Skip to main content
NCBI home page
Journal of Inflammation Research logoLink to Journal of Inflammation Research
. 2026 Oct 8;19:644335. doi: 10.2147/JIR.S644335

Association of the C-Reactive Protein–Triglyceride–Glucose Index with Kidney Injury in Patients with Coronary Heart Disease: A Two-Center Cross-Sectional Study

Mengmeng Fan 1, Xueping Zhang 1, Xianwei Tian 1, Keli Li 2, Zhiqiang Liu 1,✉
PMCID: PMC13657802  PMID: 42866475

Abstract

Background

The composite index of inflammation and insulin resistance (IR), the C-reactive protein (CRP)–triglyceride–glucose (TyG) index (CTI), has been suggested to be closely associated with cardiovascular disease. However, the relationship between the CTI and kidney injury (KI), including its subtypes [declined kidney function (DKF) and proteinuria], remains unclear in patients with coronary heart disease (CHD), and whether sex differences exist is unknown.

Methods

This two-center cross-sectional study enrolled 3793 patients with CHD between September 2021 and May 2026. Multivariable stepwise-adjusted logistic regression assessed associations of the CTI with KI and its subtypes. Restricted cubic spline (RCS) analysis identified dose-response relationships and threshold effects, followed by piecewise comparisons. Subgroup analyses stratified by sex were performed to evaluate consistency across sexes. The area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and the Boruta algorithm compared the discriminative performance of the CTI versus CRP and TyG alone for KI and its subtypes.

Results

Multivariable logistic regression showed that the CTI was significantly associated with KI and its subtypes in CHD patients. The overall prevalence of KI was 814/3,793 (21.5%). RCS revealed that CTI exceeding 4.8, 4.7, and 4.55, respectively, significantly increased risks of KI, DKF, and proteinuria. Notably, the increased risk of KI was particularly evident when CTI exceeded 4.45 in females and 4.96 in males. AUC, DCA, and Boruta rankings all confirmed stronger discriminative performance of the CTI than CRP or TyG alone for KI and its subtypes.

Conclusion

Elevated CTI was independently associated with KI and its subtypes in CHD patients, with clear threshold effects that differed by sex and superior discriminative performance to single indicators. These associations were more pronounced in overweight/obese patients. Although CTI may help identify individuals at higher risk, the sex-specific thresholds should be regarded as exploratory threshold estimates and require external validation before clinical application.

Keywords: coronary heart disease, C-reactive protein-triglyceride-glucose index, kidney injury, declined kidney function, proteinuria

Introduction

Kidney injury (KI), primarily manifested as reduced estimated glomerular filtration rate (eGFR) and/or proteinuria, is a common and clinically important comorbidity in patients with coronary heart disease (CHD).1–3 For brevity, this composite phenotype is referred to as KI in the present study; however, it was operationalized solely as a single measurement of eGFR <60 mL/min/1.73 m2 and/or proteinuria (24-hour urinary protein excretion >150 mg, or urinary albumin-to-creatinine ratio >30 mg/g). This definition does not denote acute kidney injury (AKI) or chronic kidney disease (CKD), because AKI requires a change in kidney function over a defined short interval and CKD requires persistence for at least 3 months; neither can be inferred from a single contemporaneous assessment. Long-term renal impairment can result in complications such as edema, hypertension, and cardiovascular disease (CVD).4,5 If persistently aggravated, it may progress to end-stage renal disease, involving multiple systems including the cardiovascular, skeletal, and hematopoietic systems, thereby significantly increasing the risk of mortality.6–9 CHD, a common chronic cardiovascular disorder particularly in middle-aged and elderly populations, often coexists with various chronic conditions.10,11 These high-risk patients also constitute the major population susceptible to reduced eGFR and/or proteinuria.12 Therefore, early detection, prevention, and intervention for KI in CHD patients hold substantial clinical and public health significance.

Previous strategies to mitigate KI in CHD patients have largely focused on traditional risk factors such as blood pressure (BP) and glycemic control.13–15 However, relying solely on these conventional approaches may be insufficient to adequately reduce the risk of KI. This is because CHD patients frequently experience a state of chronic low-grade inflammation and often present with insulin resistance (IR) secondary to obesity, diabetes, and hyperlipidemia.16,17 Accumulating basic research has highlighted the pivotal roles of inflammation and IR in the pathogenesis of KI.18–21 Chronic inflammation directly damages glomeruli, increases vascular permeability, and induces proteinuria.18,19,22 Concurrently, IR not only exacerbates inflammatory responses but also activates the renin-angiotensin-aldosterone system (RAAS), thereby imposing additional burdens on the kidneys.20,21 The synergistic effect of these two factors substantially elevates the risk of KI. Clinical evidence has also emerged. Studies in Chinese populations have shown that an elevated systemic inflammation response index (SIRI) is closely associated with CKD.23 Similarly, analyses in American populations have revealed that this index increases the risk of diabetic kidney disease in patients with type 2 diabetes mellitus (DM).24 Moreover, research on triglyceride glucose index (TyG)-related IR has confirmed its association with decreased kidney function (DKF) and the progression of CKD.25 These findings provide direct evidence linking inflammation and IR to KI.

Nevertheless, a single biomarker may not fully capture the complex pathophysiological interplay underlying these conditions. Recently, a novel composite index reflecting both inflammation and IR—the [C-reactive protein (CRP)-TyG index] (CTI)—has been developed.26,27 Conceptually, CRP reflects systemic inflammation, whereas TyG is a surrogate marker of IR and related metabolic dysregulation. These two processes are closely interconnected and may amplify each other; therefore, combining them into a composite index may capture inflammatory–metabolic crosstalk more comprehensively than either component alone and may provide incremental information beyond CRP or TyG alone. The CTI has demonstrated strong predictive capability in various diseases, including CVD, cardio-renal metabolic syndrome, sarcopenia, and depression.26–29 However, it remains unclear whether, in the specific high-risk population of CHD patients, the CTI is independently associated with KI and whether this association is stronger than that of either CRP or the TyG. Moreover, sex differences in this association have not been adequately explored. Such differences may be biologically and clinically plausible because sex hormones, body fat distribution, inflammatory responses, insulin sensitivity, renal hemodynamics, and the progression of renal impairment differ between women and men.30,31 These factors may influence how inflammation–IR-related indices relate to kidney injury, making sex-stratified analyses more than a purely statistical exploration.32 Therefore, this dual-center study was conducted to explore the association between the CTI and KI in CHD patients, including sex-stratified analyses to assess consistency across sexes, and to evaluate the discriminative performance of the CTI relative to CRP and TyG alone. The findings may provide evidence-based support for early KI monitoring and future risk stratification in this high-risk population. However, clinically useful CTI thresholds would require validation in independent populations before they could be considered for clinical risk stratification.

Materials and Methods

Study Design and Population

A total of 4255 patients with a confirmed diagnosis of CHD from the two centers were initially enrolled in this study. First, after excluding patients with missing data on fasting plasma glucose (FPG), triglycerides (TG), and CRP, which precluded the calculation of the CTI, 4126 patients remained for CTI calculation. Second, to assess KI, we further excluded those lacking data on estimated glomerular filtration rate (eGFR), 24-hour urinary protein excretion, or the urinary albumin-to-creatinine ratio (ACR). In addition, considering other potential confounding factors, we excluded patients with a prior diagnosis of end-stage renal disease, those who had received renal replacement therapy, and those with severe liver disease, malignancy, ongoing anti-infective therapy, or autoimmune diseases. Following these rigorous exclusion criteria, a total of 3793 patients were ultimately eligible for the study and included in the final analysis (Figure 1).

Figure 1.

Flowchart of patient enrollment and exclusion for a study on coronary heart disease. The flowchart outlines the enrollment and exclusion process for a study on coronary heart disease. Initially, 4255 patients diagnosed with coronary heart disease at Huangshan City People′s Hospital and Xinxiang Central Hospital were enrolled. Patients with missing data on fasting plasma glucose, triglycerides and C-reactive protein, totaling 129, were excluded, leaving 4126 patients. Further exclusions were made for 333 patients due to missing data on estimated glomerular filtration rate and proteinuria, prior diagnosis of end-stage renal disease or renal replacement therapy, coexisting severe liver disease, cancer, anti-infective treatment, or autoimmune disease. Ultimately, 3793 patients met the study requirements.

Open in a new tab

Flowchart of patient enrollment and exclusion.

Covariate Information Collection

Covariate information in this study was collected from multiple sources, including medical visit records, electronic medical records, and patient interviews. First, basic anthropometric measurements were obtained by trained nurses, including height, weight, body mass index (BMI), systolic and diastolic blood pressure (SBP and DBP). Personal lifestyle habits, such as smoking and alcohol consumption, were acquired through structured interviews. Second, laboratory parameters were collected, encompassing liver function tests, total cholesterol (TC), TG, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and FPG. Based on these measurements, the eGFR was calculated using the CKD-EPI equation specific to the Chinese population.33–35 Third, information on medication use was recorded, including antiplatelet agents, lipid-lowering drugs, beta-blockers, and angiotensin-converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs). Finally, medical history was obtained with a specific focus on the diagnoses of hypertension, diabetes mellitus, and hyperlipidemia (Supplementary Material).

Calculation of the CTI

Based on previous studies, the CTI was calculated using the following formula:26,27

CTI = 0.412 × Ln (CRP [mg/L]) + Ln (TG [mg/dL] × FPG [mg/dL])/2.

Outcomes

The primary outcome of this study was the occurrence of KI, defined as the presence of any of the following: 1) Decreased kidney function (DKF): eGFR <60 mL/min/1.73 m2; 2) Proteinuria: 24-hour urinary protein excretion >150 mg, or urinary ACR >30 mg/g. Participants meeting at least one of the above criteria were considered to have KI. This composite definition was intended to capture both impaired kidney function and markers of kidney damage. It does not denote acute kidney injury (AKI). Because eGFR and proteinuria/ACR were measured at a single time point in this cross-sectional study, we could not determine the duration of abnormalities or distinguish acute from chronic kidney disease.

Statistical Analysis

First, all patients with CHD were divided into three equal groups according to CTI tertiles, and comparisons of covariates were made across the three groups. Secondly, we conducted a collinearity diagnosis to determine whether all the variables had significant contributions (Table S1). To evaluate the independent associations of the CTI with KI and its subtypes (DKF and proteinuria), multivariable stepwise-adjusted logistic regression models were used, and trend tests were performed. Next, restricted cubic spline (RCS) analysis was employed to explore the dose–response relationship and potential threshold effects between the CTI and each outcome. To further validate the robustness of the findings, extensive subgroup analyses were conducted. Finally, to compare the diagnostic performance of the CTI with that of the individual indicators (CRP and TyG), we applied receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and the Boruta algorithm—a machine-learning method for assessing variable importance. Detailed information on the statistical analyses is provided in the Supplementary Material.

R software (version 4.3.2) was used to perform all statistical analyses. Statistical significance was defined as a two-sided P value < 0.05.

Results

Comparison of Baseline Characteristics Among the Three CTI Groups

Table 1 presents the comparison of baseline characteristics among three groups of patients equally divided according to CTI tertiles (T1, T2, and T3). Compared with the T1 group (lowest CTI), the higher CTI groups (T2 and T3) exhibited a higher proportion of male patients but a relatively younger age. Regarding lifestyle factors, the rates of current smoking and alcohol consumption were significantly higher in the T2 and T3 groups. In terms of physiological parameters, both SBP and DBP were higher in the higher CTI groups than in the T1 group.

Table 1.

Comparison of Baseline Characteristics Across Tertiles of the CTI

Characteristic T1 (<4.56) T2 (4.56–5.12) T3 (>5.12) P value
Numbers 1265 1264 1264
Sex (%) <0.001
Female 770 (60.87%) 614 (48.58%) 579 (45.81%)
Male 495 (39.13%) 650 (51.42%) 685 (54.19%)
Age (years) 67.31±10.74 64.44±10.64 64.06±11.07 <0.001
BMI (kg/m2) 25.60±3.80 26.26±3.65 26.71±3.67 <0.001
SBP (mmHg) 125.65±17.46 133.59±18.12 141.41±18.52 <0.001
DBP (mmHg) 81.47±13.39 85.50±14.03 90.54±14.04 <0.001
Current smoking (%) 277 (21.90%) 369 (29.19%) 404 (31.96%) <0.001
Current drinking (%) 248 (19.60%) 309 (24.45%) 316 (25.00%) 0.002
Laboratory tests
ALT (U/L) 16.15 (11.40–23.75) 18.00 (13.00–27.40) 19.00 (13.82–27.00) 0.001
AST (U/L) 17.10 (14.25–20.90) 18.00 (15.00–22.00) 18.90 (15.75–23.10) <0.001
TC (mmol/L) 3.90±0.95 4.02±0.98 4.23±1.07 <0.001
TG (mmol/L) 0.71 (0.59–0.88) 0.91 (0.80–1.04) 1.05 (0.93–1.21) <0.001
LDL-C (mg/dL) 2.36±0.83 2.42±0.94 2.46±0.95 0.024
HDL-C (mg/dL) 1.39±0.35 1.30±0.34 1.26±0.34 <0.001
FPG (mmol/L) 5.18±1.24 5.30±1.28 5.39±1.36 <0.001
eGFR (mL/min/1.73 m2) 105.52±32.05 102.65±34.01 101.44±35.16 0.008
CRP (mg/dL) 2.74±2.68 3.02±2.63 6.61±9.07 <0.001
CTI 3.97±0.38 4.69±0.14 5.19±0.24 <0.001
Medical history
Hypertension (%) 556 (43.95%) 570 (45.09%) 650 (51.42%) <0.001
DM (%) 341 (26.96%) 382 (30.22%) 411 (32.52%) 0.009
Hyperlipidemia (%) 251 (19.84%) 273 (21.60%) 297 (23.50%) 0.083
Medications
Lipid-lowering drugs (%) 1234 (97.55%) 1252 (99.05%) 1241 (98.18%) 0.015
Antiplatelet drugs (%) 1237 (97.79%) 1229 (97.23%) 1230 (97.31%) 0.632
Beta-blockers (%) 1232 (97.39%) 1239 (98.02%) 1251 (98.97%) 0.013
ACEIs/ARBs (%) 872 (68.93%) 887 (70.17%) 943 (74.60%) 0.004
Open in a new tab

Notes: Data are presented as mean ± standard deviation, median (interquartile range), or as numbers, and percentages.

Abbreviations: CTI, C-reactive protein-triglyceride-glucose index; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; ALT, alanine transaminase; AST, aspartate transaminase; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TC, total cholesterol; TG, triglyceride; FPG, fasting plasma glucose; eGFR, estimated glomerular filtration rate; CRP, C-reactive protein; DM, diabetes mellitus; ACEIs, angiotensin-converting enzyme inhibitors; ARBs, angiotensin receptor blockers.

Further analysis of laboratory findings revealed the following: first, liver function levels were significantly elevated in the higher CTI groups; second, regarding lipid metabolism, TC, TG, and LDL-C were markedly higher, whereas HDL-C was relatively lower; additionally, FPG and CRP levels increased with higher CTI, and the CTI itself was, by definition, higher; in contrast, eGFR was lower in the higher CTI groups.

With respect to medical history, the prevalence of hypertension and diabetes mellitus (DM) was significantly higher in the higher CTI groups compared with the T1 group. Regarding medication use, the rates of lipid-lowering drugs, beta-blockers, and ACEIs/ARBs were also higher in these groups. No significant differences were observed in the remaining variables across the three groups.

Association of the CTI with KI and Its Subtypes in Patients with CHD

Among the 3793 patients, 814 had KI. Table S2 shows the numbers of patients with KI and its subtypes. To investigate the association of the CTI with KI and its subtypes—DKF and proteinuria—in patients with CHD, we first divided the patients into three groups (T1, T2, T3) based on CTI tertiles and compared the prevalence of KI and its subtypes across the three groups. The results showed a gradually increasing trend in the prevalence of KI from the T1 to T3 group (Figure 2A). Similarly, for the two subtypes (DKF and proteinuria), both the T2 and T3 groups exhibited significantly higher prevalence than the T1 group, with the T3 group having the highest prevalence (Figure 2B and C).

Figure 2.

A grouped bar chart showing kidney injury, decreased kidney function and proteinuria across tertile groups. Image A: Bar chart showing kidney injury prevalence across Tertiles 1, 2 and 3. Y-axis ranges from 0 to 30. Values: 13.99% (Tertile 1), 23.10% (Tertile 2), 27.29% (Tertile 3). Trend significance: P < 0.001. Image B: Bar chart depicting decreased kidney function prevalence for Tertiles 1, 2 and 3. Y-axis ranges from 0 to 15. Values: 6.53% (Tertile 1), 10.92% (Tertile 2), 13.53% (Tertile 3). Trend significance: P < 0.001. Image C: Bar chart illustrating proteinuria prevalence across Tertiles 1, 2 and 3. Y-axis ranges from 0 to 16. Values: 7.51% (Tertile 1), 12.58% (Tertile 2), 14.56% (Tertile 3). Trend significance: P < 0.001.

Open in a new tab

Prevalence of kidney injury and its subtypes by the three groups. (A) Kidney injury; (B) Decreased kidney function; (C) Proteinuria. Note (numbers): T1: 1265; T2: 1264; T3: 1264.

Logistic regression models were then used to evaluate the association between CTI and KI. In the unadjusted model (Model 1), each one-unit increase in CTI was associated with a 2.724-fold increased risk of KI (OR = 3.724, 95% CI: 3.149–4.421), and each one-standard deviation (SD) increase in CTI was associated with a 1.120-fold increased risk (OR = 2.120, 95% CI: 1.927–2.339) (Table 2). After full adjustment for covariates (Model 4), the association remained significant: each one-unit increase in CTI was associated with a 206.2% increased risk (OR = 3.062, 95% CI: 2.523–3.734), and each one-SD increase was associated with an 89.6% increased risk (OR = 1.896, 95% CI: 1.697–2.124). When CTI was analyzed as a categorical variable, the association became more explicit: compared with the T1 group, the T2 and T3 groups had a 63.4% (OR = 1.634) and 85.2% (OR = 1.852) higher risk of KI, respectively (Table 2).

Table 2.

Association of the CTI with Kidney Injury in Patients with CHD

Kidney Injury Model 1 Model 2 Model 3 Model 4
OR (95% CI) P OR (95% CI) P OR (95% CI) P OR (95% CI) P
CTI (per 1 increase) 3.724 [3.149, 4.421] <0.001 3.717 [3.133, 4.428] <0.001 3.092 [2.553, 3.764] <0.001 3.062 [2.523, 3.734] <0.001
CTI (per 1SD increase) 2.120 [1.927, 2.339] <0.001 2.118 [1.921, 2.341] <0.001 1.907 [1.709, 2.133] <0.001 1.896 [1.697, 2.124] <0.001
Tertile of CTI
Tertile 1 Reference Reference Reference Reference
Tertile 2 1.847 [1.505, 2.271] <0.001 1.807 [1.468, 2.229] <0.001 1.689 [1.332, 2.145] <0.001 1.634 [1.294, 2.066] <0.001
Tertile 3 2.308 [1.889, 2.827] <0.001 2.232 [1.820, 2.745] <0.001 1.868 [1.485, 2.356] <0.001 1.852 [1.466, 2.345] <0.001
P for trend <0.001 <0.001 <0.001 <0.001
Open in a new tab

Notes: Model 1: no covariates were adjusted. Model 2: age, sex, BMI, smoking status, drinking status SBP, and DBP were adjusted. Model 3: Model 2 plus adjustment for ALT, AST, TC, TG, HDL-C, LDL-C, and FPG. Model 4: Model 3 plus adjustment for Hypertension, DM, Hyperlipidemia, Lipid-lowering drugs, antiplatelet drugs, beta-blockers, and ACEIs/ARBs.

Abbreviations: CTI, C-reactive protein-triglyceride-glucose index; CHD, coronary heart disease; OR, Odds Ratio; CI, confidence interval.

Similar findings were observed for the subtypes of KI. Each one-SD increase in CTI was associated with a 42.6% increased risk of DKF (OR = 1.426, 95% CI: 1.258–1.620) and an 81.7% increased risk of proteinuria (OR = 1.817, 95% CI: 1.591–2.084) (Table 3). When CTI was categorized by tertiles, the risk of both DKF and proteinuria also showed a graded increasing trend (P for trend < 0.01) (Table 3).

Table 3.

 Association of the CTI with Subtypes of Kidney Injury in Patients with CHD

Subtypes of Kidney Injury Model 1 Model 2 Model 3 Model 4
OR (95% CI) P OR (95% CI) P OR (95% CI) P OR (95% CI) P
Decreased kidney function
CTI (per 1 increase) 2.572 [2.085, 3.190] <0.001 2.586 [2.088, 3.218] <0.001 1.885 [1.514, 2.358] <0.001 1.859 [1.493, 2.327] <0.001
CTI (per 1SD increase) 1.716 [1.522, 1.941] <0.001 1.721 [1.523, 1.950] <0.001 1.437 [1.268, 1.633] <0.001 1.426 [1.258, 1.620] <0.001
Tertile of CTI
Tertile 1 Reference Reference Reference Reference
Tertile 2 1.768 [1.333, 2.359] <0.001 1.767 [1.328, 2.366] <0.001 1.657 [1.219, 2.265] 0.001 1.550 [1.148, 2.104] 0.004
Tertile 3 2.257 [1.719, 2.986] <0.001 2.238 [1.696, 2.975] <0.001 1.889 [1.401, 2.564] <0.001 1.835 [1.370, 2.472] <0.001
P for trend <0.001 <0.001 <0.001 <0.001
Proteinuria
CTI (per 1 increase) 4.117 [3.312, 5.149] <0.001 4.011 [3.216, 5.033] <0.001 2.938 [2.334, 3.722] <0.001 2.843 [2.252, 3.613] <0.001
CTI (per 1SD increase) 2.246 [1.983, 2.552] <0.001 2.212 [1.950, 2.519] <0.001 1.852 [1.623, 2.120] <0.001 1.817 [1.591, 2.084] <0.001
Tertile of CTI
Tertile 1 Reference Reference Reference Reference
Tertile 2 1.772 [1.360, 2.321] <0.001 1.683 [1.287, 2.212] <0.001 1.446 [1.084, 1.937] 0.013 1.445 [1.088, 1.928] 0.012
Tertile 3 2.098 [1.620, 2.733] <0.001 1.964 [1.509, 2.570] <0.001 1.563 [1.183, 2.076] 0.002 1.512 [1.139, 2.018] 0.005
P for trend <0.001 <0.001 0.003 0.008
Open in a new tab

Notes: Model 1: no covariates were adjusted. Model 2: age, sex, BMI, smoking status, drinking status SBP, and DBP were adjusted. Model 3: Model 2 plus adjustment for ALT, AST, TC, TG, HDL-C, LDL-C, and FPG. Model 4: Model 3 plus adjustment for Hypertension, DM, Hyperlipidemia, Lipid-lowering drugs, antiplatelet drugs, beta-blockers, and ACEIs/ARBs.

Abbreviations: CTI, C-reactive protein-triglyceride-glucose index; CHD, coronary heart disease; OR, Odds Ratio; CI, confidence interval.

Threshold Effect of CTI on KI and Its Subtypes (DKF and Proteinuria) in Patients with CHD

To further explore the dose–response relationship and potential threshold effects between the CTI and KI as well as its subtypes (DKF and proteinuria), we performed RCS analysis. The plotted curves revealed a nonlinear dose-dependent increase between the CTI and KI, DKF, and proteinuria (Figure 3). Moreover, specific thresholds were identified: 4.8 for KI, 4.7 for DKF, and 4.55 for proteinuria (Figure 3). These thresholds indicate that when the CTI exceeds 4.8, 4.7, and 4.55, respectively, the risk of KI, DKF, and proteinuria in patients with CHD increases significantly (Figure 3).

Figure 3.

A set of three line graphs showing odds ratio versus coronary thrombus index for kidney outcomes. Image A: Line graph with P < 0.001 for both overall and nonlinear. X-axis: CTI (3-6), Y-axis: OR (0-20). Horizontal line at OR=1, vertical line at CTI=4.8. Fitted curve and confidence intervals rise slowly to CTI 4.8 near OR=1, then steeply beyond CTI 5. Image B: Line graph with P < 0.001 overall, P=0.032 nonlinear. X-axis: CTI (3-6), Y-axis: OR (0-7.5). Horizontal line at OR=1, vertical line at CTI=4.7. Fitted curve and confidence intervals rise gradually to CTI 4.7 near OR=1, then sharply after CTI 5. Image C: Line graph with P < 0.001 for both overall and nonlinear. X-axis: CTI (3-6), Y-axis: OR (0-15). Horizontal line at OR=1, vertical line at CTI=4.55. Fitted curve and confidence intervals stay below OR=1 up to CTI 4.55, then rise steeply after CTI 5.

Open in a new tab

Dose-response relationship of the CTI with kidney injury and its subtypes (decreased kidney function and proteinuria) in patients with CHD. (A) Kidney injury; (B) Decreased kidney function; (C) Proteinuria. Note: The solid line represents the fitted curve; the dashed lines on either side of the solid line represent the 95% confidence interval; the red dashed line represents the threshold.

Based on the identified cut-off points, we further conducted piecewise comparative analyses. The results showed that for KI, patients with a CTI > 4.8 had a 2.322-fold higher risk compared with those with a CTI ≤ 4.8 (Table 4). Similarly, for DKF, patients with a CTI > 4.7 had a 1.989-fold higher risk than those with a CTI ≤ 4.7, and for proteinuria, patients with a CTI > 4.55 had a 2.071-fold higher risk than those with a CTI ≤ 4.55 (Table 4).

Table 4.

Threshold Effects of the CTI on Kidney Injury and Its Subtypes in Patients with CHD

CTI Model 1 Model 2 Model 3 Model 4
OR (95% CI) P OR (95% CI) P OR (95% CI) P OR (95% CI) P
Kidney injury
Turning point 4.8 4.8 4.8 4.8
<= 4.8 Reference Reference Reference Reference
> 4.8 2.975 [2.421, 3.668]
<0.001		 2.859 [2.321, 3.534]
<0.001		 2.372 [1.903, 2.965]
<0.001		 2.322 [1.857, 2.911]
<0.001
Decreased kidney function
Turning point 4.7 4.7 4.7 4.7
<= 4.7 Reference Reference Reference Reference
> 4.7 2.601 [2.104, 3.231]
<0.001		 2.478 [1.998, 3.088]
<0.001		 2.040 [1.626, 2.572]
<0.001		 1.989 [1.579, 2.515]
<0.001
Proteinuria
Turning point 4.55 4.55 4.55 4.55
<= 4.55 Reference Reference Reference Reference
> 4.55 2.771 [2.188, 3.543]
<0.001		 2.641 [2.077, 3.388]
<0.001		 2.109 [1.638, 2.737]
<0.001		 2.071 [1.603, 2.697]
<0.001
Open in a new tab

Notes: Model 1: no covariates were adjusted. Model 2: age, sex, BMI, smoking status, drinking status SBP, and DBP were adjusted. Model 3: Model 2 plus adjustment for ALT, AST, TC, TG, HDL-C, LDL-C, and FPG. Model 4: Model 3 plus adjustment for Hypertension, DM, Hyperlipidemia, Lipid-lowering drugs, antiplatelet drugs, beta-blockers, and ACEIs/ARBs.

Abbreviations: CTI, C-reactive protein-triglyceride-glucose index; CHD, coronary heart disease; OR, Odds Ratio; CI, confidence interval.

We further performed sex-specific threshold analyses. The RCS curves showed that the risk of KI increased significantly when CTI exceeded 4.45 in females and 4.96 in males, suggesting that the optimal CTI threshold may differ by sex (Figure 4).

Figure 4.

A line graph of kidney injury risk by CTI for female and male, with sharp rise after thresholds. Kidney injury. Line graph with two curves and shaded confidence bands, plus a horizontal dashed reference line and two vertical dashed threshold lines. X-axis label, CTI, with tick labels 3, 4, 5, 6. Y-axis label, OR (95 percentCI), with tick labels 0, 5, 10, 15, 20. Legend label, Sex, with two series: Female and Male. A horizontal dashed line is drawn at OR equals 1. Two vertical dashed lines are drawn at CTI equals 4.45 and CTI equals 4.96, with the numbers 4.45 and 4.96 printed near the x-axis. Female curve: near CTI 3 the OR is slightly below 1, then increases gradually toward OR about 1 by CTI around 4.5. After CTI about 5, the curve rises steeply, reaching roughly OR about 14 at CTI 6. Male curve: near CTI 3 the OR is slightly below 1, increases gradually toward OR about 1 by CTI around 5, then rises steeply after CTI about 5, reaching roughly OR about 12 to 13 at CTI 6. The shaded bands around both curves widen markedly as CTI approaches 6.

Open in a new tab

Dose‑response relationships and threshold effects by sex.

Notes: CTI × sex interaction for KI: P = 0.144.

These threshold findings suggest that maintaining the CTI below the respective cut-off values may exert a protective effect against the risk of renal injury and its subtypes in patients with CHD.

Subgroup Analysis and Sensitivity Analysis

Given the individual differences among patients with CHD, including baseline characteristics, comorbidities, and medication use, which might have influenced the results to some extent, we conducted extensive subgroup analyses stratified by sex, age, BMI, current smoking status, alcohol consumption, presence of hypertension, DM, hyperlipidemia, and use of ACEIs/ARBs. The results remained consistent with the main findings, showing that elevated CTI was positively associated with KI and its subtypes (DKF and proteinuria) across all subgroups (Table 5). Importantly, the association was consistent in both male and female subgroups, indicating that the relationship between CTI and KI is not sex‑dependent.

Table 5.

Association of the CTI with Kidney Injury and Its Subtypes in Various Subgroup Analyses

Subgroups OR (95% CI) P value P for Interaction
Kidney Injury
Sex 0.144
Male 3.358 (2.688–4.227) <0.001
Female 4.338 (3.362–5.648) <0.001
Age (years) 0.093
<60 4.840 (3.492–6.811) <0.001
≥60 3.469 (2.853–4.241) <0.001
BMI (kg/m2) <0.001
<24 1.364 (1.037–1.808) 0.0284
≥24 5.808 (4.676–7.266) <0.001
Current smoking 0.752
No 3.710 (3.061–4.522) <0.001
Yes 3.956 (2.816–5.647) <0.001
Hypertension 0.6105
No 3.615 (2.801–4.708) <0.001
Yes 3.953 (3.165–4.975) <0.001
DM 0.103
No 3.362 (2.756–4.126) <0.001
Yes 4.601 (3.384–6.352) <0.001
Hyperlipidemia 0.008
No 3.292 (2.736–3.981) <0.001
Yes 5.973 (4.060–8.991) <0.001
ACEIs/ARBs 0.201
No 2.927 (2.042–4.264) <0.001
Yes 3.840 (3.177–4.667) <0.001
Decreased kidney function
Sex 0.568
Male 2.481 (1.878–3.309) <0.001
Female 2.813 (2.041–3.919) <0.001
Age (years) 0.068
<60 3.625 (2.405–5.563) <0.001
≥60 2.303 (1.805–2.960) <0.001
BMI (kg/m2) <0.001
<24 1.142 (0.799–1.651) 0.4732
≥24 3.630 (2.792–4.758) <0.001
Current smoking 0.269
No 2.775 (2.172–3.570) <0.001
Yes 2.111 (1.404–3.230) <0.001
Hypertension 0.133
No 2.144 (1.567–2.969) <0.001
Yes 2.973 (2.252–3.965) <0.001
DM 0.127
No 2.281 (1.775–2.953) <0.001
Yes 3.273 (2.242–4.869) <0.001
Hyperlipidemia 0.191
No 2.362 (1.863–3.016) <0.001
Yes 3.323 (2.139–5.280) <0.001
ACEIs/ARBs 0.773
No 2.345 (1.465–3.846) <0.001
Yes 2.538 (2.010–3.226) <0.001
Proteinuria
Sex 0.081
Male 3.407 (2.541–4.622) <0.001
Female 5.058 (3.665–7.068) <0.001
Age (years) 0.408
<60 4.851 (3.226–7.436) <0.001
≥60 3.942 (3.050–5.141) <0.001
BMI (kg/m2) <0.001
<24 1.565 (1.069–2.330) 0.0241
≥24 5.695 (4.363–7.501) <0.001
Current smoking 0.079
No 3.727 (2.917–4.800) <0.001
Yes 6.023 (3.795–9.802) <0.001
Hypertension 0.289
No 4.816 (3.394–6.936) <0.001
Yes 3.770 (2.868–5.007) <0.001
DM 0.835
No 4.029 (3.095–5.292) <0.001
Yes 4.237 (2.899–6.314) <0.001
Hyperlipidemia 0.036
No 3.651 (2.873–4.674) <0.001
Yes 6.751 (4.081–11.542) <0.001
ACEIs/ARBs 0.287
No 3.104 (1.899–5.215) <0.001
Yes 4.211 (3.306–5.407) <0.001
Open in a new tab

Note: CTI (per 1SD increase).

Abbreviations: CTI, C-reactive protein-triglyceride-glucose index; BMI, body mass index; DM, diabetes mellitus; ACEIs, angiotensin-converting enzyme inhibitors; ARBs, angiotensin receptor blockers; OR, Odds Ratio; CI, confidence interval.

Notably, BMI showed significant interactions across all three outcomes (P for interaction < 0.05). Specifically, the associations of CTI with KI and its subtypes were stronger in patients who were overweight or obese (BMI ≥ 24 kg/m2) and had hyperlipidemia (Table 5). This finding further emphasizes that overweight and obese patients with CHD should receive greater attention regarding their CTI levels, and timely monitoring and early intervention are warranted to reduce the risk of renal injury. Additionally, considering the possibility of overadjustment, we removed TG and FPG from the covariates adjusted for in Model 3 and repeated the model analyses; the results remained consistent (Table S3). Moreover, we further excluded patients with acute coronary syndrome and those with recent surgery to mitigate the potential influence of acute conditions and repeated the analyses; the results remained essentially unchanged (Table S4). Finally, because certain potential confounders, including prior use of various medications, could not be captured, we conducted an E-value analysis for unmeasured confounding. The E-value results suggested that unmeasured confounding was unlikely to explain away our findings (Table S5).

Comparison of the Diagnostic Performance of CTI versus Single Indicators (CRP and TyG) for KI and Its Subtypes

To further evaluate the discriminative performance of the CTI compared with the single indicators CRP and TyG for KI and its subtypes (DKF and proteinuria), we first performed ROC curve analysis and compared the areas under the curve (AUCs). The results showed that for KI, DKF, and proteinuria, the CTI yielded the highest AUC values (0.691, 0.655, and 0.703, respectively), indicating modest discriminative ability; these values were higher than those of CRP and TyG alone (Figure 5 and Table 6). Subsequently, DCA was used to assess the potential clinical utility, and the findings were consistent with the ROC analysis: the CTI also demonstrated the greatest net benefit for both KI and its subtypes (Figure 6). However, these DCA findings should be interpreted as exploratory and do not establish a diagnostic pathway or a clinical diagnostic reference standard. Finally, we applied the Boruta algorithm, a machine learning-based method for variable importance ranking. The results showed that compared with other covariates, the CTI consistently ranked among the top three in variable importance and was significantly higher than CRP and TyG (Figure 7). These findings provide variable-importance information and suggest that CTI may be a potentially useful variable for discrimination/classification, but they do not independently validate CTI as a clinical prediction or diagnostic tool. Collectively, these comparative analyses support that the CTI is an important factor associated with the concurrent presence of KI, DKF, and proteinuria in this cross-sectional study.

Figure 5.

A multi line graph figure comparing receiver operating characteristic curves for CTI, CRP and TyG. The images A, B and C depict receiver operating characteristic (ROC) graphs for kidney injury, decreased kidney function and proteinuria, respectively. Each graph has a horizontal axis labeled ′Specificity′ (range 1.0 to 0.0) and a vertical axis labeled ′Sensitivity′ (range 0.0 to 1.0), with a diagonal reference line from (1.0, 0.0) to (0.0, 1.0). In image A, the curves are CTI (AUC: 0.691), TyG (AUC: 0.676) and CRP (AUC: 0.566), ordered by AUC from highest to lowest. Image B features CTI (AUC: 0.655), TyG (AUC: 0.625) and CRP (AUC: 0.565), again ordered by AUC. Image C shows CTI (AUC: 0.703), TyG (AUC: 0.692) and CRP (AUC: 0.558), maintaining the same order by AUC.

Open in a new tab

Comparison of the area under the receiver operating characteristic curve between CTI and single indicators (CRP and TyG). (A) Kidney injury; (B) Decreased kidney function; (C) Proteinuria.

Table 6.

Comparison of the Diagnostic Performance of the CTI Versus Single Indicators (CRP and TyG) for Kidney Injury and Its Subtypes

Outcomes AUC 95% CI low 95% CI up Accuracy Specificity Sensitivity Positive-pv Negative-pv
Kidney injury
CTI 0.691 0.668 0.714 0.668 0.692 0.588 0.363 0.849
CRP 0.566 0.543 0.588 0.659 0.738 0.363 0.278 0.810
TyG 0.676 0.654 0.698 0.723 0.787 0.488 0.385 0.849
Decreased kidney function
CTI 0.655 0.626 0.684 0.736 0.770 0.469 0.204 0.920
CRP 0.565 0.534 0.595 0.689 0.724 0.386 0.139 0.911
TyG 0.625 0.594 0.656 0.733 0.767 0.435 0.177 0.922
Proteinuria
CTI 0.703 0.678 0.728 0.665 0.668 0.649 0.220 0.929
CRP 0.558 0.528 0.587 0.803 0.878 0.224 0.194 0.897
TyG 0.692 0.664 0.719 0.736 0.762 0.534 0.227 0.926
Open in a new tab

Abbreviations: CTI, C-reactive protein-triglyceride-glucose index; CRP, C-reactive protein; TyG, triglyceride-glucose index; AUC, area under the curve; Positive-pv, positive predictive value; Negative-pv, negative predictive value.

Figure 6.

A multi-line graph set comparing decision curves for CTI, CRP and TyG across three outcomes. Image A, B and C depict multi-line graphs with x-axis labeled ′High Risk Threshold′ (0.0 to 1.0) and y-axis labeled ′Net Benefit′ (0.0 to 0.3). Legend lines include CTI, CRP, TyG, All and None. ′None′ is a flat line at 0.0 net benefit. ′All′ declines to 0.0 near a threshold of 0.35. In Image A, CTI remains above CRP and TyG, staying above 0.0 until about 0.7. CRP approaches 0.0 near 0.5 and TyG near 0.6. Coordinates: CTI (0.0, 0.30), (0.7, 0.00); CRP (0.0, 0.30), (0.5, 0.00); TyG (0.0, 0.30), (0.6, 0.00). Image B shows CTI above 0.0 until about 0.6, generally higher than CRP and TyG after 0.25. CRP drops to 0.0 near 0.35, TyG near 0.5. Coordinates: CTI (0.0, 0.30), (0.6, 0.00); CRP (0.0, 0.30), (0.35, 0.00); TyG (0.0, 0.30), (0.5, 0.00). Image C illustrates CTI above 0.0 until about 0.7, surpassing CRP across most thresholds. CRP nears 0.0 at 0.5, TyG at 0.6 with a mid-range bump. Coordinates: CTI (0.0, 0.30), (0.7, 0.00); CRP (0.0, 0.30), (0.5, 0.00); TyG (0.0, 0.30), (0.6, 0.00).

Open in a new tab

Comparison of decision curves between the CTI and single indicators (CRP and TyG). (A) Kidney injury; (B) Decreased kidney function; (C) Proteinuria.

Figure 7.

Three box plots showing Boruta variable importance ranking for various attributes. The image contains three box plots labeled A, B and C, each showing Boruta variable importance ranking. The x-axis represents attributes and the y-axis represents Importance Z-Score. In plot A, the range is from 0 to 60. Attributes like SBP and CTI have the highest median scores, while shadowMin and shadowMean have the lowest. Outliers are present in attributes such as CRP and HDL-C. Plot B ranges from 0 to 35, with SBP and CTI again showing high median scores. Attributes like shadowMin and shadowMean have lower scores, with outliers visible in attributes like CRP. Plot C ranges from 0 to 40, with CTI and SBP having the highest medians. Attributes such as shadowMin and shadowMean have lower scores. Outliers are noted in attributes like CRP. Across all plots, SBP and CTI consistently rank high, while shadowMin and shadowMean rank low. The spread and presence of outliers vary, indicating differences in attribute importance across panels.

Open in a new tab

Boruta variable importance ranking. (A) Kidney injury; (B) Decreased kidney function; (C) Proteinuria.

Discussion

Traditionally, inflammation and IR have been recognized as core risk factors for the development and progression of CVD.16,36 However, accumulating evidence has recently revealed that these two pathological states also play critical roles in KI.37,38 Patients with CHD often experience a state of chronic low-grade inflammation and frequently present with IR secondary to obesity, diabetes, and hyperlipidemia, thereby subjecting their kidneys to a dual insult.16,39 Consequently, this high-risk population should not only be prioritized for cardiovascular event prevention but also be regarded as susceptible to KI. Given that impaired renal function further worsens cardiovascular outcomes, early monitoring and intervention for renal function in CHD patients are of great clinical importance.

To overcome the limitation that single indicators (eg, CRP or TyG) reflect only one aspect (inflammation or IR) and lack comprehensiveness, researchers have recently developed a novel composite index—the CTI—that integrates both dimensions.26,29 The CTI simultaneously assesses systemic inflammatory burden and IR and has demonstrated strong prognostic and predictive capabilities in various diseases, including CVD, stroke, sarcopenia, and depression.26–29 However, the association between the CTI and KI (including its subtypes, DKF and proteinuria) in CHD patients remains unexplored, and whether the CTI outperforms single indicators is unknown. To address this gap, the present study employed a dual‑center design to systematically investigate these associations. Our results showed that elevated CTI was significantly and positively associated with KI and its subtypes in CHD patients, with clear threshold effects: the cut‑off values for KI, DKF, and proteinuria were 4.8, 4.7, and 4.55, respectively. Subgroup analysis revealed that the adverse impact of elevated CTI was more pronounced in patients with BMI ≥ 24 kg/m2 (overweight or obese), highlighting the importance of maintaining CTI below the respective thresholds in this subgroup to reduce the risk of KI. Moreover, comparative analyses confirmed that the diagnostic performance of the CTI for KI and its subtypes was significantly superior to that of CRP or TyG alone. Importantly, sex‑specific threshold analyses revealed notable differences: the risk of KI increased when CTI exceeded 4.45 in females and 4.96 in males, indicating that the optimal CTI cut‑off for predicting KI differs between sexes. Despite these distinct thresholds, the positive association between CTI and KI was present in both male and female subgroups. These findings suggest that monitoring CTI levels in CHD patients—with consideration of sex‑specific thresholds—may facilitate early identification of high‑risk individuals and timely intervention.

Previous clinical studies have provided direct evidence supporting the independent roles of inflammation and IR in KI or CKD.23–25 For instance, studies in Chinese populations have shown that an elevated SIRI is closely associated with CKD, and analyses in American populations have revealed that this index increases the risk of diabetic kidney disease in patients with type 2 DM.23,24 Additionally, IR has been confirmed to be associated with declining renal function and progression of CKD.25,40,41 The TyG index, a surrogate marker of IR, has been demonstrated to be associated with renal function decline and CKD progression, and to mediate the obesity‑related risk of KI.25,40 However, most of these studies focused on a single pathological pathway without integrating both, and few have examined whether these associations differ by sex. Furthermore, no study has specifically investigated a composite index reflecting both inflammation and IR in the context of CHD—a population characterized by a high confluence of these two abnormalities. The present study is the first to validate an independent association between the CTI and renal injury in CHD patients, to identify threshold effects, to demonstrate stronger associations in the overweight/obese subgroup, and to show that these associations remain consistent across male and female subgroups, thereby filling an important evidence gap.

An elevated CTI reflects the coexistence of chronic inflammation and IR, which may synergistically promote KI in CHD patients through the following mechanisms: 1) Under chronic inflammation, pro‑inflammatory cytokines (eg, IL‑6, TNF‑α) directly damage glomerular endothelial cells, increase vascular permeability, and lead to proteinuria;42–44 2) Inflammation also induces podocyte injury and thickening of the glomerular basement membrane, further compromising filtration function;45–47 3) IR activates the RAAS, elevating intraglomerular pressure, exacerbating renal hemodynamic burden, and promoting tubulointerstitial fibrosis;48–50 4) IR is often accompanied by hyperinsulinemia, which stimulates renal tubular sodium reabsorption, leading to sodium and water retention and hypertension, thereby indirectly impairing renal function;51,52 5) Inflammation and IR amplify each other: inflammatory cytokines interfere with insulin signaling, worsening resistance, while insulin resistance, via metabolites such as free fatty acids, triggers inflammatory cascades, forming a vicious cycle;53,54 6) A high CTI also reflects lipid metabolic dysregulation; elevated triglycerides and glucose can directly damage renal tubular epithelial cells through lipotoxicity, oxidative stress, and mitochondrial dysfunction.55–57 Collectively, these mechanisms explain why the CTI outperforms single indicators in predicting KI.

This study has several major strengths: (1) The dual‑center design enhances sample representativeness and generalizability of the findings; (2) It is the first to employ the novel CTI that integrates inflammation and IR, focusing on the high‑risk CHD population, which represents a significant innovation; (3) Beyond the overall association with KI, the study separately examined two distinct subtypes (DKF and proteinuria), allowing for phenotypic refinement; (4) RCS analysis identified clear threshold effects, providing specific targets for clinical intervention; (5) Comprehensive validation was performed through subgroup analyses, interaction tests, and multiple diagnostic performance comparisons (ROC, DCA, Boruta algorithm), confirming the robustness of the results and the superiority of the CTI; (6) The conclusions directly inform early monitoring and high‑risk screening, offering strong clinical applicability.

Several limitations should be acknowledged: (1) As an observational study, causality between CTI and renal injury cannot be established; future prospective cohort or interventional studies are needed. (2) Despite adjustment for multiple confounders, residual confounding or unmeasured variables (dietary patterns, physical activity, and genetic background) may persist. (3) The CTI was based on a single blood measurement, failing to capture dynamic changes in inflammation and IR over time; the relationship between long‑term exposure and cumulative renal injury risk remains unclear. (4) The study population consisted exclusively of Chinese CHD patients; whether the CTI thresholds and other conclusions apply to other ethnicities or populations requires external validation. (5) KI was defined using eGFR and proteinuria markers; tubular injury biomarkers or renal biopsy data were not included. (6) Although the sample size was reasonably large, some subgroup analyses might have been underpowered, necessitating confirmation in larger studies.

Conclusion

In this dual-center study, higher CTI was independently associated with KI and its subtypes (DKF and proteinuria) in patients with CHD, with apparent nonlinear threshold effects (4.8, 4.7, and 4.55, respectively). These associations were stronger in overweight/obese patients (BMI ≥ 24 kg/m2), and CTI showed stronger discriminative performance for KI than CRP or TyG alone. Sex-specific associations were evident above 4.45 in female and 4.96 in male; these are exploratory threshold estimates, not established clinical cut-offs. Because CTI and renal outcomes were assessed at approximately the same time and CTI was based on a single measurement, these findings indicate association only and cannot establish temporal or causal relationships. Longitudinal studies with external validation are needed.

Funding Statement

No funding was received for this study.

Data Sharing Statement

The dataset generated and analyzed in this study is available upon reasonable request to the corresponding author.

Ethics Approval and Consent to Participate

This study was conducted in strict accordance with the ethical principles outlined in the Declaration of Helsinki. The study protocol was reviewed and approved by the Ethics Committees of both participating hospitals: Huangshan City People’s Hospital (HS-20219-08) and Xinxiang Central Hospital (2024-352). All participants were fully informed of the study objectives and procedures before the commencement of the study, and written informed consent was obtained from each participant voluntarily.

Author Contributions

Mengmeng Fan: Conceptualization, Methodology, Investigation, Data Curation, Formal Analysis, Writing – Original Draft, Writing – review and editing. Xueping Zhang: Conceptualization, Data Curation, Methodology, Investigation, Supervision, Data Curation, Writing – review and editing. Xianwei Tian: Methodology, Investigation, Data Curation, Writing – review and editing. Keli Li: Methodology, Data Curation, and Writing – review and editing. Zhiqiang Liu: Conceptualization, Data Curation, Methodology, Investigation, Supervision, Data Curation, Writing – Original Draft, Writing – review and editing. All authors gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors report no conflicts of interest in this work.

References

  • 1.Eisenstein M. Preventing kidney injuries in hospital. Nature. 2023;615(7954):S111. doi: 10.1038/d41586-023-00803-w [DOI] [PubMed] [Google Scholar]
  • 2.Lameire N, Biesen WV, Vanholder R. Acute kidney injury. Lancet. 2008;372(9653):1863–19. doi: 10.1016/S0140-6736(08)61794-8 [DOI] [PubMed] [Google Scholar]
  • 3.Warnock DG. Towards a definition and classification of acute kidney injury. J Am Soc Nephrol. 2005;16(11):3149–3150. doi: 10.1681/ASN.2005090934 [DOI] [PubMed] [Google Scholar]
  • 4.Johnson RJ, Bakris GL, Borghi C, et al. Hyperuricemia, acute and chronic kidney disease, hypertension, and cardiovascular disease: report of a scientific workshop organized by the national kidney foundation. Am J Kidney Dis. 2018;71(6):851–865. doi: 10.1053/j.ajkd.2017.12.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Jensen SK, Heide-Jørgensen U, Gammelager H, et al. Acute kidney injury duration and 20-year risks of CKD and cardiovascular disease. Kidney Int Rep. 2024;9(4):817–829. doi: 10.1016/j.ekir.2024.01.034 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Samanta R, Chan C, Chauhan VS. Arrhythmias and sudden cardiac death in end stage renal disease: epidemiology, risk factors, and management. Can J Cardiol. 2019;35(9):1228–1240. doi: 10.1016/j.cjca.2019.05.005 [DOI] [PubMed] [Google Scholar]
  • 7.Lees JS, Welsh CE, Celis-Morales CA, et al. Glomerular filtration rate by differing measures, albuminuria and prediction of cardiovascular disease, mortality and end-stage kidney disease. Nat Med. 2019;25(11):1753–1760. doi: 10.1038/s41591-019-0627-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Isakova T, Nickolas TL. Reducing elevated parathyroid hormone to protect bone strength in end-stage kidney disease. Am J Kidney Dis. 2024;83(4):432–434. doi: 10.1053/j.ajkd.2023.12.004 [DOI] [PubMed] [Google Scholar]
  • 9.Fishbane S, Spinowitz B. Update on anemia in ESRD and earlier stages of CKD: core curriculum 2018. Am J Kidney Dis. 2018;71(3):423–435. doi: 10.1053/j.ajkd.2017.09.026 [DOI] [PubMed] [Google Scholar]
  • 10.Dalen JE, Alpert JS, Goldberg RJ, et al. The epidemic of the 20(th) century: coronary heart disease. Am J Med. 2014;127(9):807–812. doi: 10.1016/j.amjmed.2014.04.015 [DOI] [PubMed] [Google Scholar]
  • 11.Zhu KF, Wang YM, Zhu JZ, et al. National prevalence of coronary heart disease and its relationship with human development index: a systematic review. Eur J Prev Cardiol. 2016;23(5):530–543. doi: 10.1177/2047487315587402 [DOI] [PubMed] [Google Scholar]
  • 12.Li Y, Xiao M, Li Y, et al. Machine learning for the prediction of acute kidney injury in critically ill patients with coronary heart disease: algorithm development and validation. JMIR Med Inform. 2025;13(72349). doi: 10.2196/72349 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Mondorf UF, Frey K, Radounikli A, et al. Blood pressure control in essential hypertension and impairment of renal function with age. Blood Press. 2000;9(5):293–297. doi: 10.1080/080370500448696 [DOI] [PubMed] [Google Scholar]
  • 14.Busse LW, Ostermann M. Vasopressor therapy and blood pressure management in the setting of acute kidney injury. Semin Nephrol. 2019;39(5):462–472. doi: 10.1016/j.semnephrol.2019.06.006 [DOI] [PubMed] [Google Scholar]
  • 15.Galindo RJ, Beck RW, Scioscia MF, et al. Glycemic monitoring and management in advanced chronic kidney disease. Endocr Rev. 2020;41(5):756–774. doi: 10.1210/endrev/bnaa017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Yang T, Li G, Wang C, et al. Insulin resistance and coronary inflammation in patients with coronary artery disease: a cross-sectional study. Cardiovasc Diabetol. 2024;23(1):79. doi: 10.1186/s12933-024-02159-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Gaudino M, Crea F. Inflammation in coronary artery disease: which biomarker and which treatment? Eur J Prev Cardiol. 2019;26(8):869–871. doi: 10.1177/2047487319829307 [DOI] [PubMed] [Google Scholar]
  • 18.Miguel V, Shaw IW, Kramann R. Metabolism at the crossroads of inflammation and fibrosis in chronic kidney disease. Nat Rev Nephrol. 2025;21(1):39–56. doi: 10.1038/s41581-024-00889-z [DOI] [PubMed] [Google Scholar]
  • 19.Kadatane SP, Satariano M, Massey M, et al. The role of inflammation in CKD. Cells. 2023;12(12):1581. doi: 10.3390/cells12121581 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Spoto B, Pisano A, Zoccali C. Insulin resistance in chronic kidney disease: a systematic review. Am J Physiol Renal Physiol. 2016;311(6):F1087–F1108. doi: 10.1152/ajprenal.00340.2016 [DOI] [PubMed] [Google Scholar]
  • 21.Teta D. Insulin resistance as a therapeutic target for chronic kidney disease. J Ren Nutr. 2015;25(2):226–229. doi: 10.1053/j.jrn.2014.10.019 [DOI] [PubMed] [Google Scholar]
  • 22.Diaz-Ricart M, Torramade-Moix S, Pascual G, et al. Endothelial damage, inflammation and immunity in chronic kidney disease. Toxins (Basel). 2020;12(6):361. doi: 10.3390/toxins12060361 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Li X, Cui L, Xu H. Association between systemic inflammation response index and chronic kidney disease: a population-based study. Front Endocrinol (Lausanne). 2024;15:1329256. doi: 10.3389/fendo.2024.1329256 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Guo W, Song Y, Sun Y, et al. Systemic immune-inflammation index is associated with diabetic kidney disease in Type 2 diabetes mellitus patients: evidence from NHANES 2011-2018. Front Endocrinol (Lausanne). 2022;13:1071465. doi: 10.3389/fendo.2022.1071465 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Liu S, Sun H, Liu J, et al. Accessing the relationship between six surrogate insulin resistance indexes and the incidence of rapid kidney function decline and the progression to chronic kidney disease among middle-aged and older adults in China: results from the China health and retirement longitudinal study. Diabetes Res Clin Pract. 2024;212:111705. doi: 10.1016/j.diabres.2024.111705 [DOI] [PubMed] [Google Scholar]
  • 26.Huo G, Tang Y, Liu Z, et al. Association between C-reactive protein-triglyceride glucose index and stroke risk in different glycemic status: insights from the China Health and Retirement Longitudinal Study (CHARLS). Cardiovasc Diabetol. 2025;24(1):142. doi: 10.1186/s12933-025-02686-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Ou H, Wei M, Li X, et al. C-reactive protein-triglyceride glucose index in evaluating cardiovascular disease and all-cause mortality incidence among individuals across stages 0-3 of cardiovascular-kidney-metabolic syndrome: a nationwide prospective cohort study. Cardiovasc Diabetol. 2025;24(1):296. doi: 10.1186/s12933-025-02848-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Huang C, You H, Zhang Y, et al. Association between C-reactive protein-triglyceride glucose index and depressive symptoms in American adults: results from the NHANES 2005 to 2010. BMC Psychiatry. 2024;24(1):890. doi: 10.1186/s12888-024-06336-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Chen Y, Huang T, Wang J, et al. Association between C-reactive protein-triglyceride glucose index and risk of sarcopenia in older adults: findings from CHARLS and NHANES. Exp Gerontol. 2026;213:113003. doi: 10.1016/j.exger.2025.113003 [DOI] [PubMed] [Google Scholar]
  • 30.Li G, Xu Z, Yang H, et al. Sex-specific mechanisms in the pathogenesis and progression of chronic kidney disease. Autoimmun Rev. 2026;25(1):103938. doi: 10.1016/j.autrev.2025.103938 [DOI] [PubMed] [Google Scholar]
  • 31.Harvey BJ, Alvarez de la Rosa D. Sex differences in kidney health and disease. Nephron. 2025;149(2):77–103. doi: 10.1159/000541352 [DOI] [PubMed] [Google Scholar]
  • 32.Pattar BSB, Ahmed SB, Holodinsky JK, et al. Sex differences in the association between albuminuria and vascular health in individuals living with chronic kidney disease. Curr Res Physiol. 2025;8:100145. doi: 10.1016/j.crphys.2025.100145 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Song S, Cai X, Shen D, et al. Relationship between cumulative exposure and time course of plasma aldosterone concentrations and chronic kidney disease in hypertensive patients: a real-world cohort study. Am J Nephrol. 2025;56(5):641–656. doi: 10.1159/000545451 [DOI] [PubMed] [Google Scholar]
  • 34.Song S, Li N, Shen D, et al. Plasma aldosterone elevation in hypertensive patients and association with urinary stone formation: a large-scale population study from Northwest China. Clin Epidemiol. 2025;17:497–512. doi: 10.2147/CLEP.S522455 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Kong X, Ma Y, Chen J, et al. Evaluation of the chronic kidney disease epidemiology collaboration equation for estimating glomerular filtration rate in the Chinese population. Nephrol Dial Transplant. 2013;28(3):641–651. doi: 10.1093/ndt/gfs491 [DOI] [PubMed] [Google Scholar]
  • 36.Haffner SM. Pre-diabetes, insulin resistance, inflammation and CVD risk. Diabetes Res Clin Pract. 2003;61(Suppl 1):S9–S18. doi: 10.1016/s0168-8227(03)00122-0 [DOI] [PubMed] [Google Scholar]
  • 37.Hall JE, Mouton AJ, da Silva AA, et al. Obesity, kidney dysfunction, and inflammation: interactions in hypertension. Cardiovasc Res. 2021;117(8):1859–1876. doi: 10.1093/cvr/cvaa336 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Chueakula N, Jaikumkao K, Arjinajarn P, et al. Diacerein alleviates kidney injury through attenuating inflammation and oxidative stress in obese insulin-resistant rats. Free Radic Biol Med. 2018;115:146–155. doi: 10.1016/j.freeradbiomed.2017.11.021 [DOI] [PubMed] [Google Scholar]
  • 39.Malhotra A, Redberg RF, Meier P. Saturated fat does not clog the arteries: coronary heart disease is a chronic inflammatory condition, the risk of which can be effectively reduced from healthy lifestyle interventions. Br J Sports Med. 2017;51(15):1111–1112. doi: 10.1136/bjsports-2016-097285 [DOI] [PubMed] [Google Scholar]
  • 40.Wang X, Lin R, Ji X, et al. Insulin resistance mediates the association between adiposity markers and incident chronic kidney disease: findings from the UK Biobank prospective cohort study. Diabetes Res Clin Pract. 2025;225:112255. doi: 10.1016/j.diabres.2025.112255 [DOI] [PubMed] [Google Scholar]
  • 41.Leyking S, Fliser D. Insulin resistance in CKD. Clin J Am Soc Nephrol. 2014;9(4):638–640. doi: 10.2215/CJN.01290214 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Imig JD, Ryan MJ. Immune and inflammatory role in renal disease. Compr Physiol. 2013;3(2):957–976. doi: 10.1002/cphy.c120028 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Cachofeiro V, Goicochea M, de Vinuesa SG, et al. Oxidative stress and inflammation, a link between chronic kidney disease and cardiovascular disease. Kidney Int Suppl. 2008;111(9). doi: 10.1038/ki.2008.516 [DOI] [PubMed] [Google Scholar]
  • 44.Pergola P, Johnson RJ, Levin A, et al. Chronic inflammation as an overlooked link between cardiovascular and kidney disease: a focus on IL-6 and C-reactive protein. Clin J Am Soc Nephrol. 2026. doi: 10.2215/CJN.0000001054 [DOI] [PubMed] [Google Scholar]
  • 45.Mathieson PW. The podocyte as a target for therapies--new and old. Nat Rev Nephrol. 2011;8(1):52–56. doi: 10.1038/nrneph.2011.171 [DOI] [PubMed] [Google Scholar]
  • 46.Jiang H, Shen Z, Zhuang J, et al. Understanding the podocyte immune responses in proteinuric kidney diseases: from pathogenesis to therapy. Front Immunol. 2024;14:1335936. doi: 10.3389/fimmu.2023.1335936 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Shankland SJ. The podocyte’s response to injury: role in proteinuria and glomerulosclerosis. Kidney Int. 2006;69(12):2131–2147. doi: 10.1038/sj.ki.5000410 [DOI] [PubMed] [Google Scholar]
  • 48.Underwood PC, Adler GK. The renin angiotensin aldosterone system and insulin resistance in humans. Curr Hypertens Rep. 2013;15(1):59–70. doi: 10.1007/s11906-012-0323-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Aazar RFM, Arzouni R, Nicolaou PA. Dual inhibition of the renin-angiotensin-aldosterone system and sodium-glucose cotransporter-2: mechanistic and clinical evidence for cardiorenal protection. Biomedicines. 2026;14(1):101. doi: 10.3390/biomedicines14010101 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Piani F, Reinicke T, Lytvyn Y, et al. Vasopressin associated with renal vascular resistance in adults with longstanding type 1 diabetes with and without diabetic kidney disease. J Diabetes Complications. 2021;35(3 107807). doi: 10.1016/j.jdiacomp.2020.107807 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.DeFronzo RA. Insulin resistance, lipotoxicity, type 2 diabetes and atherosclerosis: the missing links. The claude bernard lecture 2009. Diabetologia. 2010;53(7):1270–1287. doi: 10.1007/s00125-010-1684-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Horita S, Seki G, Yamada H, et al. Insulin resistance, obesity, hypertension, and renal sodium transport. Int J Hypertens. 2011;2011 391762. doi: 10.4061/2011/391762 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Hotamisligil GS. Inflammation and metabolic disorders. Nature. 2006;444(7121):860–867. doi: 10.1038/nature05485 [DOI] [PubMed] [Google Scholar]
  • 54.Tramontano D, D’Erasmo L, Larouche M, et al. The vicious circle of chronic kidney disease and hypertriglyceridemia: what is first, the hen or the egg? Atherosclerosis. 2025;403:119146. doi: 10.1016/j.atherosclerosis.2025.119146 [DOI] [PubMed] [Google Scholar]
  • 55.Ruan XZ, Varghese Z, Moorhead JF. An update on the lipid nephrotoxicity hypothesis. Nat Rev Nephrol. 2009;5(12):713–721. doi: 10.1038/nrneph.2009.184 [DOI] [PubMed] [Google Scholar]
  • 56.Bobulescu IA. Renal lipid metabolism and lipotoxicity. Curr Opin Nephrol Hypertens. 2010;19(4):393–402. doi: 10.1097/MNH.0b013e32833aa4ac [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Rutledge JC, Ng KF, Aung HH, et al. Role of triglyceride-rich lipoproteins in diabetic nephropathy. Nat Rev Nephrol. 2010;6(6):361–370. doi: 10.1038/nrneph.2010.59 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The dataset generated and analyzed in this study is available upon reasonable request to the corresponding author.

Articles from Journal of Inflammation Research are provided here courtesy of Dove Press

RESOURCES