduced eGFR and elevated albumin-to-creatinine ratio indicated early and established renal involvement, even in the absence of overt renal symptoms. The association with CKD was significantly higher in patients with MASLD and DM compared with those without diabetes (28% vs 12%). A previous study has demonstrated that the association with CKD is significantly higher among individuals with MASLD compared with control subjects[26]. Diabetic individuals without MASLD were found to have a significantly higher incidence of CKD compared with non-diabetic subjects with MASLD. Additionally, the incidence of CKD was significantly greater in patients with diabetes and MASLD than in those without diabetes and MASLD. These findings indicate that DM is an independent risk factor for the development of CKD and further amplifies renal risk in patients with MASLD[26].

Hwang et al[27] reported that MASLD is associated with a higher prevalence of microalbuminuria in individuals with prediabetes and newly diagnosed DM. This association remained significant after adjusting for potential confounders, including age, gender, ethnicity, education, smoking status, and other components of the metabolic syndrome, as defined by the Adult Treatment Panel III criteria. These findings suggest that MASLD may contribute to an increased risk of CKD in patients with microalbuminuria[27]. Recently, it has been reported that MASLD is a risk factor for the development of incident CKD, and the severity of MASLD can further increase the risk of CKD, regardless of the coexisting metabolic diseases, such as obesity, hypertension, type-2 DM, or metabolic syndrome, with a frequency of CKD in MASLD patients of 29.6%[28].

A previous study reported that among 1521 patients with type-2 DM, the prevalence of liver steatosis was 75.1%, while liver fibrosis was present in 28.0% of patients. These findings highlight the high prevalence of MASLD, including steatosis and fibrosis, in type-2 DM patients, which increases the risk of progression to advanced fibrosis. In patients with diabetes and additional risk factors, such as increased waist circumference and BMI, early screening and timely intervention are warranted to prevent liver-related complications[29]. In the present study, obesity, DM, inflammatory markers (PLR and NLR), insulin resistance (HOMA-IR), and hepatic fibrosis and steatosis indices (FIB-4, APRI, and fibrosis and steatosis scores) were significant predictors of CKD. The strongest associations were observed with the fibrosis score, steatosis score, and HOMA-IR. In line with the present study, Badawi et al[30] revealed that patients diagnosed with CKD exhibit significantly elevated levels of liver fibrosis and steatosis compared to those without CKD[30].

A cohort study found that MASLD increases the risk of CKD, with type-2 DM being the main driver. The MASLD with type-2 DM group had a higher risk of CKD and a higher urinary albumin-to-creatinine ratio than the prediabetes and regular glucose groups. Achieving metabolic goals in patients with MASLD significantly reduces the risk of CKD[31]. Additionally, a previous Egyptian study found that the prevalence of CKD was 38.1% in the MASLD group. This study revealed that the presence of DM and high BMI were significant risk factors for CKD in non-alcoholic fatty liver disease subjects[32].

CKD has similar risk factors to MASLD, such as increasing age, obesity, hypertension, DM, and metabolic syndrome. The relationship between MASLD and CKD is complex and has not been definitively established. Several cohort studies have reported the influence of MASLD on the risk of all-cause mortality, incidence of non-fatal cardiovascular events, and progression of kidney disease within the CKD population[30-33]. The present study had several strengths, including a well-defined case-control design with clearly specified inclusion and exclusion criteria, an adequately powered sample size, and a comprehensive assessment of clinical, laboratory, radiological, and noninvasive fibrosis parameters (FIB-4, APRI, and FibroScan), enabling accurate evaluation of liver disease without invasive biopsy.

However, the observational, single-center design limits causal inference and generalizability. Noninvasive fibrosis assessment may exhibit measurement variability compared to biopsy, and the relatively small number of patients with advanced CKD stages may compromise the precision of subgroup analyses. Additionally, residual confounders such as diet, physical activity, and relevant systemic medications (nephroprotective drugs, such as renin-angiotensin-aldosterone system blockers, sodium-glucose co-transporter-2 inhibitors, or statins) effects could not be fully accounted for, potentially influencing the observed associations. Furthermore, the unavailability of the baseline eGFR may confound the identification of the changes in renal function in those patients.

CONCLUSION

This study highlights the strong association between MASLD - particularly when accompanied by DM and advanced hepatic fibrosis - and the presence and severity of CKD. Patients with MASLD and DM demonstrated a higher burden of metabolic derangements, inflammatory markers, and liver fibrosis, which were significantly linked to CKD. Routine screening for renal dysfunction should be considered in patients with MASLD, especially those with DM or evidence of advanced liver fibrosis. Incorporating non-invasive fibrosis scores and FibroScan into standard care may help stratify CKD risk and guide closer follow-up.