Abstract
Background
Metabolic dysfunction-associated steatotic liver disease (MASLD) is closely tied to systemic metabolic dysregulation, yet reliable biomarkers of steatosis severity remain limited. The creatinine-to-body weight (Cr/BW) ratio, a surrogate for muscle mass and renal function, has been proposed as a predictor of MASLD; however, its correlation with hepatic steatosis severity, quantified by the controlled attenuation parameter (CAP), remains poorly defined.
Methods
In this cross-sectional study of 1,492 participants (946 MASLD; 546 non-MASLD), we evaluated the association between the Cr/BW ratio and CAP-measured steatosis severity (mild, moderate, or severe) using ordinal logistic regression. Nonlinear relationships were explored using restricted cubic splines (RCS), and predictive performance was assessed using the area under the receiver operating characteristic curve (AUROC). To ensure robustness, subgroup analyses were conducted by age and sex.
Results
Severe MASLD (CAP ≥ 295 dB/m) was associated with a significantly lower Cr/BW ratio (median 0.89 vs. 1.03 in mild steatosis; P < 0.001). The Cr/BW ratio independently predicted steatosis severity (adjusted OR per 0.1-unit decrease: 0.03; 95% CI: 0.02–0.05; P < 0.001). RCS analysis revealed a threshold effect, and the risk of severe steatosis rose sharply below a Cr/BW ratio of 1.01 (OR = 0.02; 95% CI: 0.01–0.05). The AUROC for the Cr/BW ratio was 0.72, outperforming traditional metabolic markers. The findings were also consistent across age and sex subgroups.
Conclusion
The Cr/BW ratio is inversely and independently associated with the severity of hepatic steatosis in MASLD. A threshold of 1.01 may serve as a marker to identify high-risk patients, supporting its integration into clinical screening and management strategies.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12876-026-04692-x.
Keywords: Metabolic dysfunction-associated steatotic liver disease, Transient Elastography, Controlled Attenuation Parameter, Creatinine-to-body weight ratio
Introduction
Metabolic dysfunction-associated steatotic liver disease (MASLD) emerged as the globally most common chronic liver disorder, with prevalence projected to exceed 55% by 2040 [1]. The pathological hallmark of MASLD is hepatic steatosis, defined as intrahepatic lipid accumulation exceeding 5% of hepatocyte volume. This condition independently predicts progressive liver complications, including fibrosis [2] and hepatocellular carcinoma (HCC) [3], while also driving systemic metabolic dysfunction. Critically, the severity of hepatic steatosis demonstrates a dose-dependent association with extrahepatic morbidities, such as type 2 diabetes [4], cardiovascular disease [5, 6], chronic kidney disease [7], venous thromboembolism [8], rheumatoid arthritis [9], and Helicobacter pylori infection [10]. This underscores the urgent need for precise, non-invasive biomarkers to stratify disease severity and guide early intervention. This underscores the urgent need for non-invasive, reproducible biomarkers to quantify steatosis severity and stratify high-risk patients. Liver biopsy, though traditionally regarded as the diagnostic gold standard for hepatic steatosis, is limited by its invasiveness, risk of complications (such as bleeding and perforation), and sampling variability. These limitations hinder its applicability in large-scale screening and routine clinical settings. As a non-invasive alternative, the CAP, derived from transient elastography (TE), has gained widespread clinical acceptance. Meta-analyses have demonstrated that CAP achieves an AUROC of 0.80–0.85 for the detection of moderate-to-severe hepatic steatosis, underscoring its diagnostic reliability. Unlike binary diagnostic tools, CAP provides quantitative measurements that allow for dynamic monitoring of liver fat content, enabling stratification of patients by risk, assessment of disease progression, and evaluation of treatment efficacy. In hepatology research, CAP has proven to be a reproducible and robust surrogate marker of metabolic dysfunction [11], offering high diagnostic precision for quantifying hepatic steatosis [12]. Its non-invasive nature makes it particularly suitable for longitudinal studies and routine clinical use, especially in the context of MASLD, where repeated evaluations are essential for guiding therapeutic decisions and monitoring patient outcomes [12]. Serum creatinine (Cr) levels have shown a paradoxical yet consistent positive correlation with MASLD prevalence [13], suggesting a complex interplay between renal function and liver metabolism. Elevated Cr is further associated with accelerated MASLD progression [14], potentially reflecting underlying muscle wasting and impaired glomerular filtration. The Cr/BW ratio, a validated surrogate for muscle mass index, offers a more refined assessment by accounting for body size [15]; low ratios correlate strongly with incident MASLD and systemic insulin resistance (IR) [16]. The liver is a central organ in the regulation of glucose and lipid metabolism. During the synthesis of various lipids, the liver can activate specific cytokines that subsequently interfere with insulin signaling pathways, thereby contributing to the development of hepatic IR. Concurrently, in MASLD, adipose tissue IR is closely associated with a lipid profile enriched in saturated fatty acids, increased macrophage activity, and the progression of hepatic fibrosis [17]. Skeletal muscle, a key site of energy metabolism, is primarily responsible for insulin-mediated glucose uptake. In MASLD, reduced muscle mass and intramuscular fat infiltration lead to a decline in muscle quality and function, which directly contributes to disturbances in glucose metabolism and further exacerbates systemic IR [18]. Despite this evidence, the direct relationship between the Cr/BW ratio and CAP-defined steatosis severity remains unexamined, leaving a critical gap in risk-stratification tools for MASLD.
To address this, we conducted a cross-sectional analysis of 1,492 participants (946 MASLD, 546 controls) to determine whether the Cr/BW ratio independently predicts hepatic steatosis severity. Using TE-derived CAP and multivariate-adjusted ordinal logistic regression, we tested the hypothesis that a lower Cr/BW ratio is inversely and nonlinearly associated with advanced steatosis, even after adjusting for metabolic confounders. Our findings aim to establish the Cr/BW ratio as a clinically actionable, muscle-renal biomarker for early MASLD diagnosis and personalized management strategies.
Methods
Study design and participant population
In this single-center, cross-sectional study, a total of 2,137 adult participants were consecutively recruited between January 2018 and January 2024 from the outpatient health-screening clinic and the Department of Hepatology at Xinjiang Uygur Autonomous Region Hospital of Traditional Chinese Medicine. A multidisciplinary panel established the diagnosis of MASLD in accordance with the 2023 international Delphi consensus criteria [19]. Participants were excluded based on the following criteria: (1) the consumption of > 20 g/30 g of alcohol per day in females and males; (2) presence of viral or autoimmune hepatitis; (3) diagnosis of cirrhosis, HCC, or acute liver failure; (4) severe extrahepatic infections or malignancies; (5) incomplete clinical or imaging data; (6) duplicated patient records; (7) major psychiatric disorder, etc. Following the application of these exclusion criteria, 1,492 individuals were included in the final analysis (Fig. 1), comprising 546 non-MASLD controls and 946 patients diagnosed with MASLD. Hepatic steatosis was assessed using TE (Fibrotouch), with the CAP employed to quantify liver fat content. Based on CAP values, participants were categorized as follows: Non-MASLD: CAP < 240 dB/m, Mild MASLD: 240 ≤ CAP < 265 dB/m, Moderate MASLD: 265 ≤ CAP < 295 dB/m, and Severe MASLD: CAP ≥ 295 dB/m [20].
Fig. 1.
Flowchart of study design and participant enrollment
Data collection
Comprehensive clinical data were collected from all enrolled participants, including demographic variables (age, sex), anthropometric measurements (weight and height), alcohol consumption history, medical history, and other relevant clinical information. Body mass index (BMI) was calculated using the standard formula: weight (kg) divided by the square of height (m2). Biochemical parameters were obtained through fasting blood samples. They included aspartate aminotransferase (AST), alanine aminotransferase (ALT), uric acid (UA), Cr, triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and fasting plasma glucose (FPG).
BMI classification
BMI was categorized based on the World Health Organization (WHO) criteria. Participants with a BMI of less than 18.5 kg/m2 were classified as underweight. Those with a BMI between 18.5 kg/m2 and 24.9 kg/m2 were considered to have normal weight. A BMI ranging from 25.0 kg/m2 to 29.9 kg/m2 was defined as overweight, while a BMI of 30.0 kg/m2 or higher was classified as obese.
Definitions of derived metabolic outcomes
Several derived indices and ratios were calculated to assess metabolic and biochemical status:
-
(i)
AST/ALT ratio = AST(U/L)/ALT(U/L)
-
(ii)
Triglyceride-glucose index (TyG index) [21] = Ln [TG (mg/dL) *FBG (mg/dL)/2]; TyG-BMI index [21] = TyG index*BMI
-
(iii)
Non-High-Density Lipoprotein Cholesterol (Non-HDL-C) ratio [22] = TC(mmol/L) -HDL-C(mmol/L)
-
(iv)
Non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol (NHHR) ratio [23] = Non-HDL-C(mmol/L)/HDL-C(mmol/L)
-
(v)
TC/HDL-C ratio [24] = TC(mmol/L)/HDL-C(mmol/L)
-
(vi)
TG/HDL-C ratio [25] = TG(mmol/L)/HDL-C(mmol/L)
-
(vii)
Cr/BW ratio [26] = Cr (μmol/L)/BW(kg)
-
(viii)
UA/Cr ratio [27] = UA(μmol/L)/Cr(μmol/L)
-
(ix)
UA/HDL-C ratio [28] = UA(μmol/L)/HDL-C(mmol/L)
Statistical analysis
The continuous variables were examined for normality using the Shapiro–Wilk test (p > 0.05 indicating a normal distribution). Normally distributed data are expressed as mean ± standard deviation, whereas non-normally distributed data are presented as median (interquartile range). Categorical data were summarized as absolute counts and proportions (%). Between-group comparisons of continuous variables were conducted with independent-sample t-tests (reporting 95% confidence intervals of the mean difference) when normality was satisfied; otherwise, the Mann–Whitney U test was applied, accompanied by the Hodges–Lehmann estimator of effect size. The categorical associations were evaluated with the χ2 test or, for expected frequencies below five, Fisher’s exact test. The ordinal outcomes were analyzed using the nonparametric Mann–Whitney or Kruskal–Wallis trend test, as appropriate. To model the ordinal degree of hepatic steatosis, candidate predictors were first screened in univariable proportional-odds logistic regression (VGAM v 1.1–9). Variables were retained when the Wald statistic reached P < 0.05, and the Brant test showed no violation of the proportional-odds assumption (P > 0.05). Significant variables were then incorporated into a multivariable ordinal model (MASS v 7.3–60) built by stepwise selection, minimizing the Akaike Information Criterion (AIC). The final coefficients were graphically summarized with a nomogram (rms package). Model discrimination was quantified with receiver-operating-characteristic (ROC) curves (pROC v 1.18–5); cumulative area under the curve (AUC) values were micro-averaged and compared using DeLong’s test. Potential non-linear associations were explored with RCS (rms v 6.7–1) employing three AIC-optimized knots; likelihood-ratio tests formally examined departures from linearity. Finally, interaction by sex, age, hypertension status, and T2DM status was assessed through stratified ordinal regression; interaction terms were evaluated with Wald tests, and subgroup-specific odds ratios (OR) (95% CIs) were displayed in forest plots (ggplot2 v 3.4.2, ggpubr v 0.6.0). All tests were two-sided, and the alpha level was 0.05.
Ethical compliance
The study was conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants or their legal representatives. The protocol was reviewed and approved by the Medical Ethics Committee of Xinjiang Uygur Autonomous Region Hospital of Traditional Chinese Medicine (approval No. 2023XE-GS190).
Results
Baseline characteristics of the study
A total of 1,492 eligible individuals participated in the study. The average age was 54.95 years with a standard deviation of 13.8 years, and the mean BMI was 25.80 kg/m2 with a standard deviation of 4.09. Exactly half of the participants were female (n = 746). Hypertension, type 2 diabetes mellitus (T2DM), and hyperuricemia were present in 47.9%, 48.2%, and 49.2% of participants, respectively. According to WHO criteria, 2.4% were underweight, 40.5% had a normal weight, 42.3% were overweight, and 14.7% were obese. After diagnostic evaluation, 546 participants were classified as non-MASLD controls, whereas 946 fulfilled criteria for MASLD. FibroTouch-derived CAP further graded these MASLD cases into mild (N = 315), moderate (N = 298), and severe (N = 333) hepatic steatosis. Compared with mild and moderate disease, severe MASLD was characterized by younger age, male predominance, and higher prevalences of obesity, hypertension, T2DM, and hyperuricaemia (all P < 0.001). Laboratory profiling showed lower AST/ALT ratios, HDL-C, and diminished Cr/BW ratios, but markedly elevated BMI, ALT, AST, TG, TC, LDL-C, FPG, TyG index, TyG-BMI index, non-HDL-C, NHHR, TC/HDL-C, TG/HDL-C, Cr, UA, UA/Cr, and UA/HDL-C (Table 1). Consistently, CAP values rose progressively across steatosis grades, peaking in severe MASLD.
Table 1.
Baseline characteristics of the study population, stratified by hepatic steatosis severity
| ALL (N = 1492) | Non MASLD group (N = 546) | MASLD group (N = 946) |
P-value overall | |||
|---|---|---|---|---|---|---|
| Mild-MASLD (N = 315) |
Moderate-MASLD (N = 298) | Severe-MASLD (N = 333) | ||||
| Age (Year) | 54.9 5 ± 10.38 | 56.34 ± 9.27 | 54.78 ± 10.97 | 54.77 ± 10.69 | 53.00 ± 10.96 | < 0.001 |
| BMI (kg/m2) | 25.80 ± 4.09 | 23.05 ± 2.96 | 25.77 ± 2.82 | 27.02 ± 3.34 | 29.24 ± 4.18 | < 0.001 |
| ALT (U/L) | 20.00 (14.77,30.00) | 17.00 (13.00,22.00) | 20.62 (14.50,30.00) | 24.40 (17.00,37.51) | 27.00 (17.70,40.95) | < 0.001 |
| AST (U/L) | 22.00 (17.80,27.20) | 21.20 (18.00,26.00) | 21.00 (16.73,27.00) | 22.79 (17.79,29.27) | 24.00 (19.00,30.47) | < 0.001 |
| AST/ALT | 1.06 (0.81,1.38) | 1.32 (1.00,1.64) | 1.05 (0.85,1.27) | 0.95 (.073,1.17) | 0.91 (0.68,1.44) | < 0.001 |
| TC (mmol/L) | 4.65 ± 0.89 | 4.47 ± 0.66 | 4.56 ± 0.90 | 4.63 ± 0.99 | 5.05 ± 0.91 | < 0.001 |
| TG (mmol/L) | 1.49 (1.08.2.15) | 1.16 (0.83,1.63) | 1.67 (1.26,2.43) | 1.71 (1.28,2.48) | 1.81 (1.26,2.52) | < 0.001 |
| LDL-C (mmol/L) | 2.80 ± 0.72 | 2.69 ± 0.69 | 2.82 ± 0.71 | 2.85 ± 0.71 | 2.89 ± 0.77 | < 0.001 |
| HDL-C (mmol/L) | 1.20 (0.98,1.50) | 1.45 (1.19,1.73) | 1.12 (0.90,1.34) | 1.10 (0.91,1.34) | 1.05 (0.85,1.26) | < 0.001 |
| FPG (mmol/L) | 5.67 (5.03,6.81) | 5.26 (4.75,5.84) | 6.04 (5.10,6.91) | 6.11 (5.17,6.96) | 6.36 (5.46,7.83) | < 0.001 |
| TyG index | 8.89 ± 0.68 | 8.52 ± 0.58 | 9.02 ± 0.62 | 9.10 ± 0.61 | 9.21 ± 0.66 | < 0.001 |
| TyG-BMI index | 230.34 ± 45.45 | 196.66 ± 30.83 | 232.63 ± 32.17 | 245.87 ± 34.86 | 269.49 ± 45.03 | < 0.001 |
| Non-HDL-C (mmol/L) | 3.39 (2.75,3.98) | 3.03 (2.47,3.53) | 3.42 (2.79,3.96) | 3.46 (2.79,4.10) | 4.00 (3.41,4.57) | < 0.001 |
| NHHR | 2.84 (2.00,3.81) | 2.08 (1.53,2.82) | 3.08 (2.37,3.86) | 3.19 (2.31,4.16) | 3.80 (2.98,4.67) | < 0.001 |
| TC/HDL-C | 3.84 (3.00,4.81) | 3.08 (2.53,3.82) | 4.08 (3.37,4.86) | 4.19 (3.31,5.16) | 4.80 (3.98,5.67) | < 0.001 |
| TG/HDL-C | 1.27 (0.79,2.12) | 0.79 (0.53,1.28) | 1.55 (1.04,2.40) | 1.58 (1.08,2.39) | 1.66 (1.13,2.82) | < 0.001 |
| Cr(μmol/L) | 69.53 (60.00,79.42) | 66.00 (57.00,78.0) | 70.00(60.00,78.82) | 71.00 (64.00,81.00) | 71.77 (63.00,81.29) | < 0.001 |
| UA (μmol/L) | 335.27 ± 85.81 | 313.60 ± 80.55 | 331.19 ± 82.64 | 345.15 ± 87.63 | 365.80 ± 85.32 | < 0.001 |
| Cr/BW ratio (μmol/L/kg) | 1.01 (0.87,1.18) | 1.13 (0.98,1.33) | 0.99 (0.87,1.15) | 0.97 (0.85,1.10) | 0.89 (0.78,1.03) | < 0.001 |
| UA/Cr | 4.79 ± 1.15 | 4.68 ± 1.21 | 4.70 ± 1.06 | 4.72 ± 1.09 | 5.10 ± 1.15 | < 0.001 |
| UA/HDL-C | 263.76 (201.78,371.34) | 214.28 (160.37,275.11) | 285.49 (223.48,390.09) | 301.03 (236.93,399.22) | 346.15 (261.68,446.75) | < 0.001 |
| CAP | 257.00 (201.00,282.00) | 177.00 (140.00,212.00) | 254.00 (250.00,260.00) | 277.00 (272.00,280.00) | 311.00 (301.00,327.00) | < 0.001 |
| Sex (%) | 0.808 | |||||
| Female | 50.0 | 51.1 | 49.2 | 51.0 | 48.0 | |
| Male | 50.0 | 48.9 | 50.8 | 49.0 | 52.0 | |
| Hypertension (%) | < 0.001 | |||||
| No | 52.1 | 53.8 | 54.0 | 67.8 | 33.3 | |
| Yes | 47.9 | 46.2 | 46.0 | 32.2 | 66.7 | |
| T2DM (%) | < 0.001 | |||||
| No | 51.8 | 78.8 | 40.0 | 36.9 | 32.1 | |
| Yes | 48.2 | 21.2 | 60.0 | 63.1 | 67.9 | |
| Hyperuricemia (%) | < 0.001 | |||||
| No | 50/8 | 87.2 | 31.7 | 30.5 | 27.3 | |
| Yes | 49.2 | 12.8 | 68.3 | 69.5 | 72.7 | |
| BMI Categories (%) | < 0.001 | |||||
| Underweight | 2.4 | 5.7 | 1.0 | 0.7 | 0.0 | |
| Normal | 40.5 | 71.8 | 33.0 | 23.5 | 11.7 | |
| Overweight | 42.3 | 21.4 | 60.0 | 53.0 | 50.2 | |
| Obese | 14.7 | 1.1 | 6.0 | 22.8 | 38.1 | |
Determinants of hepatic steatosis severity in MASLD
Univariate ordinal logistic regression identified six variables significantly associated with increasing steatosis burden (all P < 0.001): age, AST, LDL-C, FPG, UA, and the Cr/BW. After multivariable adjustment, five predictors remained independently associated with severity: AST (P < 0.001), LDL-C (P = 0.001), FPG (P < 0.001), UA (P < 0.001), and Cr/BW ratio (P < 0.001). Age lost statistical significance (P = 0.767) and was excluded from the final model. The metabolic risk associated with advancing age is primarily mediated by the pathophysiological alterations it induces, such as dyslipidemia and sarcopenia. Consequently, when the model already incorporates these direct downstream effect indicators, the independent contribution of age itself ceases to be significant. The directionality of these associations was consistent: AST, LDL-C, and FPG displayed positive dose–response relationships with steatosis grade, whereas each unit increment in Cr/BW ratio conferred a marked protective effect (OR < 1) (Table 2). Integrating these five independent variables, we constructed a multivariable nomogram that accurately stratifies individual risk for progressive hepatic steatosis in patients with MASLD (Fig. 2).
Table 2.
Univariate and multivariable ordinal logistic regression of factors associated with hepatic steatosis severity in MASLD
| Factor | Univariate analysis | Multivariate analysis | ||||
|---|---|---|---|---|---|---|
| OR | 95% CI | P-value | OR | 95% CI | P-value | |
| Age (Year) | 0.98 | 0.97–0.99 | < 0.001 | 1 | 0.99–1.01 | 0.767 |
| AST (U/L) | 1.02 | 1.01–1.03 | < 0.001 | 1.01 | 1.01–1.02 | < 0.001 |
| LDL-C (mmol/L) | 1.33 | 1.17–1.51 | < 0.001 | 1.26 | 1.09–1.44 | 0.001 |
| FPG (mmol/L) | 1.36 | 1.29–1.44 | < 0.001 | 1.33 | 1.26–1.41 | < 0.001 |
| UA (μmol/L) | 1.01 | 1.01–1.01 | < 0.001 | 1.01 | 1.01–1.01 | < 0.001 |
| Cr/BW ratio | 0.05 | 0.04–0.08 | < 0.001 | 0.03 | 0.02–0.05 | < 0.001 |
ORs for all continuous variables are presented for a 1-unit increase
Fig. 2.
Nomogram for the individualized prediction of hepatic steatosis severity in MASLD
Predictive performance of the Cr/BW ratio for hepatic steatosis severity in MASLD
The receiver-operating characteristic analyses were conducted to quantify the discriminative capacity of each candidate biomarker across CAP-defined steatosis grades (Fig. 3). Among the individual predictors, the Cr/BW ratio delivered the highest accuracy, with an AUROC of 0.72 (95% CI:0.69–0.75), surpassing AST (0.55; 95% CI:0.52–0.56), LDL-C (0.56; 95% CI:0.53–0.59), FPG (0.68; 95% CI 0.66–0.71) and UA (0.62; 95% CI:0.59–0.65). It is noteworthy that the AUROCs for both AST and LDL-C were only marginally above 0.5, suggesting that these markers individually have limited discriminatory power for distinguishing between steatosis grades. When the Cr/BW ratio was combined with each of the remaining four variables, the resulting bivariate models achieved AUROCs of 0.73 (Cr/BW + AST), 0.72 (Cr/BW + LDL-C), 0.76 (Cr/BW + FPG), and 0.77 (Cr/BW + UA), all significantly exceeding the clinically relevant threshold of 0.70 (P < 0.05). A composite model that integrated AST, LDL-C, FPG, UA, and the Cr/BW ratio further improved performance, yielding an AUROC of 0.86 (95% CI 0.85–0.88) and maintaining robust predictive accuracy (AUROC > 0.75) across mild, moderate, and severe steatosis subgroups. These findings underscore the superior discriminative value of the Cr/BW ratio for MASLD risk stratification compared with conventional metabolic markers.
Fig. 3.
Comparative ROC analysis of candidate biomarkers for hepatic steatosis severity in MASLD. Note: Comparative ROC analysis for differentiating between group 0 (non-MASLD), group 1 (Mild), and groups 2–3 (Moderate-to-Severe). As a single indicator, the predictive performance of AST (A), LDL-C (B), FPG (C), UA (D), and Cr/BW ratio (E) for liver steatosis in MASLD. As a dual indicator, the predictive performance of AST-Cr/BW ratio (F), LDL-C-Cr/BW ratio (G), FPG-Cr/BW ratio (H), and UA-Cr/BW ratio (I) for liver steatosis in MASLD.As a composite indicator, the predictive performance of AST, LDL-C, FPG, UA, and Cr/BW ratio (J)for liver steatosis in MASLD
Non-linear dose–response relationships of metabolic indicators with MASLD-associated steatosis
To delineate potential threshold effects, we applied RCS regression to examine the curvilinear associations between five key metabolic variables (AST, LDL-C, FPG, UA, and Cr/BW) and the ordinal severity of hepatic steatosis, as quantified by CAP. All predictors displayed appreciable non-linearity; among them, FPG, UA, and Cr/BW ratio achieved statistical significance for departure from linearity (P_non-linear < 0.001, = 0.001, and < 0.001, respectively; Fig. 4). FPG and UA exhibited monotonic, positive dose–response curves: each incremental rise in concentration was accompanied by a progressively higher probability of advanced steatosis. Conversely, the Cr/BW ratio demonstrated an inverse J-shaped relationship. Threshold-effect modelling identified an inflection point at 1.01 (log-likelihood ratio P = 0.025; Table 3). Above 1.01: every 0.1-unit increase in Cr/BW conferred a 0.91-fold risk reduction of steatosis (OR = 0.09 per 0.1-unit increase, 95% CI: 0.05–0.17). Below 1.01: each 0.1-unit decrement was associated with a 0.98-fold elevated risk OR = 0.02 per 0.1-unit decrease; 95% CI 0.01–0.05).
Fig. 4.
Dose–Response curves for key metabolic indices and hepatic steatosis severity in MASLD: Restricted cubic spline analysis. Note: Non-linear association between AST (A), LDL-C (B), FPG (C), UA (D), Cr/BW ratio (E), and liver steatosis in MASLD. The solid lines in the figure represent OR, and the shaded regions represent the 95% CI
Table 3.
Breakpoint analysis of the Cr/BW ratio: Inflection-point identification for stratifying severe steatosis Risk in MASLD
| Adjusted OR (95%), P value | |
|---|---|
| Inflection point | 1.01 |
| P for log likelihood ratio | P = 0.025 |
| Cr/BW ratio < 1.01 | 0.02 (0.01–0.05) |
| Cr/BW ratio > 1.01 | 0.09 (0.05–0.17) |
Subgroup analyses of the association between Cr/BW ratio and liver steatosis in MASLD
To further explore the relationship between the Cr/BW ratio and hepatic steatosis severity in MASLD, we conducted stratified analyses by sex and age. The results consistently revealed a significant inverse association between the Cr/BW ratio and the risk of MASLD across all subgroups. The Sex-specific analysis showed that both males and females exhibited statistically significant negative correlations between the Cr/BW ratio and liver steatosis severity. The association was slightly stronger in males (OR = 0.0326, 95% CI: 0.0323–0.0330) compared to females (OR = 0.0343, 95% CI: 0.0340–0.0347), with P < 0.001 for both. Despite a statistically significant interaction P-value (P_interaction = 0.042), the adjusted OR point estimates for males (0.0326) and females (0.0343) were numerically very close. This suggests that the magnitude of the effect modification by sex is likely clinically negligible. Further, the age-stratified analysis showed that, there was a inverse association between the Cr/BW ratio and MASLD steatosis persisted across all age groups: Young adults: OR = 0.0207 (95% CI: 0.0205–0.0209), Middle-aged adults: OR = 0.0236 (95% CI: 0.0234–0.0239), Older adults: OR = 0.0746 (95% CI: 0.0732–0.0761). All associations were statistically significant (P < 0.001). However, the interaction test for age did not reach statistical significance (P = 0.149), suggesting that the inverse relationship between the Cr/BW ratio and MASLD is relatively consistent across age categories. Stratification based on hypertension status demonstrated that the inverse relationship between the Cr/BW ratio and MASLD steatosis persisted in both subgroups: individuals without hypertension presented an OR of 0.0273 (95% CI: 0.0270–0.0276), while those with hypertension (receiving antihypertensive treatment) exhibited an OR of 0.0407 (95% CI: 0.0403–0.0411); both findings were statistically significant (P < 0.001). The results suggest that the existence of hypertension and the administration of antihypertensive drugs do not alter the relationship between the Cr/BW ratio and the severity of MASLD steatosis. In examining the relationship between T2DM status and glucose-lowering therapy on the risk of MASLD steatosis, a significant inverse association with the Cr/BW ratio was observed. The OR was calculated at 0.0586 (95% CI: 0.0578–0.0594) for participants without diabetes, while those with diabetes on antidiabetic medication exhibited an OR of 0.0368 (95% CI: 0.0365–0.0371), with both results yielding P values less than 0.001. The results suggest that the presence of diabetes and glucose-lowering therapy does not alter the protective influence of an elevated Cr/BW ratio on the severity of hepatic steatosis. (Fig. 5 and Supplementary data).
Fig. 5.
Cr/BW ratio and steatosis severity across sex (A), age (B), hypertension status (C), and T2DM status (D) subgroups: Forest plot of stratified OR in MASLD. Note: The ORs for all continuous variables, including the Cr/BW ratio
Discussion
MASLD is now recognized as the hepatic manifestation of a multisystem metabolic disorder whose prevalence is rising alongside the global epidemics of obesity and T2DM. Large prospective cohorts [29] have shown a graded, dose-dependent rise in both liver-related and all-cause mortality with each additional marker of metabolic dysregulation, such as dysglycemia, dyslipidemia, and hypertension, which tend to cluster in affected individuals. Central to its underlying pathobiology is chronic IR that impairs intrahepatic lipid regulation while also promoting oxidative stress and systemic inflammation [30]. These mechanisms not only hasten the progression from isolated fat accumulation to non-alcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and eventually HCC, but also increase the risk of extra-hepatic complications, particularly chronic kidney disease, sarcopenia, and premature cardiovascular events [31]. Therefore, accurately measuring hepatic fat burden has become a clinical necessity. FibroTouch (Wuxi Hisen Healthcare Technology Co., Ltd., Wuxi, China), a vibration-controlled TE device introduced in 2013, similar to FibroScan, has been extensively validated against magnetic resonance proton-density fat fraction and histology. According to the manufacturer-recommended CAP thresholds, hepatic steatosis is categorized into mild, moderate, and severe stages. Its CAP provides precise, operator-independent estimation of liver fat content, allowing for dynamic risk assessment and longitudinal monitoring without the sampling errors and risks associated with biopsy [32]. This non-invasive method significantly advances clinical management of MASLD. Intrahepatic fat accumulation is not merely a storage issue but an active endocrine process. Excess lipid within hepatocytes produces reactive lipid species that surpass antioxidant defenses [30]; these metabolites activate stress kinases and transcription factors, leading to sustained cytokine and vasoactive mediator release [33]. The resulting systemic inflammation increases IR in skeletal muscle and fat tissue, while also activating renal renin-angiotensin signaling, which accelerates microvascular damage. Conversely, hepatic steatosis results from nutrient spill-over when glycogen stores in the liver and skeletal muscles become saturated. The inability to store additional glucose redirects carbohydrate flux toward de novo lipogenesis, further raising intrahepatic triglyceride levels. This lipid overload disturbs everyday communication between the liver and muscle [31]: hepatokines suppress muscle gene expression, and decreased muscle mass impairs systemic glucose disposal, increasing hepatic substrate delivery. This bidirectional hepato-muscular axis creates a self-perpetuating cycle where sarcopenia and ongoing steatosis reinforce each other [18]. Serum Cr is the end-product of non-enzymatic conversion of creatine and phosphocreatine within skeletal muscle; under normal conditions, its production is directly proportional to muscle mass [15], and its clearance occurs mainly through glomerular filtration. For this reason, Cr clearance has long been a practical marker of kidney function, but it is also influenced by factors like age, sex, diet, and most importantly, muscle volume [34]. Emerging evidence indicates that serum Cr not only reflects renal injury but also correlates with body weight [35]. Studies [26] have shown that a lower Cr/BW ratio is strongly linked to MASLD development and serves as a valuable predictor of the severity of hepatic steatosis. Due to its low cost, wide availability, and reproducibility, the Cr/BW ratio can be used as a practical indicator to track MASLD progression over time. Incorporating it routinely into electronic health records could enable dynamic risk evaluation and guide personalized lifestyle or drug interventions that simultaneously protect the liver and musculoskeletal health, turning a simple lab value into a key element of tailored MASLD management.
In this cross-sectional study of 1,492 adults, we demonstrate that the Cr/BW ratio declines in direct proportion to CAP-quantified hepatic fat content. The findings revealed that individuals in the Severe-MASLD group had significantly lower Cr/BW ratios compared to those with milder forms of steatosis. Moreover, a strong negative correlation was observed between the Cr/BW ratio and the severity of hepatic steatosis in MASLD patients (P < 0.001), aligning well with trends reported in earlier studies [26]. To assess the Cr/BW ratio’s predictive capability for hepatic steatosis severity, a multivariate logistic regression analysis was conducted, confirming that the Cr/BW ratio serves as an independent predictor (OR = 0.03, 95% CI: 0.02–0.05, P < 0.001). The identified Cr/BW ratio threshold of 1.01 holds clinical relevance, serving as a critical cut-off that distinguishes a steep escalation in the risk of severe hepatic steatosis among individuals with MASLD. Patients with a Cr/BW ratio falling below this cutoff demonstrated substantially elevated odds of severe disease—an association that proved consistent across different age and sex subgroups. ROC analysis confirmed that the Cr/BW ratio achieved superior discrimination (AUROC 0.72) over conventional metabolic surrogates such as AST (0.55), LDL-C (0.56), FPG (0.68), and UA (0.62). The association remained robust across age-, sex-, hypertension status-, and T2DM status- stratified subgroups. The robustness of this association across both subgroups, despite the theoretical impact of ACE Inhibitors/ARBs on Cr, indicates that the observed relationship is independent of hypertension status and its treatment medications and is unlikely to be materially confounded by this factor. In clinical practice, all patients diagnosed with hypertension receive antihypertensive therapy under physician guidance. Studies [36] indicate that ACE inhibitors and ARBs may cause an early, modest rise in serum Cr by modulating glomerular filtration; however, Cr levels stabilize within 2–3 months once blood pressure reaches the target. Furthermore, the consistent strength of the observed association across subgroups suggests the confounding effects of glucose-lowering therapy do not primarily mediate it. Evidence [37] notes that insulin sensitizers, such as thiazolidinediones or biguanides, may attenuate muscle loss. Although glucagon-like peptide-1 (GLP-1) receptor agonists and sodium-glucose cotransporter-2 (SGLT2) inhibitors are also recognized to affect muscle mass, our observational design precluded a detailed dissection of how specific agents modulate Cr levels and, consequently, hepatic steatosis risk. In summary, despite these potential pharmacological confounders, the primary inverse association between the Cr/BW ratio and hepatic steatosis appears robust. The precise mechanistic interplay between serum Cr and glucose-lowering drugs in determining intrahepatic fat accumulation remains to be elucidated in future interventional studies. These data extend prior observations linking sarcopenia with MASLD by providing a quantitative, non-invasive metric that simultaneously reflects muscle mass and metabolic reserve. The negligible cost and routine availability of serum Cr render the Cr/BW ratio an immediately implementable tool for risk stratification in both primary-care and specialist settings. Integrating this biomarker into existing care pathways could facilitate early identification of individuals at imminent risk of progressive steatosis, enabling timely lifestyle or pharmacological intervention to preserve both hepatic and musculoskeletal health. Despite the study’s findings, certain limitations should be acknowledged. First, the study is of a single-center cross-sectional design, which may limit the generalizability of its findings between the Cr/BW ratio and CAP-measured hepatic steatosis, and cannot determine causality. Second, while CAP is validated against magnetic resonance, it lacks the granularity of liver biopsy for assessing necro-inflammation or fibrosis. Third, waist circumference data were not available, precluding its use as a covariate and preventing a comparison with a Cr-to-waist circumference ratio. Finally, potential confounders, including lifestyle factors such as physical activity level and dietary protein intake, as well as data on direct muscle mass measurements (such as dual-energy X-ray absorptiometry [DEXA]), were not captured and may influence the observed associations. Moreover, a low ratio could also result from obesity (high body weight), fluid overload, or altered Cr metabolism. Isolating a low Cr/BW ratio as indicative of sarcopenia should be avoided. In clinical assessment, the Cr/BW ratio must be interpreted in conjunction with BMI, body composition analysis, signs of edema, and renal function parameters. Furthermore, compared with a single measurement, longitudinal and dynamic monitoring of the Cr/BW ratio in the same patient holds greater clinical significance, as its trend more accurately reflects the evolution of the body's muscular and metabolic status. Meanwhile, we did not test whether interventions that raise the Cr/BW ratio (e.g., resistance exercise) translate into reduced hepatic fat. Longitudinal, multicenter cohorts supported by biopsies are necessary to address these questions. Future prospective studies should combine regular muscle mass measurements, such as bioelectrical impedance or DEXA, with multiple CAP assessments to better understand the causal relationship within the hepato-muscular axis in MASLD. It is crucial to urgently conduct prospective randomized controlled trials, validated by liver biopsy, to test whether increasing the Cr/BW ratio improves hepatic steatosis. These studies should also evaluate their effect on liver histology improvements and long-term clinical outcomes, helping to turn the Cr/BW ratio from merely a risk marker into a practical therapeutic target. Future research should, within prospective cohorts, further quantify how factors like obesity and fluid status affect the Cr/BW ratio and create more precise correction models. At the same time, investigating the use of other simple biomarkers (such as handgrip strength and gait speed) to develop a comprehensive assessment system for muscular health and metabolic risk is recommended. We also recognize that our study findings and the nomogram we created need external validation using independent, multi-center populations to verify their broader applicability and clinical utility.
Conclusion
This cross-sectional study of 1,492 adults shows that the Cr/BW ratio is a strong, independent inverse indicator of CAP-measured hepatic steatosis severity MASLD. A clinically relevant inflection point at 1.01 demarcates a steep rise in risk: values below this threshold conferred a significantly increased odds (OR corresponding to 1/0.02 of severe disease, an association that remained stable across age and sex strata. ROC modelling further confirmed the superior discriminative capacity of Cr/BW (AUROC 0.72) over traditional metabolic markers, and its integration into a multivariable nomogram enhanced overall prediction (AUROC 0.86). These data position the Cr/BW ratio as an inexpensive, reproducible biomarker that can be readily deployed in primary-care and specialist settings for dynamic risk stratification. Prospective cohorts verified by biopsy are now needed to assess whether interventions that increase the Cr/BW ratio, like resistance training or specific protein supplements, lead to observable decreases in liver fat and better long-term health results.
Supplementary Information
Acknowledgements
Thanks to all our colleagues whose support and collaboration made this work possible.
Abbreviations
- AIC
Akaike Information Criterion
- ALT
Alanine aminotransferase
- AST
Aspartate aminotransferase
- AUC
Cumulative area under the curve
- AUROC
Area under the receiver operating characteristic curve
- BMI
Body mass index
- CAP
Controlled attenuation parameter
- Cr
Creatinine
- Cr/BW ratio
The creatinine-to-body weight ratio
- DEXA
Dual-energy X-ray absorptiometry
- FPG
Fasting plasma glucose
- GLP-1
Glucagon-like peptide-1
- HCC
Hepatocellular carcinoma
- HDL-C
High-density lipoprotein cholesterol
- IR
Insulin resistance
- LDL-C
Low-density lipoprotein cholesterol
- MASLD
Metabolic dysfunction-associated steatotic liver disease
- NASH
Non-alcoholic steatohepatitis
- Non-HDL-C
Non-High-Density Lipoprotein Cholesterol
- NHHR ratio
Non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio
- OR
Odds ratios
- RCS
Restricted cubic splines
- ROC
Receiver-operating-characteristic
- SGLT2
Sodium-Glucose Cotransporter-2
- TC
Total cholesterol
- TE
Transient Elastography
- TG
Triglycerides
- TyG index
Triglyceride-glucose index
- T2DM
Type 2 diabetes mellitus
- UA
Uric acid
- WHO
World Health Organization
Authors’ contributions
S.X: Methodology, Formal analysis, Writing the original draft; W.X.: Data curation; A.Y.: Review & editing; W.S.: Investigation; S.M: Review & editing; L.J.: Funding acquisition; H.Z.: Visualization. L.Y.: Conceptualization, Formal analysis, Project administration, Supervision, Writing-review & editing, Funding acquisition.
Funding
Open access funding provided by Zhejiang “Small but Strong” Clinical Cultivation Innovation Team (Project No.: CXTD202502034). Joint TCM Science & Technology Projects of National Demonstration Zones for Comprehensive TCM Reform (Project No.: GZY-KJS-ZJ-2026–152).
Data availability
Data used to support the findings of this study are available from the corresponding author upon request.
Declarations
Ethics approval and consent to participate
Approved by the Medical Ethics Committee of Xinjiang Uygur Autonomous Region Hospital of Traditional Chinese Medicine (approval No. 2023XE-GS190), this study adhered to the 1964 Helsinki Declaration and its later amendments. Informed consent was obtained from all participants.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Younossi ZM, Kalligeros M, Henry L. Epidemiology of metabolic dysfunction-associated steatotic liver disease. Clin Mol Hepatol. 2025;31(Suppl):S32–50. 10.3350/cmh.2024.0431. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Trivedi HD, Niezen S, Jiang ZG, Tapper EB. Severe hepatic steatosis by controlled attenuation parameter predicts quality of life independent of fibrosis. Dig Dis Sci. 2022;67(8):4215–22. 10.1007/s10620-021-07228-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Wiegand J, Petroff D, Karlas T. Metabolic dysfunction associated steatotic liver disease-Clinicians should not underestimate the role of steatosis. United European Gastroenterol J. 2024;12(3):277–8. 10.1002/ueg2.12520. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Shih CI, Wu KT, Hsieh MH, et al. Severity of fatty liver is highly correlated with the risk of hypertension and diabetes: a cross-sectional and longitudinal cohort study. Hepatol Int. 2024;18(1):138–54. 10.1007/s12072-023-10576-z. [DOI] [PubMed] [Google Scholar]
- 5.Toh JZK, Pan XH, Tay PWL, et al. A meta-analysis on the global prevalence, risk factors and screening of coronary heart disease in nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol. 2022;20(11):2462-2473.e10. 10.1016/j.cgh.2021.09.021. [DOI] [PubMed] [Google Scholar]
- 6.Jamalinia M, Zare F, Noorizadeh K, Bagheri Lankarani K. Systematic review with meta-analysis: steatosis severity and subclinical atherosclerosis in metabolic dysfunction-associated steatotic liver disease. Aliment Pharmacol Ther. 2024;59(4):445–58. 10.1111/apt.17869. [DOI] [PubMed] [Google Scholar]
- 7.Gao J, Li Y, Zhang Y, et al. Severity and remission of metabolic dysfunction-associated fatty/steatotic liver disease with chronic kidney disease occurrence. J Am Heart Assoc. 2024;13(5):e032604. 10.1161/JAHA.123.032604. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Suresh MG, Gogtay M, Singh Y, Yadukumar L, Mishra AK, Abraham GM. Case-control analysis of venous thromboembolism risk in non-alcoholic steatohepatitis diagnosed by transient elastography. World J Clin Cases. 2023;11(34):8126–38. 10.12998/wjcc.v11.i34.8126. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Erre GL, Castagna F, Sauchella A, et al. Prevalence and risk factors of moderate to severe hepatic steatosis in patients with rheumatoid arthritis: an ultrasonography cross-sectional case-control study. Ther Adv Musculoskelet Dis. 2021;13:1759720X211042739. 10.1177/1759720X211042739. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Fialho A, Fialho A, Ribeiro B, et al. Association between Helicobacter pylori and steatosis severity on transient elastography. Cureus. 2023;15(1):e34042. 10.7759/cureus.34042. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Yang L, Zhu Y, Zhou L, Yin H, Lin Y, Wu G. Transient elastography in the diagnosis of pediatric non-alcoholic fatty liver disease and its subtypes. Front Pediatr. 2022;10:808997. 10.3389/fped.2022.808997. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Tavaglione F, De Vincentis A, Bruni V, et al. Accuracy of controlled attenuation parameter for assessing liver steatosis in individuals with morbid obesity before bariatric surgery[J]. Liver Int. 2022;42(2):374–83. [DOI] [PubMed] [Google Scholar]
- 13.Guan L, Zhang X, Tian H, et al. Prevalence and risk factors of metabolic-associated fatty liver disease during 2014-2018 from three cities of Liaoning Province: an epidemiological survey. BMJ Open. 2022;12(2):e047588. 10.1136/bmjopen-2020-047588. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Niu Y, Zhang W, Zhang H, et al. <article-title update="added">Serum creatinine levels and risk of nonalcohol fatty liver disease in a middle‐aged and older Chinese population: a cross‐sectional analysis. Diabetes Metab Res Rev. 2022;38(2):e3489. 10.1002/dmrr.3489. [DOI] [PubMed] [Google Scholar]
- 15.Hashimoto Y, Okamura T, Hamaguchi M, Obora A, Kojima T, Fukui M. Creatinine to body weight ratio is associated with incident diabetes: population-based cohort study. J Clin Med. 2020;9(1):227. 10.3390/jcm9010227. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Okamura T, Hashimoto Y, Hamaguchi M, Obora A, Kojima T, Fukui M. Creatinine-to-body weight ratio is a predictor of incident non-alcoholic fatty liver disease: a population-based longitudinal study. Hepatol Res. 2020;50(1):57–66. 10.1111/hepr.13429. [DOI] [PubMed] [Google Scholar]
- 17.Carli F, Della Pepa G, Sabatini S, Vidal Puig A, Gastaldelli A. Lipid metabolism in MASLD and MASH: from mechanism to the clinic. JHEP Rep. 2024;6(12):101185. 10.1016/j.jhepr.2024.101185. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Chakravarthy MV, Siddiqui MS, Forsgren MF, Sanyal AJ. Harnessing muscle-liver crosstalk to treat nonalcoholic steatohepatitis. Front Endocrinol (Lausanne). 2020;11:592373. 10.3389/fendo.2020.592373. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Rinella M E, Lazarus J V, Ratziu V, et al. A multi-society Delphi consensus statement on new fatty liver disease nomenclature. Ann Hepatol. 2023;101133. [DOI] [PubMed]
- 20.Wang YY, Tian F, Qian XL, Ying HM, Zhou ZF. <article-title update="added">Effect of 5:2 intermittent fasting diet versus daily calorie restriction eating on metabolic-associated fatty liver disease—a randomized controlled trial. Front Nutr. 2024;11:1439473. 10.3389/fnut.2024.1439473. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Chen Q, Hu P, Hou X, et al. Association between triglyceride-glucose related indices and mortality among individuals with non-alcoholic fatty liver disease or metabolic dysfunction-associated steatotic liver disease. Cardiovasc Diabetol. 2024;23(1):232. 10.1186/s12933-024-02343-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Romaszko J, Gromadziński L, Buciński A. Friedewald formula may be used to calculate non-HDL-C from LDL-C and TG(J). Front Med. 2023;10:1247126. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Yang Y, Li S, An Z, Li S. The correlation between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) with non-alcoholic fatty liver disease: an analysis of the population-based NHANES (2017-2018). Front Med (Lausanne). 2024;11:1477820. 10.3389/fmed.2024.1477820. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Zhao C, Zhang X, Zhang X, et al. U-shaped association between TC/HDL-C ratio and osteoporosis risk in older adults. Sci Rep. 2025;15(1):4791. 10.1038/s41598-025-89537-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Kosmas CE, Rodriguez Polanco S, Bousvarou MD, et al. The triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio as a risk marker for metabolic syndrome and cardiovascular disease. Diagnostics (Basel). 2023;13(5):929. 10.3390/diagnostics13050929. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Lin J, Zheng J, Lin X, Chen Y, Li Z. A low creatinine to body weight ratio predicts the incident nonalcoholic fatty liver disease in nonelderly Chinese without obesity and dyslipidemia: a retrospective study. Gastroenterol Res Pract. 2020;2020:4043871. 10.1155/2020/4043871. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Chen Y, Lu P, Lin C, et al. <article-title update="added">Hyperuricemia and elevated uric acid/creatinine ratio are associated with stages III/IV periodontitis: a population-based cross-sectional study (NHANES 2009–2014). BMC Oral Health. 2024;24(1):1389. 10.1186/s12903-024-05173-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Posadas-Sánchez R, Fuentevilla-Álvarez G, Vargas-Alarcón G, et al. Is UA/HDL-C a reliable surrogate marker for fatty liver? A comparative evaluation with metabolic scores in a Mexican population: the genetics of atherosclerotic disease study. Diagnostics. 2025;15(11):1419. 10.3390/diagnostics15111419. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Golabi P, Otgonsuren M, de Avila L, Sayiner M, Rafiq N, Younossi ZM. Components of metabolic syndrome increase the risk of mortality in nonalcoholic fatty liver disease (NAFLD). Medicine (Baltimore). 2018;97(13):e0214. 10.1097/MD.0000000000010214. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Hydes T, Buchanan R, Kennedy OJ, Fraser S, Parkes J, Roderick P. Systematic review of the impact of non-alcoholic fatty liver disease on mortality and adverse clinical outcomes for individuals with chronic kidney disease. BMJ Open. 2020;10(9):e040970. 10.1136/bmjopen-2020-040970. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Sandireddy R, Sakthivel S, Gupta P, Behari J, Tripathi M, Singh BK. Systemic impacts of metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH) on heart, muscle, and kidney related diseases. Front Cell Dev Biol. 2024;12:1433857. 10.3389/fcell.2024.1433857. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Kim NJ, Vutien P, Borgerding JA, et al. Hepatic steatosis estimated by VCTE-derived CAP scores was associated with lower risks of liver-related events and all-cause mortality in patients with chronic liver diseases. Am J Gastroenterol. 2025;120(7):1529–37. 10.14309/ajg.0000000000003161. [DOI] [PubMed] [Google Scholar]
- 33.You Y, Pei X, Jiang W, et al. Non-obese non-alcoholic fatty liver disease and the risk of chronic kidney disease: a systematic review and meta-analysis. PeerJ. 2024;12:e18459. 10.7717/peerj.18459. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Jayasekhar Babu P, Tirkey A, Mohan Rao TJ, Chanu NB, Lalchhandama K, Singh YD. Conventional and nanotechnology based sensors for creatinine (a kidney biomarker) detection: a consolidated review. Anal Biochem. 2022;645:114622. 10.1016/j.ab.2022.114622. [DOI] [PubMed] [Google Scholar]
- 35.De Rosa S, Greco M, Rauseo M, Annetta MG. The good, the bad, and the serum creatinine: exploring the effect of muscle mass and nutrition. Blood Purif. 2023;52(9–10):775–85. 10.1159/000533173. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Mangrum AJ, Bakris GL. Angiotensin-converting enzyme inhibitors and angiotensin receptor blockers in chronic renal disease: safety issues. Semin Nephrol. 2004;24(2):168–75. 10.1016/j.semnephrol.2003.11.001. [DOI] [PubMed] [Google Scholar]
- 37.Song DK, Hong YS, Sung YA, Lee H. Association of serum creatinine levels and risk of type 2 diabetes mellitus in Korea: a case control study. BMC Endocr Disord. 2022;22(1):4. 10.1186/s12902-021-00915-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
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Supplementary Materials
Data Availability Statement
Data used to support the findings of this study are available from the corresponding author upon request.