{
  "abstract": "Background and aims The Kidney Disease: Improving Global Outcomes (KDIGO) 2024 guideline recommends risk stratification for chronic kidney disease (CKD) management; however, patients within the same KDIGO category may still experience heterogeneous outcomes. Hypertension and type 2 diabetes mellitus (T2DM) are predominant causes of CKD, and hepatic fibrosis is highly prevalent in this population, but not integrated into current KDIGO risk assessment. The Fibrosis-4 (FIB-4) index, a widely validated noninvasive marker of hepatic fibrosis, may capture residual risk not reflected by KDIGO stratification. This study aims to explore whether FIB-4 can identify residual risk beyond KDIGO stratification in patients with T2DM and hypertension.Methods This cross-sectional study included 1208 patients with T2DM and hypertension. FIB-4 was dichotomized at 1.3 based on established guidelines. The primary outcome was KDIGO high/very high risk (categories 3–4). The secondary outcome was CKD, defined as an estimated glomerular filtration rate <60 mL/min/1.73 m² and/or albumin-to-creatinine ratio ≥30 mg/g. Multivariable logistic regression, restricted cubic spline analysis, and stratified analyses were conducted.Results Among 1208 patients (mean age 58.3 years; 60.1% male), 514 (42.5%) had FIB-4>1.3, 286 (23.7%) were classified as KDIGO 3–4, and 588 (48.7%) met criteria for CKD. After multivariable adjustment, FIB-4>1.3 was independently associated with both KDIGO 3–4 (OR 1.57, 95% CI 1.12 to 2.19, p=0.008) and CKD (OR 1.51, 95% CI 1.14 to 2.01, p=0.004). In stratified analyses, these associations persisted among patients achieving body mass index/low-density lipoprotein cholesterol targets, although statistical power was limited for KDIGO 3–4. Within KDIGO low-risk categories, elevated FIB-4 was associated with a significantly higher prevalence of vascular comorbidities (p value for trend<0.01 for all). Restricted cubic spline modeling demonstrated a non-linear relationship, with a threshold effect at approximately 1.3 (p value for non-linearity<0.001 for KDIGO 3–4; p=0.010 for CKD).Conclusions Elevated FIB-4 is independently associated with KDIGO 3–4 and CKD, and identifies patients with higher vascular comorbidity burden even within KDIGO low-risk categories. FIB-4 may serve as a complementary risk stratification marker within the KDIGO framework for patients with T2DM and hypertension.",
  "authors": [
    {
      "affiliations": [
        "Medical Department 1, Friedrich-Alexander-Universitat Erlangen-Nuremberg, Erlangen, Germany",
        "Pu'er People’s Hospital, Pu’er, Yunnan, China"
      ],
      "name": "Hongmei Fu"
    },
    {
      "affiliations": [
        "Pu'er People’s Hospital, Pu’er, Yunnan, China"
      ],
      "name": "Chengzhi Xing"
    },
    {
      "affiliations": [
        "Pu'er People’s Hospital, Pu’er, Yunnan, China"
      ],
      "name": "Hengye Wang"
    },
    {
      "affiliations": [
        "Pu'er People’s Hospital, Pu’er, Yunnan, China"
      ],
      "name": "Xianwen Wei"
    },
    {
      "affiliations": [
        "Medical Department 1, Friedrich-Alexander-Universitat Erlangen-Nuremberg, Erlangen, Germany"
      ],
      "name": "Stefan Wirtz"
    }
  ],
  "full_text": "WHAT IS ALREADY KNOWN ON THIS TOPIC The Kidney Disease: Improving Global Outcomes (KDIGO) 2024 guideline stratifies chronic kidney disease (CKD) risk using estimated glomerular filtration rate and albuminuria, but substantial heterogeneity remains within the same risk category. Hepatic fibrosis is highly prevalent in patients with type 2 diabetes mellitus (T2DM) and hypertension but is not incorporated into current KDIGO risk assessment. The FIB-4 index is a simple non-invasive marker of hepatic fibrosis and has been associated with incident CKD in diabetic populations; however, its utility within the KDIGO framework has not been examined.WHAT THIS STUDY ADDS In a cross-sectional cohort of 1208 patients with T2DM and hypertension, elevated FIB-4 (>1.3) was independently associated with both KDIGO high/very high risk and CKD after multivariable adjustment. Elevated FIB-4 also identified a higher burden of vascular comorbidities even among patients classified as KDIGO low risk. A threshold effect was observed around FIB-4=1.3.HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY FIB-4, derived from routinely available laboratory tests at no additional cost, may serve as a practical complementary risk marker within the KDIGO framework to identify residual risk in patients with T2DM and hypertension. Integrating FIB-4 into CKD risk stratification may support more individualized clinical decision-making, but prospective validation is required.Introduction Chronic kidney disease (CKD) represents a major global public health challenge, affecting approximately 14.2% of the adult population worldwide. 1 The prevalence of CKD continues to rise in parallel with the global epidemics of type 2 diabetes mellitus (T2DM) and hypertension, which together constitute the two most common etiologies of CKD and frequently coexist in clinical practice.2 Patients with concurrent T2DM and hypertension exhibit a disproportionately high burden of renal impairment, cardiovascular comorbidities, and microvascular complications, reflecting the convergence of metabolic, hemodynamic, and inflammatory pathways. This population is therefore at substantially elevated risk for CKD onset and progression, underscoring the need for more refined risk stratification strategies.3 Importantly, patients with coexisting T2DM and hypertension constitute a critical cohort. While current CKD risk stratification is rigorously applied to this group, significant residual risk remains beyond conventional markers. Consequently, this population offers an ideal setting to evaluate complementary markers for improved kidney risk assessment.The Kidney Disease Guideline provides a comprehensive framework for CKD evaluation and management, recommending risk stratification based on estimated glomerular filtration rate (eGFR) and albuminuria to guide clinical decision-making.4 This heatmap-based classification system enables clinicians to categorize patients into distinct risk strata for adverse kidney and cardiovascular outcomes. However, despite its clinical utility, substantial heterogeneity persists among individuals within the same eGFR and albuminuria categories. Accumulating evidence indicates that patients with comparable eGFR and albuminuria levels can experience markedly different trajectories of renal decline and cardiovascular events.5 6 This is likely reflected by underlying pathophysiological mechanisms not captured by eGFR and albuminuria alone, emphasizing the need for complementary biomarkers to refine risk stratification within the KDIGO framework.Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as non-alcoholic fatty liver disease (NAFLD), is highly prevalent in patients with T2DM, affecting approximately 60–70% of this population worldwide, with a substantial proportion exhibiting advanced hepatic fibrosis.7 8 Compelling evidence from large epidemiological studies and meta-analyses demonstrates that MASLD is independently associated with an increased risk of incident CKD, and this association correlates with higher severity of hepatic fibrosis.9 10 Despite this, hepatic fibrosis has not yet been incorporated into routing KDIGO risk assessment so far. The biological interplay between hepatic fibrosis and kidney injury has been shown to involve shared pathophysiological mechanisms, collectively referred to as the liver-kidney axis, including hepatic insulin resistance, chronic low-grade inflammation, altered adipokine and hepatokine secretion, disrupted lipid mediator signaling, oxidative stress, endothelial dysfunction, and activation of the renin-angiotensin system.11–13The Fibrosis-4 (FIB-4) index is a simple, non-invasive score estimating hepatic fibrosis from four routine laboratory parameters: age, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) concentration, and platelet counts. The American Association for the Study of Liver Diseases (AASLD) recommends FIB-4 as the first-line screening tool for hepatic fibrosis, using a cut-off of 1.3 to exclude advanced fibrosis.14 Beyond its established utility in hepatology, emerging evidence suggests that elevated FIB-4 has been independently associated with incident CKD among individuals with diabetes.15 However, previous studies have largely examined the association between FIB-4 and conventional binary CKD definitions (eGFR<60 mL/min/1.73m² or albuminuria), without assessing its potential to refine risk stratification within the KDIGO classification. Given that FIB-4 is derived from routinely available tests at no additional cost, its potential as a practical risk marker within the KDIGO framework deserves further investigation.The concept of risk-enhancing factors, which identify residual risk among individuals classified as low or intermediate risk by conventional prediction algorithms, has been successfully implemented in cardiovascular disease prevention, most notably in the 2019 American College of Cardiology/American Heart Association (ACC/AHA) guideline for the primary prevention of atherosclerotic cardiovascular disease (ASCVD).16 The potential for FIB-4 to serve as a risk enhancer within the KDIGO framework, however, remains incompletely defined, particularly in patients with T2DM and hypertension. In this context, FIB-4 is viewed as a conceptual analog for risk stratification, rather than a marker of causality or temporal prediction. We hypothesize that elevated FIB-4, as a marker of subclinical hepatic fibrosis, identifies individuals with increased kidney disease risk beyond what is captured by standard KDIGO stratification. Accordingly, this study aimed to examine the association between FIB-4 and KDIGO-defined kidney risk (primary outcome) and CKD defined by conventional criteria (secondary outcome), and to evaluate the potential utility of FIB-4 as a risk marker within the KDIGO framework among patients with T2DM and hypertension.Methods Study design and population We conducted a single-center, retrospective cross-sectional analysis of adults with T2DM and hypertension undergoing routine clinical evaluation. Inclusion criteria were: (1) age≥18 years; (2) T2DM diagnosed according to the 2025 American Diabetes Association Standards of Care 17 and (3) hypertension defined by documented diagnosis or current use of antihypertensive medications.Exclusion criteria were: active malignancy, acute coronary or cerebrovascular events within 3 months, acute infection, autoimmune diseases, maintenance dialysis, history of kidney or liver transplantation, decompensated liver cirrhosis, hematological disorders affecting platelet count (eg, idiopathic thrombocytopenic purpura), and excessive alcohol intake (≥140 g/week for men or ≥70 g/week for women); however, in this study, patients with alcohol consumption below these thresholds were retained, and alcohol use in the baseline characteristics refers to social or habitual drinking below the exclusion criteria, viral hepatitis (hepatitis B surface antigen or hepatitis C antibody positive), or known hepatotoxic medication use.This study was approved by the Ethics Committee of Pu’er People’s Hospital. Informed consent was waived due to the retrospective design.Data collection Clinical assessment Demographic characteristics (age, sex), anthropometrics (height, weight), blood pressure (systolic and diastolic), lifestyle factors (smoking, alcohol), diabetes duration, and current medications were extracted from electronic medical records. Blood pressure was measured using standardized protocols (seated position, arm at heart level, average of two readings≥5 min apart). Body mass index (BMI) was calculated as weight (kg) divided by height squared (m²).Laboratory assays Venous blood samples were collected after overnight fasting (≥8 hours). Glycated hemoglobin (HbA1c) was measured by high-performance liquid chromatography using the Tosoh Automated Glycohemoglobin Analyzer (HLC-723G11, Tosoh Corporation, Tokyo, Japan). Serum ALT (alanine substrate method), AST (aspartate substrate method), serum creatinine (Cr; enzymatic method) were measured using a cobas c 702 module (Roche Diagnostics, Mannheim, Germany). Lipid profiles, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C), were also measured on the same Roche automated analyzers. Platelet counts were determined by electrical impedance method using the XN-10 Automated Hematology Analyzer (Sysmex Corporation, Kobe, Japan). Spot urine samples were collected, preferably first morning void. Urine albumin was measured by immunoturbidimetric method using the cobas c 702 module (Roche Diagnostics, Mannheim, Germany), and albumin-to-creatinine ratio (ACR) was calculated and expressed as mg/g. Hepatitis B surface antigen and hepatitis C antibody were assessed by magnetic microparticle chemiluminescence immunoassay (Xiamen Wantai Kerry Biotechnology, Xiamen, China).Ultrasonography Abdominal ultrasonography Abdominal ultrasonography was performed using a high-resolution ultrasound system (Aplio i700, Canon Medical Systems, Tokyo, Japan). Hepatic steatosis was diagnosed by experienced radiologists based on established ultrasonographic criteria, defined as diffusely increased echogenicity of the liver parenchyma relative to the renal cortex or spleen. 18Carotid ultrasonography Carotid ultrasonography was performed using a Doppler ultrasound system (EPIQ 7C, Philips Healthcare, Netherlands) equipped with a 3–12 MHz linear transducer. Carotid plaque was defined as a focal structure encroaching into the arterial lumen by at least 0.5 mm or 50% of the surrounding intima-media thickness value, or demonstrating a thickness >1.5 mm as measured from the media-adventitia interface to the intima-lumen interface, according to the Mannheim Carotid Intima-Media Thickness Consensus. 19Lower extremity arterial ultrasonography Lower extremity arterial ultrasonography was performed using the same ultrasound system with a 3–12 MHz linear transducer. Atherosclerotic plaque of the lower extremity arteries was identified using B-mode and color Doppler imaging of the femoral and popliteal arteries, defined as focal echogenic structures protruding into the arterial lumen. 20The presence of atherosclerotic plaque at any examined carotid or lower extremity arterial site was recorded.Definitions and classifications Renal assessment and KDIGO risk classification eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration 2009 creatinine equation. 21 KDIGO risk categories were derived from the eGFR×albuminuria grid according to the KDIGO 2024 Clinical Practice Guideline4; risk categories were defined as follows: low risk (category 1) = G1 G2 with A1; moderate risk (category 2) = G3a with A1 or G1-G2 with A2; high risk (category 3) = G3 b with A1, G3a with A2, or G1-G2 with A3; very high risk (category 4) = G4 G5 with A1-A3, G3b with A2-A3, or G3a with A3.FIB-4 index and BARD score The FIB-4 index was calculated using the following formula:FIB-4 = [age (years)×AST (U/L)] / [platelet count (×10⁹/L) × √ALT (U/L)]Participants were categorized as FIB-4 >1.3 versus ≤1.3, consistent with previous studies investigating FIB-4 and kidney outcomes in patients with T2DM.15 The BARD (uses BMI, AST/ALT ration and diabetes) score was calculated as: BMI ≥28 kg/m² (1 point) + AST/ALT ratio ≥0.8 (2 points) + diabetes mellitus (1 point), with participants categorized as BARD ≥2 versus <2.22Clinical target definitions Clinical targets were defined following current practice guidelines: blood pressure <130/80 mm Hg; HbA1c <7.0% 23; BMI <24 kg/m², and LDL-C <2.6 mmol/L.24Clinical history definitions History of cardiovascular disease and stroke was ascertained through comprehensive review of electronic medical records, including documented physician diagnoses in hospitalization and outpatient records.Outcomes The primary outcome was KDIGO high/very high risk (categories 3–4). The secondary outcome was CKD, defined as eGFR <60 mL/min/1.73 m² and/or ACR ≥30 mg/g.Statistical analysis Continuous variables were assessed for normality using the Shapiro-Wilk test and summarized as mean±SD or median (IQR), as appropriate. Categorical variables were presented as counts (percentages). Between-group comparisons were performed using Student’s t-test or Mann-Whitney U test for continuous variables, and χ² test for categorical variables.Associations between FIB-4 (> 1.3 vs ≤ 1.3) and renal outcomes (KDIGO 3–4 and CKD) were estimated using multivariable logistic regression, with results reported as ORs and 95% CIs. Three models were constructed: model 1, unadjusted; model 2, adjusted for age, sex, BMI, systolic blood pressure (BP), diastolic BP, HbA1c, TG, LDL-C, HDL-C, and diabetes duration; model 3, further adjusted for smoking, alcohol consumption, oral antidiabetic drugs, insulin, antihypertensives, statins, and fibrates.Restricted cubic spline (RCS) regression with four knots (placed at the fifth, 35th, 65th, and 95th percentiles) was used to explore the dose-response relationship between FIB-4 and renal outcomes, with FIB-4=1.3 as the reference. Non-linearity was assessed using the likelihood ratio test.Subgroup analyses stratified by clinical target achievement (BP, HbA1c, LDL-C, BMI) were performed, with interaction effects evaluated using multiplicative interaction.Key variables for FIB-4 calculation and renal outcome assessment were complete. Missing data were minimal for lipid parameters (TG, LDL-C, and HDL-C; < 1% each). No imputation was performed; analyses were conducted using available cases.All analyses were performed using SPSS V.31.0 (IBM Corp, Armonk, New York, USA) and R V.4.5.2 (rms package). Two-sided p<0.05 was considered statistically significant.Results Baseline characteristics A total of 1208 patients with T2DM and hypertension were included in this analysis. The mean age was 58.28±10.64 years, and 726 (60.1%) were men. Among all participants, 514 (42.5%) had FIB-4>1.3, 286 (23.7%) were classified as KDIGO high/very high risk (categories 3–4), and 588 (48.7%) met the criteria for CKD ( table 1).Table 1Baseline characteristics of patients with T2DM and hypertension stratified by FIB-4 indexTotal (n=1208)FIB-4 ≤1.3 (n=694)FIB-4 >1.3 (n=514)P valueDemographics     Men (%)726 (60.1%)452 (65.1%)274 (53.3%)<0.001 Age (years)58.28±10.6454.14±10.5263.85±7.91<0.001 Duration of diabetes (years)9.16±7.388.05±7.0510.62±7.55<0.001Anthropometrics & BP     BMI (kg/m2)25.75±3.5826.07±3.7025.33±3.37<0.001 SBP (mm Hg)137.79±21.37138.20±21.41137.25±21.310.447 DBP (mm Hg)83.92±13.2186.12±13.5480.95±12.13<0.001Metabolic parameters     HbA1c (%)8.79±2.218.98±2.328.52±2.03<0.001 TC (mmol/L)4.42±1.344.55±1.354.24±1.31<0.001 TG (mmol/L)2.11 (1.35–3.43)2.35 (1.45–3.74)1.89 (1.25–2.90)<0.001 HDL-C (mmol/L)1.04±0.271.02±0.271.07±0.280.005 LDL-C (mmol/L)2.53±0.962.61±1.002.43±0.900.001 ALT (U/L)20 (14–28)20 (15–28)19 (13–29)0.324 AST (U/L)19 (16–24)17 (15–21)21 (17–29)<0.001 PLT (×109 /L)223.99±64.95253.82±62.19183.71±43.25<0.001 Cr (μmol/L)78 (62–98)77 (62–93)79 (62–103)0.041 ACR (mg/g)22.4 (8.23–118.83)20.20 (7.90–88.50)26.20 (9.60–166.50)0.029 eGFR (mL/min/1.73 m2)86.67 (65.55–99.74)91 (71.92–103.75)80.26 (58.14–94.61)<0.001Renal parameters     ACR classification (n=1208)   0.019 Normal (<30 mg/g), n (%)677 (56.0%)409 (58.9%)268 (52.1%)  Microalbuminuria/macroalbuminuria (≥ 30 mg/g), n (%)531 (44.0%)285 (41.1%)246 (47.9%)  eGFR×ACR classification (n=1208)   <0.001 eGFR ≥60 (mL/min/1.73 m²) + ACR < 30 mg/g, n (%)620 (51.3%)387 (55.8%)233 (45.3%)  eGFR ≥60 (mL/min/1.73 m²)+ACR ≥ 30 mg/g, n (%)347 (28.7%)205 (29.5%)142 (27.6%)  eGFR <60 (mL/min/1.73 m²) + ACR < 30 mg/g, n (%)57 (4.7%)22 (3.2%)35 (6.8%)  eGFR <60 (mL/min/1.73 m²) + ACR ≥ 30 mg/g, n (%)184 (15.2%)80 (11.5%)104 (20.2%) Target achieved subgroup     BP <130/80 mm Hg, n (%)281 (23.3%)142 (20.5%)139 (27.0%)0.007 BMI <24 kg/m2, n (%)370 (30.6%)190 (27.4%)180 (35.0%)0.004 HbA1c <7%, n (%)256 (21.2%)136 (20.0%)120 (24.4%)0.071 LDL <2.6 mmol/L, n (%)668 (55.3%)357 (52.0%)311 (60.9%)0.002Lifestyle and medications     Smoking (%)362 (30.0%)217 (31.3%)145 (28.2%)0.251 Alcohol (%)244 (20.2%)135 (19.5%)109 (21.2%)0.453 Antidiabetic drugs (%)831 (68.8%)481 (69.3%)350 (68.1%)0.652 Insulin (%)521 (43.1%)286 (41.2%)235 (45.7%)0.118 Antihypertensive drugs (%)1087 (90.0%)608 (87.6%)479 (93.2%)0.001 Statin (%)362 (30.0%)193 (27.8%)169 (32.9%)0.057 Fibrates (%)27 (2.2%)21 (3.0%)6 (1.2%)0.031Data are presented as mean±SD or median (IQR) for continuous variables, and n (%) for categorical variables. P values were calculated using Student’s t-test or Mann-Whitney U test for continuous variables and χ² test for categorical variables.ACR, albumin-to-creatinine ratio.ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; BP, blood pressure; Cr, serum creatinine; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FIB-4, Fibrosis-4 index; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; PLT, platelet; SBP, systolic blood pressure; TC, total cholesterol; TG, triglycerides.Compared with patients with FIB-4≤1.3, those with FIB-4>1.3 were older (63.85±7.91 vs 54.14±10.52 years, p<0.001), had a longer diabetes duration (10.62±7.55 vs 8.05±7.05 years, p<0.001). They also had lower BMI and diastolic BP.In terms of metabolic parameters, patients with elevated FIB-4 had lower HbA1c, TG, TC, and LDL-C, but higher HDL-C levels. AST levels were significantly higher in the FIB-4 >1.3 group, whereas ALT levels were comparable between groups (p=0.324).For renal parameters, patients with FIB-4 >1.3 had lower eGFR, higher ACR, and Cr. The distribution of combined eGFR and ACR categories differed significantly between groups (p<0.001). A smaller proportion of patients with FIB-4>1.3 had preserved renal function (eGFR ≥60 mL/min/1.73 m² and ACR <30 mg/g; 45.3% vs 55.8%), while a larger proportion had reduced eGFR (<60 mL/min/1.73 m²; 27.0% vs 14.7%).Notably, patients with FIB-4>1.3 were more likely to achieve clinical targets for BP <130/80 mm Hg (27.0% vs 20.5%, p=0.007), BMI <24 kg/m² (35.0% vs 27.4%, p=0.004), and LDL-C <2.6 mmol/L (60.9% vs 52.0%, p=0.002). The proportion achieving HbA1c targets did not differ significantly (p=0.071). In addition, the use of antihypertensive medications was higher in the FIB-4 >1.3 group (93.2% vs 87.6%, p=0.001).Distribution of KDIGO risk categories by FIB-4 status The distribution of KDIGO risk categories differed significantly between FIB-4 groups (p<0.001; figure 1). Compared with patients with FIB-4 ≤1.3, those with FIB-4>1.3 had a lower proportion classified as KDIGO low risk (45.3% vs 55.8%) and a higher proportion classified as KDIGO high or very high risk (categories 3–4: 28.4% vs 20.2%).Figure 1Distribution of KDIGO risk categories by FIB-4 status. The stacked bar chart shows the proportion of patients classified into KDIGO risk categories (KDIGO 1–4) stratified by FIB-4 index (≤1.3 vs >1.3). FIB-4, Fibrosis-4 index; KDIGO, Kidney Disease: Improving Global Outcomes.Association between FIB-4 and renal outcomes In multivariable logistic regression analysis, FIB-4 >1.3 was significantly associated with both KDIGO 3–4 and CKD across all models. After full adjustment (model 3), FIB-4 >1.3 remained independently associated with KDIGO 3–4 (OR 1.57, 95% CI 1.12 to 2.19, p=0.008) and CKD (OR 1.51, 95% CI 1.14 to 2.01, p=0.004) ( table 2).Table 2Association between elevated FIB-4 (>1.3) and renal outcomesOutcomeModel 1OR (95% CI)P valueModel 2OR (95% CI)P valueModel 3OR (95% CI)P valueCKD1.52 (1.21 to 1.91)<0.0011.50 (1.13 to 1.99)0.0051.51 (1.14 to 2.01)0.004KDIGO 3–41.57 (1.20 to 2.05)<0.0011.61 (1.17 to 2.21)0.00371.57 (1.12 to 2.19)0.008Model 1: unadjusted. Model 2: adjusted for age, sex, BMI, systolic blood pressure, diastolic blood pressure, glycated hemoglobin, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and diabetes duration. Model 3: additionally adjusted for smoking, alcohol, oral antidiabetic drugs, insulin, antihypertensive drugs, statins, and fibrates.BMI, body mass index; CKD, chronic kidney disease; KDIGO, Kidney Disease: Improving Global Outcomes.Stratified analyses by clinical target achievement By stratified analyses we further examined whether the association between FIB-4 and renal outcomes differed according to achievement of clinical targets ( table 3, figure 2).Figure 2Stratified analyses of FIB-4 association with renal outcomes by clinical target achievement. Forest plots show adjusted ORs and 95% CIs for the associations between FIB-4>1.3 and KDIGO 3–4 as well as CKD across predefined clinical subgroups. Models were adjusted for relevant covariates as described in the Methods section. P values for interaction are shown for each subgroup analysis. BMI, body mass index; BP, blood pressure; CKD, chronic kidney disease; FIB-4, Fibrosis-4 index; HbA1c, glycated hemoglobin; KDIGO, Kidney Disease: Improving Global Outcomes; LDL, low-density lipoprotein.Table 3Stratified analyses of FIB-4 association with renal outcomes by clinical target achievementSubgroupEvents N (%)CKDOR (95% CI)CKDP valueP valueInteractionEvents N (%)KDIGO 3–4OR (95% CI)KDIGO 3–4P valueP valueInteractionBP0.8940.472BP< 130/80mm Hg118 (42.0%)1.43 (0.77 to 2.62)ns54 (19.2%)1.26 (0.58 to 2.74)nsBP≥130mm Hg470 (50.7%)1.59 (1.15 to 2.21)0.006232 (25.0%)1.66 (1.14 to 2.43)0.009HbA1c0.0490.238HbA1c <7%121 (47.3%)2.04 (0.98 to 4.27)ns64 (25.0%)1.63 (0.67 to 3.98)nsHbA1c ≥7%443 (48.3%)1.39 (1.01 to 1.90)0.043215 (23.4%)1.49 (1.03 to 2. 16)0.035LDL0.2080.851LDL <2.6mmol/L339 (50.7%)1.79 (1.21 to 2.65)0.003167 (25.0%)1.50 (0.97 to 2.35)nsLDL ≥2.6mmol/L243 (45.8%)1.23 (0.80 to 1.89)ns116 (21.9%)1.66 (0.97 to 2.83)nsBMI0.8890.499BMI<24kg/m²171 (46.2%)1.71 (1.03 to 2.84)0.03791 (24.6%)1.97 (1.08 to 3.60)0.027BMI≥24kg/m²417 (49.8%)1.43 (1.00 to 2.04)0.049195 (23.3%)1.33 (0.89 to 2.03)nsAdjusted age, sex, BMI, systolic BP, diastolic BP, HbA1c, triglycerides, LDL-C, HDL-C, diabetes duration, smoking, alcohol, oral antidiabetic drugs, insulin, antihypertensive drugs, statin, and fibrates.BMI, body mass index; BP, blood pressure; CKD, chronic kidney disease; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein cholesterol; KDIGO, Kidney Disease: Improving Global Outcomes; LDL-C, low-density lipoprotein cholesterol; ns, no significant (p≥0.05).For CKD, elevated FIB-4 was significantly associated with higher risk among patients not achieving BP targets (≥130/80 mm Hg; OR 1.59, p=0.006) and those not achieving HbA1c targets (≥7%; OR 1.39, p=0.043). Notably, the association remained significant in patients achieving LDL-C (<2.6 mmol/L; OR 1.79, p=0.003) and BMI (<24 kg/m²; OR 1.71, p=0.037) targets.For KDIGO 3–4, significant associations were observed among patients not achieving BP (OR 1.66, p=0.009) or HbA1c (OR 1.49, p=0.035) targets and among those achieving BMI targets (OR 1.97, p=0.027). Associations in other subgroups were directionally consistent but did not reach statistical significance.No significant interactions were detected between FIB-4 and clinical target achievement across all models (all p value for interaction>0.05).Vascular comorbidities by FIB-4 status within KDIGO categories To evaluate whether elevated FIB-4 identifies patients with greater vascular comorbidity burden within the same KDIGO risk stratum, we next compared the prevalence of vascular comorbidities between FIB-4 groups across KDIGO categories ( table 4).Table 4Prevalence of vascular comorbidities by FIB-4 status within each KDIGO risk categoryKDIGO riskFIB-4NCPN (%)LEAPN (%)CVD historyN (%)Stroke historyN (%)1 (low)≤1.3387200 (54.3)209 (56.8)38 (9.8)38 (9.8) > 1.3233144 (66.1)158 (70.9)42 (18.0)34 (14.6) P value 0.005<0.0010.0030.0722 (moderate)≤1.316794 (61.4)97 (62.2)23 (13.8)26 (15.6) >1.313588 (68.8)106 (82.2)32 (23.7)22 (16.3) P value 0.201<0.0010.0260.8643 (high)≤1.36942 (64.6)43 (68.3)8 (11.6)6 (8.7) >1.36849 (74.2)42 (68.9)15 (22.1)13 (19.1) P value 0.2320.9430.1010.0784 (very high)≤1.37144 (66.7)49 (75.4)9 (12.7)10 (14.1) >1.37863 (81.8)64 (83.1)14 (17.9)17 (21.8) P value 0.0370.2550.3740.222P value for trend  <0.001<0.001<0.0010.009Data are presented as N (%). P values within each KDIGO category were calculated using χ² test. P value for trend was assessed using the Cochran-Armitage trend test across FIB-4 groups within each KDIGO stratum.CVD, cardiovascular disease.CP, carotid plaque; FIB-4, Fibrosis-4 index; KDIGO, Kidney Disease: Improving Global Outcomes; LEAP, lower extremity arterial plaque.Within KDIGO 1, participants with FIB-4>1.3 had significantly higher prevalence of carotid plaque (66.1% vs 54.3%, p=0.005), lower extremity arterial plaque (70.9% vs 56.8%, p<0.001), and a history of cardiovascular disease (18.0% vs 9.8%, p=0.003).Within KDIGO 2, elevated FIB-4 was similarly associated with higher prevalence of lower extremity arterial plaque (82.2% vs 62.2%, p<0.001) and prior cardiovascular disease (23.7% vs 13.8%, p=0.026).Among participants in KDIGO 3–4, the same directional trends were observed, although differences did not reach statistical significance.Across all KDIGO categories 1-4, p value for trend was significant for carotid plaque, lower extremity arterial plaque, cardiovascular disease history, and stroke history (all p<0.01).Across all KDIGO categories1–4 1-4, p value for trend was significant for carotid plaque, lower extremity arterial plaque, cardiovascular disease history, and stroke history (all p<0.01).Figure 3Restricted cubic spline analysis of the association between FIB-4 index and renal outcomes. Panels (A) and (B) show the adjusted ORs (solid lines) with 95% CIs for KDIGO 3–4 and CKD, respectively. The reference point was set at FIB-4=1.3 (vertical dashed line). Restricted cubic splines were fitted with four knots placed at the fifth, 35th, 65th, and 95th percentiles of FIB-4 distribution. Models were adjusted for age, sex, body mass index, systolic blood pressure, diastolic blood pressure, glycated hemoglobin, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, diabetes duration, smoking, alcohol, oral antidiabetic drugs, insulin, antihypertensive drugs, statins, and fibrates. The gray histograms represent the distribution of FIB-4 values in the study population. CKD, chronic kidney disease; FIB-4, Fibrosis-4 index; KDIGO, Kidney Disease: Improving Global Outcomes.Non-linear association between FIB-4 and renal outcomes RCS analysis revealed a significant non-linear association between FIB-4 and renal outcomes ( figure 3). Notably, a significant non-linear association was observed for both KDIGO categories 3–4 (p value for nonlinearity<0.001) and CKD (p=0.010). The spline curves showed that ORs remained close to 1.0 at FIB-4 values below approximately 1.3 and increased progressively above this level. In contrast, when FIB-4 was modeled as a linear term, no significant association was observed with KDIGO 3–4 (p=0.997) or CKD (p=0.132).Additional sensitivity analyses To further evaluate the robustness of our primary findings, several sensitivity analyses were conducted ( online supplemental table S1, online supplemental table S2 and figure S1).SP110.1136/bmjdrc-2025-005880.supp1Supplementary dataFirst, FIB-4 was compared with the BARD score, another non-invasive hepatic fibrosis index (online supplemental table S1). In separate models, FIB-4>1.3 was significantly associated with KDIGO 3–4 (OR 1.57, 95% CI 1.12 to 2.19; p=0.008), whereas BARD ≥2 demonstrated only borderline significance (OR 1.48, 95% CI 1.00 to 2.21; p=0.052). In mutually adjusted models, the association with FIB-4 remained significant (OR 1.53; p=0.014), while that for BARD was attenuated (OR 1.39; p=0.122). Similar patterns were observed for CKD.Second, direct comparison of continuous and dichotomized FIB-4 indicated that only dichotomized FIB-4 (>1.3), not continuous FIB-4, was significantly associated with renal outcomes (online supplemental table S2).Furthermore, subgroup analyses stratified by age, sex, diabetes duration, MASLD status, medication use, and lifestyle factors revealed consistent associations across all subgroups, with no significant interactions except for smoking status (online supplemental figure S1).Discussion In this cross-sectional study of 1208 patients with T2DM and hypertension, FIB-4 was independently associated with both KDIGO 3–4 (OR 1.57, 95% CI 1.12 to 2.19) and CKD (OR 1.51, 95% CI 1.14 to 2.01). These associations remained even among patients achieving BMI/LDL-C targets, suggesting that FIB-4 reflects residual renal risk beyond conventional metabolic control. Moreover, elevated FIB-4 was associated with greater vascular comorbidity burden even within KDIGO 1–2. Collectively, these findings demonstrate that FIB-4 may function as a complementary marker for risk stratification within the KDIGO framework.Our results extend previous evidence linking hepatic fibrosis markers to renal impairment. Saito et al reported that FIB-4>1.3 independently predicted incident diabetic kidney disease in a Japanese cohort (HR 1.54, 95% CI 1.15 to 2.08).15 Takahashi et al observed a significant inverse correlation between FIB-4 and eGFR in middle-aged and older adults25, while Mima et al confirmed this relationship across diverse renal pathologies, including nephrosclerosis and IgA nephropathy.26 Similarly, Oikawa et al demonstrated that high FIB-4 predicted 5-year CKD incidence even in metabolically healthy individuals.27 However, these studies primarily relied on binary CKD definitions (eGFR<60 mL/min/1.73m² or albuminuria≥30 mg/g), without integrating both parameters into a comprehensive KDIGO risk classification model. In the present study, we evaluated FIB-4 across both KDIGO-defined risk categories and conventional CKD definitions, thereby extending the analysis within the contemporary KDIGO framework.4 In sensitivity analyses, FIB-4 demonstrated more robust and consistent associations with renal outcomes than the BARD score (online supplemental table S1), and the dichotomized threshold (FIB-4>1.3) outperformed the continuous measure (online supplemental table S2), validating the clinical relevance of the 1.3 cut-off in patients with T2DM and hypertension.To our knowledge, this is the first study to position FIB-4 as a potential risk marker within the KDIGO 2024 framework for CKD management. This concept parallels the 2019 ACC/AHA cardiovascular prevention guidelines, which introduced risk-enhancing factors to refine risk estimation among individuals at borderline or intermediate risk of ASCVD.16 Similarly, FIB-4 may serve an analogous role in nephrology by identifying patients whose renal risk is not fully captured by KDIGO categories based solely on eGFR and albuminuria. This framework is supported by growing evidence that liver-kidney crosstalk contributes to cardiorenal-metabolic disease.10 28 MASLD, particularly in the presence of advanced fibrosis, has been shown to independently increase the risk of both cardiovascular disease and CKD.29 30 However, accumulating data also point to marked heterogeneity in the relationship between MASLD and CKD. Accordingly, a recent Lancet review emphasized that MASLD represents a spectrum of heterogeneous disease phenotypes, with varying contributions from metabolic dysfunction, hepatic insulin resistance, and systemic inflammation across individuals, which may translate into differential downstream renal risk.31Moreover, the reported strength of the association between MASLD and CKD varies substantially across studies, reflecting differences in disease definitions, diagnostic criteria, adjustment for shared cardiometabolic confounders, and the evolving nomenclature from NAFLD to metabolic dysfunction-associated fatty liver disease (MAFLD) to MASLD.32 In this context, our stratified analyses by MASLD status (online supplemental figure S1) demonstrated consistent associations between FIB-4 and renal outcomes irrespective of the presence of MASLD presence, with no significant interaction (p=0.176). These findings suggest that fibrosis-based markers such as FIB-4 may capture clinically relevant renal risk beyond that explained by steatosis status alone, supporting their integration into multidimensional frameworks for cardiorenal-metabolic risk stratification.The analysis of vascular comorbidities across KDIGO 1–4 (table 4) demonstrated that elevated FIB-4 was associated with a significantly higher prevalence of carotid plaque, lower extremity arterial plaque, and cardiovascular disease history, even within KDIGO 1–2 (all p values for trend<0.01). Within KDIGO 3–4, similar patterns were observed but did not reach statistical significance within individual categories, likely due to smaller sample sizes (KDIGO 3: n=137; KDIGO 4: n=149) and limited statistical power. Nevertheless, the overall trend across KDIGO 1–4 remained significant, supporting a consistent gradient of vascular comorbidity burden associated with elevated FIB-4. Subgroup analyses (online supplemental figure S1) demonstrated consistent associations across age, sex, diabetes duration, MASLD status, medication use, and alcohol consumption (all p values for interaction>0.05). The interaction between smoking status and FIB-4 reached nominal statistical significance (p=0.022), with a stronger association among smokers (OR 2.66, p=0.002) than non-smokers (OR 1.26, p=0.269). However, this observation should be interpreted cautiously, as interaction tests in cross-sectional analyses are often underpowered and may produce statistically unstable results. Nevertheless, smoking and hepatic fibrosis share biological pathways characterized by oxidative stress and systemic inflammation, which may underlie the observed effect modification.33 Confirmation in prospective studies is warranted.Notably, despite exhibiting more favorable metabolic profiles, including lower HbA1c, LDL-C, TG levels, and higher rates of achieving clinical targets, patients with elevated FIB-4 displayed worse renal parameters and greater vascular comorbidity burden (table 1). This apparent paradox likely reflects disease-related metabolic alterations rather than a genuinely protective metabolic phenotype. Lower lipid levels in the high FIB-4 group may result from disrupted lipid metabolism and impaired hepatic synthetic function associated with advanced liver disease, rather than improved cardiometabolic health.34 Similarly, lower HbA1c may reflect intensive glucose-lowering treatment (eg, higher insulin use), rather than intrinsically better glycemic control. Reduced BMI in this group may further indicate sarcopenia or chronic catabolic states commonly observed in advanced hepatic fibrosis, rather than metabolic resilience.35 Collectively, these findings suggest that seemingly favorable metabolic markers may paradoxically signal greater systemic disease severity.Beyond these clinical observations, several interrelated biological pathways may underlie the association between hepatic fibrosis and adverse renal and vascular outcomes.11–13 Subclinical hepatic fibrosis is increasingly recognized as a driver of chronic low-grade inflammation, characterized by increased release of proinflammatory cytokines (eg, Interleukin (IL)-6, Tumour necrosis factor (TNF)-α) and dysregulated hepatokine secretion, including fetuin-A and fibroblast growth factor 21.36 37 These liver-derived factors act as endocrine mediators influencing systemic inflammation and insulin sensitivity, thereby contributing to multiorgan injury, including renal dysfunction.38 Emerging experimental evidence further supports a direct role of hepatokin-mediate liver-kidney crosstalk in promoting renal fibrogenesis.39 Concurrently, hepatic insulin resistance may amplify systemic metabolic dysfunction and lipotoxicity,40 while activation of the renin-angiotensin-aldosterone system, endothelial dysfunction, and oxidative stress represent shared pathogenic pathways linking hepatic fibrosis with renal and vascular injury.41 42 Taken together, these mechanisms provide biological plausibility for the observation that FIB-4 captures residual cardiorenal risk beyond traditional metabolic risk factors, even in patients who appear metabolically well controlled by conventional measures.RCS analysis confirmed a nonlinear association between FIB-4 and renal outcomes, with a threshold effect around 1.3 (p value for non-linearity<0.001 for KDIGO 3–4; p=0.010 for CKD; figure 3). This pattern supports the use of FIB-4>1.3 as a clinically meaningful cut-off for identifying patients at increased risk of adverse renal outcomes. This threshold aligns with the current AASLD guideline recommending 1.3 for identifying individuals at elevated risk of clinically significant hepatic fibrosis.14 43 Sensitivity analyses testing alternative cutoffs (1.0, 1.5, 2.0) demonstrated that 1.3 provided optimal statistical discrimination for both outcomes (online supplemental table S3). Moreover, dichotomized FIB-4 showed stronger associations with renal outcomes than continuous FIB-4 (online supplemental table S2), underscoring the clinical utility of threshold-based interpretation. Bootstrap validation (1000 resamples) confirmed the stability of the effect estimates. Because ALT is a component of FIB-4, additional analyses were conducted to evaluate potential confounding (online supplemental figures S2 and S3); the association persisted after adjusting for low ALT levels, indicating minimal confounding.Our findings have important clinical implications. FIB-4 is calculated from routinely available laboratory parameters and requires no additional cost or specialized testing, making it particularly practical in resource-limited settings. Current guidelines already recommend FIB-4 screening for hepatic fibrosis in patients with T2DM14; extending its use to kidney risk assessment would require no additional testing. Among patients classified as KDIGO low risk but presenting with elevated FIB-4, clinicians might consider closer monitoring of kidney function, intensified management of cardiovascular risk factors, or earlier initiation of nephroprotective therapies such as sodium-glucose cotransporter 2 (SGLT2) inhibitors. These agents have demonstrated dual hepatic and renal benefits.44–46 Emerging evidence further suggests that SGLT2 inhibitors may lower FIB-4 and improve hepatic outcomes while simultaneously conferring cardiovascular and renal protection.47 48 These findings collectively support the integration of FIB-4 into comprehensive cardiorenal-metabolic risk assessment strategies.Several limitations should be acknowledged. First, the cross-sectional, single-center design precludes causal inference and limits external generalizability; moreover, because the study specifically enrolled patients with coexisting T2DM and hypertension, the findings may not be applicable to individuals with either condition alone or to the general population. Second, liver biopsy was not performed; however, as FIB-4 is intended for noninvasive risk stratification, this approach reflects real-world clinical practice and current guideline recommendations.49 Third, residual confounding from unmeasured factors, such as dietary patterns, physical activity, genetic susceptibility, detailed pharmacotherapy, and socioeconomic status, cannot be fully excluded despite multivariable adjustment. In addition, this single-center study was conducted in a Chinese population, with all participants of Chinese ethnicity, which may limit the applicability of the findings to other ethnic groups or healthcare settings. Fourth, because age is a component of FIB-4, potential confounding by age-related renal decline warrants attention. Nevertheless, our models adjusted for age as a continuous variable, and subgroup analyses showed consistent associations across age groups (p value for interaction>0.05), suggesting that the observed relationship is not driven by age alone. However, the association was statistically significant only among participants aged ≥65 years, indicating that the discriminatory performance of FIB-4 may be attenuated in younger populations and warrants further investigation. Fifth, information on specific subclasses of antidiabetic medications (eg, SGLT2 inhibitors, glucagon-like peptide-1 receptor agonists (GLP)-1 receptor agonists) and antihypertensive medications (eg, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (ACEi/ARB)) was not available. Although overall use of oral antidiabetic drugs, insulin, and antihypertensive medications was adjusted for in multivariable analyses, residual confounding related to differential use of renoprotective therapies cannot be excluded.Future prospective studies are needed to validate whether incorporating FIB-4 into the KDIGO risk stratification framework improves prognostic discrimination and reclassification, and to determine whether targeted interventions in patients with elevated FIB-4 translate into improved renal and cardiovascular outcomes. Strengths of our study include the comprehensive evaluation of vascular comorbidities, application of the updated KDIGO 2024 framework, and validation of results through extensive sensitivity analyses.Conclusion In patients with T2DM and hypertension, elevated FIB-4 was independently associated with KDIGO high/very high risk and CKD, and identifies individuals with greater vascular comorbidity burden even within KDIGO low-risk. Importantly, this association persisted among patients achieving metabolic targets, suggesting that FIB-4 captures residual renal and vascular risk beyond conventional factors. As a low-cost, noninvasive index derived from routine laboratory tests, FIB-4 may provide complementary information to KDIGO risk categories, thereby facilitating more precise identification of patients who could benefit from intensified monitoring and early nephroprotective interventions. Further longitudinal/prospective studies are now warranted to determine whether integrating FIB-4 into the KDIGO framework improves risk stratification and clinical outcomes.",
  "title": "Elevated FIB-4 index as a risk marker within the KDIGO framework in patients with type 2 diabetes and hypertension",
  "uid": "1ba8072c-83f7-5e14-ab9a-f90361a1e9cc"
}