Revised: June 2, 2026
Accepted: June 29, 2026
Published online: July 27, 2026
Processing time: 83 Days and 22.6 Hours
Metabolic dysfunction-associated steatotic liver disease (MASLD) increases the risk of developing chronic kidney disease (CKD) substantially. Although various noninvasive scoring systems have been validated for assessing liver fibrosis, their longitudinal performance in predicting time-dependent renal outcomes remains poorly characterized.
To evaluate and compare the prognostic performance of baseline noninvasive scores in predicting 5-year incident CKD in an MASLD cohort.
This retrospective longitudinal study included 922 patients with MASLD. Baseline estimated glomerular filtration rate (eGFR) was recorded, with annual assessments performed over a 5-year follow-up period to identify CKD onset (defined as an eGFR < 60 mL/minute/1.73 m2). The prognostic accuracy of liver stiffness measurement (LSM), Fibrosis-4 (FIB-4) Index, Nonalcoholic fatty liver disease fibrosis score (NFS), and albumin-bilirubin (ALBI) score was evaluated via time-dependent area under the receiver operating characteristic (AUROC) curve analysis.
Over the 5-year period, 7.16% (n = 66) of the cohort developed CKD. The baseline NFS consistently outperformed other modalities in predicting cumulative CKD incidence. The NFS demonstrated robust discriminatory performance (AUROC, 0.73-0.79). In head-to-head comparisons, NFS was significantly superior to both LSM (AUROC, 0.64-0.67) and ALBI score (AUROC, 0.54-0.58) across nearly all time points. Although NFS was higher than the FIB-4 index (AUROC, 0.70-0.77), the difference was not statistically significant. Using a standard threshold of -1.455, NFS showed high sensitivity (approximately 89.1% after 3 years). Interestingly, the predictive capability of the NFS peaked at year 3 and then showed a slight progressive decline by years 4 and 5. In the adjusted models, both NFS and FIB-4 index remained independent predictors of CKD onset, whereas LSM lost its predictive significance by the end of the follow-up period.
Compared with LSM, FIB-4 index, and ALBI score, NFS is a superior and reliable noninvasive tool for predicting 5-year CKD onset in patients with MASLD. Considering its high sensitivity and reliance on routine clinical parameters, baseline NFS assessment can effectively identify high-risk patients, facilitating early nephrological intervention and individualized metabolic management.
Core Tip: Nonalcoholic fatty liver disease fibrosis score (NFS) is an independent and reliable predictor of 5-year incident chronic kidney disease in patients with metabolic dysfunction-associated steatotic liver disease. NFS demonstrated superior predictive accuracy compared with liver stiffness measurement and albumin-bilirubin score in this longitudinal study, with performance peaking at 3 years. Baseline NFS assessment should be incorporated into clinical practice to enable early identification of high-risk patients for nephrological intervention and individualized metabolic management.
- Citation: Navadurong H, Teerawongsakul P, Trakarnvanich T, Ruamtawee W, Treerasoradaj N, Sethasine S. Time-dependent predictive accuracy of noninvasive liver scores for chronic kidney disease onset in metabolic dysfunction-associated steatotic liver disease. World J Hepatol 2026; 18(7): 122926
- URL: https://www.wjgnet.com/1948-5182/full/v18/i7/122926.htm
- DOI: https://dx.doi.org/10.4254/wjh.122926
Metabolic dysfunction-associated steatotic liver disease (MASLD) has emerged as a major global health burden. Currently, it is the most prevalent chronic liver disease worldwide[1-3]. MASLD can progress to severe hepatic complications, including advanced fibrosis, cirrhosis, and hepatocellular carcinoma[4], and is associated with increased liver-related mortality[5-7]. In addition to its primary effect on the liver, recent evidence has indicated that MASLD is inde
Noninvasive liver scoring systems, such as liver stiffness measurement (LSM) on transient elastography, biochemical indexes [including the Fibrosis-4 (FIB-4) index and the nonalcoholic fatty liver disease fibrosis score (NFS)], and albumin-bilirubin (ALBI) score, are now extensively used for assessing liver disease severity and prognosis[12,13]. Previous studies have shown that these noninvasive approaches are associated with a high risk of renal impairment in individuals with MASLD[14-16]. However, the comparative prognostic performance of these noninvasive tools in predicting time-dependent renal risk among patients with MASLD remains insufficiently characterized. To address this gap, the present study compares metabolism-informed serum scores and elastography-derived structural measurements longitudinally with annual, time-dependent CKD outcomes over a 5-year follow-up period in a large, well-characterized cohort with MASLD.
The present retrospective longitudinal cohort study was conducted at a tertiary care institution between January 1, 2017, and December 31, 2023. In total, 922 individuals diagnosed with MASLD were enrolled in this study. MASLD diagnosis was established according to the following international consensus criteria[17].
Hepatic steatosis: Confirmed by a controlled attenuation parameter (CAP) exceeding 248 dB/m on transient elas
Cardiometabolic risk factors: Presence of at least one factor, including overweight/obesity, prediabetes or type 2 diabetes mellitus, dyslipidemia, or hypertension.
Alcohol consumption: Absence of hazardous alcohol intake, defined as an average daily consumption of > 20 g/day for women and > 30 g/day for men.
The exclusion criteria included individuals aged < 18 years, patients with pre-existing CKD at baseline, and pregnant women.
Baseline liver steatosis and fibrosis were evaluated via vibration-controlled transient elastography (FibroScan®, Echosens, Paris, France). To ensure diagnostic reliability, each patient required a minimum of 10 valid measurements, with an interquartile range (IQR)-to-median ratio of 30%.
Hepatic steatosis was quantified using CAP (expressed in dB/m) and stratified into three grades based on established meta-analysis thresholds[17,18]: (1) S1 (mild): 248-268 dB/m; (2) S2 (moderate): > 268-280 dB/m; and (3) S3 (severe): > 280 dB/m.
Simultaneously, LSM was used to assess hepatic fibrosis stages, with results expressed in kilopascals (kPa). Patients were categorized according to fibrosis stage, ranging from normal (F0) to minimal (F1) fibrosis and significant to advanced (F2-F4) fibrosis. Based on the clinical practice guidelines, non-advanced fibrosis was defined as a threshold value of < 8.0 kPa, and significant-to-advanced fibrosis was defined as a threshold value of ≥ 8.0 kPa[17,19].
The primary endpoint of this study was the occurrence of incident CKD, which was defined as stage 3 or higher (G3a-G5) according to the Kidney Disease: Improving Global Outcomes 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease[20]. The onset of CKD was established based on an eGFR < 60 mL/minute/1.73 m2 that persisted for at least 3 months. Renal function was monitored via baseline and annual eGFR assessments throughout the 5-year follow-up period. Outpatient laboratory data collected within a 60-day window of each annual enrollment were used to standardize annual eGFR tracking. Patients were right-censored at the time of documented death, loss to follow-up, or at the administrative end of the study. Loss to follow-up was treated as uninformative censoring at the date of the last recorded clinical visit. Notably, to account for the significantly lower baseline renal reserve in patients who developed the outcome, baseline eGFR was included in all adjusted models.
The prognostic performance of several noninvasive scoring systems was evaluated at baseline to determine their utility in predicting CKD onset.
NFS was calculated using the clinical and biochemical parameters established by Angulo et al[21]: NFS = -1.675 + 0.037 × age (years) + 0.094 × body mass index (BMI) (kg/m2) + 1.13 × presence of impaired fasting glucose levels or diabetes (coded as 1 for yes, and 0 for no) + 0.99 × aspartate aminotransferase (AST)-to-alanine aminotransferase (ALT) ratio - 0.013 × platelet count (× 109/L) - 0.66 × serum albumin levels (g/dL).
The patients were stratified into three risk categories based on validated cutoff values: (1) Low risk: < -1.455; (2) Intermediate risk: -1.455 to 0.676; and (3) High risk: > 0.676.
The FIB-4 index was determined using the following equation[17,22]: FIB-4 index = (age × AST level in IU/L) ÷ [platelet count (× 109/L) × √ALT level in IU/L].
The participants were categorized into three groups according to the established thresholds: (1) Low risk: < 1.30; (2) Intermediate risk: 1.30-2.67; and (3) High risk: > 2.67.
The ALBI score, originally designed to evaluate hepatic functional reserve in hepatocellular carcinoma[13], has recently gained a broader application in predicting outcomes in chronic liver disease and cirrhosis[23-26]. In this study, the score was calculated as follows: ALBI score = (log10 bilirubin in μmol/L × 0.66) + [albumin level in g/L × (-0.085)].
The patients were classified into three grades based on the original cutoffs: (1) Grade 1: ≤ -2.60; (2) Grade 2: > -2.60 to ≤ -1.39; and (3) Grade 3: > -1.39.
Comprehensive baseline data were extracted from the Electronic Public Hospital Information System of Vajira Hospital, which included demographic characteristics, cardiometabolic risk factors such as hypertension, diabetes, and dyslipidemia; and biochemical markers such as AST levels, ALT levels, albumin levels, and bilirubin levels, platelet count, and baseline eGFR. Baseline CAP and LSM assessments were recorded at enrollment. Furthermore, the baseline noninvasive scores of each participant, including the FIB-4 index, NFS, and ALBI score, were calculated using the aforementioned equations. To identify incident CKD, the renal function of patients was monitored annually throughout the 5-year follow-up period. A diagnosis of CKD was established and verified if eGFR remained consistently below 60 mL/minute/1.73 m2 for ≥ 3 months, in accordance with the Kidney Disease: Improving Global Outcomes 2024 clinical practice guidelines.
This study adhered strictly to the principles of the Declaration of Helsinki. All personal data were anonymized to ensure participant confidentiality. The study protocol was approved by the Human Research Ethics Committee of Navamindradhiraj University (COA 111/2567).
Continuous variables were presented as medians with IQR, and categorical variables were expressed as frequencies and percentages. The baseline characteristics of patients who developed CKD and those who did not were compared using the Mann-Whitney U test for continuous variables and Pearson’s χ2 test for categorical variables. The cumulative probability of incident CKD was illustrated using Kaplan-Meier failure curves, with between-group differences evaluated using the log-rank test. To determine the prognostic association between noninvasive liver scores and CKD onset, Cox proportional hazards regression models were used. Both univariate and multivariate analyses were conducted, with the latter adjusting for potential confounders, including age, sex, BMI, type 2 diabetes mellitus, hypertension, dyslipidemia, and baseline liver parameters. The proportional hazards assumption for all Cox models was validated using Schoenfeld residuals and was found to be satisfied. This assumption was verified for all models. The predictive performance of each scoring system was assessed using time-dependent area under the receiver operating characteristic (AUROC) curve analysis. The AUROC at each time point was estimated using the nearest-neighbor method via the stroccurve command in Stata. The standard clinical cutoff values for the NFS, FIB-4 index, LSM, and ALBI score were used as thresholds for these evaluations. The liver score with the highest AUROC at year 1 was identified as the reference, and its AUROC was subsequently compared with those of the remaining liver scores at each time point using a bootstrap-based test with 1000 replications. P values were derived from the ratio of the observed difference in AUROC to its bootstrap standard error, approximated using a standard normal distribution. Internal validation was performed using bootstrap optimism correction with 1000 replications to evaluate the stability and potential optimism bias of the time-dependent AUROC values. Because of the minimal frequency of missing data in the cohort, imputation was not required. All statistical analyses were performed using Stata version 18.0 (StataCorp, College Station, TX, United States). A two-tailed P value of < 0.05 indicated statistical significance.
This study included 922 individuals with MASLD. Table 1 shows the baseline clinical and biochemical characteristics of the cohort. The median age of the patients was 54.5 (IQR: 44.0-62.0) years, and 447 (48.5%) participants were male. The median BMI was 26.4 kg/m2. Regarding cardiometabolic risk factors, 82.8% of patients were obese, 57.2% presented with hypertension, 31.3% with type 2 diabetes mellitus, and 61.0% with dyslipidemia. In addition, the prevalence rates of concomitant chronic hepatitis B and C infections were 20.9% and 16.4%, respectively. The baseline median ALT level was 34.0 U/L, with 79.2% of patients having ALT levels below twice the upper limit of normal.
| Characteristics | Total (n = 922) | 5-year CKD (n = 66) | Non-CKD (n = 856) | P value |
| Age (years) | 54.5 (44.0, 62.0) | 65.5 (57.0, 70.0) | 54.0 (44.0, 61.0) | < 0.001 |
| Male | 447 (48.5) | 32 (48.5) | 415 (48.5) | 1.000 |
| BMI (kg/m2) | 26.4 (24.0, 29.4) | 26.6 (24.2, 30.0) | 26.3 (24.0, 29.3) | 0.374 |
| Obesity | 763 (82.8) | 56 (84.9) | 707 (82.6) | 0.640 |
| Hypertension | 518 (57.2) | 48 (72.7) | 470 (56.0) | 0.008 |
| Diabetes | 248 (31.3) | 23 (37.1) | 225 (30.8) | 0.306 |
| Dyslipidemia | 509 (61.0) | 38 (58.5) | 471 (61.3) | 0.658 |
| eGFR (mL/minute/1.73 m2) | 94.0 (82.1, 103.0) | 69.0 (64.0, 77.0) | 95.0 (85.0, 104.0) | < 0.001 |
| AST (U/L) | 29.0 (23.0, 39.0) | 31.0 (24.0, 47.0) | 29.0 (22.5, 39.0) | 0.167 |
| ALT (U/L) | 34.0 (21.0, 53.0) | 30.5 (21.0, 50.0) | 35.0 (21.0, 53.0) | 0.421 |
| CAP (dB/m) | 299.0 (275.0, 328.0) | 308.5 (276.0, 336.0) | 299.0 (275.0, 328.0) | 0.488 |
| LSM (kPa) | 6.8 (5.1, 10.8) | 10.1 (6.3, 14.3) | 6.7 (5.0, 10.4) | < 0.001 |
| NFS | -1.5 (-2.5, -0.5) | -0.4 (-1.2, 0.5) | -1.5 (-2.5, -0.6) | < 0.001 |
| NFS group | < 0.001 | |||
| Low | 460 (49.9) | 13 (19.7) | 447 (52.2) | |
| Intermediate | 369 (40.0) | 39 (59.1) | 8.6) | |
| High | 93 (10.1) | 14 (21.2) | 79 (9.2) | |
| FIB-4 | 1.2 (0.8, 1.8) | 1.7 (1.2, 2.7) | 1.1 (0.8, 1.7) | < 0.001 |
| FIB-4 group | < 0.001 | |||
| Low | 531 (57.6) | 17 (25.8) | 514 (60.0) | |
| Intermediate | 276 (29.9) | 34 (51.5) | 242 (28.3) | |
| High | 115 (12.5) | 15 (22.7) | 100 (11.7) | |
| ALBI | -3.0 (-3.2, -2.7) | -2.9 (-3.1, -2.6) | -3.0 (-3.2, -2.7) | 0.006 |
| ALBI grade | 0.105 | |||
| Grade 1 | 774 (84.0) | 50 (75.8) | 724 (84.6) | |
| Grade 2 | 137 (14.9) | 14 (21.2) | 123 (14.4) | |
| Grade 3 | 11 (1.1) | 2 (3.0) | 9 (1.0) |
The baseline assessment of hepatic steatosis and fibrosis was conducted via transient elastography. The median CAP was 299 (IQR: 275-328) dB/m, and the median LSM was 6.8 (IQR: 5.1-10.8) kPa. Based on LSM, patients were categorized into nonadvanced fibrosis (F0-F1) and significant-to-advanced fibrosis (F2-F4), with a median value of 10.1 (IQR: 6.3-14.3) kPa observed in those who eventually developed CKD. In terms of baseline liver scores, the median values were -1.5 for the NFS, 1.2 for the FIB-4 index, and -3.0 for the ALBI score.
During the 5-year follow-up period, 66 (7.16%) participants presented with incident CKD. Patients stratified into intermediate-to-high-risk categories for both NFS and FIB-4 index had a significantly higher cumulative incidence of CKD than those classified as low-risk (log-rank P < 0.001; Figure 1A and B). Similarly, patients with significant-to-advanced fibrosis (LSM ≥ 8.0 kPa) had a significantly higher cumulative incidence of CKD than those with low-risk fibrosis (log-rank P < 0.001; Figure 1C). However, the cumulative CKD incidence did not differ significantly between patients with ALBI grades 2-3 and those with ALBI grade 1 (log-rank P = 0.105; Figure 1D).
Table 2 and Figure 2 present the prognostic performance of noninvasive scores for predicting CKD onset over 5 years. The baseline NFS emerged as the most effective tool, consistently outperforming the FIB-4 index, LSM, and ALBI. Using a threshold of -1.455, NFS exhibited a robust discriminatory performance with AUROC values of 0.73-0.79 over the 5-year period. Although the FIB-4 index showed acceptable performance (AUROC: 0.70-0.77), it remained inferior to NFS.
| Scores | Cumulative CKD occurrence stage ≥ 3 | ||||
| 1-year | 2-year | 3-year | 4-year | 5-year | |
| NFS | |||||
| AUROC | 0.77 | 0.78 | 0.79 | 0.73 | 0.73 |
| (95%CI) | (0.67-0.85) | (0.71-0.85) | (0.73-0.85) | (0.65-0.81) | (0.65-0.81) |
| Standard cutoff values | ≥ -1.455 | ≥ -1.455 | ≥ -1.455 | ≥ -1.455 | ≥ -1.455 |
| Sensitivity (%) | 84.21 | 88.24 | 89.13 | 79.31 | 80.30 |
| Specificity (%) | 47.21 | 47.79 | 48.47 | 47.85 | 41.97 |
| FIB-4 | |||||
| AUROC | 0.72 | 0.76 | 0.77 | 0.70 | 0.73 |
| (95%CI) | (0.61-0.80) | (0.69-0.82) | (0.70-0.82) | (0.63-0.78) | (0.65-0.79) |
| Standard cutoff values | ≥ 1.30 | ≥ 1.30 | ≥ 1.30 | ≥ 1.30 | ≥ 1.30 |
| Sensitivity (%) | 73.68 | 82.35 | 82.61 | 75.86 | 74.24 |
| Specificity (%) | 53.37 | 54.20 | 54.58 | 53.31 | 41.45 |
| LSM | |||||
| AUROC | 0.67 | 0.66 | 0.67 | 0.64 | 0.65 |
| (95%CI) | (0.53-0.78) | (0.56-0.75) | (0.59-0.76) | (0.56-0.72) | (0.57-0.72) |
| Standard cutoff values | ≥ 8.0 | ≥ 8.0 | ≥ 8.0 | ≥ 8.0 | ≥ 8.0 |
| Sensitivity (%) | 63.16 | 64.71 | 65.22 | 62.07 | 59.09 |
| Specificity (%) | 58.06 | 57.58 | 56.71 | 55.79 | 51.81 |
| ALBI | |||||
| AUROC | 0.57 | 0.56 | 0.54 | 0.57 | 0.58 |
| (95%CI) | (0.45-0.71) | (0.48-0.65) | (0.46-0.64) | (0.48-0.67) | (0.49-0.66) |
| Standard cutoff values | ≥ -2.61 | ≥ -2.61 | ≥ -2.61 | ≥ -2.61 | ≥ -2.61 |
| Sensitivity (%) | 15.79 | 17.65 | 21.74 | 25.86 | 25.76 |
| Specificity (%) | 80.35 | 79.02 | 78.62 | 77.65 | 79.02 |
| Comparison of AUROCa | |||||
| NFS vs FIB-4 | 0.147 | 0.521 | 0.384 | 0.329 | 0.838 |
| NFS vs LSM | 0.059 | 0.012a | 0.008a | 0.034a | 0.038a |
| NFS vs ALBI | 0.018a | <0.001a | <0.001a | 0.003a | 0.005a |
Based on the time-dependent AUROC analysis, the predictive accuracy of both NFS and the FIB-4 index peaked within the first 3 years of follow-up. Subsequently, it declined slightly in years 4 and 5 (Figure 2F). Head-to-head comparisons confirmed that the AUROC of NFS was significantly superior to those of the LSM and ALBI score across nearly all time points (Table 2).
In the univariate Cox proportional hazards analysis, higher baseline NFS, FIB-4 index, LSM, and ALBI scores were significantly associated with 1- to 5-year incident CKD (Supplementary Table 1). After adjusting for clinical covariates including age, sex, BMI, diabetes, and hypertension; NFS remained a strong independent predictor of CKD onset, with adjusted hazard ratios of 1.40-1.50 over 5 years. Although the FIB-4 index was not significantly associated with CKD onset in the first year (P = 0.662), it showed a robust independent prognostic association from years 2 to 5 (all P < 0.05). In contrast, LSM and ALBI scores did not maintain statistical significance in the multivariate models, with P values of 0.231 and 0.759, respectively, at the 5-year mark (Supplementary Table 1).
Internal validation using bootstrap optimism correction revealed the negligible optimism across all liver scores at each time point, and the optimism-corrected AUROC values were virtually identical to the reported values (Supplementary Table 2). These findings confirm the stability of the AUROC estimates and the absence of overfitting within this cohort.
This longitudinal cohort study performed a comprehensive evaluation of noninvasive liver scores in predicting the time-dependent incidence of CKD among patients with MASLD. Our primary finding showed that NFS consistently outperformed the FIB-4 index, LSM, and ALBI scores in predicting 5-year incident CKD. Machine learning models[27] and the Kidney Failure Risk Equation[28] have traditionally focused on baseline renal parameters and albuminuria for predicting kidney failure. However, our study emphasizes the prognostic value of baseline metabolism-informed liver scores in identifying patients with MASLD at high risk for future renal impairment.
The observed 5-year CKD incidence rate of 7.16% in our cohort is consistent with previous longitudinal studies reporting incidence rates of 4.2%-16% over similar follow-up periods[10,29]. Extensive evidence has shown that MASLD is an independent predictor of CKD[30-32], even after adjusting for individual cardiometabolic risk factors. In particular, this association is evident in Asian populations, in which MASLD is associated with long-term renal function decline[33,34]. Our study advances this knowledge by showing that noninvasive scores originally designed to assess hepatic fibrosis can serve as alternatives for renal risk stratification.
The superior performance of NFS and the FIB-4 index over structural and functional markers such as LSM and ALBI score is an important observation of our study. In the multivariate Cox regression models, both NFS and the FIB-4 index remained independent predictors of CKD onset. In contrast, LSM and ALBI score lost statistical significance at the 5-year mark. This disparity likely reflects the different biological pathways represented by these indexes. NFS and the FIB-4 index integrate parameters such as age, BMI, glycemic status, and aminotransferase levels, which collectively reflect systemic insulin resistance, chronic inflammation, and endothelial dysfunction, the “common soil” linking MASLD to renal injury. In contrast, LSM measures structural liver stiffness, and the ALBI score reflects hepatic functional reserve, neither of which completely captures the extrahepatic drivers of renal disease. The NFS integrates metabolic markers associated with renal risk, yet it remains a recognized clinical standard for staging hepatic fibrosis. Our findings underscore the novel application of NFS as a dual-purpose indicator reflecting both the severity of liver fibrosis and the longitudinal risk of CKD, thereby supporting the 'same soil' hypothesis of shared metabolic determinants. Despite its robust discriminative ability, further validation using calibration measures and decision-curve analyses is required to quantify its incremental benefit over traditional renal risk calculators.
Based on these findings, multidimensional, metabolism-informed scores are more suitable than liver-specific markers alone for predicting systemic outcomes such as CKD. Despite the known limitations of NFS in diagnosing advanced fibrosis[35,36], its prognostic accuracy for CKD was robust, with AUROC values peaking at year 3 of follow-up. The high sensitivity of NFS makes it an excellent screening tool in clinical practice. However, its relatively lower specificity across the 5-year duration may be influenced by temporal fluctuations in comorbidities or clinical interventions. To address this limited specificity, further studies should investigate the incorporation of this metabolism-informed score with novel, highly specific renal and inflammatory biomarkers. Integrating advanced biomarker panels can yield a superior predictive model and substantially reduce false-positive rates, thereby improving patient selection for intensive nephrological monitoring.
Furthermore, our investigation underscores the role of obesity as a metabolic driver of CKD. Obesity-related glomerulopathy is characterized by alterations in renal hemodynamics, leading to glomerular hyperfiltration and increased tubular salt reabsorption. The associated adipokine dysregulation and oxidative stress further diminish the glomerular filtration rate and promote albuminuria[37-39]. Our findings are consistent with previous finding in populations with MASLD, which show strong correlations between BMI[40], type 2 diabetes[41], and renal impairment.
This study represents a novel analysis of time-dependent CKD risk stratification. By utilizing baseline NFS, physicians can identify patients with MASLD who are at increased risk of renal decline within a critical 5-year window. This early identification facilitates prompt interventions, such as intensified monitoring of renal function and optimized metabolic management, potentially delaying progression to advanced CKD stages.
The distinct contribution of this study lies in its temporal resolution, demonstrating when specific liver scoring systems achieve maximum prognostic utility for renal outcomes. Unlike previous studies that have predominantly focused on static, cross-sectional associations or single-point baseline correlations between MASLD and CKD, our year-by-year longitudinal framework highlights that metabolism-informed indexes provide superior, time-dependent risk stratification. By tracking these changes across annual intervals over a 5-year period, this study bridges a critical clinical gap, moving beyond simple association toward characterizing the temporal trajectory of renal risk in patients with MASLD.
The present study had several limitations. First, the use of baseline scores alone may not have captured the effect of longitudinal changes in these variables on predictive performance. Second, the retrospective design limited our ability to account for dynamic covariates or competing risks during follow-up. Detailed data on glycemic control variability, incident cardiovascular events, and exposure to nephrotoxic agents or cardiorenal protective therapies were unavailable, introducing potential residual confounding. Furthermore, a key limitation is the absence of albuminuria data, which restricted the primary outcome definition to eGFR < 60 mL/minute/1.73 m2. Consequently, patients with early-stage kidney damage presenting solely with microalbuminuria may have been missed, potentially underestimating the true early renal disease burden. Future prospective studies should refine outcome definitions by incorporating composite renal endpoints to better capture early disease trajectories. Although external validation was not feasible due to the single-center design, internal validation demonstrated negligible optimism, supporting the stability of the AUROC estimates within this cohort. However, generalizability may be limited by the specific characteristics of the study population, including high prevalence of obesity, hypertension, and viral hepatitis. Therefore, multicenter and external validation studies are needed to confirm these findings in more diverse populations. Finally, although specific kidney biomarkers models were not applied in the present study, integrating these models with established clinical scores represents a promising direction for future research.
In this 5-year longitudinal study of patients with MASLD, baseline NFS demonstrated superior prognostic performance in predicting incident CKD compared with LSM and ALBI score. Both NFS and the FIB-4 index were independent predictors of renal impairment. However, structural liver markers such as LSM did not retain significant predictive value in multivariate models. These findings suggest that metabolism-informed scores, which integrate key systemic drivers of the disease, may provide a more effective approach to renal risk stratification than liver-specific markers alone. Incorporating baseline NFS into routine clinical practice may facilitate early identification of high-risk individuals and support timely interventions to reduce renal decline in patients with MASLD.
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