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. 2023 May 24;54(7-8):268–274. doi: 10.1159/000531191

Interleukin-6-to-Albumin Ratio as a Superior Predictor of Mortality in End-Stage Kidney Disease Patients

Xiejia Li a,b,✉, Abdul Rashid Qureshi a, Mohamed E Suliman a, Olof Heimbürger a, Peter Barany a, Peter Stenvinkel a, Bengt Lindholm a,✉
PMCID: PMC10623391  PMID: 37231796

Abstract

Introduction

In patients with chronic kidney disease (CKD), high interleukin-6 (IL-6) and low albumin circulating concentrations are associated with worse outcomes. We examined the IL-6-to-albumin ratio (IAR) as a predictor of risk of death in incident dialysis patients.

Methods

In 428 incident dialysis patients (median age 56 years, 62% men, 31% diabetes mellitus, 38% cardiovascular disease [CVD]), plasma IL-6 and albumin were measured at baseline to calculate IAR. We compared the discrimination of IAR with other risk factors for predicting 60-month mortality using receiver operating characteristic curve (ROC) and analyzed the association of IAR with mortality using Cox regression analysis. We divided patients into IAR tertiles and analyzed: (1) cumulative incidence of mortality and the association of IAR with mortality risk in Fine-Gray analysis, taking kidney transplantation as competing risk and (2) the restricted mean survival time (RMST) to 60-month mortality and differences of RMST (∆RMST) between IAR tertiles to describe quantitative differences of survival time.

Results

For all-cause mortality, the area under the ROC curve (AUC) for IAR was 0.700, which was greater than for IL-6 and albumin separately, while for CV mortality, the AUC for IAR (0.658) showed negligible improvement over IL-6 and albumin separately. In Cox regression analysis, IAR was significantly associated with all-cause mortality but not with CV mortality. Both high versus low and middle versus low tertiles of IAR associated with higher risk of all-cause mortality, subdistribution hazard ratio of 2.22 (95% CI 1.40–3.52) and 1.85 (95% CI 1.16–2.95), respectively, after adjusting for age, sex, diabetes mellitus, CVD, smoking, and estimated glomerular filtration rate. ∆RMST at 60 months showed significantly shorter survival time in middle and high IAR tertiles compared with low IAR tertile for all-cause mortality.

Conclusions

Higher IAR was independently associated with significantly higher all-cause mortality risk in incident dialysis patients. These results suggest that IAR may provide useful prognostic information in patients with CKD.

Keywords: End-stage kidney disease, Dialysis, Inflammation, Interleukin-6, Serum albumin, Mortality

Introduction

The high morbidity and premature mortality in chronic kidney disease (CKD) population place a heavy burden on public health resources. Rigorous evaluations of mortality risk in CKD patients are needed to predict clinical outcomes and to facilitate better patient management and provide timely and effective medical interventions. In addition to well-acknowledged traditional risk factors, nontraditional risk factors including systemic inflammation play an important role in CKD, especially in end-stage kidney disease (ESKD) patients [1]. Systemic inflammation is thought to be a contributor to worse outcomes in ESKD patients and high circulating concentrations of inflammation biomarkers, such as C-reactive protein (CRP) and interleukin-6 (IL-6), and low concentrations of serum albumin (s-albumin) are tightly associated with protein-energy wasting, atherosclerosis [2], and vascular calcification [3] and predict risk of CV events and mortality [4, 5].

Whereas s-albumin is widely used as a biomarker of nutritional status [6] and hypoalbuminemia is highly prevalent in ESKD patients and considered a valuable predictor of mortality [7], s-albumin is not an ideal marker of nutritional status in ESKD [6] as it is greatly influenced by inflammation and fluid status [8, 9]. On the other hand, persistent inflammation, usually assessed by CRP, may affect nutritional status through various mechanisms such as inhibition of protein synthesis and induction of catabolism, while the higher mortality associated with low s-albumin is dependent on systemic inflammation in ESKD [10]. This suggests that s-albumin should be combined with measurements of high-sensitivity C-reactive protein (hsCRP) when analyzing s-albumin to predict risk of mortality [11]. Meanwhile, other studies showed that an increase in serum CRP-to-albumin ratio (CAR) is independently associated with increased risk of muscle wasting in patients undergoing hemodialysis [12] and with increased all-cause mortality risk in patients undergoing peritoneal dialysis [13].

IL-6 is a pivotal pro-inflammatory cytokine which is increasingly recognized as being intimately involved in the pathogenesis of many cardiometabolic diseases. Among inflammatory biomarkers, IL-6 appears to best capture the inflammatory status and risk for adverse cardiac remodeling in CKD patients [14] as well as mortality risk in ESKD [15], and an inflammation score composed of CRP, IL-6, IL-1, IL-18, and TNF-α was not better than the sole use of IL-6 for predicting death in patients with ESKD [16]. We hypothesized that by combining IL-6 and s-albumin into a ratio of IL-6 to albumin (IAR), this biomarker might reflect both the severity of inflammation and malnutrition and thereby could stratify risk of death better than IL-6 and s-albumin separately. Therefore, we explored the value of IAR as a biomarker to predict outcomes in incident dialysis patients.

Materials and Methods

Study Population and Data Collection

The study population was recruited from a cohort of 560 incident dialysis patients – investigated in conjunction with dialysis initiation – who were recruited from the malnutrition, inflammation, and atherosclerosis (MIA) cohort study, the methods, and results of which have been published in part elsewhere [2, 6, 10, 15]. After excluding 132 patients, 125 patients due to lack of data on IL-6 and 7 patients due to other reasons (see online suppl. Fig. S1; for all online suppl. material, see https://doi.org/10.1159/000531191), we investigated IAR in the remaining 428 patients (median age 56 years, 62% men) who were followed from study recruitment until death, commencement of kidney transplantation (KTx) or completing 60 months of follow-up. Time to KTx or death and type of death (cardiac or noncardiac reason) were documented. The Ethics Committee of the Karolinska Institutet, Stockholm, Sweden, approved the study protocol. Studies adhered to the Declaration of Helsinki. Informed written consent was obtained from each participant.

Measurements of Clinical and Laboratory Data

Biochemical and clinical parameters were analyzed at baseline in conjunction with start of dialysis as previously described [2, 6, 10, 15]. Estimated glomerular filtration rate (eGFR) was calculated from serum creatinine, obtained prior to dialysis initiation, using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [17]. The plasma IL-6 level was divided by the s-albumin level to calculate the baseline IAR. The patients were divided into tertiles according to IAR value. Baseline cardiovascular disease (CVD) diagnoses included myocardial infarction, heart failure, peripheral vascular disease, ischemic stroke, unspecified stroke, hemorrhagic stroke, and presence of coronary angioplasty implant and graft.

Endpoints and Outcome Measures

CV mortality was defined as death caused by coronary events, arrhythmia, sudden cardiac death, cardiac failure, or cerebrovascular accident. All other causes of death were designated as non-CV mortality.

Statistical Analysis

Continuous variables are presented as median (25th–75th percentile). Categorical variables are presented as number (n)/percentage (%). Statistical significance was set at the level of p < 0.05. We used Cochran-Armitage test for categorical variables and linear-by-linear test for continuous variables to compare the characteristics of participants at baseline by IAR tertiles. Cause of death was established by the death certificate issued by the attending physician. Receiver operating characteristic curve (ROC) and the area under the ROC curve (AUC) were calculated using DeLong method to compare the discriminatory performance of risk factors for the prediction of all-cause and CV mortality. We performed Spearman rank correlation test to measure the relationship between IAR and continuous variables and the Kruskal-Wallis test to measure the relationship between IAR and dichotomous variables. We used Cox proportional hazards regression models to estimate the hazard ratios (HRs) with 95% confidence intervals (95% CIs) of all-cause and CV mortality risk associated with IAR. As KTx was offered predominantly to younger and healthier patients and multivariable Cox regression proportional hazard assumption was violated, we performed Fine-Gray analysis, taking KTx as a competing risk to establish cumulative incidence curves for survival [18]. Estimates for mortality risk associated with high and middle IAR tertiles were expressed as subdistribution HR (sHR) and 95% CI with the low IAR tertile serving as the reference. We further performed adjusted restricted mean survival time (RMST) with inverse probability of treatment weighting and expressed the results as difference (∆RMST) between IAR groups at 60 months. Statistical analyses were performed using Stata 17 (Stata Corp, College Station, TX, USA) and SAS 9.4 (SAS Institute, Cary, NC, USA).

Results

Baseline Characteristics and Clinical Outcomes during Follow-Up

The clinical and biochemical characteristics of the patients in IAR tertiles are shown in Table 1. As expected, patients in the high IAR tertile were older and more often smokers and had higher prevalence of diabetes mellitus (DM) and CVD. They also had higher white blood cell count, higher levels of hsCRP and IL-6, and lower hemoglobin and s-albumin levels when compared to the low IAR tertile. In these 428 patients, 142 patients (33.2%) died and 174 (40.7%) patients underwent KTx up to 60 months of follow-up; 80 (56.3%) of the 142 deaths were due to CVD. As shown in online supplementary Figure S2, IAR was significantly higher among non-survivors (median 2.7, 95% CI 1.6–4.7) compared to survivors (median 1.7, 95% CI 1.1–3.6) (p < 0.001).

Table 1.

Baseline characteristics of incident dialysis patients categorized by tertiles of interleukin-6-to-albumin ratio (IAR)

All Low tertile Middle tertile High tertile p value
(N = 428) (N = 143) (N = 143) (N = 142)
Age, years 56 (45–64) 50 (38–61) 55 (47–63) 60 (52–66) <0.001
Male, n (%) 263 (61.5) 79 (55.2) 92 (64.3) 92 (64.8) 0.098
Diabetes mellitus, n (%) 132 (30.8) 25 (17.5) 43 (30.1) 64 (45.1) <0.001
CVD, n (%) 162 (37.9) 36 (25.2) 43 (30.1) 83 (58.5) <0.001
Smokinga, n (%) 193 (45.1) 55 (38.5) 60 (41.3) 79 (55.6) 0.004
BMI, kg/m2 24.1 (21.7–27.3) 23.3 (21.0–26.0) 24.9 (22.3–28.4) 24.1 (21.9–28.0) 0.020
SBP, mm Hg 148 (134–165) 145 (133–157) 151 (136–170) 148 (134–166) 0.096
DBP, mm Hg 87 (79–97) 88 (80–98) 88 (78–96) 86 (79–97) 0.437
eGFR, mL/min/1.73 m2 6.3 (4.9–8.0) 6.3 (5.0–8.2) 6.2 (4.8–8.2) 6.3 (4.9–8.0) 0.836
Hemoglobin, g/L 104 (95–114) 109 (98-117) 105 (95–113) 100 (92–110) <0.001
White blood cell, *109/L 7.5 (6.1–9.4) 6.8 (5.7–8.5) 7.6 (6.2–9.1) 8.9 (6.7–11.0) <0.001
Triglyceride, mmol/L 1.7 (1.3–2.3) 1.7 (1.3–2.2) 1.8 (1.2–2.6) 1.7 (1.4–2.3) 0.598
Total cholesterol, mmol/L 4.8 (4.0–5.8) 4.9 (4.0–6.1) 4.8 (4.0–6.0) 4.7 (3.8–5.6) 0.117
HDL-C, mmol/L (n = 332) 1.2 (0.9–1.5) 1.3 (1.1–1.6) 1.1 (1.0–1.5) 1.2 (0.9–1.5) 0.203
iPTH, pg/mL 229 (108–373) 216 (108–360) 240 (97–373) 221 (108–382) 0.178
Albumin, g/dL 3.4 (3.0–3.7) 3.6 (3.4–3.9) 3.3 (3.1–3.7) 2.9 (2.6–3.4) <0.001
hsCRP, mg/L 5.0 (1.7–14.0) 1.8 (0.7–4.2) 5.3 (2.3–12.0) 14.0 (6.3–27.9) <0.001
IL-6, pg/mL 6.4 (3.5–10.8) 2.7 (1.9–3.6) 6.4 (5.1–7.3) 14.2 (10.8–18.4) <0.001
CAR 1.5 (0.5–4.2) 0.5 (0.2–1.1) 1.5 (0.7–3.6) 4.7 (2.1–9.3) <0.001
IAR 1.9 (1.0–3.4) 0.8 (0.5–1.0) 1.9 (1.6–2.2) 4.6 (3.4–6.5) <0.001
Anti-hypertension medication, n (%) (n = 321) 293 (91.3) 96 (88.9) 103 (93.6) 94 (91.3) 0.531
Statin, n (%) (n = 333) 104 (31.2) 28 (24.8) 43 (38.4) 33 (30.6) 0.341
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Continuous variables are presented as median (25th – 75th percentile). Categorical variables are presented as number (n)/percentage (%).

IAR, interleukin-6-to-albumin ratio; CVD, cardiovascular disease; SBP, systolic blood pressure; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein cholesterol; iPTH, intact parathyroid hormone; hsCRP, high-sensitivity C-reactive protein; IL-6, interleukin-6; CAR, hsCRP-to-albumin ratio; BMI, body mass index.

aPrevious and current smoking.

Comparison of the Prediction Value of IAR and Other Factors for Mortality

We calculated AUC for s-albumin, hsCRP, CAR, IL-6, and IAR as predictors of all-cause and CV mortality and found that AUC for IAR was the highest for both all-cause mortality and CV mortality (online suppl. Fig. S3). The AUC for IAR (0.70) for all-cause mortality was greater than for s-albumin (0.639, p = 0.017) and IL-6 (0.687, p = 0.028) separately, while for CV mortality, the AUC for IAR (0.658) showed negligible improvement over s-albumin (0.604, p = 0.083) and IL-6 (0.646, p = 0.092) separately. AUC for IAR was consistently higher than for other variables also at other time points (12, 24, 36, and 48 months; data not shown).

IAR Associated with All-Cause Mortality Risk

A correlation matrix is presented in online supplementary Table S1. IAR correlated significantly with age, being or having been a smoker, presence of DM and CVD, hemoglobin, white blood cell count, and levels of hsCRP, high-density lipoprotein cholesterol, and BMI. Cox regression analysis revealed that IAR significantly associated with all-cause mortality (HR 1.31, 95% CI 1.05–1.63, p = 0.019) but not with CV mortality (HR 1.23, 95% CI 0.92–1.64, p = 0.159). After dividing patients into tertiles of IAR, we performed Fine-Gray analysis, taking KTx as competing risk, and calculated cumulative incidence curves of all-cause mortality after adjusting for age, sex, DM, CVD, smoking, and eGFR (Fig. 1). All-cause mortality was significantly higher in the middle (sHR 1.85, 95% CI 1.16–2.95) and high (sHR 2.22, 95% CI 1.40–3.52) IAR tertiles compared to the low IAR tertile. Additional adjustments for BMI, systolic blood pressure, and hemoglobin had no major impact on all-cause mortality risk (data not shown).

Fig. 1.

Fig. 1.

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Fine-Gray competing risk analysis of cumulative incidence of all-cause mortality – with inserts showing subdistribution hazard ratio (sHR) and 95% confidence intervals (95% CI) – for tertiles of interleukin-6 to albumin ratio (IAR) after adjusting for age, sex, diabetes mellitus, cardiovascular disease, smoking, and estimated glomerular filtration rate.

∆RMST between IAR Tertiles

The RMST to all-cause mortality at 60 months associated with middle and high IAR tertiles were significantly shorter compared with RMST associated with the low tertile after adjusting for age, sex, DM, CVD, smoking, and eGFR. The difference in RMST (∆RMST) for high versus low IAR tertile was −12.73 (±3.19) months (p < 0.001) and for middle versus low IAR tertile was −6.91 (±2.58) months (p < 0.01) (Fig. 2).

Fig. 2.

Fig. 2.

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Difference in restricted mean survival time (∆RMST) to all-cause mortality at 60 months for middle and high tertiles compared with the low tertile of interleukin-6-to-albumin ratio (IAR) after adjusting for age, sex, diabetes mellitus, cardiovascular disease, smoking, and estimated glomerular filtration rate.

Discussion

CKD is associated with high morbidity and mortality and is one of the top ten leading causes of disability-adjusted life years globally according to the Global Burden of Disease Study 2019 [19]. CVD deaths accounted for half of all known causes of mortality in ESKD [20, 21]. Traditional risk factors do not completely explain the high risk for CV events and death in ESKD, and novel biomarkers that may allow better risk stratification, guide therapy, and provide insights into the pathogenesis of CVD in ESKD are warranted. The circulating concentrations of the inflammation biomarkers CRP and IL-6 investigated in the present study are associated with protein-energy wasting, cardiometabolic complications, and CVD and predict mortality in ESKD. The predictive value of elevated plasma CRP and IL-6 was demonstrated to be better than that of other inflammatory biomarkers [22, 23]. Moreover, IL-6 captures almost entirely the high-risk associated with inflammation in ESKD patients [14, 16, 24]. This is in line with our results of ROC analysis comparing s-albumin, hsCRP, and IL-6 as predictors of mortality in incident dialysis patients showing that AUC of IL-6 was greater compared with AUC’s of s-albumin and hsCRP.

Another suggested biomarker, CAR, a composite indicator of inflammation and nutritional status, has recently been recognized as an independent prognostic marker for use in multiple types of diseases [25–29], including predicting outcomes in ESKD patients [12, 13]. However, we found AUC for IAR to be higher than that for CAR for predicting 60-month all-cause mortality in ESKD, and therefore focused on IAR in the current study. Our main finding is that when categorizing our patients into tertiles of IAR, taking KTx as competing risk, Fine-Gray analysis revealed that patients in the middle and high IAR tertiles (sHR 1.85 and 2.22, respectively) had significantly higher all-cause mortality compared to the low IAR tertile after adjusting for confounders. We did not find significant associations between IAR tertiles and CV mortality risk. We speculate that IAR is more closely associated with other causes of deaths than CV such as infections. Further studies are needed to confirm this.

As a predominant pro-inflammatory cytokine, IL-6 has attracted much interest. In addition to reduced removal of IL-6 by the kidneys, individual genetic predisposition [30, 31], and the toxic uremic environment, there are many factors that may enhance circulating levels of IL-6 [32, 33]. Apart from being a reliable marker of systemic inflammation, accumulating evidence from animal models and clinical trials has firmly associated IL-6 with both vascular pathophysiology [34] and as a direct cause of CVD. Moreover, there is an increasing attention toward anti-cytokine therapy targeting inflammation including IL-6 [35, 36]. The Canakinumab Anti-Inflammatory Thrombosis Outcomes Study (CANTOS) provided evidence in humans that modulation of the IL-6 signaling pathway is associated with risk reduction of CV events [37]. Prior studies suggested an association between the nutritional status and extent of inflammation, which may influence the risk of developing complications. IAR is a simple marker that reflects both the inflammatory and nutritional status and may serve as a potential marker of disease severity. Meanwhile, as IL-6 and s-albumin are inversely related in inflamed ESKD patients, it is conceivable that IAR could be a more reliable marker than its two components in predicting outcomes in ESKD. Furthermore, as both IL-6 and albumin levels are affected by numerous conditions, IAR may reduce potential bias. It should be noted that as measurements of IL-6 are usually not available in the clinic, the main potential application of IAR at present is in clinical research. However, the introduction of anti-inflammatory therapies targeting IL-6 should increase the indication for measuring IL-6 and this could stimulate the development of affordable and fast methods for determining IL-6.

Our results are supported by the findings of the RMST analysis. Whereas risk estimates such as HR or sHR represent measures of relative risk, they do not represent quantitative measures of either survival time or survival rate. In some cases, even if the absolute effect of different factors on survival time is small, HR or sHR can be very high. Therefore, these measures need to be considered together with other indicators, such as median survival time or survival rate at a given time point to understand their clinical implications. RMST analysis, on the other hand, provides an absolute measure of survival time, or differences in survival time, for different groups at a given time point [38]. In our study, RMST analysis showed that patients with higher IAR (middle and high IAR tertiles vs. low tertiles) had significantly shorter survival time. After 60 months of follow-up, patients in the high IAR tertile lived 12.73 ± 3.19 months shorter compared to those in the low tertile, and patients in the middle tertile lived 6.91 ± 2.58 months less. RMST offers a more realistic and clinically meaningful estimate than HR or sHR. The quantitative difference in terms of longer survival for low versus high IAR tertiles with ∆RMST exceeding 1 year can be considered to be clinically significant.

The present study has some limitations and strengths that should be considered. First, since this is an observational study, no conclusions can be made about causality. Second, biomarkers were measured at one time point only while additional measurements may have enabled more precise assessment of the relationship between IAR and outcomes. Strengths include a relatively large cohort with long follow-up time and no loss of follow-up. In many cases, follow-up was censored at the time of KTx because patients are no longer at risk to die while receiving dialysis at that time. It can be argued that such censoring may have influenced the results. We performed Fine-Gray analysis taking KTx as a competing risk to reduce the bias. Furthermore, in addition to the HR analysis, we analyzed RMST and ΔRMST, which may be more informative and intuitive to clinical communities as these measures describe quantitative differences of survival time, here expressed as number of months over defined follow-up periods, between IAR tertiles. Although many traditional risk factors were considered, the results need to be confirmed in larger cohorts of ESKD patients with different ethnicities to ascertain the generalizability of the results.

In conclusion, this study shows that IAR is independently associated with all-cause mortality risk in ESKD patients and that it may be a better predictor than s-albumin and IL-6 separately. These findings suggest that IAR might help identify ESKD patients with a high risk of death and support decisions about appropriate prevention and treatment to improve their prognosis. Whereas measurements of IL-6 are usually not available in the clinic at present, the possible advent of more affordable and faster methods to determine IL-6 may allow IAR to become a biomarker for clinical risk assessments in patients with ESKD in the future.

Acknowledgments

We thank all patients who participated in the study and those who carried out the extensive clinical and laboratory work at the Department of Renal Medicine, Karolinska University Hospital, Stockholm, Sweden.

Statement of Ethics

The Ethics Committee of the Karolinska Institutet, Stockholm, Sweden, approved the study protocol (Dnr 273/94, Dnr 2007/166331/4, Dnr 2008/1748-31/2). Written informed consent was obtained from each patient and the protocol adhered to the statutes of the Declaration of Helsinki.

Conflict of Interest Statement

Bengt Lindholm is affiliated with Baxter Healthcare Corporation. Peter Stenvinkel is or has been on scientific advisory boards of REATA, Baxter, GSK, Astra Zeneca, Invizius, Behring, and Vifor and educational advisory boards for Astellas and FMC. Other authors do not declare any conflict of interest.

Funding Sources

This work was supported by the National Natural Science Foundation (81900696) and the Natural Science Foundation of Hunan Province (2020JJ5814), China. Baxter Novum is the result of a grant from Baxter Healthcare to the Department of Clinical Science, Intervention and Technology, Karolinska Institutet. The study also benefited from the support by Swedish Medical Research Council (PS), Heart and Lung Foundation (PS), CIMED (PS), Njurfonden (PS), and Westmans Foundation (PS).

Author Contributions

Xiejia Li and Abdul Rashid Quresh were responsible for the statistical analysis. Xiejia Li and Bengt Lindholm were responsible for drafting the manuscript. Xiejia Li, Abdul Rashid Qureshi, Mohamed E Suliman, Olof Heimbürger, Peter Barany, Peter Stenvinkel, and Bengt Lindholm have made substantial contributions to the conception, design, interpretation of data and results, and critical revision of the manuscript and intellectual content and gave final approval of the version to be published and agreed to take responsibility for the content.

Funding Statement

This work was supported by the National Natural Science Foundation (81900696) and the Natural Science Foundation of Hunan Province (2020JJ5814), China. Baxter Novum is the result of a grant from Baxter Healthcare to the Department of Clinical Science, Intervention and Technology, Karolinska Institutet. The study also benefited from the support by Swedish Medical Research Council (PS), Heart and Lung Foundation (PS), CIMED (PS), Njurfonden (PS), and Westmans Foundation (PS).

Data Availability Statement

Data are not publicly available due to ethical reasons. Further inquiries can be directed to the corresponding author.

Supplementary Material

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Associated Data

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

Supplementary Materials

Supplementary_material-Suppl.1-s1.docx (190.8KB, docx)

Data Availability Statement

Data are not publicly available due to ethical reasons. Further inquiries can be directed to the corresponding author.

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