Introduction
Potassium is an essential mineral micronutrient in all living cells and the most abundant cation in intracellular fluid. It is rich in fruits and vegetables, and it is excreted by the kidney. Urinary potassium excretion is known as a surrogate for dietary potassium intake, because the average urinary potassium excretion is approximately 77% of the intake (1). Many studies have shown favorable effects of potassium intake on cardiovascular outcomes and mortality; thus, potassium intake is often encouraged in the general population to reduce cardiovascular risk. In a large cohort of approximately 100,000 adults from 18 countries, low urinary potassium excretion was associated with an increased risk for hypertension (2) and cardiovascular events (3). Conversely, in a post hoc analysis of a combination of two cohorts at a high risk for cardiovascular disease, high urinary potassium excretion was associated with a reduced risk of stroke (4). A similar association was also found in persons at a high risk for CKD. In patients with type 2 diabetes and preserved kidney function, higher urinary potassium excretion is associated with slower decline of kidney function (5,6) and lower incidence of cardiovascular complications (6).
Accordingly, one might expect that a potassium-rich diet can also delay the progression of CKD given the benefits of potassium supplement on BP and glycemic control, which is an important therapeutic strategy in patients with CKD. However, dietary potassium can raise a serious concern in these patients because of high incidence of hyperkalemia, particularly in advanced CKD. The National Kidney Foundation–Kidney Disease Outcomes Quality Initiative guidelines allow patients flexibility concerning the amount of potassium intake according to CKD stages (7). However, the Kidney Disease Improving Global Outcomes guidelines do not recommend potassium supplementation to reduce BP in patients with CKD because of the low level of evidence (8). Interestingly, several cohort studies have yielded conflicting results concerning CKD development or progression. The Prevention of Renal and Vascular End-Stage Disease (PREVEND) study (9) showed positive results of potassium intake against CKD development, the Chronic Renal Insufficiency Cohort (CRIC) study (10) presented negative findings indicating that high potassium intake is associated with CKD progression, and the Modification of Diet in Renal Disease (MDRD) study reported a neutral effect of potassium intake on kidney failure (11).
Generally, 24-hour urine collection is a gold standard method for evaluating potassium intake. However, it is burdensome to patients, making serial monitoring infeasible. Spot urinary potassium concentration has been alternatively proposed as a simple measurement in clinical practice (12–14). Although this measure correlates well with 24-hour urinary potassium excretion in healthy individuals, it has not been tested in patients with CKD. Therefore, this study aimed to clarify the association between urinary potassium excretion and CKD progression using spot and 24-hour urinary potassium tests in participants of the Korean Cohort Study for Outcome in Patients with CKD (KNOW-CKD).
Materials and Methods
Study Participants
The KNOW-CKD is a nationwide, multicenter, prospective, observational cohort study from nine tertiary hospitals in Korea. The rationale, design, methods, and protocol summary of the study (15) and baseline characteristics of the participants (16–20) are described elsewhere. Briefly, patients with CKD stages 1–5 (nondialysis) ages 20–75 years old were enrolled between 2011 and 2015. CKD was defined as eGFR<60 ml⋅min−1⋅1.73 m−2 for ≥3 months by the CKD Epidemiology Collaboration Equation (21). Participants without decreased eGFR (CKD stages 1 and 2) were also enrolled if they had evidence of kidney damage, such as albuminuria, or pathologic or structural abnormalities detected by kidney biopsy or imaging examination. Eligibility for enrollment was assessed by the KNOW-CKD investigators at each participating center. We excluded patients with a previous history of maintenance dialysis or kidney transplantation, advanced heart failure or liver failure, history of malignancy, current pregnancy, or single kidney. The study was conducted following the principles of the Declaration of Helsinki, and the study protocol was approved by the institutional review boards of participating centers.
The cohort comprised 2238 participants who voluntarily provided informed consent. We excluded 134 patients who underwent baseline tests but did not have follow-up visits thereafter. We further excluded 283 patients who had missing values of spot urinary potassium-to-creatinine ratio (n=85) and other baseline covariates (n=198). Therefore, 1821 patients were finally included in the final primary analysis, and they were classified according to quartiles of spot urinary potassium-to-creatinine ratio (Supplemental Figure 1).
Data Collection and Measurements
Demographic and anthropometric data were recorded at enrollment. Comorbidities were assessed at baseline by self-report and review of medical records by trained nurses. Baseline laboratory measurements included hemoglobin, BUN, creatinine, uric acid, total cholesterol, triglyceride, HDL cholesterol, LDL cholesterol, albumin, high-sensitivity C-reactive protein, calcium, and intact parathyroid hormone levels. We used a creatinine method that was calibrated and traceable to isotope dilution mass spectrometry reference. For measurement of spot urinary metrics, such as potassium, second-voided urine samples were collected; 24-hour urinary samples were also collected at baseline for urinary potassium (milliequivalent per day) and protein excretion (grams per day) measurements.
Outcomes
The primary outcome was a composite of the first occurrence of a kidney event, defined by a ≥50% decrease in eGFR from baseline values, and the onset of ESKD. ESKD was defined as the initiation of maintenance dialysis or kidney transplantation. Survival time was defined as the time from enrollment to primary outcome. The study protocol indicated measurement of creatinine at 0, 6, and 12 months and then yearly thereafter. However, patients with CKD stage ≥3 were under close observation and had been followed at 1- to 3-month intervals by all participating centers. Regardless of the study protocol, patients who reached the end points were reported by each center.
Statistical Analyses
Continuous variables were presented as means with SDs for normally distributed data or medians with interquartile ranges (IQRs) for skewed data. The normality of the parameters was assessed using the Shapiro–Wilk test. Categorical variables were expressed as number of participants. For the main analysis, all parameters were compared according to quartiles of baseline spot urinary potassium-to-creatinine ratio. To explore the association of urinary potassium excretion with CKD progression, we primarily used cause-specific hazard models. We then constructed subdistribution hazard models to confirm the association observed in the primary analysis. Death before primary outcome was considered as a competing risk. The main difference between two hazard models is that subjects experiencing a competing risk event remain in the risk set in the subdistribution hazard model, whereas they are removed in the cause-specific hazard model (22, 23). These models were constructed after adjustment of the following variables. Model 1 represents unadjusted hazard ratios (HRs). Model 2 was adjusted for age, sex, smoking history, body mass index, and comorbid diseases (diabetes and cardiovascular disease). We also created model 3 after adjustment of mean arterial pressure and laboratory parameters, such as eGFR, random urinary protein-to-creatinine ratio, high-sensitivity C-reactive protein level, intact parathyroid hormone level, serum albumin level, and LDL cholesterol level, in addition to factors included in model 2. In model 4, medications, including statins, renin-angiotensin system blockers, and diuretics, were further added. Mean arterial pressure and eGFR were transformed into categorical variables, because the proportional hazard assumption was violated (24). The results from multivariable hazard models were presented as HRs and 95% confidence intervals (95% CIs). P value for trend was calculated by treating quartiles as a continuous variable in each model. Patients who were lost to follow-up were censored at the date of the last examination. Cumulative kidney outcomes were derived using the cumulative incidence function for a competing risk, and the difference between curves was analyzed using the Gray test (25). Restricted cubic splines were used to reveal the association between urinary potassium excretion as a continuous variable and the HR for kidney event. The rate of kidney function decline per year was assessed using the slope of eGFR obtained from a generalized linear mixed model after adjustment of all factors applied in the cause-specific hazard model 4 above. To confirm our findings, we performed sensitivity analyses with two additional urinary potassium excretion metrics: spot urinary potassium and 24-hour urinary potassium excretion. These were analyzed in 855 patients who had 24-hour urinary data without missing values. In addition to primary analysis, we further performed multiple imputation analysis in 2019 participants who had data for spot urinary potassium-to-creatinine ratio. To this end, chained equations were applied to fill in the 198 missing values of the baseline covariates. All statistical analyses were performed using R (version 3.4.3; www.r-project.org; R Foundation for Statistical Computing, Vienna) and Stata 14 statistical software (StataCorp, College Station, TX), with a P value <0.05 considered significant.
Results
Baseline Characteristics
Table 1 presents the characteristics of the participants according to quartiles of spot urinary potassium-to-creatinine ratio. The average number of visits per participant was 5.2 (SD, 1.6). The median age was 55 (IQR, 45–63) years old, and 61% were men. The median eGFR was 47 (IQR, 29–75) ml⋅min−1⋅1.73 m−2, and the median random urinary protein-to-creatinine ratio was 0.46 (IQR, 0.14–1.41) g/g. Kidney function was more preserved when urinary potassium concentration was higher. There were fewer patients with diabetes in the lowest quartile than in higher quartiles. Moreover, diuretics were used significantly more often in the lowest quartile. However, there was no difference in renin-angiotensin system blocker use across the quartiles.
Table 1. -
Baseline characteristics of 1821 participants in the Korean Cohort Study for Outcome in Patients with CKD
| Variable |
Overall, n=1821 |
Spot Urinary Potassium-to-Creatinine Ratio, mmol/g |
| Q1<33.99, n=455 |
Q2=33.99–45.94, n=455 |
Q3=45.95–61.69, n=455 |
Q4≥61.70, n=456 |
| Age, median [IQR], yr |
55 [45–63] |
53 [40–62] |
53 [45–63] |
56 [48–64] |
56 [48–64] |
| Men, n (%) |
1109 (61) |
352 (77) |
329 (72) |
257 (57) |
171 (38) |
| BMI, mean (SD), kg/m2
|
24.5 (3.4) |
24.7 (3.5) |
24.6 (3.5) |
24.5 (3.2) |
24.5 (3.4) |
| Diabetes, n (%) |
620 (34) |
171 (8) |
142 (31) |
142 (31) |
165 (36) |
| Cardiovascular disease, n (%) |
197 (11) |
52 (11) |
44 (10) |
58 (13) |
43 (9) |
| Smoking history, n (%) |
855 (47) |
276 (61) |
256 (56) |
192 (42) |
131 (29) |
| eGFR, median [IQR], ml⋅min−1⋅1.73 m−2
|
47 [29–75] |
38 [24–63] |
44 [29–68] |
46 [29–73] |
58 [36–93] |
|
eGFR, category, ml⋅min−1⋅1.73 m−2, n (%)
|
|
|
|
|
|
| ≥90 |
309 (17) |
53 (12) |
65 (14) |
69 (15) |
122 (27) |
| 60–90 |
341 (19) |
70 (15) |
81 (18) |
96 (21) |
94 (21) |
| 30–59 |
684 (38) |
167 (37) |
189 (42) |
172 (38) |
156 (34) |
| 15–29 |
372 (20) |
128 (28) |
92 (20) |
88 (19) |
64 (14) |
| <15 (nondialysis) |
115 (6) |
37 (8) |
28 (6) |
30 (7) |
20 (4) |
| BUN, median [IQR], mg/dl |
24 [17–35] |
27 [18–42] |
24 [18–35] |
23 [16–33] |
21 [15–30] |
| Hemoglobin, mean (SD), g/dl |
13 (2) |
13 (2) |
13 (2) |
13 (2) |
13 (2) |
| hs-CRP, median [IQR], mg/L |
0.6 [0.2–1.7] |
0.7 [0.2–2.0] |
0.8 [0.3–1.8] |
0.6 [0.2–1.4] |
0.5 [0.2–1.6] |
| Intact PTH, median [IQR], pg/ml |
51 [33–84] |
55 [36–99] |
51 [34–87] |
54 [36–81] |
45 [29–71] |
| Calcium, mean (SD), mg/dl |
9.1 (0.5) |
9.1 (0.6) |
9.1 (0.6) |
9.1 (0.5) |
9.2 (0.5) |
| Albumin, mean (SD), g/dl |
4.2 (0.4) |
4.2 (0.4) |
4.2 (0.5) |
4.2 (0.4) |
4.2 (0.4) |
| Uric acid, mean (SD), mg/dl |
7.0 (1.9) |
7.6 (2.0) |
7.1 (1.8) |
6.9 (1.8) |
6.4 (1.7) |
| Total cholesterol, mean (SD), mg/dl |
174 (39) |
167 (38) |
173 (39) |
176 (40) |
179 (36) |
| LDL cholesterol, mean (SD), mg/dl |
97 (31) |
93 (31) |
97 (32) |
98 (31) |
100 (31) |
| Triglyceride, median [IQR], mg/dl |
132 [92–192] |
133 [94–197] |
134 [91–198] |
128 [91–189] |
129 [91–180] |
| HDL cholesterol, mean (SD), mg/dl |
50 (16) |
47 (16) |
49 (15) |
50 (15) |
53 (16) |
|
Serum potassium, mean (SD), mmol/L
|
4.6 (0.6) |
4.7 (0.6) |
4.6 (0.6) |
4.6 (0.6) |
4.6 (0.5) |
| Hyperkalemia (≥5.5 mmol/L), n (%) |
160 (9) |
52 (12) |
37 (9) |
37 (9) |
34 (8) |
| Normal (3.5–5.4 mmol/L), n (%) |
1575 (90) |
386 (88) |
394 (90) |
395 (91) |
400 (92) |
| Hypokalemia (<3.5 mmol/L), n (%) |
11 (0.6) |
3 (0.6) |
5 (1) |
2 (0.5) |
1 (0.2) |
| Urinary protein-to-creatinine ratio, median [IQR], g/g |
0.46 [0.14–1.41] |
0.51 [0.17–1.53] |
0.43 [0.12–1.44] |
0.46 [0.15–1.38] |
0.44 [0.12–1.29] |
| Spot urinary Na/Cr, median [IQR] |
0.88 [0.55–1.32] |
0.61 [0.32–0.91] |
0.81 [0.51–1.20] |
1.02 [0.66–1.38] |
1.23 [0.85–1.78] |
| Mean arterial pressure, mean (SD), mm Hg |
94 (12) |
93 (12) |
95 (12) |
94 (12) |
93 (11) |
|
Baseline kidney disease, n (%)
|
|
|
|
|
|
| Diabetic nephropathy |
428 (24) |
129 (28) |
101 (22) |
95 (21) |
103 (23) |
| Hypertensive kidney disease |
329 (18) |
82 (18) |
89 (20) |
83 (18) |
75 (16) |
| GN |
633 (35) |
162 (36) |
159 (35) |
153 (34) |
159 (35) |
| Polycystic kidney disease |
322 (18) |
59 (13) |
82 (18) |
91 (20) |
90 (20) |
| Others |
109 (6) |
23 (5) |
24 (5) |
33 (7) |
29 (6) |
| RAS blocker use, n (%) |
1553 (85) |
382 (84) |
377 (83) |
396 (87) |
398 (87) |
| Diuretics use, n (%) |
570 (31) |
174 (38) |
127 (28) |
136 (30) |
133 (29) |
| Statin use, n (%) |
938 (52) |
231 (51) |
239 (53) |
241 (53) |
227 (50) |
Q, quartile; IQR, interquartile range; BMI, body mass index; hs-CRP, high-sensitivity C-reactive protein; PTH, parathyroid hormone; RAS, renin-angiotensin system.
Urinary Potassium Excretion and Kidney Outcomes
During 5326 person-years of follow-up, the primary outcome occurred in 143 (110.1/1000 person-years), 86 (64.2/1000 person-years), 100 (74.5/1000 person-years), and 63 (46.8/1000 person-years) from the lowest to the highest quartiles of spot urinary potassium-to-creatinine ratio, respectively (Table 2). Cumulative kidney events were significantly lower in the highest quartiles (P<0.001) (Figure 1). Adjusted cumulative incidence function showed the similar pattern (Supplemental Figure 2).
Table 2. -
Kidney outcomes according to quartiles of spot urinary potassium-to-creatinine ratio
| Kidney Outcomes |
Overall |
Spot Urinary Potassium-to-Creatinine Ratio, mmol/g |
| Q1<33.99 |
Q2=33.99–45.94 |
Q3=45.95–61.69 |
Q4≥61.70 |
| No. of participants |
1821 |
455 |
455 |
455 |
456 |
| Person-year |
5326 |
1298 |
1340 |
1342 |
1345 |
|
Composite kidney outcome
|
|
|
|
|
|
| Events |
392 |
143 |
86 |
100 |
63 |
| Events per 1000 person-yr |
73.6 |
110.1 |
64.2 |
74.5 |
46.8 |
|
≥50% decline in eGFR
|
|
|
|
|
|
| Events |
78 |
29 |
12 |
24 |
13 |
| Events per 1000 person-yr |
14.6 |
22.3 |
9.0 |
17.9 |
9.7 |
|
ESKD
|
|
|
|
|
|
| Events |
280 |
100 |
64 |
69 |
47 |
| Events per 1000 person-yr |
52.6 |
77.0 |
47.8 |
51.4 |
34.9 |
;)
Figure 1.: Cumulative incidence of CKD progression according to baseline spot urinary potassium-to-creatinine ratio. Adverse kidney events occurred more as urinary spot urinary potassium-to-creatinine ratio was lower (P<0.001 for the Gray test). Q, quartile.
The unadjusted HRs for the cause-specific hazard model were 2.35 (95% CI, 1.75 to 3.16), 1.35 (95% CI, 0.98 to 1.87), and 1.58 (95% CI, 1.16 to 2.17) for the first, second, and third quartiles, respectively, compared with the fourth quartile (model 1 in Table 3). The results were similar after adjustments for demographic factors, comorbidities, and laboratory parameters (models 2 and 3 in Table 3). The final model with adjustment of medication revealed that the lowest quartile of spot urinary potassium-to-creatinine ratio had a 1.47-fold higher risk of CKD progression than the highest quartile (95% CI, 1.01 to 2.12) (model 4 in Table 3). Similar results were found in the analysis with spot urinary potassium level (Supplemental Table 1). We confirmed this association using a subdistribution hazard model. Compared with the highest quartile, the lowest quartile was significantly associated with increased risk of the primary kidney outcome (HR, 1.68; 95% CI, 1.12 to 2.51) (model 4 in Supplemental Table 2).
Table 3. -
Associations of spot urinary potassium-to-creatinine ratio with CKD progression among 1821 participants in the Korean Cohort Study for Outcome in Patients with CKD
| Spot Urinary Potassium-to-Creatinine Ratio, mmol/g |
No. of Events (%) |
Model 1 |
Model 2 |
Model 3 |
Model 4 |
| HR (95% CI) |
P Value for Trend
a
|
HR (95% CI) |
P Value for Trend
a
|
HR (95% CI) |
P Value for Trend
a
|
HR (95% CI) |
P Value for Trend
a
|
| Q1: <33.99 |
143 (31) |
2.35 (1.75 to 3.16) |
<0.001 |
2.64 (1.93 to 3.62) |
<0.001 |
1.47 (1.02 to 2.12) |
0.03 |
1.47 (1.01 to 2.12) |
0.03 |
| Q2: 33.99–45.94 |
86 (19) |
1.35 (0.98 to 1.87) |
|
1.55 (1.11 to 2.17) |
|
0.95 (0.66 to 1.38) |
|
0.96 (0.66 to 1.40) |
|
| Q3: 45.95–61.69 |
100 (22) |
1.58 (1.16 to 2.17) |
|
1.86 (1.35 to 2.56) |
|
1.07 (0.76 to 1.50) |
|
1.06 (0.76 to 1.49) |
|
| Q4: ≥61.70 |
63 (14) |
1.00 |
|
1.00 |
|
1.00 |
|
1.00 |
|
Model 1: unadjusted crude HR. Model 2: adjusted for age, sex, smoking status, body mass index, and comorbid disease (diabetes and cardiovascular disease). Model 3: model 2 plus mean arterial pressure, eGFR, random urinary protein-to-creatinine ratio, C-reactive protein level, intact parathyroid hormone level, serum albumin level, serum calcium level, random urinary Na/Cr, and LDL cholesterol level. Model 4: model 3 plus renin-angiotensin system blocker, statin, and diuretics usage. HR, hazard ratio; 95% CI, 95% confidence interval; Q, quartile.
aP values for trend across quartiles of spot urinary potassium-to-creatinine ratio. P values for trend were calculated by treating quartiles as a continuous variable in each model.
Spline regression analyses clearly showed a relatively linear inverse relationship between urinary potassium excretion and risk of kidney outcome (Figure 2). We also compared the kidney function decline among spot urinary potassium-to-creatinine ratio quartiles, and the lowest quartile had a faster decline in eGFR than the highest quartile (Supplemental Table 3).
Figure 2.: The relationships between three urinary potassium metrics and CKD progression. Cubic spline analyses showed that there were inverse associations of spot urinary potassium-to-creatinine ratio (A), spot urinary potassium concentration (B), and 24-hour urinary potassium excretion (C) with CKD progression. All curves represent multivariable-adjusted hazard ratios. Hazard ratios were adjusted for age, sex, smoking status, body mass index, comorbid disease (diabetes and cardiovascular disease), mean arterial pressure, eGFR, C-reactive protein level, intact parathyroid hormone level, serum albumin level, serum calcium level, spot urinary Na-to-Cr ratio (24-hour urinary sodium excretion for 24-hour urinary potassium excretion), spot urinary protein-to-creatinine ratio (24-hour urinary protein for 24-hour urinary potassium excretion), LDL cholesterol level, and renin-angiotensin system blocker, statin, and diuretics usage. The histograms in A–C represent the frequency distribution of spot urinary potassium-to-creatinine ratio, spot urinary potassium, and 24-hour urinary potassium, respectively. Median value of each urinary potassium metric was set as a reference.
Sensitivity Analyses
We performed sensitivity analyses in 855 patients with available 24-hour urinary data without missing values (Supplemental Table 4). We created multivariable cause-specific hazard models with spot urinary potassium-to-creatinine ratio, spot urinary potassium, and 24-hour urinary potassium excretion in the same manner as above, and we found that the lowest quartile of each urinary potassium metric was significantly associated with a higher risk of CKD progression (Table 4).
Table 4. -
Associations of urinary potassium excretion with CKD progression among a subset of 855 participants with 24-hour urine samples
| Urinary Potassium Metrics |
No. of Participants |
No. of Events (%) |
Model 1 |
Model 2 |
Model 3 |
Model 4 |
| HR (95% CI) |
P Value for Trend
a
|
HR (95% CI) |
P Value for Trend
a
|
HR (95% CI) |
P Value for Trend
a
|
HR (95% CI) |
P Value for Trend
a
|
|
24-h urinary potassium excretion, mEq/d
|
|
|
|
|
|
|
|
|
|
|
| Q1: <37 |
206 |
65 (32) |
4.21 (2.49 to 7.10) |
<0.001 |
4.60 (2.69 to 7.88) |
<0.001 |
3.12 (1.59 to 6.09) |
<0.001 |
3.05 (1.54 to 6.04) |
0.002 |
| Q2: 37–49 |
210 |
36 (17) |
2.10 (1.19 to 3.70) |
|
2.62 (1.47 to 4.70) |
|
2.41 (1.25 to 4.67) |
|
2.30 (1.18 to 4.47) |
|
| Q3: 50–65 |
218 |
33 (15) |
1.72 (0.97 to 3.06) |
|
2.11 (1.18 to 3.78) |
|
1.86 (0.99 to 3.51) |
|
1.89 (0.99 to 3.59) |
|
| Q4: ≥66 |
221 |
18 (8) |
1.00 |
|
1.00 |
|
1.00 |
|
1.00 |
|
|
Spot urinary potassium-to-creatinine ratio, mmol/g
|
|
|
|
|
|
|
|
|
|
|
| Q1: <35.1 |
208 |
58 (28) |
2.63 (1.61 to 4.30) |
<0.001 |
3.35 (1.95 to 5.75) |
<0.001 |
1.96 (1.08 to 3.56) |
0.02 |
1.95 (1.05 to 3.62) |
0.03 |
| Q2: 35.1–47.9 |
208 |
29 (14) |
1.36 (0.78 to 2.37) |
|
1.80 (1.02 to 3.19) |
|
1.21 (0.67 to 2.18) |
|
1.23 (0.68 to 2.23) |
|
| Q3: 48.0–63.2 |
208 |
38 (18) |
1.59 (0.94 to 2.70) |
|
1.86 (1.09 to 3.18) |
|
1.18 (0.67 to 2.07) |
|
1.16 (0.65 to 2.07) |
|
| Q4: ≥63.3 |
208 |
22 (11) |
1.00 |
|
1.00 |
|
1.00 |
|
1.00 |
|
|
Spot urinary potassium concentration, mmol/L
|
|
|
|
|
|
|
|
|
|
|
| Q1: <30.0 |
205 |
57 (28) |
11.30 (4.87 to 26.22) |
<0.001 |
10.59 (4.54 to 24.66) |
<0.001 |
3.44 (1.41 to 8.36) |
0.03 |
3.79 (1.51 to 9.51) |
0.01 |
| Q2: 30.0–43.4 |
211 |
55 (26) |
9.46 (4.07 to 21.99) |
|
9.54 (4.09 to 22.28) |
|
3.17 (1.31 to 7.65) |
|
3.35 (1.38 to 8.15) |
|
| Q3: 43.5–59.9 |
206 |
28 (14) |
5.19 (2.15 to 12.54) |
|
4.95 (2.04 to 12.01) |
|
3.30 (1.31 to 8.30) |
|
3.13 (1.24 to 7.89) |
|
| Q4: ≥60.0 |
210 |
7 (3) |
1.00 |
|
1.00 |
|
1.00 |
|
1.00 |
|
Model 1: unadjusted crude HR. Model 2: adjusted for age, sex, smoking status, body mass index, and comorbid disease (diabetes and cardiovascular disease). Model 3: model 2 plus mean arterial pressure, eGFR, random urinary protein-to-creatinine ratio (24-hour urinary protein for 24-hour urinary potassium excretion), C-reactive protein level, intact parathyroid hormone level, serum albumin level, serum calcium level, random urinary Na/Cr (24-hour urinary sodium excretion for 24-hour urinary potassium excretion), and LDL cholesterol level. Model 4: model 3 plus renin-angiotensin system blocker, statin, and diuretics usage. In total, 855 patients with available 24-hour urinary data were used in analysis. HR, hazard ratio; 95% CI, 95% confidence interval; Q, quartile.
aP values for trend across quartiles of each urinary potassium metric. P values for trend were calculated by treating quartiles as a continuous variable in each model.
To substantiate our findings, we performed additional analyses using a multiple imputation method. After 198 missing values of baseline covariates were imputed, we analyzed 2019 patients and found the robust association between low urinary potassium excretion and increased risk of CKD progression (Supplemental Table 5).
Subgroup Analyses
To evaluate modification effects of subgroups on the relationship between urinary potassium and CKD progression, subgroup analyses were performed in subgroups stratified by age (<60 or ≥60 years old), eGFR (<45 or ≥45 ml⋅min−1⋅1.73 m−2), proteinuria (<1.0 or ≥1.0 g/g), diabetes (with or without), and sex (men or women). The cause-specific HR per 1-SD lower spot urinary potassium-to-creatinine ratio (22.7 mmol/g) was significant, with a lower hazard of kidney outcome in the overall patients (HR, 1.20; 95% CI, 1.03 to 1.40). P values for interactions were >0.05 for the subgroups by age, eGFR, proteinuria, and diabetes, suggesting that the increased risk of kidney outcome associated with low urinary potassium excretion was evident regardless of these factors (Figure 3). However, a significant interaction was observed between sex and spot urinary potassium-to-creatinine ratio (P value for interaction =0.01), suggesting that the increased risk of kidney outcome associated with low potassium excretion was evident particularly in men.
Figure 3.: Subgroup associations of urinary potassium-to-creatinine ratio with CKD progression. The significant association between urinary potassium excretion and CKD progression was seen in most subgroups. Cause-specific hazard ratios (HRs; 95% confidence intervals [95% CIs]) per 1-SD lower spot urinary potassium-to-creatinine ratio (millimoles per gram) were on log scale. HRs were adjusted for age, sex, smoking status, body mass index, comorbid disease (diabetes and cardiovascular disease), mean arterial pressure, eGFR, high-sensitivity C-reactive protein level, intact parathyroid hormone level, serum albumin level, serum calcium level, spot urinary Na-to-Cr ratio, spot urinary protein-to-creatinine ratio, LDL cholesterol level, and renin-angiotensin system blocker, statin, and diuretics usage. UPCR, urinary protein-to-creatinine ratio.
Discussion
In this study, we found that lower urinary potassium excretion is associated with a significantly increased risk of CKD progression, which was defined by a ≥50% decrease in eGFR from baseline values, and the onset of ESKD. Our findings are robust, because we showed consistent results across three different measures of urinary potassium excretion.
During the past decades, potassium intake was encouraged in the general population given its cardiovascular benefits. Recently, several studies examined the association between potassium intake assessed using urinary potassium excretion and CKD progression, but there were considerable disagreements among studies. Using the PREVEND study cohort database, Kieneker et al. (9) observed that low urinary potassium excretion was associated with a higher risk of development of incident CKD. However, in the CRIC cohort study of only patients with CKD, higher incidence rates of ESKD and death occurred in those with higher potassium excretion than in those with lower potassium excretion (10). Notably, this finding was not consistently confirmed by two other studies involving patients with CKD. In a post hoc analysis of the MDRD study, higher urinary potassium excretion was not related to progression to ESKD, although it was associated with a lower risk of mortality (11). Another study from the Dutch kidney allograft cohort favored higher urinary potassium excretion with respect to decreased risk of graft failure (26). With this background, we addressed this issue in a large Korean CKD cohort and found that higher urinary potassium excretion was significantly associated with a lower risk of adverse kidney outcomes, such a ≥50% decline in eGFR or ESKD.
It is uncertain why discrepant findings exist. Potassium homeostasis can differ and be affected by many factors, such as enhanced rectal secretion (27), when kidney failure occurs. Moreover, the effects of medications that regulate the renin-angiotensin system cannot be ignored, and there is a racial difference in potassium homeostasis (28,29). Notably, our study included a single race; however, the CRIC cohort included 40% blacks (30), and the CRIC study showed opposite results of those of previous studies. Furthermore, it is possible that relatively healthy subjects were less restricted in eating fresh fruits or vegetables. In fact, in all studies, including ours, subjects with high urinary potassium excretion had better kidney function than those with low potassium excretion. This can make the association between potassium excretion and CKD more difficult to interpret. Interestingly, several studies have shown that a diet including potassium-rich fruits and vegetables is associated with a decreased risk of death (31) or CKD development (32). Despite such controversy, we clearly showed that patients with low urinary potassium excretion are at high risk of CKD progression. In addition, the mean serum potassium level was 4.6±0.5 mmol/L in the highest quartile of spot urinary potassium-to-creatinine ratio compared with 4.7±0.6 mmol/L in the lowest quartile, and there was no difference in the proportion of hyperkalemia of ≥5.5 mmol/L among four groups. Given the significant association between low urinary potassium excretion and CKD progression, potassium-rich diet may be considered in clinical practice. In fact, several studies have shown that a potassium-rich diet decreased albuminuria and preserved kidney function (33,34). This diet also produces less acid; thus, it can have advantages over oral supplement. However, because of concern of hyperkalemia, this should be cautiously provided, with close monitoring of serum potassium level in patients with CKD. Although causality is uncertain, there are several explanations for the beneficial effects of higher urinary potassium excretion on clinical outcomes. High potassium intake can attenuate vascular resistance (35,36) and decrease BP (37), suggesting the potential benefits of potassium intake in cardiovascular disease. In fact, a previous observational study showed a significant relationship between high potassium intake and a reduced risk of stroke and death in women (38). Particularly, animal studies showed that potassium deficiency augmented ammoniagenesis, subsequently resulting in tubule-interstitial fibrosis (39,40). Additionally, potassium can activate the kinin-kallikrein system, which is reported to reduce kidney injury (41). These findings can support our data indicating the protective association between high urinary potassium excretion and CKD progression.
Several limitations of our study should be discussed. First, because this study is observational in nature, it precludes conclusions concerning causality and cannot exclude the possibility of residual confounding. This significant association between low urinary potassium excretion and CKD progression may be simply due to the inability to excrete potassium at lower eGFR. Patients with more severe disease were likely to be restricted in eating fruit or vegetables and might have lower potassium excretion. However, general nutritional status did not differ among the four quartiles of urinary potassium as evidenced by similar body mass index and serum albumin levels across the quartiles. These findings suggest that potassium deficiency can play a potential role in CKD progression. Second, we primarily analyzed the association between spot urinary potassium-to-creatinine ratio and kidney outcome. Unfortunately, data for 24-hour urinary potassium excretion, a gold standard for estimating daily potassium intake, were available in only 855 patients. By measuring urinary creatinine excretion, we found that samples were properly collected (data not shown). However, this measurement is burdensome to patients, resulting in poor adherence. Incomplete collection and improper storage are also obstacles (42). We sought to overcome this limitation by performing analyses with three measures of urinary potassium excretion. The role of spot urinary potassium concentration may become more important, and its use is more likely to increase in patients needing serial monitoring, because repeated 24-hour urine collection is unrealistic in clinical practice. Third, this study did not include a dietary survey, which is another standard estimation method for potassium intake. However, it is easily subject to bias owing to the lack of information on potassium contents in many products, inaccurate reporting by participants, recall bias, and other reasons (43). Nevertheless, 77% of dietary potassium is excreted via urine, whereas the remaining is removed via other routes. Therefore, further adjusted estimation using urinary potassium should be developed to correctly reflect potassium intake. Fourth, we do not exactly know why the significant relationship between urinary potassium and CKD progression was specific to men. It is possible that difference in dietary pattern will contribute to this finding, although data on dietary intake were lacking in this study. In line with this notion, previous studies have shown more protein and sodium intake in men than in women (44,45). Moreover, a recent study showed that there was a considerable variation in potassium intake between men and women among 252 healthy Greek adults (46). Notably, a previous study suggested a possible role of estrogen in the lower plasma potassium level in female rodents (47). In addition, total body potassium differs by sex and race (48). However, we did not find differences in plasma potassium level and urinary potassium excretion between men and women. Additional studies should address whether this is a true effect modification. Fifth, this study included only Korean people; thus, our findings may not be extrapolated to the Western population.
In conclusion, this study showed that low urinary potassium excretion is significantly associated with an increased risk of adverse kidney outcome. This association was also significant regardless of age, diabetes, and proteinuria. Our robust findings suggest potential prognostic value of urinary potassium excretion for progression of CKD.
Disclosures
None.
Acknowledgments
This work was supported by the Research Program funded by the Korea Centers for Disease Control and Prevention (grants 2011E3300300, 2012E3301100, 2013E3301600, 2013E3301601, 2013E3301602, 2016E3300200, and 2016E3300201).
Supplemental Material
This article contains the following supplemental material online at http://cjasn.asnjournals.org/lookup/suppl/doi:10.2215/CJN.07820618/-/DCSupplemental.
Supplemental Figure 1. A flow diagram of study subjects.
Supplemental Figure 2. Cumulative incidence function of kidney outcome for the competing risk model with adjustment for covariates in patients according to spot urinary potassium-to-creatinine ratio quartiles.
Supplemental Table 1. Cause-specific hazard ratios and 95% confidence intervals of CKD progression according to quartiles of spot urinary potassium concentration (millimoles per liter) among 1821 patients.
Supplemental Table 2. Subdistribution hazard ratios and 95% confidence intervals of CKD progression according to quartiles of spot urinary potassium-to-creatinine ratio (millimoles per gram) and spot urinary potassium concentration (millimoles per liter) among 1821 patients and 24-hour urinary potassium excretion (milliequivalent per day) among 855 patients.
Supplemental Table 3. The adjusted rate of kidney function decline on the basis of four categories of spot urinary potassium-to-creatinine ratio.
Supplemental Table 4. Baseline characteristics among a subset of 855 participants with 24-hour urine samples.
Supplemental Table 5. Cause-specific hazard ratios and 95% confidence intervals of CKD progression according to quartiles of spot urinary potassium-to-creatinine ratio (millimoles per gram) among 2019 patients using a multiple imputation.
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