Kidney Res Clin Pract > Epub ahead of print
Jo, Kim, Kim, Yoo, Kim, Kim, Oh, Seong, and Song: The association between transferrin saturation and all-cause mortality in chronic kidney disease: findings from Korean Cohort Study for Outcome in Patients with Chronic Kidney Disease

Abstract

Background

Transferrin saturation (TSAT) has been used as an indicator of iron deficiency. However, there is no consensus regarding its optimal range for patient with chronic kidney disease (CKD). We aimed to analyze the effect of TSAT on the prognosis of patients with non-dialysis CKD (NDCKD).

Methods

From 2011 to 2016, 2157 NDCKD patients with baseline TSAT measurements were followed for 10 years. Patients were divided into three groups based on baseline TSAT values: <25%, ≥25% and <45%, and ≥45%. All-cause mortality and 4-point major adverse cardiovascular events (MACE) were analyzed using multivariable Cox regression analysis. Other iron biomarkers and mortality were also analyzed.

Results

During a mean follow-up of 7.1 ± 2.9 years, 182 of a total of 2,157 patients (8.4%) died. Compared with the TSAT ≥25% and <45% group, the TSAT <25% group showed significantly increased all-cause mortality (hazard ratio [HR], 1.44; 95% confidence interval (CI), 1.02–2.03; p = 0.04). The occurrence of 4-point MACE was significantly increased in univariable analysis in the TSAT <25% group (HR, 1.48; 95% CI, 1.02–2.15; p = 0.04), but it was not significant in the multivariable analysis (HR, 1.38; 95% CI, 0.89–2.15; p = 0.15). Tertile comparisons of the iron-to-log-ferritin ratio showed increased mortality in the first tertile group.

Conclusion

TSAT <25% is an independent risk factor for all-cause mortality in patients with NDCKD and care should be taken to prevent TSAT values of <25%. Other indicators, such as serum iron and iron-to-log-ferritin ratio, may also be used to assess iron deficiency.

Introduction

In patients with chronic kidney disease (CKD), anemia is a common complication that plays a major role in determining the prognosis. The lower the hemoglobin concentration, the higher the mortality, the faster the decline in renal function, the more severe the associated symptoms, and the lower the productivity of daily life [14].
Iron deficiency anemia is a leading cause of anemia, and the importance of iron deficiency (ID) itself is increasing regardless of the occurrence of anemia [57]. Serum ferritin and transferrin saturation (TSAT) are the most common tools used to evaluate ID. Ferritin is a marker of stored iron and TSAT is a marker of circulating iron, indicating iron availability to the tissue [8]. However, serum ferritin has limitations in diagnosing functional ID (FID) because it can be increased in the inflammatory state as an acute-phase reactant, independent of iron metabolism [9]. In CKD, where both absolute and FID are important, TSAT can be a useful marker for assessing low iron availability [5,7].
However, the exact ranges of serum biomarkers for iron supply are inconsistent. The 2012 Kidney Disease–Improving Global Outcomes (KDIGO) guidelines recommend iron supplementation when ferritin is less than 500 ng/mL and TSAT is less than 30% in both dialysis and non-dialysis CKD (NDCKD) patients [10]. However, subsequent studies have shown a better prognosis in a higher range than the current guidelines, with mortality rising again above a certain level of TSAT [1114].
Serum iron is reduced in ID, even in the setting of inflammation, and its usefulness in evaluating ID is emerging [7,15]. However, in the progression of anemia serum iron begins to decrease later than stored iron and alone its concentrations do not indicate ID [7]. It may be helpful to evaluate the prognosis through the relationship between serum iron and ferritin as well as TSAT.
Therefore, we aimed to analyze the effect of TSAT on the prognosis of patients with NDCKD through all-cause mortality, cardiovascular outcomes, and its association with other iron biomarkers.

Methods

Study participants and design

The Korean Cohort Study for Outcome in Patients with Chronic Kidney Disease (KNOW-CKD) is a multicenter, prospective cohort study that enrolled pre-dialysis patients with CKD stages 1 to 5 in South Korea (NCT01630486 at http://www.clinicaltrials.gov). From 2011 to 2016, 2,238 CKD adult patients aged 20 to 75 years were recruited from nine clinical centers in university-affiliated hospitals. Details of the rationale and design of the KNOW-CKD study have been described previously [16]. A total of 2,157 patients were finally included in the analysis after excluding 69 patients without baseline TSAT values and 12 patients without data on follow-up duration (Supplementary Fig. 1, available online).
The study was conducted in accordance with the principles of the Declaration of Helsinki and informed consent was obtained from all participants. The study protocol was approved by the Institutional Review Board of each participating clinical center: Seoul National University Hospital (No. 1104-089-359), Seoul National University Bundang Hospital (No. B-1106/129-008), Severance Hospital (No. 4-2011-0163), Kangbuk Samsung Medical Center (No. 2011-01-076), The Catholic University of Korea, Seoul St. Mary’s Hospital (No. KC11OIMI0441), Gachon University Gil Hospital (No. GIRBA2553), Nowon Eulji Medical Center (No. 201105-01), Chonnam National University Hospital (No. CNUH-2011-092), and Pusan Paik Hospital (No. 11-091).

Data collection

Baseline demographic data including age, sex, comorbidities, anthropometric measurements, and medical history were recorded at enrollment. Blood samples were obtained after overnight fasting and sent to a central laboratory (Lab Genomic). The glomerular filtration rate was estimated by the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) creatinine equation using serum creatinine levels in the central laboratory [17]. The spot urine albumin-to-creatinine ratio was used to measure urinary albumin excretion.

Study outcome

The primary outcome was all-cause mortality according to TSAT (%). Baseline TSAT was calculated as serum iron × 100/total iron-binding capacity (TIBC) values at enrollment. The secondary outcome was 4-point major adverse cardiovascular events (MACE). In this study, 4-point MACE was defined as cardiovascular death, nonfatal acute myocardial infarction, nonfatal stroke, and unstable angina requiring hospitalization. We also analyzed the differences in outcomes according to their relationship with serum ferritin, iron, and TSAT levels.

Statistical analysis

We categorized patients into three groups based on baseline TSAT level: <25%, ≥25% and <45% (reference group), and ≥45%. The three groups were classified by referring to the points showing a clear difference in mortality between adjacent sections. We performed a t-test for mortality between adjacent groups by dividing the TSAT level into 5% units, and there was a clear difference starting from 25% and 45%. In addition, analysis using the cubic spline curve according to TSAT showed that the adjusted hazard ratio (HR) began to rise from the section below 25% to 30% (Supplementary Fig. 2, available online). Data are expressed as mean ± standard deviation or median (interquartile range, IQR) for continuous variables and as number (percentage) for categorical variables. Baseline characteristics and laboratory findings were compared using the analysis of variance or the Kruskal-Wallis test for continuous variables and the chi-square test for categorical variables. Cumulative survival curves for all-cause mortality were generated using the Kaplan-Meier method, and between-group survival was compared using the log-rank test. A Cox proportional hazard regression model was used to assess the independent relationship between TSAT levels and all-cause mortality. The analyses were adjusted for baseline confounders, including age, sex, body mass index (BMI), mean blood pressure, underlying comorbidities, laboratory tests, erythropoietin-stimulating agent (ESA) use, and iron supplementation. Four models were designed and verified to satisfy the proportional hazards assumption of the Cox model using the Schoenfeld test. The results of the Cox analysis were presented as HRs and 95% confidence intervals (CIs). We performed a subgroup analysis and subgroups were defined by age (<65 years vs. ≥65 years), sex (male vs. female), BMI (<25 kg/m2 vs. ≥25 kg/m2), diabetes mellitus (DM) vs. non-DM, anemia vs. non-anemia, ferritin (<100 mg/dL vs. ≥100 mg/dL), high-sensitivity C-reactive protein (hsCRP; <0.5 mg/dL vs. ≥0.5 mg/dL), estimated glomerular filtration rate (eGFR; <30 mL/min/1.73 m2 vs. ≥30 mL/min/1.73 m2), and serum hepcidin (<15 ng/mL vs. ≥15 ng/mL). Differences in mortality were analyzed for the combination of TSAT and serum ferritin and TSAT and serum iron. To examine the association between mortality and other serum iron profiles, such as serum iron and ferritin, we used the serum iron-to-log-ferritin ratio and classified the patients into three tertile groups (T1, T2, and T3). Ferritin values were normally distributed through the log and the ratio of serum iron-to-log-ferritin was obtained. and classified the patients into three tertile groups (T1, T2, and T3). Statistical analyses were performed using SPSS for Windows (version 27.0; IBM Corp.) and R version 4.3.1 (R Foundation for Statistical Computing).

Results

Baseline characteristics

Of the 2,157 patients, 644 patients were in the TSAT <25% group, 1,240 were in the TSAT ≥25% and <45% group (reference), and 273 were in the TSAT ≥45% group (Supplementary Fig. 1, available online). The baseline demographic and clinical characteristics of the patients are shown in Table 1. The mean age was 53.7 ± 12.2 years and the mean eGFR was 53.0 ± 30.7 mL/min per 1.73 m2. The mean value of TSAT was 31.7% ± 12.1% and the median value of ferritin was 99.9 ng/mL (IQR, 54.1–176.6 ng/mL). The incidence of DM (p < 0.001) and cardiovascular disease (p = 0.03) was higher in the lower TSAT group. There were no differences in the prescriptions of ESA and supplementary iron between the TSAT groups.

Primary outcome

During a mean follow-up duration of 7.1 ± 2.9 years, 182 patients (8.4%) died. The causes of death were evaluated in 120 patients, with 41 (deaths 34.2%) attributed to infection, 25 (20.8%) to malignancy, 23 (19.2%) to cardiovascular disease, and 31 (25.8%) to other causes. For patients with TSAT categories of <25%, ≥25% and <45%, and ≥45%, the mortality rate was 11.0%, 7.7%, and 5.5%, respectively. Cardiovascular death was more common in the TSAT <25% group than in other groups (Supplementary Fig. 3, available online). Kaplan-Meier analysis showed higher all-cause mortality in the TSAT <25% group than in the other groups and a decrease with higher TSAT (log-rank p = 0.005) (Fig. 1). Four models of multivariable Cox analysis revealed that the TSAT <25% was associated with higher mortality risk, even after adjustment for hemoglobin, ferritin, ESA use, and iron supplementation (HR, 1.44; 95% CI, 1.00–2.03; p = 0.04) (Table 2). The all-cause mortality showed a trend toward decrease with higher TSAT, but no statistically significant difference at TSAT ≥45% (HR, 0.90; 95% CI, 0.50–1.64; p = 0.73). Supplementary Fig. 3 (available online) shows the restricted cubic spline curve for the multivariable Cox regression analysis of all-cause mortality according to TSAT. The results of the subgroup analyses are shown in Supplementary Table 1 (available online). In the subgroup analysis of patients with BMI <25 kg/m2, serum ferritin <100 ng/mL, eGFR ≥30 mL/min per 1.73 m2, and hepcidin <15 ng/mL, the HRs of the TSAT <25% group showed significantly increased mortality compared with the TSAT ≥25% and <45% group.

Secondary outcome

For patients with TSAT categories of <25%, ≥25% and <45%, and ≥45%, the incidence of 4-point MACE was 12.9%, 9.7%, and 5.9%, respectively. Similar to the all-cause mortality outcomes, 4-point MACE decreased in the higher TSAT group (log-rank p = 0.03) (Fig. 2). In the Cox regression analysis, the TSAT <25% group had increased MACE compared with the TSAT ≥25% and <45% group in univariable analysis (HR, 1.48; 95% CI, 1.02–2.15; p = 0.04). However, this was not significant in the multivariable analysis (HR, 1.38; 95% CI, 0.89–2.15; p = 0.15). The TSAT ≥45% group also showed no significant difference in MACE compared with the TSAT ≥25% and <45% group (HR, 0.72; 95% CI, 0.32–1.60; p = 0.42) (Table 3).

Association of transferrin saturation with other iron biomarkers: ferritin and iron levels

Differences in all-cause mortality were analyzed for the combination of TSAT and serum ferritin and TSAT and serum iron. Supplementary Fig. 4 (available online) shows the HR of all-cause mortality according to serum ferritin and iron levels using a restricted cubic spline plot. The serum ferritin level was divided by 100 ng/mL, which is the current general reference value for ID in NDCKD, and the iron level was divided by 80 μg/dL based on the restricted cubic spline of the HR. Kaplan-Meier analysis showed that mortality was highest in the group with TSAT <25% and ferritin <100 ng/mL (Fig. 3A). In a multivariable Cox analysis with the TSAT ≥25% and ferritin ≥100 ng/mL group as the reference, the adjusted HR was highest when TSAT <25% and ferritin <100 ng/mL (HR, 1.69; 95% CI, 1.12–2.56; p = 0.01) (Fig. 3B). The TSAT <25% and ferritin ≥100 ng/mL group was the next highest, but it was not statistically significant. For TSAT and iron, the Kaplan-Meier analysis showed higher mortality in the two iron <80 μg/dL groups (Fig. 3C). In Cox analysis with the TSAT ≥25% and iron ≥80 μg/dL group as the reference, the adjusted HR was highest in the TSAT <25% and iron <80 μg/dL group (HR, 1.67; 95% CI, 1.13–2.46; p = 0.009) (Fig. 3D). The TSAT ≥25% and iron <80 μg/dL group had the next highest, but this was not statistically significant.

Iron-to-log-ferritin ratio

Cox regression analysis of the iron-to-log-ferritin ratio showed a U-shaped distribution, with the lowest mortality in the second tertile group (T2) (Fig. 4A). The HR of the first tertile group (T1) was significantly increased compared with T2 as the reference group (HR, 1.71; 95% CI, 1.16–2.51; p = 0.007) (Fig. 4B). There was also an increase in mortality in the third tertile (T3), but this was not statistically significant. The median values of the iron biomarkers in the T2 range were as follows: TSAT, 30% (IQR, 25%–35%); iron, 90 μg/dL (IQR, 79–101 μg/dL); and ferritin, 108 ng/mL (IQR, 60–176 ng/mL).

Discussion

In our study, the TSAT <25% group had a significantly higher mortality rate than the ≥25% and <45% group. Overall, all-cause mortality decreased with increasing TSAT, but there was no significance at TSAT 45% or higher. The incidence of 4-point MACE was significantly increased in the univariable analysis in the TSAT <25% group, but the significance was lost after multivariable adjustment. The 4-point MACE incidence tended to decrease as TSAT increased, but the difference was not statistically significant.
Various studies on TSAT have been published since the 2012 KDIGO guidelines. The PIVOTAL trial, a randomized controlled trial (RCT) in patients with CKD on dialysis (HDCKD), showed that high-dose intravenous (IV) iron administered proactively (with a ferritin cutoff of 700 ng/mL or TSAT 40%) was superior to low-dose IV iron administered reactively (administered to maintain ferritin 200 ng/mL and TSAT 20%) [14]. Observational studies have suggested relatively clear optimal ranges. In patients with NDCKD, mortality has been shown to be higher in TSAT <10%–17% [12,1820], with increased mortality above 45%–55% [12,21]. In the 2021 CKDopps study, all-cause mortality and MACE were the lowest in the TSAT 35%–45% range and increased again above 46% [12]. In patients with HDCKD, TSAT <20% was associated with higher mortality and was a predictor of coronary artery calcium scoring >400 [2224], with the best survival at 30%–50% [2].
Our results are consistent with those of the CKDopps study, which showed an increase in all-cause mortality below 25% when the TSAT 25%–35% group was used as a reference [12]. However, there was no upper cutoff for TSAT, and the HR continued to decrease. Nevertheless, caution is required in concluding that the higher the TSAT, the lower the mortality rate. Malignancy accounted for half of the deaths in the TSAT ≥45% group, and high TSAT and iron supply may contribute to increase oxidative stress and the risk of malignancy [25,26]. The average age of our participants was 53 years old, and in particular, the TSAT ≥45% group averaged 51 years old, which is younger than the average ages of 69 years [12] and 61 years [2] in previous studies. Therefore, if the incidence of malignancy increases with age due to longer follow-up periods, all-cause mortality in the high TSAT range may increase. In the subgroup analysis, the TSAT <25% group had a higher HR in the nonobese subgroup, relatively preserved renal function, and lower serum ferritin and hepcidin levels. This suggests that the impact of a low TSAT is more pronounced in patients with better overall health. Under inflammatory conditions, such as CKD or obesity, cytokines increase ferritin and hepcidin expression [27]. In a controlled environment of chronic inflammation, which may contribute to mortality, the impact of low TSAT levels may be more apparent.
Of the 182 deaths, only 35 patients (1.6%) were confirmed as cardiovascular deaths, making comparisons between groups difficult. Supplementary Table 2 (available online) shows the echocardiographic parameters. Lower TSAT has been shown to be associated with increased left ventricular geometric abnormalities, and increased E/E’ although in the normal range. This is consistent with previous findings that ID and anemia are associated with diastolic dysfunction and increased left ventricular mass [28,29]. Although there was no significant difference in 4-MACE incidence, avoiding TSAT <25% may be beneficial in preventing cardiac dysfunction.
ID can be classified as absolute ID (AID) or FID. AID is a condition in which iron stores in the whole body are insufficient and iron is reduced. In contrast, FID refers to a condition in which the body is not deficient in iron but cannot use iron for hematopoiesis and is mainly related to chronic inflammation [7]. In CKD, the use of ESAs increases iron demand by stimulating hematopoiesis; however, insufficient iron mobilization worsens FID [3033]. At the KDIGO conference in 2019, AID was defined as TSAT <20% and ferritin <100 ng/mL, and FID was defined as TSAT <20% and ferritin >100 ng/mL in NDCKD. However, the definition of ID in CKD patients is still not consistently used [8]. Previous studies have also been conducted on serum ferritin levels. The FIND-CKD RCT indicated that IV iron targeting higher ferritin (400–600 ng/mL) delayed the need for other anemia management than low ferritin (100–200 ng/mL). Other observational studies have shown no directional association, but mortality increased at levels above 250 [19], 300 [12], or 500 [34].
In our study, regardless of the ferritin level, the group with low TSAT had a poor prognosis, and the AID group had a higher mortality rate than the FID group. For TSAT ≥25%, the prognosis was better with ferritin <100 than ≥100 ng/mL, and a similar result was found in a heart failure study [35]. This may be related to the implications of high ferritin because ferritin is always low in AID; however, when minimal iron is available, ferritin is regulated by non-iron-dependent factors, such as inflammation [36]. Therefore, when TSAT ≥25%, that is, when there is no shortage of iron supply, the prognosis may be better if the ferritin is low.
Under inflammatory conditions, cytokines upregulate ferritin and hepcidin and downregulate ferroportin, a transmembrane exporter of iron. Iron absorption in the gastrointestinal tract and its dissociation from macrophages and hepatocytes is impaired, limiting its supply to plasma and causing FID [7,27,32,37]. Because of this pathophysiology, even if ferritin levels are high in CKD, the possibility of FID cannot be ruled out [32,38]. In contrast, TSAT is more useful for identifying low plasma iron availability in both AID and FID [5,7]. In a study evaluating bone marrow aspiration in patients with heart failure, a low TSAT alone showed the best performance in diagnosing ID [39]. However, because TSAT can also be affected by inflammatory conditions, its limitations and the usefulness of serum iron have been noted [15,32,40]. In our study, the two groups with low iron levels, regardless of TSAT, had the worst prognoses. In patients with CKD, the value of TSAT required to achieve a serum iron level increases as a result of decreased TIBC [15]. Therefore, high TSAT levels have limitations as a stand-alone marker to exclude ID, and should also be considered a marker of inflammation in CKD [41]. In contrast, a low TSAT can be associated with low iron or high TIBC, both of which reflect the pathophysiology of ID.
As serum iron begins to deplete later than stored iron during the progression of anemia [7], the prognosis of ID was also analyzed using the iron-to-log-ferritin ratio, including ferritin, in our study. It showed a U-shape with the T2 range as a reference and a statistically significant increase in mortality in the T1 range. This can be interpreted as a poor prognosis in inflammatory conditions with decreased iron and high ferritin levels. In the T2 segment, each biomarker ranged from TSAT 25% to 35%, ferritin 60 to 176 ng/mL, and iron 79 to 101 μg/dL. These ranges were within the low mortality interval in our study. Based on the above results, serum iron and iron-to-log-ferritin ratios would be helpful in addition to TSAT when evaluating ID in patients with CKD.
Our study has several strengths. A long-term analysis was conducted on a large cohort of patients with CKD, and the relationship between TSAT and other iron biomarkers provided an overall insight into ID and its effects. However, this study also has its limitations. The relatively small number of patients with TSAT ≥45% may have contributed to the low incidence of the outcomes. In addition, iron status may vary according to ethnicity [42], but this study may have limitations in that it was conducted in a single ethnic group. Another limitation is that although we adjusted for multiple factors, this was an observational study, and confounding factors may remain.
In conclusion, TSAT <25% was an independent risk factor for all-cause mortality in patients with NDCKD compared to the TSAT ≥25% and <45% group. Care should be taken to prevent TSAT values of <25%. In the treatment of ID in patients with CKD, other indicators, such as serum iron and iron-to-log-ferritin ratio, may also be used to assess ID.

Notes

Conflicts of interest

All authors have no conflicts of interest to declare.

Funding

This work was supported by the Research Program funded by the Korea Disease Control and Prevention Agency (2011E3300300, 2012E3301100, 2013E3301600, 2013E3301601, 2013E3301602, 2016E3300200, 2016E3300201, 2016E3300202,2019E320100, 2019E320101, 2019E320102, 2022-11-007), a New Faculty Research Grant of Pusan National University, 2023 (No. 202310510001), and the Bio&Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. RS-2023-00223764).

Data sharing statement

The data presented in this study are available from the corresponding author upon reasonable request.

Authors’ contributions

Conceptualization, Investigation, Methodology: EJ, HJK, EYS, SHS

Data curation: HJK, JK, THY, YK, SWK, KHO, EYS, SHS

Formal analysis: EJ, HJK

Funding acquisition: HJK, KHO, SHS

Supervision: JK, THY, YK, SWK, KHO, EYS, SHS

Writing–original draft: EJ, HJK

Writing–review & editing: EJ, HJK, EYS, SHS

All the authors read and approved the final manuscript.

Figure 1.

Kaplan-Meier survival curve for all-cause mortality according to baseline TSAT value.

TSAT, transferrin saturation.
j-krcp-23-278f1.jpg
Figure 2.

Cumulative incidence of 4-point MACE by baseline TSAT value.

MACE, major adverse cardiovascular event; TSAT, transferrin saturation.
j-krcp-23-278f2.jpg
Figure 3.

Kaplan-Meier survival curve and Cox analysis for all-cause mortality according to iron biomarkers: serum ferritin and iron.

Kaplan-Meier survival curve according to TSAT and ferritin (A) and TSAT and iron (C). Unadjusted and adjusted hazard ratio for TSAT and ferritin (B) and TSAT and iron (D). In the Cox analysis, adjustment for TSAT and ferritin groups included age, sex, BMI, MBP, underlying comorbidities, estimated glomerular filtration rate (eGFR), albumin, uric acid, phosphate, corrected calcium, low-density lipoprotein cholesterol (LDL-C), log high-sensitivity C-reactive protein (log-hsCRP), log urine albumin-to-creatinine ratio (log-UACR), hemoglobin, total iron-binding capacity (TIBC), erythropoietin-stimulating agent (ESA) use, and iron supplementation. Adjustments for the TSAT and iron groups included age, sex, body mass index, mean blood pressure, underlying comorbidities, eGFR, albumin, uric acid, phosphate, corrected calcium, LDL-C, log-hsCRP, log-UACR, hemoglobin, TIBC, log-ferritin, ESA use, and iron supplementation.
TSAT, transferrin saturation.
j-krcp-23-278f3.jpg
Figure 4.

Adjusted hazard ratio of all-cause mortality by iron-to-log-ferritin ratio.

(A) Restricted cubic spline curve for the hazard ratio for iron-to-log-ferritin ratio using multivariable Cox regression analysis. (B) Hazard ratio and values of iron biomarkers by iron-to-log-ferritin ratio tertiles. Iron-to-log-ferritin ratio was divided into tertiles (T1, T2, and T3, respectively) and compared with T2 as the reference group. The median value (interquartile range) of TSAT, serum iron, and ferritin according to each tertile section are indicated under the column.
TSAT, transferrin saturation.
j-krcp-23-278f4.jpg
Table 1.
Baseline characteristics
Characteristic Total TSAT group
p-value
<25% ≥25% and <45% ≥45%
No. of patients 2,157 644 1,240 273
Age (yr) 53.7 ± 12.2 53.8 ± 11.8 54.1 ± 12.3 51.2 ± 12.7 0.002
Male sex 1,324 (61.4) 334 (51.9) 785 (63.3) 205 (75.1) <0.001
Primary cause of CKD <0.001
 Diabetes melliuts 499 (23.1) 171 (26.6) 297 (24.0) 31 (11.4)
 Glomerulonephritis 782 (36.3) 222 (34.5) 431 (34.8) 129 (47.3)
 Polycystic kidney disease 348 (16.1) 106 (16.5) 198 (16.0) 44 (16.1)
 Hypertension 395 (18.3) 107 (16.6) 234 (18.9) 54 (19.8)
 Others 133 (6.2) 38 (5.9) 80 (6.5) 15 (5.5)
Body mass index (kg/m2) 24.6 ± 3.4 24.7 ± 3.5 24.6 ± 3.4 24.4 ± 3.1 0.34
SBP (mmHg) 127.8 ± 16.2 128.5 ± 16.7 127.7 ± 16.1 127.0 ± 15.8 0.38
DBP (mmHg) 77.0 ± 11.1 77.1 ± 11.7 76.8 ± 10.8 77.1 ± 10.9 0.84
MBP (mmHg) 93.9 ± 11.7 94.2 ± 12.2 93.8 ± 11.4 93.8 ± 11.6 0.72
Comorbidities
 Diabetes melliuts 728 (33.8) 259 (40.3) 421 (34.0) 48 (17.6) <0.001
 Hypertension 2068 (95.9) 610 (94.7) 1,197 (96.5) 261 (95.6) 0.17
 Cardiovascular disease 283 (13.1) 98 (15.2) 161 (13.0) 24 (8.8) 0.03
Prescription
 Iron, IV or oral 316 (14.7) 96 (14.9) 191 (15.5) 29 (10.7) 0.13
 ESA 163 (7.6) 56 (8.7) 93 (7.5) 14 (5.1) 0.17
 ACEi/ARBs 1,845 (85.5) 548 (85.1) 1,063 (85.7) 234 (85.7) 0.93
 Diuretics 685 (31.8) 228 (35.4) 394 (31.8) 63 (23.1) 0.001
 Statin 1,112 (51.6) 321 (49.8) 654 (52.7) 137 (50.2) 0.44
 Antiplatelet drug 644 (29.9) 200 (31.1) 381 (30.7) 63 (23.1) 0.03
 Anticoagulant 29 (1.3) 11 (1.7) 13 (1.0) 5 (1.8) 0.38
Laboratory test
 White blood cell (×103/mm3) 6.6 ± 1.9 6.9 ± 2.0 6.5 ± 1.9 6.4 ± 1.7 <0.001
 Hemoglobin (g/dL) 12.8 ± 2.0 12.4 ± 1.9 12.9 ± 2.0 13.5 ± 2.0 <0.001
 Iron (μg/dL) 92.6 ± 35.1 59.6 ± 17.2 97.4 ± 21.5 148.3 ± 34.6 <0.001
 TIBC (μg/dL) 297.5 ± 52.3 315.2 ± 57.0 293.0 ± 47.8 276.1 ± 48.1 <0.001
 TSAT (%) 31.7 ± 12.1 19.1 ± 4.7 33.3 ± 5.3 53.9 ± 9.7 <0.001
 Ferritin (ng/mL) 99.9 (54.1–176.6) 69.4 (36.6–126.1) 109.8 (62.5–186.1) 127.0 (74.8–221.3) <0.001
 Creatinine (mg/dL) 1.6 ± 1.0 1.6 ± 1.1 1.6 ± 0.9 1.6 ± 1.1 0.84
 eGFR (mL/min per 1.73 m2) 53.0 ± 30.7 51.2 ± 30.7 52.1 ± 30.2 61.3 ± 31.9 <0.001
 Albumin (g/dL) 4.2 ± 0.4 4.2 ± 0.4 4.2 ± 0.4 4.1 ± 0.5 0.52
 hsCRP (mg/dL) 0.6 (0.2–1.7) 1.1 (0.3–2.7) 0.5 (0.2–1.3) 0.6 (0.3–1.3) <0.001
 Uric acid (mg/dL) 7.0 ± 1.9 7.0 ± 2.0 7.1 ± 1.9 6.9 ± 1.9 0.26
 Total cholesterol (mg/dL) 174.2 ± 39.3 172.4 ± 37.3 174.2 ± 39.0 178.3 ± 44.3 0.12
 LDL-C (mg/dL) 97.0 ± 31.9 94.6 ± 30.0 97.2 ± 32.0 101.4 ± 35.0 0.01
 HDL-C (mg/dL) 49.2 ± 15.5 47.7 ± 14.8 49.6 ± 15.4 51.2 ± 17.0 0.003
 Triglyceride (mg/dL) 157.7 ± 98.8 168.9 ± 108.7 153.1 ± 94.6 152.7 ± 91.0 0.003
 Fasting glucose (mg/dL) 100 (92–114) 100 (91–118) 100 (91–114) 98 (93–106) 0.09
 Phosphorus (mg/dL) 3.7 ± 0.7 3.7 ± 0.7 3.7 ± 0.7 3.5 ± 0.6 <0.001
 Corrected calciuma (mg/dL) 9.0 ± 0.4 9.0 ± 0.4 9.0 ± 0.5 9.0 ± 0.4 0.99
 UACR (g/g) 0.7 (0.6–0.8) 0.7 (0.6–0.8) 0.7 (0.6–0.8) 0.7 (0.5–0.8) 0.62

Data are expressed as number only, mean ± standard deviation, number (%), or median (interquartile range).

ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; BMI, body mass index; CKD, chronic kidney disease; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; ESA, erythropoietin-stimulating agent; HDL-C, high-density lipoprotein cholesterol; hsCRP, high-sensitivity C-reactive protein; IV, intravenous; LDL-C, low-density lipoprotein cholesterol; MBP, mean blood pressure; SBP, systolic blood pressure; TIBC, total iron-binding capacity; TSAT, transferrin saturation; UACR, urine albumin-to-creatinine ratio.

a Corrected calcium = 0.8 × (normal albumin – patient’s albumin) + serum calcium.

Table 2.
HRs for all-cause mortality by TSAT levels
TSAT Events, n (%) Model 1a
Model 2b
Model 3c
Model 4d
HR (95% CI) p-value HR (95% CI) p-value HR (95% CI) p-value HR (95% CI) p-value
<25% 71 (11.0) 1.49 (1.09–2.02) 0.01 1.67 (1.23–2.27) 0.001 1.45 (1.04–2.03) 0.03 1.44 (1.02–2.03) 0.04
≥25% and <45% 96 (7.7) Reference Reference Reference Reference
≥45% 15 (5.5) 0.69 (0.40–1.18) 0.17 0.78 (0.45–1.35) 0.38 0.93 (0.04–2.03) 0.80 0.90 (0.50–1.64) 0.73

CI, confidence interval; HR, hazard ratio; TSAT, transferrin saturation.

a Unadjusted.

b Adjusted for age, sex, body mass index, mean blood pressure.

c Model 2 + adjusted for underlying comorbidities (diabetes mellitus, hypertension, and cardiovascular disease), estimated glomerular filtration rate, corrected calcium, phosphate, uric acid, albumin, low-density lipoprotein cholesterol, log-high-sensitivity C-reactive protein, and log-urine albumin-to-creatinine ratio.

d Model 3 + adjusted for hemoglobin, log-ferritin, erythropoietin-stimulating agent use, and iron supplementation.

Table 3.
HRs for 4-point MACE by TSAT levels
TSAT Events, n (%) Model 1a
Model 2b
Model 3c
Model 4d
HR (95% CI) p-value HR (95% CI) p-value HR (95% CI) p-value HR (95% CI) p-value
<25% 48 (7.5) 1.48 (1.02–2.15) 0.04 1.67 (1.14–2.43) 0.008 1.44 (0.95–2.18) 0.08 1.38 (0.89–2.15) 0.15
≥25% and <45% 65 (5.2) Reference Reference Reference Reference
≥45% 10 (3.7) 0.68 (0.35–1.32) 0.25 0.77 (0.40–1.50) 0.45 0.72 (0.32–1.58) 0.41 0.72 (0.32–1.60) 0.42

CI, confidence interval; HR, hazard ratio; MACE, major adverse cardiovascular event; TSAT, transferrin saturation.

a Unadjusted.

b Adjusted for age, sex, body mass index, mean blood pressure.

c Model 2 + adjusted for underlying comorbidities (diabetes mellitus, hypertension, and cardiovascular disease), estimated glomerular filtration rate, corrected calcium, phosphate, uric acid, albumin, low-density lipoprotein cholesterol, log-high-sensitivity C-reactive protein, and log-urine albumin-to-creatinine ratio.

d Model 3 + adjusted for hemoglobin, log-ferritin, erythropoietin-stimulating agent use, and iron supplementation.

References

1. Haalen HV, Sloand J, Moon R, et al. Drug treatment patterns and work productivity in chronic kidney disease patients with anemia in China: cross sectional analysis of real-world data. Kidney Res Clin Pract 2020;39:318–333.
crossref pmid pmc
2. Kalantar-Zadeh K, Regidor DL, McAllister CJ, Michael B, Warnock DG. Time-dependent associations between iron and mortality in hemodialysis patients. J Am Soc Nephrol 2005;16:3070–3080.
crossref pmid
3. Palaka E, Grandy S, van Haalen H, McEwan P, Darlington O. The impact of CKD anaemia on patients: incidence, risk factors, and clinical outcomes. A systematic literature review. Int J Nephrol 2020;2020:7692376.
crossref pmid pmc pdf
4. Ko EJ, Kim YK, Cho JH, et al. The differential effects of anemia on mortality in young and elderly end-stage renal disease patients. Kidney Res Clin Pract 2020;39:192–201.
crossref pmid pmc
5. Cacoub P, Choukroun G, Cohen-Solal A, et al. Iron deficiency screening is a key issue in chronic inflammatory diseases: a call to action. J Intern Med 2022;292:542–556.
crossref pmid pmc pdf
6. Camaschella C. Iron-deficiency anemia. N Engl J Med 2015;372:1832–1843.
crossref pmid
7. Pasricha SR, Tye-Din J, Muckenthaler MU, Swinkels DW. Iron deficiency. Lancet 2021;397:233–248.
crossref pmid
8. Babitt JL, Eisenga MF, Haase VH, et al. Controversies in optimal anemia management: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Conference. Kidney Int 2021;99:1280–1295.
pmid
9. Kalantar-Zadeh K, Kalantar-Zadeh K, Lee GH. The fascinating but deceptive ferritin: to measure it or not to measure it in chronic kidney disease? Clin J Am Soc Nephrol 2006;1 Suppl 1:S9–S18.
pmid
10. Kidney Disease: Improving Global Outcomes (KDIGO) anemia work group. KDIGO clinical practice guideline for anemia in chronic kidney disease. Kidney Int Suppl 2012;2:279–335.
11. Guedes M, Muenz D, Zee J, et al. Serum biomarkers of iron stores are associated with worse physical health-related quality of life in nondialysis-dependent chronic kidney disease patients with or without anemia. Nephrol Dial Transplant 2021;36:1694–1703.
crossref pmid pmc pdf
12. Guedes M, Muenz DG, Zee J, et al. Serum biomarkers of iron stores are associated with increased risk of all-cause mortality and cardiovascular events in nondialysis CKD patients, with or without anemia. J Am Soc Nephrol 2021;32:2020–2030.
crossref pmid pmc
13. Macdougall IC, Bhandari S, White C, et al. Intravenous iron dosing and infection risk in patients on hemodialysis: a prespecified secondary analysis of the PIVOTAL trial. J Am Soc Nephrol 2020;31:1118–1127.
crossref pmid pmc
14. Macdougall IC, White C, Anker SD, et al. Intravenous iron in patients undergoing maintenance hemodialysis. N Engl J Med 2019;380:447–458.
crossref pmid
15. Besarab A, Drueke TB. The problem with transferrin saturation as an indicator of iron \'sufficiency\' in chronic kidney disease. Nephrol Dial Transplant 2021;36:1377–1383.
crossref pmid pdf
16. Oh KH, Park SK, Park HC, et al. KNOW-CKD (KoreaN cohort study for Outcome in patients With Chronic Kidney Disease): design and methods. BMC Nephrol 2014;15:80.
crossref pmid pmc pdf
17. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150:604–612.
crossref pmid pmc
18. Eisenga MF, Nolte IM, van der Meer P, Bakker SJ, Gaillard CA. Association of different iron deficiency cutoffs with adverse outcomes in chronic kidney disease. BMC Nephrol 2018;19:225.
crossref pmid pmc pdf
19. Kovesdy CP, Estrada W, Ahmadzadeh S, Kalantar-Zadeh K. Association of markers of iron stores with outcomes in patients with nondialysis-dependent chronic kidney disease. Clin J Am Soc Nephrol 2009;4:435–441.
crossref pmid pmc
20. Fishbane S, Pollack S, Feldman HI, Joffe MM. Iron indices in chronic kidney disease in the National Health and Nutritional Examination Survey 1988-2004. Clin J Am Soc Nephrol 2009;4:57–61.
crossref pmid pmc
21. Mainous AG, Gill JM, Carek PJ. Elevated serum transferrin saturation and mortality. Ann Fam Med 2004;2:133–138.
crossref pmid pmc
22. Koo HM, Kim CH, Doh FM, et al. The relationship of initial transferrin saturation to cardiovascular parameters and outcomes in patients initiating dialysis. PLoS One 2014;9:e87231.
crossref pmid pmc
23. Mizuiri S, Nishizawa Y, Doi T, et al. Iron, coronary artery calcification, and mortality in patients undergoing hemodialysis. Ren Fail 2021;43:371–380.
crossref pmid pmc
24. Sato M, Hanafusa N, Tsuchiya K, Kawaguchi H, Nitta K. Impact of transferrin saturation on all-cause mortality in patients on maintenance hemodialysis. Blood Purif 2019;48:158–166.
crossref pmid pdf
25. Chua AC, Knuiman MW, Trinder D, Divitini ML, Olynyk JK. Higher concentrations of serum iron and transferrin saturation but not serum ferritin are associated with cancer outcomes. Am J Clin Nutr 2016;104:736–742.
crossref pmid
26. Mainous AG, Gill JM, Everett CJ. Transferrin saturation, dietary iron intake, and risk of cancer. Ann Fam Med 2005;3:131–137.
crossref pmid pmc
27. Weiss G, Goodnough LT. Anemia of chronic disease. N Engl J Med 2005;352:1011–1023.
crossref pmid
28. Bekfani T, Pellicori P, Morris D, et al. Iron deficiency in patients with heart failure with preserved ejection fraction and its association with reduced exercise capacity, muscle strength and quality of life. Clin Res Cardiol 2019;108:203–211.
crossref pmid pdf
29. Cho IJ, Mun YC, Kwon KH, Shin GJ. Effect of anemia correction on left ventricular structure and filling pressure in anemic patients without overt heart disease. Korean J Intern Med 2014;29:445–453.
crossref pmid pmc
30. Camaschella C. Iron deficiency: new insights into diagnosis and treatment. Hematology Am Soc Hematol Educ Program 2015;2015:8–13.
crossref pmid pdf
31. Macdougall IC, Bircher AJ, Eckardt KU, et al. Iron management in chronic kidney disease: conclusions from a “Kidney Disease: Improving Global Outcomes” (KDIGO) Controversies Conference. Kidney Int 2016;89:28–39.
pmid
32. Thomas DW, Hinchliffe RF, Briggs C, et al. Guideline for the laboratory diagnosis of functional iron deficiency. Br J Haematol 2013;161:639–648.
crossref pmid pdf
33. Kong JY, Hwang HS. Options for intravenous iron supplementation in hemodialysis patients. Kidney Res Clin Pract 2020;39:239–243.
crossref pmid pmc
34. Awan AA, Walther CP, Richardson PA, Shah M, Winkelmayer WC, Navaneethan SD. Prevalence, correlates and outcomes of absolute and functional iron deficiency anemia in nondialysis-dependent chronic kidney disease. Nephrol Dial Transplant 2021;36:129–136.
crossref pmid pdf
35. Campodonico J, Nicoli F, Motta I, et al. Prognostic role of transferrin saturation in heart failure patients. Eur J Prev Cardiol 2021;28:1639–1646.
crossref pmid pdf
36. Kalantar-Zadeh K, Rodriguez RA, Humphreys MH. Association between serum ferritin and measures of inflammation, nutrition and iron in haemodialysis patients. Nephrol Dial Transplant 2004;19:141–149.
crossref pmid
37. Ueda N, Takasawa K. Impact of inflammation on ferritin, hepcidin and the management of iron deficiency anemia in chronic kidney disease. Nutrients 2018;10:1173.
crossref pmid pmc
38. Batchelor EK, Kapitsinou P, Pergola PE, Kovesdy CP, Jalal DI. Iron deficiency in chronic kidney disease: updates on pathophysiology, diagnosis, and treatment. J Am Soc Nephrol 2020;31:456–468.
crossref pmid pmc
39. Grote Beverborg N, Klip IT, Meijers WC, et al. Definition of iron deficiency based on the gold standard of bone marrow iron staining in heart failure patients. Circ Heart Fail 2018;11:e004519.
pmid
40. Yu PH, Lin MY, Chiu YW, et al. Low serum iron is associated with anemia in CKD stage 1-4 patients with normal transferrin saturations. Sci Rep 2021;11:8343.
crossref pmid pmc pdf
41. Hayes W. Measurement of iron status in chronic kidney disease. Pediatr Nephrol 2019;34:605–613.
crossref pmid pdf
42. Kang W, Barad A, Clark AG, et al. Ethnic differences in iron status. Adv Nutr 2021;12:1838–1853.
crossref pmid pmc pdf
TOOLS
METRICS Graph View
  • 0 Crossref
  •  0 Scopus
  • 244 View
  • 4 Download
ORCID iDs

Eunmi Jo
https://orcid.org/0009-0000-4861-7219

Hyo Jin Kim
https://orcid.org/0000-0001-9289-9073

Jayoun Kim
https://orcid.org/0000-0003-2234-7091

Tae-Hyun Yoo
https://orcid.org/0000-0002-9183-4507

Yaeni Kim
https://orcid.org/0000-0002-2903-8374

Soo Wan Kim
https://orcid.org/0000-0002-3540-9004

Kook-Hwan Oh
https://orcid.org/0000-0001-9525-2179

Eun Young Seong
https://orcid.org/0000-0002-6006-0051

Sang Heon Song
https://orcid.org/0000-0002-8218-6974

Related articles

Longitudinal progression trajectory of estimated glomerular filtration rate in children with chronic kidney disease: results from the KNOW-Ped CKD (KoreaN cohort study for Outcomes in patients With Pediatric Chronic Kidney Disease)

Comparison of cardiovascular event predictability between the 2009 and 2021 Chronic Kidney Disease Epidemiology Collaboration equations in a Korean chronic kidney disease cohort: the KoreaN Cohort Study for Outcome in Patients With Chronic Kidney Disease 2023 November;42(6)

Association between serum osteoprotegerin level and renal prognosis in nondialysis patients with chronic kidney disease in the Korean Cohort Study for Outcomes in Patients with Chronic Kidney Disease (the KNOW-CKD Study)2022 March;41(2)

Quality of life in patients with diabetic nephropathy: findings from the KNOW-CKD (Korean Cohort Study for Outcomes in Patients with Chronic Kidney Disease) cohort 2022 January;41(1)

Intensity of statin therapy and renal outcome in chronic kidney disease: Results from the Korean Cohort Study for Outcome in Patients With Chronic Kidney Disease2020 March;39(1)



ABOUT
BROWSE ARTICLES
EDITORIAL POLICY
FOR CONTRIBUTORS
Editorial Office
#301, (Miseung Bldg.) 23, Apgujenog-ro 30-gil, Gangnam-gu, Seoul 06022, Korea
Tel: +82-2-3486-8736    Fax: +82-2-3486-8737    E-mail: registry@ksn.or.kr                

Copyright © 2024 by The Korean Society of Nephrology.

Developed in M2PI

Close layer