Sodium Polystyrene Sulfonate and Heart Failure Risk: A Comparative Study With Calcium Polystyrene Sulfonate

ABSTRACT

Aim

Sodium polystyrene sulfonate (SPS) is one of the classic potassium‐binding agents that remains commonly used in the treatment of hyperkalaemia. However, the potential concern about its sodium load has not been fully investigated. In this study, we evaluated the association between SPS initiation and heart failure (HF), compared with calcium polystyrene sulfonate (CPS).

Methods

This retrospective study enrolled individuals from a nationwide claims database who had been newly prescribed either SPS or CPS between April 2014 and August 2023. The incidence of HF between the two groups was compared using inverse probability of treatment weighting based on the propensity scores.

Results

We analysed 3481 eligible individuals, of whom 478 received SPS and 3003 CPS. Median age (interquartile range) was 78 (71–83) years, 2160 (62%) were male, and median estimated glomerular filtration rate was 47.0 (32.1–63.1) mL/min/1.73 m². Over a median follow‐up of 361 (147–726) days, 709 HF events were documented. Weighted Cox regression analysis demonstrated that SPS users had a higher risk of developing HF (hazard ratio [HR] 1.24; 95% confidence interval [CI] 1.00–1.53). In sub‐group analyses, the association of SPS administration with incident HF was more profound in individuals aged ≥ 78 years (HR 1.37; 95% CI 1.05–1.79) than in those aged < 78 years (HR 1.01; 95% CI 0.69–1.48).

Conclusion

Our analysis of a nationwide real‐world dataset demonstrated that the use of SPS was associated with an increased likelihood of developing HF, suggesting a possible risk related to SPS‐induced sodium loading.

The clinical impact of sodium load from sodium polystyrene sulfonate (SPS), a long‐standing treatment for hyperkalaemia, remains unclear. In a nationwide cohort of 3481 individuals, SPS use was associated with a significantly higher incidence of heart failure compared with calcium polystyrene sulphonate.

1. Introduction

Hyperkalaemia is a prevalent electrolyte disorder that can present as neuromuscular weakness or cardiac arrhythmias [1]. A primary driver of hyperkalaemia is impaired urinary potassium excretion caused by a decline in glomerular filtration rate or the use of renin‐angiotensin system (RAS) inhibitors or mineralocorticoid receptor antagonists (MRAs) [2, 3, 4]. Beyond its immediate clinical effects, hyperkalaemia may lead to the sub‐optimal use of these cardiorenoprotective agents [2, 4, 5, 6]. With the projected rise in patients with chronic kidney disease (CKD), the appropriate management of potassium levels is expected to become an increasingly critical medical issue [7, 8].

Polystyrene sulfonate formulations – sodium polystyrene sulfonate (SPS) and calcium polystyrene sulfonate (CPS) – have been used as cation‐exchange resins for the treatment of hyperkalaemia since the 1950s. Despite the introduction of novel potassium binders (patiromer and sodium zirconium cyclosilicate [SZC]), these classic agents are still one of the common options due to their affordability and availability [9, 10, 11, 12, 13]. It is noteworthy that there are differences in the ion‐exchange processes within the colonic lumen between SPS and CPS: the former exchanges potassium for sodium, the latter for calcium. Sodium loading affects blood pressure and fluid retention, albeit to a lesser extent in the absence of chloride [14, 15]. Theoretically, SPS could contribute to the development of heart failure (HF); however, this potential concern has been insufficiently explored [16].

In this study, we aimed to evaluate whether SPS is associated with an increased likelihood of developing HF, using a health check‐up and administrative claims dataset.

2. Materials and Methods

2.1. Study Design and Database

This study was a nationwide retrospective cohort analysis utilising the DeSC database provided by DeSC Healthcare Inc., Tokyo, Japan [17]. This database includes Japanese administrative health records for both inpatients and outpatients from April 2014 to August 2023, integrating data from three types of health insurance systems: the health insurance for employees of large companies, the National Health Insurance for unemployed individuals under 75 years old, and the Advanced Elderly Medical Service System for individuals aged 75 years and older. Diagnoses were documented based on the International Classification of Diseases, 10th Revision (ICD‐10) codes. Additionally, the DeSC database contains data from annual health check‐ups, comprising anthropometric measurements, lifestyle questionnaires and laboratory results.

We employed a new‐user, active‐comparator design to mitigate confounding by indication and unmeasured factors [18]. CPS is a widely used potassium binder in Japan, with properties comparable to those of SPS in terms of cost, dosing frequency and potassium‐lowering efficacy. Therefore, we selected individuals who received CPS as the control group. To emulate a new user, active comparator design and minimise potential biases, we excluded individuals with a history of HF, those prescribed both SPS and CPS on the same day and those with prior use of SZC. These criteria ensured a clear temporal relationship between treatment initiation and outcomes, reduced carryover effects and minimised confounding by treatment selection.

2.2. Ethics Statement

This study was approved by the Ethics Committee of the University of Tokyo (2021010NI) and adhered to the ethical principles of the Declaration of Helsinki. Informed consent was waived as all data in the DeSC database were anonymised.

2.3. Data Collection and Definitions

We examined the health check‐up data before SPS or CPS were prescribed and obtained the following data: body mass index (BMI), blood pressure, serum creatinine, urine dipstick protein (negative, trace, 1+, 2+, and 3+), haemoglobin A1c (HbA1c), low‐density lipoprotein cholesterol (LDL‐C), high‐density lipoprotein cholesterol (HDL‐C) and triglycerides. Information on cigarette smoking (current or non‐current) was collected via a self‐reported questionnaire. From the administrative claims records, we collected information on concurrent medications and kidney replacement therapy (KRT) (dialysis and kidney transplantation) history at the index date.

Hypertension was defined based on the following criteria: systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg or the use of anti‐hypertensive medications. Diabetes mellitus was determined by HbA1c ≥ 6.5% or the use of blood glucose‐lowering medications, including insulin. Dyslipidaemia was ascertained according to LDL‐C ≥ 140 mg/dL, HDL‐C < 40 mg/dL, triglycerides ≥ 150 mg/dL, or the use of lipid‐lowering medications. Estimated glomerular filtration rate (eGFR) was calculated using the following formula: 194 × Cr^−1.094 × Age^−0.287 (×0.739 if female) [19].

2.4. Outcomes

The primary outcome was the incidence of HF, determined by ICD‐10 codes I110, I500, I501, and I509 (confirmed diagnoses only, excluding suspected cases), with both inpatient and outpatient diagnoses considered [20]. The secondary outcome was the incidence of cardiovascular disease (CVD) (HF, myocardial infarction [MI] [ICD‐10: I210–I214 and I219] and stroke [ICD‐10: I630, I631–I636, I638, I639, I600, I611, I613–I616, I619, I629 and G459]). For this analysis, individuals with a history of MI or stroke were additionally excluded. The observation period spanned from the index date to the outcome of interest, insurance disenrollment (including death) or the study endpoint.

2.5. Inverse Probability of Treatment Weighting

We used stabilised inverse probability of treatment weighting (IPTW) method based on the propensity score to adjust for the differences in baseline profiles between SPS and CPS users [21]. The propensity score for receiving SPS was estimated using a logistic regression model. This estimation included the following variables: age, sex, BMI, systolic blood pressure, diastolic blood pressure, smoking status, KRT history, comorbidities (hypertension, diabetes mellitus, dyslipidaemia, MI, stroke, and osteoporosis), laboratory data (HbA1c, eGFR, and proteinuria) and concomitant medications (calcium channel blockers, RAS inhibitors, MRAs, beta‐blockers, diuretics, sodium‐glucose co‐transporter 2 inhibitors and laxative). We adjusted for blood pressure and the use of antihypertensive medications as surrogate measures for sodium intake, and for a history of osteoporosis as a surrogate for calcium intake. The covariate balance between SPS and CPS users before and after IPTW adjustment was evaluated using standardised mean differences, with an absolute value < 0.1 considered indicative of adequate balance [22].

2.6. Statistical Analyses

Continuous and binary variables are presented as medians (25%–75% interquartile range) and numbers (percentages), respectively. Cox proportional hazard models were employed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Following the overall cohort analysis, we performed sub‐group analyses stratified by age (< 78 vs. ≥ 78 years), sex, BMI (< 23 vs. ≥ 23 kg/m²), eGFR (< 47.0 vs. ≥ 47.0 mL/min per 1.73 m²), proteinuria (negative vs. non‐negative), diabetes mellitus (absence vs. presence), systolic blood pressure (< 140 vs. ≥ 140 mmHg) and RAS inhibitors/MRAs (non‐use vs. use).

We conducted a total of six sensitivity analyses. First, we carried out an analysis using overlap weighting based on the propensity scores to adjust for baseline characteristics between SPS and CPS users; SPS users were weighted by (1−propensity score), and the corresponding CPS users were weighted by the propensity score. Second, we used a propensity score‐matching algorithm to construct a matched cohort. SPS and CPS users were matched using a 1:4 protocol with a calliper width set at 0.2 standard deviations of the logit score. Third, to capture effects beyond transient use, we included participants who received at least one additional prescription for SPS or CPS between 1 and 6 months after initiation. Fourth, we performed an analysis that included only individuals not receiving KRT. Fifth, we restricted the analysis to participants with CKD, defined by any positive proteinuria (from trace to 3+), eGFR < 60 mL/min/1.73 m², or prior KRT. Sixth, we performed multiple imputation for the chained equations to impute missing data. Missing data were assumed to be missing at random, and multiple imputation was performed using 20 iterations. Propensity scores and IPTW were calculated separately for each imputed dataset, and HRs obtained from respective weighted analysis were subsequently combined using Rubin's rules [23].

A p value of < 0.05 was considered statistically significant, and all statistical analyses were performed using Stata version 18 (StataCorp, College Station, TX, USA).

3. Results

3.1. Participant Characteristics

The flowchart detailing the selection process is shown in Figure 1. For this study, we identified 7084 individuals who were newly prescribed SPS or CPS at least 12 months after insurance enrolment and had available data on eGFR and proteinuria. We then excluded 3504 individuals with a history of HF, 25 who were prescribed both SPS and CPS on the same day, and 74 who had received SZC before SPS or CPS initiation. As a result, 3481 individuals were included in the final analysis.

FIGURE 1.

Study flowchart. From the DeSC database, we identified individuals with available eGFR and proteinuria values who were newly initiated on SPS (sodium polystyrene sulfonate) or CPS (calcium polystyrene sulfonate) (n = 7084). After excluding individuals with a prior history of HF (heart failure) (n = 3504), those prescribed both SPS and CPS on the same day (n = 25), and those with prior SZC (sodium zirconium cyclosilicate) use before SPS or CPS initiation (n = 74), a total of 3481 individuals were included in the final analysis.

In this cohort, SPS was prescribed to 478 (14%) and CPS to 3003 (86%) of the 3481 individuals initiated on potassium binders. Baseline characteristics are presented in Table 1. The median age was 78 (71–83) years, 2160 (62%) participants were male, and the median eGFR was 47.0 (32.1–63.1) mL/min/1.73 m². Before applying IPTW, SPS users were more likely to be younger, have lower HbA1c, lower eGFR, and a higher prevalence of proteinuria. After IPTW adjustment, the distributions of all variables were well balanced between the two groups.

TABLE 1.

Characteristics of study participants.

Variable	Before IPTW				After IPTW
	Overall n = 3481	SPS users n = 478	CPS users n = 3003	SMD	SPS users	CPS users	SMD
Age, years	78 (71–83)	76 (70–82)	78 (71–83)	−0.167	76.4	76.5	−0.013
Male	2160 (62)	311 (65)	1849 (62)	0.072	61.8	62.1	−0.006
Cigarette smoking
Non‐current	1850 (53)	268 (56)	1582 (53)	0.068	52.6	53.1	−0.010
Current	381 (11)	63 (13)	318 (11)	0.08	10.9	11.0	−0.001
Unknown	1250 (36)	147 (31)	1103 (37)	‐0.127	36.5	35.9	0.012
BMI, kg/m2	23.0 (20.9–25.3)	22.9 (20.8–25.2)	23.0 (20.9–25.3)	−0.025	23.3	23.2	0.032
SBP, mmHg	133 (122–145)	134 (122–147)	133 (123–145)	0.053	134.8	134.7	0.005
DBP, mmHg	72 (65–80)	73 (66–81)	72 (65–80)	0.053	73.0	73.0	−0.001
Kidney replacement therapy	23 (1)	7 (1)	16 (1)	0.094	0.6	0.7	−0.002
Comorbidities
Hypertension	2985 (86)	409 (86)	2576 (86)	−0.006	85.7	85.7	−0.001
Diabetes mellitus	1334 (38)	174 (36)	1160 (39)	−0.046	39.6	38.4	0.026
Dyslipidaemia	2363 (68)	322 (67)	2041 (68)	−0.013	68.1	67.9	0.005
Myocardial infarction	52 (1)	9 (2)	43 (1)	0.035	1.3	1.5	−0.018
Stroke	636 (18)	87 (18)	549 (18)	−0.002	18.4	18.3	0.003
Osteoporosis	1057 (30)	136 (29)	921 (31)	−0.049	30.4	30.3	0.001
Medications
Calcium channel blockers	1860 (53)	260 (54)	1600 (53)	0.022	53.3	53.4	−0.002
RAS inhibitors	2001 (57)	282 (59)	1719 (57)	0.036	57.8	57.5	0.007
MRAs	242 (7)	25 (5)	217 (7)	−0.083	7.1	7.0	0.004
Beta blockers	386 (11)	59 (12)	327 (11)	0.045	11.1	11.1	−0.001
SGLT2 inhibitors	261 (7)	34 (7)	227 (8)	−0.017	7.4	7.5	−0.002
Diuretics	867 (25)	128 (27)	739 (25)	0.05	25.2	24.9	0.006
Statin	1254 (36)	168 (35)	1086 (36)	−0.021	35.2	36.0	−0.016
Laxatives	1447 (42)	194 (41)	1253 (42)	−0.023	41.3	41.6	−0.005
Laboratory data
LDL–C, mg/dL	108 (88–130)	110 (88–132)	108 (88–130)	0.038	111.7	110.3	0.048
HDL–C, mg/dL	55 (45–67)	56 (46–69)	55 (45–67)	0.069	58.5	57.4	0.062
Triglycerides, mg/dL	108 (78–153)	104 (77–153)	109 (79–153)	−0.059	124.2	129.8	−0.072
HbA1c, %	5.8 (5.5–6.3)	5.7 (5.4–6.2)	5.8 (5.5–6.3)	−0.138	6.1	6.0	0.047
eGFR, mL/min/1.73 m2	47.0 (32.1–63.1)	45.0 (28.0–63.3)	47.3 (33.0–63.1)	−0.123	48.8	48.3	0.023
Proteinuria
Negative	2011 (58)	253 (53)	1758 (59)	−0.113	57.4	57.8	−0.007
Trace	429 (12)	56 (12)	373 (12)	−0.022	12.5	12.3	0.005
1+	406 (12)	61 (13)	345 (11)	0.039	11.7	11.7	0.002
2+	368 (11)	63 (13)	305 (10)	0.094	10.7	10.6	0.003
3+	267 (8)	45 (9)	222 (7)	0.073	7.7	7.7	0.001

Abbreviations: BMI = body mass index; CPS = calcium polystyrene sulfonate; DBP = diastolic blood pressure; eGFR = estimated glomerular filtration rate; HbA1c = haemoglobin A1c; HDL–C = high–density lipoprotein cholesterol; IPTW = inverse probability of treatment weighting; LDL–C = low–density lipoprotein cholesterol; MRA = mineralocorticoid receptor antagonist; RAS = renin–angiotensin system; SBP = systolic blood pressure; SGLT2 = sodium–glucose cotransporter–2; SMD = standardised mean difference; SPS = sodium polystyrene sulfonate.

3.2. Difference in Heart Failure Incidence Between SPS and CPS

During a median follow‐up of 361 (147–726) days, 709 HF events were observed. The incidence rates per 1000 person‐years were 196.8 (163.6–236.6) for SPS and 152.6 (140.8–165.3) for CPS. Weighted Cox regression analyses demonstrated that the risk of developing HF was higher in participants prescribed SPS compared to those prescribed CPS (HR 1.24; 95% CI 1.00–1.53) (Table 2). Moreover, a significant association between SPS use and the development of CVD was observed (HR 1.27; 95% CI 1.00–1.60). In sub‐group analyses stratified by age, SPS administration was notably associated with an increased risk of incident HF in participants aged ≥ 78 years (HR 1.37; 95% CI 1.05–1.79), whereas this relationship was not evident in those aged < 78 years (HR 1.01; 95% CI 0.69–1.48) (Figure 2). Other sub‐group analyses by sex, BMI, eGFR, proteinuria, diabetes mellitus, systolic blood pressure and use of RAS inhibitors or MRAs showed a generally consistent trend towards an increased risk of HF associated with SPS use.

TABLE 2.

Results of the Cox proportional hazards analyses for heart failure and cardiovascular disease before and after applying inverse probability of treatment weighting.

	No. of participant	No. of event	Unadjusted HR (95% CI)	Adjusted HR (95% CI)
Heart failure
SPS	478	113	1.27 (1.04–1.55)	1.24 (1.00–1.53)
CPS	3003	596	Reference	Reference
Cardiovascular disease
SPS	383	94	1.29 (1.03–1.60)	1.27 (1.00–1.60)
CPS	2419	507	Reference	Reference

Note: Adjusted HRs were estimated using inverse probability of treatment weighting. The model accounted for age, sex, body mass index, systolic blood pressure, diastolic blood pressure, smoking status, kidney replacement therapy history, hypertension, diabetes mellitus, dyslipidaemia, myocardial infarction, stroke, osteoporosis, haemoglobin A1c, estimated glomerular filtration rate, proteinuria and the use of calcium channel blockers, renin‐angiotensin system inhibitors, mineralocorticoid receptor antagonists, beta‐blockers, diuretics, sodium‐glucose co‐transporter 2 inhibitors and laxatives. In the analysis with cardiovascular disease as the outcome, we excluded 679 individuals with a history of myocardial infarction or stroke.

Abbreviations: CI = confidence interval; CPS = calcium polystyrene sulfonate; HR = hazard ratio; SPS = sodium polystyrene sulfonate.

FIGURE 2.

Subgroup analyses of heart failure risk associated with SPS versus CPS: IPTW‐adjusted hazard ratios. We conducted Cox proportional hazards regression model to estimate the hazard ratio (HR) and 95% confidence interval (95% CI) for incident heart failure associated with SPS (sodium polystyrene sulfonate) use versus CPS (calcium polystyrene sulfonate) use, after applying inverse probability of treatment weighting (IPTW) on the propensity score (estimated separately for each subgroup). Adjustment was made for age, sex, body mass index (BMI), systolic blood pressure, diastolic blood pressure, smoking status, kidney replacement therapy history, hypertension, diabetes mellitus, dyslipidaemia, myocardial infarction, stroke, osteoporosis, haemoglobin A1c, estimated glomerular filtration rate (eGFR), proteinuria and the use of calcium channel blockers, renin‐angiotensin system (RAS) inhibitors, mineralocorticoid receptor antagonists (MRAs), beta‐blockers, diuretics, sodium‐glucose co‐transporter 2 inhibitors and laxatives. In the subgroup analyses by sex, proteinuria (negative group only), diabetes mellitus and RAS inhibitor/MRA use (non‐use group only), the respective variables were not included in the propensity score models used for IPTW. Hypertension was not included as a covariate in the systolic blood pressure subgroup analysis.

3.3. Sensitivity Analyses

First, we applied an overlap weighting approach to achieve well‐balanced clinical characteristics between SPS and CPS users. The overlap‐weighted Cox proportional hazards analysis showed that participants receiving SPS were more likely to develop HF compared to those receiving CPS (HR 1.24; 95% CI 1.01–1.51) (Table S1). Second, after propensity score matching, the risk of incident HF was higher in SPS users than in CPS users (HR 1.30; 95% CI 1.05–1.60) (Table S2). Third, we analysed participants who had received additional SPS or CPS prescriptions within1 to 6 months post initiation; the direction of effect remained unchanged (HR 1.38; 95% CI 1.05–1.81) (Table S3). Fourth, in the analysis of participants excluding those who had KRT, SPS use was related to an increased risk of developing HF (HR 1.24; 95% CI 1.00–1.53) (Table S4). Fifth, the analysis restricted to participants with CKD yielded similar results (HR 1.23; 95% CI 0.98–1.54) (Table S5). Sixth, we employed the multiple imputation method for missing variables, and the main finding was consistent in this sensitivity analysis (HR 1.25; 95% CI 1.01–1.54) (Table S6).

4. Discussion

This study included individuals who were newly prescribed either SPS or CPS, identified from a nationwide health check‐up and insurance claims database. Using the IPTW method, we compared the risk of incident HF between the two agents and found that SPS administration was associated with a higher likelihood of developing HF than CPS administration. The robustness of our findings was confirmed through multiple sensitivity analyses. To our knowledge, this is the first clinical epidemiological study to indicate an increased risk of HF associated with SPS use.

Potassium binders are gaining increasing clinical relevance; they can facilitate the appropriate use of RAS inhibitors while also easing dietary restrictions on vegetables rich in nutrients such as fibre [24, 25]. Despite the emergence of novel binders, long‐established, low‐cost polystyrene sulfonate‐based agents are expected to remain in clinical use [11, 12, 13]. There is little doubt that an in‐depth understanding of the safety profile is essential in the use of medications. Gastrointestinal complications are well‐documented adverse effects common to both SPS and CPS [11, 26, 27]. In contrast, the potential risk of sodium overload, which is a distinct concern with SPS, has been poorly investigated. Previous studies have reported cases of hypernatremia and increased demand for antihypertensive medications following the initiation of SPS [28, 29]. In a head‐to‐head randomised controlled trial (RCT) involving 20 hyperkalaemic patients, 4 weeks of SPS administration at 15 g per day increased human atrial natriuretic peptide levels from 80 to 113 pg/mL, whereas CPS did not induce a similar change [10]. The precise impact of SPS‐driven sodium burden is still uncertain, highlighting the need for further investigation to ensure high‐quality hyperkalaemia management.

We used a nationwide real‐world dataset to compare the risk of HF onset between new users of SPS and CPS, which had similar clinical profiles. In our analysis of the entire cohort of approximately 3500 participants without a history of HF, we identified a significant association between SPS use and subsequent new‐onset HF. Among ~3000 participants without prior CVD, the use of SPS was linked to a higher incidence of CVD compared to CPS. Most sub‐group analyses consistently demonstrated that SPS use was linked to HF development. Interestingly, this relationship was more pronounced in participants aged ≥ 78 years, while it appeared neutral in those aged < 78 years. Although the underlying mechanisms are unclear, this observed age‐dependent difference might reflect diminished tolerance to sodium loading in older adults, as previously reported [30].

Our findings indicate that SPS can serve as a clinically significant source of sodium burden. Unlike CPS, a calcium–potassium exchange resin, SPS contains about 100 mg of sodium per gram, delivering approximately 3000 mg of sodium per day at the 30 g/day dose used in the RCT [9]. In clinical settings, SPS (and CPS) is frequently used at less than the recommended dose. As reported by the French National Registry, the average SPS dose was assumed to be 15 g every other day over 1 year (equivalent to 750 mg of sodium per day) [11]. Notably, the Japanese Society of Hypertension recommends that salt intake for hypertensive patients be kept below 6 g per day (equivalent to 2400 mg of sodium), whereas the Kidney Disease: Improving Global Outcomes guidelines set a stricter limit of 5 g (equivalent to 2000 mg of sodium) for CKD patients [31, 32]. Considering that even modest reductions in salt intake are associated with lower blood pressure, sodium contribution from SPS appears to warrant due attention [33]. While the sodium load from SPS is likely a major driver of the observed effects, other unspecified differences between SPS and CPS might also have contributed.

This study has clinical implications. Although multiple potassium binders are currently available, insufficient data on agent‐specific clinical effects hampers optimal treatment selection – particularly in relation to cardiovascular risk. In this context, we found that SPS use had a stronger association with the development of HF compared to CPS. Individuals with hyperkalaemia frequently have comorbidities such as diabetes, hypertension and CKD, many of whom struggle to adhere to sodium restrictions [1]. For these individuals, CPS might be preferable to SPS in order to minimise sodium load and reduce HF risk. Nevertheless, given interpatient variability in gastrointestinal tolerability and palatability, SPS continues to play a necessary role in certain clinical scenarios. Furthermore, SPS remains the only viable option in some settings or countries [32]. When prescribing this sodium‐based potassium binder, careful monitoring of blood pressure is advisable, accompanied by appropriate salt restriction and judicious diuretic use. These approaches may help mitigate the sodium burden associated with SPS and support optimal fluid balance.

The strength of this study lies in the direct comparison of HF risk between SPS and CPS, based on a nationwide real‐world dataset. Presumably because of the limited distribution and underuse of CPS relative to SPS, previous clinical research on SPS has often employed the absence of treatment as a comparator [26, 27]. In Japan, CPS as well as SPS have been widely used, providing a unique opportunity to conduct the present study. On the other hand, this study has several acknowledged limitations. Although we conducted Cox regression analysis using IPTW methods, it was not feasible to fully adjust for residual confounding factors such as dietary habits, family history of heart disease or socioeconomic status. It is worth noting that daily salt and calcium intake can influence the choice of potassium binders. Owing to the infeasibility of direct assessment, these dietary factors were evaluated through surrogate markers. Nevertheless, physicians are unlikely to intentionally prescribe sodium‐containing SPS to individuals with habitually high salt intake. Therefore, we believe that this unmeasured factor could not result in a misleadingly high estimation of HF risk with SPS. Next, we were unable to consider the dosage of potassium binders. Due to inconsistent use and poor adherence, the assessment of the exact administered dose is not straightforward. This issue may have influenced the observed outcomes and their interpretation. Whether SPS carries a dose‐dependent risk of HF is an intriguing area for further research. In our cohort, it is difficult to ascertain the nature of the actual prescribing practices due to unavailable baseline potassium levels. However, potassium binders are indicated only for hyperkalaemia and are potentially burdensome, making them likely to be prescribed to individuals with a clinical need. In light of the comparable potassium‐lowering efficacy of both agents, this factor appears to have minimal impact on medication selection. Our analysis did not evaluate novel potassium binders. Although SZC has been available in Japan since May 2020 (and patiromer since November 2024), its use as an initial potassium binder during this study period was not enough for analysis. Comparative studies with these new agents regarding efficacy, safety, and cost‐effectiveness remain an important research priority. Moreover, the incidence of HF observed in this study was higher than that reported in presumed at‐risk groups such as individuals with hypertension or cancer in other Japanese epidemiological studies [34, 35]. This observation may reflect that our cohort—older individuals with hyperkalaemia – represented a particularly high‐risk population for incident HF. Admittedly, some individuals may have had unrecorded pre‐existing HF and the use of ICD‐10 codes could raise concerns about diagnostic accuracy. Nevertheless, as these issues likely affected the SPS and CPS groups equally, their impact on our main findings appears limited. It should be noted that the exclusion criteria employed in this study, while applied to achieve a valid new‐user, active comparator design, may still have introduced selection bias, potentially affecting the representativeness of the study population. Considering that the median follow‐up in this study was approximately 1 year, the potential implications of longer‐term observation also warrant further investigation. This study does not account for racial differences. Asians have a higher salt intake and a greater prevalence of salt‐sensitive hypertension compared to other populations [36]. As our dataset almost exclusively includes individuals of Asian descent, these findings should be interpreted with caution when extrapolated to other ethnic groups. Finally, well‐designed prospective studies incorporating potassium levels, medication profiles and dietary patterns are required to better understand and validate the results of this study.

In conclusion, our analyses revealed that individuals with hyperkalaemia who were newly prescribed SPS had an increased risk of developing HF compared to those receiving CPS. These findings underscore clinical considerations when prescribing potassium‐binding agents.

Author Contributions

H.K., T.N., A.O., M.N. and K.H. conceived and designed the study. T.N., A.O., Y.S. and H.Y. analysed the data. H.K., T.N., A.O., Y.S., H.M., K.F., Norifumi T., T.A., T.Y., Norihiko T., K.N. and K.H. interpreted the data.

Conflicts of Interest

H.K. and K.F. received research funding and scholarship funds from Medtronic Japan CO. LTD, Boston Scientific Japan CO. LTD, Biotronik Japan, and Simplex QUANTUM CO. LTD. H.K. holds shares in PrevMed Co. Ltd. and Japan Preventive Medical Development Institute Co. Ltd. A.O. is a member of the Department of Prevention of Diabetes and Lifestyle‐related Diseases, a cooperative programme between the University of Tokyo and the Asahi Mutual Life Insurance Company. M.N. received consulting fees or speaking honorarium or both from Mitsubishi Tanabe Pharma, Astellas, Kyowa Kirin, AstraZeneca, JT, and Boehringer Ingelheim and has received research grants from Daiichi Sankyo, Mitsubishi Tanabe Pharma, Kyowa Kirin, JT, Takeda, Chugai Pharmaceutical and Torii. The authors have no conflicts of interest to declare.

Supporting information

Table S1: Results of the Cox proportional hazards analyses for heart failure before and after applying overlap weighting.

Table S2: Results of the Cox proportional hazards analyses for heart failure after applying propensity score–matching.

Table S3: Results of the Cox proportional hazards analyses for heart failure before and after applying inverse probability of treatment weighting (including individuals with two or more prescriptions for SPS or CPS).

Table S4: Results of the Cox proportional hazards analyses for heart failure before and after applying inverse probability of treatment weighting (excluding individuals receiving kidney replacement therapy).

Table S5: Results of the Cox proportional hazards analyses for heart failure before and after applying inverse probability of treatment weighting (restricted to individuals with chronic kidney disease).

Table S6: Results of the Cox proportional hazards analyses for heart failure before and after applying inverse probability of treatment weighting (multiple imputation for missing data).

Nakayama T., Kaneko H., Suzuki Y., et al., “Sodium Polystyrene Sulfonate and Heart Failure Risk: A Comparative Study With Calcium Polystyrene Sulfonate,” Nephrology 30, no. 9 (2025): e70117, 10.1111/nep.70117.

Data Availability Statement

The data are not deposited in a public repository due to licensing restrictions, but are available for purchase from DeSC Healthcare Inc. (Tokyo, Japan).